A methodology for reduced order modeling and calibration of the upper atmosphere
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DOI: 10.1002/2017sw001642
Abstract: Atmospheric drag is the largest source of uncertainty in accurately predicting the orbit of satellites in low Earth orbit (LEO). Accurately predicting drag for objects that traverse LEO is critical to Space Situational Awareness. Atmospheric… read more here.
Keywords: reduced order; methodology; physics based; physics ... See more keywords
Estimating the accuracy of a reduced-order model for the calculation of fractional flow reserve (FFR).
Sign Up to like & getrecommendations! Published in 2018 at "International journal for numerical methods in biomedical engineering"
DOI: 10.1002/cnm.2908
Abstract: Image-based noninvasive fractional flow reserve (FFR) is an emergent approach to determine the functional relevance of coronary stenoses. The present work aimed to determine the feasibility of using a method based on coronary computed tomography… read more here.
Keywords: reduced order; fractional flow; flow reserve; geometry ... See more keywords
Reduced‐order modeling of lead‐acid battery using cluster analysis and orthogonal cluster analysis method
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DOI: 10.1002/er.4645
Abstract: Real‐time fast simulation of lead‐acid battery (LAB) plays an important role in monitoring, control, optimization, and many other engineering fields. Hence, any improvement toward a reduction in computational time of LAB simulation while maintaining the… read more here.
Keywords: reduced order; analysis; cluster analysis; battery ... See more keywords
Convergence acceleration of continuous adjoint solvers using a reduced‐order model
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DOI: 10.1002/fld.4468
Abstract: Summary A novel acceleration technique using a reduced-order model is presented to speed up convergence of continuous adjoint solvers. The acceleration is achieved by projecting to an improved solution within an iterative process solely using… read more here.
Keywords: reduced order; adjoint; order model; order ... See more keywords
An efficient mixed variational reduced‐order model formulation for nonlinear analyses of elastic shells
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DOI: 10.1002/nme.5629
Abstract: Summary The Koiter-Newton method had recently demonstrated a superior performance for non-linear analyses of structures, compared to traditional path-following strategies. The method follows a predictor-corrector scheme to trace the entire equilibrium path. During a predictor… read more here.
Keywords: reduced order; order model; order; formulation ... See more keywords
Reduced order modeling of random linear dynamical systems based on a new a posteriori error bound
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DOI: 10.1002/nme.5942
Abstract: Reduced order models (ROMs) are becoming increasingly useful for saving computational cost in response prediction of vibrating systems. In a number of applications such as uncertainty quantification, ROMs require robustness over a wide variation of… read more here.
Keywords: error; reduced order; error bound; posteriori error ... See more keywords
Sparse POD modal subsets for reduced‐order nonlinear explicit dynamics
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DOI: 10.1002/nme.6243
Abstract: Projected reduced order methods (PROM) such as the proper orthogonal decomposition (POD) rely on the quality of the underlying reduced basis (RB) used to approximate the solution. The RB is generally constructed by the low‐rank… read more here.
Keywords: order; sparse pod; reduced order; explicit dynamics ... See more keywords
A coupled thermo‐chemo‐mechanical reduced‐order multiscale model for predicting process‐induced distortions, residual stresses, and strength
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DOI: 10.1002/nme.6274
Abstract: We study residual stresses and part distortion induced by a manufacturing process of a polymer matrix composite and its effect on the component strength. Unlike most of the thermo‐chemo‐mechanical models in the literature where governing… read more here.
Keywords: reduced order; chemo mechanical; strength; thermo chemo ... See more keywords
A hybrid stabilization approach for reduced‐order models of compressible flows with shock‐vortex interaction
Sign Up to like & getrecommendations! Published in 2019 at "International Journal for Numerical Methods in Engineering"
DOI: 10.1002/nme.6281
Abstract: Model order reduction approaches, such as proper orthogonal decomposition (POD)‐Galerkin projection, provide a systematic manner to construct Reduced‐Order Models (ROM) from pregenerated high‐fidelity datasets. The current study focuses on the stabilization of ROMs built from… read more here.
Keywords: order; method; reduced order; order models ... See more keywords
Mesh sampling and weighting for the hyperreduction of nonlinear Petrov–Galerkin reduced‐order models with local reduced‐order bases
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DOI: 10.1002/nme.6603
Abstract: The energy‐conserving sampling and weighting (ECSW) method is a hyper‐reduction method originally developed for accelerating the performance of Galerkin projection‐based reduced‐order models (PROMs) associated with large‐scale finite element models, when the underlying projected operators need… read more here.
Keywords: order; sampling weighting; petrov galerkin; method ... See more keywords
Non‐intrusive reduced‐order modeling using convolutional autoencoders
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DOI: 10.1002/nme.7072
Abstract: The use of reduced‐order models (ROMs) in physics‐based modeling and simulation almost always involves the use of linear reduced basis (RB) methods such as the proper orthogonal decomposition (POD). For some nonlinear problems, linear RB… read more here.
Keywords: reduced order; order; intrusive reduced; convolutional autoencoders ... See more keywords