%0 Generic %1 praditia2020inputoutput %A Praditia, Timothy %D 2020 %K PN6 EXC2075 darus %R 10.18419/darus-633 %T Input-Output Dataset for Physics-inspired Artificial Neural Network for Dynamic System %X This dataset contains two .mat files, one pre-processed (direct simulation results) and the other one is with added noise. The simulated problem is a thermochemical energy storage problem using CaO/Ca(OH)2 as the material choice. This dataset is used as input-output data pairs necessary for training, validating, and testing the ANN. The input data consist of CaO density, Ca(OH)2 density, CaO specific heat capacity, Ca(OH)2, porosity, permeability, reaction rate constant, initial and outlet pressure, initial temperature, inlet temperature, N2 molar inflow rate, H2O molar inflow rate, and specific reaction enthalpy. The output data consist of pressure, temperature, CaO volume fraction, and H2O molar fraction. Additionally, there is an automated script file for the DuMuX run.

@misc{praditia2020inputoutput, abstract = {This dataset contains two .mat files, one pre-processed (direct simulation results) and the other one is with added noise. The simulated problem is a thermochemical energy storage problem using CaO/Ca(OH)2 as the material choice. This dataset is used as input-output data pairs necessary for training, validating, and testing the ANN. The input data consist of CaO density, Ca(OH)2 density, CaO specific heat capacity, Ca(OH)2, porosity, permeability, reaction rate constant, initial and outlet pressure, initial temperature, inlet temperature, N2 molar inflow rate, H2O molar inflow rate, and specific reaction enthalpy. The output data consist of pressure, temperature, CaO volume fraction, and H2O molar fraction. Additionally, there is an automated script file for the DuMuX run.}, added-at = {2024-07-03T10:45:52.000+0200}, affiliation = {Praditia, Timothy/Universität Stuttgart}, author = {Praditia, Timothy}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2cdd13dd38e0b03302d9ecb41c4bc7f2a/exc2075}, doi = {10.18419/darus-633}, howpublished = {Dataset}, interhash = {3ca8f1b169c650ca2cb27512d841fcd4}, intrahash = {cdd13dd38e0b03302d9ecb41c4bc7f2a}, keywords = {PN6 EXC2075 darus}, note = {Related to: Praditia, T., Walser, T., Oladyshkin, S. and Nowak, W.: Improving Thermochemical Energy Storage dynamics forecast with Physics-Inspired Neural Network architecture. Energies 2020}, orcid-numbers = {Praditia, Timothy/0000-0003-3619-9122}, timestamp = {2024-07-03T10:45:52.000+0200}, title = {Input-Output Dataset for Physics-inspired Artificial Neural Network for Dynamic System}, year = 2020 }