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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Effect of water vapor on high-temperature oxidation of NiAl alloy

The high-temperature oxidation of NiAl is studied with dry oxygen and water vapor. The oxidation in H 2 O results in a thicker Al 2 O 3 oxide scale than that in O 2 . The oxide scale formed initially is a single layer of γ-Al 2 O 3 that subsequently transforms into a α-Al 2 O 3 /γ-Al 2 O 3 bilayer structure, in which the inner α-Al2O3 layer formed in H2O has a higher porosity than that in O 2 . Finally, further density functional theory calculations show that H protons derived from H 2 O molecules penetrate into the oxide lattice and boost the formation of lattice vacancies in both α-Al 2 O 3 and γ-Al 2 O 3 , thus enhancing the oxide scale growth.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Intergranular precipitation-enhanced wetting and phase transformation in an Al0.4CoCrFeNi high-entropy alloy exposed to lead-bismuth eutectic

We report after exposure to oxygen-poor (10 -13 –10 -14 wt%) liquid lead-bismuth eutectic (LBE) at 500 °C for 500 h, LBE penetrates more than one order of magnitude deeper in an FCC Al 0.4 CoCrFeNi high-entropy alloy (HEA) decorated with a network of BCC (Ni, Al)-rich intergranular (IG) precipitates than in a single-phase, FCC Al 0.3 CoCrFeNi HEA without the IG precipitate network. This deterioration of corrosion resistance is attributed to the energetic nature of the BCC/FCC interphase boundaries (IBs) and resultant IB wetting. The LBE ingress film selectively leaches nickel located at those low-indexed crystalline planes, resulting in phase transformation from FCC to BCC structure.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Chromium segregation-induced oxide evolution in Ni-10Cr alloys during high-temperature oxidation

The oxidation behavior of a Ni-10(wt%)Cr alloy under high-temperature O 2 conditions is investigated using transmission electron microscopy and first-principles calculations. Results reveal that chromium segregation plays a central role in driving the evolution of complex oxide phase structures during oxidation. At low Cr concentrations, Cr preferentially segregates to NiO grain boundaries or internal pores, substituting for Ni atoms and forming Ni(Cr)O solid solutions. As Cr content increases, enhanced diffusion promotes Cr penetration into the NiO lattice, leading to the formation of multiphase oxide structures. First-principles modeling corroborates these findings: at low Cr concentrations, Cr atoms favor surface and grain-boundary segregation, while higher concentrations lead to Cr aggregation within the NiO bulk. Furthermore, the integrated experimental-theoretical approach provides atomistic insights into Cr-mediated mass transport mechanisms during alloy oxidation and offers valuable guidance for controlling oxide growth kinetics and phase stability in Ni-Cr alloys, with implications for improving oxidation resistance in high-temperature structural applications.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Elucidating the impact of stress states on grain boundary passivation in Ni-5Cr using the Rhines pack method

The oxidation behavior of a Ni–5Cr (at.%) alloy was evaluated at 420 ° C using the Rhines pack method and simultaneous tensile and compressive stress states via a miniature four-point bending fixture. At this moderate temperature, grain boundaries dominate mass transport and the resulting oxidation response. Oxidation produced approximately 1μ⁢m-wide protective Cr 2 O 3 films capping some grain boundaries and penetrative, intergranular Cr-rich oxides at other grain boundaries. Externally applied tensile and compressive stress during oxidation increased the prevalence of Cr 2 O 3 cap formation compared to no applied stress, with tensile stress resulting in more Cr 2 O 3 caps than compressive stress. The observed oxide cap morphology was similar across all test conditions. Regions under compressive stress showed an order of magnitude greater Cr depletion depth along the grain boundary. Chromium nitrides (CrN), likely from N contamination of the Rhines pack cell, were observed both at the oxide–metal interface and intergranularly within the alloy. Collectively these results demonstrate that applied stress promotes localized protective oxide cap formation over grain boundaries, with compressive stress additionally promoting deeper Cr depletion. Furthermore the experimental approach helped separate out the effects of stress on local oxide formation and grain boundary passivation.

4 point bend↗
DOE OSTI2023

High strain rate nanoindentation testing: Recent advancements, challenges and opportunities

Recent advancements in electronics have renewed the interest in high strain rate nanoindentation testing, resulting in the development of new high strain rate nanoindentation test equipment and test methodologies. Here, in this work, the current state-of-the-art in high strain rate nanoindentation testing is critically reviewed, with focus on three key aspects - the testing equipment's dynamic mechanical and electronic response, test methodology, and post-processing of raw data to obtain hardness and strain rate. The challenges in instrument hardware design and post-test data analysis are discussed, along with optimal strain rate window for accurate high strain rate measurements. Specific focus will be on instrumented high strain rate testing using self-similar indenters at strain rates in excess of 100 s -1 , wherein load and depth of penetration into the sample are both measured or applied.

36 MATERIALS SCIENCE↗
DOE OSTI2026

New perspectives on materials and device dynamics using time-resolved full-field diffraction X-ray imaging

Understanding how materials evolve during synthesis, processing, or device operation requires experimental access to structural dynamics across wide ranges of length and time scales, often in bulk samples or even within packaged devices. Time-resolved full-field diffraction X-ray microscopy has recently emerged as a powerful way to meet this need by combining the penetration and structural sensitivity of X-ray diffraction with objective-lens-based magnification, sensitive high-resolution X-ray imaging detectors, and pump-probe and real-time imaging strategies. Advances in full-field X-ray diffraction microscopy, often termed dark-field X-ray microscopy (DFXM), enable simultaneous imaging of extended fields of view while retaining crystallographic selectivity. Time-resolved DFXM promises to enable advances in materials processing and dynamics, electrochemical and photochemical processes, and the design of electronic devices. This Perspective summarizes the key instrumental concepts that define the performance of these methods, including the choice of imaging optics, detector considerations, and the impact of source time structure at synchrotron light sources and X-ray free-electron lasers (XFELs). We discuss the simultaneous use of complementary imaging modes that are increasingly used in practice. The early demonstrations of the potential of time-resolved DFXM include real-time defect and grain-boundary dynamics during metal annealing, stroboscopic imaging of functional devices with high strain sensitivity, and ultrafast pump-probe implementations at XFELs that directly visualize acoustic-wave propagation and energy dissipation in bulk crystals.

Dark-field X-ray microscopy↗
DOE OSTI2022

SuperScreen: An open-source package for simulating the magnetic response of two-dimensional superconducting devices

Quantitative understanding of the spatial distribution of magnetic fields and Meissner screening currents in two-dimensional (2D) superconductors and mesoscopic thin film superconducting devices is critical to interpreting the results of magnetic measurements of such systems. Here, we introduce SuperScreen, an open-source Python package for simulating the response of 2D superconductors to trapped flux and applied time-independent or quasi-DC magnetic fields for any value of the effective magnetic penetration depth, Λ. Given an applied magnetic field, SuperScreen solves the 2D London equation using an efficient matrix inversion method to obtain the Meissner currents and magnetic fields in and around structures composed of one or more superconducting thin films of arbitrary geometry. Further, SuperScreen can be used to model screening effects and calculate self- and mutual-inductance in thin film superconducting devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Two-fluid physical modeling of superconducting resonators in the ARTEMIS framework

In this work, we implement a new London equation module for superconductivity in the GPU-enabled ARTEMIS framework, and couple it to a finite-difference time-domain solver for Maxwell's equations. We apply this two-fluid approach to model a superconducting coplanar waveguide (CPW) resonator. We validate our implementation by verifying that the theoretical skin depth and reflection coefficients can be obtained for several superconductive materials, with different London penetration depths, over a range of frequencies. Our convergence studies show that the algorithm is second-order accurate in both space and time, except at superconducting interfaces where the approach is spatially first-order. In our CPW simulations, we leverage the GPU scalability of our code to compare the two-fluid model to more traditional approaches that approximate superconducting behavior and demonstrate that superconducting physics can show comparable performance to the assumption of quasi-infinite conductivity as measured by the Q-factor.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

QMMM 2023: A program for combined quantum mechanical and molecular mechanical modeling and simulations

Combined quantum mechanical and molecular mechanical (QM/MM) methods play an important role in multiscale modeling and simulations. QMMM 2023 is a general-purpose program for single-point calculations, geometry optimizations, transition-state optimizations, and molecular dynamics (MD) at the QM/MM level. It calls a QM package and an MM package to perform the required single-level calculations and combines them into a QM/MM energy by a variety of schemes. QMMM 2023 supports GAMESS-US, Gaussian, and ORCA as QM packages and Tinker as the MM package. Four types of treatments are available for embedding the QM subsystem in the MM environment: mechanical embedding with gas-phase calculations of the QM region, electronic embedding that allows polarization of the QM region by the MM environment, polarizable embedding for mutual polarization of the QM and MM regions, and flexible embedding for both mutual polarization and partial charge transfer between the QM and MM regions. Boundaries between QM and MM regions that pass through covalent bonds can be treated by several methods, including the redistributed charge (RC) scheme, redistributed charge and dipole (RCD) scheme, balanced-RC scheme, balanced-RCD scheme, screened charge scheme that takes account of charge penetration effects, and smeared charge scheme that delocalizes the MM charges near the QM–MM boundary. Geometry optimization can be done using the optimizer implemented in QMMM 2023 or the Berny optimizer in Gaussian through external calls to Gaussian. Molecular dynamics simulations can be performed at the pure-MM level, pure-QM level, fixed-partitioning QM/MM level, and adaptive-partitioning QM/MM level. As a result, the adaptive-partitioning treatments permit on-the-fly relocation of the QM–MM boundary by dynamically reclassifying atoms or groups into the QM or MM subsystems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Dataset of mechanically induced thermal runaway measurement and severity level on Li-ion batteries

The deployment of Li-ion batteries covers a wide range of energy storage applications, from mobile phones, e-bikes, electric vehicles (EV) and stationary energy storage systems. However, safety issue such as thermal runaway is always one of the most important concerns to prevent Li-ion batteries from further market penetration. A standardized single-side indentation test protocol was developed to mechanically induce an internal short-circuit. The cell voltage, compressive load, indenter stroke, and temperature at the indentation point are measured in time series. The test data of each cell, along with cell parameters such as dimensions, mass, chemistry, state of charge (SOC), capacity, are integrated together to calculate a thermal runaway severity score from 0 to100. Complete data collection process including the original measured record, test method, severity score calculation scheme is presented in this article. The thermal runaway severity analysis and the more than 100 tested Li-ion battery records provide a good data source for further comparison and ranking of thermal runaway risks.

25 ENERGY STORAGE↗
DOE OSTI2021

Using concentrating-solar-power plants as economic carbon-free capacity resources

Deep decarbonization of electricity systems raises concerns about the need for generating capacity that can maintain reliable energy supply. Given this concern, we explore the ability of concentrating-solar-power plants with thermal energy storage to provide a carbon-free source of such capacity. We develop an approach to assessing the capacity contribution of concentrating-solar-power plants that considers future systems. Such considerations are important, because of potential differences in net-load patterns compared to today (e.g., due to higher renewable-energy penetrations). Using historical data spanning an 18-year period, we demonstrate that concentrating-solar-power plants with thermal energy storage can provide this necessary capacity with little impact on their financial viability. We examine the impact of myopic decisions and imperfect foresight of future system conditions on the operation of concentrating-solar-power plants and the resultant impact on their capacity contributions. We find that imperfect foresight can have limited impacts on such a use of concentrating-solar-power plants, so long as proper forecasting techniques are developed and used. Overall, our work shows that concentrating-solar-power plants with thermal energy storage may have a role to play in delivering reliable electricity supply in a decarbonized energy system.

14 SOLAR ENERGY↗
DOE OSTI2022

Data-driven modeling of power generation for a coal power plant under cycling

Increased penetration of renewables for power generation has negatively impacted the dynamics of conventional fossil fuel-based power plants. The power plants operating on the base load are forced to cycle, to adjust to the fluctuating power demands. This results in an inefficient operation of the coal power plants, which leads up to higher operating losses. To overcome such operational challenge associated with cycling and to develop an optimal process control, this work analyzes a set of models for predicting power generation. Moreover, the power generation is intrinsically affected by the state of the power plant components, and therefore our model development also incorporates additional power plant process variables while forecasting the power generation. We present and compare multiple state-of-the-art forecasting data-driven methods for power generation to determine the most adequate and accurate model. We also develop an interpretable attention-based transformer model to explain the importance of process variables during training and forecasting. The trained deep neural network (DNN) LSTM model has good accuracy in predicting gross power generation under various prediction horizons with/without cycling events and outperforms the other models for long-term forecasting. The DNN memory-based models show significant superiority over other state-of-the-art machine learning models for short, medium and long range predictions. The transformer-based model with attention enhances the selection of historical data for multi-horizon forecasting, and also allows to interpret the significance of internal power plant components on the power generation. This newly gained insights can be used by operation engineers to anticipate and monitor the health of power plant equipment during high cycling periods.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

A neural-network-enhanced parameter-varying framework for multi-objective model predictive control applied to buildings

Management of the electrical grid is becoming more complex due to the increased penetration of alternative energy generation technologies and a broadening diversity of electric loads. This complexity creates challenges in balancing demand and generation that can increase the potential for grid instabilities. One effective way to address this issue is to leverage previously unexploited demand flexibility through advanced control strategies. In this work, we propose an advanced control method, called adaptive neural parameter-varying model predictive control (ANPV-MPC), to control the temperature and energy consumption of a building via its Heating, Ventilation, and Air Conditioning system. ANPV-MPC combines key ideas in parameter-varying control, adaptive control, and online learning strategies to bridge the gap between computationally efficient linear model predictive control and more accurate nonlinear model predictive control. The novelty in ANPV-MPC is the use of a physics-inspired Bayesian neural network to estimate the coefficients of the parameter-varying linear control model. The Bayesian neural network additionally provides uncertainty estimates, triggering online training to capture evolving building system conditions. We show that ANPV-MPC can approximate the building system dynamics with a 28.39% higher accuracy than traditional linear model predictive control, resulting in 36.23% better control performance without increasing complexity of the optimal control problem. ANPV-MPC also adapts in real time to previously unseen conditions using online learning, further improving its performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Turbine scale and siting considerations in wind plant layout optimization and implications for capacity density

Improvements in wind energy technology, reduced costs, and ambitious clean energy goals have led to projections of high wind contribution in coming years. Developing methodologies to design wind plants with a variety of siting constraints and turbine sizes helps enable high wind penetration, and gain a better understanding of how wind plants are sensitive to setback constraints and turbine design. In this paper, we present a two-step optimization method to simultaneously determine the optimal number of turbines and their locations in a wind plant domain divided into many small, discrete parcels. We present the optimized performance metrics of a wind plant optimized with different turbine sizes and ratings, and with different siting restrictions within the wind plant. Our results indicate that taller and larger turbines are more sensitive to increasing siting constraints. We also compare the optimal wind plant layouts and performance for wind plants optimized for minimum COE and maximum profit. Wind plants optimized for profit had 130%-190% of the capacity of plants optimized for COE, which demonstrates that the optimal results are greatly affected by the objective function, which should be carefully considered. Finally, in this paper we demonstrate the effect of increasing siting constraints on wind plant capacity density, and how the results change when different land areas are used to calculate capacity density. When using the entire wind plant boundary area to determine capacity density, increasing siting constraints decreases the capacity density. However, when we only use the available area (the area left after removing the siting constraints) to calculate the capacity density, increasing the siting constraints increases capacity density. This is a critical insight because of how capacity density is typically defined and used in research, and has important implications for assessment of technical potential and capacity expansion modeling, as well as future wind deployment potential.

17 WIND ENERGY↗
DOE OSTI2023

Electrical substation grid testbed for DLT applications of electrical fault detection, power quality monitoring, DERs use cases and cyber-events

Electrical utilities continue to deploy more intelligent electronic devices (IEDs) inside and outside electrical substation, and are associated with customer-owned distributed energy resources (DERs). The integrity and confidentiality of data from these IEDs, like power meters and protective relays, is crucial. Blockchain technology could improve the resilience of microgrids by improving the security of data sharing. The penetration of customer-owned DERs (renewable energy sources) and the increasing deployment of IEDs can lead to integrate power system applications with Distributed Ledger Technology (DLT). In this study, we implemented the electrical faulted phase detection and power quality monitoring algorithms with a Cyber Grid Guard (CGG) system using DLT. In addition, the DERs (wind turbine farms) use case and protective relay cyber-event tests were assessed, by using the CGG system with DLT. In the experimental model, the testbed was created by using a real-time simulator and CGG system with power meters/ protective relays in-the-loop. The data collected from the CGG system and IEDs were compared with the same time stamp source. These results had shown the successful assessment of protection, control and monitoring applications using a CGG system with DLT. In the future, the ESGT with DERs and the CGG system will be used in other power system applications, based on implementing smart contracts between electrical utilities with customer-owned DERs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

The value of hydropower flexibility for electricity system decarbonization

Hydropower is an abundant, dispatchable, clean energy resource that will play an important role in supporting the clean energy transition. In particular, dispatchable hydropower can provide the operational flexibility that will be required in future systems with high variable renewable energy penetrations. However, the theoretical operational flexibility of hydropower can be restricted in practice by various non-power constraints. In this paper, we quantify how increasing the operational flexibility of dispatchable hydropower resources with reservoirs impacts least-cost generation portfolios and supports power system decarbonization. Specifically, we conduct a capacity expansion analysis of a two-zone system: a hydro-dominated region and a neighboring region with aggressive decarbonization targets that are represented by the United States Pacific Northwest and California respectively. We then introduce a quantifiable index for characterizing the operational flexibility of reservoir hydropower and assess how changes in this metric impact the system-optimal generation portfolio. We find that increasing hydropower flexibility leads to more investment in wind generation, less investment in natural gas generation, lower system costs, and lower system emissions. We further demonstrate a substitution effect between the grid services provided by flexible hydropower operation, increased transmission capacity on a congested line, and energy storage resources. Finally, we show that increasing the operational flexibility of hydropower increases the effective load carrying capability of both hydropower and wind resources. This research supports a more nuanced understanding of how hydropower can support electricity system decarbonization and may motivate reassessing the cost-benefit tradeoffs of non-power constraints that restrict operational flexibility.

Capacity expansion modeling↗
DOE OSTI2025

Facets of hydro power and future trends in a Nordic Context

Hydropower technologies bolster high penetration of variable renewable energies (VREs) in the net zero emissions scenarios. Nevertheless, there are various challenges to meeting the ambitious goal, such as stability, reliability, resiliency, security, lack of reactive power, voltage support and inertia, large-scale storage deployment and coordination, interconnectedness, demand-side response, higher thermal cycles with increased start/stops, and inadequate Levelized Cost of Energy (LCOE) for system-wise VRE integration and profitability. This survey conducts a bottom-up analysis to unveil the opportunities to utilize hydropower facilities and disentangle the nested problem for intertwining design features, control algorithms, operation, optimization approaches, incentives, services, and market mechanisms using a three-pillar framework perspective: grid owners, power producers, and machine designers. The survey identified emerging trends in real-time and capacity markets, flexible power systems, and enhanced grid capabilities, including advanced voltage support and updated grid codes. These developments present significant opportunities for hydropower, such as achieving super-flexibility through hybridization, expanded reactive power capabilities, and advanced operational modes like a synchronous condenser and power adequator functionalities. These opportunities require novel design philosophies — including new winding, stator, and rotor configurations, optimized ventilation, and active cooling systems — to enhance performance under stressed grid and climate conditions. Finally, integrating climate and energy models for multi-basin optimization with finer spatial and temporal granularity enhances the planning accuracy for water management of hydropower while addressing environmental challenges. The review delivers helpful prospective suggestions and tools that would serve researchers, power engineers, and stakeholders in making decisions about hydropower technologies and services in 2050 and beyond.

13 HYDRO ENERGY↗
DOE OSTI2024

Multistage Stochastic optimization for mid-term integrated generation and maintenance scheduling of cascaded hydroelectric system with renewable energy uncertainty

The uncertainties resulting from the escalating penetration of renewable energy resources pose severe challenges to the efficient operation of modern power systems. Hydroelectricity is characterized by its flexibility, controllability, and reliability, and thus becomes one of the most ideal energy resources to hedge against such uncertainties. This paper studies the mid-term integrated generation and maintenance scheduling of a cascaded hydroelectric system (CHS) consisting of multiple cascaded reservoirs and hydroelectric units. To precisely describe the mid-term water regulation policies, the hydraulic coupling relationship and water-energy nexus of CHS are incorporated into the proposed optimization model. The uncertainties of natural water inflow and the power outputs of wind/solar energy generation are taken into consideration and captured via a stochastic process modeled by a scenario tree. A multistage stochastic optimization (MSO) approach is developed to coordinate the complementary operations of multiple energy resources, by optimizing the mid-term water resource management, generation scheduling, and maintenance scheduling of CHS. The proposed MSO model is formulated as a large-scale mixed-integer linear program that presents significant computational intractability. To address this issue, a tailored Benders decomposition algorithm is developed. Two real-world case studies are conducted to demonstrate the capability and characteristics of the proposed model and algorithm. The computational results show that the proposed MSO model can exploit the flexibility of hydroelectricity to efficiently respond to variable wind and solar power, and reserve water resources for the generation in peak months to reduce the consumption of fossil fuel. Furthermore, the proposed solution approach also exhibits promising computational efficiency when handling large-scale models.

13 HYDRO ENERGY↗