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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Identification of hydrochemical facies in the Roswell Artesian Basin, New Mexico (USA), using graphical and statistical methods
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Recent progress and challenges in exploiting graphics processors in computational fluid dynamics
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Continuous toolpath planning in a graphical framework for sparse infill additive manufacturing
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iMatTOUGH: An open-source Matlab-based graphical user interface for pre- and post-processing of TOUGH2 and iTOUGH2 models
TOUGH2 and iTOUGH2 are powerful models that simulate the heat and fluid flows in porous and fracture media, and perform parameter estimation, sensitivity analysis and uncertainty propagation analysis. However, setting up the input files is not only tedious, but error prone, and processing output files is time consuming. Here, we present an open source Matlab-based tool (iMatTOUGH) that supports the generation of all necessary inputs for both TOUGH2 and iTOUGH2 and visualize their outputs. The tool links the inputs of TOUGH2 and iTOUGH2, making sure the two input files are consistent. It supports the generation of rectangular computational mesh, i.e., it automatically generates the elements and connections as well as their properties as required by TOUGH2. The tool also allows the specification of initial and time-dependent boundary conditions for better subsurface heat and water flow simulations. The effectiveness of the tool is illustrated by an example that uses TOUGH2 and iTOUGH2 to estimate soil hydrological and thermal properties from soil temperature data and simulate the heat and water flows at the Rifle site in Colorado.
Urban land teleconnections in the United States: A graphical network approach
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Graphics Processing Unit acceleration of the Random Phase Approximation in the projector augmented wave method
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Xclaim: A graphical interface for the calculation of core-hole spectroscopies
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LandMOD ET mapper: A new matlab-based graphical user interface (GUI) for automated implementation of SEBAL and METRIC models in thermal imagery
Remote sensing based surface energy balance (SEB) models, such as SEB Algorithm for Land (SEBAL) and Mapping evapotranspiration (ET) at high Resolution with Internalized Calibration (METRIC) are widely used to monitor ET. However, their operational use is challenged by model complexity and the tedious process of selecting hot and cold pixels. We developed a new Matlab-based ET mapping toolbox named “LandMOD ET Mapper” for automated implementation of these models to facilitate their widespread applications among new users. The toolbox is demonstrated using one Landsat and one Moderate Resolution Imaging Spectroradiometer (MODIS) Terra image covering three AmeriFlux sites in Nebraska, U.S. Daily ET estimates were comparable with the observed ET for five days when near cloud-free Landsat and MODIS Terra images were available in the 2006 maize growing season. The toolbox provides a viable ET mapping option to new and inexperienced users with reduced time and labor demands.
Exasim: Generating discontinuous Galerkin codes for numerical solutions of partial differential equations on graphics processors
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Quantum Chemistry for Solvated Molecules on Graphical Processing Units Using Polarizable Continuum Models
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Ab Initio Interactive Molecular Dynamics on Graphical Processing Units (GPUs)
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Automated Code Engine for Graphical Processing Units: Application to the Effective Core Potential Integrals and Gradients
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High-Performance Semiempirical Excited-State Molecular Dynamics Powered by Graphics Processing Units
Here, this Letter introduces excited-state molecular dynamics in PYSEQM, a GPU-accelerated semiempirical quantum chemistry engine implemented in PyTorch. The new module enables Born–Oppenheimer molecular dynamics (BOMD) using configuration-interaction singles and random phase approximation for excited states, allowing long trajectories and large statistical ensembles to be simulated efficiently on a single GPU. We also implement an extended Lagrangian excited-state BOMD (XL-ESMD) scheme that propagates auxiliary electronic variables, enabling relaxed ground and excited-state convergence thresholds without compromising energy conservation. The excited-state BOMD implementation scales smoothly from small chromophores to a nearly 900-atom dendrimer (taking 6.5 s per MD step). PYSEQM also supports batched execution, allowing many geometries or trajectories to be evaluated in a single GPU launch, substantially increasing throughput and making ensemble-based protocols routine. As a demonstration, we compute absorption, emission, and infrared spectra from trajectories propagated on the ground and first excited states. The XL-ESMD scheme yields identical spectra at significantly lower computational cost, establishing the role of extended Lagrangian based dynamics for efficient excited-state BOMD simulations. Beyond raw performance, PYSEQM’s PyTorch foundation provides automatic differentiation for forces, efficient GPU batching, and seamless interfacing with machine learning models. These capabilities position PYSEQM as a practical platform for machine learning-augmented excited-state dynamics and lay the foundation for future data-driven nonadiabatic excited-state dynamics modeling of ultrafast spectroscopic probes.
Acceleration of the Parameterization of Unified Microphysics Across Scales (PUMAS) on the Graphics Processing Unit (GPU) With Directive-Based Methods
Cloud microphysics is one of the most time-consuming components in a climate model. In this study, we port the cloud microphysics parameterization in the Community Atmosphere Model (CAM), known as Parameterization of Unified Microphysics Across Scales (PUMAS), from CPU to GPU to seek a computational speedup. The directive-based methods (OpenACC and OpenMP target offload) are determined as the best fit specifically for our development practices, which enable a single version of source code to run either on the CPU or GPU, and yield a better portability and maintainability. Their performance is first examined in a PUMAS stand-alone kernel and the directive-based methods can outperform a CPU node as long as there is enough computational burden on the GPU. A consistent behavior is observed when we run PUMAS on the GPU in a practical CAM simulation. A 3.6× speedup of the PUMAS execution time, including data movement between CPU and GPU, is achieved at a coarse horizontal resolution (8 NVIDIA V100 GPUs against 36 Intel Skylake CPU cores). This speedup further increases up to 5.4× at a high resolution (24 NVIDIA V100 GPUs against 108 Intel Skylake CPU cores), which highlights the fact that GPU favors larger problem size. This study demonstrates that using GPU in a CAM simulation can save noticeable computational costs even with a small portion of code being GPU-enabled. Therefore, we are encouraged to port more parameterizations to GPU to take advantage of its computational benefit.
Graphical Gaussian Process Regression Model for Aqueous Solvation Free Energy Prediction of Organic Molecules in Redox Flow Battery
The solvation free energy of organic molecules is a critical parameter in determining emergent properties such as solubility, liquid-phase equilibrium constants, and pKa and redox potentials in an organic redox flow battery. In this work, we present a machine learning (ML) model that can learn and predict the aqueous solvation free energy of an organic molecule using Gaussian process regression method based on a new molecular graph kernel. To investigate the performance of the ML model on electrostatic interaction, the nonpolar interaction contribution of solvent and the conformational entropy of solute in solvation free energy, three data sets with implicit or explicit water solvent models, and contribution of conformational entropy of solute are tested. We demonstrate that our ML model can predict the solvation free energy of molecules at chemical accuracy with a mean absolute error of less than 1 kcal/mol for subsets of the QM9 dataset and the Freesolv database. To solve the general data scarcity problem for a graph-based ML model, we propose a dimension reduction algorithm based on the distance between molecular graphs, which can be used to examine the diversity of the molecular data set. It provides a promising way to build a minimum training set to improve prediction for certain test sets where the space of molecular structures is predetermined.