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Copy file name to clipboardExpand all lines: content/research_papers/2024/2024-12-19-implementation-of-two-numerical-solvers-for-the-study-of-non-equilibrium-gas-dynamics-on-gpu-accelerated-platforms-using-sycl.md
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The application of GPUs has extended beyond traditional graphics rendering because their
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parallel processing capabilities can accelerate many general-purpose tasks, such as machine
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learning and scientific computing. This thesis presents the implementation of two numerical
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-
solvers for the solution of non-equilibrium gas flows. It also demonstrates the computational
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performance of the two solvers when developed to target GPU-based supercomputers using the SYCL
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programming model. The first solver incorporates a novel ray-tracing technique and accurate
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mathematical relations to efficiently compute any observable property of free-molecular flow
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past convex shapes (FMFC). It computes integrals of the Maxwell-Boltzmann distribution function
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to create an algorithm that quickly evaluates any moment of the local particle-velocity
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distribution. This highly efficient technique is extended for GPUs to accelerate the
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computation of accurate results. Results produced with the solver serve as robust benchmarks
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in the validation of other scientific models that describe fluid motion in non-equilibrium
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regimes. The second solver extends a CPU-based implementation of the discontinuous Galerkin Hancock (DGH)
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method into an efficient GPU code. The DGH scheme is a high-order numerical method that
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solves hyperbolic partial differential equations (PDEs) with stiff source terms. This class
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of equations is common in many models that are used to describe non-equilibrium gas flows.
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The GPU implementation of the DGH solver that is presented in this work provides a
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computationally efficient and numerically accurate method to compute the solution for these
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models. Results produced by the FMFC and DGH solvers showcase their accuracy and parallel
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scalability as efficient GPU algorithms. Furthermore, the effectiveness of the FMFC
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solver as a validation tool is demonstrated by producing benchmarks to confirm the
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accuracy of scientific models that are solved with numerical schemes such as DGH.
14
+
The application of GPUs has extended beyond traditional graphics rendering because their parallel processing
15
+
capabilities can accelerate many general-purpose tasks, such as machine learning and scientific computing. This thesis
16
+
presents the implementation of two numerical solvers for the solution of non-equilibrium gas flows. It also demonstrates
17
+
the computational performance of the two solvers when developed to target GPU-based supercomputers using the SYCL
18
+
programming model. The first solver incorporates a novel ray-tracing technique and accurate mathematical relations to
19
+
efficiently compute any observable property of free-molecular flow past convex shapes (FMFC). It computes integrals of
20
+
the Maxwell-Boltzmann distribution function to create an algorithm that quickly evaluates any moment of the local
21
+
particle-velocity distribution. This highly efficient technique is extended for GPUs to accelerate the computation of
22
+
accurate results. Results produced with the solver serve as robust benchmarks in the validation of other scientific
23
+
models that describe fluid motion in non-equilibrium regimes. The second solver extends a CPU-based implementation of
24
+
the discontinuous Galerkin Hancock (DGH)method into an efficient GPU code. The DGH scheme is a high-order numerical
25
+
method that solves hyperbolic partial differential equations (PDEs) with stiff source terms. This class of equations is
26
+
common in many models that are used to describe non-equilibrium gas flows. The GPU implementation of the DGH solver that
27
+
is presented in this work provides a computationally efficient and numerically accurate method to compute the solution
28
+
for these models. Results produced by the FMFC and DGH solvers showcase their accuracy and parallel scalability as
29
+
efficient GPU algorithms. Furthermore, the effectiveness of the FMFC solver as a validation tool is demonstrated by
30
+
producing benchmarks to confirm the accuracy of scientific models that are solved with numerical schemes such as DGH.
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