Inscrição na biblioteca: Guest
International Journal for Uncertainty Quantification

Publicou 6 edições por ano

ISSN Imprimir: 2152-5080

ISSN On-line: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

Indexed in

MULTIFIDELITY MODELING OF IRRADIATED PARTICLE-LADEN TURBULENCE SUBJECT TO UNCERTAINTY

Volume 10, Edição 6, 2020, pp. 499-514
DOI: 10.1615/Int.J.UncertaintyQuantification.2020032236
Get accessGet access

RESUMO

The study of thermal radiation interacting with particle-laden turbulence is of great importance in a wide range of scientific and engineering applications. The computational study of such systems is challenging as a result of the large number of thermo-fluid mechanisms governing the underlying physics. To build confidence and improve the prediction accuracy of such simulations, the impact of uncertainties on the quantities of interest must be measured. This, however, requires a computational budget that is typically a large multiple of the cost of a single calculation, and thus may become infeasible for expensive simulation models featuring a large number of uncertain inputs and highly nonlinear behavior. In this regard, multifidelity methods have become increasingly popular in recent years as acceleration strategies to reduce the computational cost. These methods are based on a hierarchy of generalized numerical resolutions, or model fidelities, and attempt to leverage the correlation between high- and low-fidelity models to obtain a more accurate statistical estimator with a relatively small number of high-fidelity calculations. In this work, the performance of a collection of different multifidelity strategies and modeling approaches is assessed to propagate the uncertainties encountered in the simulation of irradiated particle-laden turbulence relevant to volumetric solar energy receivers. The results obtained indicate that multifidelity methods provide speedups on the order of 101−103 × with respect to straightforward Monte Carlo approaches, resulting in remarkable reductions in computational cost.

Referências
  1. Najm, H.N., Uncertainty Quantification and Polynomial Chaos Techniques in Computational Fluid Dynamics, Annu. Rev. FluidMech, 41:35-52, 2009.

  2. Exascale Computing Engineering Center. Predictive Science Academic Alliance Program (PSAAP) II, Stanford University, 2019.

  3. Shaw, R.A., Particle-Turbulence Interactions in Atmospheric Clouds, Annu. Rev. Fluid Mech, 35:183-227, 2003.

  4. Tieszen, S.R., On the Fluid Mechanics of Fires, Annu. Rev. Fluid Mech., 33:67-92, 2001.

  5. Lasheras, J.C. and Hopfinger, E.J., Liquid Jet Instability and Atomization in a Coaxial Gas Stream, Annu. Rev. Fluid Mech, 32:275-308, 2000.

  6. Dodd, M.S. and Jofre, L., Small-Scale Flow Topologies in Decaying Isotropic Turbulence Laden with Finite-Size Droplets, Phys. Rev. Fluids, 4:064303, 2019.

  7. Raman, V. and Fox, R.O., Modeling of Fine-Particle Formation in Turbulent Flames, Annu. Rev. Fluid Mech, 48:159-190, 2016.

  8. Ho, C.K., Advances in Central Receivers for Concentrating Solar Applications, Sol. Energy, 152:38-56, 2017.

  9. Caporaloni, M., Tampieri, F., Trombetti, F., and Vittori, O., Transfer of Particles in Nonisotropic Air Turbulence, J. Atmos. Sci., 32:565-568, 1975.

  10. Balachandar, S. and Eaton, J.K., Turbulent Dispersed Multiphase Flow, Annu. Rev. Fluid Mech, 42:111-133,2010.

  11. Dunton, A.M., Jofre, L., Doostan, A., and Iaccarino, G., Pass-Efficient Methods for Compression of High-Dimensional Turbulent Flow Data, CTRAnnu. Res. Briefs, pp. 313-325, 2017.

  12. Zamansky, R., Coletti, F., Massot, M., and Mani, A., Radiation Induces Turbulence in Particle-Laden Fluids, Phys. Fluids, 26:071701,2014.

  13. Frankel, A.,Pouransari, H., Coletti, F., and Mani, A., Settling of Heated Particles in Homogeneous Turbulence, J. Fluid Mech, 792:869-893,2016.

  14. Pouransari, H. and Mani, A., Effects of Preferential Concentration on Heat Transfer in Particle-Based Solar Receivers, J. Sol. Energy Eng., 139:021008, 2017.

  15. Rahmani, M., Geraci, G., Iaccarino, G., and Mani, A., Effects of Particle Polydispersity on Radiative Heat Transfer in Particle-Laden Turbulent Flows, Int. J. Multiphase Flow, 104:42-59,2018.

  16. Jofre, L., Geraci, G., Fairbanks, H.R., Doostan, A., and Iaccarino, G., Multi-Fidelity Uncertainty Quantification of Irradiated Particle-Laden Turbulence, CTR Annu. Res. Briefs, pp. 21-34, 2017.

  17. Fairbanks, H.R., Jofre, L., Geraci, G., Iaccarino, G., and Doostan, A., Bi-Fidelity Approximation for Uncertainty Quantification and Sensitivity Analysis of Irradiated Particle-Laden Turbulence, J. Comput. Phys, 402:108996, 2020.

  18. Jofre, L., del Rosario, Z.R., and Iaccarino, G., Data-Driven Dimensional Analysis of Heat Transfer in Irradiated Particle-Laden Turbulent Flow, Int. J. Multiphase Flow, 125:103198,2020.

  19. Jofre, L., Domino, S.P., and Iaccarino, G., A Framework for Characterizing Structural Uncertainty in Large-Eddy Simulation Closures, Flow Turbul. Combust., 100(2):341-363, 2018.

  20. Jofre, L., Domino, S.P., and Iaccarino, G., Eigensensitivity Analysis of Subgrid-Scale Stresses in Large-Eddy Simulation of a Turbulent Axisymmetric Jet, Int. J. Heat Fluid Flow, 7:314-335, 2019.

  21. Peherstorfer, B., Willcox, K., and Gunzburger, M., Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization, SIAMRev., 60(3):550-591,2018.

  22. Giles, M.B., Multi-Level Monte Carlo Path Simulation, Oper. Res., 56:607-617,2008.

  23. Pasupathy, R., Taaffe, M., Schmeiser, B.W., and Wang, W., Control-Variate Estimation Using Estimated Control Means, IIE Trans, 44:381-385,2014.

  24. Geraci, G., Eldred, M., and Iaccarino, G., A Multifidelity Multilevel Monte Carlo Method for Uncertainty Propagation in Aerospace Applications, in Proc. 19th AIAA Non-Deterministic Approaches Conf., AIAA Paper 2017-1951,2017.

  25. Fairbanks, H.R., Doostan, A., Ketelsen, C., and Iaccarino, G., A Low-Rank Control Variate for Multilevel Monte Carlo Simulation of High-Dimensional Uncertain Systems, J. Comput. Phys, 341:121-139, 2017.

  26. Subramaniam, S., Lagrangian-Eulerian Methods for Multiphase Flows, Prog. Energy Combust. Sci., 39:215-245, 2013.

  27. Maxey, M.R. and Riley, J.J., Equation of Motion for a Small Rigid Sphere in a Nonuniform Flow, Phys. Fluids, 26:883-889, 1983.

  28. Amsden, A.A., O'Rourke, P. J., and Butler, T.D., KIVA-II: A Computer Program for Chemically Reactive Flows with Sprays, Los Alamos National Laboratory, Los Alamos, NM, Tech. Rep. LA-12503-MS, 1989.

  29. Esmaily, M., Jofre, L., Mani, A., and Iaccarino, G., A Scalable Geometric Multigrid Solver for Nonsymmetric Elliptic Systems with Application to Variable-Density Flows, J. Comput. Phys, 357:142-158, 2018.

  30. Bassenne, M., Urzay, J., Park, G.I., and Moin, P., Constant-Energetics Physical-Space Forcing Methods for Improved Convergence to Homogeneous-Isotropic Turbulence with Applications to Particle-Laden Flows, Phys. Fluids, 28:035114, 2016.

  31. Roy, P.T., Jofre, L., Jouhaud, J.C., and Cuenot, B., Versatile Sequential Sampling Algorithm Using Kernel Density Estimation, Eur. J. Oper. Res, 284(1):201-211,2020.

  32. Gorodetsky, A.A., Geraci, G., Eldred, M.S., and Jakeman, J.D., A Generalized Approximate Control Variate Framework for Multifidelity Uncertainty Quantification, Stat. Computat., 2019. arXiv:1811.04988.

  33. Jofre, L., Papadakis, M., Aiken, A., and Iaccarino, G., Multifidelity Ensemble-Based Prediction of Turbulent Flows at the Exascale, in Proc. of 72nd Annual Meeting of the American Physical Society, Division of Fluid Dynamics, P16-005,2019.

CITADO POR
  1. Jofre Lluís, Doostan Alireza, Rapid aerodynamic shape optimization under uncertainty using a stochastic gradient approach, Structural and Multidisciplinary Optimization, 65, 7, 2022. Crossref

  2. Valero Mario Miguel, Jofre Lluís, Torres Ricardo, Multifidelity prediction in wildfire spread simulation: Modeling, uncertainty quantification and sensitivity analysis, Environmental Modelling & Software, 141, 2021. Crossref

  3. Chen Jingjing, Kumar Apurv, Coventry Joe, Lipiński Wojciech, Heat transfer in directly-irradiated high-temperature solid–gas flows laden with polydisperse particles, Applied Mathematical Modelling, 110, 2022. Crossref

  4. Yao Yuan, Huan Xun, Capecelatro Jesse, Multi-fidelity uncertainty quantification of particle deposition in turbulent pipe flow, Journal of Aerosol Science, 166, 2022. Crossref

Portal Digital Begell Biblioteca digital da Begell eBooks Diários Referências e Anais Coleções de pesquisa Políticas de preços e assinaturas Begell House Contato Language English 中文 Русский Português German French Spain