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GPU based general-purpose parallel computing to solve nuclear reactor in-core fuel management design and operation problem
Prayudhatama D.a, Waris A.a, Kurniasih N.a, Kurniadi R.a
a Bosscha Laboratory, Department of Physics, Institut Teknologi Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]In-core fuel management study is a crucial activity in nuclear power plant design and operation. Its common problem is to find an optimum arrangement of fuel assemblies inside the reactor core. Main objective for this activity is to reduce the cost of generating electricity, which can be done by altering several physical properties of the nuclear reactor without violating any of the constraints imposed by operational and safety considerations. This research try to address the problem of nuclear fuel arrangement problem, which is, leads to the multi-objective optimization problem. However, the calculation of the reactor core physical properties itself is a heavy computation, which became obstacle in solving the optimization problem by using genetic algorithm optimization. This research tends to address that problem by using the emerging General Purpose Computation on Graphics Processing Units (GPGPU) techniques implemented by C language for CUDA (Compute Unified Device Architecture) parallel programming. By using this parallel programming technique, we develop parallelized nuclear reactor fitness calculation, which is involving numerical finite difference computation. This paper describes current prototype of the parallel algorithm code we have developed on CUDA, that performs one hundreds finite difference calculation for nuclear reactor fitness evaluation in parallel by using GPU G9 Hardware Series developed by NVIDIA. © 2010 American Institute of Physics.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]CUDA,genetic algorithm optimization,GPU,in-core fuel management,parallel computation[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1063/1.3462749[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]