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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Offshore application of landslide susceptibility mapping using gradient-boosted decision trees: a Gulf of Mexico case study

Abstract Among natural hazards occurring offshore, submarine landslides pose a significant risk to offshore infrastructure installations attached to the seafloor. With the offshore being important for current and future energy production, there is a need to anticipate where future landslide events are likely to occur to support planning and development projects. Using the northern Gulf of Mexico (GoM) as a case study, this paper performs Landslide Susceptibility Mapping (LSM) using a gradient-boosted decision tree (GBDT) model to characterize the spatial patterns of submarine landslide probability over the United States Exclusive Economic Zone (EEZ) where water depths are greater than 120 m. With known spatial extents of historic submarine landslides and a Geographic Information System (GIS) database of known topographical, geomorphological, geological, and geochemical factors, the resulting model was capable of accurately forecasting potential locations of sediment instability. Results of a permutation modelling approach indicated that LSM accuracy is sensitive to the number of unique training locations with model accuracy becoming more stable as the number of training regions was increased. The influence that each input feature had on predicting landslide susceptibility was evaluated using the SHapely Additive exPlanations (SHAP) feature attribution method. Areas of high and very high susceptibility were associated with steep terrain including salt basins and escarpments. This case study serves as an initial assessment of the machine learning (ML) capabilities for producing accurate submarine landslide susceptibility maps given the current state of available natural hazard-related datasets and conveys both successes and limitations.

Dyer, Alec S. (ORCID:0000000219813904)↗
DOE OSTI2024

High Speed Impact Testing of UHMWPE Composite Using Orthogonal Arrays

Abstract Background Ultra high molecular weight polyethylene composites are fiber based composites used in armor applications. While some characterization has been conducted experimentally, this study varies multiple parameters simultaneously to investigate material response under a wide range of conditions. Objective This work focuses on characterizing the response of Dyneema ® HB26 hard laminate composites under high-speed impacts to examine the influence of plate diameter, clamping pressure, and plate spacing on target performance. Additionally, micro Computer Tomography scans are used to nondestructively evaluate the damage evolution in the targets. Methods These scan results are used in concert with more traditional armor performance metrics to evaluate the effect of various parameters using the method of orthogonal array analysis. This technique allows for multiple variables to be investigated in the same test series, saving time and budget while still providing quality results across a range of variables and variable values. Results We conclude that of the parameters investigated, the plate spacing parameter has the largest effect on performance, followed by the plate diameter. Bolt torque was found to not have a significant impact on results, indicating that an edge clamping pressure is not critical to material response. Additionally, by examining the high resolution scans, we can quantify the damage with an effective damage angle and that this angle is a good predictor of performance. Conclusion Finally a damage theory involving the effective bending strength of the plates is discussed as an explanation for all of the results observed in this test series.

Hannah, T. (ORCID:0000000230261362)↗
DOE OSTI2020

Ab Initio Study of Stability, Local Order, and Phase Diagram For a Series of bcc-based Transition Metal Alloys

In this work, a parameter-free electronic structure approach is applied to the study of stability and chemical order in the 15 substitutional body-centered cubic (bcc)-based alloys made of the six transition metals of groups 5 (V, Nb, Ta) and 6 (Cr, Mo, W) of the periodic table. The method is based on a Green’s function description of the electronic structure of the random alloys. Configurational order is treated within the generalized perturbation method, and temperature effects are examined with a generalized mean-field approach. In contrast to the results summarized in the assessed phase diagrams, stability and ordering trends are predicted in a broad range of alloy composition for at least seven alloys, and explanation is found in their electronic structure properties. Short-range order results, thermodynamics analysis, and bcc-based phase diagrams are also presented.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Initial conceptual demonstration of control co-design for WEC optimization

Abstract While some engineering fields have benefited from systematic design optimization studies, wave energy converters have yet to successfully incorporate such analyses into practical engineering workflows. The current iterative approach to wave energy converter design leads to sub-optimal solutions. This short paper presents an open-source MATLAB toolbox for performing design optimization studies on wave energy converters where power take-off behavior and realistic constraints can be easily included. This tool incorporates an adaptable control co-design approach, in that a constrained optimal controller is used to simulate device dynamics and populate an arbitrary objective function of the user’s choosing. A brief explanation of the tool’s structure and underlying theory is presented. To demonstrate the capabilities of the tool, verify its functionality, and begin to explore some basic wave energy converter design relationships, three conceptual case studies are presented. In particular, the importance of considering (and constraining) the magnitudes of device motion and forces in design optimization is shown.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Macroscopic Traffic Modeling Using Probe Vehicle Data: A Machine Learning Approach

Abstract The macroscopic fundamental diagram (MFD) captures an orderly relationship among traffic flow, density, and speed at the network level. It is a simple yet powerful tool for modeling traffic dynamics in large urban networks with broad application in traffic control and management. However, empirically derived MFDs in urban regions require high-resolution traffic data from the network. Having the network flow and vehicular density estimated at the (granular) census tract level using vehicle probe data, we apply machine learning methods to predict the MFDs across U.S. urban areas and capture the impacts of location-specific input features on the network flow–density relationships at a large scale. The results show that, among the four tested machine learning approaches (Random Forest, XGBoost, Support Vector Machine, and Neural Network), XGBoost delivers the best performance in predicting network traffic flow based on vehicular density and location attributes. Using interaction Shapley Additive explanation (SHAP) values and partial correlation analysis, we examine the factors influencing MFD shapes across different locations. Our empirical findings reveal that across U.S. urban areas, network topology, transportation infrastructure, and land use are primary factors shaping MFD curves, while demand and trip-related factors play a lesser role. Specifically, higher ranking roads, centrality, and development levels correlate positively with network capacity and critical density, whereas negative associations are observed for network connectivity, mixed-use development, and road roughness levels.

Jin, Ling↗
DOE OSTI2023

Quantum computing for a profusion of postman problem variants

In this paper we study the viability of solving the Chinese Postman Problem, a graph routing optimization problem, and many of its variants on a quantum annealing device. Routing problem variants considered include graph type, directionally varying weights, number of parties involved in routing, among others. We put emphasis on the explanation of how to convert such problems into quadratic unconstrained binary optimization (QUBO) problems. QUBO is one of two equivalent natural paradigms for quantum annealing devices, the other being the Ising Model. We also expand upon a previously discovered algorithm for solving the Chinese Postman Problem on a closed undirected graph to decrease the number of constraints and variables used in the problem. Optimal annealing parameter settings and constraint weight values are discussed based on results from implementation on the D-Wave 2000Q and Advantage. Results from classical, purely quantum, and hybrid algorithms are compared.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Pyridoxal 5'-phosphate dependent reactions: Analyzing the mechanism of aspartate aminotransferase

Enzyme catalysis is the primary activity in energy and information metabolism and enzyme cofactors are key to the catalytic ability of most enzymes. Pyridoxal 5'-phosphate (PLP) cofactor, derived from Vitamin B6, is widely distributed in nature and has significant latitude in catalytic diversity. X-ray crystallography has revealed the structures of diverse PLP dependent enzymes from multiple families. But these structures are incomplete, lacking the positions of protons essential for understanding enzymatic mechanisms. Here, we review the diversity of PLP and discuss the use of neutron crystallography and joint X-ray/neutron refinement of Fold Type I aspartate aminotransferase to visualize the positions of protons in both the internal and external aldimine forms. Strategies used to prepare extremely large crystals required for neutron diffraction and the approach to data refinement including the PLP cofactor are discussed. The observed positions of protons, including one located in a previously unknown low-barrier hydrogen bond, have been used to create more accurate models for computational analysis. The results revealed a new mechanism for the transaminase reaction where hyperconjugation is key to reducing the energy barrier which finally provides a clear explanation of the Dunathan alignment.

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

The combined effects of hydrogen and aging condition on the deformation and fracture behavior of a precipitation-hardened nickel-base superalloy

The effect of hydrogen (H) on the deformation behavior of Monel K-500 in various isothermal heat treatment conditions (non-aged, under-aged, peak-aged, and over-aged) was assessed via uniaxial mechanical testing. H-charged and non-charged specimens were strained to failure to facilitate a comparison of ductility, fracture surface morphology, strength, and work hardening behavior. For all examined heat treatment conditions, H charging leads to a significant reduction in ductility, which is accompanied by a consistent change in fracture surface morphology from ductile microvoid coalescence to brittle intergranular fracture. While H charging led to a systematic enhancement in the yield strength of all heat treatments, the three age-hardened conditions exhibited a more than 2-fold increase relative to the non-aged heat treatment. This suggests that H modifies the dislocation–precipitate interactions, which also manifest themselves through changes in work hardening metrics related to the dislocation storage and recovery rates. In particular, the H-charged peak-aged specimen exhibited a significant increase in initial hardening (dislocation storage) rate relative to the H-charged under-aged specimen. Transmission electron microscopy of these samples revealed the onset of widespread dislocation looping in the H-charged peak-aged sample, in addition to the planar slip bands characteristic of the non-charged condition. This result suggests that hydrogen induces the particle shearing-to-looping transition at smaller particle sizes. Possible mechanistic explanations for this observed behavior are presented.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Facile healing of cracks in organic–inorganic halide perovskite thin films

Organic-inorganic halide perovskite (OIHP) thin films at the heart of the burgeoning thin-film perovskite solar cells (PSCs) technology possess poor mechanical properties, which is likely to limit the long-term reliability of PSCs as they are poised for commercialization. In an effort to address this issue, in this work we demonstrate that through-thickness cracks induced by bending-tension in thin films of the two prototypical OlHPs, methylammonium lead triiodide (MAPbl(3)) and formamidinium lead triiodide (FAPbl(3)), can be healed easily. This is through the application of either a moderate compressive stress (bending-compression) at room temperature or a simple heat-treatment at modest temperatures. The crack-healing process is found to be time-dependent, which indicates that facile mass-transport in OlHPs plays a key role in this phenomenon. An explanation for this phenomenon is provided, one based on the fundamentals of brittle fracture. This discovery has broad implications for the prevention and/or restoration of the overall performance, environmental stability, and mechanical reliability of PSCs, and other devices.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Atomistic modeling of metal–nonmetal interphase boundary diffusion

Atomistic computer simulations are applied to investigate the atomic structure, thermal stability, and diffusion processes in Al–Si interphase boundaries as a prototype of metal–ceramic interfaces in composite materials. Some of the most stable orientation relationships between the phases found in this work were previously observed in epitaxy experiments. Here, a non-equilibrium interface can transform to a more stable state by a mechanism that we call interface-induced recrystallization. Diffusion of both Al and Si atoms in stable Al–Si interfaces is surprisingly slow compared with diffusion of both elements in Al grain boundaries but can be accelerated in the presence of interface disconnections. A qualitative explanation of the sluggish interphase boundary diffusion is proposed. Atomic mechanisms of interphase boundary diffusion are similar to those in metallic grain boundaries and are dominated by correlated atomic rearrangements in the form of strings and rings of collectively moving atoms.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Morphologies of dealloying corrosion attack at grain boundaries

Dealloying corrosion at grain boundaries severely compromises the performance of polycrystalline materials across a wide variety of technological applications. The impact of this phenomenon depends upon the morphology and rate of intergranular dealloying, which can range from planar to wormhole-like patterns that rapidly advance into the alloy. Using 2D and 3D multi-phase field simulations, we reveal how diverse microstructures result from a fundamental interplay between alloy composition and a grain boundary migration mechanism that alters diffusional pathways of dealloying. Inside intergranular dealloying channels, corrosion product buildup can spawn new channels that branch into grain interiors, such that alloys can be degraded from the inside out. These processes further lead to an atypical coarsening mode assisted by diffusion in the dealloying agent. We summarize a unifying explanation for dealloying morphology selection in polycrystalline alloys, which provides an important step towards their optimization for dealloying corrosion environments.

Corrosion↗
DOE OSTI2020

Using the red chromatic coordinate to characterize the phenology of forest canopy photosynthesis

Vegetation phenology has received increasing attention in climate change research. Near-surface sensing using digital repeat photography has proven to be useful for ecosystem-scale monitoring of vegetation phenology. However, our understanding of the link between phenological metrics derived from digital repeat photography and the phenology of forest canopy photosynthesis is still incomplete, especially for evergreen plant species. Using 49 site-years of digital images from the PhenoCam Network from eight evergreen needleleaf forest (ENF) and six deciduous broadleaf forest (DBF) sites in North America, we explored the potential of the green chromatic (GCC) and red chromatic coordinates (RCC) in tracking forest canopy photosynthesis by comparing camera-derived start- and end-of-growing season (SOS and EOS, respectively) with corresponding estimates derived from eddy covariance-derived daily gross primary productivity (GPP). We found that for DBF sites, both GCC and RCC performed comparable in capturing SOS and EOS. However, similar to earlier studies, GCC had limited potential in capturing GPP-based SOS or EOS for ENF sites. In contrast, we found RCC was a better predictor of both GPP-based SOS and EOS for ENF sites. Environmental and ecological explanations were both provided that phenological transitions derived from RCC were highly correlated with spring and autumn meteorological conditions, as well as having overall higher correlations with phenology based on LAI, a critical variable for describing canopy development. Our results demonstrate that RCC is an underappreciated metric for tracking vegetation phenology, especially for ENF sites where GCC failed to provide reliable estimates for GPP-based SOS or EOS. Our results improve confidence in using digital repeat photography to characterize the phenology of canopy photosynthesis across forest types.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Fine resolution remote sensing spectra improves estimates of gross primary production of croplands

Gross primary production (GPP) is a fundamental measure of the terrestrial carbon cycle critical to our understanding of ecosystem function under the changing climate and land use. Remote sensing enables access to continuous spatial coverage, but remains challenged in heterogeneous croplands. Coarse resolution products, like MOD17A (500 m), may aggregate fragmented land cover types commonly found in heavily managed landscapes and misrepresent their respective contribution to carbon production. Consequently, this study demonstrates the capability of fine-resolution imagery (20-30 m) and available red-edge vegetation indices to characterize GPP across seven Midwest cropping systems. Four sites were established on a 22-year-old USDA Conservation Reserve Program (CRP); and the other three on land conventionally farmed with corn-soybean-wheat rotation (AGR). We compare in situ GPP estimates from eddy-covariance towers with ten satellite models: eight variants of the vegetation photosynthesis models (VPM), of which five include a red-edge vegetation index, as well as conventional products Landsat CONUS GPP (30 m) and MOD17A2H V6 (500 m). Daily and cumulative fine-resolution imagery integrated within VPM generally agreed with tower-based GPP in heterogeneous landscapes more than those from MODIS 500 m VPM or conventional GPP products from MOD17AH V6 or Landsat 8 CONUS. Here, replacing EVI2 with red-edge indices NDRE2, NDRE1, and MTCI in Sentinel 2 VPMs notably improved explanation of variance and estimation of cumulative GPP. While existing methods using MODIS- and Landsat-derived GPP are important baselines for regional and global studies, future research may benefit from the higher spatial, temporal, and radiometric resolution.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Moiré superconductivity

Recently, superconductivity was discovered at very low densities in slightly misaligned graphene multilayers. Surprisingly, despite extremely low electronic density (about 10 –4 electrons per unit cell), these systems realize strong-coupling superconductivity, with the transition temperature being a large fraction of the Fermi energy $(T_C ~ 0.1_{ϵ_{F}})$. In this work, we propose a qualitative explanation for this remarkable phenomenon, highlighting similarities and qualitative differences with the conventional uniform high-density superconductivity. Most importantly, we find that periodic superimposed potential generically enhances local interactions relative to nonlocal (for instance, Coulomb) interactions. In addition, the density of states is enhanced as well, exponentially in modulation strength for low lying bands in some cases. Combination of these two effects makes moiré systems natural intermediate or strong-coupled superconductors, with potential for very high transition temperatures.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Non-local entanglement and fast scrambling in de-Sitter holography

We study holographic entanglement and information scrambling in de-Sitter (dS) space in the context of the DS/dS correspondence. We find that our previously identified non-local entanglement structure of dS vacua can be extended out of the time-reflection symmetric slice. We extend the geometry to a two-sided configuration and calculate the zero-time mutual information between two intervals on different sides when there is a localized shock wave in the bulk. Interestingly, we find that the information scrambling time saturates the fast scrambler bound proposed by Sekino and Susskind and that the shock wave renders a wormhole to be traversable. Furthermore, we calculate a two-sided out-of-time-ordered correlator (OTOC) in the late time regime and we see that, before scrambling, it exponentially grows with an exponent whose value saturates the maximal bound of chaos proposed by Maldacena, Shenker and Stanford. At the end, we provide an explanation why the exponential growing of the late-time OTOC with the maximal bound of chaos saturated and the traversability of the wormhole are simple results of the non-local entanglement structure and we point out that this is a realization of the ER=EPR proposal.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Analyzing the impact of design factors on solar module thermomechanical durability using interpretable machine learning techniques

Solar modules in utility-scale systems are expected to maintain decades of lifetime to rival conventional energy sources. However, cyclic thermomechanical loading often degrades their long-term performance, highlighting the importance of effective design to mitigate thermal expansion mismatches between module materials. Given the complex composition of solar modules, isolating the impact of individual components on overall durability remains a challenging task. In this work, we analyze a comprehensive data set that comprises bill-of-materials (BOM) and thermal cycling power loss from 251 distinct module designs to identify the predominant design factors and their impacts on the thermomechanical durability of modules. The methodology of our analysis combines machine learning modeling (random forest) and Shapley additive explanation (SHAP) to correlate design factors with power loss and interpret the model’s decision-making. The interpretation reveals that silicon type (monocrystalline or polycrystalline), encapsulant thickness, busbar numbers, and wafer thickness predominantly influence the degradation. With lower power loss of around 0.6% on average in the SHAP analysis, monocrystalline cells present better durability than polycrystalline cells. This finding is further substantiated by statistical testing on our raw data set. The SHAP analysis also demonstrates that while thicker encapsulants lead to reduced power loss, further increasing their thickness over around 0.6 to 0.7 mm does not yield additional benefits, particularly for the front side one. In addition, other important BOM features such as the number of busbars are analyzed. This study provides a blueprint for utilizing explainable machine learning techniques in a complex material system and can potentially guide future research on optimizing the design of solar modules.

14 SOLAR ENERGY↗
DOE OSTI2025

Explainable multi-fidelity Bayesian neural network for distribution system state estimation

Distribution System State Estimation (DSSE) is frequently constrained by limited real-time measurements, the uncertainties introduced by distributed energy resources, and the presence of bad data. To address them, this paper proposes an enhanced Multi-Fidelity Bayesian Neural Network (MFBNN) DSSE approach. A low-fidelity layer based on a Deep Neural Network (DNN) is first pre-trained on pseudo-measurement data to learn fundamental state features. Subsequently, a high-fidelity Bayesian Neural Network (BNN) layer leverages limited but high-quality real-time measurements to refine these features, thereby achieving accurate DSSE. Additionally, the deep SHapley Additive exPlanation (SHAP) is developed to quantify the influence of measurement data on DSSE through dual perspectives of global feature importance and local nodal contributions, establishing a hierarchical explainability framework for machine learning-based DSSE. Comparative studies conducted on the IEEE 13-bus system and a real-world 2135-node system from Dominion Energy demonstrate that the proposed method excels in estimation accuracy, even under situations of high noise levels, bad data, and missing data. Further comparisons with Weighted Least Squares (WLS) and other machine learning-based DSSE approaches verify that the proposed framework offers higher accuracy, improved interpretability, and enhanced robustness.

Bad data↗
DOE OSTI2025

Understanding neutron capture processes in uranium deposits using combined U-Sm-Nd isotopic compositions

Valuable insights into the history and evolution of a geologic deposit can be found by investigating neutron capture reactions. Thermal neutron capture reactions occur within both the samarium (Sm) and the uranium (U) systems, where 149 Sm and 235 U can capture neutrons to become 150 Sm and 236 U, respectively. Although largely unexplored, paired measurements of 150 Sm and 236 U could be important for understanding neutron capture effects within uranium ore bodies, and such measurements are potentially useful in nuclear forensics for assessing a material's provenance or mineral exploration. In this work, we refined measurement procedures of Sm isotope compositions utilizing MC-ICPMS. While geologic reference materials were found to have indistinguishable Sm isotope compositions, we found significant isotope variations consistent with nuclear field shift among synthetic Sm standards. Here, this observation highlights that future high-precision Sm isotope investigations need to carefully evaluate synthetic standard(s) against geologic reference materials until an unfractionated and agreed-upon standard is identified. Here, we applied this method to a set of nine uranium ores from the South Australian Beverley North uranium deposits. Although 236 U excesses had been previously reported for these U ores, we found no measurable isotopic shifts in 149 Sm- 150 Sm at the current level of precision (±5 parts per million). One possible explanation for this disparity in the observed neutron capture signatures between U and Sm is that the source(s) of the U and Sm in these ores may be decoupled. This is consistent with the finding that these ores have variable 143 Nd/ 144 Nd, thus demonstrating that diverse sources were involved in the formation of the Beverley North deposits. Alternatively, this deposit may be too young (<50Ma) to have accumulated measurable neutron capture effects in Sm to be detected with the methods employed here.

Mineral exploration↗