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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Preliminary description of a new creep-fatigue design method that reduces over conservatism and simplifies the high temperature design process

The report provides the initial description of a new creep-fatigue design method for structural components in high temperature nuclear service. The new method is based on an integrated elastic-perfectly plastic (EPP) analysis and Simplified Model Test (SMT) approach that reduces over conservatism, improves the treatment of elastic follow up, and simplifies the design procedure, when compared with the current creep-fatigue design methods in ASME Boiler and Pressure Vessel Code. Developing the design charts for the EPP-SMT design method requires extrapolating SMT test data as a function of hold time and follow up factor. The report develops the preliminary design charts for Alloy 617 at temperatures between 800°C and 950°C by combining two extrapolation approaches developed in a previous work. The report also presents a comparative analysis between the EPP-SMT design method and the current ASME creep-fatigue design methods by evaluating design life of two sample geometries under different loading conditions. Results from the comparative analysis verify the EPP-SMT design charts but suggest the requirement of additional test data in the low strain range regime for improving the extrapolation procedure that will further reduce the over conservatism in the creep-fatigue damage evaluation. The report also concludes that the EPP-SMT design procedure can account for effect of primary load on creep-fatigue life by using a fixed, bounding value of follow up in constructing the design charts. The conclusions to this report describe the future work required to complete this new design method so it can be codified through a nuclear Code Case.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Melting and density of MgSiO 3 determined by shock compression of bridgmanite to 1254GPa

The essential data for interior and thermal evolution models of the Earth and super-Earths are the density and melting of mantle silicate under extreme conditions. Here, we report an unprecedently high melting temperature of MgSiO 3 at 500 GPa by direct shockwave loading of pre-synthesized dense MgSiO3 (bridgmanite) using the Z Pulsed Power Facility. We also present the first high-precision density data of crystalline MgSiO 3 to 422 GPa and 7200 K and of silicate melt to 1254 GPa. The experimental density measurements support our density functional theory based molecular dynamics calculations, providing benchmarks for theoretical calculations under extreme conditions. The excellent agreement between experiment and theory provides a reliable reference density profile for super-Earth mantles. Furthermore, the observed upper bound of melting temperature, 9430 K at 500 GPa, provides a critical constraint on the accretion energy required to melt the mantle and the prospect of driving a dynamo in massive rocky planets.

58 GEOSCIENCES↗
DOE OSTI2023

Analysis of hydrogen infrastructure for the feasibility, economics, and sustainability of a fuel cell powered data center

Data centers used for internet data services, cloud computing, and/or data storage consume vast amounts of electricity and are increasing rapidly in capacity. Consequently, their power consumption has raised concerns about energy sustainability and environmental impacts. Large-scale, on-site renewable energy could help reduce data centers’ carbon footprint; however, wind and solar power alone cannot provide an uninterrupted power supply to computer servers due to their natural variability. Instead, reliable power integration can be achieved by using fuel cells powered by hydrogen from sustainable resources (e.g., wind and solar energy). Establishing a hydrogen infrastructure will be critical for realizing these benefits and establishing fuel cells as a viable power source for data centers. Here, to facilitate the development of novel carbon-free fuel cell data enters, this paper presents renewable power integrated with hydrogen infrastructures in four scenarios to provide reliable hydrogen supply from production to storage. Various paths were analyzed toward a hydrogen supply infrastructure by determining the proper component sizes and calculating the cost of meeting the server load. We used a microgrid modeling software, Hybrid Optimization of Multiple Energy Resources (HOMER), and studied the feasibility of fuel cell powered data centers employing renewable hydrogen. The modeling results show various renewable integration configurations to meet reliable and sustainable power requirement under four scenarios for a carbon-free data center.

08 HYDROGEN↗
DOE OSTI2024

PV Generation and Load Forecasting for Adjuntas PR Community Microgrids

Existing frameworks to forecast time-series photovoltaic (PV) output power and consumer load for microgrid operations and controls assume a near-continuous availability of real-time input features from the field assets such as PV inverters, energy meters, and weather station. These incoming data points are used to periodically retrain models and update forecast snapshots over a moving horizon window, be it one hour-ahead, one-day ahead, or one-week ahead. However, such frameworks are not resilient to disruptions in data availability caused by losses in communications between the field sensors and data loggers. Hence, there is a need for programs that assume no availability of real-time microgrid asset data and still make reliable forecasts that can be used for decision-making. Such programs would be apt to function in extreme weather events such as hurricanes and would use lightweight recursive time-series models to independently forecast solar irradiance and ambient temperature, then compute PV power from those forecasts, as well as independently forecast consumer load. The codebase performs forecasting for the scenario of when the microgrid does not have a reliable access to forecasts or real-time observations of solar irradiance (I) and ambient temperature (AT) and load (Load) to be able to adequately forecast, in real-time, the PV power production or a business' load. In this case, using historical values of PV power and load, a univariate forecasting of generation and consumption are respectively made. The use-case in particular has two sub-scenarios: one, a normal 7-day ahead forecast where the unavailability of real-time data is assumed due to infrastructure issues such as loss of communication or sensor maintenance or service downtimes. Whereas a hurricane-caused unavailability of real-time data requires a second model trained specifically on historical hurricane days to be able to capture the extreme day behavior of generation in particular, and load if applicable. A gradient boosted regression tree comprises an ensemble of additive models that map between the input of historical values (be it irradiance, temperature, or load) and their corresponding output forecasts of a given horizon such that the individual learner predictions are summed up over the total number of such learners in the ensemble to produce an aggregate forecast. A weighting mechanism is applied to the training data in each iteration, where actual and forecast values are compared to penalize incorrect forecasts by increasing the weight and reducing it to reward correct forecasts. The code's benefits are that it: (a) accounts for a contingency where communication loss renders newly measured real-time data unavailable for model tuning and snapshot updates; (b) presents blind forecasting that recursively determines the next time-step value in a horizon using the forecast of the same attribute from a prior step; and (c) employs lightweight models that, once trained, can reliably generalize for different horizons, which make them suitable for enhancing the resilience of field microgrids prone to extreme events that encounter disruptions to data availability.

Sundararajan, Aditya [Oak Ridge National Laborator↗
DOE OSTI2025

Constraining the impact of chlorine as a neutron absorber in next-gen fast reactor designs

The role of chlorine as a neutron poison and as a seed for producing radioactive waste in nuclear systems has driven a renewed interest to improve its nuclear data uncertainties. Additionally, basic and applied science programs that use CLYC (Cs 2 LiYCl 6 :Ce) detectors for neutron spectroscopy and monitoring are also very sensitive to any change in chlorine nuclear data for simulations of the detector response. In this work, sensitivities relevant for these different applications are addressed through simulations of the efficiency of CLYC detectors in a fast fission spectrum when applying new chlorine nuclear data as input. These simulations are validated by an experimental measurement using CLYC detectors coupled to an ionization chamber loaded with a 252 Cf spontaneous fission source. The results are then used to obtain the first reliable direct measurement of the 35 Cl(n,p 0 ) and summed Cl(n,p+n,α) fission spectrum average cross sections, found to be 54.7(32) and 105.0(98) mb, respectively. The results are within uncertainty of calculated fission spectrum averaged cross sections based on recently re-evaluated chlorine nuclear data, which confirm recent impact studies performed for the Molten Chloride Reactor Experiment. Meanwhile, there currently exists only one published criticality benchmark experiment that is sufficiently sensitive to chlorine nuclear data. Discrepancies are found with this set of criticality safety benchmarks, which are more sensitive to thermal and epithermal neutron energies than the energies, above 100 keV, tested in this current work. Hence, there is still a need to re-evaluate the chlorine nuclear data at lower energies to assess these discrepancies. Interpretation of the data from future “faster” criticality benchmarks, which are needed for next-gen fast reactor designs, benefit from the improved constraints on the chlorine nuclear data validated in this work.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Simulation of the NuScale SMR and Investigation of the Effect of Load-Following on Component Lifetimes

The NuScale SMR has been modelled using the Virtual Environment for Reactor Applications (VERA) multiphysics environment and the results compared with the publicly reported data in the Design Certification Application (DCA). The results show an excellent agreement for the compared axial and radial power distributions, temperature coefficients of reactivity, boron and control rod worths, and fast neutron flux. This NuScale model is then used to investigate the effect of different operational modes on reactor components to determine how flexible load-following operation may affect control rod and reactor pressure vessel (RPV) lifetimes. The control rod degradation is confirmed to primarily affect the silver-indium-cadmium (AIC) rod tip. The degradation rate is observed to follow a non-linear function of core power level where the increase in degradation decreases with insertion depth. For the variation in core power levels expected with current load-following schemes, the total control rod degradation is found to be mild, at 5-10% of usable life per cycle for a reactor operating at <80% power. Nonetheless, this enables load following strategies to be confirmed and/or modified to ensure that control rods do not need to be replaced during the 60+ year life of the reactor. The RPV degradation was found to be almost directly proportional to the core power level and was not overly sensitive to flux shape perturbations. Future work is planned using these damage functions to optimize operation over multiple NuScale SMR units and develop strategies for prognostics and health management.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Bayesian calibration of strength model parameters from Taylor impact data

Materials strength plays a key role in determining the mechanical response of engineered structures. As such, accurate strength models are crucial in simulations involving complex loading conditions, particularly when deformation in the plastic regime is deemed important. In this work, a Gaussian process based surrogate for a finite element simulation of a Taylor impact test is developed and used for Bayesian calibration of the Preston–Tonks–Wallace strength model. Here, the surrogate model is shown to closely approximate the salient features of the Taylor cylinder deformation and is validated against simulation output before being used in the strength model calibration routine. The results show that Taylor impact test data can be used in the calibration of constitutive equations through the use of a combination of data science techniques, namely Gaussian processes and Bayesian inference.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Investigation of the effects of passive pre-chamber nozzle pattern and ignition system on engine performance and emissions

The impact of passive pre-chamber (PC) internal volume, nozzle hole pattern (i.e. with and without a central axial nozzle), and PC igniter plug type on performance and emissions was investigated in an optically accessible, single-cylinder, gasoline research engine. The two PC igniter plugs investigated were a conventional inductive coil spark plug and a nanosecond repetitively pulsed (NRP) plasma discharge system previously demonstrated to accelerate early flame propagation. The baseline PC design featured a funnel shaped internal volume with a PC tip that contained six radial nozzles and a larger central axial nozzle. Two additional PC tip geometries were evaluated where either the baseline internal volume was increased or the axial nozzle was removed and the radial nozzle diameters were increased. A sweep of charge equivalence ratios ( ϕ) from nearly stoichiometric to the lean limit was performed for a fixed engine speed (1300 revolutions per minute), and engine load (3.5 bar gross indicated mean effective pressure). Time-resolved PC and main chamber (MC) pressure data as well as MC emissions data were collected to analyze engine performance and emissions characteristics. Combustion in the MC was further investigated using high-speed excited methylidyne radical (CH*) chemiluminescence imaging. Collected results highlighted that while all PC tips and ignition systems exhibited similar performance and emissions down to ϕ = 0.8, relevant differences in thermal efficiency and emissions for leaner charge mixtures were observed, with the results highly dependent on the nozzle pattern and ignition system. Major deviations were correlated to preferential de-pressurization of the PC through the axial nozzle for lean conditions that was not observed for mixture conditions closer to stoichiometric. Results show that a combination of radial and axial nozzle patterns in the PC extended lean-stability limits at the low-load condition evaluated. Further benefits were observed with the use of NRP ignition systems due to faster combustion within the PC volume provided that the volume was sufficiently large.

Di Sabatino, Francesco↗
DOE OSTI2025

A Parametric, Data-Driven, Non-Intrusive Reduced-Order Model Framework for Crystal Plasticity Simulations of Voids

The influence of the internal structure at micrometer length scales on the deformation of polycrystalline materials can be effectively captured using crystal plasticity finite element methods (CPFEM). However, the complexity and nonlinearity of the deformation equations CPFEM solves demand significant computational power and resources to achieve accurate predictions, limiting its broader application. To address this challenge, we have identified a reduced-order representation of the complex data in order to establish a computationally efficient reduced-order models (ROM) and drastically reduce the computational expense of CPFEM. Specifically, in this work, we developed a parametric, data-driven, and non-intrusive ROM framework for CPFEM using proper orthogonal decomposition (POD) and sparse variational Gaussian process (SVGP) regression for single-crystal microstructures under tensile loading conditions. The developed protocol enables one to compress field into a latent/low-dimensional space described by principal component analysis (PCA) via the singular value decomposition (SVD) algorithm. As a result, the high-dimensional data are reduced to a significantly smaller amount of dimensions with POD bases and POD coefficients. Furthermore, we deployed an ensemble of SVGPs—extended from the classical Gaussian process (GP) regression for scalability and handling big data—in a massively parallel manner to train and predict latent POD coefficients using known POD bases from a set of previously obtained simulations results. Lastly, using the predicted POD coefficients, we reconstructed the full-field results and showed reasonable agreement compared with the true values obtained from running CPFEM. The developed framework is validated with a set of CPFEM simulations of a single embedded void in single-crystal aluminum alloy. While the framework is broadly applicable, this work specifically focuses on single-crystal microstructures, a single load case (e.g., tensile), and a specific void geometry (spherical).

Anisotropy↗
DOE OSTI2025

Artificial Intelligence and Machine Learning Applications in Modern Power Systems

Machine learning (ML) and artificial intelligence (AI) algorithms offer valuable tools for the analysis and interpretation of large datasets. These tools have the capability to uncover insights that may not be readily apparent within these datasets. In recent years, the integration of ML and AI has become increasingly prevalent in various applications within the power system domain. One of the earliest instances of machine learning in power systems can be traced back to demand forecasting, where artificial neural networks were employed for short-term load forecasting. In contemporary power systems, an abundance of high-resolution geospatial and temporal data is generated at various time intervals, ranging from sub-seconds (Phasor Measurement Units or PMUs) to seconds (Supervisory Control and Data Acquisition or SCADA), minutes (Process Information or PI), and extending to days, months, and years. These datasets contain valuable information concerning system reliability and performance. This information holds the potential to offer critical insights into system operations, as well as solutions for predicting and mitigating contingencies to prevent cascading outages. Despite the immense power of machine learning tools, system operators, planners, and utilities often exhibit hesitancy in fully embracing AI-enabled system operations and planning. This cautious approach persists, even as numerous diverse applications of machine learning continue to emerge in the realm of power systems. In this chapter, our focus will delve deep into ML and AI applications tailored for power systems. These applications aim to furnish system operators with enhanced situational awareness and augment their decision-making capabilities, especially during challenging operating conditions. Specific areas of interest encompass root cause analyses of electricity market datasets and the strategic selection of representative samples from vast power system databases for training ML/AI models. Finally, the chapter will conclude with a short discussion on the future of ML/AI in power systems and possible directions that the industry is moving towards.

power system applications, machine learning (ML), ↗
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 OSTI2025

Phase transformation kinetics model for metals

We develop a new model for phase transformation kinetics in metals by generalizing the Levitas–Preston (LP) phase field model of martensite phase transformations (see Levitas and Preston (2002a,b) and Levitas et al. (2003)) to arbitrary pressure. Furthermore, we account for and track: the interface speed of the pressure-driven phase transformation, properties of critical nuclei, as well as nucleation at grain sites and on dislocations and homogeneous nucleation. The volume fraction evolution of each phase is described by employing KJMA (Kolmogorov, 1937; Johnson and Mehl, 1939; Avrami, 1939, 1940, 1941) kinetic theory. We then test our new model for iron under ramp loading conditions and compare our predictions for the α → ϵ iron phase transition to experimental data of Smith et al. (2013). In conclusion, more than one combination of material and model parameters (such as dislocation density and interface speed) led to good agreement of our simulations to the experimental data, thus highlighting the importance of having accurate microstructure data for the sample under consideration.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Three-dimensional full-field velocity measurements in shock compression experiments using stereo digital image correlation

Shock compression plate impact experiments conventionally rely on point-wise velocimetry measurements based on laser-based interferometric techniques. This study presents an experimental methodology to measure the free surface full-field particle velocity in shock compression experiments using high-speed imaging and three-dimensional (3D) digital image correlation (DIC). The experimental setup has a temporal resolution of 100 ns with a spatial resolution varying from 90 to 200 μm/pixel. Experiments were conducted under three different plate impact configurations to measure spatially resolved free surface velocity and validate the experimental technique. First, a normal impact experiment was conducted on polycarbonate to measure the macroscopic full-field normal free surface velocity. Second, an isentropic compression experiment on Y-cut quartz–tungsten carbide assembly is performed to measure the particle velocity for experiments involving ramp compression waves. To explore the capability of the technique in multiaxial loading conditions, a pressure shear plate impact experiment was conducted to measure both the normal and transverse free surface velocities under combined normal and shear loading. The velocities measured in the experiments using digital image correlation are validated against previous data obtained from laser interferometry. Numerical simulations were also performed using established material models to compare and validate the experimental velocity profiles for these different impact configurations. The novel ability of the employed experimental setup to measure full-field free surface velocities with high spatial resolutions in shock compression experiments is demonstrated for the first time in this work.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

A Hierarchical Local Electricity Market for a DER-rich Grid Edge

We report with increasing penetration of distributed energy resources (DER) in the distribution system, it is critical to design market structures that enable smooth integration of DERs. A hierarchical local electricity market (LEM) structure is proposed in this paper with a secondary market (SM) at the lower level representing secondary feeders and a primary market (PM) at the upper level, representing primary feeders, in order to effectively use DERs to increase grid efficiency and resilience. The lower level SM enforces budget, power balance, and flexibility constraints and accounts for costs related to consumers, such as their disutility, flexibility limits, and commitment reliability, while the upper level PM enforces grid physics constraints such as power balance and capacity limits, and also minimizes line losses. The hierarchical LEM is extensively evaluated using a modified IEEE-123 bus with high DER penetration, with each primary feeder consisting of at least three secondary feeders. Data from a GridLAB-D model is used to emulate realistic power injections and load profiles over the course of 24 hours. The performance of the LEM is illustrated by delineating the family of power-injection profiles across the primary and secondary feeders as well as corresponding local electricity tariffs that vary across the distribution grid. Through numerical simulations, the hierarchical LEM is shown to improve the efficiency of the market in terms of lowering overall costs, including both the distribution-level locational marginal prices (d-LMP) as well as retail tariffs paid by customers. Together, it represents an overall framework for a Distribution System Operator (DSO) who can provide the oversight for the entire LEM.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Radioisotope production at the Spallation Neutron Source: Design concept of isotope production target

Upon completion of the Second Target Station (STS) Project in the mid 2030s, the Spallation Neutron Source accelerator at Oak Ridge National Laboratory will deliver a 2.7 MW proton beam to the neutron production targets. In the post-STS phase, the accelerator will have a reserve beam power capacity of at least 100 kW beyond what the two neutron production targets will receive, which could potentially be ramped up to 300 kW. In this paper, a design concept for a radioisotope production target that could utilize 250 kW of the reserve beam power capacity is presented. The target consists of thorium discs encapsulated in 316L austenitic steel shells that are cooled by water. The estimated post-irradiation activity of Ac-225 and Ra-225, critical medical radioisotopes used in targeted alpha therapy cancer treatment, is calculated at the end of bombardment after a 14 day long irradiation time. Thermal and structural analyses are performed on the basis of calculated nuclear heating data. The technical feasibility of a high-power target under a 250-kW beam load with an extremely low duty factor of $3.5\cdot 10^{-6}$ is presented from thermal, structural and fatigue lifetime perspectives.

Lee, Yong Joong [ORNL] (ORCID:0000000298381723)↗
DOE OSTI2026

CRADA Number NFE-24-10110 with Qubit Engineering Inc. (CRADA Final Report)

Over the past year, the Qubit Engineering team has pushed the frontiers of power‑grid optimization, working in close collaboration with Oak Ridge National Laboratory (ORNL) and the Tennessee Valley Authority (TVA). Their progress is reflected in three newly submitted conference papers, “Unified Relational GNN Architecture for AC Optimal Power Flow Calculations in Electric Grids,” “Graph‑Based Attention Mechanisms for Solving the AC Optimal Power Flow Problem in Electrical‑Power Networks,” and “Enhanced Power‑Grid Maintenance Planning and Quantum‑Inspired Combinatorial Prospects.” These publications showcase state‑of‑the‑art graph‑neural‑network methods for AC‑OPF and novel quantum‑inspired heuristics for maintenance scheduling. Beyond the academic results, the Qubit team has converted the research into two production‑grade tools built on TVA data: Neuro‑Grid, an AI‑driven power‑flow simulator that provides instant, interactive full‑grid load‑flow visualizations, and Quanta‑Grid, a quantum‑inspired maintenance‑scheduling engine to support logistics optimization for power utilities. Together, these advances demonstrate how Qubit’s partnership with ORNL and TVA is delivering practical, physics‑grounded analytics for next‑generation grid management.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Failure Strain and Related Triaxiality of Aluminum 6061-T6, A36 Carbon Steel, 304 Stainless Steel, and Nitronic 60 Metals, Part I: Experimental Investigation

The objective of this study is to develop failure-limit material models for Aluminum 6061-T6, A36 Carbon Steel, 304 Stainless Steel, and Nitronic 60 metals, based on parameters of plastic equivalent strain (failure strain) and stress triaxiality. The research is conducted in two parts. This paper presents Part One of the study. In Part One, custom-designed test specimens undergo controlled uniaxial tension and compression testing at ambient temperature. These tests are performed at quasi-static speeds using Universal Testing Machines (UTMs) in accordance with ASTM E8 and ASTM E9 standards. Experimental data, specifically engineering stress–strain and force–displacement curves, are recorded from the onset of loading until specimen fracture, or in the case of compression tests, until the capacity of the testing machine is reached. In Part Two, the emphasis shifts to the calibration of Finite Element Analysis (FEA) models of the custom-designed test specimens. Plastic equivalent strain and the corresponding stress triaxiality values at failure are extracted from each test specimen for the given metal. These values are then systematically plotted onto a single graph to construct the failure-limit curve, which delineates the boundary conditions for material failure. This approach will facilitate the development of a comprehensive material property definition that correlates plastic equivalent strain with stress triaxiality at failure for Aluminum 6061-T6, A36 Carbon Steel, 304 Stainless Steel, and Nitronic 60 metals.

Harwell, Ron↗
DOE OSTI2024

Fuel Cell Inverter Dataset

This data set contains the three phase AC voltage, three phase AC current, DC voltage and DC current. These data sets were captured during fuel cell inverter operation in grid-connected dispatch, islanded load changes, transition from grid-connected mode to islanded mode and vice-versa.

25 ENERGY STORAGE↗