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Kiril S. Shterev

Publications and source records attributed to Kiril S. Shterev.

2 recordsLinked to original sources

On convective terms approximation approach that corresponds to pure convection

Recent decades are put lots of efforts to develop a higher-order scheme for convective terms approximation that is stable and reliable. The idea presented here is that approximation approach has to correspond to the physical phenomenon described by approximated terms. Pure convection (advection) that is described by convective terms is transporting a property along the streamline, and the information propagation is unidirectional, i.e., transported property depends on previous values along the streamline but does not depend on the next ones. The proposed approach represents streamlines on mesh as discrete streamlines and is called Discrete Stream(line) Method (DStreaM). A discrete streamline here is represented as a narrow triangle with one vertex of the approximated node and two others neighbor upstream nodes. Discrete streamlines are orientated using local flow direction as skew upwind schemes. DStreaM corresponds to pure convection. Here are considered standard test problems: advection of a step profile, advection of a double-step profile, advection of a sinusoidal profile, and Smith and Hutton problem. DStreaM solutions were compared with upwind-first order scheme and second-order Total Variation Diminishing (TVD) schemes with limiters Min-Mod, QUICK, and SUPERBEE solutions. DStreaM demonstrated second-order accuracy and rapid convergence. Upwind and DStreaM need 2 or 4 iterations to reach a final solution while TVD schemes need from 15 to 93.5 more iterations. DStreaM approach looks promising for calculation of convective-dominated problems because it approximates naturally first derivatives and is straightforwardly applicable as a meshfree method or on unstructured meshes.

math.NA↗

GPU implementation of algorithm SIMPLE-TS for calculation of unsteady, viscous, compressible and heat-conductive gas flows

The recent trend of using Graphics Processing Units (GPU's) for high performance computations is driven by the high ratio of price performance for these units, complemented by their cost effectiveness. At first glance, computational fluid dynamics (CFD) solvers match perfectly to GPU resources because these solvers make intensive calculations and use relatively little memory. Nevertheless, there are scarce results about the practical use of this serious advantage of GPU over CPU, especially for calculations of viscous, compressible, heat-conductive gas flows with double precision accuracy. In this paper, two GPU algorithms according to time approximation of convective terms were presented: explicit and implicit scheme. To decrease data transfers between device memories and increase the arithmetic intensity of a GPU code we minimize the number of kernels. The GPU algorithm was implemented in one kernel for the implicit scheme and two kernels for the explicit scheme. The numerical equations were put together using macros and optimization, data copy from global to private memory, and data reuse were left to the compiler. Thus keeps the code simpler with excellent maintenance. As a test case, we model the flow past squares in a microchannel at supersonic speed. The tests show that overall speedup of AMD Radeon R9 280X is up to 102x compared to Intel Core i5-4690 core and up to 184x compared to Intel Core i7-920 core, while speedup of NVIDIA Tesla M2090 is up to 11x compared to Intel Core i5-4690 core and up to 20x compared to Intel Core i7-920 core. Memory requirements of GPU code are improved compared to CPU one. It requires 1[GB] global memory for 5.9 million finite volumes that are two times less compared to C++ CPU code. After all the code is simple, portable (written in OpenCL), memory efficient and easily modifiable moreover demonstrates excellent performance.

cs.CE↗