Academic literature on the topic 'Data-Driven reduced order modeling'
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Journal articles on the topic "Data-Driven reduced order modeling"
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Guo, Mengwu, and Jan S. Hesthaven. "Data-driven reduced order modeling for time-dependent problems." Computer Methods in Applied Mechanics and Engineering 345 (March 2019): 75–99. http://dx.doi.org/10.1016/j.cma.2018.10.029.
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Xie, X., M. Mohebujjaman, L. G. Rebholz, and T. Iliescu. "Data-Driven Filtered Reduced Order Modeling of Fluid Flows." SIAM Journal on Scientific Computing 40, no. 3 (January 2018): B834—B857. http://dx.doi.org/10.1137/17m1145136.
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Ivagnes, Anna, Giovanni Stabile, Andrea Mola, Traian Iliescu, and Gianluigi Rozza. "Hybrid data-driven closure strategies for reduced order modeling." Applied Mathematics and Computation 448 (July 2023): 127920. http://dx.doi.org/10.1016/j.amc.2023.127920.
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Borcea, Liliana, Josselin Garnier, Alexander V. Mamonov, and Jörn Zimmerling. "When Data Driven Reduced Order Modeling Meets Full Waveform Inversion." SIAM Review 66, no. 3 (May 2024): 501–32. http://dx.doi.org/10.1137/23m1552826.
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Peters, Nicholas, Christopher Silva, and John Ekaterinaris. "A data-driven reduced-order model for rotor optimization." Wind Energy Science 8, no. 7 (July 20, 2023): 1201–23. http://dx.doi.org/10.5194/wes-8-1201-2023.
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Abstract. For rotor design applications, such as wind turbine rotors or urban air mobility (UAM) rotorcraft and flying-car design, there is a significant challenge in quickly and accurately modeling rotors operating in complex, turbulent flow fields. One potential path for deriving reasonably accurate but low-cost rotor performance predictions is available through the application of data-driven surrogate modeling. In this study, an initial investigation is undertaken to apply a proper orthogonal decomposition (POD)-based reduced-order model (ROM) for predicting rotor distributed loads. The POD ROM was derived based on computational fluid dynamics (CFD) results and utilized to produce distributed-pressure predictions on rotor blades subjected to topology change due to variations in the twist and taper ratio. Rotor twist, θ, was varied between 0, 10, 20, and 30∘, while the taper ratio, λ, was varied as 1.0, 0.9, 0.8, and 0.7. For a demonstration of the approach, all rotors consisted of a single blade. The POD ROM was validated for three operation cases: a high-pitch or a high-thrust rotor in hover, a low-pitch or a low-thrust rotor in hover, and a rotor in forward flight at a low speed resembling wind turbine operation with wind shear. Results showed that reasonably accurate distributed-load predictions could be achieved and the resulting surrogate model can predict loads at a minimal computational cost. The computational cost for the hovering blade surface pressure prediction was reduced from 12 h on 440 cores required for CFD to a fraction of a second on a single core required for POD. For rotors in forward flight, cost was reduced from 20 h on 440 cores to less than a second on a single core. The POD ROM was used to carry out a design optimization of the rotor such that the figure of merit was maximized for hovering-rotor cases and the lift-to-drag effective ratio was maximized in forward flight.
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Zhang, Xinshuai, Tingwei Ji, Fangfang Xie, Changdong Zheng, and Yao Zheng. "Data-driven nonlinear reduced-order modeling of unsteady fluid–structure interactions." Physics of Fluids 34, no. 5 (May 2022): 053608. http://dx.doi.org/10.1063/5.0090394.
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A novel data-driven nonlinear reduced-order modeling framework is proposed for unsteady fluid–structure interactions (FSIs). In the proposed framework, a convolutional variational autoencoder model is developed to determine the coordinate transformation from a high-dimensional physical field into a reduced space. This enables the efficient extraction of nonlinear low-dimensional manifolds from the high-dimensional unsteady flow field of the FSIs. The sparse identification of a nonlinear dynamics (SINDy) algorithm is then used to identify the dynamical governing equations of the reduced space and the vibration responses. To investigate and validate the effectiveness of the proposed framework for modeling and predicting unsteady flow fields in FSI problems, the two-dimensional laminar vortex shedding of a fixed cylinder is considered. Furthermore, the proposed data-driven nonlinear reduced-order modeling framework is applied to the three-dimensional vortex-induced vibration of a flexible cylinder. Using the SINDy model to analyze the vibration responses, the dynamics of the flexible cylinder are found to be correlated with the flow wake patterns, revealing the underlying FSI mechanism. The present work is a significant step toward the establishment of machine learning-based nonlinear reduced-order models for complex flow phenomena, the discovery of underlying unsteady FSI physics, and real-time flow control.
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Baumann, Henry, Alexander Schaum, and Thomas Meurer. "Data-driven control-oriented reduced order modeling for open channel flows." IFAC-PapersOnLine 55, no. 26 (2022): 193–99. http://dx.doi.org/10.1016/j.ifacol.2022.10.399.
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German, Péter, Mauricio E. Tano, Carlo Fiorina, and Jean C. Ragusa. "Data-Driven Reduced-Order Modeling of Convective Heat Transfer in Porous Media." Fluids 6, no. 8 (July 28, 2021): 266. http://dx.doi.org/10.3390/fluids6080266.
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This work presents a data-driven Reduced-Order Model (ROM) for parametric convective heat transfer problems in porous media. The intrusive Proper Orthogonal Decomposition aided Reduced-Basis (POD-RB) technique is employed to reduce the porous medium formulation of the incompressible Reynolds-Averaged Navier–Stokes (RANS) equations coupled with heat transfer. Instead of resolving the exact flow configuration with high fidelity, the porous medium formulation solves a homogenized flow in which the fluid-structure interactions are captured via volumetric flow resistances with nonlinear, semi-empirical friction correlations. A supremizer approach is implemented for the stabilization of the reduced fluid dynamics equations. The reduced nonlinear flow resistances are treated using the Discrete Empirical Interpolation Method (DEIM), while the turbulent eddy viscosity and diffusivity are approximated by adopting a Radial Basis Function (RBF) interpolation-based approach. The proposed method is tested using a 2D numerical model of the Molten Salt Fast Reactor (MSFR), which involves the simulation of both clean and porous medium regions in the same domain. For the steady-state example, five model parameters are considered to be uncertain: the magnitude of the pumping force, the external coolant temperature, the heat transfer coefficient, the thermal expansion coefficient, and the Prandtl number. For transient scenarios, on the other hand, the coastdown-time of the pump is the only uncertain parameter. The results indicate that the POD-RB-ROMs are suitable for the reduction of similar problems. The relative L2 errors are below 3.34% for every field of interest for all cases analyzed, while the speedup factors vary between 54 (transient) and 40,000 (steady-state).
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Gruber, Anthony, Max Gunzburger, Lili Ju, and Zhu Wang. "A comparison of neural network architectures for data-driven reduced-order modeling." Computer Methods in Applied Mechanics and Engineering 393 (April 2022): 114764. http://dx.doi.org/10.1016/j.cma.2022.114764.
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Li, Mengnan, and Lijian Jiang. "Data-driven reduced-order modeling for nonautonomous dynamical systems in multiscale media." Journal of Computational Physics 474 (February 2023): 111799. http://dx.doi.org/10.1016/j.jcp.2022.111799.
Full textDissertations / Theses on the topic "Data-Driven reduced order modeling"
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Mou, Changhong. "Cross-Validation of Data-Driven Correction Reduced Order Modeling." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/87610.
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In this thesis, we develop a data-driven correction reduced order model (DDC-ROM) for numerical simulation of fluid flows. The general DDC-ROM involves two stages: (1) we apply ROM filtering (such as ROM projection) to the full order model (FOM) and construct the filtered ROM (F-ROM). (2) We use data-driven modeling to model the nonlinear interactions between resolved and unresolved modes, which solves the F-ROM's closure problem.
In the DDC-ROM, a linear or quadratic ansatz is used in the data-driven modeling step. In this thesis, we propose a new cubic ansatz. To get the unknown coefficients in our ansatz, we solve an optimization problem that minimizes the difference between the FOM data and the ansatz. We test the new DDC-ROM in the numerical simulation of the one-dimensional Burgers equation with a small diffusion coefficient. Furthermore, we perform a cross-validation of the DDC-ROM to investigate whether it can be successful in computational settings that are different from the training regime.M.S.
Practical engineering and scientific problems often require the repeated simulation of unsteady fluid flows. In these applications, the computational cost of high-fidelity full-order models can be prohibitively high. Reduced order models (ROMs) represent efficient alternatives to brute force computational approaches. In this thesis, we propose a data-driven correction ROM (DDC-ROM) in which available data and an optimization problem are used to model the nonlinear interactions between resolved and unresolved modes. In order to test the new DDC-ROM's predictability, we perform its cross-validation for the one-dimensional viscous Burgers equation and different training regimes.
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Koc, Birgul. "Commutation Error in Reduced Order Modeling." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/87537.
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We investigate the effect of spatial filtering on the recently proposed data-driven correction reduced order model (DDC-ROM). We compare two filters: the ROM projection, which was originally used to develop the DDC-ROM, and the ROM differential filter, which uses a Helmholtz operator to attenuate the small scales in the input signal. We focus on the following questions: ``Do filtering and differentiation with respect to space variable commute, when filtering is applied to the diffusion term?'' or in other words ``Do we have commutation error (CE) in the diffusion term?" and ``If so, is the commutation error data-driven correction ROM (CE-DDC-ROM) more accurate than the original DDC-ROM?'' If the CE exists, the DDC-ROM has two different correction terms: one comes from the diffusion term and the other from the nonlinear convection term. We investigate the DDC-ROM and the CE-DDC-ROM equipped with the two ROM spatial filters in the numerical simulation of the Burgers equation with different diffusion coefficients and two different initial conditions (smooth and non-smooth).M.S.
We propose reduced order models (ROMs) for an efficient and relatively accurate numerical simulation of nonlinear systems. We use the ROM projection and the ROM differential filters to construct a novel data-driven correction ROM (DDC-ROM). We show that the ROM spatial filtering and differentiation do not commute for the diffusion operator. Furthermore, we show that the resulting commutation error has an important effect on the ROM, especially for low viscosity values. As a mathematical model for our numerical study, we use the one-dimensional Burgers equations with smooth and non-smooth initial conditions.
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Mou, Changhong. "Data-Driven Variational Multiscale Reduced Order Modeling of Turbulent Flows." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103895.
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In this dissertation, we consider two different strategies for improving the projection-based reduced order model (ROM) accuracy:
(I) adding closure terms to the standard ROM;
and
(II) using Lagrangian data to improve the ROM basis.
Following strategy (I), we propose a new data-driven reduced order model (ROM) framework that centers around the hierarchical structure of the variational multiscale (VMS) methodology and utilizes data to increase the ROM accuracy at a modest computational cost.
The VMS methodology is a natural fit for the hierarchical structure of the ROM basis:
In the first step, we use the ROM projection to separate the scales into three categories: (i) resolved large scales, (ii) resolved small scales, and (iii) unresolved scales.
In the second step, we explicitly identify the VMS-ROM closure terms, i.e., the terms representing the interactions among the three types of scales.
In the third step, we use available data to model the VMS-ROM closure terms.
Thus, instead of phenomenological models used in VMS for standard numerical discretizations (e.g., eddy viscosity models), we utilize available data to construct new structural VMS-ROM closure models.
Specifically, we build ROM operators (vectors, matrices, and tensors) that are closest to the true ROM closure terms evaluated with the available data.
We test the new data-driven VMS-ROM in the numerical simulation of four test cases:
(i) the 1D Burgers equation with viscosity coefficient $nu = 10^{-3}$;
(ii) a 2D flow past a circular cylinder at Reynolds numbers $Re=100$, $Re=500$, and $Re=1000$;
(iii) the quasi-geostrophic equations at Reynolds number $Re=450$ and Rossby number $Ro=0.0036$; and
(iv) a 2D flow over a backward facing step at Reynolds number $Re=1000$.
The numerical results show that the data-driven VMS-ROM is significantly more accurate than standard ROMs.
Furthermore, we propose a new hybrid ROM framework for the numerical simulation of fluid flows.
This hybrid framework incorporates two closure modeling strategies: (i) A structural closure modeling component that involves the recently proposed data-driven variational multiscale ROM approach, and (ii) A functional closure modeling component that introduces an artificial viscosity term.
We also utilize physical constraints for the structural ROM operators in order to add robustness to the hybrid ROM.
We perform a numerical investigation of the hybrid ROM for the three-dimensional turbulent channel flow at a Reynolds number $Re = 13,750$.
In addition, we focus on the mathematical foundations of ROM closures.
First, we extend the verifiability concept from large eddy simulation to the ROM setting.
Specifically, we call a ROM closure model verifiable if a small ROM closure model error (i.e., a small difference between the true ROM closure and the modeled ROM closure) implies a small ROM error.
Second, we prove that a data-driven ROM closure (i.e., the data-driven variational multiscale ROM) is verifiable.
For strategy (II), we propose new Lagrangian inner products that we use together with Eulerian and Lagrangian data to construct new Lagrangian ROMs.
We show that the new Lagrangian ROMs are orders of magnitude more accurate than the standard Eulerian ROMs, i.e., ROMs that use standard Eulerian inner product and data to construct the ROM basis.
Specifically, for the quasi-geostrophic equations, we show that the new Lagrangian ROMs are more accurate than the standard Eulerian ROMs in approximating not only Lagrangian fields (e.g., the finite time Lyapunov exponent (FTLE)), but also Eulerian fields (e.g., the streamfunction).
We emphasize that the new Lagrangian ROMs do not employ any closure modeling to model the effect of discarded modes (which is standard procedure for low-dimensional ROMs of complex nonlinear systems).
Thus, the dramatic increase in the new Lagrangian ROMs' accuracy is entirely due to the novel Lagrangian inner products used to build the Lagrangian ROM basis.Doctor of Philosophy
Reduced order models (ROMs) are popular in physical and engineering applications: for example, ROMs are widely used in aircraft designing as it can greatly reduce computational cost for the aircraft's aeroelastic predictions while retaining good accuracy. However, for high Reynolds number turbulent flows, such as blood flows in arteries, oil transport in pipelines, and ocean currents, the standard ROMs may yield inaccurate results. In this dissertation, to improve ROM's accuracy for turbulent flows, we investigate three different types of ROMs. In this dissertation, both numerical and theoretical results show that the proposed new ROMs yield more accurate results than the standard ROM and thus can be more useful.
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Swischuk, Renee C. (Renee Copland). "Physics-based machine learning and data-driven reduced-order modeling." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122682.
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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 123-128).
This thesis considers the task of learning efficient low-dimensional models for dynamical systems. To be effective in an engineering setting, these models must be predictive -- that is, they must yield reliable predictions for conditions outside the data used to train them. These models must also be able to make predictions that enforce physical constraints. Achieving these tasks is particularly challenging for the case of systems governed by partial differential equations, where generating data (either from high-fidelity simulations or from physical experiments) is expensive. We address this challenge by developing learning approaches that embed physical constraints. We propose two physics-based approaches for generating low-dimensional predictive models. The first leverages the proper orthogonal decomposition (POD) to represent high-dimensional simulation data with a low-dimensional physics-based parameterization in combination with machine learning methods to construct a map from model inputs to POD coefficients. A comparison of four machine learning methods is provided through an application of predicting flow around an airfoil. This framework also provides a way to enforce a number of linear constraints by modifying the data with a particular solution. The results help to highlight the importance of including physics knowledge when learning from small amounts of data. We also apply a data-driven approach to learning the operators of low-dimensional models. This method provides an avenue for constructing low-dimensional models of systems where the operators of discretized governing equations are unknown or too complex, while also having the ability to enforce physical constraints. The methodology is applied to a two-dimensional combustion problem, where discretized model operators are unavailable. The results show that the method is able to accurately make predictions and enforce important physical constraints.
by Renee C. Swischuk.
S.M.
S.M. Massachusetts Institute of Technology, Computation for Design and Optimization Program
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Ali, Naseem Kamil. "Thermally (Un-) Stratified Wind Plants: Stochastic and Data-Driven Reduced Order Descriptions/Modeling." PDXScholar, 2018. https://pdxscholar.library.pdx.edu/open_access_etds/4634.
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Wind energy is one of the significant sources of renewable energy, yet a number of challenges preclude optimal operation of wind plants. Research is warranted in order to minimize the power losses and improve the productivity of wind plants. Here, a framework combining turbulence theory and data mining techniques is built to elucidate physics and mechanisms driving the energy extraction of the wind plants under a number of atmospheric/operating conditions. The performance of wind turbines is subjected to adverse effects caused by wake interactions. Therefore, it is crucial to understand wake-to-wake interactions as well as wake-to-atmospheric boundary layer interactions. Experimental and numerical data sets are examined in order to provide descriptions of the wakes and extract relevant features. As wakes merge, it is of interest to observe characteristics within the turbulent velocity signal obtained via wind tunnel experiments. Higher order moments, structure functions, intermittency and multifractality analysis are investigated to distinguish the flow dynamics. In this manner, considered approaches highlight the flow deceleration induced by the wind turbines, which subsequently changes the energy transfer rate imposed by the coherent eddies, and adapt the equilibrium range in the energy cascade. Also, wind turbines induce scale interactions and cause the intermittency that lingers at large and small scales. When wind plants interact dynamically with small scales, the flow becomes highly intermittent and multifractality is increased, especially near the rotor. Multifractality parameters, including the Hurst exponent and the combination factor, show the ability to describe the flow state in terms of its development. Based on Markov theory, the time evolution of the probability density function of the velocity is described via the Fokker-Planck equation and its Kramers-Moyal coefficients. Stochastic analysis proves the non-universality of the turbulent cascade immediate to the rotor, and the impact of the generation mechanism on flow cascade. Classifying the wake flow based the velocity and intermittency signs emphasizes that a negative correlation is dominant downstream from the rotor. These results reflect large-scale organization of the velocity-intermittency events corresponding to a recirculation region near the hub height and bottom tip. A linear regression approach based on the Gram-Charlier series expansion of the joint probability density function successfully models the contribution of the second and fourth quadrants. Thus, the model is able to predict the imbalance in the velocity and intermittency contribution to momentum transfer. Via large eddy simulations, the structure of the turbulent flow within the array under stratified conditions is quantified through the use of the Reynolds stress anisotropy tensor, proper orthogonal decomposition and cluster-based modeling. Perturbations induced by the turbine wakes are absorbed by the background turbulence in the unstable and neutrally stratified cases. Contrary, the flow in the stable stratified case is fully dominated by the presence of turbines and extremely influenced by the Coriolis force. Also, during the unstable period the turbulent kinetic energy is maximum. Thus, leading to fast convergence of the cumulative energy with only few modes. Reynolds stress anisotropy tensor reveals that under unstable thermal stratification the turbulence state tends to be more isotropic. The turbulent mixing due to buoyancy determines the degree of anisotropy and the energy distribution between the flow layers. The wakes of the turbines display large degree of anisotropy due to the correlation with the turbulent kinetic energy production. A combinatorial technique merging image segmentation via K-Means clustering and colormap of the barycentric map is posed. Clustering aids in extracting identical features from the spatial distribution of anisotropy colormap images by minimizing the sum of squared error over all clusters. Clustering also enables to highlight the wake expansion and interaction as produced by the wind turbines as a function of thermal stratification. A cluster-based reduced-order dynamical model is proposed for flow field and passive scalars; the model relies on full-state measurements. The dynamical behavior is predicted through the cluster transition matrix and modeled as a Markov process. The geometric nature of the attractor shows the ability to assess the quality of the clustering and identify transition regions. Periodical trends in the cluster transition matrix characterize the intrinsic periodical behavior of the wake. The modeling strategy points out a feasible path for future design and control that can be used to maximize power output. In addition, characterization of intermittency with power integration model can allow for power fluctuation arrangement/prediction in wind plants.
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Xie, Xuping. "Large Eddy Simulation Reduced Order Models." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/77626.
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This dissertation uses spatial filtering to develop a large eddy simulation reduced order model (LES-ROM) framework for fluid flows. Proper orthogonal decomposition is utilized to extract the dominant spatial structures of the system. Within the general LES-ROM framework, two approaches are proposed to address the celebrated ROM closure problem. No phenomenological arguments (e.g., of eddy viscosity type) are used to develop these new ROM closure models.
The first novel model is the approximate deconvolution ROM (AD-ROM), which uses methods from image processing and inverse problems to solve the ROM closure problem. The AD-ROM is investigated in the numerical simulation of a 3D flow past a circular cylinder at a Reynolds number $Re=1000$. The AD-ROM generates accurate results without any numerical dissipation mechanism. It also decreases the CPU time of the standard ROM by orders of magnitude.
The second new model is the calibrated-filtered ROM (CF-ROM), which is a data-driven ROM. The available full order model results are used offline in an optimization problem to calibrate the ROM subfilter-scale stress tensor. The resulting CF-ROM is tested numerically in the simulation of the 1D Burgers equation with a small diffusion parameter. The numerical results show that the CF-ROM is more efficient than and as accurate as state-of-the-art ROM closure models.Ph. D.
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Bertram, Anna Verfasser], and Ralf [Akademischer Betreuer] [Zimmermann. "Data-driven variable-fidelity reduced order modeling for efficient vehicle shape optimization / Anna Bertram ; Betreuer: Ralf Zimmermann." Braunschweig : Technische Universität Braunschweig, 2018. http://d-nb.info/1175392154/34.
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Bertram, Anna [Verfasser], and Ralf [Akademischer Betreuer] Zimmermann. "Data-driven variable-fidelity reduced order modeling for efficient vehicle shape optimization / Anna Bertram ; Betreuer: Ralf Zimmermann." Braunschweig : Technische Universität Braunschweig, 2018. http://d-nb.info/1175392154/34.
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D'Alessio, Giuseppe. "Data-driven models for reacting flows simulations: reduced-order modelling, chemistry acceleration and analysis of high-fidelity data." Doctoral thesis, Universite Libre de Bruxelles, 2021. https://dipot.ulb.ac.be/dspace/bitstream/2013/328064/5/contratGA.pdf.
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Combustion science must necessarily go through a deep process of innovation, as only improving the energy efficiency and the fuel flexibility it will be possible to mitigate the impact of the anthropogenic activities on the climate and the environment. Because of the strong relation that is observed in chemically reacting flows between the fluid-dynamic conditions and the chemical kinetics, the use of Computational Fluid Dynamics (CFD) simulations with detailed kinetic mechanisms represents the best tool to optimize and develop novel combustion systems. In fact, while the CFD provides for the possibility to retrieve information that cannot be extracted by using experimental means (such as the turbulence-chemistry interaction and the local straining rates) and it avoids the costs associated to the scale-up process from laboratory scale experiments, the use of detailed kinetic mechanisms offers the possibility to correctly describe process conditions which are relevant from an industrial point of view (i.e. in which the chemical and mixing time scales are comparable), as well as to predict the formation of complex chemical species, such as the pollutants. Nevertheless, the use of detailed kinetic mechanisms in numerical simulations adds a considerable number of differential equations to be solved (because of the large number of species which are taken into account), and therefore increases the computational complexity of the CFD model. Thus, Machine Learning (ML) algorithms and Reduced-Order Models (ROMs) can be effectively included in the numerical description of chemically reacting flows. In fact, they can be used either to reduce the computational cost associated to the large number of equations in CFD simulations carried out with detailed chemistry, or to leverage the detailed information which can be found in massive, high-fidelity, data obtained from Direct Numerical Simulations (DNS), for model development and validation. In this Thesis, unsupervised and supervised learning algorithms were employed to design a novel adaptive-chemistry approach: the Sample-Partitioning Adaptive Reduced Chemistry (SPARC). This framework can be used to reduce the computational effort required by detailed CFD simulations thanks to a kinetic reduction accomplished in light of the local conditions of the thermochemical field. Several machine-learning algorithms, such as the Principal Component Analysis (PCA), the Local Principal Component Analysis (LPCA), and Artificial Neural Networks (ANNs) were coupled with the Direct Relation Graph with Error Propagation (DRGEP), a graph-based tool for the automatic reduction of kinetic mechanisms. The aforementioned algorithms were compared to achieve the optimal formulation of the adaptive approach, such that the best performances, in terms of accuracy and computational speed-up with respect to the CFD simulation carried out with detailed kinetics, could be obtained. Finally, PCA-based algorithms were proposed and tested to perform feature extraction and local feature selection from high-fidelity data, which were obtained by means of a DNS of a n-heptane jet reacting in air. The PCA, as well as two formulations of LPCA, and the Procrustes analysis were employed and compared with the aim to extract the main features of the turbulent reacting jet in an unsupervised fashion (i.e. to perform data mining tasks), as well as to aid the formulation of local optimized ROMs. All the codes employed to perform the unsupervised and supervised machine learning tasks in the current work were also included in an open-source Python framework, called OpenMORe, designed to perform reduction, clustering and data analysis, and specifically conceived for reacting flows. In fact, although many open-source Python software are already available, they often cannot be adapted to the user’s specific needs, unlike OpenMORe. In addition, many features such as the PCA-based clustering algorithm, or the local feature selection via PCA, are not yet available on any commercial or open-source software, to the best of the author’s knowledge.Doctorat en Sciences de l'ingénieur et technologie
This thesis is submitted to the Université Libre de Bruxelles (ULB) and to the Politecnico di Milano for the degree of philosophy doctor. This doctoral work has been performed at the Université Libre de Bruxelles, École polytechnique de Bruxelles, Aero-Thermo-Mechanics Laboratory, Bruxelles, Belgium with Professor Alessandro Parente and at the Politecnico di Milano, CRECK Modelling Lab, Department of Chemistry, Materials and Chemical Engineering, Milan, Italy with Professor Alberto Cuoci.
info:eu-repo/semantics/nonPublished
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Ghosh, Rajat. "Transient reduced-order convective heat transfer modeling for a data center." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50380.
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A measurement-based reduced-order heat transfer modeling framework is developed to optimize cooling costs of dynamic and virtualized data centers. The reduced-order model is based on a proper orthogonal decomposition-based model order reduction technique. For data center heat transfer modeling, the framework simulates air temperatures and CPU temperatures as a parametric response surface with different cooling infrastructure design variables as the input parameters. The parametric framework enables an efficient design optimization tool and is used to solve several important problems related to energy-efficient thermal design of data centers.
The first of these problems is about determining optimal response time during emergencies such as power outages in data centers. To solve this problem, transient air temperatures are modeled with time as a parameter. This parametric prediction framework is useful as a near-real-time thermal prognostic tool.
The second problem pertains to reducing temperature monitoring cost in data centers. To solve this problem, transient air temperatures are modeled with spatial location as the parameter. This parametric model improves spatial resolution of measured temperature data and thereby reduces sensor requisition for transient temperature monitoring in data centers.
The third problem is related to determining optimal cooling set points in response to dynamically-evolving heat loads in a data center. To solve this problem, transient air temperatures are modeled with heat load and time as the parameters. This modeling framework is particularly suitable for life-cycle design of data center cooling infrastructure.
The last problem is related to determining optimal cooling set points in response to dynamically-evolving computing workload in a virtualized data center. To solve this problem, transient CPU temperatures under a given computing load profile are modeled with cooling resource set-points as the parameters.
Books on the topic "Data-Driven reduced order modeling"
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Quarteroni, Alfio, and Gianluigi Rozza. Reduced Order Methods for Modeling and Computational Reduction. Springer London, Limited, 2014.
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Quarteroni, Alfio, and Gianluigi Rozza. Reduced Order Methods for Modeling and Computational Reduction. Springer International Publishing AG, 2016.
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Reduced Order Methods for Modeling and Computational Reduction. Springer, 2014.
Find full textBook chapters on the topic "Data-Driven reduced order modeling"
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Zdybał, K., M. R. Malik, A. Coussement, J. C. Sutherland, and A. Parente. "Reduced-Order Modeling of Reacting Flows Using Data-Driven Approaches." In Lecture Notes in Energy, 245–78. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-16248-0_9.
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AbstractData-driven modeling of complex dynamical systems is becoming increasingly popular across various domains of science and engineering. This is thanks to advances in numerical computing, which provides high fidelity data, and to algorithm development in data science and machine learning. Simulations of multicomponent reacting flows can particularly profit from data-based reduced-order modeling (ROM). The original system of coupled partial differential equations that describes a reacting flow is often large due to high number of chemical species involved. While the datasets from reacting flow simulation have high state-space dimensionality, they also exhibit attracting low-dimensional manifolds (LDMs). Data-driven approaches can be used to obtain and parameterize these LDMs. Evolving the reacting system using a smaller number of parameters can yield substantial model reduction and savings in computational cost. In this chapter, we review recent advances in ROM of turbulent reacting flows. We demonstrate the entire ROM workflow with a particular focus on obtaining the training datasets and data science and machine learning techniques such as dimensionality reduction and nonlinear regression. We present recent results from ROM-based simulations of experimentally measured Sandia flames D and F. We also delineate a few remaining challenges and possible future directions to address them. This chapter is accompanied by illustrative examples using the recently developed Python software, PCAfold. The software can be used to obtain, analyze and improve low-dimensional data representations. The examples provided herein can be helpful to students and researchers learning to apply dimensionality reduction, manifold approaches and nonlinear regression to their problems. The Jupyter notebook with the examples shown in this chapter can be found on GitHub at https://github.com/kamilazdybal/ROM-of-reacting-flows-Springer.
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Grinberg, Leopold, Mingge Deng, George Em Karniadakis, and Alexander Yakhot. "Window Proper Orthogonal Decomposition: Application to Continuum and Atomistic Data." In Reduced Order Methods for Modeling and Computational Reduction, 275–303. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02090-7_10.
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Samadiani, Emad. "Reduced Order Modeling Based Energy Efficient and Adaptable Design." In Energy Efficient Thermal Management of Data Centers, 447–96. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-7124-1_10.
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Cangellaris, Andreas C., and Mustafa Celik. "Reduced-Order Electromagnetic Modeling for Design-Driven Simulations of Complex Integrated Electronic Systems." In ICASE/LaRC Interdisciplinary Series in Science and Engineering, 126–54. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5584-7_6.
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Aumann, Quirin, Peter Benner, Jens Saak, and Julia Vettermann. "Model Order Reduction Strategies for the Computation of Compact Machine Tool Models." In Lecture Notes in Production Engineering, 132–45. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-34486-2_10.
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AbstractThe deviation of the tool center point (TCP) of a machine tool from its desired location needs to be assessed correctly to ensure an accurate and safe operation of the machine. A major source of TCP deviation are thermal loads, which are constantly changing during operation. Numerical simulation models help predicting these loads, but are typically large and expensive to solve. Especially in (real-time feedback) control settings, but also to ensure an efficient design phase of machine tools, it is inevitable to use compact reduced-order surrogate models which approximate the behavior of the original system but are much less computationally expensive to evaluate. Model order reduction (MOR) methods generate computationally efficient surrogates. Classic intrusive methods require explicit access to the assembled system matrices. However, commercial software packages, which are typically used for the design of machine tools, do not always allow an unrestricted access to the required matrices. Non-intrusive data-driven methods compute surrogates requiring only input and output data of a dynamical system and are therefore independent of the discretization method. We evaluate the performance of such data-driven approaches to compute cheap-to-evaluate surrogate models of machine tools and compare their efficacy to intrusive MOR strategies. A focus is put on modeling the machine tool via individual substructures, which can be reduced independently of each other.
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Jaiman, Rajeev, Guojun Li, and Amir Chizfahm. "Data-Driven Reduced Order Models." In Mechanics of Flow-Induced Vibration, 433–77. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8578-2_8.
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Chen, Nan. "Data-Driven Low-Order Stochastic Models." In Stochastic Methods for Modeling and Predicting Complex Dynamical Systems, 99–118. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22249-8_7.
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Masoumi-Verki, Shahin, Fariborz Haghighat, and Ursula Eicker. "Data-Driven Reduced-Order Model for Urban Airflow Prediction." In Proceedings of the 5th International Conference on Building Energy and Environment, 3039–47. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9822-5_324.
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Liu, Wing Kam, Zhengtao Gan, and Mark Fleming. "Knowledge-Driven Dimension Reduction and Reduced Order Surrogate Models." In Mechanistic Data Science for STEM Education and Applications, 131–70. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87832-0_5.
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Sledge, Isaac J., Liqian Peng, and Kamran Mohseni. "An Empirical Reduced Modeling Approach for Mobile, Distributed Sensor Platform Networks." In Dynamic Data-Driven Environmental Systems Science, 195–204. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25138-7_18.
Full textConference papers on the topic "Data-Driven reduced order modeling"
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Riva, Stefano, Sophie Deanesi, Carolina Introini, Stefano Lorenzi, Antonio Cammi, and Lorenzo Loi. "Neutron Flux Reconstruction from Out-Core Sparse Measurements Using Data-Driven Reduced Order Modelling." In International Conference on Physics of Reactors (PHYSOR 2024), 1632–41. Illinois: American Nuclear Society, 2024. http://dx.doi.org/10.13182/physor24-43444.
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Wang, Hong, Xipeng Guo, Chenn Zhou, Bill King, and Judy Li. "Reduced Order Modeling via CFD Simulation Data for Inclusion Removal in Steel Refining Ladle." In 2024 12th International Conference on Control, Mechatronics and Automation (ICCMA), 432–37. IEEE, 2024. https://doi.org/10.1109/iccma63715.2024.10843944.
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Xiao, Jian, Ning Liu, Jim Lua, Caleb Saathoff, and Waruna p. Seneviratne. "Data-Driven and Reduced-Order Modeling of Composite Drilling." In AIAA Scitech 2020 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2020. http://dx.doi.org/10.2514/6.2020-1859.
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Liao, J., J. Spring, and C. Worrell. "Data-Driven Safety Margin Management Using Reduced Order Modeling." In Tranactions - 2019 Winter Meeting. AMNS, 2019. http://dx.doi.org/10.13182/t30732.
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Hines Chaves, D., and P. Bekemeyer. "Data-Driven Reduced Order Modeling for Aerodynamic Flow Predictions." In 8th European Congress on Computational Methods in Applied Sciences and Engineering. CIMNE, 2022. http://dx.doi.org/10.23967/eccomas.2022.077.
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Carloni, Ana C., and João Luiz F. Azevedo. "Data-Driven Reduced-Order Modeling Techniques for Aeroelastic Analyses." In AIAA SCITECH 2025 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2025. https://doi.org/10.2514/6.2025-0670.
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Farcas, Ionut, Ramakanth Munipalli, and Karen E. Willcox. "On filtering in non-intrusive data-driven reduced-order modeling." In AIAA AVIATION 2022 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2022. http://dx.doi.org/10.2514/6.2022-3487.
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Newton, Rachel, Zhe Du, Laura Balzano, and Peter Seiler. "Manifold Optimization for Data Driven Reduced-Order Modeling*." In 2023 59th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2023. http://dx.doi.org/10.1109/allerton58177.2023.10313500.
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Simac, Joshua, Andrew Kaminsky, Jinhyuk Kim, and Yi Wang. "Extending SHARPy to Support Data-Driven Aeroelastic Reduced-Order Modeling." In AIAA SCITECH 2025 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2025. https://doi.org/10.2514/6.2025-0883.
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Kadeethum, Teeratorn, and Hongkyu Yoon. "Progressive reduced order modeling: a road to redemption for data-driven modeling." In Proposed for presentation at the AGU Fall Meeting 2022 in ,. US DOE, 2022. http://dx.doi.org/10.2172/2006238.
Full textReports on the topic "Data-Driven reduced order modeling"
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Ali, Naseem. Thermally (Un-) Stratified Wind Plants: Stochastic and Data-Driven Reduced Order Descriptions/Modeling. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6518.
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Parish, Eric. Multiscale modeling high-order methods and data-driven modeling. Office of Scientific and Technical Information (OSTI), October 2020. http://dx.doi.org/10.2172/1673827.
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Russo, David, Daniel M. Tartakovsky, and Shlomo P. Neuman. Development of Predictive Tools for Contaminant Transport through Variably-Saturated Heterogeneous Composite Porous Formations. United States Department of Agriculture, December 2012. http://dx.doi.org/10.32747/2012.7592658.bard.
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The vadose (unsaturated) zone forms a major hydrologic link between the ground surface and underlying aquifers. To understand properly its role in protecting groundwater from near surface sources of contamination, one must be able to analyze quantitatively water flow and contaminant transport in variably saturated subsurface environments that are highly heterogeneous, often consisting of multiple geologic units and/or high and/or low permeability inclusions. The specific objectives of this research were: (i) to develop efficient and accurate tools for probabilistic delineation of dominant geologic features comprising the vadose zone; (ii) to develop a complementary set of data analysis tools for discerning the fractal properties of hydraulic and transport parameters of highly heterogeneous vadose zone; (iii) to develop and test the associated computational methods for probabilistic analysis of flow and transport in highly heterogeneous subsurface environments; and (iv) to apply the computational framework to design an “optimal” observation network for monitoring and forecasting the fate and migration of contaminant plumes originating from agricultural activities. During the course of the project, we modified the third objective to include additional computational method, based on the notion that the heterogeneous formation can be considered as a mixture of populations of differing spatial structures. Regarding uncertainly analysis, going beyond approaches based on mean and variance of system states, we succeeded to develop probability density function (PDF) solutions enabling one to evaluate probabilities of rare events, required for probabilistic risk assessment. In addition, we developed reduced complexity models for the probabilistic forecasting of infiltration rates in heterogeneous soils during surface runoff and/or flooding events Regarding flow and transport in variably saturated, spatially heterogeneous formations associated with fine- and coarse-textured embedded soils (FTES- and CTES-formations, respectively).We succeeded to develop first-order and numerical frameworks for flow and transport in three-dimensional (3-D), variably saturated, bimodal, heterogeneous formations, with single and dual porosity, respectively. Regarding the sampling problem defined as, how many sampling points are needed, and where to locate them spatially in the horizontal x₂x₃ plane of the field. Based on our computational framework, we succeeded to develop and demonstrate a methdology that might improve considerably our ability to describe quntitaively the response of complicated 3-D flow systems. The results of the project are of theoretical and practical importance; they provided a rigorous framework to modeling water flow and solute transport in a realistic, highly heterogeneous, composite flow system with uncertain properties under-specified by data. Specifically, they: (i) enhanced fundamental understanding of the basic mechanisms of field-scale flow and transport in near-surface geological formations under realistic flow scenarios, (ii) provided a means to assess the ability of existing flow and transport models to handle realistic flow conditions, and (iii) provided a means to assess quantitatively the threats posed to groundwater by contamination from agricultural sources.
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Heitman, Joshua L., Alon Ben-Gal, Thomas J. Sauer, Nurit Agam, and John Havlin. Separating Components of Evapotranspiration to Improve Efficiency in Vineyard Water Management. United States Department of Agriculture, March 2014. http://dx.doi.org/10.32747/2014.7594386.bard.
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Vineyards are found on six of seven continents, producing a crop of high economic value with much historic and cultural significance. Because of the wide range of conditions under which grapes are grown, management approaches are highly varied and must be adapted to local climatic constraints. Research has been conducted in the traditionally prominent grape growing regions of Europe, Australia, and the western USA, but far less information is available to guide production under more extreme growing conditions. The overarching goal of this project was to improve understanding of vineyard water management related to the critical inter-row zone. Experiments were conducted in moist temperate (North Carolina, USA) and arid (Negev, Israel) regions in order to address inter-row water use under high and low water availability conditions. Specific objectives were to: i) calibrate and verify a modeling technique to identify components of evapotranspiration (ET) in temperate and semiarid vineyard systems, ii) evaluate and refine strategies for excess water removal in vineyards for moist temperate regions of the Southeastern USA, and iii) evaluate and refine strategies for water conservation in vineyards for semi-arid regions of Israel. Several new measurement and modeling techniques were adapted and assessed in order to partition ET between favorable transpiration by the grapes and potentially detrimental water use within the vineyard inter-row. A micro Bowen ratio measurement system was developed to quantify ET from inter-rows. The approach was successful at the NC site, providing strong correlation with standard measurement approaches and adding capability for continuous, non-destructive measurement within a relatively small footprint. The environmental conditions in the Negev site were found to limit the applicability of the technique. Technical issues are yet to be solved to make this technique sufficiently robust. The HYDRUS 2D/3D modeling package was also adapted using data obtained in a series of intense field campaigns at the Negev site. The adapted model was able to account for spatial variation in surface boundary conditions, created by diurnal canopy shading, in order to accurately calculate the contribution of interrow evaporation (E) as a component of system ET. Experiments evaluated common practices in the southeastern USA: inter-row cover crops purported to reduce water availability and thereby favorably reduce grapevine vegetative growth; and southern Israel: drip irrigation applied to produce a high value crop with maximum water use efficiency. Results from the NC site indicated that water use by the cover crop contributed a significant portion of vineyard ET (up to 93% in May), but that with ample rainfall typical to the region, cover crop water use did little to limit water availability for the grape vines. A potential consequence, however, was elevated below canopy humidity owing to the increased inter-row evapotranspiration associated with the cover crops. This creates increased potential for fungal disease occurrence, which is a common problem in the region. Analysis from the Negev site reveals that, on average, E accounts for about10% of the total vineyard ET in an isolated dripirrigated vineyard. The proportion of ET contributed by E increased from May until just before harvest in July, which could be explained primarily by changes in weather conditions. While non-productive water loss as E is relatively small, experiments indicate that further improvements in irrigation efficiency may be possible by considering diurnal shading effects on below canopy potential ET. Overall, research provided both scientific and practical outcomes including new measurement and modeling techniques, and new insights for humid and arid vineyard systems. Research techniques developed through the project will be useful for other agricultural systems, and the successful synergistic cooperation amongst the research team offers opportunity for future collaboration.
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Tarko, Andrew P., Mario A. Romero, Vamsi Krishna Bandaru, and Xueqian Shi. Guidelines for Evaluating Safety Using Traffic Encounters: Proactive Crash Estimation on Roadways with Conventional and Autonomous Vehicle Scenarios. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317587.
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With the expected arrival of autonomous vehicles, and the ever-increasing levels of automation in today’s human driven vehicles, road safety is changing at a rapid pace. This project aimed to address the need for an efficient and rapid method of safety evaluation and countermeasure identification via traffic encounters, specifically traffic conflicts that are considered useful surrogates of crashes. Recent research-delivered methods for estimating crash frequencies based on these events were observed in the field. In this project we developed a method for observing traffic encounters with two LiDAR-based traffic monitoring units, called TScan, which were recently developed in JTRP-funded projects SPR-3831 and SPR-4102. The TScan units were deployed in the field for several hours to collect data at selected intersections. These large data sets were used to improve object detection and tracking algorithms in order to better assist in detecting traffic encounters and conflicts. Consequently, the software of the TScan trailer-based units was improved and the results generated with the upgraded system include a list of potential encounters for further analysis. We developed an engineering application for analyzing the trajectories of vehicles involved in the pre-selected encounters to identify final traffic encounters and conflicts. Another module of the engineering application visualized the traffic encounters and conflicts to inspect the spatial patterns of these events and to estimate the number of crashes for the observation period. Furthermore, a significant modeling effort resulted in a method of producing factors that expand the conflict-based crash estimates in short observation periods to an entire year. This report provides guidelines for traffic encounters and conflicts, the user manuals for setting up and operating the TScan research unit. and manuals for the engineering applications mentioned above.
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Jalkanen, Jukka-Pekka, Erik Fridell, Jaakko Kukkonen, Jana Moldanova, Leonidas Ntziachristos, Achilleas Grigoriadis, Maria Moustaka, et al. Environmental impacts of exhaust gas cleaning systems in the Baltic Sea, North Sea, and the Mediterranean Sea area. Finnish Meteorological Institute, 2024. http://dx.doi.org/10.35614/isbn.9789523361898.
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Description: Shipping is responsible for a range of different pressures affecting air quality, climate, and the marine environment. Most social and economic analyses of shipping have focused on air pollution assessment and how shipping may impact climate change and human health. This risks that policies may be biased towards air pollution and climate change, whilst impacts on the marine environment are not as well known. One example is the sulfur regulation introduced in January 2020, which requires shipowners to use a compliant fuel with a sulfur content of 0.5% (0.1% in SECA regions) or use alternative compliance options (Exhaust Gas Cleaning Systems, EGCS) that are effective in reducing sulfur oxide (SOx) emissions to the atmosphere. The EGCS cleaning process results in large volumes of discharged water that includes a wide range of contaminants. Although regulations target SOx removal, other pollutants such as polycyclic aromatic hydrocarbons (PAHs), metals and combustion particles are removed from the exhaust to the wash water and subsequently discharged to the marine environment. Based on dilution series of the Whole Effluent Testing (WET), the impact of the EGCS effluent on marine invertebrate species and on phytoplankton was found to vary between taxonomic groups, and between different stages of the invertebrate life cycle. Invertebrates were more affected than phytoplankton, and the most sensitive endpoint detected in the present project was the fertilisation of sea urchin eggs, which were negatively affected at a sample dilution of 1 : 1,000,000. Dilutions of 1: 100,000 were harmful to early development of several of the tested species, including mussels, polychaetes, and crustaceans. The observed effects at these low concentrations of EGCS effluent were reduced egg production, and deformations and abnormal development of the larvae of the species. The ecotoxicological data produced in the EMERGE project were used to derive Predicted No Effect Concentration values. Corresponding modelling studies revealed that the EGCS effluent can be considered as a single entity for 2-10 days from the time of discharge, depending on the environmental conditions like sea currents, winds, and temperature. Area 10-30 km outside the shipping lanes will be prone to contaminant concentrations corresponding to 1 : 1,000,000 dilution which was deemed harmful for most sensitive endpoints of WET experiments. Studies for the Saronikos Gulf (Aegean Sea) revealed that the EGCS effluent dilution rate exceeded the 1 : 1,000,000 ratio 70% of the time at a distance of about 10 km from the port. This was also observed for 15% of the time within a band of 10 km wide along the shipping lane extending 500 km away from the port of Piraeus. When mortality of adult specimens of one of the species (copepod Acartia tonsa) was used as an endpoint it was found to be 3-4 orders of magnitude less sensitive to EGCS effluent than early life stage endpoints like fertilisation of eggs and larval development. Mortality of Acartia tonsa is commonly used in standard protocols for ecotoxicological studies, but our data hence shows that it seriously underestimates the ecologically relevant toxicity of the effluent. The same is true for two other commonly used and recommended endpoints, phytoplankton growth and inhibition of bioluminescence in marine bacteria. Significant toxic effects were reached only after addition of 20-40% effluent. A marine environmental risk assessment was performed for the Öresund region for baseline year 2018, where Predicted Environmental Concentrations (PECs) of open loop effluent discharge water were compared to the PNEC value. The results showed modelled concentrations of open loop effluent in large areas to be two to three orders of magnitude higher than the derived PNEC value, yielding a Risk Characterisation Ratio of 500-5000, which indicates significant environmental risk. Further, it should be noted that between 2018-2022 the number of EGCS vessels more than quadrupled in the area from 178 to 781. In this work, the EGCS discharges of the fleet in the Baltic Sea, North Sea, the English Channel, and the Mediterranean Sea area were studied in detail. The assessments of impacts described in this document were performed using a baseline year 2018 and future scenarios. These were made for the year 2050, based on different projections of transport volumes, also considering the fuel efficiency requirements and ship size developments. From the eight scenarios developed, two extremes were chosen for impact studies which illustrate the differences between a very high EGCS usage and a future without the need for EGCS while still compliant to IMO initial GHG strategy. The scenario without EGCS leads to 50% reduction of GHG emissions using low sulfur fuels, LNG, and methanol. For the high EGCS adoption scenario in 2050, about a third of the fleet sailing the studied sea areas would use EGCS and effluent discharge volumes would be increased tenfold for the Baltic Sea and hundredfold for the Mediterranean Sea when compared to 2018 baseline discharges. Some of the tested species, mainly the copepods, have a central position in pelagic food webs as they feed on phytoplankton and are themselves the main staple food for most fish larvae and for some species of adult fish, e.g., herring. The direct effect of the EGSE on invertebrates will therefore have an important indirect effect on the fish feeding on them. Effects are greatest in and near shipping lanes. Many important shipping lanes run close to shore and archipelago areas, and this also puts the sensitive shallow water coastal ecosystems at risk. It should be noted that no studies on sub-lethal effects of early 19 life stages in fish were included in the EMERGE project, nor are there any available data on this in the scientific literature. The direct toxic effects on fish at the expected concentrations of EGCS effluent are therefore largely unknown. According to the regional modelling studies, some of the contaminants will end up in sediments along the coastlines and archipelagos. The documentation of the complex chemical composition of EGCS effluent is in sharp contrast to the present legislation on threshold levels for content in EGCS effluent discharged from ships, which includes but a few PAHs, pH, and turbidity. Traditional assessments of PAHs in environmental and marine samples focus only on the U.S. Environmental Protection Agency (EPA) list of 16 priority PAHs, which includes only parent PAHs. Considering the complex PAHs assemblages and the importance of other related compounds, it is important to extend the EPA list to include alkyl-PAHs to obtain a representative monitoring of EGCS effluent and to assess the impact of its discharges into the marine environment. An economic evaluation of the installation and operational costs of EGCS was conducted noting the historical fuel price differences of high and low sulfur fuels. Equipment types, installation dates and annual fuel consumption from global simulations indicated that 51% of the global EGCS fleet had already reached break-even by the end of 2022, resulting in a summarised profit of 4.7 billion €2019. Within five years after the initial installation, more than 95% of the ships with open loop EGCS reach break-even. The pollutant loads from shipping come both through atmospheric deposition and direct discharges. This underlines the need of minimising the release of contaminants by using fuels which reduce the air emissions of harmful components without creating new pollution loads through discharges. Continued use of EGCS and high sulfur fossil fuels will delay the transition to more sustainable options. The investments made on EGCS enable ships to continue using fossil fuels instead of transitioning away from them as soon as possible as agreed in the 2023 Dubai Climate Change conference. Continued carriage of residual fuels also increases the risk of dire environmental consequences whenever accidental releases of oil to the sea occur.
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Wu, Yingjie, Selim Gunay, and Khalid Mosalam. Hybrid Simulations for the Seismic Evaluation of Resilient Highway Bridge Systems. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, November 2020. http://dx.doi.org/10.55461/ytgv8834.
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Bridges often serve as key links in local and national transportation networks. Bridge closures can result in severe costs, not only in the form of repair or replacement, but also in the form of economic losses related to medium- and long-term interruption of businesses and disruption to surrounding communities. In addition, continuous functionality of bridges is very important after any seismic event for emergency response and recovery purposes. Considering the importance of these structures, the associated structural design philosophy is shifting from collapse prevention to maintaining functionality in the aftermath of moderate to strong earthquakes, referred to as “resiliency” in earthquake engineering research. Moreover, the associated construction philosophy is being modernized with the utilization of accelerated bridge construction (ABC) techniques, which strive to reduce the impact of construction on traffic, society, economy and on-site safety. This report presents two bridge systems that target the aforementioned issues. A study that combined numerical and experimental research was undertaken to characterize the seismic performance of these bridge systems. The first part of the study focuses on the structural system-level response of highway bridges that incorporate a class of innovative connecting devices called the “V-connector,”, which can be used to connect two components in a structural system, e.g., the column and the bridge deck, or the column and its foundation. This device, designed by ACII, Inc., results in an isolation surface at the connection plane via a connector rod placed in a V-shaped tube that is embedded into the concrete. Energy dissipation is provided by friction between a special washer located around the V-shaped tube and a top plate. Because of the period elongation due to the isolation layer and the limited amount of force transferred by the relatively flexible connector rod, bridge columns are protected from experiencing damage, thus leading to improved seismic behavior. The V-connector system also facilitates the ABC by allowing on-site assembly of prefabricated structural parts including those of the V-connector. A single-column, two-span highway bridge located in Northern California was used for the proof-of-concept of the proposed V-connector protective system. The V-connector was designed to result in an elastic bridge response based on nonlinear dynamic analyses of the bridge model with the V-connector. Accordingly, a one-third scale V-connector was fabricated based on a set of selected design parameters. A quasi-static cyclic test was first conducted to characterize the force-displacement relationship of the V-connector, followed by a hybrid simulation (HS) test in the longitudinal direction of the bridge to verify the intended linear elastic response of the bridge system. In the HS test, all bridge components were analytically modeled except for the V-connector, which was simulated as the experimental substructure in a specially designed and constructed test setup. Linear elastic bridge response was confirmed according to the HS results. The response of the bridge with the V-connector was compared against that of the as-built bridge without the V-connector, which experienced significant column damage. These results justified the effectiveness of this innovative device. The second part of the study presents the HS test conducted on a one-third scale two-column bridge bent with self-centering columns (broadly defined as “resilient columns” in this study) to reduce (or ultimately eliminate) any residual drifts. The comparison of the HS test with a previously conducted shaking table test on an identical bridge bent is one of the highlights of this study. The concept of resiliency was incorporated in the design of the bridge bent columns characterized by a well-balanced combination of self-centering, rocking, and energy-dissipating mechanisms. This combination is expected to lead to minimum damage and low levels of residual drifts. The ABC is achieved by utilizing precast columns and end members (cap beam and foundation) through an innovative socket connection. In order to conduct the HS test, a new hybrid simulation system (HSS) was developed, utilizing commonly available software and hardware components in most structural laboratories including: a computational platform using Matlab/Simulink [MathWorks 2015], an interface hardware/software platform dSPACE [2017], and MTS controllers and data acquisition (DAQ) system for the utilized actuators and sensors. Proper operation of the HSS was verified using a trial run without the test specimen before the actual HS test. In the conducted HS test, the two-column bridge bent was simulated as the experimental substructure while modeling the horizontal and vertical inertia masses and corresponding mass proportional damping in the computer. The same ground motions from the shaking table test, consisting of one horizontal component and the vertical component, were applied as input excitations to the equations of motion in the HS. Good matching was obtained between the shaking table and the HS test results, demonstrating the appropriateness of the defined governing equations of motion and the employed damping model, in addition to the reliability of the developed HSS with minimum simulation errors. The small residual drifts and the minimum level of structural damage at large peak drift levels demonstrated the superior seismic response of the innovative design of the bridge bent with self-centering columns. The reliability of the developed HS approach motivated performing a follow-up HS study focusing on the transverse direction of the bridge, where the entire two-span bridge deck and its abutments represented the computational substructure, while the two-column bridge bent was the physical substructure. This investigation was effective in shedding light on the system-level performance of the entire bridge system that incorporated innovative bridge bent design beyond what can be achieved via shaking table tests, which are usually limited by large-scale bridge system testing capacities.