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32 results for “Thermodynamic modeling”
Additional steady-state simulations of Miocene Antarctic ice-sheet variability using 3D thermodynamical ice-sheet model IMAU-ICE
<div> </div> <div> <div> <div>We supplement our previous dataset (<a href="https://doi.pangaea.de/10.1594/PANGAEA.939114">doi:10.1594/PANGAEA.939114</a>), with six additional steady-state simulations of the Miocene Antarctic ice sheet using the reference Miocene settings.</div> <div> </div> <div>IMAU-ICE was run using a 40x40km grid covering the Antarctic continent. Initial conditions were obtained from reconstructions of the Antarctic bathymetry and bedrock topography pertaining to 23 to 24 million years (Myr) ago (dataset <a href="https://doi.pangaea.de/10.1594/PANGAEA.923109" target="_self">doi:10.1594/PANGAEA.923109</a>). The simulations were forced by climate input data obtained from GENESIS simulations with varying CO2 levels (280 to 840 ppm) and Antarctic ice sheet cover (no ice to a large East-Antarctic ice sheet), and with present-day insolation. We utilized a matrix interpolation method to construct the time-varying climate forcing, based on the prescribed CO2 levels and ice cover simulated by IMAU-ICE.</div> <div> </div> <div>For each simulation, we provide the run script, 1D output variables including CO2 level and the sea level contribution of the Antarctic ice sheet, and 3D output variables including ice thickness, bedrock and surface height, surface mass balance, basal mass balance, ice velocities, and ice temperatures. For more information, please contact L.B. Stap at l.b.stap@uu.nl.</div> </div> </div>
Data for: Deriving early hydration cement paste phase assemblage, microstructure development and elastic properties using thermodynamic simulation and multi-scale material modeling
<h2>Description</h2> <p>DATA REPOSITORY FOR</p> <p>Title: Deriving early hydration cement paste phase assemblage, microstructure development and elastic properties using thermodynamic simulation and <br> multi-scale material modeling<br>By: Eva Jägle, Jithender J. Timothy, Daniel Jansen, Alisa Machner<br>Accepted by: Cement and Concrete Research</p> <p>This dataset presents the data of the paper 'Deriving early hydration cement paste phase assemblage, microstructure development and elastic properties using thermodynamic simulation and multi-scale material modeling' submitted to and accepted by Cement and Concrete Research. The dataset follows the structure of the paper such that the calculations described therein can be reproduced.</p> <p>Data is available on three types of cement: Two ordinary Portland cements of different grinding fineness (CEM I 42.5 R und CEM I 52.5 R) and one limestone-containing blended cement (CEM II/A-LL 42.5 R). The data refer to the first 24 hours of hydration and temperature conditions of 20°C (for CEM I 42.5 R, CEM I 52.5 R, CEM II/A-LL 42.5 R) and 35°C (for CEM I 52.5 R). All data were retrieved for cement pastes with a water-to-cement ratio of 0.45.</p> <p>The dataset contains raw and processed data from quantitative X-ray diffraction, 5PL cement dissolution fitting, thermodynamic simulation with GEMS, multi-scale material modeling, ultrasonic testing and Vicat penetration tests. The data is mainly available in .xlsx files together with short descriptions in ReadMe.txt files.</p>
Modeled dynamic and thermodynamic sea ice growth in the Arctic 1980-2019 from NAOSIM
<p>This data set is related to the paper "Evidence for an Increasing Role of Ocean Heat in Arctic Winter Sea Ice Growth" by Ricker et al. (2021). Please refer to this study for further details.</p> <p>Ricker, R., Kauker, F., Schweiger, A., Hendricks, S., Zhang, J., & Paul, S. (2021). Evidence for an Increasing Role of Ocean Heat in Arctic Winter Sea Ice Growth, Journal of Climate, 34(13), 5215-5227. Retrieved Nov 24, 2022, from https://journals.ametsoc.org/view/journals/clim/34/13/JCLI-D-20-0848.1.xml</p>
A simple model for daily basin-wide thermodynamic sea ice thickness growth retrieval: Data
<p>Data for:</p> <p>Anheuser, J., Liu, Y., and Key, J.: A daily basin-wide sea ice thickness retrieval methodology: Stefan's Law Integrated Conducted Energy (SLICE), The Cryosphere Discuss. [preprint], <a href="https://doi.org/10.5194/tc-2021-333">https://doi.org/10.5194/tc-2021-333</a>, in review, 2021.</p> <p> </p> <p>Scripts for producing data and figures can be found at:</p> <p>https://doi.org/10.5281/zenodo.6561431</p> <p> </p> <p> </p>
Exact Thermodynamics and Transport in the Classical Sine-Gordon Model
<p>Raw data and Mathematica Notebook for the thermodynamics of the classical sine-Gordon model.</p> <p>It can be found:</p> <ol> <li>A transfer matrix code for equilibrium correlation functions.</li> <li>A solver for the classical Thermodynamic Bethe Ansatz and partitioning protocol.</li> <li>Monte Carlo data of the partitioning protocol.</li> </ol> <p> </p>
NOAA PSL thermodynamic profiles retrieved from a combination of active and passive remote sensors and numerical weather prediction models with the optimal estimation physical retrieval TROPoe at Platteville, CO, USA
<p>This dataset contains retrieved profiles of thermodynamic variables obtained using the Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe) physical retrieval from various combinations of input data collected by passive and active remote sensing instruments, in-situ surface platforms, and numerical weather prediction models deployed at the Platteville, CO, USA, site in fall 20221-winter 2022. Among the employed instruments are Microwave Radiometers (MWRs), Infrared Spectrometers (IRS), Radio Acoustic Sounding Systems (RASS), ceilometers, surface sensors, and information from the operational Rapid Refresh numerical weather prediction model.</p> <p>The dataset also includes 15 radiosounding launched for assessing the retrievals.</p> <p>For further information, please see:</p> <p>Bianco, L., Adler, B., Bariteau, L., Djalalova, I. V., Myers, T., Pezoa, S., Turner, D. D., and Wilczak, J. M.: Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2023-263, in review, 2024.</p>
Thermodynamics of the metal-insulator transition in the extended Hubbard model from determinantal quantum Monte Carlo
<p>This zip archive contains the DQMC data sets for the \beta=1/T resolved double<br> occupancy, internal energy per site, antiferromagnetic structure factor and<br> charge density wave structure factor obtained with the ALF Code.<br> The computations have been carried out on a square lattice at half filling for<br> the Hubbard model, the U-V model and the long-range Coulomb(LRC)-Hubbard model.<br> Every model is computed at fixed U/t=1.9.</p> <p>The naming convention of the csv-files is the following:<br> V0.0 = Hubbard model<br> V0.x = U-V model with V/t=0.x<br> Vcx.x = LRC-Hubbard model with V_C/t=x.x<br> <br> - dat_docc_{\Delta\tau}_V{x}.csv = double occupancy at \Delta\tau={0.05, 0.1, 0.2}<br> - dat_docc_{\Delta\tau}_V{x}_err.csv = corresponding statistical error<br> - dat_energy_0.1_V{x}.csv = internal energy at \Delta\tau=0.1<br> - dat_energy_0.1_V{x}_err.csv = corresponding statistical error<br> - dat_saf_0.1_V{x}.csv = antiferromagnetic structure factor at \Delta\tau=0.1<br> - dat_saf_0.1_V{x}_err.csv = corresponding statistical error<br> - dat_scdw_0.1_V{x}.csv = charge density wave structure factor at \Delta\tau=0.1<br> - dat_scdw_0.1_V{x}_err.csv = corresponding statistical error</p> <p>The structure of each file is to be read column-wise:<br> \beta, L=8, L=10, L=12, L=16, L=18, L=20</p> <p> </p>
The planted XY model: thermodynamics and inference
<p>Data and codes for figures in manuscript "The planted XY model: thermodynamics and inference"</p>
Thermophysical properties of hydrogen mixtures relevant for the development of the hydrogen economy: Review of available experimental data and thermodynamic models
<p>File: 1-s2.0-S096014812201271X-mmc1.docx</p> <p>This file (DOCX) contains additional figures associated with the hydrogen-containing systems.</p> <p>File: 1-s2.0-S096014812201271X-mmc2.xlsx</p> <p>This file (XLSX) contains tables with the coordinates of the VLE associated with the hydrogen-containing systems.</p> <p>File: 1-s2.0-S096014812201271X-mmc3.xlsx</p> <p>This file (XLSX) contains tables with the density data associated with the hydrogen-containing systems.</p> <p>File: 1-s2.0-S096014812201271X-mmc4.xlsx</p> <p>This file (XLSX) contains tables with the calorific data associated with the hydrogen-containing systems.</p> <p> </p> <p>File: 2022_Renewable Energy_Manuscript_repository.docx</p> <p>This is an author-created, un-copyedited version of an article accepted for publication in Renewable Energy (2022, 198, 1398-1429). The editor of the Journal is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The definitive publisher-authenticated, Open-Access version is available online at: https://doi.org/10.1016/j.renene.2022.08.096</p>
Thermodynamic characterization of the (H2 + C3H8) system significant for the hydrogen economy: Experimental (p, rho, T) determination and equation of-state modelling
<p>File: 2023_IJHE_Manuscript_repository.docx</p> <p>This is an author-created, un-copyedited version of an article accepted for publication in the International Journal of Hydrogen Energy (2023, 48 (23), 8645-8667). The editor of the Journal is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The definitive publisher-authenticated, Open-Access version is available online at: https://doi.org/10.1016/j.ijhydene.2022.11.170<br><br>File: 2023_IJHE_Results_Repository.xlsx</p> <p>This is the MS Excel data file of the paper. </p> <p> </p> <p> </p>
Dataset - Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models
<p><strong>Simulation data needed to reproduce the results from the manuscript “Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models”</strong><br> by V. Pandey, N. Hadadi and V. Hatzimanikatis</p> <p>"REMI manuscript - simData" folder contains all simulation data which can be used to generate results of the paper: <br> • Expression_data: This folder contains Transcriptomics data from both studies: Ishii et al (see test_expr.mat) and Holm et al.<br> • Fluxdata: Fluxomics data can be found form the studies Ishii et al and Holm et al.<br> • Metabolomics: This contains metabolomics data of aforementioned both studies.<br> • ModelsSolutions: We generated different models using with thermodynamics (TGex, TGexM, TM) and without thermodynamics models (Gex, GexM, M). Gex indicates integration with only gene expression, GexM indicates gene expression and metabolite, and M indicates only metabolites. ‘T’ is used for thermodynamic models. Models for different mutants and conditions (e.g. pgm, pgi) can be found in the corresponding folders (TGex, TGexM, TM, Gex, GexM, and M). Variables with the ‘store’ tag comprises flux solutions, correlation values and percentage error between simulation and experiment fluxes.<br> • AlternativeMCS: We generated alternative states for MCS and saved results.<br> • FVAMM: This is the result flux variability analysis can be found in this folder.<br> • Scatter_plot: Scatter plots indicates correlation between measured and model predicted fluxes.</p> <p> </p>
Thermodynamic model input files
<p>PerpleX (6.7.2) input files for P-X modelling of Mars mantle, olivine and orthopyroxene systems. Contains refined olivine solution model parameters O(fei), Wad(fei) and Ring(fei) to fit experiments of Katsura and Ito (1989) and Fei et al. (1991); other contents either were pre-existing or irrelevant parameterizations. </p>
Sample programs of an eco-redox model for the article: Microbial redox cycling enhances ecosystem thermodynamic efficiency and productivity
<p><span>Microbial life in low-energy ecosystems relies on individual energy conservation, optimizing </span><span>energy use in response to interspecific competition, and mutualistic interspecific syntrophy. Our study proposes a novel community-level strategy for increasing energy use efficiency. By</span> <span>utilizing a</span><span>n</span> <span>oxidation-reduction (redox) reaction network model that represents microbial redox metabolic interactions, we </span><span>investigated multiple species-level competition and cooperation within the network</span><span>. Our results suggest that microbial functional diversity allows for metabolic handoffs</span><span>, which in turn lead to increased energy use efficiency. Furthermore, the mutualistic division of labor and the resulting </span><span>complexity of redox pathways actively </span><span>drive material cycling, further promoting energy exploitation. Our findings reveal the potential of self-organized ecological interactions to develop efficient energy utilization strategies, with important implications for microbial ecosystem functioning and </span><span>co-</span><span>evolution of life and Earth.</span></p>
Modeling the thermodynamic properties of saturated lactones in non-ideal mixtures with the SAFT-γ Mie approach. JCED 2023
<p>All computational data in the publication.</p>
Sample programs of an eco-redox model for the article: Microbial redox cycling enhances ecosystem thermodynamic efficiency and productivity
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Modeled Atmospheric Optical and Thermodynamic Responses to an Exceptional Trans-Atlantic Dust Outbreak
<p>Nine WRF-Chem 3.8.0 hindcasts, each utilizing a different dust emission configuration, from 1 March – 31 May 2015, coinciding with a Saharan air layer (SAL) dust outbreak during the 2015 Caribbean drought. WRF-Chem modeled aerosol optical depth (AOD) and Gálvez-Davison Index (GDI), a convective forecasting parameter, are provided in the files. These data correspond to the results published in "Modeled Atmospheric Optical and Thermodynamic Responses to an Exceptional Trans-Atlantic Dust Outbreak" by Paul Miller, Marcus Williams, and Thomas Mote, published the <em>Journal of Geophysical Research: Atmospheres</em>.</p>
Predicting p53-Dependent Cell Transitions From Thermodynamic Models
<p>Matlab codes containing Grand Canonical Monte Carlo (GCMC) simulation codes and theoretical codes to study the first-order phase transition behavior of a malignant cell to a normal healthy cell. In this work, we develop thermodynamic models to determine the fate of a malignant cell as governed by the tumor suppressor p53 signaling network.</p>
Equation of State of Liquid Fe7C3 and Thermodynamic Modeling of the Liquidus Phase Relations in the Fe-C System
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Data from: A statistical mechanics framework for constructing non-equilibrium thermodynamic models
<p><span>Far-from-equilibrium phenomena are critical to all natural and engi</span><span>neered systems, and essential to biological processes responsible </span><span>for life. For over a century and a half, since Carnot, Clausius, Maxwell, </span><span>Boltzmann, and Gibbs, among many others, laid the foundation for </span><span>our understanding of equilibrium processes, scientists and engineers </span><span>have dreamed of an analogous treatment of non-equilibrium systems. </span><span>But despite tremendous efforts, a universal theory of non-equilibrium </span><span>behavior akin to equilibrium statistical mechanics and thermodynam</span><span>ics has evaded description. Several methodologies have proved their </span><span>ability to accurately describe complex non-equilibrium systems at </span><span>the macroscopic scale, but their accuracy and predictive capacity is </span><span>predicated on either phenomenological kinetic equations fit to mi</span><span>croscopic data, or on running concurrent simulations at the particle </span><span>level. Instead, we provide a framework for deriving stand-alone macro</span><span>scopic thermodynamics models directly from microscopic physics </span><span>without fitting in overdamped Langevin systems.</span> <span>The only neces</span><span>sary ingredient is a functional form for a parameterized, approximate </span><span>density of states, in analogy to the assumption of a uniform density </span><span>of states in the equilibrium microcanonical ensemble. We highlight </span><span>this framework's effectiveness by deriving analytical approximations </span><span>for evolving mechanical and thermodynamic quantities in a model of </span><span>coiled-coil proteins and double stranded DNA, thus producing, to the </span><span>authors' knowledge, the first derivation of the governing equations for </span><span>a phase propagating system under general loading conditions without </span><span>appeal to phenomenology. The generality of our treatment allows </span><span>for application to any system described by Langevin dynamics with </span><span>arbitrary interaction energies and external driving, including colloidal </span><span>macromolecules, hydrogels, and biopolymers.</span></p>
Data set for "Intertwined spin, charge, and pair correlations in the two-dimensional Hubbard model in the thermodynamic limit"
<p>This data set is for the paper "Intertwined spin, charge, and pair correlations in the two-dimensional Hubbard model in the thermodynamic limit". It contains the raw DCA HD5 and DQMC plain text output files, as well as the scripts and final processed data used to generate figures 1-5 of the main text and supplementary figures 1-12. Copies of the figures and latex files are also included for completeness. </p>
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