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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

CORSAIR: A Framework for Human Mobility Prediction through Visit Characterization and Spatial Behavior Modeling

The rapid advancement of location acquisition technologies has led to the daily collection of vast amounts of mobile trajectory data, facilitating in-depth research on human mobility and enabling more accurate mobility prediction models. However, existing methodologies often fall short in capturing the intricate dynamics of human navigation and spatial behavior. This paper addresses this gap by exploring the multifaceted relationships between individuals and their environments, considering the diverse influences of personal preferences and experiences. Some places hold sentimental value and are visited frequently, while others serve as transient points of passage. To model these differences, we introduce CORSAIR, a novel visit characterization framework that leverages visitation patterns and dwell times to delineate an individual’s relationship with specific places. CORSAIR classifies visits into seven distinct types: casual, occasional, routine, special, anchor, important, and resettling. Also, we show that explicitly recognizing these distinct visit types and incorporating nuanced visit intents into mobility prediction models leads to a substantial improvement in prediction accuracy. This distinction allows for more precise modeling of the individual’s transitions, enhancing the personalization and relevance of location-based services. Our findings suggest that a deeper understanding of the complexities of individual-environment interactions is crucial for developing effective predictive tools in mobility research.

Amichi, Licia [ORNL] (ORCID:0000000177631394)↗
DOE OSTI2025

Assessing High Burnup U-19Pu-10Zr Fuel Performance against Historical and Modeled Behavior

Advancing the deployment of sodium-cooled fast reactors (SFRs) requires thorough testing of metallic fuel pins under accident conditions to establish safe operational limits of high burnup fuel. To conduct transient testing, a comprehensive understanding of steady-state fuel behavior obtained through both experimental characterization and accurate predictive capabilities is needed. This study comparatively assesses the steady-state irradiation performance of two high burnup U-19Pu-10Zr fuel pins, DP-36 and DP-40, irradiated under prototypic fast reactor conditions in preparation for planned safety testing at the Transient Reactor Test Facility. Since DP-40 was designated for use in the test and DP-36 serves as its sibling pin, non-destructive, engineering-scale post-irradiation examinations (PIE) were conducted on both pins while destructive examinations were performed exclusively on DP-36. The results were then assessed against historical performance data from similar fuel pins irradiated in the Experimental Breeder Reactor-II. Additionally, the steady-state irradiation of each pin was modeled using the BISON fuel performance code to assess the accuracy of current modeling capabilities in predicting the baseline irradiation behavior. Non-destructive examinations included neutron radiography to measure fuel column elongation, gamma scanning to verify pin integrity and fission product migration, and profilometry to assess dimensional changes. Benchmarking against existing PIE data revealed consistent patterns in axial fuel column growth and cladding diametral strain, though both pins exhibited longer low-density “fluff” structures, which can have implications for core reactivity and source term calculations. Destructive examinations on DP-36 included fission gas release analysis and sectioning for optical microscopy, which showed more complex constituent redistribution patterns than the traditionally accepted 3-ring model. The axial evolution of fractional areas and porosities of each of the redistributed zones were quantified and presented. Modeling comparisons showed agreement in fractional fission gas release but consistently overestimated axial and radial swelling and disagreed with measured axial porosity patterns. These conservative overpredictions suggested that the pins would appear closer to failure or operational limits at the start of transient tests, potentially leading to higher strain accumulation during the transient. While conservative estimates provide safety margins, they can negatively impact fuel economics. A review of the swelling models identified areas for improvement in the gaseous swelling, solid swelling, and fuel hot-pressing models when applied to ternary fuel. The results of this study highlight the critical importance of conducting pre-test characterization on both test and sibling pins to accurately capture steady-state fuel behavior, providing a precise baseline for post-test evaluations and essential inputs for transient modeling of the planned experiments. The analysis also revealed significant data gaps that require further investigation to enhance the understanding and prediction of fuel swelling and pore dynamics. Collecting comprehensive data across different irradiation conditions, burnup levels, and fuel compositions are essential for refining existing models and developing mechanistic models for both binary and ternary metallic fuels, ultimately improving the integration of modeling and experimental approaches in accident testing.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Electric Vehicle Charging Data Falsification Attacks Utilizing Behavioral Models

A charging station (CS) and its associated electric vehicle supply equipment (EVSE) and charging electric vehicle (EV) interactions are potential targets for data falsification attacks since CSs are typically unmanned public facilities that are connected to the internet and EVs incorporate the vulnerable CAN bus network, which are both susceptible to remote attacks. The research question being addressed is how is the EV owner and CS negatively affected by CAN bus EV battery current sensor and battery temperature sensor data falsification attacks. Negative effects include economic losses from reduced life span of the battery, battery thermal runaway and fire (and potential loss of surrounding structure), and reduced utilization of the CS due to delayed departure time (longer charging times).

25 ENERGY STORAGE↗
DOE OSTI2025

Data-driven modeling of dynamic occupant thermostat override behavior for demand response applications

Buildings consume nearly 40% of global energy and produce similar emissions. Whiletechnological advances address efficiency, occupant behavior causes energy use variations up to 300% between identical buildings. This gap between predicted and actual building performance impacts building design, operations, and grid demand management programs. Through analyses of smart thermostat data from 1,400 single-occupant homes, the researchdemonstrates that occupants respond to 8°F thermostat setpoint changes within a median of 15 minutes, while 2°F changes trigger responses within a median of 30 minutes. This highlights an understudied temporal relationship between thermostat setbacks and response time of occupant behaviors. Models of such behavior dynamics are required to incorporate occupant impacts into building performance simulation. A key contribution of this dissertation is the Thermal Frustration Theory (TFT), which positsthat thermal discomfort driven behaviors are caused by the time-accumulation of discomfort, not simply a temperature deviation threshold or a delay from an initiating event. Using a dataset of 634 thermostats, each with 25+ manual setpoint changes, a comparative analysis of TFT and comfort zone and a delayed response theories demonstrated that personalized TFT models better predict when manual setpoint change occur. This was measured by the area under the curve statistical measure (AUC); all three models perform similarly by a Matthews Correlation Coefficient measure. Higher AUC performance is especially important for modeling occupant behavior in demand response programs where false negatives of rare occupant interactions could adversely affect grid stability. EnergyPlus based simulations were conducted with TFT-derived occupant models, demonstrating the ability to identify parameters of known TFT models from only data observable with smart thermostats, even under the presence of noise from routine overrides. Overall, the dissertation highlights that thermostat interactions are neither static,instantaneous, nor driven solely by the environment. Instead, temporal accumulation of discomfort and routine-based behavior play important roles. The methodology and results offer a pathway towards more accurate modeling of human-building interactions for policy assessment, building design, and demand response programs.

Sharma, Kunind [Northeastern University] (ORCID:00↗
DOE OSTI2025

Disturbance and Response Model (DRM)

The DRM is a framework to interpret ecosystem process model output for 3D fire behavior model input and interpret the 3D fire behavior model output for ecosystem process model inpu

Atchley, Adam↗
DOE OSTI2024

W2VPCA: A Machine Learning Method for Measuring Attitudes With Natural Language

Company strategy influences many decisions in freight transportation. Behavioral models of company decision-making therefore could benefit from including strategy variables. However, strategy is difficult to observe and quantify. Attitudinal surveys of company executives can be used to collect measurements of latent strategy to use in quantitative models. However, surveys are costly and burdensome. Text mining methods to collect measurements overcome these issues somewhat, but typically require manual intervention and ignore the context of words, which can be problematic. This study introduces a new machine learning method to generate strategy measurement data from existing big text data. The new method, called W2VPCA, combines Natural Language Processing and Principal Components Analysis. W2VPCA produces measurement data that serve as quantitative indicators of latent strategy in behavioral models. W2VPCA is unsupervised, data-driven, and uses information on word context. We apply W2VPCA to generate measurements of latent strategies using readily available, large-scale text data: annual company reports. The empirical measurements are used successfully to associate two latent strategies, one focusing on distribution and the other on products, with truck fleet and distribution center outsourcing decisions. The main empirical outcome is that the W2VPCA measurements outperform Bag-of-Words measurements in a psychometric analysis of latent firm strategies. While this study focuses on freight behavioral models, W2VPCA may also have applications in behavioral modeling in other domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

WHA Sphere Impacting RHA Plate: Modeling Brittle Behavior in Metal Alloys

Brittle behavior of metal alloys is often critical to modeling ballistic impact and penetration. The ALEGRA multiphysics finite element software incorporates calibrated models for the equation of state, elasticity, yield stress, plasticity and fracture, but simulations do not always capture expected metal fracture. Here we report concerted efforts to do so for one important case where experiments clearly show shear fractures: a tungsten sphere impacting a steel plate at various angles. Our best simulations show fractures that are qualitatively similar to experiments, but there are significant differences in quantitative metrics. Specifically, velocities of tracers used to quantify simulated plug parameters consistently fall short of measured plug velocities. Also, simulated plugs break apart more than expected from experimental evidence. We attribute these shortfalls to the lack of an explicit shear fracture mechanism in the material models, leading to over-estimated resistance to plug formation and movement.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Remote Influence of Andean Convection on Amazonian Rainfall and Its Mechanisms

Models from Coupled Model Intercomparison Project Phase 6 produce too much precipitation over the Andes but too little over the Amazon or the Wet Andes-Dry Amazon (WADA) bias pattern. Unlike the conventional view that convection parameterization and land model deficiencies can contribute to Amazonian rainfall biases, we approach this long-standing biased model behavior through the lens of Andean convection. Using Community Earth System Model v1.1 and focusing on the wet season, our mechanism-denial experiments demonstrate that Andean convection notably reduces precipitation over the Amazon during austral summer. The Andean forced Amazonian response operates on weather timescale. Furthermore, the reduction of Amazonian rainfall is detectable within a few hours after initial Andean forcing. The precipitation response is primarily driven by variations in the moisture budget and is moderated by changes in convective available potential energy over the Amazon. Changes in the total advection of moisture over the Amazon are dominated by the vertical advection term and can be attributed to discrepancies in the dynamic omega field. In the experiments, the Andean east flank region is scrutinized where the vertical velocity and moisture fields play an intermediary role for the Andean driven WADA connection. The Andean forcing induces descending anomalies on the Andean east flank. The disturbances of wind and geopotential fields over the Andean east flank propagate eastward via Kelvin waves. Over the Amazon, descending anomalies and advective drying lead to reduction of mid-to-high level cloud, increase of shortwave cloud forcing and surface net radiation, and enhancement of themodynamic stability and rainfall reduction.

58 GEOSCIENCES↗
DOE OSTI2025

Moving beyond post hoc explainable artificial intelligence: a perspective paper on lessons learned from dynamical climate modeling

AI models are criticized as being black boxes, potentially subjecting climate science to greater uncertainty. Explainable artificial intelligence (XAI) has been proposed to probe AI models and increase trust. In this review and perspective paper, we suggest that, in addition to using XAI methods, AI researchers in climate science can learn from past successes in the development of physics-based dynamical climate models. Dynamical models are complex but have gained trust because their successes and failures can sometimes be attributed to specific components or sub-models, such as when model bias is explained by pointing to a particular parameterization. We propose three types of understanding as a basis to evaluate trust in dynamical and AI models alike: (1) instrumental understanding, which is obtained when a model has passed a functional test; (2) statistical understanding, obtained when researchers can make sense of the modeling results using statistical techniques to identify input–output relationships; and (3) component-level understanding, which refers to modelers' ability to point to specific model components or parts in the model architecture as the culprit for erratic model behaviors or as the crucial reason why the model functions well. We demonstrate how component-level understanding has been sought and achieved via climate model intercomparison projects over the past several decades. Such component-level understanding routinely leads to model improvements and may also serve as a template for thinking about AI-driven climate science. Currently, XAI methods can help explain the behaviors of AI models by focusing on the mapping between input and output, thereby increasing the statistical understanding of AI models. Yet, to further increase our understanding of AI models, we will have to build AI models that have interpretable components amenable to component-level understanding. We give recent examples from the AI climate science literature to highlight some recent, albeit limited, successes in achieving component-level understanding and thereby explaining model behavior. The merit of such interpretable AI models is that they serve as a stronger basis for trust in climate modeling and, by extension, downstream uses of climate model data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Modeling the behavior of concentrated aqueous HNO 3 using machine learning interatomic potentials

We develop two multi-defect machine learning interatomic potentials (MLIPs) trained at the BLYP-D2 and PBE-D3 density functional theories using the DeepMD-kit, allowing for the investigation of structural and thermodynamic properties of nitric acid over a wide range of concentrations via molecular dynamics (MD) simulations. We directly compute the degree of dissociation, α, and pK a from MD simulations, revealing that HNO 3 behaves as a weaker acid at higher concentrations, noting that our standard-state pK a value is in excellent agreement with the experimental one. In general, good agreement is observed with experimental results such as α and density outside the training dataset, with only modest deviations at low-to-medium concentrations. We benchmark our custom multi-defect DeepMD MLIPs against foundational models MACE-MP0 and MACE-OFF23. The foundation models capture some aspects of HNO 3 /NO 3 − solvation in concentrated nitric acid but show noticeable density errors and miss subtle structural features relevant to spectroscopy, whereas the bespoke DeepMD MLIPs yield more compact solvation shells, reproduce density-concentration trends, and run ∼12–15× faster than MACE-MP0. Although classical FFs are still more efficient and match experimental densities better, they lack chemical reactivity and thus cannot predict α or pK a , underscoring the need for system-specific reactive MLIPs beyond universal MLIPs.

Dinpajooh, Mohammadhasan [Pacific Northwest Nation↗
DOE OSTI2024

Forest structural complexity and ignition pattern influence simulated prescribed fire effects

Background: Forest structural characteristics, the burning environment, and the choice of ignition pattern each influence prescribed fire behaviors and resulting fire effects; however, few studies examine the influences and interactions of these factors. Understanding how interactions among these drivers can influence prescribed fire behavior and effects is crucial for executing prescribed fires that can safely and effectively meet management objectives. To analyze the interactions between the fuels complex and ignition patterns, we used FIRETEC, a three-dimensional computational fluid dynamics fire behavior model, to simulate fire behavior and effects across a range of horizontal and vertical forest structural complexities. For each forest structure, we then simulated three different prescribed fires each with a unique ignition pattern: strip-head, dot, and alternating dot. Results: Forest structural complexity and ignition pattern affected the proportions of simulated crown scorch, consumption, and damage for prescribed fires in a dry, fire-prone ecosystem. Prescribed fires in forests with complex canopy structures resulted in increased crown consumption, scorch, and damage compared to less spatially complex forests. The choice of using a strip-head ignition pattern over either a dot or alternating-dot pattern increased the degree of crown foliage scorched and damaged, though did not affect the proportion of crown consumed. We found no evidence of an interaction between forest structural complexity and ignition pattern on canopy fuel consumption, scorch, or damage. Conclusions: We found that forest structure and ignition pattern, two powerful drivers of fire behavior that forest managers can readily account for or even manipulate, can be leveraged to influence fire behavior and the resultant fire effects of prescribed fire. These simulation findings have critical implications for how managers can plan and perform forest thinning and prescribed burn treatments to meet risk management or ecological objectives.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Modeling the Behavior of Complex Aqueous Electrolytes Using Machine Learning Interatomic Potentials: The Case of Sodium Sulfate

Understanding the structure and thermodynamics of solvated ions is essential for advancing applications in electrochemistry, water treatment, and energy storage. While ab initio molecular dynamics methods are highly accurate, they are limited by short accessible time and length scales whereas classical force fields struggle with accuracy. Herein, we explore the structure and thermodynamics of complex monovalent-divalent ion pairs using Na 2 SO 4 (aq) as a case study by applying a machine learning interatomic potential (MLIP) trained on density functional theory (DFT) data. Our MLIP-based approach reproduces key bulk properties such as density and radial distribution functions of water. We provide the hydration structure of the sodium and sulfate ions in the 0.1–2 M concentration range and the one-dimensional and two-dimensional potentials of mean force for the sodium–sulfate ion pairing at the low concentration limit (0.1 M), which are inaccessible to DFT. At low concentrations, the sulfate ion is strongly solvated, leading to the stabilization of solvent-separated ion pairs over contact ion pairs. Minimum energy pathway analysis revealed that coordinating two sodium ions with a sulfate ion is a multistep process whereby the sodium ions coordinate to the sulfate ion sequentially. Finally, we demonstrate that MLIPs allow the study of solvated ions beyond simple monovalent pairs with DFT-level accuracy in their low concentration limit (0.1 M) via statistically converged properties from ns-long simulations.

anions↗
DOE OSTI2024

Modeling the Behavior of Noble Metals During HLW Vitrification in the DM1200 Melter

The noble metals ruthenium (Ru), rhodium (Rh), and palladium (Pd) are present to various extents in defense and commercial high-level nuclear waste streams. Usually, their concentrations are higher in commercial waste. Ru, Rh, and Pd are sparingly soluble in silicate glass melts and they therefore form separate metal, oxide, or other phases. These phases are generally denser than the glass melt and tend to sediment to the bottom of the melter, where they form a "sludge" layer. Since the sludge layer has a higher electrical conductivity than the molten glass, accumulation of sludge can ultimately lead to electrical disruption of the operation of the melter. In addition, the sludge reduces the melt volume, which also changes the operational characteristics of the melter. The noble metals sludge is a highly viscous mixture of melt and noble metal phases. The concentration of noble metals in the sludge may be 10 to 100 times higher than that in the drained glass.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Machine learning-enabled discovery of ionic liquid–solvent electrolytes exhibiting high ionic conductivity

Ionic liquids (ILs), which are a class of materials with versatile nature and growing popularity, are facing impediments toward widespread usage as electrolytes due to various factors such as low ionic conductivity, high viscosity, high market price etc. One of the ways these limitations can be addressed is by mixing ILs with a molecular solvent. In a combinatorial sense, there exists an immense number of specific IL–solvent combinations. An exhaustive experimental or even simulation-based investigation of the chemical space spanned by such combinations can be extremely time-consuming, expensive, and nearly impossible. An alternative approach is to employ machine learning-based models developed from available databases. Although there exists prior literature that integrates machine learning to investigate mixtures of specific solvents with ILs, these models lack generalization necessitating development of a large number of ML models to handle various solvents. To remedy this shortcoming, as a part of designing green electrolytes with high ionic conductivity that can have potential applications in next-generation batteries and solar cells, this work aims to develop a unified machine learning model to predict ionic conductivity of any IL–solvent mixture system. In this regard, three models, namely, Random Forest, extreme gradient boosting (XGBoost), and artificial neural network (ANN) were formulated using the NIST ILThermo database. The dataset contained 549 unique ionic liquids from 16 cation families and 81 unique solvents, representing a total of 23 712 datapoints. SHAPLEY additive explanation (SHAP) method was used to assess the impact of various features on model prediction and their significance was compared with literature to gain physical insight about the model behavior. Finally, using the developed models, approximately 2.5 million IL–solvent mixtures at five different compositions were screened at room temperature. The high-throughput screening yielded nearly 19 000 IL–solvent mixtures for which ionic conductivity was found to exceed the ionic conductivity of conventional Li-ion battery electrolyte.

25 ENERGY STORAGE↗
DOE OSTI2024

Airborne LiDAR to Improve Canopy Fuels Mapping for Wildfire Modeling

Increasing conflict between wildfire and the built environment has increased the need for more up-to-date and finer resolution canopy fuels data to improve wildfire modeling and associated risk forecasts. The US Forest Service and US Department of the Interior’s LANDFIRE product, which provides 30-m resolution canopy fuels data for the entire US, is one of the most widely used sources of fuels data. However, the last complete mapping effort for LANDFIRE is based on 2016 conditions, and subsequent updates reflect disturbances 1-2 years behind the release year. Airborne systems equipped with Light Detection and Ranging (LiDAR) sensors can be deployed to actively sense canopy structure and estimate canopy fuels data (cover, height, base height, bulk density) at finer resolutions. Canopy base height (CBH) and canopy bulk density (CBD) are difficult to measure both in the field and in LiDAR point clouds. Still, they are important for accurately modeling crown fires, which are often intense and difficult to contain. Additionally, point cloud datasets are large, and calculations require efficient utilization of computational resources. To address these challenges, we are working on an approach that uses openly available National Ecological Observatory Network (NEON) airborne LiDAR data, with calculations processed in the R programming language and parallelized through the lidR package. CBH and CBD are often derived from tree height, diameter at breast height, and species-specific allometries using the Fire and Fuels Extension of the Forest Vegetation Simulator (FFE-FVS). We aim to test if airborne LiDAR can estimate CBH and CBD without the use of empirical equations. Reliable estimates of canopy fuels data directly from airborne LiDAR could streamline quick, fine-resolution updates for use in wildfire behavior models.

54 ENVIRONMENTAL SCIENCES↗