Reducing memory requirements of unsteady adjoint by synergistically using check‐pointing and compression

The unsteady adjoint method used in gradient‐based optimization in 2D and, particularly, 3D industrial problems modeled by unsteady PDEs may have significant storage requirements and/or computational cost. The reason for this is that the backward in time integration of the adjoint equations requires...

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Vydané v:International journal for numerical methods in fluids Ročník 95; číslo 1; s. 23 - 43
Hlavní autori: Margetis, Andreas‐Stefanos I., Papoutsis‐Kiachagias, Evangelos M., Giannakoglou, Kyriakos C.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Bognor Regis Wiley Subscription Services, Inc 01.01.2023
Predmet:
Algorithms
binomial check‐pointing
Compression
Computational efficiency
computational fluid dynamics
Computer applications
Computing costs
Controllability
Data reduction
Flow control
Fluid flow
Fluid mechanics
Hybridization
Image reconstruction
lossless and lossy data compression algorithms
OpenFOAM
Optimization
Proper Generalized Decomposition
Shape
Shape optimization
Storage requirements
Three dimensional models
Time integration
unsteady adjoint
Unstructured data
Unstructured grids (mathematics)
ISSN:0271-2091, 1097-0363
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Shrnutí:The unsteady adjoint method used in gradient‐based optimization in 2D and, particularly, 3D industrial problems modeled by unsteady PDEs may have significant storage requirements and/or computational cost. The reason for this is that the backward in time integration of the adjoint equations requires the previously computed instantaneous flow fields to be available at each time‐step. This article proposes remedies to this problem, by extending/upgrading relevant techniques proposed by the group of authors as well as other researchers. Their applicability is wide, even if these remedies are herein demonstrated in shape optimization problems in unsteady fluid mechanics. Check‐pointing is in widespread use as it reduces the memory footprint and CPU cost of the optimization with a controllable computational overhead. Alternatively, flow field time‐series can be stored in a lossless or lossly compressed form. The novelty of this article is the development of a Compressed Coarse‐grained Check‐Pointing strategy for second‐order accurate schemes in time, by optimally combining check‐pointing and lossy compression. The latter includes (a) the incremental Proper Generalized Decomposition (iPGD) algorithm and (b) a hybridization of the iPGD with the ZFP and Zlib algorithms. This is implemented within OpenFOAM, which is used to solve the flow and adjoint equations and conduct the optimization, and assessed in 2D/3D aerodynamic shape optimization problems on unstructured grids. Effectiveness in data reduction, computational cost, and reconstruction accuracy are compared, vis‐à‐vis also to the “standard” binomial check‐pointing technique after adjusting it to second‐order accurate schemes in time. This article proposes a Compressed Coarse‐grained Check‐Pointing strategy, to reduce the significant storage requirements of the unsteady adjoint method used in gradient‐based optimization, by optimally combining check‐pointing and lossy compression. The latter includes (a) the incremental Proper Generalized Decomposition (iPGD) algorithm and (b) a newly proposed hybridization of the iPGD with the ZFP and Zlib algorithms. This is implemented within OpenFOAM and assessed in 2D/3D aerodynamic shape optimization problems on unstructured grids.
Bibliografia:Funding information
Special Account for Research Funding (ELKE) of the National Technical University of Athens (NTUA)
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SourceType-Scholarly Journals-1
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ISSN:0271-2091
1097-0363
DOI:10.1002/fld.5136