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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

KETCHUP: Parameterizing of large-scale kinetic models using multiple datasets with different reference states

Large-scale kinetic models provide the computational means to dynamically link metabolic reaction fluxes to metabolite concentrations and enzyme levels while also conforming to substrate level regulation. However, the development of broadly applicable frameworks for efficiently and robustly parameterizing models remains a challenge. Challenges arise due to both the heterogeneity, paucity, and difficulty in obtaining flux and/or concentration data but also due to the computational difficulties of the underlying parameter identification problem. Both the computational demands for parameterization, degeneracy of obtained parameter solutions and interpretability of results has so far limited widespread adoption of large-scale kinetic models despite their potential. Herein, we introduce the Kinetic Estimation Tool Capturing Heterogeneous Datasets Using Pyomo (KETCHUP), a flexible parameter estimation tool that leverages a primal-dual interior-point algorithm to solve a nonlinear programming (NLP) problem that identifies a set of parameters capable of recapitulating the (non)steady-state fluxes and concentrations in wild-type and perturbed metabolic networks. KETCHUP is benchmarked against previously parameterized large-scale kinetic models demonstrating an at least an order of magnitude faster convergence than the tool K-FIT while at the same time attaining better data fits. Finally, this versatile toolbox accepts different kinetic descriptions, metabolic fluxes, enzyme levels and metabolite concentrations, under either steady-state or instationary conditions to enable robust kinetic model construction and parameterization. KETCHUP supports the SBML format and can be accessed at https://github.com/maranasgroup/KETCHUP.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Quantifying Dissolution Dynamics in Porous Media Using a Spatial Flow Focusing Profile

Abstract The diverse range of patterns in porous media formed by dissolution processes depends on the relative magnitude of flow, transport, and chemical reactions at pore surfaces. However, distinguishing between regimes often relies solely on qualitative, visual comparisons of emergent structures. Here, we propose a quantitative measure capable of identifying different regimes using the concept of the spatial flow focusing profile, which segments the medium into cross sections along the flow direction to calculate the flow focusing index for each section. We employ this measure in numerical simulations of a dissolving porous medium using a pore network model. We obtain a morphological phase diagram of dissolution patterns, which we characterize using the flow focusing profile. In particular, we demonstrate that analyzing the temporal changes in the profile allows one to quantitatively distinguish between wormholing and channeling. The transition between them is shown to be affected by the heterogeneity of the system.

58 GEOSCIENCES↗
DOE OSTI2021

Machine learned features from density of states for accurate adsorption energy prediction

Materials databases generated by high-throughput computational screening, typically using density functional theory (DFT), have become valuable resources for discovering new heterogeneous catalysts, though the computational cost associated with generating them presents a crucial roadblock. Hence there is a significant demand for developing descriptors or features, in lieu of DFT, to accurately predict catalytic properties, such as adsorption energies. Here, we demonstrate an approach to predict energies using a convolutional neural network-based machine learning model to automatically obtain key features from the electronic density of states (DOS). The model, DOSnet, is evaluated for a diverse set of adsorbates and surfaces, yielding a mean absolute error on the order of 0.1 eV. In addition, DOSnet can provide physically meaningful predictions and insights by predicting responses to external perturbations to the electronic structure without additional DFT calculations, paving the way for the accelerated discovery of materials and catalysts by exploration of the electronic space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Union: An Automatic Workload Manager for Accelerating Network Simulation

With the rapid growth of the machine learning applications, the workloads of future HPC systems are anticipated to be a mix of scientific simulation, big data analytics, and machine learning applications. Simulation is a great research vehicle to understand the performance implications of co-running scientific applications with big data and machine learning workloads on large-scale systems. In this paper, we present Union, a workload manager that provides an automatic framework to facilitate hybrid workload simulation in CODES. Furthermore, we use Union, along with CODES, to investigate various hybrid workloads composed of traditional simulation applications and emerging learning applications on two dragonfly systems. The experiment results show that both message latency and communication time are important performance metrics to evaluate network interference. Network interference on HPC applications is more reflected by the message latency variation, whereas ML application performance depends more on the communication time.

heterogeneous workloads↗
DOE OSTI2023

Resolving intragranular stress fields in plastically deformed titanium using point-focused high-energy diffraction microscopy

Abstract The response of a polycrystalline material to a mechanical load depends not only on the response of each individual grain, but also on the interaction with its neighbors. These interactions lead to local, intragranular stress concentrations that often dictate the initiation of plastic deformation and consequently the macroscopic stress–strain behavior. However, very few experimental studies have quantified intragranular stresses across bulk, three-dimensional volumes. In this work, a synchrotron X-ray diffraction technique called point-focused high-energy diffraction microscopy (pf-HEDM) is used to characterize intragranular deformation across a bulk, plastically deformed, polycrystalline titanium specimen. The results reveal the heterogenous stress distributions within individual grains and across grain boundaries, a stress concentration between a low and high Schmid factor grain pair, and a stress gradient near an extension twinning boundary. This work demonstrates the potential for the future use of pf-HEDM for understanding the local deformation associated with networks of grains and informing mesoscale models. Graphical abstract

36 MATERIALS SCIENCE↗
DOE OSTI2025

Continuous snow depth and temperature measurements from dense network of above-ground distributed temperature profiling systems from 2021-09-23 to 2024-08-23, Seward Peninsula, Alaska

The dataset contains temperature measurements from distributed temperature profiling (DTP) systems (Dafflon et al., 2022; Wielandt et al., 2022; Wang et al., 2024a; Fiolleau et al., 2024) deployed vertically above the ground surface at a large number of locations from 2021 to 2024. The research is designed to improve understanding of the local heterogeneity in snow depth and snow thermal insulation dynamics, as well as their interactions in a discontinuous permafrost region (Wang et al., 2025). The DTP systems were deployed at 96 locations in a watershed along the Nome-Teller road at mile marker 27 (T27) and at 54 locations on a hillslope along the Kougarok road at mile marker 64 (K64) in the Seward Peninsula, Alaska. The probe location information is stored in Probe_locations_T27.csv and Probe_locations_K64.csv. Temperature measurements were recorded at 15-minute intervals using high-precision digital sensors (accuracy: ±0.1°C, resolution: 0.0078°C). The temperature probes, either 1.4 m or 1.6 m long, contain sensors spaced every 5 cm or 10 cm along their length. The temperature data are stored in compressed files following the format: DTP_snow_air_temperature_(site)_(start)_(end).zip, where site is either T27 or K64, and start and end represent the time series period. Within each ZIP file, individual CSV files are named by probe ID and contain temperature records at different heights above the ground surface.This dataset also includes derived snow depth time series over three snow seasons, estimated from temperature measurements. Snow depth was estimated by identifying the consecutive sensor pair that exhibited the largest drop in high-frequency temperature fluctuations (detailed in the methods). These data are stored in: Snow_depths_flags_(site)_(start)_(end).csv, which includes snow depth time series and corresponding quality flags (defined in the methods) from different probes. Additionally, the dataset includes derived metrics and supporting measurements at selected locations over two snow seasons, contributing to the manuscript of Wang et al., 2025. These locations were chosen based on the availability of high-quality snow depth time series during both seasons. The additional data include: (1) Air temperature proxies measured from the top sensors on the pole when they were not buried by snow, stored in Air_temperature_proxies_(site)_(start)_(end).csv (2) Ground interface temperature, recorded at 3 cm above the ground, stored in Ground_interface_temperature_(site)_(start)_(end).csv (3) Site characteristics, including vegetation height, elevation, and the topographic position index (TPI) within a 50 m radius, stored in Selected_probe_locations_gps_vegheight_tpi_elevation_(site).csv. These metrics were derived from 1 m resolution summer LiDAR-based digital elevation models and digital surface models from Singhania et al., 2023, DOI:10.5440/1832016. Metadata files include data descriptions (_dd.csv) for tabular data. All included files are listed and described in xxxx_flmd.csv.This dataset is an updated version of a previous archive (Wang et al., 2024b, DOI: 10.15485/2475020), incorporating multiple seasons and improved snow depth estimation. Please note that due to large amount of information present in this dataset, many specificities associated with the acquisition of snow temperature, air temperature proxy and estimation of snow depth, and the future archiving of additional datasets on the soil temperature, thaw depth and soil characteristics at these locations, the author would welcome being contacted by people planning to use this dataset.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Deep Learning Modeling Framework to Capture Mixing Patterns in Reactive-Transport Systems

Prediction and control of chemical mixing are vital for many scientific areas such as subsurface reactive transport, climate modeling, combustion, epidemiology, and pharmacology. Due to the complex nature of mixing in heterogeneous and anisotropic media, the mathematical models related to this phenomenon are not analytically tractable. Numerical simulations often provide a viable route to predict chemical mixing accurately. However, contemporary modeling approaches for mixing cannot utilize available spatial-temporal data to improve the accuracy of the future prediction and can be compute-intensive, especially when the spatial domain is large and for long-term temporal predictions. To address this knowledge gap, in this work we will present in this paper a deep learning (DL) modeling framework applied to predict the progress of chemical mixing under fast bimolecular reactions. This framework uses convolutional neural networks (CNN) for capturing spatial patterns and long short-term memory (LSTM) networks for forecasting temporal variations in mixing. By careful design of the framework—placement of non-negative constraint on the weights of the CNN and the selection of activation function, the framework ensures non-negativity of the chemical species at all spatial points and for all times. Our DL-based framework is fast, accurate, and requires minimal data for training. The time needed to obtain a forecast using the model is a fraction (≈ O(-6)) of the time needed to obtain the result using a high-fidelity simulation. To achieve an error of 10% (measured using the infinity norm) for capturing local-scale mixing features such as interfacial mixing, only 24% to 32% of the sequence data for model training is required. To achieve the same level of accuracy for capturing global-scale mixing features, the sequence data required for model training is 64% to 70% of the total spatial-temporal data. Hence, the proposed approach—a fast and accurate way to forecast long-time spatial-temporal mixing patterns in heterogeneous and anisotropic media—will be a valuable tool for modeling reactive-transport in a wide range of applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Catalytic Activation of Polyethylene Model Compounds Over Metal-Exchanged Beta Zeolites

Decomposition of polymers by heterogeneous catalysts presents a promising approach for reuse of waste plastics. Here, we demonstrated non-hydrogenative decomposition of model polyolefins over proton-form and metal (Cu, Ni) ion-exchanged beta zeolites at moderate temperatures (ca. 300 degrees C). Near complete polyolefin decomposition was observed in batch reactions monitored by thermogravimetric analysis, while decomposition at partial conversion was studied in flow reactions. Ni-exchanged zeolites produced H 2 at substantially higher rates (> 10x) than other catalysts while also uniquely resisting deactivation over time. Application of the delplot formalism offered insights into the reaction network for polyolefin decomposition over Ni/BEA, most notably that H 2 is solely a primary product. We deduce that H 2 production is catalyzed by activation of C-H bonds at ionic Ni sites, and H 2 prevents buildup of polyaromatic coke species in Ni-exchanged zeolites that deactivate Cu-exchanged and protonic zeolites.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Synthesis of PHI-type zeolite harmotome using barium as an inorganic structure-directing agent inspired from natural mineral compositions

Zeolites are nanoporous aluminosilicates widely used as commercial adsorbents, heterogeneous catalysts, and ion-exchange materials due to their unique porosity, acidity, and thermal stability. The vast majority of synthetically-realized zeolite structures are prepared under hydrothermal conditions in alkaline media, and often in the presence of an organic structure-directing agent that facilitates the crystallization of diverse porous networks. Due to economic and environmental disadvantages of organic-based syntheses, it is often desirable to produce zeolites in organic-free media using alkali metal ions as the most commonly employed inorganic structure-directing agents (ISDAs). Here, in this study, we were inspired by the elemental compositions of natural zeolite minerals to examine the use of an alkaline earth metal ion as the ISDA to prepare zeolites with the PHI framework type. Two natural minerals exhibiting this crystal structure are phillipsite and harmotome. Both structures contain sodium and potassium as extra-framework cations, but only harmotome contains barium cations. Here we examined how the combination of alkali and alkaline earth ISDAs promote the formation of PHI-type zeolites. Our findings reveal that each isostructure is prepared under different synthesis conditions where simply adding barium to a growth mixture does not always lead to harmotome formation. Harmotome is formed in more dilute and alkaline growth media; however, time-resolved analyses of zeolite crystallization reveal that harmotome can be synthesized at low temperatures (ca. 65 °C) with rates that are 5-times faster than syntheses of phillipsite. This seemingly indicates that barium reduces the energetic barrier for PHI-type zeolite nucleation in ways that are not fully understood. Moreover, harmotome crystals exhibit an average particle size of ca. 60 nm, which is nearly 40-fold smaller than phillipsite crystals prepared under similar conditions. Collectively, these findings identify routes to control crystallization of PHI-type zeolites and may provide a broader strategy for using the composition of natural zeolite minerals to optimize the preparation of synthetic analogues.

36 MATERIALS SCIENCE↗
DOE OSTI2020

FPGAs-as-a-Service Toolkit (FaaST)

Computing needs for high energy physics are already intensive and are expected to increase drastically in the coming years. In this context, heterogeneous computing, specifically as-a-service computing, has the potential for significant gains over traditional computing models. Although previous studies and packages in the field of heterogeneous computing have focused on GPUs as accelerators, FPGAs are an extremely promising option as well. A series of workflows are developed to establish the performance capabilities of FPGAs as a service. Multiple different devices and a range of algorithms for use in high energy physics are studied. For a small, dense network, the throughput can be improved by an order of magnitude with respect to GPUs as a service. For large convolutional networks, the throughput is found to be comparable to GPUs as a service. This work represents the first open-source FPGAs-as-a-service toolkit.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Cloud Services Enable Efficient AI-Guided Simulation Workflows across Heterogeneous Resources

Applications which fuse machine learning and simulation are rarely best served by a single computing resource. Highly parallel simulation codes are best deployed on super- computers, while AI tasks used to decide which simulations to perform may be best suited to specialized accelerators. Here we present a Function-as-a-Service (FaaS) system for executing complex, distributed computational campaigns that achieves performance parity with conventional workflow systems without the complexities of secure network connections between compute providers. One innovation enabling high performance is a subsystem that directly moves task data between sites, separate from the cloud-hosted FaaS system used to distribute task instructions. We also introduce a flexible scheduling system that allows us access factor of 2 trade offs between the amount of resources required to solve a problem at each compute site. We anticipate that this system will upgrade multi-site applications from demonstration projects to routine practice in computational science.

Ward, Logan↗
DOE OSTI2023

AEflow (Autoencoder fluid flow compression network) [SWR-22-29]

As the size of turbulent flow simulations continues to grow, in situ data compression is becoming increasingly important for visualization, analysis, and restart checkpointing. For these applications, single-pass compression techniques with low computational and communication overhead are crucial. In this paper we present a deep-learning approach to in situ compression using an autoencoder architecture that is customized for three-dimensional turbulent flows and is well suited for contemporary heterogeneous computing resources. The autoencoder is compared against a recently introduced randomized single-pass singular value decomposition (SVD) for three different canonical turbulent flows: decaying homogeneous isotropic turbulence, a Taylor-Green vortex, and turbulent channel flow. Our proposed fully convolutional autoencoder architecture compresses turbulent flow snapshots by a factor of 64 with a single pass, allows for arbitrarily sized input fields, is cheaper to compute than the randomized single-pass SVD for typical simulation sizes, performs well on unseen flow configurations, and has been made publicly available. The results reported here show that the autoencoder dramatically outperforms a randomized single-pass SVD with similar compression ratio and yields comparable performance to a higher-rank decomposition with an order of magnitude less compression in regard to preserving a number of important statistical quantities such as turbulent kinetic energy, enstrophy, and Reynolds stresses.

King, Ryan↗
DOE OSTI2025

Heterogeneous Point Set Transformers for Segmentation of Multiple View Particle Detectors

NOvA is a long-baseline neutrino oscillation experiment that detects neutrino particles from the NuMI beam at Fermilab. Before data from this experiment can be used in analyses, raw hits in the detector must be matched to their source particles, and the type of each particle must be identified. This task has commonly been done using a mix of traditional clustering approaches and convolutional neural networks (CNNs). Due to the construction of the detector, the data is presented as two sparse 2D images: an XZ and a YZ view of the detector, rather than a 3D representation. We propose a point set neural network that operates on the sparse matrices with an operation that mixes information from both views. Our model uses less than 10% of the memory required using previous methods while achieving a 96.8% AUC score, a higher score than obtained when both views are processed independently (85.4%).

Robles, Edgar E. [UC, Irvine (main)]↗
DOE OSTI2024

Neural network kinetics for exploring diffusion multiplicity and chemical ordering in compositionally complex materials

Diffusion involving atom transport from one location to another governs many important processes and behaviors such as precipitation and phase nucleation. The inherent chemical complexity in compositionally complex materials poses challenges for modeling atomic diffusion and the resulting formation of chemically ordered structures. Here, we introduce a neural network kinetics (NNK) scheme that predicts and simulates diffusion-induced chemical and structural evolution in complex concentrated chemical environments. The framework is grounded on efficient on-lattice structure and chemistry representation combined with artificial neural networks, enabling precise prediction of all path-dependent migration barriers and individual atom jumps. To demonstrate the method, we study the temperature-dependent local chemical ordering in a refractory NbMoTa alloy and reveal a critical temperature at which the B2 order reaches a maximum. The atomic jump randomness map exhibits the highest diffusion heterogeneity (multiplicity) in the vicinity of this characteristic temperature, which is closely related to chemical ordering and B2 structure formation. The scalable NNK framework provides a promising new avenue to exploring diffusion-related properties in the vast compositional space within which extraordinary properties are hidden.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Radiosonde Observations of Environments Supporting Deep Moist Convection Initiation during RELAMPAGO-CACTI

The Remote Sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observations (RELAMPAGO) and Cloud, Aerosol, and Complex Terrain Interactions (CACTI) projects deployed a high-spatiotemporal-resolution radiosonde network to examine environments supporting deep convection in the complex terrain of central Argentina. This study aims to characterize atmospheric profiles most representative of the near-cloud environment (in time and space) to identify the mesoscale ingredients affecting storm initiation and growth. Spatiotemporal autocorrelation analysis of the soundings reveals that there is considerable environmental heterogeneity, with boundary layer thermodynamic and kinematic fields becoming statistically uncorrelated on scales of 1–2 h and 30 km. Using this as guidance, we examine a variety of environmental parameters derived from soundings collected within close proximity (30 km in space and 30 min in time) of 44 events over 9 days where the atmosphere either: 1) supported the initiation of sustained precipitating convection, 2) yielded weak and short-lived precipitating convection, or 3) produced no precipitating convection in disagreement with numerical forecasts from convection-allowing models (i.e., Null events). There are large statistical differences between the Null event environments and those supporting any convective precipitation. Null event profiles contained larger convective available potential energy, but had low free-tropospheric relative humidity, higher freezing levels, and evidence of limited horizontal convergence near the terrain at low levels that likely suppressed deep convective growth. Here, we also present evidence from the radiosonde and satellite measurements that flow–terrain interactions may yield gravity wave activity that affects CI outcome.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Quantifying local and global mass balance errors in physics-informed neural networks

Physics-informed neural networks (PINN) have recently become attractive for solving partial differential equations (PDEs) that describe physics laws. By including PDE-based loss functions, physics laws such as mass balance are enforced softly in PINN. This paper investigates how mass balance constraints are satisfied when PINN is used to solve the resulting PDEs. We investigate PINN’s ability to solve the 1D saturated groundwater flow equations (diffusion equations) for homogeneous and heterogeneous media and evaluate the local and global mass balance errors. We compare the obtained PINN’s solution and associated mass balance errors against a two-point finite volume numerical method and the corresponding analytical solution. We also evaluate the accuracy of PINN in solving the 1D saturated groundwater flow equation with and without incorporating hydraulic heads as training data. We demonstrate that PINN’s local and global mass balance errors are significant compared to the finite volume approach. Tuning the PINN’s hyperparameters, such as the number of collocation points, training data, hidden layers, nodes, epochs, and learning rate, did not improve the solution accuracy or the mass balance errors compared to the finite volume solution. Mass balance errors could considerably challenge the utility of PINN in applications where ensuring compliance with physical and mathematical properties is crucial.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

An adaptive Hessian approximated stochastic gradient MCMC method

Bayesian approaches have been successfully integrated into training deep neural networks. One popular family is stochastic gradient Markov chain Monte Carlo methods (SG-MCMC), which have gained increasing interest due to their ability to handle large datasets and the potential to avoid overfitting. Although standard SG-MCMC methods have shown great performance in a variety of problems, they may be inefficient when the random variables in the target posterior densities have scale differences or are highly correlated. Here, we present an adaptive Hessian approximated stochastic gradient MCMC method to incorporate local geometric information while sampling from the posterior. The idea is to apply stochastic approximation (SA) to sequentially update a preconditioning matrix at each iteration. The preconditioner possesses second-order information and can guide the random walk of a sampler efficiently. Instead of computing and saving the full Hessian of the log posterior, we use limited memory of the samples and their stochastic gradients to approximate the inverse Hessian-vector multiplication in the updating formula. Moreover, by smoothly optimizing the preconditioning matrix via SA, our proposed algorithm can asymptotically converge to the target distribution with a controllable bias under mild conditions. To reduce the training and testing computational burden, we adopt a magnitude-based weight pruning method to enforce the sparsity of the network. Our method is user-friendly and demonstrates better learning results compared to standard SG-MCMC updating rules. The approximation of inverse Hessian alleviates storage and computational complexities for large dimensional models. Numerical experiments are performed on several problems, including sampling from 2D correlated distribution, synthetic regression problems, and learning the numerical solutions of heterogeneous elliptic PDE. The numerical results demonstrate great improvement in both the convergence rate and accuracy.

97 MATHEMATICS AND COMPUTING↗