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Автор: Grafiati

Опубліковано: 14 березня 2023

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1

Lee, Daeyeon, Nhu Van Nguyen, Maxim Tyan, Hyung-Geun Chun, Sangho Kim, and Jae-Woo Lee. "Enhanced multi-fidelity model for flight simulation using global exploration and the Kriging method." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 231, no. 4 (August 6, 2016): 606–20. http://dx.doi.org/10.1177/0954410016641441.

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Анотація:

Using the global exploration and Kriging-based multi-fidelity analysis methods, this study developed a multi-fidelity aerodynamic database for use in the performance analysis of flight vehicles and for use in flight simulations. Athena vortex lattice, a program based on vortex lattice method, was used as the low-fidelity analysis tool in the multi-fidelity analysis method. The in-house high-fidelity AADL-3D code was based on the Navier–Stokes equations. The AADL-3D code was validated by comparing the data and the analysis results of the Onera M-6 wing and NACA TN 3649. The design of experiment method and the Kriging method were applied to integrate low- and high-fidelity analysis results. General data tendencies were established from the low-fidelity analysis results. The high-fidelity analysis results and the Kriging method were used to generate a surrogate model, from which the low-fidelity analysis results were interpolated. To reduce repeated calculations, three design points were simultaneously added for each calculation. The convergence of three design points was avoided by considering only the peak points as additional design points. The reliability of the final surrogate model was determined by applying the leave-one-out cross-validation method and by obtaining the cross-validation root mean square error. Using the multi-fidelity model developed in this study, a multi-fidelity aerodynamic database was constructed for use in the three degrees of freedom flight simulation of flight vehicles.

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2

Rumpfkeil, Markus P., Dean Bryson, and Phil Beran. "Multi-Fidelity Sparse Polynomial Chaos and Kriging Surrogate Models Applied to Analytical Benchmark Problems." Algorithms 15, no. 3 (March 21, 2022): 101. http://dx.doi.org/10.3390/a15030101.

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In this article, multi-fidelity kriging and sparse polynomial chaos expansion (SPCE) surrogate models are constructed. In addition, a novel combination of the two surrogate approaches into a multi-fidelity SPCE-Kriging model will be presented. Accurate surrogate models, once obtained, can be employed for evaluating a large number of designs for uncertainty quantification, optimization, or design space exploration. Analytical benchmark problems are used to show that accurate multi-fidelity surrogate models can be obtained at lower computational cost than high-fidelity models. The benchmarks include non-polynomial and polynomial functions of various input dimensions, lower dimensional heterogeneous non-polynomial functions, as well as a coupled spring-mass-system. Overall, multi-fidelity models are more accurate than high-fidelity ones for the same cost, especially when only a few high-fidelity training points are employed. Full-order PCEs tend to be a factor of two or so worse than SPCES in terms of overall accuracy. The combination of the two approaches into the SPCE-Kriging model leads to a more accurate and flexible method overall.

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3

Forrester, Alexander I. J., András Sóbester, and Andy J. Keane. "Multi-fidelity optimization via surrogate modelling." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 463, no. 2088 (October 2, 2007): 3251–69. http://dx.doi.org/10.1098/rspa.2007.1900.

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Анотація:

This paper demonstrates the application of correlated Gaussian process based approximations to optimization where multiple levels of analysis are available, using an extension to the geostatistical method of co-kriging . An exchange algorithm is used to choose which points of the search space to sample within each level of analysis. The derivation of the co-kriging equations is presented in an intuitive manner, along with a new variance estimator to account for varying degrees of computational ‘noise’ in the multiple levels of analysis. A multi-fidelity wing optimization is used to demonstrate the methodology.

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4

Bonfiglio, Luca, Paris Perdikaris, and Stefano Brizzolara. "Multi-fidelity Bayesian Optimization of SWATH Hull Forms." Journal of Ship Research 64, no. 02 (June 1, 2020): 154–70. http://dx.doi.org/10.5957/jsr.2020.64.2.154.

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This study presents a multi-fidelity framework that enables the construction of surrogate models capable of capturing complex correlations between design variables and quantities of interest. Resistance in calm water is investigated for a SWATH hull in a multidimensional design space using a new method to derive high-quality response surfaces through machine learning techniques based on a low number of high-fidelity computations and a larger number of less-expensive low-fidelity computations. First, a verification and validation study is presented with the goal of comparing and ranking numerical methods against experiments performed on a conventional SWATH geometry. Then, the hull geometry of a new family of unconventional SWATH hull forms with twin counter-canted struts is parametrically defined and sequentially refined using multi-fidelity Bayesian optimization. Ship resistance in calm water is finally predicted using observations from two different fidelity levels. We demonstrate that the multi-fidelity optimization framework is successful in obtaining an optimized design using a small number of high-fidelity computations and a larger number of low-fidelity computations. Simulation and optimization costs are reduced by orders of magnitude, providing accurate certificates of fidelity for the performance of the proposed design.

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5

Thelen, Andrew S., Dean E. Bryson, Bret K. Stanford, and Philip S. Beran. "Multi-Fidelity Gradient-Based Optimization for High-Dimensional Aeroelastic Configurations." Algorithms 15, no. 4 (April 16, 2022): 131. http://dx.doi.org/10.3390/a15040131.

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The simultaneous optimization of aircraft shape and internal structural size for transonic flight is excessively costly. The analysis of the governing physics is expensive, in particular for highly flexible aircraft, and the search for optima using analysis samples can scale poorly with design space size. This paper has a two-fold purpose targeting the scalable reduction of analysis sampling. First, a new algorithm is explored for computing design derivatives by analytically linking objective definition, geometry differentiation, mesh construction, and analysis. The analytic computation of design derivatives enables the accurate use of more efficient gradient-based optimization methods. Second, the scalability of a multi-fidelity algorithm is assessed for optimization in high dimensions. This method leverages a multi-fidelity model during the optimization line search for further reduction of sampling costs. The multi-fidelity optimization is demonstrated for cases of aerodynamic and aeroelastic design considering both shape and structural sizing separately and in combination with design spaces ranging from 17 to 321 variables, which would be infeasible using typical, surrogate-based methods. The multi-fidelity optimization consistently led to a reduction in high-fidelity evaluations compared to single-fidelity optimization for the aerodynamic shape problems, but frequently resulted in a cost penalty for cases involving structural sizing. While the multi-fidelity optimizer was successfully applied to problems with hundreds of variables, the results underscore the importance of accurately computing gradients and motivate the extension of the approach to constrained optimization methods.

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6

Wei, Yunfei, and Shifeng Xiong. "Bayesian integrative analysis for multi-fidelity computer experiments." Journal of Applied Statistics 46, no. 11 (February 4, 2019): 1973–87. http://dx.doi.org/10.1080/02664763.2019.1575340.

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7

Klimczyk, Witold Artur, and Zdobyslaw Jan Goraj. "Analysis and optimization of morphing wing aerodynamics." Aircraft Engineering and Aerospace Technology 91, no. 3 (March 4, 2019): 538–46. http://dx.doi.org/10.1108/aeat-12-2017-0289.

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Анотація:

PurposeThe purpose of this paper is to present a method for analysis and optimization of morphing wing. Moreover, a numerical advantage of morphing airfoil wing, typically assessed in simplified two-dimensional analysis is found using higher fidelity methods.Design/methodology/approachBecause of multi-point nature of morphing wing optimization, an approach for optimization by analysis is presented. Starting from naïve parametrization, multi-fidelity aerodynamic data are used to construct response surface model. From the model, many significant information are extracted related to parameters effect on objective; hence, design sensitivity and, ultimately, optimal solution can be found.FindingsThe method was tested on benchmark problem, with some easy-to-predict results. All of them were confirmed, along with additional information on morphing trailing edge wings. It was found that wing with morphing trailing edge has around 10 per cent lower drag for the same lift requirement when compared to conventional design.Practical implicationsIt is demonstrated that providing a smooth surface on wing gives substantial improvement in multi-purpose aircrafts. Details on how this is achieved are described. The metodology and results presented in current paper can be used in further development of morphing wing.Originality/valueMost of literature describing morphing airfoil design, optimization or calculations, performs only 2D analysis. Furthermore, the comparison is often based on low-fidelity aerodynamic models. This paper uses 3D, multi-fidelity aerodynamic models. The results confirm that this approach reveals information unavailable with simplified models.

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8

Fu, Wenbo, Qiushi Li, Yongshun Song, Yaogen Shu, Zhongcan Ouyang, and Ming Li. "Theoretical analysis of RNA polymerase fidelity: a steady-state copolymerization approach." Communications in Theoretical Physics 74, no. 1 (December 10, 2021): 015601. http://dx.doi.org/10.1088/1572-9494/ac3993.

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Abstract The fidelity of DNA transcription catalyzed by RNA polymerase (RNAP) has long been an important issue in biology. Experiments have revealed that RNAP can incorporate matched nucleotides selectively and proofread the incorporated mismatched nucleotides. However, systematic theoretical researches on RNAP fidelity are still lacking. In the last decade, several theories on RNA transcription have been proposed, but they only handled highly simplified models without considering the high-order neighbor effects and the oligonucleotides cleavage both of which are critical for the overall fidelity. In this paper, we regard RNA transcription as a binary copolymerization process and calculate the transcription fidelity by the steady-state copolymerization theory recently proposed by us for DNA replication. With this theory, the more realistic models considering higher-order neighbor effects, oligonucleotides cleavage, multi-step incorporation and multi-step cleavage can be rigorously handled.

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9

Kandasamy, Kirthevasan, Gautam Dasarathy, Junier Oliva, Jeff Schneider, and Barnabás Póczos. "Multi-fidelity Gaussian Process Bandit Optimisation." Journal of Artificial Intelligence Research 66 (September 15, 2019): 151–96. http://dx.doi.org/10.1613/jair.1.11288.

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In many scientific and engineering applications, we are tasked with the maximisation of an expensive to evaluate black box function f. Traditional settings for this problem assume just the availability of this single function. However, in many cases, cheap approximations to f may be obtainable. For example, the expensive real world behaviour of a robot can be approximated by a cheap computer simulation. We can use these approximations to eliminate low function value regions cheaply and use the expensive evaluations of f in a small but promising region and speedily identify the optimum. We formalise this task as a multi-fidelity bandit problem where the target function and its approximations are sampled from a Gaussian process. We develop MF-GP-UCB, a novel method based on upper confidence bound techniques. In our theoretical analysis we demonstrate that it exhibits precisely the above behaviour and achieves better bounds on the regret than strategies which ignore multi-fidelity information. Empirically, MF-GP-UCB outperforms such naive strategies and other multi-fidelity methods on several synthetic and real experiments.

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10

Younis, Adel, and Zuomin Dong. "High-Fidelity Surrogate Based Multi-Objective Optimization Algorithm." Algorithms 15, no. 8 (August 7, 2022): 279. http://dx.doi.org/10.3390/a15080279.

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The employment of conventional optimization procedures that must be repeatedly invoked during the optimization process in real-world engineering applications is hindered despite significant gains in computing power by computationally expensive models. As a result, surrogate models that require far less time and resources to analyze are used in place of these time-consuming analyses. In multi-objective optimization (MOO) problems involving pricey analysis and simulation techniques such as multi-physics modeling and simulation, finite element analysis (FEA), and computational fluid dynamics (CFD), surrogate models are found to be a promising endeavor, particularly for the optimization of complex engineering design problems involving black box functions. In order to reduce the expense of fitness function evaluations and locate the Pareto frontier for MOO problems, the automated multiobjective surrogate based Pareto finder MOO algorithm (AMSP) is proposed. Utilizing data samples taken from the feasible design region, the algorithm creates three surrogate models. The algorithm repeats the process of sampling and updating the Pareto set, by assigning weighting factors to those surrogates in accordance with the values of the root mean squared error, until a Pareto frontier is discovered. AMSP was successfully employed to identify the Pareto set and the Pareto border. Utilizing multi-objective benchmark test functions and engineering design examples such airfoil shape geometry of wind turbine, the unique approach was put to the test. The cost of computing the Pareto optima for test functions and real engineering design problem is reduced, and promising results were obtained.

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11

Osama, M., M. Y. Ahmed, S. Saleh, and M. Khalil. "Multi-fidelity structural analysis of a beam-shaped wing." Journal of Physics: Conference Series 2299, no. 1 (July 1, 2022): 012009. http://dx.doi.org/10.1088/1742-6596/2299/1/012009.

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Abstract As far as integrity and proper operation of a flying vehicle are concerned, modal analysis of the wing is a crucial design procedure. This procedure aims to define the natural frequencies of the wing structure to avoid resonance under fluctuating aerodynamic loads. This procedure becomes more significant if the wing to forced to perform pitching oscillation. The present study aims to define the dynamic behavior of a rectangular wing with NACA 0009 airfoil using the Euler-Bernoulli beam theory by treating the wing as a cantilever beam. To assess the validity of this treatment and the theoretical method as a design tool, both numerical simulations and experimental measurements are applied on the wing. Results of this multi-fidelity modal analysis define the natural frequencies of the wing. The good agreement of the three methods confirms the validity of treating the wing in concern as a simple cantilever beam.

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12

Lee, Jae-Woo, Seok-Min Choi, Nguyen Nhu Van, Ji-Min Kim, and Yung-Hwan Byun. "Multidisciplinary UAV Design Optimization Implementing Multi-Fidelity Analysis Techniques." Journal of the Korean Society for Aeronautical & Space Sciences 40, no. 8 (August 1, 2012): 695–702. http://dx.doi.org/10.5139/jksas.2012.40.8.695.

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13

Tsushima, Natsuki, Masato Tamayama, and Tomohiro Yokozeki. "Static Geometrically Nonlinear Aeroelastic Framework for Multi-Fidelity Analysis." JOURNAL OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES 68, no. 4 (2020): 142–47. http://dx.doi.org/10.2322/jjsass.68.142.

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14

Palar, Pramudita Satria, Lavi Rizki Zuhal, Koji Shimoyama, and Takeshi Tsuchiya. "Global sensitivity analysis via multi-fidelity polynomial chaos expansion." Reliability Engineering & System Safety 170 (February 2018): 175–90. http://dx.doi.org/10.1016/j.ress.2017.10.013.

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15

Meng, Xuhui, Hessam Babaee, and George Em Karniadakis. "Multi-fidelity Bayesian neural networks: Algorithms and applications." Journal of Computational Physics 438 (August 2021): 110361. http://dx.doi.org/10.1016/j.jcp.2021.110361.

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16

Serafino, Aldo, Benoit Obert, and Paola Cinnella. "Multi-Fidelity Gradient-Based Strategy for Robust Optimization in Computational Fluid Dynamics." Algorithms 13, no. 10 (September 30, 2020): 248. http://dx.doi.org/10.3390/a13100248.

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Efficient Robust Design Optimization (RDO) strategies coupling a parsimonious uncertainty quantification (UQ) method with a surrogate-based multi-objective genetic algorithm (SMOGA) are investigated for a test problem in computational fluid dynamics (CFD), namely the inverse robust design of an expansion nozzle. The low-order statistics (mean and variance) of the stochastic cost function are computed through either a gradient-enhanced kriging (GEK) surrogate or through the less expensive, lower fidelity, first-order method of moments (MoM). Both the continuous (non-intrusive) and discrete (intrusive) adjoint methods are evaluated for computing the gradients required for GEK and MoM. In all cases, the results are assessed against a reference kriging UQ surrogate not using gradient information. Subsequently, the GEK and MoM UQ solvers are fused together to build a multi-fidelity surrogate with adaptive infill enrichment for the SMOGA optimizer. The resulting hybrid multi-fidelity SMOGA RDO strategy ensures a good tradeoff between cost and accuracy, thus representing an efficient approach for complex RDO problems.

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17

Dinh, Bao Anh, Hieu Khanh Ngo, and Van Nhu Nguyen. "An efficient low-speed airfoil design optimization process using multi-fidelity analysis for UAV flying wing." Science and Technology Development Journal 19, no. 3 (September 30, 2016): 43–52. http://dx.doi.org/10.32508/stdj.v19i3.519.

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This paper proposes an efficient low-speed airfoil selection and design optimization process using multi-fidelity analysis for a long endurance Unmanned Aerial Vehicle (UAV) flying wing. The developed process includes the low speed airfoil database construction, airfoil selection and design optimization steps based on the given design requirements. The multi-fidelity analysis solvers including the panel method and computational fluid dynamics (CFD) are presented to analyze the low speed airfoil aerodynamic characteristics accurately and perform inverse airfoil design optimization effectively without any noticeable turnaround time in the early aircraft design stage. The unconventional flying wing UAV design shows poor reaction in longitudinal stability. However, It has low parasite drag, long endurance, and better performance. The multi-fidelity analysis solvers are validated for the E387 and CAL2463m airfoil compared to the wind tunnel test data. Then, 29 low speed airfoils for flying wing UAV are constructed by using the multi-fidelity solvers. The weighting score method is used to select the appropriate airfoil for the given design requirements. The selected airfoil is used as a baseline for the inverse airfoil design optimization step to refine and obtain the optimal airfoil configuration. The implementation of proposed method is applied for the real flying-wing UAV airfoil design case to demonstrate the effectiveness and feasibility of the proposed method.

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18

Morse, Llewellyn, Zahra Sharif Khodaei, and M. H. Aliabadi. "Multi-Fidelity Modeling-Based Structural Reliability Analysis with the Boundary Element Method." Journal of Multiscale Modelling 08, no. 03n04 (September 2017): 1740001. http://dx.doi.org/10.1142/s1756973717400017.

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In this work, a method for the application of multi-fidelity modeling to the reliability analysis of 2D elastostatic structures using the boundary element method (BEM) is proposed. Reliability analyses were carried out on a rectangular plate with a center circular hole subjected to uniaxial tension using Monte Carlo simulations (MCS), the first-order reliability method (FORM), and the second-order reliability method (SORM). Two BEM models were investigated, a low-fidelity model (LFM) of 20 elements and a high-fidelity model (HFM) of 100 elements. The response of these models at several design points was used to create multi-fidelity models (MFMs) utilizing second-order polynomial response surfaces and their reliability, alongside that of the LFM and the HFM, was evaluated. Results show that the MFMs that directly called the LFM were significantly superior in terms of accuracy to the LFM, achieving very similar levels of accuracy to the HFM, while also being of similar computational cost to the LFM. These direct MFMs were found to provide good substitutes for the HFM for MCS, FORM, and SORM.

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19

Zhang, Lili, Seung-Kyum Choi, Tingli Xie, Ping Jiang, Jiexiang Hu, and Jasuk Koo. "Multi-fidelity surrogate model-assisted fatigue analysis of welded joints." Structural and Multidisciplinary Optimization 63, no. 6 (February 23, 2021): 2771–87. http://dx.doi.org/10.1007/s00158-020-02840-9.

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20

Sevieri, Giacomo, Roberto Gentile, and Carmine Galasso. "A multi‐fidelity Bayesian framework for robust seismic fragility analysis." Earthquake Engineering & Structural Dynamics 50, no. 15 (October 24, 2021): 4199–219. http://dx.doi.org/10.1002/eqe.3552.

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21

Konrad, Julia, Ionuţ-Gabriel Farcaş, Benjamin Peherstorfer, Alessandro Di Siena, Frank Jenko, Tobias Neckel, and Hans-Joachim Bungartz. "Data-driven low-fidelity models for multi-fidelity Monte Carlo sampling in plasma micro-turbulence analysis." Journal of Computational Physics 451 (February 2022): 110898. http://dx.doi.org/10.1016/j.jcp.2021.110898.

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22

Avramova, Maria, Agustin Abarca, Jason Hou, and Kostadin Ivanov. "Innovations in Multi-Physics Methods Development, Validation, and Uncertainty Quantification." Journal of Nuclear Engineering 2, no. 1 (March 7, 2021): 44–56. http://dx.doi.org/10.3390/jne2010005.

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This paper provides a review of current and upcoming innovations in development, validation, and uncertainty quantification of nuclear reactor multi-physics simulation methods. Multi-physics modelling and simulations (M&S) provide more accurate and realistic predictions of the nuclear reactors behavior including local safety parameters. Multi-physics M&S tools can be subdivided in two groups: traditional multi-physics M&S on assembly/channel spatial scale (currently used in industry and regulation), and novel high-fidelity multi-physics M&S on pin (sub-pin)/sub-channel spatial scale. The current trends in reactor design and safety analysis are towards further development, verification, and validation of multi-physics multi-scale M&S combined with uncertainty quantification and propagation. Approaches currently applied for validation of the traditional multi-physics M&S are summarized and illustrated using established Nuclear Energy Agency/Organization for Economic Cooperation and Development (NEA/OECD) multi-physics benchmarks. Novel high-fidelity multi-physics M&S allow for insights crucial to resolve industry challenge and high impact problems previously impossible with the traditional tools. Challenges in validation of novel multi-physics M&S are discussed along with the needs for developing validation benchmarks based on experimental data. Due to their complexity, the novel multi-physics codes are still computationally expensive for routine applications. This fact motivates the use of high-fidelity novel models and codes to inform the low-fidelity traditional models and codes, leading to improved traditional multi-physics M&S. The uncertainty quantification and propagation across different scales (multi-scale) and multi-physics phenomena are demonstrated using the OECD/NEA Light Water Reactor Uncertainty Analysis in Modelling benchmark framework. Finally, the increasing role of data science and analytics techniques in development and validation of multi-physics M&S is summarized.

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23

Yildiz, Sihmehmet, Hayriye Pehlivan Solak, and Melike Nikbay. "Multi-Fidelity Low-Rank Approximations for Uncertainty Quantification of a Supersonic Aircraft Design." Algorithms 15, no. 7 (July 19, 2022): 250. http://dx.doi.org/10.3390/a15070250.

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Uncertainty quantification has proven to be an indispensable study for enhancing reliability and robustness of engineering systems in the early design phase. Single and multi-fidelity surrogate modelling methods have been used to replace the expensive high fidelity analyses which must be repeated many times for uncertainty quantification. However, since the number of analyses required to build an accurate surrogate model increases exponentially with the number of random input variables, most surrogate modelling methods suffer from the curse of dimensionality. As an alternative approach, the Low-Rank Approximation method can be applied to high-dimensional uncertainty quantification studies with a low computational cost, where the number of coefficients for building the surrogate model increases only linearly with the number of random input variables. In this study, the Low-Rank Approximation method is implemented for multi-fidelity applications with additive and multiplicative correction approaches to make the high-dimensional uncertainty quantification analysis more efficient and accurate. The developed uncertainty quantification methodology is tested on supersonic aircraft design problems and its predictions are compared with the results of single- and multi-fidelity Polynomial Chaos Expansion and Monte Carlo methods. For the same computational cost, the Low-Rank Approximation method outperformed both in surrogate modeling and uncertainty quantification cases for all the benchmarks and real-world engineering problems addressed in the present study.

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24

Imielinska, Celina, Andrzej Przekwas, and XG Tan. "Multi-Scale Visual Analysis of Trauma Injury." Information Visualization 5, no. 4 (November 30, 2006): 279–89. http://dx.doi.org/10.1057/palgrave.ivs.9500137.

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We develop a multi-scale high-fidelity biomechanical and physiologically based modeling tools for trauma (ballistic/impact and blast) injury to brain, lung and spinal cord for resuscitation, treatment planning and design of personnel protection. Several approaches have been used to study blast and ballistic/impact injuries. Dummy containing pressure sensors and synthetic phantoms of human organs have been used to study bomb blast and car crashes. Large animals like pigs also have been equipped with pressure sensors exposed to blast waves. But these methods do not provide anatomically and physiologically, full optimization of body protection design and require animal sacrifice. Anatomy and medical image-based high-fidelity computational modeling can be used to analyze injury mechanisms and to optimize the design of body protection. This paper presents novel approach of coupled computational fluid dynamics and computational structures dynamics to simulate fluid (air, cerebrospinal fluid)–solid (cranium, brain tissue) interaction during ballistic/blast impact. We propose a trauma injury simulation pipeline concept staring from anatomy and medical image-based high-fidelity 3D geometric modeling, extraction of tissue morphology, generation of computational grids, multi-scale biomechanical and physiological simulations, and data visualization.

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25

Hartl, Darren J., Geoffrey J. Frank, and Jeffery W. Baur. "Embedded magnetohydrodynamic liquid metal thermal transport: validated analysis and design optimization." Journal of Intelligent Material Systems and Structures 28, no. 7 (July 28, 2016): 862–77. http://dx.doi.org/10.1177/1045389x16657429.

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This work addresses the multi-fidelity analysis-driven design of a thermal transport system based on the flow of liquid metal through a structural laminate as induced by a solid-state magneto-hydro-dynamic (MHD) pump. A full three-dimensional model of the thermal transport system is both simplified to a reduced-order algebraic model, which correctly captures trends in the global system response, and alternatively implemented in an finite element framework, which captures essential global and local aspects of the system response not attainable via reduced-order modeling. The predictions of each model are validated against previously published experimental data. It is shown in detail for the first time in the context of MHD systems that a multi-fidelity approach to the multi-objective design optimization problem can leverage both the speed of the algebraic model and the accuracy of the finite element model, leading to effective predictions of optimal system designs in a reasonable amount of time. A relatively new algorithm for multi-objective and parameterized Pareto optimization is employed, and a clear path of continued development is identified.

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26

Lopez-Caballero, Fernando. "Probabilistic seismic analysis for liquefiable embankment through multi-fidelity codes approach." Soil Dynamics and Earthquake Engineering 149 (October 2021): 106849. http://dx.doi.org/10.1016/j.soildyn.2021.106849.

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27

Gutoski, Gus, Ansis Rosmanis, and Jamie Sikora. "Fidelity of quantum strategies with applications to cryptography." Quantum 2 (September 3, 2018): 89. http://dx.doi.org/10.22331/q-2018-09-03-89.

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We introduce a definition of the fidelity function for multi-round quantum strategies, which we call the strategy fidelity, that is a generalization of the fidelity function for quantum states. We provide many properties of the strategy fidelity including a Fuchs-van de Graaf relationship with the strategy norm. We also provide a general monotinicity result for both the strategy fidelity and strategy norm under the actions of strategy-to-strategy linear maps. We illustrate an operational interpretation of the strategy fidelity in the spirit of Uhlmann's Theorem and discuss its application to the security analysis of quantum protocols for interactive cryptographic tasks such as bit-commitment and oblivious string transfer. Our analysis is general in the sense that the actions of the protocol need not be fully specified, which is in stark contrast to most other security proofs. Lastly, we provide a semidefinite programming formulation of the strategy fidelity.

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28

Nachar, Stéphane, Pierre-Alain Boucard, David Néron, and Christian Rey. "Multi-fidelity bayesian optimization using model-order reduction for viscoplastic structures." Finite Elements in Analysis and Design 176 (September 2020): 103400. http://dx.doi.org/10.1016/j.finel.2020.103400.

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29

Peart, Tanya, Nicolas Aubin, Stefano Nava, John Cater, and Stuart Norris. "Multi-Fidelity Surrogate Models for VPP Aerodynamic Input Data." Journal of Sailing Technology 6, no. 01 (February 9, 2021): 21–43. http://dx.doi.org/10.5957/jst/2021.6.1.21.

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Predicting the performance of a sail design is important for optimising the performance of a yacht, and Velocity Prediction Programs (VPPs) are commonly used for this purpose. The aerodynamic force data for a VPP is often calculated using Computational Fluid Dynamics (CFD) models, but these can be computationally expensive. A full VPP analysis for sail design is therefore usually restricted to high-budget design projects or research activities and is not practical for many industry projects. This work presents a method to reduce the computational cost of creating lift and drag force coefficient curves for input into a VPP using both multi-fidelity kriging surrogate modelling and data from existing sail designs. This method is shown to reduce the number of CFD simulations required for a desired accuracy when compared to a single-fidelity model. A maximum reduction in the required computational effort of 57% was achieved for model-scale symmetric spinnaker sails. For the same number of simulations, the accuracy of the model predictions was improved by up to 72% for scale-symmetric spinnaker sails, and 90% for asymmetric spinnakers.

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30

Xu, Jie, Si Zhang, Edward Huang, Chun-Hung Chen, Loo Hay Lee, and Nurcin Celik. "MO2TOS: Multi-Fidelity Optimization with Ordinal Transformation and Optimal Sampling." Asia-Pacific Journal of Operational Research 33, no. 03 (June 2016): 1650017. http://dx.doi.org/10.1142/s0217595916500172.

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Simulation optimization can be used to solve many complex optimization problems in automation applications such as job scheduling and inventory control. We propose a new framework to perform efficient simulation optimization when simulation models with different fidelity levels are available. The framework consists of two novel methodologies: ordinal transformation (OT) and optimal sampling (OS). The OT methodology uses the low-fidelity simulations to transform the original solution space into an ordinal space that encapsulates useful information from the low-fidelity model. The OS methodology efficiently uses high-fidelity simulations to sample the transformed space in search of the optimal solution. Through theoretical analysis and numerical experiments, we demonstrate the promising performance of the multi-fidelity optimization with ordinal transformation and optimal sampling (MO2TOS) framework.

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31

Zhang, Chi, Chaolin Song, and Abdollah Shafieezadeh. "Adaptive reliability analysis for multi-fidelity models using a collective learning strategy." Structural Safety 94 (January 2022): 102141. http://dx.doi.org/10.1016/j.strusafe.2021.102141.

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32

Pourhabib, Arash, Jianhua Z. Huang, Kan Wang, Chuck Zhang, Ben Wang, and Yu Ding. "Modulus prediction of buckypaper based on multi-fidelity analysis involving latent variables." IIE Transactions 47, no. 2 (November 5, 2014): 141–52. http://dx.doi.org/10.1080/0740817x.2014.917777.

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33

Yang, Qiang, Songhe Meng, Hua Jin, Weihua Xie, and Xinghong Zhang. "Multi-fidelity uncertainty quantification method with application to nonlinear structural response analysis." Applied Mathematical Modelling 75 (November 2019): 853–64. http://dx.doi.org/10.1016/j.apm.2019.06.038.

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34

Guo, Zhendong, Liming Song, Chanyoung Park, Jun Li, and Raphael T. Haftka. "Analysis of dataset selection for multi-fidelity surrogates for a turbine problem." Structural and Multidisciplinary Optimization 57, no. 6 (May 25, 2018): 2127–42. http://dx.doi.org/10.1007/s00158-018-2001-8.

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35

JIN, ZHAO, YAN-QIANG JI, SHI-LEI SU, BIN SI, HONG-FU WANG, SHOU ZHANG, and AI-DONG ZHU. "NONDESTRUCTIVE ENTANGLEMENT ANALYSIS AND GENERATION WITH ATOMS IN LOW-Q CAVITIES ASSISTED BY COHERENT LIGHT." International Journal of Quantum Information 11, no. 06 (September 2013): 1350055. http://dx.doi.org/10.1142/s021974991350055x.

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We propose a scheme to implement a nondestructive Greenberger–Horne–Zeilinger (GHZ) state analyzer with atoms in low-Q cavities by taking the coherent light as quantum channel. It can also be applied to generate multi-atom entanglement state with high fidelity and entanglement degree. The analysis for the effect of atomic spontaneous emission on the fidelity and homodyne detection (HD) shows that the present scheme is not only robust in the condition of a bad cavity, but also feasible with the current technology due to the relaxed experimental condition.

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36

Xu, Jing, Daming Feng, and Andrey Chernikov. "Efficient tetrahedralization of multi-material images with quality, fidelity, and topological guarantees." Finite Elements in Analysis and Design 203 (June 2022): 103723. http://dx.doi.org/10.1016/j.finel.2022.103723.

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37

Morse, Llewellyn, Zahra Sharif Khodaei, and M. H. Aliabadi. "A multi-fidelity boundary element method for structural reliability analysis with higher-order sensitivities." Engineering Analysis with Boundary Elements 104 (July 2019): 183–96. http://dx.doi.org/10.1016/j.enganabound.2019.03.036.

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38

Nguyen, Vinh-Tan, Jason Yu Chuan Leong, Satoshi Watanabe, Toshimitsu Morooka, and Takayuki Shimizu. "A Multi-Fidelity Model for Simulations and Sensitivity Analysis of Piezoelectric Inkjet Printheads." Micromachines 12, no. 9 (August 29, 2021): 1038. http://dx.doi.org/10.3390/mi12091038.

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The ink drop generation process in piezoelectric droplet-on-demand devices is a complex multiphysics process. A fully resolved simulation of such a system involves a coupled fluid–structure interaction approach employing both computational fluid dynamics (CFD) and computational structural mechanics (CSM) models; thus, it is computationally expensive for engineering design and analysis. In this work, a simplified lumped element model (LEM) is proposed for the simulation of piezoelectric inkjet printheads using the analogy of equivalent electrical circuits. The model’s parameters are computed from three-dimensional fluid and structural simulations, taking into account the detailed geometrical features of the inkjet printhead. Inherently, this multifidelity LEM approach is much faster in simulations of the whole inkjet printhead, while it ably captures fundamental electro-mechanical coupling effects. The approach is validated with experimental data for an existing commercial inkjet printhead with good agreement in droplet speed prediction and frequency responses. The sensitivity analysis of droplet generation conducted for the variation of ink channel geometrical parameters shows the importance of different design variables on the performance of inkjet printheads. It further illustrates the effectiveness of the proposed approach in practical engineering usage.

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39

Badrya, Camli, Ananth Sridharan, James D. Baeder, and Christopher M. Kroninger. "Multi-Fidelity Coupled Trim Analysis of a Flapping-Wing Micro Air Vehicle Flight." Journal of Aircraft 54, no. 5 (September 2017): 1614–30. http://dx.doi.org/10.2514/1.c034236.

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40

Iyappan, Praveen, and Ranjan Ganguli. "Multi-fidelity analysis and uncertainty quantification of beam vibration using correction response surfaces." International Journal for Computational Methods in Engineering Science and Mechanics 21, no. 1 (January 2, 2020): 26–42. http://dx.doi.org/10.1080/15502287.2020.1729898.

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41

Du, Wenting, and Jin Su. "Uncertainty Quantification for Numerical Solutions of the Nonlinear Partial Differential Equations by Using the Multi-Fidelity Monte Carlo Method." Applied Sciences 12, no. 14 (July 12, 2022): 7045. http://dx.doi.org/10.3390/app12147045.

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The Monte Carlo simulation is a popular statistical method to estimate the effect of uncertainties on the solutions of nonlinear partial differential equations, but it requires a huge computational cost of the deterministic model, and the convergence may become slow. For this reason, we developed the multi-fidelity Monte Carlo (MFMC) methods based on data-driven low-fidelity models for uncertainty analysis of nonlinear partial differential equations. Firstly, the nonlinear partial differential equations are transformed into ordinary differential equations (ODEs) by using finite difference discretization or Fourier transformation. Then, the reduced dimension model and discrete empirical interpolation method (DEIM) are coupled to construct effective nonlinear low-fidelity models in ODEs system. Finally, the MFMC method is used to combine the output information of the high-fidelity model and the low-fidelity models to give the optimal estimation of the statistics. Experimental results of the nonlinear Schrodinger equation and the Burgers’ equation show that, compared with the standard Monte Carlo method, the MFMC method based on the data-driven low-fidelity model in this paper can improve the calculation efficiency significantly.

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42

Siddappaji, Kiran, and Mark G. Turner. "An Advanced Multifidelity Multidisciplinary Design Analysis Optimization Toolkit for General Turbomachinery." Processes 10, no. 9 (September 13, 2022): 1845. http://dx.doi.org/10.3390/pr10091845.

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The MDAO framework has become an essential part of almost all fields, apart from mechanical, transportation, and aerospace industries, for efficient energy conversion or otherwise. It enables rapid iterative interaction among several engineering disciplines at various fidelities using automation tools for design improvement. An advanced framework from low to high fidelity is developed for ducted and unducted turbomachinery blade designs. The parametric blade geometry tool is a key feature which converts low-fidelity results into 3D blade shapes and can readily be used in high-fidelity multidisciplinary simulations as part of an optimization cycle. The geometry generator and physics solvers are connected to DAKOTA, an open-source optimizer with parallel computation capability. The entire cycle is automated and new design iterations are generated with input parameter variations controlled by DAKOTA. Single- and multi-objective genetic algorithm and gradient method-based optimization cases are demonstrated for various applications. B-splines are used to define smooth perturbation of parametric variables chordwise and spanwise of the blade. The ability to create parametric 3D blade shapes quickly from low-fidelity analyses with advanced control is demonstrated to be unique and enables a rapid 3D design cycle. Non-intuitive designs are feasible in this framework and designers can really benefit from parametric geometry manipulation. Optimization at each fidelity is realized through automation. As part of the multidisciplinary analysis, 3D structural analysis is also performed using the unidirectional fluid–structure interaction for a few cases with imported pressure loads from the 3D RANS solution. Examples of axial turbofans, compressor rotors, turbines, radial compressors, propellers, wind and hydrokinetic turbines are demonstrated to prove generality.

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43

Liu, Yize, Theoklis Nikolaidis, Seyed Hossein Madani, Mohammad Sarkandi, Abdelaziz Gamil, Muhamad Firdaus Sainal, and Seyed Vahid Hosseini. "Multi-Fidelity Combustor Design and Experimental Test for a Micro Gas Turbine System." Energies 15, no. 7 (March 23, 2022): 2342. http://dx.doi.org/10.3390/en15072342.

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A multi-fidelity micro combustor design approach is developed for a small-scale combined heat and power CHP system. The approach is characterised by the coupling of the developed preliminary design model using the combined method of 3D high-fidelity modelling and experimental testing. The integrated multi-physics schemes and their underlying interactions are initially provided. During the preliminary design phase, the rapid design exploration is achieved by the coupled reduced-order models, where the details of the combustion chamber layout, flow distributions, and burner geometry are defined as well as basic combustor performance. The high-fidelity modelling approach is then followed to provide insights into detailed flow and emission physics, which explores the effect of design parameters and optimises the design. The combustor is then fabricated and assembled in the MGT test bench. The experimental test is performed and indicates that the designed combustor is successfully implemented in the MGT system. The multi-physics models are then verified and validated against the test data. The details of refinement on lower-order models are given based on the insights acquired by high-fidelity methods. The shortage of conventional fossil fuels and the continued demand for energy supplies have led to the development of a micro-turbine system running renewable fuels. Numerical analysis is then carried out to assess the potential operation of biogas in terms of emission and performance. It produces less NOx emission but presents a flame stabilisation design challenge at lower methane content. The details of the strategy to address the flame stabilisation are also provided.

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44

Riva, Riccardo, Stefano Cacciola, and Carlo Luigi Bottasso. "Periodic stability analysis of wind turbines operating in turbulent wind conditions." Wind Energy Science 1, no. 2 (October 20, 2016): 177–203. http://dx.doi.org/10.5194/wes-1-177-2016.

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Abstract. The formulation is model-independent, in the sense that it does not require knowledge of the equations of motion of the periodic system being analyzed, and it is applicable to an arbitrary number of blades and to any configuration of the machine. In addition, as wind turbulence can be viewed as a stochastic disturbance, the method is also applicable to real wind turbines operating in the field. The characteristics of the new method are verified first with a simplified analytical model and then using a high-fidelity multi-body model of a multi-MW wind turbine. Results are compared with those obtained by the well-known operational modal analysis approach.

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45

Habitu, Yohannes Ayanaw, Gashaw Andargie Biks, Abebaw Gebeyehu Worku, and Kassahun Alemu Gelaye. "Individual and contextual factors affect the implementation fidelity of youth-friendly services, northwest Ethiopia: A multilevel analysis." PLOS ONE 17, no. 2 (February 10, 2022): e0263733. http://dx.doi.org/10.1371/journal.pone.0263733.

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Background The evaluation of all potential determinants of implementation fidelity of Youth-Friendly Services (YFS) is crucial for Ethiopia. Previous studies overlooked investigating the determinants at different levels. Therefore, this study aimed to assess the determinants of implementation fidelity of YFS considering individual and contextual levels. Methods This study was conducted among 1,029 youths, from 11 health centers that are implementing the YFS in Central Gondar Zone. Data were collected by face to face interview and facility observation using a semi-structured questionnaire. A Bivariable multi-level mixed effect modelling was employed to assess the main determinants. Four separate models were fitted to reach the full model. The fitness of the model was assessed using Akaike Information Criterion (AIC) and level of significance was declared at p-values < 0.05. The results of fixed effects were presented as adjusted odds ratio (AOR) at their 95% CI. Results Four hundred one (39.0%) of the respondents got the YFS with high level of fidelity. Had high level of involvement in the YFS provision (AOR = 1.35, 95% CI: 1.15, 1.57), knew any peer educator trained in YFS (AOR = 1.60, 95% CI: 1.36, 1.86), and involved as a peer educator (AOR = 1.46, 95% CI: 1.24, 1.71), were the individual level determinants. Whereas, got capacity building training; (AOR = 1.93, 95% CI (1.12, 3.48), got supportive supervision, (AOR 2.85, 95% CI (1.99, 6.37), had a separate waiting room (AOR = 9.84, 95%CI: 2.14, 17.79), and system in place to provide continuous support to staff (AOR = 2.81, 95%CI: 1.25, 6.34) were the contextual level determinants. Conclusions The level of implementation fidelity remains low. Both individual and contextual level determinants affect the implementation fidelity of YFS. Therefore, policy makers, planners, managers and YFS providers could consider both individual and contextual factors to improve the implementation fidelity.

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46

Krishnan, K. V. Vishal, and Ranjan Ganguli. "Multi-fidelity analysis and uncertainty quantification of beam vibration using co-kriging interpolation method." Applied Mathematics and Computation 398 (June 2021): 125987. http://dx.doi.org/10.1016/j.amc.2021.125987.

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47

Skandalos, Konstantinos, Souvik Chakraborty, and Solomon Tesfamariam. "Seismic reliability analysis using a multi-fidelity surrogate model: Example of base-isolated buildings." Structural Safety 97 (July 2022): 102222. http://dx.doi.org/10.1016/j.strusafe.2022.102222.

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48

Magedanz, J., M. Avramova, Y. Perin, and A. K. Velkov. "High-fidelity multi-physics system TORT-TD/CTF/FRAPTRAN for light water reactor analysis." Annals of Nuclear Energy 84 (October 2015): 234–43. http://dx.doi.org/10.1016/j.anucene.2015.01.033.

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49

Yi, Jiaxiang, Fangliang Wu, Qi Zhou, Yuansheng Cheng, Hao Ling, and Jun Liu. "An active-learning method based on multi-fidelity Kriging model for structural reliability analysis." Structural and Multidisciplinary Optimization 63, no. 1 (July 25, 2020): 173–95. http://dx.doi.org/10.1007/s00158-020-02678-1.

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50

Lu, Xinzheng, Donglian Gu, Zhen Xu, Chen Xiong, and Yuan Tian. "CIM-Powered Multi-Hazard Simulation Framework Covering both Individual Buildings and Urban Areas." Sustainability 12, no. 12 (June 21, 2020): 5059. http://dx.doi.org/10.3390/su12125059.

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To improve the ability to prepare for and adapt to potential hazards in a city, efforts are being invested in evaluating the performance of the built environment under multiple hazard conditions. An integrated physics-based multi-hazard simulation framework covering both individual buildings and urban areas can help improve analysis efficiency and is significant for urban planning and emergency management activities. Therefore, a city information model-powered multi-hazard simulation framework is proposed considering three types of hazards (i.e., earthquake, fire, and wind hazards). The proposed framework consists of three modules: (1) data transformation, (2) physics-based hazard analysis, and (3) high-fidelity visualization. Three advantages are highlighted: (1) the database with multi-scale models is capable of meeting the various demands of stakeholders, (2) hazard analyses are all based on physics-based models, leading to rational and scientific simulations, and (3) high-fidelity visualization can help non-professional users better understand the disaster scenario. A case study of the Tsinghua University campus is performed. The results indicate the proposed framework is a practical method for multi-hazard simulations of both individual buildings and urban areas and has great potential in helping stakeholders to assess and recognize the risks faced by important buildings or the whole city.

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