Reducing spatial discretization error on coarse CFD simulations using an openFOAM-embedded deep learning framework

We propose a method for reducing the spatial discretization error of coarse computational fluid dynamics (CFD) problems by enhancing the quality of low-resolution simulations using deep learning. We feed the model with fine-grid data after projecting it to the coarse-grid discretization. We substitute the default differencing scheme for the convection term by a feed-forward neural network that interpolates velocities from cell centers to face values to produce velocities that approximate the down-sampled fine-grid data well. The deep learning framework incorporates the open-source CFD code OpenFOAM, resulting in an end-to-end differentiable model. We automatically differentiate the CFD physics using a discrete adjoint code version. We present a fast communication method between TensorFlow (Python) and OpenFOAM (c++) that accelerates the training process. We applied the model to the flow past a square cylinder problem, reducing the error from 120% to 25% in the velocity for simulations inside the training distribution compared to the traditional solver using an x8 coarser mesh. For simulations outside the training distribution, the error reduction in the velocities was about 50%. The training is affordable in terms of time and data samples since the architecture exploits the local features of the physics.PID2023-146678OB-I00 PRE2020-09309

Untersuchung der atmosphaerischen Belastung, des weitraeumigen Transports und des Verbleibs von polychlorierten Dibenzodioxinen, Dibenzofuranen und coplanaren Biphenylen in ausgewaehlten Gebieten Deutschlands. Anhang Schlussbericht

The atmospheric deposition of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/F) was investigated for the purpose of development and field validation of methods for quantifying deposition rates and for obtaining a better understanding of deposition processesIn diesem Vorhaben wurde die atmosphaerische Deposition von polychlorierten Dibenzo-p-dioxinen und Dibenzofuranen (PCDD/F) untersucht. Die Ziele waren die Entwicklung und Feldvalidierung von Methoden zur Quantifierung der Depositionsfluesse sowie die Gewinnung eines besseren Verstaendnisses fuer die Depositionsvorgaenge. (orig.)SIGLEAvailable from TIB Hannover: RN 8908(99-027,2) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekUmweltbundesamt, Berlin (Germany)DEGerman