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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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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

WaterTAP: Tool for analysis of water treatment systems

This presentation describes the Water treatment Technoeconomic Assessment Platform (WaterTAP), an open-source software tool to analyze water treatment trains. The presentation was provided to the NAWI Alliance Fall Meeting on November 29th 2023 and it includes an overview of the tool and a couple of analyses demonstrating its capabilities. The analyses include a technoeconomic assessment of a novel membrane-based technology called low salt rejection reverse osmosis and a brackish water desalination train with pretreatment.

Dudchenko, Alexander↗
DOE OSTI2024

Design Methods, Tools, and Data for Ceramic Solar Receivers

This report presents the development of tools and methods for evaluating the reliability and performance of ceramic materials in high temperature solar receivers. As Concentrating Solar Power (CSP) technologies aim for higher operating temperatures to enhance efficiency and meet industrial process heat requirements, current high temperature metallic materials face challenges in maintaining structural integrity. This report explores advanced ceramics as a promising alternative, given their superior high temperature strength and lower thermal expansion, compared to metals. To address the need for effective ceramic receiver design tools, this report integrates statistical failure models of ceramics into the existing srlife tool: an open-source software package designed to estimate the life of high temperature CSP components. These failure models account for the inherent variability and flaw distribution in ceramics, as well as the impact of subcritical crack growth under high temperature cyclic loads. The report also presents experimental data collected for a commercially available ceramic material, SiC, and details the process of estimating reliability model parameters from these data. A comparative design analysis is then performed between ceramic (SiC) and metallic (current nickel-based superalloys A740H and A282) receiver. This comparison demonstrates that SiC receivers can achieve service life exceeding 30 years under high incident heat flux conditions, compared to just a few years for metallic receivers.

14 SOLAR ENERGY↗
DOE OSTI2024

Self-Aware Local Autonomous and Semi-Cooperative Control for Cross-Layered Resilience (SLAC3R)

The objective of this work is to develop and demonstrate novel, adaptive, lightweight algorithms that enable the decision-making agents in a large cyber-physical network to act both autonomously and in collaborative harmony to enforce assured resilience across spatiotemporal layers, even under unforeseen adversarial scenarios (e.g., high- impact-low-probability events). Towards this end, the proposed solution will serve as minimally invasive add-on layers that bridge the existing (faster, reactive) local myopic controls and (slower, predictive) centralized optimization. Importantly, the proposed algorithms will enable the multi-agent network to autonomously and collaboratively enforce resilient operation under no or limited communication environment typical of severe cyber- physical adversarial events. The expected outcome of this effort is a suite of prototype, open-source, software algorithms for safety-aware local autonomous and semi-cooperative control (SLAC3R), demonstrated on networked microgrids (via RD2C/Thrust-1 OPAL-RT testbed).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

End-to-End Optimization for Battery Materials and Molecules by Combining Graph Neural Networks and Reinforcement Learning

The National Renewable Energy Laboratory (NREL), together with the Colorado School of Mines (CSM) and Colorado State University (CSU), has developed a machine learning-enhanced approach to the design of new battery materials. Currently, such materials are designed in part via numerous expensive high-fidelity computational simulations that predict the performance of a given composition. Even with computational screening tools, the vast landscape of possible molecular or crystal structures exceeds current and future computational capacity. Improving the efficiency by which new materials can be optimized will therefore disrupt the cost, risk, and time required to bring new energy solutions to the marketplace. Predicting the properties of an organic molecule or periodic crystalline material given its structure has grown increasingly common. These approaches leverage large-scale computational and experimental databases and ML approaches such as graph neural networks. The inverse design problem of finding a material that possesses desired properties is substantially more challenging, since enumerating all valid material structures is not feasible. In this project, we leveraged recent success in reinforcement learning to efficiently navigate this high-dimensional search space. Just as algorithms can find the optimal chess moves from nearly limitless options, we train an approach to evolve a simple starting structure into a complex structure that possess the desired properties. Our solution has been demonstrated by applying it to two related design application tasks for short- and long-term energy storage, respectively: (1) the design of solid-state ion conductors and (2) the design of organic redox-active materials. The project has resulted an open-source software library for material design, documented examples of applying the library to both organic and inorganic material optimization, and peer-reviewed publications detailing the data, computational models, and resulting candidate materials.

25 ENERGY STORAGE↗
DOE OSTI2025

User Guide for BLADE_LC_processor.py

BLADE_LC_processor.py is an optional script within the BLADE (Bolide Light-curve Analysis and Discrimination Explorer) open-source software package. It converts per-event light curve CSVs plus a metadata table into maps, plots, a per-sample trajectory file, and an aggregate summary. It anchors each trajectory at peak brightness, assumes constant speed and entry angle across the event, and propagates altitude and ground track relative to that anchor. The script converts the input azimuth internally to a travel bearing for the map and trajectory. Outputs include event folders with figures and a consolidated CSV containing start, peak, and end altitudes and all original metadata, sorted newest to oldest. For further background, users are referred to the foundational publication: Silber, E. A., Sawal, V. (2025), “BLADE: An Automated Framework for Classifying Light Curves from the Center for Near-Earth Object Studies Fireball Database,” The Astronomical Journal, doi: 10.3847/1538-3881/adeb55.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Strong Correlation DMRG and DFT

This project developed new ways to improve computer simulations of materials where electrons interact strongly with each other, a challenge for today’s most widely used method, density functional theory (DFT). We used an exact numerical method, the density matrix renormalization group (DMRG), to create highly accurate reference results for simple model systems, and used these to test DFT, prove when it will converge, and even train machine-learned functionals. We also invented new kinds of localized basis functions (“gausslets” and “multi-sliced gausslets”) and a “sliced-basis” approach that make high-accuracy simulations faster and more practical. These methods were applied to extended hydrogen systems, enabling the direct derivation of accurate low-energy models from first-principles calculations. We also introduced a new formalism, Conditional-Probability DFT, which could bypass traditional approximations. The tools and results from this work, including open-source software releases, will help scientists design and understand complex quantum materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Rigorous incorporation of pH effects into ab initio electrochemical models

This report summarizes the work carried out with support of the United States Department of Energy under Award DE-SC0023410. The theme of this project was to update electronically grand canonical atomistic simulations to properly incorporate pH effects, on a rigorous thermodynamic basis, and to compare this to experimental findings. This project was primarily conducted by PI Andrew Peterson and his group at Brown University, with an unfunded collaboration with the Quantum Simulations Group at Lawrence Livermore National Laboratory with Dr. Joel Varley, who hosted the primary graduate student (Alexander Lackey) funded by this project and contributed to all major intellectual pursuits, utilizing the expertise and capabilities of the national laboratory to advance the project goals. Postdoctoral associate Dr. Juye Kim was also a major contributor to this project, and implementation of the final section included cooperation with several electronic-structure open-source software developers, including Colin Baker (Brown), Sandeep Sharma (CalTech), Georg Kastlunger (DTU), and Jens Jørgen Mortensen (DTU).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Energy-Transit Nexus Tools for Bus Fleet Electrification (NEXTBUS)

NEXTBUS is an open-source software project that integrates NLR's bus energy modeling and simulation tools with multi-objective optimization for fleet operations. NLR is collaborating with a transit technology startup, ReVolt, to commercialize these capabilities by deploying NEXTBUS in ReVolt's software platform. The goal is to manage the added complexities of running a heterogeneous fleet, encompassing battery electric and diesel buses, across a large, multi-depot transit network.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

National Solar Thermal Test Facility: Operations & Maintenance Report

The NSTTF O&M Project continues the operation and maintenance activities of the existing critical capabilities and infrastructure at the NSTTF. This project is to support and assure the success of Solar Heat for Industrial Process in the United States and the larger global community by ensuring the NSTTF is a safe and operational facility. The primary goal of this project will be to maintain the solar tower and heliostat field while also allowing NSTTF staff to improve processes for operations and maintenance. This includes expanding our preventative maintenance program, inventory systems, and our data sharing capabilities. Additionally, this will support an outreach program with regular seminars, sharing of data, and the release of open-source software to support heliostat metrology

14 SOLAR ENERGY↗
DOE OSTI2026

Tabletop Testing for EV Charging Ecosystem PKI (Project T34PKI Final Report)

To test the communications and cybersecurity functionality, Electric Vehicle and charging station vendors have had to ship their products to in-person testing events. This is cumbersome, expensive, inefficient, and an impediment to rapid time-to-deployment. In this project Sandia used COTS hardware and Open-Source Software to develop and demonstrate a more agile, productive approach: testing low-voltage controllers independently from high-voltage power delivery sub-systems. This approach allows communications controllers to be transported easily (e.g. shipped at low cost, checked as airline baggage); set up on a table-top (“bench testing”); and use ordinary 120 VAC outlets to conduct agile testing. Table-top platforms become end nodes that can connect to laboratory and cloud-based servers to test communications and cybersecurity, specifically Public Key Infrastructure (PKI) functionality and interoperability, separately from EV battery charging (power/energy transfer) functionality.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Large Load Impacts to Distribution System Hosting Capacity

This work examined the impact of large loads on utility distribution system models using the Sandia-developed open-source software DREAMS. It was shown that hosting capacity varies with location and changes after any asset is added to, or removed from, a system. Despite the tested models having similar rated voltages and other characteristics, their thermal and voltage constrained hosting capacity varied over 2 MW. The addition of a 3-phase balanced constant power large load with power factor of 1.0 exhibited non-linear reductions to all voltage constrained hosting capacities. The reductions to thermal constrained hosting capacity from a load with similar characteristics was more linear, related to the size of the added load, and did not impact all model buses. Co-located capacitors were shown to accommodate demand that was beyond the baseline voltage constrained hosting capacity limits, however, the costs and benefits from this approach were found to not be 1:1 and required additional available thermal capacity.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Omics-to-Reactive-Transport (ORT): A workflow linking genome-scale metabolic models with reactive transport codes

Motivation: Nutrient and contaminant behavior in the subsurface are governed by multiple coupled hydrobiogeochemical processes which occur across different temporal and spatial scales. Accurate description of macroscopic system behavior requires accounting for the effects of microscopic and especially microbial processes. Microbial processes mediate precipitation and dissolution and change aqueous geochemistry, all of which impacts macroscopic system behavior. As `omics data describing microbial processes is increasingly affordable and available, novel methods for using this data quickly and effectively for improved ecosystem models are needed. Results: We propose a workflow (`Omics to Reactive Transport – ORT) for utilizing metagenomic and environmental data to describe the effect of microbiological processes in macroscopic reactive transport models. This workflow utilizes and couples two open-source software packages: KBase (a software platform for systems biology) and PFLOTRAN (a reactive transport modeling code). We describe the architecture of ORT and demonstrate an implementation using metagenomic and geochemical data from a river system. Our demonstration uses microbiological drivers of nitrification and denitrification to predict nitrogen cycling patterns which agree with those provided with generalized stoichiometries. While our example uses data from a single measurement, our workflow can be applied to spatiotemporal metagenomic datasets to allow for iterative coupling between KBASE and PFLOTRAN. Live, interactive models, which incorporate the results from this narrative into a PFLOTRAN simulation, are available (without login) at https://pflotranmodeling.paf.subsurfaceinsights.com/pflotran-simple-model/.

Rubinstein, Rebecca L↗
DOE OSTI2023

A Modeling Toolkit for Comparing AC and DC Electrical Distribution Efficiency in Buildings

Recently, there has been considerable research interest in the potential for DC distribution systems in buildings instead of the traditional AC distribution systems. Due to the need for performing power conversions between DC and AC electricity, DC distribution may provide electrical efficiency advantages in some systems. To support comparative evaluations of AC-only, DC-only, and hybrid AC/DC distribution systems in buildings, a new modeling toolkit called the Building Electrical Efficiency Analysis Model (BEEAM) was developed and is described in this paper. To account for harmonics in currents or voltages arising from nonlinear devices, the toolkit implements harmonic power flow, along with nonlinear device behavioral descriptions derived from empirical measurements. This paper describes the framework, network equations, device representations, and an implementation of the toolkit in an open source software package, including a component library and graphical interface for creating circuits. Simulations of electrical behavior and device and system efficiencies using the toolkit are compared with experimental measurements of a small office environment in a variety of operating and load configurations. A detailed analysis of uncertainty estimation is also provided. Key findings were that a comparison of predicted versus measured efficiencies and power losses in the validation testbed using the initial toolkit implementation predicted device- and system-level efficiencies with reasonably good accuracy under both balanced and unbalanced AC scenarios. An uncertainty analysis also revealed that the maximum estimated error for system efficiency across all scenarios was 3%, and measured and modeled system efficiency agreed within the experimental uncertainty in approximately half of the scenarios. Based on the correspondence between simulation and measurement, the toolkit is proposed by the authors as a potentially useful tool for comparing efficiency in AC, DC, and hybrid AC/DC distribution systems in buildings.

DC distribution↗
DOE OSTI2025

Nuclear Thermal Rocket Emulator for a Hardware-in-the-Loop Test Bed

To support NASA’s mission to use nuclear thermal rockets for future Mars missions, an instrumentation and control test bed has been built at Oak Ridge National Laboratory. The system is designed as a hardware-in-the-loop test bed for testing control elements and autonomous control algorithms for nuclear thermal propulsion rockets. The mock reactor system consists of a modular and scalable framework, using inexpensive components and open-source software. The hardware system consists of a two-phase flow loop and a mock reactor with six control drums. A single-board computer (NVIDIA Jetson) handles reactor core emulation and hosts a message queuing telemetry transport broker that allows user-deployed control algorithms to interact with the system hardware. The reactor emulator receives sensor data from the hardware and provides the simulated performance of the reactor under steady-state, transient, and fault conditions. The emulator uses a reactivity lookup table and the point kinetics equations to solve for the reactor dynamics in real time. Emulated reactor dynamics and sensor input inform the autonomous control algorithm’s decision-making in a closed-loop manner. The current system is capable of operating at 10 Hz, but faster cycle rates are an area of ongoing research. This test bed will enable NASA and other space vendors to rigorously test their autonomous control systems for NTP rockets under transient (reactor startup and shutdown), steady-state, and fault conditions to reduce development time and risk for autonomous control systems in future missions.

autonomous control↗
DOE OSTI2025

Framework for Assessing Impact of Wave-Powered Desalination on Resilience of Coastal Communities

Coastal communities face unique challenges in maintaining continuous service from critical infrastructure. This research advances capabilities for evaluating the impact of using wave energy to desalinate water on the resilience of coastal communities. The study focuses on the feasibility of using wave energy conversion to provide drinking water to communities in need and applying resilience metrics to quantify its impact on the community. To assess the feasibility of wave-powered desalination, this research couples the open-source software Wave Energy Converter SIMulator (WEC-Sim) and Water Network Tool for Resilience (WNTR). This research explores variations in both the wave resource (location, seasonality, and duration) and the ability to maintain drinking water service during a disruption scenario by applying the simulation framework to three case studies, which are based on communities in Puerto Rico. The simulation framework provides a contextualized assessment of the ability of wave-powered desalination to improve the resilience of coastal communities, which can serve as a methodology for future studies seeking the integration of wave-powered desalination with water distribution systems.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Similarity Downselection: Finding the n Most Dissimilar Molecular Conformers for Reference-Free Metabolomics

Computational methods for creating in silico libraries of molecular descriptors (e.g., collision cross sections) are becoming increasingly prevalent due to the limited number of authentic reference materials available for traditional library building. These so-called “reference-free metabolomics” methods require sampling sets of molecular conformers in order to produce high accuracy property predictions. Due to the computational cost of the subsequent calculations for each conformer, there is a need to sample the most relevant subset and avoid repeating calculations on conformers that are nearly identical. The goal of this study is to introduce a heuristic method of finding the most dissimilar conformers from a larger population in order to help speed up reference-free calculation methods and maintain a high property prediction accuracy. Finding the set of the n items most dissimilar from each other out of a larger population becomes increasingly difficult and computationally expensive as either n or the population size grows large. Because there exists a pairwise relationship between each item and all other items in the population, finding the set of the n most dissimilar items is different than simply sorting an array of numbers. For instance, if you have a set of the most dissimilar n = 4 items, one or more of the items from n = 4 might not be in the set n = 5. An exact solution would have to search all possible combinations of size n in the population exhaustively. We present an open-source software called similarity downselection (SDS), written in Python and freely available on GitHub. SDS implements a heuristic algorithm for quickly finding the approximate set(s) of the n most dissimilar items. We benchmark SDS against a Monte Carlo method, which attempts to find the exact solution through repeated random sampling. We show that for SDS to find the set of n most dissimilar conformers, our method is not only orders of magnitude faster, but it is also more accurate than running Monte Carlo for 1,000,000 iterations, each searching for set sizes n = 3–7 out of a population of 50,000. We also benchmark SDS against the exact solution for example small populations, showing that SDS produces a solution close to the exact solution in these instances. Using theoretical approaches, we also demonstrate the constraints of the greedy algorithm and its efficacy as a ratio to the exact solution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

ezAlign: A Tool for Converting Coarse-Grained Molecular Dynamics Structures to Atomistic Resolution for Multiscale Modeling

Soft condensed matter is challenging to study due to the vast time and length scales that are necessary to accurately represent complex systems and capture their underlying physics. Multiscale simulations are necessary to study processes that have disparate time and/or length scales, which abound throughout biology and other complex systems. Herein we present ezAlign, an open-source software for converting coarse-grained molecular dynamics structures to atomistic representation, allowing multiscale modeling of biomolecular systems. The ezAlign v1.1 software package is publicly available for download at github.com/LLNL/ezAlign. Its underlying methodology is based on a simple alignment of an atomistic template molecule, followed by position-restraint energy minimization, which forces the atomistic molecule to adopt a conformation consistent with the coarse-grained molecule. The molecules are then combined, solvated, minimized, and equilibrated with position restraints. Validation of the process was conducted on a pure POPC membrane and compared with other popular methods to construct atomistic membranes. Additional examples, including surfactant self-assembly, membrane proteins, and more complex bacterial and human plasma membrane models, are also presented. By providing these examples, parameter files, code, and an easy-to-follow recipe to add new molecules, this work will aid future multiscale modeling efforts.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Methods in PES-Learn: Direct-Fit Machine Learning of Born–Oppenheimer Potential Energy Surfaces

The release of PES-L EARN version 1.0 as an open-source software package for the automatic construction of machine learning models of semi-global molecular potential energy surfaces (PESs) is presented. Improvements to PES-L EARN ’s interoperability are stressed with new Python API that simplifies workflows for PES construction via interaction with QCSchema input and output infrastructure. In addition, a new machine learning method is introduced to PES-L EARN : kernel ridge regression (KRR). The capabilities of KRR are emphasized with examination of select semi-global PESs. All machine learning methods available in PES-L EARN are benchmarked with benzene and ethanol datasets from the rMD17 database to illustrate PES-L EARN ’s performance ability. Fitting performance and timings are assessed for both systems. Finally, the ability to predict gradients with neural network models is presented and benchmarked with ethanol and benzene. PES-L EARN is an active project and welcomes community suggestions and contributions.

kernel ridge regression↗