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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 OSTI2020

Low-temperature carbonization of polyacrylonitrile/graphene carbon fibers: A combined ReaxFF molecular dynamics and experimental study

Graphene inclusion in a polymer matrix is a promising route to significantly enhance the mechanical properties of low-grade carbon fibers (CFs). Using ReaxFF molecular dynamics simulation, the atomistic mechanism leading to this enhancement is investigated. We demonstrate that the graphene edges along with the nitrogen and oxygen functional groups play a catalytic role and act as seeds to expedite alignment of the all-carbon rings, which are starting sites for the growth of graphitic structures. To examine the role of this proposed mechanism that enhances the graphitic structure of PAN/graphene CFs, we discuss the experimental results wherein the PAN/graphene CFs carbonized at 1250 C demonstrate 91% (from 632 to 1207 MPa) increase in strength and 101% (from 88 to 177 GPa) enhancement in Young’s modulus compared to PAN-based CFs carbonized at 1500 C. In conclusion, these enhanced mechanical properties of low-grade carbon fibers achieved via graphene inclusion at decreased carbonization temperature provide a means to realize both energy savings and cost reduction.

42 ENGINEERING↗
DOE OSTI2022

Into the net zero emissions and climate change control: From solid carbon waste to effective solar convertors

Here, in this work, we demonstrate the potential for solar-to-heat conversion of carbon byproducts and their outstanding effects to reduce anthropogenic emissions. Those carbon byproducts are soots (biomass, evaporated carbon, etc.) as well as morphed graphenes. Our results cover optical characterization to demonstrate their suitability as heat capacitors and solar-to-heat convertors. Our raw materials are sustainable and currently considered environmental pollutants that require true attention to minimize global warming effects. Fortunately, they are ideal candidates to reduce the carbon footprints and can contribute towards a true net-zero environmental transition. We explore and demonstrate that soots are among the most competitive materials to transform solar energy into heat due to their absorption capacity, and durability. These materials are more competitive than commercial products and other known solar convertors demonstrating the superior thermosolar benefits. The best candidate is biomass with solar absorptivity, light emission, and cost reduction of 96, 85% and 15-fold, respectively, when compare to commercial products. Biomass and soot have comparable or better solar conversion characteristics, and sustainability than pristine carbon nanostructures and they are up to 3 orders of magnitude cheaper.

14 SOLAR ENERGY↗
DOE OSTI2025

An end-to-end deep learning method for solving nonlocal Allen–Cahn and Cahn–Hilliard phase-field models

Here, we propose an efficient end-to-end deep learning method for solving nonlocal Allen–Cahn (AC) and Cahn–Hilliard (CH) phase-field models. One motivation for this effort emanates from the fact that discretized partial differential equation-based AC or CH phase-field models result in diffuse interfaces between phases, with the only recourse for remediation is to severely refine the spatial grids in the vicinity of the true moving sharp interface whose width is determined by a grid-independent parameter that is substantially larger than the local grid size. In this work, we introduce non-mass conserving nonlocal AC or CH phase-field models with regular, logarithmic, or obstacle double-well potentials. Because of non-locality, some of these models feature totally sharp interfaces separating phases. The discretization of such models can lead to a transition between phases whose width is only a single grid cell wide. Another motivation is to use deep learning approaches to ameliorate the otherwise high cost of solving discretized nonlocal phase-field models. To this end, loss functions of the customized neural networks are defined using the residual of the fully discrete approximations of the AC or CH models, which results from applying a Fourier collocation method and a temporal semi-implicit approximation. To address the long-range interactions in the models, we tailor the architecture of the neural network by incorporating a nonlocal kernel as an input channel to the neural network model. We then provide the results of extensive computational experiments to illustrate the accuracy, predictive capabilities, and cost reductions of the proposed method.

42 ENGINEERING↗
DOE OSTI2023

Cost-constrained adaptive simulations of transient spray combustion in a gas turbine combustor

Predictive high-fidelity simulations of turbulent spray combustion must capture the combined effects of complex chemistry, multiphase evaporating flow and spray-flame interactions to achieve physical accuracy. Finite-rate chemistry (FRC) combined with a realistic chemical mechanism is a combustion model well-suited for this purpose, but has a high computational cost due to the large number and stiffness of transported chemical species. In contrast, flamelet-based models achieve lower cost by transporting a small number of quantities of reduced stiffness, but assumptions regarding local flame topology, boundary conditions and inter-phase coupling limit their physical accuracy. Recently, the Pareto-efficient combustion (PEC) framework was developed to dynamically assign combustion models based on local cost and accuracy metrics in gas-phase reacting flows. In this work, we extend this PEC framework to spray combustion through the rigorous analysis of the multiphase coupling terms in the governing equations. The derivation shows that spray evaporation causes errors in the prediction of species mass fractions for flamelet-based models due to the sensitivity of the local thermo-chemical state to changes in composition caused by fuel vaporization across combustion regimes present in practical spray combustion devices. Sub-model assignment is formulated as a multiple-choice knapsack problem, where computational cost is directly controlled through the fraction of the domain assigned to the FRC sub-model. The extended PEC formulation is applied to the simulation of a realistic rich-quench-lean gas turbine combustor at steady-state conditions, as well as transient operation resulting in lean blow-out (LBO). Analysis of transient simulations during LBO demonstrates the extended PEC formulation’s capacity to dynamically adapt to changing conditions within the combustor. Transient combustor dynamics are shown to approach convergence with limited increases in computational cost, while retaining substantial computational cost reduction compared to monolithic FRC simulations. Through PEC simulations with increasing fractions of the domain assigned to FRC, monolithic flamelet simulations are shown to over-predict flame stability during LBO. Finally, the extended PEC formulation is thus shown to overcome deficiencies of monolithic models by controlling modeling error for multiphase combustion modeling.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Comprehensive assessment of deep reinforcement learning approaches for economic dispatch in nuclear-driven microgrids

As the electrical grid integrates more variable renewable energy sources such as wind and solar, the demand for distributed and flexible systems to address this increased variability becomes critical. Nuclear-driven microgrids provide a promising solution by offering stable generation to complement intermittent renewables, ensuring grid reliability and operating efficiency. This paper proposes a recurrent deep reinforcement learning framework for optimal economic dispatch in a nuclear-powered microgrid integrating renewable energy sources, small modular reactors, battery storage systems, and balance-of-plant dynamics. A three-agent control architecture is developed, where demand and renewable energy agents act as forecasters, and a reinforcement learning-based dispatch agent performs real-time energy allocation. A nonlinear programming formulation is first used to generate an optimal baseline for benchmarking. The proposed dispatch controller, based on Proximal Policy Optimization enhanced with Long Short-Term Memory networks, exploits temporal correlations in system dynamics by taking advantage of the time series used as inputs to improve policy robustness under uncertainty. Comparative analysis against established deep reinforcement learning methods, including Proximal Policy Optimization with a feedforward architecture, Soft Actor-Critic, and Twin Delayed Deep Deterministic Policy Gradient, demonstrates superior performance. Numerical results indicate that the proposed controller achieves a 0.39% cost reduction relative to the nonlinear programming benchmark and outperforms other learning-based methods by generating additional revenue of up to 0.35%. All reinforcement learning controllers compute dispatch actions in less than 0.3 s, resulting in a computational speedup of more than three orders of magnitude over the nonlinear programming baseline. The findings of this paper highlight their applicability for real-time operation and control in nuclear-integrated microgrids under volatile operating conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Multilevel Monte Carlo methods for the Grad-Shafranov free boundary problem

The equilibrium configuration of a plasma in an axially symmetric reactor is described mathematically by a free boundary problem associated with the celebrated Grad-Shafranov equation. The presence of uncertainty in the model parameters introduces the need to quantify the variability in the predictions. This is often done by computing a large number of model solutions on a computational grid for an ensemble of parameter values and then obtaining estimates for the statistical properties of solutions. In this study, we explore the savings that can be obtained using multilevel Monte Carlo methods, which reduce costs by performing the bulk of the computations on a sequence of spatial grids that are coarser than the one that would typically be used for a simple Monte Carlo simulation. We examine this approach using both a set of uniformly refined grids and a set of adaptively refined grids guided by a discrete error estimator. Numerical experiments show that multilevel methods dramatically reduce the cost of simulation, with cost reductions typically on the order of 60 or more and possibly as large as 200. Furthermore, adaptive griding results in more accurate computation of geometric quantities such as x-points associated with the model.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Policy implications of net-zero emissions: A multi-model analysis of United States emissions and energy system impacts

Many countries, subnational jurisdictions, and companies are setting net-zero emissions goals; however, questions remain about strategies to reach these targets, policy measures, technology gaps, and economic impacts. Here, we investigate the potential policy implications of reaching economy-wide net-zero CO 2 emissions across the United States by 2050 using results from a multi-model comparison with 14 energy-economic models. Model results suggest that achieving net-zero CO 2 targets depends on policies that accelerate deployment of zero- and low-emitting technologies that have seen rapid cost reductions in recent years (including wind, solar, battery storage, and electric vehicles) as well as relatively nascent options (including carbon capture and storage, advanced biofuels, low-carbon hydrogen, advanced nuclear, and long-duration energy storage). While net-zero policies are likely to lower fossil fuel consumption, including considerable coal and petroleum reductions, achieving net-zero emissions does not necessarily mean phasing out all fossil fuels. Model results indicate that the Inflation Reduction Act’s energy and climate provisions amplify near-term decarbonization but that net-zero policies have larger impacts on long-run outcomes. Stringent climate policy can have large fiscal impacts on tax revenue and government spending—revenues from carbon pricing and subsidies for carbon removal range from 0.1 % to 3.7 % of GDP in 2050 across models. Each dollar per metric ton carbon price leads to a 0.06 % to 0.31 % reduction in economy-wide CO 2 emissions relative to a reference scenario with current policies. Spending on energy across the economy decreases relative to today for many models under reference and net-zero policies, especially as a share of GDP, due primarily to end-use electrification and energy efficiency.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Experimental characterization and potential energy savings of insulated cladding for U.S. residential buildings

This study evaluates a novel insulated cladding composed of foamed cement and vinyl siding that is designed as a retrofit product to enhance thermal performance in U.S. residential buildings. Experimental measurements showed that foamed cement exhibits a thermal conductivity of 0.0334–0.0365 W/m-K (R-value of 4.0–4.3 °F⋅ft 2 ⋅h/BTU), which is significantly higher than that of conventional fiber cement boards. Although mechanical testing confirmed the material’s brittleness and low strength, it also indicated its suitability for nonstructural insulation applications. Water vapor permeability testing demonstrated effective moisture resistance with the polymer coating. To evaluate the insulated cladding’s energy performance, we used the U.S. Department of Energy’s prototype single-family building model and simulated across 50 locations spanning 16 International Energy Conservation Code climate zones. The R-7 (R-value of 7 °F⋅ft 2 ⋅h/BTU per inch) retrofit cladding resulted in heating energy savings up to 275 therms (Fairbanks, AK) and cooling energy savings up to 1,547 kWh (Phoenix, AZ). The energy cost savings varied by region, but the highest annual cost savings ($\$$434/year) were observed in Santa Maria, CA, and the highest annual cost savings percentage (37 %) were noted in Monterey, CA. On a national scale, the insulated retrofit provided an average annual energy cost reduction of 16 %, highlighting the cladding system’s broad applicability and financial viability.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

A practical control strategy for demand flexibility with ensured occupant comfort in grid-interactive efficient buildings

This study proposes a practical and simplified demand response (DR) control strategy from the perspective of grid-interactive efficient buildings (GEBs), aiming to secure demand flexibility while ensuring occupant thermal comfort. Focusing on summer on-peak periods, a linear demand response (LDR) strategy that integrates cooling setpoint adjustment and lighting dimming was designed, and its performance was quantitatively evaluated. A case study was conducted using EnergyPlus-based simulations for a U.S. DOE small office prototype building under summer on-peak weather conditions. Compared with a conventional rapid demand response (RDR) strategy, the proposed LDR approach gradually reduced electrical loads while maintaining occupant thermal comfort indices, including predicted mean vote (PMV) and predicted percentage of dissatisfied (PPD), within acceptable comfort ranges. Quantitative analysis of demand flexibility using the grid-interactive impact index (GII) and the flexibility strength index (FSI) showed that the LDR strategy provided approximately 12.5% demand flexibility during DR periods and achieved an electricity cost reduction of about 8.8%. In addition, the results of the part-load ratio (PLR)-based cooling system performance analysis showed that, in the on-peak period, the LDR strategy exhibited improved cooling performance compared with the baseline. Overall, this study shows the potential of a practical DR control strategy that can simultaneously achieve occupant comfort and demand flexibility without relying on complex advanced control technologies.

Jung, Dong Eun↗
DOE OSTI2022

Forecasting battery capacity and power degradation with multi-task learning

Lithium-ion batteries degrade due to usage and exposure to environmental conditions, which affects their capability to store energy and supply power. Accurately predicting the capacity and power fade of lithium-ion battery cells is challenging due to intrinsic manufacturing variances and coupled nonlinear ageing mechanisms. In this paper, we propose a data-driven prognostics framework to predict both capacity and power fade simultaneously with multi-task learning. The model is able to predict the degradation trajectory of both capacity and internal resistance together with knee-points and end-of-life points accurately at early-life stage. The validation shows an average percentage error of 2.37% and 1.24% for the prediction of capacity fade and resistance rise, respectively. The model's ability to accurately predict the degradation, facing capacity and resistance estimation errors, further demonstrates the model's robustness and generalizability. Compared with single-task learning models for forecasting capacity and power degradation, the model shows a significant prediction accuracy improvement and computational cost reduction. This work presents the highlights of multi-task learning in the degradation prognostics for lithium-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2020

High-performance and cost-effective membrane electrode assemblies for advanced proton exchange membrane water electrolyzers: Long-term durability assessment

In this work, improved activity and durability performance of a two-cell (86 cm 2 ) proton exchange membrane water electrolyzer (PEMWE) stack is reported for the first time. Both membrane electrode assemblies (MEAs) contain one order of magnitude lower platinum group metal (PGM) loadings compared to the state-of-the-art PEMWEs and incorporate novel Pt recombination layers. The high-performance and cost-effective MEAs are fabricated by the unique reactive spray deposition technology (RSDT). This advanced methodology allows for one-step fabrication of MEAs and ensures precise control and distribution of the catalyst composition and loading. The RSDT-fabricated MEAs contain only 0.2 and 0.3 mg PGM cm –2 loading in the cathode and anode electrodes, respectively, and demonstrate excellent activity and durability for over 3000 h of operation at industrially-relevant operating conditions without showing significant loss in performance. This novel work shows that a significant cost reduction for PEMWEs is achieved while maintaining excellent durability, high catalysts activities, and low hydrogen cross-over.

Proton exchange membrane water electrolyzer↗
DOE OSTI2022

Formation of carbon and oxygen rich surface layer on high purity magnesium by atmospheric carbon dioxide plasma

Carbon and oxygen-rich corrosion barrier layer formed on Mg by a simple and scalable CO 2 atmospheric plasma (CO 2 -AP) process. The reactive CO 2 -AP interacts with the Mg surface and forms a unique layered structure with the top MgCO 3 /MgO-intermixed particulates pillars and the bottom dense layer. The surface features were simultaneously formed on the nano-/micro-structured MgO layer by carbonate molecules, plasma-active CO 2 molecules, and/or other volatile organic compounds on the nano-/micro-structured MgO particle layer. The resulting surfaces after CO 2 -AP were either hydrophobic or hydrophilic and exhibited lower anodic current or high resistance for Mg corrosion. For the hydrophobic surfaces of CO 2 -AP treated Mg, molecular dynamic simulations were performed to understand the origin of hydrophobicity and identified that the amorphous carbon layers formed on the Mg surface are the source. The environmentally benign abundant-gas-based process enables the cost reduction associated with waste treatment, generation of by-product, and supply of raw material.

36 MATERIALS SCIENCE↗
DOE OSTI2026

State of the art, gaps, and prospects in fusion materials theory and modelling

Advancing the theory and simulation of materials for fusion applications remains a key component of global roadmaps aimed at delivering much-needed fusion power. Especially as the drive for commercial application increases, prototypes must be designed against radiation damage before the relevant experimental data can be collected and cost reductions that are possible by testing materials in silico become even more important. Here, we summarise the state of the art as it emerged during the 7 th Fusion Materials Theory & Modelling Workshop that took place in 2024, with the aim to highlight present gaps and future directions for the fusion materials modelling community. Of particular interest were the effects of transmutations, chemical complexity with the development of novel alloys and interatomic potentials, advancements in modelling high-dose microstructures, comparison with experimental data and multiscale models for structural assessment relying on high-performance computing and virtual reality.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Implications of carbon content for the processing, stability, and mechanical properties of cast and wrought Ni-based superalloy Nimonic 105

As the temperature and pressure requirements in land-based turbines for power generation increase, the shift towards more advanced alloys, e.g., Ni-based superalloys, requires improved understanding of their long-term stability and mechanical properties. Additionally, the larger size of the components also presents fabrication and cost-reduction problems. In this study, we investigated Nimonic 105 as a potential rotor material at two carbon content levels – the commonly used maximum of the alloy specification and a lower carbon content variant. Reducing the carbon content presented fabrication challenges, resulting in surface crack formation during hot working. Additionally, a lower M 6 C carbide fraction was present after fabrication/solutioning, leading to a significantly larger grain size. After the standard aging treatment, similar γ' populations formed in the two variants, however the M 6 C carbides in the high-carbon variant already started transforming to intergranular M 23 C 6 . Although we did not observe a noticeable effect on the tensile properties of the two variants, the lower carbon content alloy possessed a superior creep property – mainly due to the larger grain size as the microstructural stability was inferior. Further, upon prolonged thermal exposure, the M 6 C carbides in the low-carbon variant further decomposed to intergranular and intragranular σ and μ precipitates, as opposed to mainly M 23 C 6 as in the high-carbon variant.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Radioisotope replacement with compact electron linear accelerators

The replacement of radioactive sources with alternative technologies has been identified as a priority by international authorities, due to the risk of accidents and diversion by terrorists for use in Radiological Dispersal Devices. Many of these sources can be replaced with the X-rays produced by electron beams accelerated to MeV energies. However, the size, weight and costs of electron linacs must be significantly reduced to be considered for radioisotope replacement. RadiaBeam Technologies, LLC is developing a series of inexpensive compact electron accelerators in the 1–10 MeV range for radioisotope replacement such as Ir-192, Cs-137 and Co-60 for various applications. The dramatic level of miniaturization and cost-reduction was achieved thanks to the implementation of such innovative technologies as high-frequency magnetrons, split accelerating structure fabrication technology and solid-state Marx modulators. Here, in this paper, we overview RadiaBeam's compact linac developments, discuss the enabling technologies, and report on the current progress.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Transforming E-Waste Into Strategic Resources: Techno-Economic Analysis of Gallium and By-products Recovery from LEDs via Bioleaching

The growing demand for gallium in optoelectronics and renewable energy applications raises concerns about supply security and production sustainability. This study evaluates the techno-economic feasibility of recovering gallium and by-products (copper and nickel) from waste GaN-based LEDs via bioleaching. A process flowsheet encompassing transportation, robotic disassembly, ball milling, bioleaching, solvent extraction/electrowinning, and refining was modeled. Based on mass balance analysis, more than 53 tons of LED waste are required annually to yield 1 kg of gallium alongside substantial copper and nickel co-products. Preliminary techno-economic analysis (TEA) shows an average total cost (ATC) of 6.84 USD/kg metal when costs are allocated by mass-weighted economic value (market price) fraction, corresponding to 6.75 USD/kg for copper, 15.91 USD/kg for nickel, and 470.95 USD/kg for gallium. For gallium, direct operational costs account for more than 70% of the cost. Monte Carlo simulations further quantify cost uncertainties under market price fluctuations. This work represents the first TEA of gallium recovery from GaN-based LEDs and highlights potential pathways for future cost reduction.

36 MATERIALS SCIENCE↗