Academic literature on the topic 'Hybrid Parallel Smoother'
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Journal articles on the topic "Hybrid Parallel Smoother"
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Fu, Xiaoling, Qi Zhang, Chao Wang, and Jiyun Tang. "Torque Coordination Control of Hybrid Electric Vehicles Based on Hybrid Dynamical System Theory." Electronics 8, no. 6 (June 23, 2019): 712. http://dx.doi.org/10.3390/electronics8060712.
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In order to reduce the vibration caused by mode switching of hybrid electric vehicles (HEVs) and achieve smooth mode switching, the hybrid input and output automation (HIOA) model of power control system of a parallel HEV is established based on the theory of hybrid dynamical system (HDS). Taking the switching from electric drive mode to hybrid drive mode for example, the torque coordination control is considered, and the performance is compared with the method without the torque coordination by using a rule-based control strategy. The simulation results in AVL Cruise show that, on the premise of ensuring the fuel economy and the emission, the mode switching process becomes smoother with smaller torque fluctuation and better driving comfort by considering the torque coordination.
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Mandel, J., E. Bergou, S. Gürol, S. Gratton, and I. Kasanický. "Hybrid Levenberg–Marquardt and weak-constraint ensemble Kalman smoother method." Nonlinear Processes in Geophysics 23, no. 2 (March 11, 2016): 59–73. http://dx.doi.org/10.5194/npg-23-59-2016.
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Abstract. The ensemble Kalman smoother (EnKS) is used as a linear least-squares solver in the Gauss–Newton method for the large nonlinear least-squares system in incremental 4DVAR. The ensemble approach is naturally parallel over the ensemble members and no tangent or adjoint operators are needed. Furthermore, adding a regularization term results in replacing the Gauss–Newton method, which may diverge, by the Levenberg–Marquardt method, which is known to be convergent. The regularization is implemented efficiently as an additional observation in the EnKS. The method is illustrated on the Lorenz 63 model and a two-level quasi-geostrophic model.
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Mandel, J., E. Bergou, S. Gürol, and S. Gratton. "Hybrid Levenberg–Marquardt and weak constraint ensemble Kalman smoother method." Nonlinear Processes in Geophysics Discussions 2, no. 3 (May 26, 2015): 865–902. http://dx.doi.org/10.5194/npgd-2-865-2015.
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Abstract. We propose to use the ensemble Kalman smoother (EnKS) as the linear least squares solver in the Gauss–Newton method for the large nonlinear least squares in incremental 4DVAR. The ensemble approach is naturally parallel over the ensemble members and no tangent or adjoint operators are needed. Further, adding a regularization term results in replacing the Gauss–Newton method, which may diverge, by the Levenberg–Marquardt method, which is known to be convergent. The regularization is implemented efficiently as an additional observation in the EnKS. The method is illustrated on the Lorenz 63 and the two-level quasi-geostrophic model problems.
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Vu, Trieu Minh, Reza Moezzi, Jindrich Cyrus, Jaroslav Hlava, and Michal Petru. "Parallel Hybrid Electric Vehicle Modelling and Model Predictive Control." Applied Sciences 11, no. 22 (November 12, 2021): 10668. http://dx.doi.org/10.3390/app112210668.
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This paper presents the modelling and calculations for a hybrid electric vehicle (HEV) in parallel configuration, including a main electrical driving motor (EM), an internal combustion engine (ICE), and a starter/generator motor. The modelling equations of the HEV include vehicle acceleration and jerk, so that simulations can investigate the vehicle drivability and comfortability with different control parameters. A model predictive control (MPC) scheme with softened constraints for this HEV is developed. The new MPC with softened constraints shows its superiority over the MPC with hard constraints as it provides a faster setpoint tracking and smoother clutch engagement. The conversion of some hard constraints into softened constraints can improve the MPC stability and robustness. The MPC with softened constraints can maintain the system stability, while the MPC with hard constraints becomes unstable if some input constraints lead to the violation of output constraints.
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Kehagias, Dimitris, Michael Grivas, and Grammati Pantziou. "Using a hybrid platform for cluster, NoW and GRID computing." Facta universitatis - series: Electronics and Energetics 18, no. 2 (2005): 205–18. http://dx.doi.org/10.2298/fuee0502205k.
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Clusters, Networks of Workstations (NoW) and Grids offer a new, highly available and cost-effective parallel computing paradigm. Their simplicity, versatility and unlimited power made them a rapidly adopted technology. It seems that they form the new way of computing. Although these platforms are based on the same principles, they differ significantly, needing special attention to their characteristics. Future computer scientists, programmers and analysts have to be well prepared, both about administrative and programming issues, in order to ensure faster and smoother transition. We present the architecture of a dynamic clustering system consisting of Beowulf class clusters and NoW, as well as our experience from constructing and using such a system, as an educational infrastructure for HPC.
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Yang, Y. Y., D. A. Linkens, and M. Mahfouf. "Genetic algorithms and hybrid neural network modelling for aluminium stress—strain prediction." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 217, no. 1 (February 1, 2003): 7–21. http://dx.doi.org/10.1177/095965180321700103.
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This paper addresses the design of genetic algorithms in developing a hybrid neural network model for aluminium alloy flow stress prediction. The hybrid neural network model consists of a parallel grey-box model structure, with the resulting predictions combining the outputs from the constitutive equations and a neural network. Previous work shows that the hybrid neural network model can deliver better model performance than a neural network model or the constitutive equations. However, the level of performance improvement of the hybrid model depends on the quality of the constitutive model used. This motivates the search for a better constitutive model, with genetic algorithms being employed to optimize its parameters. The advantage of genetic algorithms is that they do not require any gradient information nor continuity assumption in searching for the best parameters. A number of genetic optimization schemes, with different coding schemes (such as binary coding and real-value chromosomes) and different genetic operators for selection, crossover and mutation, have been investigated. The real-value coded genetic algorithms converge much more rapidly and are more efficient since there is no need for chromosome encoding and decoding. Compared with previous work, the resulting hybrid model performance has been improved, mainly in the generalization capability and with a simpler neural network structure. Also, the model response surfaces are much smoother and more metallurgically convincing.
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Zhang, S., M. J. Harrison, A. T. Wittenberg, A. Rosati, J. L. Anderson, and V. Balaji. "Initialization of an ENSO Forecast System Using a Parallelized Ensemble Filter." Monthly Weather Review 133, no. 11 (November 1, 2005): 3176–201. http://dx.doi.org/10.1175/mwr3024.1.
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Abstract As a first step toward coupled ocean–atmosphere data assimilation, a parallelized ensemble filter is implemented in a new stochastic hybrid coupled model. The model consists of a global version of the GFDL Modular Ocean Model Version 4 (MOM4), coupled to a statistical atmosphere based on a regression of National Centers for Environmental Prediction (NCEP) reanalysis surface wind stress, heat, and water flux anomalies onto analyzed tropical Pacific SST anomalies from 1979 to 2002. The residual part of the NCEP fluxes not captured by the regression is then treated as stochastic forcing, with different ensemble members feeling the residual fluxes from different years. The model provides a convenient test bed for coupled data assimilation, as well as a prototype for representing uncertainties in the surface forcing. A parallel ensemble adjustment Kalman filter (EAKF) has been designed and implemented in the hybrid model, using a local least squares framework. Comparison experiments demonstrate that the massively parallel processing EAKF (MPPEAKF) produces assimilation results with essentially the same quality as a global sequential analysis. Observed subsurface temperature profiles from expendable bathythermographs (XBTs), Tropical Atmosphere Ocean (TAO) buoys, and Argo floats, along with analyzed SSTs from NCEP, are assimilated into the hybrid model over 1980–2002 using the MPPEAKF. The filtered ensemble of SSTs, ocean heat contents, and thermal structures converge well to the observations, in spite of the imposed stochastic forcings. Several facets of the EAKF algorithm used here have been designed to facilitate comparison to a traditional three-dimensional variational data assimilation (3DVAR) algorithm, for instance, the use of a univariate filter in which observations of temperature only directly impact temperature state variables. Despite these choices that may limit the power of the EAKF, the MPPEAKF solution appears to improve upon an earlier 3DVAR solution, producing a smoother, more physically reasonable analysis that better fits the observational data and produces, to some degree, a self-consistent estimate of analysis uncertainties. Hybrid model ENSO forecasts initialized from the MPPEAKF ensemble mean also appear to outperform those initialized from the 3DVAR analysis. This improvement stems from the EAKF’s utilization of anisotropic background error covariances that may vary in time.
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Yan, Shi Tong. "Hybrid Base and Parallel Hybrid Electric Vehicle Control Strategy." Applied Mechanics and Materials 599-601 (August 2014): 956–59. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.956.
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Automotive hybrid technology development and mechanical, electrical,engine,energy technology is closely related with the development of these areas of technology, the development of hybrid technology also will be extensive. How to optimize the control strategy is the key to achieve hybrid cars low fuel consumption, low emissions targets . In the automotive power and meet other basic technical performance,and cost requirements of the premise,characteristics and operating conditions for the car's various components , control strategies to achieve a reasonable distribution of energy in the motor between the engine and effective,so that the vehicle the highest system efficiency,access to the largest vehicle fuel economy,low emissions and smooth driving performance.
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Kalyankar-Narwade, Supriya, Ramesh Kumar Chidambaram, and Sanjay Patil. "Neural Network- and Fuzzy Control-Based Energy Optimization for the Switching in Parallel Hybrid Two-Wheeler." World Electric Vehicle Journal 12, no. 1 (March 1, 2021): 35. http://dx.doi.org/10.3390/wevj12010035.
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Optimization of a two-wheeler hybrid electric vehicle (HEV) is a typical challenge compared to that for four-wheeler HEVs. Some of the challenges which are particular to two-wheeler HEVs are throttle integration, smooth switching between power sources, add-on weight compensation, efficiency improvisation in traffic, and energy optimization. Two power sources need to be synchronized skillfully for optimum energy utilization. A prominent variant of HEV is that it easily converts conventional scooters into parallel hybrids by “Through-the-Road (TTR)” architecture. This paper focuses on three switching control strategies of HEVs based on the state of charge, fuzzy logic, and neural network. Further, to optimize energy usage, all these control strategies are compared. Energy management control for the TTR model is developed with vehicle parameters in the Simulink environment and simulated using the “World Harmonized Motorcycle Test Cycle” (WMTC) drive cycle. The multivariable input model is presented with a fuzzy rule-based hybrid switching control. A similar system is also modeled with a neural network-based decision control and the observations are tabulated for the fuel economy and energy management. Simulation results show that the neural network-based optimization results in minimal energy consumption among all three hybrid operations.
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Pang, Ming, Yikai Shi, Wendong Wang, and Shun Pang. "Optimal sizing and control of hybrid energy storage system for wind power using hybrid Parallel PSO-GA algorithm." Energy Exploration & Exploitation 37, no. 1 (June 25, 2018): 558–78. http://dx.doi.org/10.1177/0144598718784036.
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This paper proposes a frequency-based method for sizing the hybrid energy storage system in order to smoothen wind power fluctuations. The main goal of the proposed method is to find the power and energy capacities of the hybrid energy storage system that minimizes the total cost per day of all the systems. The energy management strategy used in this paper is designed as a two-level energy distribution scheme: the first level is responsible for setting the output power of hybrid energy storage system, the second level manages the power flow between the battery and supercapacitor. The hybrid parallel particle swarm optimization-genetic algorithm (PSO-GA) optimization algorithm is proposed to solve the control parameters of energy management strategy. In addition, the proposed method uses the piecewise fitting function to describe the lifetime of battery. Obtained results show that the hybrid energy storage system with the proposed energy management strategy is able to offer the best performances for the wind power system in terms of cost and lifetime.
Dissertations / Theses on the topic "Hybrid Parallel Smoother"
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Iyer, Neha Mohan. "Parallel Smoothers for Multigrid Method in Heterogeneous CPU-GPU Environment." Thesis, 2020. https://etd.iisc.ac.in/handle/2005/4423.
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Real-world applications require the solution of large a sparse system of algebraic equations that
arise from the discretization of partial di erential equations with the help of supercomputers.
Modern supercomputers are heterogeneous - each node composed of multi-core CPUs and
many-core GPUs. Porting existing sequential applications speci cally to the GPU architecture
has lead to poor utilization of CPU computing power. In this respect, we develop hybrid
parallel smoothers for the geometric multigrid method which is one of the most e cient
solvers for a system of equations. We study the performance of multigrid method in terms
of total execution time by employing di erent hybrid parallel approaches, viz. accelerating
the smoothing operation using only GPU across all multigrid levels, alternately switching
between GPU and CPU based on the multigrid level and our proposed novel approach of
using combination of GPU and CPU across all multigrid levels. The performance of the hybrid
parallel approaches, implemented using MPI, CUDA and OpenMP, is compared against the
MPI only approach.
In the rst part of the work, we have implemented the hybrid parallel approaches for
the Jacobi and Gauss-Seidel smoothers and tested it to solve the system arising from the
discretization of the Poisson equation. A coloring strategy is developed to color the degrees of
freedom (DOFs) such that the independent set of DOFs are assigned the same color and are
updated in parallel on GPU. We adopted two of the commonly used techniques, CSR Scalar
and CSR Vector, that perform sparse matrix-vector multiplication on GPU, to implement the
smoothing iterations. Further, the strong scaling behavior of the hybrid parallel smoothers is
studied across di erent problem sizes, nite element types, standard and multilevel multigrid.
In the second part of the work, we have implemented the hybrid parallel approaches for
Vanka-type smoother which are typically used to solve the saddle-point problem arising from
the Navier-Stokes equation. We studied the time taken by the two operations viz. assembling
and solving of the local systems in cell and nodal Vanka. We have accelerated the operation of
assembling the local system using the hybrid parallel approaches. A similar coloring strategy
is developed to assign the same color to independent cells or pressure DOFs. The task of
determining the neighbors of each cell or DOF is o oaded to GPU as it is an O(N2) operation.
The operation of solving the assembled local systems is parallelized using OpenMP on CPU.
Similar to the rst part, experiments are performed to study the strong scaling results across
di erent problem sizes, number of OpenMP threads, standard and multilevel multigrid.
The experimental results for the di erent smoothers show that the scaling performance of
the hybrid parallel approaches is bounded by the degree of achievable thread parallelism which
in turn is dependent on the parallel workload per process and the algorithm itself. To improve
the scaling behavior, we propose a combination approach that uses a workload heuristic to
decide the best approach to be applied at each level of multigrid. The combination approach
improves the scaling behavior in addition to resulting in a signi cant speedup under appropriate
workload and number of MPI processes compared to the MPI only approach.
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Ivan, Lucian. "Development of High-order CENO Finite-volume Schemes with Block-based Adaptive Mesh Refinement (AMR)." Thesis, 2011. http://hdl.handle.net/1807/29759.
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A high-order central essentially non-oscillatory (CENO) finite-volume scheme in combination with a block-based adaptive mesh refinement (AMR) algorithm is proposed for solution of hyperbolic and elliptic systems of conservation laws on body- fitted multi-block mesh. The spatial discretization of the hyperbolic (inviscid) terms is based on a hybrid solution reconstruction procedure that combines an unlimited high-order k-exact least-squares
reconstruction technique following from a fixed central stencil with a monotonicity preserving limited piecewise linear reconstruction algorithm. The limited reconstruction is applied to computational cells with under-resolved solution content and the unlimited k-exact reconstruction
procedure is used for cells in which the solution is fully resolved. Switching in the
hybrid procedure is determined by a solution smoothness indicator. The hybrid approach
avoids the complexity associated with other ENO schemes that require reconstruction on
multiple stencils and therefore, would seem very well suited for extension to unstructured meshes. The high-order elliptic (viscous) fluxes are computed based on a k-order accurate average gradient derived from a (k+1)-order accurate reconstruction. A novel h-refinement criterion based on the solution smoothness indicator is used to direct the steady and unsteady refinement of the AMR mesh. The predictive capabilities of the proposed high-order AMR scheme are demonstrated for the Euler and Navier-Stokes equations governing two-dimensional
compressible gaseous flows as well as for advection-diffusion problems characterized
by the full range of Peclet numbers, Pe. The ability of the scheme to accurately represent
solutions with smooth extrema and yet robustly handle under-resolved and/or non-smooth solution content (i.e., shocks and other discontinuities) is shown for a range of problems. Moreover, the ability to perform mesh refinement in regions of smooth but under-resolved and/or non-smooth solution content to achieve the desired resolution is also demonstrated.
Book chapters on the topic "Hybrid Parallel Smoother"
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Iyer, Neha, and Sashikumaar Ganesan. "Parallel Smoothers in Multigrid Method for Heterogeneous CPU-GPU Environment." In Parallel Computing: Technology Trends. IOS Press, 2020. http://dx.doi.org/10.3233/apc200031.
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Modern-day supercomputers are equipped with sophisticated graphics processing units (GPUs) along with high-performance CPUs. Adapting existing algorithms specifically to GPU has resulted in under-utilization of CPU computing power. In this respect, we parallelize Jacobi and successive-over relaxation (SOR), which are used as smoother in multigrid method to maximize the combined utilization of both CPUs and GPUs. We study the performance of multigrid method in terms of total execution time by employing different hybrid parallel approaches, viz. accelerating the smoothing operation using only GPU across all multigrid levels, alternately switching between GPU and CPU based on the multigrid level and our proposed novel approach of using combination of GPU and CPU across all multigrid levels. Our experiments demonstrate a significant speedup using the hybrid parallel approaches, across different problem sizes and finite element types, as compared to the MPI only approach. However, the scalability challenge persists for the hybrid parallel multigrid smoothers.
Conference papers on the topic "Hybrid Parallel Smoother"
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Anselma, Pier Giuseppe. "Planning the Velocity of a Parallel Hybrid Electric in Vehicle-to-Vehicle Autonomous Driving: an Optimization-Based Approach." In FISITA World Congress 2021. FISITA, 2021. http://dx.doi.org/10.46720/f2020-adm-057.
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Improved numerical tools are required to foster flexible and effective advancement of innovative electrified and highly automated road vehicles. However, research activities concerning vehicle automation are usually decoupled from their counterparts involving vehicle electrification due to both the increased level of complexity and the partitioned organization of automotive OEMs. In this framework, implementing vehicle development approaches involving simultaneously electrification and automation would result beneficial both in facilitating the overall vehicle design procedure and in exploiting most advantage from both the development topics. This paper proposes an optimization-based approach to off-line plan of the longitudinal velocity of a hybrid electric vehicle (HEV) when travelling as Ego vehicle in a vehicle-to-vehicle (V2V) autonomous driving scenario. A parallel P2 hybrid powertrain layout is retained along with the corresponding on-board supervisory controller. A mathematical formulation for the optimal V2V autonomous driving control problem is provided and consequently solved with an optimization method based on dynamic programming (DP). The implemented DP formulation particularly exploits information about the overall longitudinal speed profile of a Lead vehicle in a predefined driving mission to determine the velocity profile of the Ego vehicle. Optimization constraints involve maintaining the inter-vehicular distance value within allowed limits while aiming at minimizing both the magnitude of Ego vehicle acceleration events and the overall Ego vehicle fuel consumption as predicted according to the on-board hybrid supervisory control logic. Simulation results for different driving missions demonstrate that, using the proposed DP formulation, the Ego vehicle can achieve both smoother speed profiles and improved fuel economy by some percentage points in V2V autonomous driving compared to the retained Lead vehicle embedding the same HEV powertrain layout. Urban driving conditions have been identified as the most promising ones both in terms of fuel economy enhancement and passenger comfort improvement by V2V automated driving according to the proposed approach. On the other hand, when the Lead vehicle encounters highway or aggressive driving conditions, remarkable improvements might be achieved only in passenger comfort, while limited saving might be attained in terms of fuel consumption according to the described methodology.
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Ruiz, Antonio, Francisco Campa Gomez, Constantino Roldan-Paraponiaris, and Oscar Altuzarra. "Experimental Validation of the Kinematics of a 3PRS Compliant Mechanism for Micromilling." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-47449.
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The present work deals with the development of a hybrid manipulator of 5 degrees of freedom for milling moulds for microlenses. The manipulator is based on a XY stage under a 3PRS compliant parallel mechanism. The mechanism takes advantage of the compliant joints to achieve higher repetitiveness, smoother motion and a higher bandwidth, due to the high precision demanded from the process, under 0.1 micrometers. This work is focused on the kinematics of the compliant stage of the hybrid manipulator. First, an analysis of the workspace required for the milling of a single mould has been performed, calculating the displacements required in X, Y, Z axis as well as two relative rotations between the tool and the workpiece from a programmed toolpath. Then, the 3PRS compliant parallel mechanism has been designed using FEM with the objective of being stiff enough to support the cutting forces from the micromilling, but flexible enough in the revolution and spherical compliant joints to provide the displacements needed. Finally, a prototype of the 3PRS compliant mechanism has been built, implementing a motion controller to perform translations in Z direction and two rotations. The resulting displacements in the end effector and the actuated joints have been measured and compared with the FEM calculations and with the rigid body kinematics of the 3PRS.
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Mehraban, Arash, Jed Brown, Valeria Barra, Henry Tufo, Jeremy Thompson, and Richard Regueiro. "Efficient Residual and Matrix-Free Jacobian Evaluation for Three-Dimensional Tri-Quadratic Hexahedral Finite Elements With Nearly-Incompressible Neo-Hookean Hyperelasticity Applied to Soft Materials on Unstructured Meshes in Parallel, With PETSc and libCEED." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-24522.
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Abstract Soft materials such as rubber, elastomers, and soft biological tissues mechanically deform at large strain isochorically for all time, or during their initial transient (when a pore fluid, typically incompressible such as water, does not have time to flow out of the deforming polymer or soft tissue porous skeleton). Simulating these large isochoric deformations computationally, such as with the Finite Element Method (FEM), requires higher order (typically quadratic) interpolation functions and/or enhancements through hybrid/mixed methods to maintain stability. Lower order (linear) finite elements with hybrid/mixed formulation may not perform stably for all mechanical loading scenarios involving large isochoric deformations, whereas quadratic finite elements with or without hybrid/mixed formulation typically perform stably, especially when large bending or folding deformations are being simulated. For topology-optimization design of soft robotics, for instance, the FEM solid mechanics solver must run efficiently and stably. Stability is ensured by the higher order finite element formulation (with possible enhancement), but efficiency for higher order FEM remains a challenge. Thus, this paper addresses efficiency from the perspective of computer science algorithms and programming. The proposed efficient algorithm utilizes the Portable, Extensible Toolkit for Scientific Computation (PETSc), along with the libCEED library for efficient compiler optimized tensor-product-basis computation to demonstrate an efficient nonlinear solution algorithm. For preconditioning, a scalable p-multigrid method is presented whereby a hierarchy of levels is constructed. In contrast to classical geometric multigrid, also known as h-multigrid, each level in p-multigrid is related to a different approximation polynomial order, p, instead of the element size, h. A Chebyshev polynomial smoother is used on each multigrid level. Algebraic MultiGrid (AMG) is then applied to the assembled Q1 (linear) coarse mesh on the nodes of the quadratic Q2 (quadratic) mesh. This allows low storage that can be efficiently used to accelerate the convergence to solution. For a Neo-Hookean hyperelastic problem, we examine a residual and matrix-free Jacobian formulation of a tri-quadratic hexahedral finite element with enhancement. Efficiency estimates on AVX-2 architecture based on CPU time are provided as a comparison to similar simulation (and mesh) of isochoric large deformation hyperelasticity as applied to soft materials conducted with the commercially-available FEM software program ABAQUS. The particular problem in consideration is the simulation of an assistive device in the form of finger-bending in 3D.
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Du, Changqing, Fuwu Yan, Zhengce Cao, and Sen Wu. "Smooth Mode-Switch Control for the Powertrain of Parallel Hybrid Electric Vehicle." In 2009 Asia-Pacific Power and Energy Engineering Conference. IEEE, 2009. http://dx.doi.org/10.1109/appeec.2009.4918773.
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Chiang, Chia-Jui, Ying-Chen Chen, and Chun-Ying Lin. "Fuzzy sliding mode control for smooth mode changes of a parallel hybrid electric vehicle." In 2014 11th IEEE International Conference on Control & Automation (ICCA). IEEE, 2014. http://dx.doi.org/10.1109/icca.2014.6871069.
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Wen, Hongjie, and Bing Ren. "3D Numerical Wave Basin Based on Parallelized SPH Method." In ASME 2014 33rd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/omae2014-23596.
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A viscous 3D numerical wave basin for high nonlinear waves was developed based on Smoothed Particle Hydrodynamics (SPH) method. The computational accuracy of SPH method is mainly improved by introducing the Corrective Smoothed Particle Hydrodynamics Method (CSPM) and a novel pressure correction scheme. The incident waves are generated from the inflow boundary by prescribing a velocity profile of the flap-type wavemaker motions, and the outgoing waves are numerically dissipated inside an artificial damping zone located at the end of the basin. Moreover, the parallelization of the improved 3D SPH scheme has been carried out using a hybrid MPI-OpenMP programming, together with a dynamic load balancing strategy to improve the computational efficiency. The generation and propagation of regular wave and solitary wave have been simulated. Wave forces induced by regular wave acting on a large-diameter circular cylinder and solitary wave passing over a submerged breakwater are also presented to verify the accuracy of SPH model. In addition, several computing cases of different particle resolutions are investigated and a high parallel efficiency is obtained.
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Wu, Justin Quach, and Douglas Nelson. "Development and Testing of a Hybrid Vehicle Energy Management Strategy." In WCX SAE World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2023. http://dx.doi.org/10.4271/2023-01-0552.
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<div class="section abstract"><div class="htmlview paragraph">An energy management strategy for a prototype P4 parallel hybrid Chevrolet Blazer is developed for the EcoCAR Mobility Challenge. The objective of the energy management strategy is to reduce energy consumption while maintaining the drive quality targets of a conventional vehicle. A comprehensive model of the hybrid powertrain and vehicle physics is constructed to aid in the development of the control strategy. To improve fuel efficiency, a Willans line model is developed for the conventional powertrain and used to develop a rule-based torque split strategy. The strategy maximizes high efficiency engine operation while reducing round trip losses. Calibratable parameters for the torque split operating regions allow for battery state of charge management. Torque request and filtering algorithms are also developed to ensure the hybrid powertrain can smoothly and reliably meet driver demand. Vehicle testing validates that the hybrid powertrain meets acceleration response targets while delivering an enjoyable driving experience. Simulation testing shows that the energy management strategy improved fuel economy in most drive cycles with improvements of 8.8% for US06, 9.8% for HWFET, and 0.1% for the EcoCAR Mobility Challenge Cycle. Battery state of charge management behavior is robust across a variety of drive cycles using inputs from both simulated and test drivers. The resulting energy management strategy delivers an efficient, responsive, and reliable hybrid electric vehicle.</div></div>
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Marialto, Renato, Gianluca Brando, Adolfo Dannier, Paolo Sementa, Massimo Cardone, and Enrico Fornaro. "Modeling and Experimental Validation of a Hybrid Electric Propulsion System for Naval Applications." In 16th International Conference on Engines & Vehicles. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2023. http://dx.doi.org/10.4271/2023-24-0131.
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<div class="section abstract"><div class="htmlview paragraph">Focusing on coastal or inland navigation cities, where emissions from ships are not negligible concerning global ones, the possibility of reducing exhaust gas pollution would have more benefits for public health and air quality. Therefore, in recent years, increasing attention on environmental sustainability is driving the shipbuilding industry towards greener propulsion based on full-electric or hybrid-electric propulsion systems. This work is presented a parallel hybrid system composed of two electric motors, one internal combustion engine, and lithium battery storage. All motors are coupled to the propeller through a specially designed transmission system based on the High Sliding Gear theory (HSG). The hybrid-electric propulsion system is designed to extender the battery pack durability, ensuring a smooth profile of the required current, through the complementary action of the batteries and the internal combustion engine. Then, the overall performance of the hybrid propulsion system is verified by laboratory experimentations, also testing the energy storage system. A fast boat (yacht) is chosen as a simulation case where a lumped parameters model is coupled with map-based motors obtained from experimental test activities performed on this hybrid propulsion system. This model is also validated by the available performance data obtained during an experimental campaign where has been emulated a typical propeller load. The results show the feasibility of this hybrid architecture, in terms of performance and sustainable maritime transport. Furthermore, the simulation results confirm the experimental performance, proving to be a good tool for system development in terms of energy management.</div></div>
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Jefferson-Loveday, Richard J. "Differential Equation-Based Specification of Turbulence Integral Length Scales for Cavity Flows." In ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/gt2016-56451.
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A new modeling approach has been developed that explicitly accounts for expected turbulent eddy length scales in cavity zones. It uses a hybrid approach with Poisson and Hamilton-Jacobi differential equations. These are used to set turbulent length scales to sensible expected values. For complex rim-seal and shroud cavity designs, the method sets an expected length scale based on local cavity width which accurately accounts for the large-scale wake-like flow structures that have been observed in these zones. The method is used to generate length scale fields for three complex rim-seal geometries. Good convergence properties are found and a smooth transition of length scale between zones is observed. The approach is integrated with the popular Menter Shear Stress Transport (MSST) RANS turbulence model and reduces to the standard Menter model in the mainstream flow. For validation of the model, a transonic deep cavity simulation is performed. Overall the Poisson-Hamilton-Jacobi model shows significant quantitative and qualitative improvement over the standard Menter RANS model for both velocity and Reynolds stress measurements. In its current development, the approach has been extended through the use of an initial stage of length scale estimation using a Poisson equation. This essentially reduces the need for user objectivity. A key aspect of the approach is that the length scale is automatically set by the model and not by the modeler. Notably, the current method is readily implementable in an unstructured, parallel processing computational framework.