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Description
Data Set Description: The dataset consists of two folders. The files used for training are stored inside the folder "train." The files used for testing are stored inside the folder "test." There are 16,000 simulations in total, divided into 15,000 training cases and 1,000 test cases. The file “inputs_15k_train.npy” contains a matrix of shape (15000,4), where the rows correspond to the first 15,000 simulations and columns are sx, sy, wu, and wv (source location in x, source location in y, wind velocity projection in x, and wind velocity projection in y). Similarly, the file “inputs_1k_test.npy” contains a matrix of shape (1000,4), where the rows correspond to the last 1,000 simulations, and columns are also sx, sy, wu, and wv. The file “RGB_deposition_15k_train.npy” contains a matrix of shape (15000,1000,1000,3) corresponding to (training case, height, width, RGB), respectively. Similarly, the file “RGB_deposition_1k_test.npy” contains a matrix of shape (1000,1000,1000,3) corresponding to (test case, height, width, RGB), respectively. Purpose: Based on simulations of the atmospheric transport and dispersion of a passive tracer using a computational fluid dynamics (CFD) model, we use autoencoder-based models to learn complex plume spatial patterns (a megapixel image) from four scalars (sx,sy,wu,wv). In other words, the goal is to predict a deposition image given its associated release location and wind velocity (four scalar quantities). We are interested in the mapping: [sx,sy,wu,wv]→[height×width×RGB channel]. The publication associated with the data set can be found in [1]. References: [1] Fernández-Godino, M. G., Lucas, D. D., & Kong, Q. Predicting wind-driven spatial deposition through simulated color images using deep autoencoders. Scientific Reports, 2023 13(1), 1394, https://doi.org/10.1038/s41598-023-28590-4. [2] Gowardhan, A., D. McGuffin, D. D. Lucas, S. Neuscamman, O. Alvarez, and L. Glascoe, Large Eddy Simulations of Turbulent and Buoyant Flows in Urban and Complex Terrain Areas Using the Aeolus Model, Atmosphere 2021, 12(9), 1107, https://doi.org/10.3390/atmos12091107. This research was funded by the National Nuclear Security Administration, Defense Nuclear Nonproliferation Research and Development (NNSA DNN R&D), was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, and is released under tracking number LLNL-MI-84834. Research Data Curation Program, UC San Diego, La Jolla, 92093-0175 (https://lib.ucsd.edu/rdcp) Fernandez-Godino, M. Giselle; Lucas, Donald D.; Gunawardena, Nipun (2023). Computational Fluid Dynamics Simulation Data of Spatial Deposition. In Lawrence Livermore National Laboratory (LLNL) Open Data Initiative. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0D50N50
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