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000902415 0247_ $$2doi$$a10.1007/978-3-030-90539-2_1
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000902415 037__ $$aFZJ-2021-04237
000902415 041__ $$aEnglish
000902415 1001_ $$0P:(DE-Juel1)177985$$aRüttgers, Mario$$b0$$eCorresponding author
000902415 1112_ $$aISC High Performance 2021$$cFrankfurt$$d2021-06-24 - 2021-07-02$$gISC2021$$wGermany
000902415 245__ $$aMachine-Learning-Based Control of Perturbed and Heated Channel Flows
000902415 260__ $$bSpringer$$c2021
000902415 29510 $$aHigh Performance Computing / Jagode, Heike (Editor)
000902415 300__ $$a7 - 22
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000902415 3367_ $$0PUB:(DE-HGF)7$$2PUB:(DE-HGF)$$aContribution to a book$$mcontb
000902415 4900_ $$aLecture Notes in Computer Science$$v12761
000902415 520__ $$aA reinforcement learning algorithm is coupled to a thermal lattice-Boltzmann method to control flow through a two-dimensional heated channel narrowed by a bump.  The algorithm is allowed to change the disturbance factor of the bump and receives feedback in terms of the pressure loss and temperature increase between the inflow and outflow region of the channel. It is trained to modify the bump such that both fluid mechanical properties are rated equally important.  After a modification,  a new simulation is initialized using the modified geometry and the flow field computed in the previous run.  The thermal lattice-Boltzmann method is validated for a fully developed isothermal channel flow.  After 265 simulations,  the trained algorithm predicts an averaged disturbance factor that deviates by less than 1% from the reference solution obtained from 3,400 numerical simulations using a parameter sweep over the disturbance factor.  The error is reduced to less than 0.1% after 1,450 simulations.  A comparison of the temperature, pres- sure, and streamwise velocity distributions of the reference solution with the solution after 1,450 simulations along the line of the maximum velocity component in streamwise direction shows only negligible differences.  The presented method is hence a valid method for avoiding expensive parameter space explorations and promises to be effective in supporting shape optimizations for more complex configurations,  e.g.,  in finding optimal nasal cavity shapes.
000902415 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
000902415 536__ $$0G:(EU-Grant)951733$$aRAISE - Research on AI- and Simulation-Based Engineering at Exascale (951733)$$c951733$$fH2020-INFRAEDI-2019-1$$x1
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000902415 7001_ $$00000-0001-7895-761X$$aWaldmann, Moritz$$b1
000902415 7001_ $$00000-0002-3472-1813$$aSchröder, Wolfgang$$b2
000902415 7001_ $$0P:(DE-Juel1)165948$$aLintermann, Andreas$$b3
000902415 773__ $$a10.1007/978-3-030-90539-2_1
000902415 8564_ $$uhttps://juser.fz-juelich.de/record/902415/files/Invoice_2936170321.pdf
000902415 8564_ $$uhttps://juser.fz-juelich.de/record/902415/files/High%20Performance%20Computing%2C%20Proceedings%20of%20the%2036th%20International%20Conference%2C%20ISC%20High%20Performance%202021%20-%202021%20-%20Machine-Learning-Based.pdf$$yOpenAccess
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000902415 9141_ $$y2021
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