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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 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Increasing the composition range of a novel τ 11 -Al 4 Fe 1.7 Si alloy with additions of Mn

Automotive applications need low-cost, lightweight, high-temperature alloys to increase vehicle efficiency. The Al–Fe–Si system provides an opportunity to develop such a material, as it consists of three low-cost elements that are all abundant in nature. Specifically, the τ 11 -Al 4 Fe 1.7 Si ternary intermetallic phase is a high-temperature, lightweight phase with high strength and good corrosion resistance. However, this phase exhibits a narrow compositional range of stability, resulting in undesirable microstructures forming during solidification and processing, limiting its use in potential applications. Density functional theory (DFT) calculations and a thermodynamically-driven experimental approach utilizing diffusion couples were employed to study the effect of Mn on the stability and composition range of τ 11 -Al 4 Fe 1.7 Si. The DFT calculations showed a decrease in the energy of the structure when alloying with Mn. Experimental results confirmed the predictions from the DFT calculations, indicating that alloying with Mn increases the compositional range, and thus the processability of this phase. New phase diagrams and equilibria are proposed by exploring and determining phase boundaries for the τ 11 -Al 4 Fe 1.7 Si phase with Mn.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problems

We explore using neural operators, or neural network representations of nonlinear maps between function spaces, to accelerate infinite-dimensional Bayesian inverse problems (BIPs) with models governed by nonlinear parametric partial differential equations (PDEs). Neural operators have gained significant attention in recent years for their ability to approximate the parameter-to-solution maps defined by PDEs using as training data solutions of PDEs at a limited number of parameter samples. The computational cost of BIPs can be drastically reduced if the large number of PDE solves required for posterior characterization are replaced with evaluations of trained neural operators. However, reducing error in the resulting BIP solutions via reducing the approximation error of the neural operators in training can be challenging and unreliable. We provide an a priori error bound result that implies certain BIPs can be ill-conditioned to the approximation error of neural operators, thus leading to inaccessible accuracy requirements in training. To reliably deploy neural operators in BIPs, we consider a strategy for enhancing the performance of neural operators: correcting the prediction of a trained neural operator by solving a linear variational problem based on the PDE residual. We show that a trained neural operator with error correction can achieve a quadratic reduction of its approximation error, all while retaining substantial computational speedups of posterior sampling when models are governed by highly nonlinear PDEs. The strategy is applied to two numerical examples of BIPs based on a nonlinear reaction–diffusion problem and deformation of hyperelastic materials. We demonstrate that posterior representations of the two BIPs produced using trained neural operators are greatly and consistently enhanced by error correction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Are quantum materials economically and environmentally sustainable?

Quantum materials have revolutionized energy, information, and healthcare technologies, yet their development has largely prioritized performance over economic and environmental impacts—key factors for industrial adoption. Using topological materials as a case study, we present a data-driven framework that evaluates over 16,000 materials based on cost, supply chain resilience, energy demand, toxicity, and environmental footprint. By integrating the recently proposed quantum weight – a metric quantifying quantum behavior – we reveal a striking trend: materials with stronger quantum effects often exhibit higher environmental impact, posing challenges for scalability and industrial adoption. To address this, we identify a small set of materials that achieve a balance between quantum functionality and sustainability. Furthermore, our approach enables high-throughput, AI-driven materials discovery that incorporates economic and environmental influences from the outset, guiding the development of quantum materials for next-generation microelectronics and energy harvesting technologies.

AI↗
DOE OSTI2021

Dipole Moment Calculations Using Multiconfiguration Pair-Density Functional Theory and Hybrid Multiconfiguration Pair-Density Functional Theory

The dipole moment is the molecular property that most directly indicates molecular polarity. The accuracy of computed dipole moments depends strongly on the quality of the calculated electron density, and the breakdown of single-reference methods for strongly correlated systems can lead to poor predictions of the dipole moments in those cases. Here, we derive the analytical expression for obtaining the electric dipole moment by multiconfiguration pair density functional theory (MCPDFT), and we assess the accuracy of MC-PDFT for predicting dipole moments at equilibrium and nonequilibrium geometries. We show that MC-PDFT dipole moment curves have reasonable behavior even for stretched geometries, and they significantly improve upon the CASSCF results by capturing more electron correlation. Herein, the analysis of a dataset consisting of 18 first-row transition metal diatomics and 6 main-group polyatomic molecules with multireference character suggests that MC-PDFT and its hybrid extension (HMC-PDFT) perform comparably to CASPT2 and MRCISD+Q methods and have a mean unsigned deviation of 0.2–0.3 D with respect to the best available dipole moment reference values. We explored the dependence of the predicted dipole moments upon the choice of the on-top density functional and active space, and we recommend the tPBE and hybrid tPBE0 on-top choices for the functionals combined with the moderate correlated participating orbital scheme for selecting the active space. With these choices, the mean unsigned deviations (in debyes) of the calculated equilibrium dipole moments from the best estimates are 0.77 for CASSCF, 0.29 for MC-PDFT, 0.24 for HMCPDFT, 0.28 for CASPT2, and 0.25 for MRCISD+Q. These results are encouraging because the computational cost of MC-PDFT or HMC-PDFT is largely reduced compared to the CASPT2 and MRCISD+Q methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Comparing (stochastic-selection) ab initio multiple spawning with trajectory surface hopping for the photodynamics of cyclopropanone, fulvene, and dithiane

Ab Initio Multiple Spawning (AIMS) simulates the excited-state dynamics of molecular systems by representing nuclear wavepackets in a basis of coupled traveling Gaussian functions, called trajectory basis functions (TBFs). New TBFs are spawned when nuclear wavepackets enter regions of strong nonadiabaticity, permitting the description of non-Born–Oppenheimer processes. The spawning algorithm is simultaneously the blessing and the curse of the AIMS method: it allows for an accurate description of the transfer of nuclear amplitude between different electronic states, but it also dramatically increases the computational cost of the AIMS dynamics as all TBFs are coupled. Recently, a strategy coined stochastic-selection AIMS (SSAIMS) was devised to limit the ever-growing number of TBFs and tested on simple molecules. In this work, we use the photodynamics of three different molecules—cyclopropanone, fulvene, and 1,2-dithiane—to investigate (i) the potential of SSAIMS to reproduce reference AIMS results for challenging nonadiabatic dynamics, (ii) the compromise achieved by SSAIMS in obtaining accurate results while using the smallest average number of TBFs as possible, and (iii) the performance of SSAIMS in comparison to the mixed quantum/classical method trajectory surface hopping (TSH)—both in terms of its accuracy and computational cost. We show that SSAIMS can accurately reproduce the AIMS results for the three molecules considered at a much cheaper computational cost, often close to that of TSH. We deduce from these tests that an overlap-based criterion for the stochastic-selection process leads to the best agreement with the reference AIMS dynamics for the smallest average number of TBFs.

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

Cooperative carbon capture and steam regeneration with tetraamine-appended metal–organic frameworks

Natural gas has become the dominant source of electricity in the United States, and technologies capable of efficiently removing carbon dioxide (CO 2 ) from the flue emissions of natural gas–fired power plants could reduce their carbon intensity. However, given the low partial pressure of CO 2 in the flue stream, separation of CO 2 is particularly challenging. Taking inspiration from the crystal structures of diamine-appended metal–organic frameworks exhibiting two-step cooperative CO 2 adsorption, we report a family of robust tetraamine-functionalized frameworks that retain cooperativity, leading to the potential for exceptional efficiency in capturing CO 2 under the extreme conditions relevant to natural gas flue emissions. The ordered, multimetal coordination of the tetraamines imparts the materials with extraordinary stability to adsorption-desorption cycling with simulated humid flue gas and enables regeneration using low-temperature steam in lieu of costly pressure or temperature swings.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Two-Stage Estimation and Variance Modeling for Latency-Constrained Variational Quantum Algorithms

The quantum approximate optimization algorithm (QAOA) has enjoyed increasing attention in noisy, intermediate-scale quantum computing with its application to combinatorial optimization problems. QAOA has the potential to demonstrate a quantum advantage for NP-hard combinatorial optimization problems. As a hybrid quantum-classical algorithm, the classical component of QAOA resembles a simulation optimization problem in which the simulation outcomes are attainable only through a quantum computer. The simulation that derives from QAOA exhibits two unique features that can have a substantial impact on the optimization process: (i) the variance of the stochastic objective values typically decreases in proportion to the optimality gap, and (ii) querying samples from a quantum computer introduces an additional latency overhead. In this paper, we introduce a novel stochastic trust-region method derived from a derivative-free, adaptive sampling trust-region optimization method intended to efficiently solve the classical optimization problem in QAOA by explicitly taking into account the two mentioned characteristics. The key idea behind the proposed algorithm involves constructing two separate local models in each iteration: a model of the objective function and a model of the variance of the objective function. Exploiting the variance model allows us to restrict the number of communications with the quantum computer and also helps navigate the nonconvex objective landscapes typical in QAOA optimization problems. In conclusion, we numerically demonstrate the superiority of our proposed algorithm using the SimOpt library and Qiskit when we consider a metric of computational burden that explicitly accounts for communication costs.

Derivative-free Optimization↗
DOE OSTI2024

Upgrading Linear Fluorescent Fixtures to LED in Schools

Lighting is a key component to creating a functional and high-quality learning environment for students and staff. Light-emitting diode or LED lighting fixtures have gained traction in common commercial building applications and quality replacement options for common fixture types are widely available. Compared to most fluorescent lighting systems, using LEDs can reduce energy consumption and therefore decrease carbon dioxide emissions, reduce maintenance and operation cost, and improve the visual appearance and lighting quality of the classroom and school environments. When additional controls are considered, more energy can be saved and some controls, like dimming, can offer amenities to occupants. As LEDs become an even more prominent lighting technology, fluorescent equipment may become challenging or expensive to replace. Additionally, school buildings are typically in use for many decades; maintaining a long-term view can be important when deciding which upgrade path to take. This fact sheet provides guidance on the various factors to consider when deciding on an LED upgrade for fluorescent lighting systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Group-Additivity–Embedded Multiscale Modeling for Electric Field-Enhanced Nanocatalysis

Elucidating structure-performance relationships remains a central challenge in field-enhanced catalysis, where nanoparticles exhibit nonuniform surface sites with site-dependent responses to electric fields. Low-coordination sites (edges, corners, and tips) are particularly electric field-sensitive (EF), leading to nonuniform charge distribution, adsorption energies, and catalytic activity. Here, using ammonia decomposition on a ruthenium cluster as a model system, we develop a transferable multiscale framework integrating density functional theory, group additivity (GA), Brønsted-Evans-Polanyi scaling, and microkinetic modeling to predict EF-dependent activity across nonuniform cluster sites. Across sites and fields, the nitrogen adsorption energy (E N ) emerges as the governing descriptor, yielding robust volcano relationships whose optimum shifts systematically with field: negative fields strengthen N binding via electron accumulation, while positive fields weaken N binding via charge depletion, moving the optimal E N toward weaker binding. Microkinetic analysis shows that N≡N bond formation remains the key kinetic bottleneck over most conditions; positive fields lower the effective barrier and, critically, increase the fraction of near-optimal active sites, leading to a net enhancement in overall activity relative to zero-field and negative-field cases. By capturing EF- and site-dependent energetics with high accuracy and low computational cost, this GA-embedded multi-scale simulation workflow provides a physically interpretable route to predict and design field-enhanced nanocatalysis.

ammonia decomposition↗
DOE OSTI2020

Assessing Uncertainties and Approximations in Solar Heating of the Climate System

In calculating solar radiation, climate models make many simplifications, in part to reduce computational cost and enable climate modeling, and in part from lack of understanding of critical atmospheric information. Whether known errors or unknown errors, the community's concern is how these could impact the modeled climate. The simplifications are well known and most have published studies evaluating them, but with individual studies it is difficult to compare. Here, we collect a wide range of such simplifications in either radiative transfer modeling or atmospheric conditions and assess potential errors within a consistent framework on climate-relevant scales. We build benchmarking capability around a solar heating code (Solar-J) that doubles as a photolysis code for chemistry and can be readily adapted to consider other errors and uncertainties. The broad classes here include: use of broad wavelength bands to integrate over spectral features; scattering approximations that alter phase function and optical depths for clouds and gases; uncertainty in ice-cloud optics; treatment of fractional cloud cover including overlap; and variability of ocean surface albedo. We geographically map the errors in W m -2 using a full climate re-creation for January 2015 from a weather forecasting model. For many approximations assessed here, mean errors are ~2 W m -2 with greater latitudinal biases and are likely to affect a model’s ability to match the current climate state. Combining this work with previous studies, we make priority recommendations for fixing these simplifications based on both the magnitude of error and the ease or computational cost of the fix.

58 GEOSCIENCES↗
DOE OSTI2026

Project 2.8: Biological Specimen Repository for the Mayak Project

Curation of the existing specimens, including Standard Operating Procedures: The Southern Urals Biophysics Institute (SUBI) will continue to manage the day-to-day operations of the biorepository, but will have to assume the financial costs of maintaining and purchasing equipment such as freezers; Standard Operating Procedures are already in place for all aspects of the operations, and the Georgetown University team will be available on a voluntary and ad hoc basis to answer any technical questions in the future. Acquisition and tracking of new specimens: SUBI will decide on the collection of new tissues and blood samples in the future, and on what scale, depending on available resources and research needs. The Georgetown University team is willing to provide advice on a voluntary and ad hoc basis. Procedures for receiving and approving specimen requests from users: SUBI can take over this function; Chris Loffredo and other qualified research scientists at Georgetown University would be willing to serve as Tissue Review Committee members on a voluntary and ad hoc basis. Biospecimens remaining at Georgetown University: pursuant to the original terms of the Biospecimen Transfer Agreement between SUBI and Georgetown University, all unused portions of specimens (FFPE blocks, slides, and frozen tissues) will be returned to SUBI at the conclusion of the approved scientific research for which they were transferred. It was anticipated that the return shipment of biospecimens would occur in the fall of 2023. However, at this time, due to international developments, there are no shipping companies who can provide such deliveries to Russia. For now, the biospecimens can remain at Georgetown University where they are stored at the Genomics and Epigenomics Shared Resource. There have not been any storage costs to date, but this could change in the future.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Molecular Fe-N 4 Moieties Coupled with Atomic Co-N 4 Sites Toward Improved Oxygen Reduction Performance

Research on high-efficiency and cost-efficient catalysts for oxygen reduction reaction (ORR) is still a vital but challenging issue for commercializing metal–air batteries. Herein, a single-molecule/atom hybrid catalyst is developed to boost the ORR, in which iron phthalocyanine molecules containing molecular Fe-N 4 moieties couple with atomic Co-N 4 sites on the surface of polyhedral carbon. Density functional theory calculations reveal that face-to-face laminated construction of Fe-N 4 and Co-N 4 in the hybrid catalyst can effectively modulate the electronic structure of active iron atoms and reduce the energy barrier of the rate-determining step for ORR. As a result, this hybrid catalyst demonstrates excellent ORR performance, featuring a half-wave potential of 0.904 V, a peak power density of 238.3 mW cm -2 for zinc–air battery, and outstanding electrocatalytic stability. Here, this work offers a distinctive and robust molecular/atomic engineering approach to creating efficient electrocatalysts, advancing the fields of metal–air batteries.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Printing polymer blends through in situ active mixing during fused filament fabrication

Fused filament fabrication (FFF) enables production of 3D objects over a range of material compositions at low-cost relative to traditional manufacturing approaches. To date, a limited but growing number of materials are able to be used with FFF, however many applications exist where specific mechanical, thermal, or chemical properties are needed that cannot currently be met with the available feedstock selection. Therefore, a need exists to tune these materials for specific chemical or mechanical properties. One common formulation strategy to address these demanding design parameters is to develop composites or polymer blend filaments. Typically, this is a time-consuming and costly optimization process. Here, we have developed hardware for reproducibly mixing two filaments of similar or dissimilar compositions at the time of printing within individual printed layers. This mixing occurs via software-controlled rotating hardware in the chamber of an extruder’s hot-end. The efficiency of mixing within the printed layers has been characterized in detail as a function of the rotational speed and geometry of the blending hardware. These parameters were exploited to program the ratio and distribution of thermoplastic-based filaments blended within printed extrudate. Example printed specimens were produced with thermoplastic polyurethane (TPU) elastomer blended with rigid polylactic acid (PLA) and Nylon blended with PLA. In addition, a conductive carbon nanotube (CNT)-PLA composite was blended as a function of mixer geometry and input feed ratios with non-conductive PLA and resistance values were measured across the resulting printed specimens.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Analysis of degradation in residential battery energy storage systems for rate-based use-cases

This article examines the impact of residential battery energy storage (BES) systems' operational modes on the life (i.e. usable energy capacity) of the battery under several climatic conditions and battery chemistries. The sharp increase in residential BES installations has been a result of decreasing costs of batteries, increase in rate structures motivated applications such as solar self-consumption and time-of-use energy management, and customers purchasing these systems for backup power. While these different modes of operations provide a combination of increased bill savings, reliability of supply, and energy sustainability to the customer, their operational characteristics vary significantly between use-cases. Functional life of BES systems is known to be strongly dependent on their operating conditions. In this article, we analyze the operation of residential BES systems under different rate-based use-cases, for different battery chemistries and cell designs, and under different environmental conditions. This is conducted by simulating the control of BES operations using rate-based cycling algorithms and analyzing the prognosis of multiple battery lifetime models that consider complex nonlinear dependencies of operational stress factors such as state-of-charge, depth-of-discharge, and temperature on degradation. Significant variations in battery life are observed owing to the differences in characteristics of the uses-cases coupled with environmental conditions and battery chemistries. Such differences lead to the conclusion that choice of residential BES technologies and chemistries should account for their intended use-cases.

25 ENERGY STORAGE↗
DOE OSTI2024

A valuation framework for customers impacted by extreme temperature-related outages

Extreme temperature outages can lead to not just economic losses but also various non-energy impacts (NEI), such as increased mortality rates, property damage, and reduced productivity, due to significant degradation of indoor operating conditions caused by service disruptions. However, existing resilience assessment approaches lack specificity for extreme temperature conditions. They often overlook temperature-related mortality and neglect the customer characteristics and grid response in the calculation, despite the significant influence of these factors on NEI-related economic losses. This paper aims to address these gaps by introducing a comprehensive framework to estimate the impact of resilience enhancement not only on the direct economic losses incurred by customers but also on potential NEI, including mortality and the value of statistical life during extreme temperature-related outages. The proposed resilience valuation integrates customer characteristics and grid response variables based on a scalable grid simulation environment. This study adopts a holistic approach to quantify customer-oriented economic impacts, utilizing probabilistic loss scenarios that incorporate health-related factors and damage/loss models as a function of exposure for valuation. The proposed methodology is demonstrated through comparative resilient outage planning, using grid response models emulating a Texas weather zone during the 2021 winter storm Uri. The case study results show that enhanced outage planning with hardened infrastructure can improve the system resilience and thereby reduce the relative risk of mortality by 16% and save the total costs related to non-energy impacts by 74%. In conclusion, these findings underscore the efficacy of the framework by assessing the financial implications of each case, providing valuable insights for decision-makers and stakeholders involved in extreme-weather related resilience planning for risk management and mitigation strategies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Bayes_Opt-SWMM: A Gaussian process-based Bayesian optimization tool for real-time flood modeling with SWMM

Real-time flood model plays a pivotal role in averting urban flood damage, particularly when there is minimal lead time for preparatory measures. However, urban flood modeling in real-time often contends with inherent uncertainties arising from input data uncertainty and parameter ambiguities. Here this study introduces a real-time calibration (RTC) tool called Bayes_Opt-SWMM, specifically tailored for real-time urban flood modeling and uncertainty optimization. This tool leverages the Gaussian process-based Bayesian optimization algorithm and interfaces seamlessly with the Stormwater Management Model (SWMM). It integrates real-time model forcing data and flood monitoring collected through sensors and gauges which are strategically placed within critical locations of urban drainage systems. Our approach hinges on the Surrogate Model based Uncertainty Optimization (SMUO) concept, providing an avenue for enhancing real-time flood modeling. Bayes_Opt-SWMM runs the optimization process using a surrogate model called Gaussian Process emulator with two inference methods: (1) the Gaussian Process (GP) model and (2) Markov Chain Monte Carlo (MCMC) algorithm in GP model (GP_MCMC). Furthermore, three acquisition functions, namely Expected Improvement (EI), Maximum Probability of Improvement (MPI), and Lower Confidence Bound (LCB), facilitate optimal parameter fitting within the surrogate models. The efficiency of GP-based surrogate models in learning SWMM model parameters, leads to an improved uncertainty quantification and accelerated real-time flood modeling in urban areas. Overall, Bayes_Opt-SWMM emerges as a cost-effective and valuable tool for real-time flood modeling and monitoring, with significant potential for managing intelligent storm water systems in urban environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A phase-field diffraction model for thermo-hydro-mechanical propagating fractures

This paper introduces a novel diffraction based thermo-hydraulic–mechanical (THM) model for fracture propagation using a phase-field fracture (PFF) approach. The key innovation of the THM-PFF model lies in its integrated treatment of four solution variables—displacements, phase-field, pressure, and temperature—each governed by a combination of conservation of momentum (mechanics problem), a variational inequality (constrained minimization problem), mass conservation (pressure problem), and energy conservation (temperature problem). This leads to a new formulation of a coupled variational inequality system. A major advancement is the development of an extended fixed-stress algorithm, where displacements, phase-field, pressures, and temperatures are solved in a staggered sequence. An important aspect of this work is the global coupling of pressures and temperatures across the domain using diffraction systems, with diffraction coefficients defined by material parameters weighted by the diffusive phase-field variable. To ensure robust local mass conservation, we employ enriched Galerkin finite elements (EG) for both pressure and temperature diffraction equations. By enriching the continuous Galerkin basis functions with discontinuous piecewise constants, EG accurately represents solution and parameter discontinuities while preserving local mass and energy conservation—crucial aspects for THM problems and realistic behavior. Moreover, the use of a predictor–corrector local mesh adaptivity scheme is employed, allowing the model to handle small phase-field length-scale parameters while maintaining high numerical accuracy and reasonable computational cost. Furthermore, these new model and algorithmic developments represent significant advances in the field and have been substantiated through rigorous numerical tests.

Diffraction systems↗