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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Intrinsic kinetics of water-inhibited ultra-lean methane oxidation over PtPd-Mg/ θ -Al 2 O 3 catalyst

Here, this study develops intrinsic methane oxidation kinetics for ultra-lean methane conditions in the presence of water over a highly active and stable PtPd–Mg/θ-Al 2 O 3 catalyst. Comprehensive laboratory experiments were conducted over a wide range of methane concentrations (150–1200 ppm CH 4 ), water contents (1–5% H 2 O), and industrially relevant space velocities (80,000 ≤ GHSV ≤ 160,000 h -1 ). These systematic experiments informed a two-dimensional, axisymmetric, multiscale reactor model that was used to develop and validate methane oxidation kinetics under practically relevant conditions, including non-isothermal operation and high conversion regimes. Combined experimental and modeling results revealed significant intraparticle diffusion resistance and transport-induced reaction exotherm at elevated temperatures, which limited catalyst utilization despite high intrinsic activity. These transport effects were explicitly incorporated into the reactor model, enabling accurate estimation of intrinsic kinetic parameters without reliance on conventional effectiveness-factor corrections. The resulting kinetic model successfully captured both kinetically controlled and mass-transfer-limited regimes and reliably predicted CH 4 conversion across broad ranges of temperature, methane concentration, and water content

Heat and mass transfer limitations↗
DOE OSTI2023

Projection pursuit adaptation on polynomial chaos expansions

Here, the present work addresses the issue of accurate stochastic approximations in high-dimensional parametric space using tools from uncertainty quantification (UQ). The basis adaptation method and its accelerated algorithm in polynomial chaos expansions (PCE) were recently proposed to construct low-dimensional approximations adapted to specific quantities of interest (QoI). The present paper addresses one difficulty with these adaptations, namely their reliance on quadrature point sampling, which limits the reusability of potentially expensive samples. Projection pursuit (PP) is a statistical tool to find the “interesting” projections in high-dimensional data and thus bypass the curse-of-dimensionality. In the present work, we combine the fundamental ideas of basis adaptation and projection pursuit regression (PPR) to propose a novel method to simultaneously learn the optimal low-dimensional spaces and PCE representation from given data. While this projection pursuit adaptation (PPA) can be entirely data-driven, the constructed approximation exhibits mean-square convergence to the solution of an underlying governing equation and thus captures the supports and probability distributions associated with the physics constraints. The proposed approach is demonstrated on a borehole problem and a structural dynamics problem, demonstrating the versatility of the method and its ability to discover low-dimensional manifolds with high accuracy with limited data. In addition, the method can learn surrogate models for different quantities of interest while reusing the same data set.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

An optimization-based approach to tailor the mechanical response of soft metamaterials undergoing rate-dependent instabilities

An optimization-based design framework is proposed to tune the response of soft metamaterials involving both geometric instabilities and nonlinear viscoelastic material behavior. Designing the response of soft metamaterials to harness instabilities and undergo large, tailored configuration changes will enable advancements in soft robotics, shock and vibration mitigation, and flexible electronics. In line with the metamaterial concept, the response of these materials is governed to a large extent by the geometric and topological makeup of their small-scale features. However, the link between structure and response is less intuitive for soft metamaterials due to their reliance upon highly nonlinear responses triggered by geometric instabilities. This is further complicated by the effects of viscoelastic relaxation, which recent studies have shown to alter the emergence of instabilities in non-intuitive ways. Here, these effects are accounted for in our framework to achieve various design objectives, including tailored force–displacement response and maximized energy absorption from both geometric and material effects. To fully automate this process, it is essential to have a completely robust equation solver for forward problems involving instabilities and viscoelastic relaxation. We achieve this by casting the search for stable mechanical equilibrium — i.e. the forward problem — as a minimization problem and utilize a trust region algorithm to robustly handle instabilities and follow energetically-favorable equilibrium paths through critical points.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Shock tube investigation of high-temperature, extremely-rich oxidation of several co-optima biofuels for spark-ignition engines

To reduce the reliance on fossil fuels in the transportation sector and increase combustion efficiency, the Co-Optima initiative from US Department of Energy identified the top 10 biofuels for downsized, boosted, spark-ignition engines. Most of these biofuels have detailed reaction mechanisms available in literature developed based on studies at temperatures lower than 1700 K and an equivalence ratio of less than five. As such, the performance of these detailed mechanisms at high temperature and extremely rich conditions are unknown. It is important to validate kinetic mechanisms at these conditions because they are conducive to soot formation. Prediction of soot by chemical kinetic models relies on the prediction of underlying benchmark species like carbon monoxide and ethylene. In this work, we conduct high temperature (1700–2050 K) and high equivalence ratio(Φ=8.6) oxidation of these biofuels, namely 2,4,4-trimethyl-1-pentene (α-diisobutylene), ethanol, cyclopentanone, methyl acetate, and 2-methylfuran, blended in ethylene behind reflected shock waves at 4–4.7 atm pressure. Carbon monoxide and ethylene time histories are measured simultaneously using a continuous feedback quantum cascade laser near 4.9 µm and a tunable CO 2 gas laser at 10.532 nm, respectively. Results show that ethanol blend forms more carbon monoxide than other biofuels and consumes ethylene faster than the biofuel blends in the temperature range considered. Overall, the performance of different mechanisms in literature are evaluated against the experimental results. The novel reaction mechanism ‘the Co-Optima model’, which includes the sub mechanisms for all the biofuels in this study, was found to be the best mechanism for the experimental conditions studied.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Large language model-driven database for thermoelectric materials

Thermoelectric materials have the ability to convert waste heat into electricity, offering a valuable solution for energy harvesting. However, their widespread use is hindered by low conversion efficiency, the reliance on expensive rare earth elements, and the environmental and regulatory concerns associated with lead-based materials. A fast and cost-effective way to identify highly efficient thermoelectric materials is through data-driven methods. These approaches rely on robust and comprehensive datasets to train models. Although there are several databases on thermoelectric materials, there is still a need to collect and integrate experimental data from peer-reviewed research articles to capture diverse compositions and properties of materials. Here, in this work, we developed a comprehensive database of 7,123 thermoelectric compounds, containing key information such as chemical composition, structural detail, seebeck coefficient, electrical and thermal conductivity, power factor, and figure of merit (ZT). We used the GPTArticleExtractor workflow, powered by large language models (LLM), to extract and curate data automatically from the scientific literature published in Elsevier journals. This process enabled the creation of a structured database that addresses the challenges of manual data collection. The open access database could stimulate data-driven research and advance thermoelectric material analysis and discovery.

Database↗
DOE OSTI2025

Hyperplane decision trees as piecewise linear surrogate models for chemical process design

Recent trends in chemical engineering research point towards an increasing reliance on data-driven modeling approaches. Neural networks, for instance, have proven to be accurate when data is plentiful and high-dimensional, but in many cases, they require computationally-intensive training procedures. Here, in this work, we describe hyperplane decision trees (HT) as a highly expressive and low-compute machine learning model architecture. These models are locally linear and have linear decision boundaries, resulting in a piecewise linear model of the data. This property allows them to be converted into mixed-integer linear constraints which can be globally optimized. Our open-source PyTorch implementation of this method is a fast, flexible, and accessible way to build accurate piecewise linear models of data.

Decision trees↗
DOE OSTI2020

Evaluation of a nonlinear variational multiscale method for fluid transport problems

Diverse transport problems, especially those based on fluid flow models, are intrinsically multiscale and nonlinear, characteristics that often lead to intricate dynamics such as the development of instabilities and turbulence. Computational simulations that resolve all scales in these problems are often unfeasible, prompting to coarse-grained simulation strategies in which small-scale features are modeled instead of resolved. Variational Multiscale (VMS) methods, and particularly residual-based Large-Eddy Simulation (LES) approaches, have proven effective and robust for the coarse-grained simulation of complex transport problems. VMS methods avoid the assumption of separable nonlinearity and the reliance on empirical small-scale models by using a variational decomposition of scales together with a residual-based approximation of the small-scales. Evaluation of a nonlinear VMS approach, denoted as VMS n , is presented for the coarse-grained simulation of transient-advective-diffusive-reactive (TADR) transport problems arising from fluid flow models. In contrast to classical VMS approaches that neglect the effect of the small scales on the transport operator, VMS n treats the inter-dependence between large- and small-scales upfront. The treatment of inter-scale coupling involves the solution of a local algebraic nonlinear system describing the evolution of the small-scales. The VMS n approach is complemented with two algebraic approximations of the small-scales: one based on the main diagonal of the transport matrices and another that preserves transport fluxes and is suitable for generic TADR systems. The suitability of the VMS n approach for handling general TADR problems and regimes is evaluated with benchmark incompressible, compressible, and magnetohydrodynamic laminar flow problems, the incompressible Taylor-Green vortex flow, the turbulent free jet, and the two-temperature arc in crossflow. Furthermore, simulation results show that VMS n leads to minor improvements in accuracy with respect to the classical VMS for the laminar flow problems, but to significantly greater accuracy for the turbulent flows and the unsteady plasma flow problems, while using the same cohesive numerical formulation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Peridynamic modeling of cementitious materials for nuclear waste management

Radioactive and hazardous waste generated from fuel processing plants, nuclear reactors, and hospitals, requires effective management strategies. Cementitious materials are widely applied for these needs, serving as structural materials, reactive barriers, or waste forms. In these applications cracking poses a significant risk to performance, driven by inconsistent shrinkage of the components and the varying strength and permeability of their interfaces. Traditional modeling approaches face challenges in representing the complex fracture behavior of cementitious materials due to the reliance on spatial derivatives and difficulties with mesh generation. Here, to overcome these limitations, we employ peridynamics, a novel continuum mechanics formulation that uses integrals to describe mechanical equilibrium, avoiding discontinuities associated with traditional methods. Through incorporation of a bi-linear softening model and quasistatics, an experimentally validated model for Portland cement concrete samples was created. Mechanical parameters, including compressive strength and elastic modulus were validated and variation due to aggregate packing was evaluated. Additionally, sensitivity analysis of the peridynamic parameters to the bulk material properties was established. The results lay the groundwork for evaluating the impact of unique conditions of cementitious waste forms that can be assessed to improve the reliability of waste management strategies.

Aggregates↗
DOE OSTI2025

Capacity optimization of nuclear power integration to meet dynamic industrial demand

To decarbonize their industrial facilities, The Dow Chemical Company has collaborated with Idaho National Laboratory (INL) to study the integration of nuclear power with an industrial chemical facility. Using Holistic Energy and Resource Optimization Network developed at INL for optimizing and analyzing integrated energy systems, a nuclear microreactor system was sized and evaluated for dynamic dispatch to Dow Silicones Corporation’s Carrollton, KY (USA) site for iloxane production. It was found that a 180 MW th system (12 × 15MW th ) with 75.1 MWh th of thermal energy storage could provide heat and power to the chemical facilities. In the process, this would reduce electricity imports by 99.9 % and reduce the Scope 1 and 2 emissions of the site by 292,100 tonnes CO 2 /yr (98.8 %). The primary novelty of this work is a first of a kind design and optimization of a microreactor powered integrated energy system to provide heat and power to a chemical plant using real plant data. This analysis will pave the way for future studies using dispatchable clean energy sources to reduce carbon emissions and commodity industries’ reliance on fossil fuels.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Ice storage model-predictive control in an office building with PV: scenario, error and sensitivity analysis

Thermal energy storage (TES) can enable more building-sited renewable electricity generation and lower utility bill costs for buildings owners and occupants, especially when there are high demand and variable time-of-use (TOU) charges. A model predictive control (MPC) strategy can offer additional savings over a schedule-based control with added complexity and reliance on forecasts. Here, this study examines savings for medium office buildings with chiller plants in three locations with building-installed solar photovoltaics (PV) to understand the impact of MPC. Control setpoints are fixed by a schedule-based control or optimized by nonlinear MPC. These control setpoints are actuated within EnergyPlus building models to simulate the utility cost of the chiller plant. NLP solutions can be unstable or unrealistic, but our results show that by regularizing the NLP, the solutions can be reasonably followed by the building model. MPC models make simplifications that lead to errors once the controller is participating in and changing the operation of the building. These errors average 9 % across the cases, showing that the most important parts of the system are represented. The no-thermal load costs are computed to show that the optimization can in some cases achieve both the minimum TOU and minimum monthly demand costs by demand management while reducing TOU energy costs by energy arbitrage. The MPC saves 35–66 % in the annual chiller plant operating costs, which is an additional savings above the schedule by 1–33 %. PV and TES are complementary and mostly independent, but a load with PV often results in better performance for the schedule. Our case study and sensitivity analysis show the importance of modeling and optimization for complex rates, but also the circumstances wherein a simpler strategy achieves the same performance with less potential for error.

14 SOLAR ENERGY↗
DOE OSTI2024

Three different directions in which the European Union could replace Russian natural gas

Russia's invasion of Ukraine fuelled an energy crisis, which considerably impacted Europe given its heavy reliance on Russian natural gas imports. This study uses an ensemble of four global integrated assessment models, which are further soft-linked to two sectoral models, and explores the synergies and trade-offs among three approaches to living without Russian gas in Europe: (a) replacing with other gas imports, (b) boosting domestic energy production, and (c) reducing demand and accelerating energy efficiency. We find that substituting Russian gas from other trade partners would miss an opportunity to accelerate decarbonisation in end-use sectors while risking further fossil-fuel lock-ins, despite featuring the lowest gas price spikes and potentially reducing heating costs for end-users in the near term. Boosting domestic, primarily renewable, energy production on the other hand would instead require considerable investments, potentially burdening consumers. Energy demand reductions, however, could offer considerable space for further emissions cuts at the lowest power-sector investment costs; nonetheless, an energy efficiency-driven strategy would also risk relocation of energy-intensive industries, an aspect of increasing relevance to EU policymakers.

03 NATURAL GAS↗
DOE OSTI2022

Automated identification of dominant physical processes

The identification of processes that locally and approximately dominate dynamical system behavior has enabled significant advances in understanding and modeling nonlinear differential dynamical systems. Conventional methods of dominant process identification involve piecemeal and ad hoc (non-rigorous, informal) scaling analyses to identify dominant balances of governing equation terms and to delineate the spatiotemporal boundaries (boundaries in space and/or time) of each dominant balance. For the first time, we present an objective global measure of the fit of dominant balances to observations, which is desirable for automation, and was previously undefined. Furthermore, we propose a formal definition of the dominant balance identification problem in the form of an optimization problem. Here, we show that the optimization can be performed by various machine learning algorithms, enabling the automatic identification of dominant balances. Our method is algorithm agnostic and it eliminates reliance upon expert knowledge to identify dominant balances which are not known beforehand.

42 ENGINEERING↗
DOE OSTI2020

North American energy system responses to natural gas price shocks

As of 2020, North American natural gas extraction and use in the electricity sector have both reached all-time highs. The combination of North America's increased reliance on natural gas with a potential disruption to the natural gas market has several energy security implications. Additionally, policymakers interested in economic resiliency will find this study's results useful for informing the implications of the energy sectors' long-term planning and investment decisions. This paper evaluates how both the electricity and natural gas sectors could respond to hypothetical gas price shocks under different system configurations. We impose unforeseen natural gas price shocks under reference and alternative configurations resulting from a renewable generation mandate or variations to renewable capacity costs. Results from several different models are presented for the electricity and natural gas sectors separately for Canada, Mexico, and the United States. Generally, the US becomes more (less) reliant on electricity imports from Canada given a high (low) gas price shock but increases (decreases) exports to Mexico. The renewable mandate is shown to buffer electricity price increases under high price shocks but price reductions under the low price shocks are dampened given less flexibility to take advantage of the low-priced natural gas. The United States is demonstrated to reduce natural gas production and net exports with high natural gas price shocks given a reduction in demand.

03 NATURAL GAS↗
DOE OSTI2025

Upcycling Mixed Spent Ni-Lean Cathodes into Ni-Rich Polycrystalline Cathodes

Sustainable battery recycling is vital for conserving resources and reducing environmental impacts. Current open- and closed-loop recycling strategies often focus on recovering individual components, making the reuse of mixed cathode materials a complex challenge. Meanwhile, the research on upcycling has been limited to using pristine cathode feedstocks and virgin materials for synthesis. Here, to address this issue, we present an upcycling approach for spent Ni-lean mixed cathode materials that integrate an upcycling hydrometallurgical recycling process with traditional hydrometallurgical methods. This strategy achieves a utilization of 92.31 mol % of recycled materials, enabling the regeneration of Ni-rich cathode materials while significantly reducing the reliance on virgin resources. The regenerated 83Ni cathode materials demonstrate physical properties comparable to those produced from virgin materials. Electrochemical evaluations using single-layer pouch cells show that both recycled and virgin cathodes exhibit initial specific capacity close to 201.1 mAh/g and maintain approximately 88 % capacity retention after 500 cycles. Additionally, 2Ah cells confirmed these findings, delivering 85 % capacity retention after about 900 cycles. Techno-economic analysis demonstrates notable environmental benefits, including reductions in greenhouse gas emissions and energy consumption, achieving 232.75 MJ/kg of product, which is 8.6 % lower than traditional methods and comparable to direct upcycling. Furthermore, the upcycling hydrometallurgical recycling process generates the highest profit, proving its economic viability. This scalable and versatile process is adaptable to varying transition metal compositions, facilitating a closed-loop recycling system that bridges mixed spent cathodes with next-generation cathode materials, and offers a sustainable solution for managing waste battery materials.

Hydrometallurgical recycling↗
DOE OSTI2020

Improving near real-time precipitation estimation using a U-Net convolutional neural network and geographical information

Reliable near real-time precipitation estimates are essential for monitoring and managing of natural disasters such as floods. Quality of inputs and capability of the retrieval algorithm are two important aspects for developing satellite-based precipitation datasets. Most retrieval algorithms utilize infrared (IR) information as their input due to its fine spatiotemporal resolution and near-instantaneous availability. However, their sole reliance on IR information limits their capability to learn different mechanisms of precipitation during training, resulting in less accurate estimates. Moreover, recent advances in the field of machine learning offer attractive opportunities to improve the precipitation retrieval algorithms. This work investigates the effectiveness of adding geographical information (i.e. latitude and longitude) to IR information and the application of a U-Net-based convolutional neural network for improving the accuracy of retrieval algorithms. This research suggests that applying an appropriate CNN architecture on geographical and IR information provides an opportunity to improve the satellite-based precipitation products.

42 ENGINEERING↗
DOE OSTI2020

A trilevel model against false gas-supply information attacks in electricity systems

The interdependence between natural gas and electricity systems is increasing rapidly due to the growing reliance on natural gas-fired generating units. Availability of natural gas for gas-fired generating units can impact the secure operation of electricity systems. Fuel supply shortage for gas-fired units can be caused by uncertain interruptible supply contracts and incorrect supply information. This article proposes a trilevel min-max-min defender-attacker-operator optimization problem to provide the power system operator a screening methodology that allocates a limited budget for best protecting critical fuel supply information, and also the strategies to sign firm supply contract to reduce natural gas supply uncertainties. We utilize a column and constraint generation (C&CG) algorithm to solve the proposed problem. We illustrate the effectiveness of this trilevel formulation using a case study based on IEEE 24-node test system.

42 ENGINEERING↗
DOE OSTI2025

Critical minerals lists for low-carbon transitions: Reviewing their structure, objectives, and limitations

Critical minerals lists have flourished in the past decade, in particular linked to the importance of critical minerals for low-carbon transitions. We identified 27 critical minerals or materials lists across 15 countries and the European Union (EU). These lists are designed to attract public and private attention and investments to secure both domestic and foreign supplies. This review article fills a gap in the existing literature by analyzing the ways in which these lists are defined and utilized by countries engaged in a mineral rush. We focus our attention on three categories of minerals – battery minerals, platinum-group metals (PGMs), and rare earth elements (REEs) that are particularly important to energy transitions. We situate this research in the broader legal and administrative developments that have driven critical minerals policies in the past decade. We provide an in-depth analysis of the commonalities and variations in the raw materials included in these lists, and identify six core limitations of critical minerals lists: (1) unclear links between criticality assessments and mineral prioritization (2) failure to account for the full mineral value-chain; (3) limited strategic alignment between allied nations; (4) limited flexibility in dynamic environments (5) limited consideration for recycling and by-product sourcing; and (6) reliance on incomplete reserve and resource data.

Battery minerals↗