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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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14 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

The Grain Boundary Relaxation (GBR) Approach for Manufacturing High Strength Nanocrystalline Lightweight Metals

The overarching goal of the project was to conduct research and development work as proposed in the Statement of Project Objective (SOPO) of the award document DE-FE-0009116. The project had 4 tasks and 12 milestones. All the milestone deliverables were completed. The accomplishments of the project objectives and technical discussions are described in Sections 3 and 4, respectively. The modeling and simulation work indicated that to increase the strength and stability of nanocrystalline aluminum (Al), selection of dopants, such as Mg, is necessary. It was predicted that the crystallite size should be less than 50 nm to give high strength. On the basis of modeling, cryo-milling of Al was conducted with the addition of Mg as a function of different times. The crystallite size of the cryo-milled powders was determined by XRD and TEM. Both measurements showed that the actual crystallite size of the grain was <40 nm. The thermal stability of the grain size was established as a function of temperature. It was established that the grain size was < 50 nm up to 500C. The crystallite size of the bulk sample prepared by spark plasma sintering (SPS) and cold spray (CS) additive manufacturing was less than <40 nm. The mechanical properties of the bulk samples prepared by SPS and CS, showed excellent microhardness, good tensile properties (>200 MPa) with moderate ductility and improved fatigue performance. Adding yttria stabilized zirconia (YSZ) improved the build thick of the CS sample, however the YSZ was getting embedded into the sample. A highly dense SPS samples sent for 3rd party testing to the Innovation Testing Services showed a minimum hardness of 180 HV with an average tensile strength of 512.5 MPa. The high cycle fatigue tests also showed an endurance limit of 179.5 MPa. The Energy cost evaluations showed an overall energy cost of around $\$$17.05 for the cryomilling and SPS processes and the total manufacturing cost calculations of $\$$78.14 for 1 kg of sample. The energy cost to prepare a Kg of CS sample is $\$$17.60 and the overall manufacturing cost is $\$$86.85.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Predicting Elastic Constants of Refractory Complex Concentrated Alloys Using Machine Learning Approach

Refractory complex concentrated alloys (RCCAs) have drawn increasing attention recently owing to their balanced mechanical properties, including excellent creep resistance, ductility, and oxidation resistance. The mechanical and thermal properties of RCCAs are directly linked with the elastic constants. However, it is time consuming and expensive to obtain the elastic constants of RCCAs with conventional trial-and-error experiments. The elastic constants of RCCAs are predicted using a combination of density functional theory simulation data and machine learning (ML) algorithms in this study. The elastic constants of several RCCAs are predicted using the random forest regressor, gradient boosting regressor (GBR), and XGBoost regression models. Based on performance metrics R-squared, mean average error and root mean square error, the GBR model was found to be most promising in predicting the elastic constant of RCCAs among the three ML models. Additionally, GBR model accuracy was verified using the other four RHEAs dataset which was never seen by the GBR model, and reasonable agreements between ML prediction and available results were found. The present findings show that the GBR model can be used to predict the elastic constant of new RHEAs more accurately without performing any expensive computational and experimental work.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Recursive Blind Forecasting of Photovoltaic Generation and Consumer Load for Microgrids

Existing forecasting frameworks that predict time-series photovoltaic (PV) generation and consumer load for micro-grids' operation and control assume near-continuous availability of real-time predictors from the field. The incoming data are used to periodically re-train the models and update forecast snapshots over a moving horizon window. However, such frameworks are not resilient to disruptions in data availability caused by losses in communications between the field sensors and data loggers. This paper bridges the shortcoming by leveraging a previously proposed forecasting framework that is resilient to abrupt changes in data quality caused by communication losses. Assuming no availability of real-time field system data, which is typical in extreme weather events such as hurricanes, the framework uses lightweight recursive time-series models to independently forecast solar irradiance, ambient temperature, PV power, and consumer load for three horizon windows: 24 hours, 12 hours, and 1 hour. Four types of ensemble-based regression trees-simple gradient boosted trees (GBR), GBR with an adaptive component (A-GBR), random forests (RF), and extra trees (ExTR)-are leveraged and their performances are compared against a simple historical weekly mean. Numerical results show that A-GBR performs better on average by 32% for 24-hour horizon and 39% for 12-hour horizon, whereas ExTR outdoes the other models on average by 10% for 1-hour horizon.

Sundararajan, Aditya↗
DOE OSTI2023

Machine learning and deep learning for mineralogy interpretation and CO 2 saturation estimation in geological carbon Storage: A case study in the Illinois Basin

Carbon capture and storage (CCS) is a promising approach to simultaneously maintaining energy security and reducing carbon dioxide (CO 2 ) emissions under the current energy portfolio that is dominated by fossil fuel energy. Pre-injection formation characterization and post-injection CO 2 monitoring are two critical tasks to guarantee storage efficiency in CCS. The CCS projects in the Illinois Basin, the first large-scale CO 2 injection into saline aquifers in the United States, employed conventional and the latest pulsed neutron logging (PNL) tools for mineralogy interpretation and CO 2 saturation estimation, which provide valuable references for future CCS projects. Because of the inherent fuzziness of petrophysical measurements and complex subsurface heterogeneity, interpreting well-logging data is time-consuming, and its accuracy can be user-biased. In recent years, data-driven methods have been widely used to capture the non-linear patterns between input features and interpretation results. This work applied and evaluated four commonly used machine learning (ML) models, including ridge regression (RR), random forest (RF), gradient boosting regression (GBR), support vector regression (SVR), and one deep learning (DL) model, the artificial neural network (ANN). We optimized the hyperparameters of the four ML models and the DL model using the simulated annealing algorithm and the grid search strategy, respectively. The input features of the mineralogy interpretation models were eleven conventional well-logging parameters, and the label data (i.e., ground truth) were the porosity and volumetric fractions of six minerals, including quartz, feldspar, dolomite, calcite, clay, and iron minerals. The results demonstrated that the GBR and RF models were superior in predicting volumetric fractions of minerals and porosity; label data with low coefficient of variation (CV) values tended to yield better performance. For CO 2 saturation estimation, the RF was the best-performing model, followed by SVR, ANN, GBR, and RR. Furthermore, we conducted feature importance ranking using the permutation importance algorithm and found that the formation sigma and well pressure were the most important features in this study. In conclusion, the study of CCS projects in the Illinois Basin bridges the gap between the limited knowledge and understanding of geological carbon storage and the increasing demand for reliable, cost-effective, and sustainable energy solutions.

58 GEOSCIENCES↗
DOE OSTI2023

Evaluating Recursive Blind Forecast Against API and Baseline: A Puerto Rican Case Study on Solar Irradiance for Normal and Extreme Weather

This paper leverages ongoing work in a community microgrid in Adjuntas, Puerto Rico to forecast global horizontal irradiance (GHI) and compare performance in normal and extreme weather. Given a positive correlation of 0.98 between GHI and PV power, forecasting GHI can be an effective, indirect forecast of photovoltaic (PV) power, especially in microgrids where the end-users, owners, operators, or other stakeholders are reluctant to share data for training or validation due to privacy and security concerns. A recursive one-shot (termed as "blind") forecast is, hence, formulated, wherein a gradient-boosted regression tree (GBR) is built to forecast GHI for a 7-day horizon in normal weather, and a 2-day horizon in extreme weather. To demonstrate its resilience, the architecture is trained on normal and hurricane weather GHI from 2002-2022. It is generalized on February 9-16, 2023, and on the landfall of Hurricane Nicole (Nov 4-5, 2022), respectively. Forecasts from GBR are compared against that from a satellite-based API resource and three baselines: persistence, averaging, and exponential smoothing. Results show GBR and persistence outperform sophisticated API in both types of weather for this case study.

Sundararajan, Aditya↗
DOE OSTI2022

Analysis of implant loss risk factors after simultaneous guided bone regeneration: A retrospective study of 5404 dental implants

Abstract Purpose The purpose was to analyze the risk factors for implant loss after simultaneous guided bone regeneration (GBR). Materials and Methods Patients who underwent implant placement with simultaneous GBR between January 2011 and December 2018 were screened for this study. The cumulative survival rate (CSR) was calculated using the life table method. Log‐rank test and Kaplan–Meier survival estimates were used to identify potential risk factors for implant loss. The association between the investigated variables and implant loss was determined using hazard ratios (HRs) obtained from a multivariate Cox regression analysis. Results A total of 3973 patients with 5404 implants were included in this study. The CSRs of the implants at 1, 5, and 10 years were 99.6%, 98.9%, and 98.7%, respectively. Male patient (HR = 2.94, 95% CI: 1.41–6.14), periodontitis (HR = 4.26, 95% CI: 2.05–9.86), tissue‐level implants (HR = 3.02, 95% CI: 1.30–6.98), narrow implants (HR = 2.71, 95% CI: 1.12–6.57), and implant length ≤10 mm (HR = 2.91, 95% CI: 1.41–6.02) significantly increased the risk of implant loss ( p < 0.05). The risk of implant loss was significantly higher in the maxillary posterior region (HR = 2.26, 95% CI: 1.04–4.90) than in the maxillary anterior region ( p < 0.05). Compared to Straumann, Nobel (HR = 4.07, 95% CI: 1.75–9.44) and other implant systems (HR = 14.23, 95% CI: 4.32–46.85) showed a significantly higher risk of implant loss ( p < 0.05). Conclusion Male patient, periodontitis, maxillary posterior region, Nobel implant system, other implant systems, tissue‐level implants, narrow implants, and implant length ≤10 mm were considered risk factors for implant loss after simultaneous GBR.

Shen, Xiaoting↗
DOE OSTI2024

Prediction of Dielectric Constant in Series of Polymers by Quantitative Structure-Property Relationship (QSPR)

This work is devoted to the investigation of dielectric permittivity which is influenced by electronic, ionic, and dipolar polarization mechanisms, contributing to the material’s capacity to store electrical energy. In this study, an extended dataset of 86 polymers was analyzed, and two quantitative structure–property relationship (QSPR) models were developed to predict dielectric permittivity. From an initial set of 1273 descriptors, the most relevant ones were selected using a genetic algorithm, and machine learning models were built using the Gradient Boosting Regressor (GBR). In contrast to Multiple Linear Regression (MLR)- and Partial Least Squares (PLS)-based models, the gradient boosting models excel in handling nonlinear relationships and multicollinearity, iteratively optimizing decision trees to improve accuracy without overfitting. The developed GBR models showed high R2 coefficients of 0.938 and 0.822, for the training and test sets, respectively. An Accumulated Local Effect (ALE) technique was applied to assess the relationship between the selected descriptors—eight for the GB_A model and six for the GB_B model, and their impact on target property. ALE analysis revealed that descriptors such as TDB09m had a strong positive effect on permittivity, while MLOGP2 showed a negative effect. These results highlight the effectiveness of the GBR approach in predicting the dielectric properties of polymers, offering improved accuracy and interpretability.

Ascencio-Medina, Estefania↗
DOE OSTI2025

Micro-structural features and material properties impact on adhesive metal joints via computational modeling and machine learning

The quality of structural bonding in practical applications depends on various factors arising from materials, pre-processing conditions, and manufacturing. Understanding how these factors influence bonding performance and determining their relative importance are of significant interest. Thus, this study evaluates the effects of microstructural features and material properties on the structural strength of adhesively-bonded metal joints at the submillimeter scale, utilizing a combination of Finite Element Modeling (FEM) and Machine Learning (ML) with Gradient Boosting Regression (GBR). The microstructural features include adhesive thickness, internal voids within the adhesive, adherend-adhesive interfacial voids, void size and volume fraction, and surface roughness. The material properties include the constitutive behavior of the adhesive, as well as the adherend-adhesive interfacial strength and fracture energy. The changes in structural strength and morphologies of the bonded metal structures with respect to different microstructural features and material properties were clarified by FEM. By further leveraging ML-GBR, the sequence of importance of these factors affecting bonding performance across various scenarios was summarized. This work provides valuable insights into the development of improved structural bonding for adhesive joints in industries such as automotive , aerospace, and beyond.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Prediction of electric and magnetic fields from spectral data using machine learning algorithms for Doppler-free saturation spectroscopy diagnostics

The prediction of electric and magnetic field amplitudes from atomic spectral data is critical for plasma control in fusion devices such as tokamaks. Conventional approaches that rely on physics-based models are computationally expensive and unsuitable for real-time applications. In this work, we develop and benchmark three machine learning algorithms—simulation-based inference (SBI), fully connected neural networks (FCNN), and histogram-based gradient boosting regression (GBR-Hist)—to infer field intensities directly from Doppler-free saturation spectroscopy (DFSS) spectra. Synthetic datasets of spectra were generated using the EZSSS code and evaluated both with and without added Poisson noise to mimic experimental conditions. We find that SBI achieves the highest accuracy and robustness, FCNN provides a strong balance of accuracy and computational efficiency for real-time applications, and GBR-Hist offers the fastest inference but is more sensitive to noise. Furthermore, these results demonstrate the potential of machine learning to accelerate DFSS analysis and enhance its utility for plasma diagnostics and control.

Doppler-free saturation spectroscopy↗
DOE OSTI2022

Enhanced descriptor identification and mechanism understanding for catalytic activity using a data-driven framework: revealing the importance of interactions between elementary steps

We report accurate identification of descriptors for catalytic activities has long been essential to the in-depth understanding of catalysis and recently to set the basis for catalyst screening. However, commonly used methods suffer from low accuracy in predictability. This study reports an enhanced approach to accurately identify the descriptors from a kinetic dataset using a machine learning (ML) surrogate model. CO hydrogenation to methanol over Cu-based catalysts was taken as a case study. Our model captures not only the contribution from individual elementary steps but also the interaction between relevant steps within a reaction network, which was found to be essential for high accuracy. As a result, six effective descriptors are identified, which are accurate enough to ensure the trained gradient boosted regression (GBR) model for good prediction of the methanol turnover frequency (TOF) over metal (M)-doped Cu(111) model surfaces (M = Au, Cu, Pd, Pt, Ni). More importantly, going beyond the purely mathematical ML model, the catalytic role of each identified descriptor can be revealed by using model-agnostic interpretation tools, which enhances the insight into the promoting effect of alloying. The trained GBR model outperforms the conventional derivative-based methods in terms of both the predictability and the mechanism understanding. It opens alternative possibilities toward accurate descriptor-based rational catalyst optimization.

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

Coupling High-Throughput Experiments and Regression Algorithms to Optimize PGM-Free ORR Electrocatalyst Synthesis

Over the past decades, significant improvement has been achieved in the performance of platinum group metal-free (PGM-free) materials as an alternative to Pt-based electrocatalysts for oxygen reduction reaction (ORR). However, further progress in ORR activity requires evaluation of precursors and synthesis approaches. In response to this challenge, we generated a first of its kind experimental data set of 36 samples using high-throughput synthesis and activity measurements. Several control parameters (e.g., Fe precursor identity, the precursor content, and pyrolysis temperature) were varied. We then developed several state-of-the-art machine learning (ML) based regression models to predict ORR activity, dependent on selected synthesis variables. Through an iterative algorithm, higher prediction accuracy (smaller root-mean-square error) was achieved. We identified that gradient boosting regression (GBR) and support vector regression (SVR), among several methods, work best for this data set. Aided by our ML-based surrogate models, we decided to alter catalyst synthesis conditions, which resulted in a 36% increase in measured ORR activity in comparison to the maximum ORR mass activity value of 21.9 A/g catalyst in the original data set. Overall, this combined experiment and machine learning approach represents a promising path forward toward developing highly efficient next-generation ORR electrocatalysts and, more generally, functional materials.

25 ENERGY STORAGE↗
DOE OSTI2022

Vickers hardness prediction from machine learning methods

Abstract The search for new superhard materials is of great interest for extreme industrial applications. However, the theoretical prediction of hardness is still a challenge for the scientific community, given the difficulty of modeling plastic behavior of solids. Different hardness models have been proposed over the years. Still, they are either too complicated to use, inaccurate when extrapolating to a wide variety of solids or require coding knowledge. In this investigation, we built a successful machine learning model that implements Gradient Boosting Regressor (GBR) to predict hardness and uses the mechanical properties of a solid (bulk modulus, shear modulus, Young’s modulus, and Poisson’s ratio) as input variables. The model was trained with an experimental Vickers hardness database of 143 materials, assuring various kinds of compounds. The input properties were calculated from the theoretical elastic tensor. The Materials Project’s database was explored to search for new superhard materials, and our results are in good agreement with the experimental data available. Other alternative models to compute hardness from mechanical properties are also discussed in this work. Our results are available in a free-access easy to use online application to be further used in future studies of new materials at www.hardnesscalculator.com .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Explaining System-Level Prognostics with Established Machine Learning Methods

System-level prognostics is crucial for ensuring reliability and enabling predictive maintenance in complex systems with interconnected components. This study presents a framework that integrates data-driven methods to predict the remaining useful life (RUL) of a subsystem under multiple and concurrent faults within a nuclear power plant system with explainable artificial intelligence (XAI). A nuclear power plant (NPP) operation was simulated to model the degradation behavior of NPP components, and four machine learning models—Gradient Boosting Regressor (GBR), Support Vector Regressor (SVR), Fully Connected Neural Network (FCNN), and Long Short-Term Memory (LSTM)—were evaluated for prognostics with a novel system RUL parameter. The LSTM model demonstrated potential superior repeatability, while SHAP (SHapley Additive exPlanations) for explainability provided consistent and trustworthy global explanations. In contrast, LIME (Local Interpretable Model-agnostic Explanations) offered localized interpretability but showed reduced stability for sequential data. Key findings include the interplay between component-level degradation and system-wide performance, with LSTM effectively capturing these dynamics through sequence-level predictions. The XAI techniques enhanced transparency by identifying critical features influencing model predictions and aligning with domain knowledge. Furthermore, this framework has significant implications for improving trust and understanding in predictive maintenance, particularly in safety-critical industries like nuclear energy.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI1975

Nuclear Safety [Vol. 16, No. 2, March-April 1975]

Nuclear Safety covers significant developments in the field of nuclear safety. The scope is limited to topics relevant to the analysis and control of hazards associated with nuclear energy, operations involving fissionable materials, and the products of nuclear fission and their effects on the environment. Primary emphasis is on safety in reactor design, construction, and operation; however, safety considerations in regard to the entire fuel cycle, including fuel fabrication, spent-fuel processing, nuclear waste disposal, handling of radioisotopes, and environmental effects of these operations, are also treated. Table of Contents for this issue follows. Table of Contents for this issue follows. GENERAL SAFETY CONSIDERATIONS: 127 Quality Assurance in the Construction of Nuclear Power Plants by Sidney A. Bernsen, 141 1974 ANS Topical Meeting on Fast Reactor Safety by M. H. Fontana; CONTROL AND INSTRUMENTATION: 150 GBR-4 Protection Systems: Failures and Their Consequences by Peter Burgsmüller, J. J. Dekais, Albert Krähe, Raffaello Pignatelli, and Gottfried Vieider, 162 Standby Emergency Power Systems. Part 2—The Later Plants by E. W. Hagen; PLANT SAFETY FEATURES: 180 Radiotoxic Hazard Measure for Buried Solid Radioactive Waste by J. Hamstra, 190 The Thirteenth AEC Air-Cleaning Conference by D. W. Moeller, D. W. Underhill, and M. W. First, 203 Book Review: Nuclear Criticality Safety; CONSEQUENCES OF EFFLUENT RELEASE: 204 Environmental Radiation Effects of Nuclear Facilities in New York State by M. S. Terpilak and B. L. Jorgensen, 222 Book Review: Thermal Ecology; OPERATING EXPERIENCES: 223 Set-Point Drift in Nuclear Power-Plant Safety-Related Instrumentation Adapted by the Nuclear Safety Staff, 224 Diesel-Generator Operating Experience at Nuclear Power Plants, 227 Summary of Operating U. S. Power Reactors as of Jan. 1, 1975, 232 Selected Safety-Related Occurrences Reported in November and December 1974 Compiled by William R. Casto, 235 Recent Occurrences at Nuclear Reactors and Their Causes Compiled by William R. Casto; CURRENT EVENTS: 243 General Administrative Activities Compiled by Wm. B. Cottrell, 251 Action on Power-Reactor Projects Undergoing Regulatory Review or Consideration Compiled by Wm. B. Cottrell, 266 Action on Nonreactor Projects Undergoing Regulatory Review or Consideration Compiled by Wm. B Cottrell, 268 Proposed Rule Changes as of Jan. 1, 1975; MISCELLANY: 149 Course in Italy on High-Energy Radiation Dosimetry and Protection (Announcement), 250 Course at Northwestern on Safety of Light-Water-Cooled Nuclear Power Plants (Announcement), 266 Symposium of the Combined Effects on the Environment of Radioactive, Chemical, and Thermal Releases from the Nuclear Industry (Announcement), 271 Short Course on Engineering for Extreme Winds and Tornadoes (Announcement), 272 Three 1-Week Courses at MIT on Nuclear Power-Reactor Safety (Announcement), 272 Harvard University Short Courses (Announcement).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗