SUMMARYThis paper presents a novel model reduction method: Deep Learning Reduced Order Model (DLROM), which is based on proper orthogonal decomposition (POD) and deep learning methods. The deep learning approach is a recent technological advancement in the field of artificial neural networks. It has the advantage of learning the non-linear system with multiple levels of representation and predicting data. In this work, the training data are obtained from high fidelity model solutions at selected time levels. The Long Short-Term Memory network (LSTM) is used to construct a set of hypersurfaces representing the reduced fluid dynamic system. The model reduction method developed here is independent of the source code of the full physical system. The reduced order model (ROM) based on deep learning has been implemented within an unstructured mesh finite element fluid model. The performance of the new ROM is evaluated using two numerical examples: an ocean gyre and flow past a cylinder. These results illustrate that the CPU cost is reduced by several orders of magnitude whilst providing reasonable accuracy in predictive numerical modelling.
A high precision calibration of the nonlinearity in the energy response of the Daya Bay Reactor Neutrino Experiment's antineutrino detectors is presented in detail. The energy nonlinearity originates from the particle-dependent light yield of the scintillator and charge-dependent electronics response. The nonlinearity model is constrained by γ calibration points from deployed and naturally occurring radioactive sources, the β spectrum from 12 B decays, and a direct measurement of the electronics nonlinearity with a new flash analog-to-digital converter readout system. Less than 0.5% uncertainty in the energy nonlinearity calibration is achieved for positrons of kinetic energies greater than 1 MeV.
We report white organic light-emitting diodes (WOLEDs) combining vacuum deposited blue electrophosphorescent devices with red surface color conversion layers (CCLs). With an iridium (III) [bis(4,6-di-fluoropheny)- pyridinato-N,C(2')] picolinate (FIrpic) doped 4,4'-bis(9-carbazolyl)-2,2'-dimethyl-biphenyl (CDBP) blue electrophosphorescent light emitting layer, and an appropriate red surface CCL containing 4-(dicyanomethylene)-2-tert-butyl-6-(1,1,7,7-tetramethyljulolidyl-9-enyl)-4H-pyran (DCJTB), the WOLED generate high efficiency and very pure white light with a peak luminous (power) efficiency of 18.1 cd/A (9.5 lm/W) and CIE coordinates of (0.32, 0.31), very close to the equal-energy white, respectively. Moreover, the output spectra and CIE coordinates of the WOLED show no significant change at a wide range of current density.
A non-intrusive model reduction computational method using hypersurfaces representation has been developed for reservoir simulation and further applied to 3D fluvial channel problems in this work. This is achieved by a combination of a radial basis function (RBF) interpolation and proper orthogonal decomposition (POD) method. The advantage of the method is that it is generic and non-intrusive, that is, it does not require modifications to the original complex source code, for example, a 3D unstructured mesh control volume finite element (CVFEM) reservoir model used here. The capability of this non-intrusive reduced order model (NIROM) based on hypersurfaces representation has been numerically illustrated in a horizontally layered porous media case, and then further applied to a 3D complex fluvial channel case. By comparing the results of the NIROM against the solutions obtained from the high fidelity full model, it is shown that this NIROM results in a large reduction in the CPU computation cost while much of the details are captured.
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