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DOE OSTI2022

Driver Distraction Behavior Detection using a Vision Transformer Model based on Transfer Learning Strategy

Driver distraction behavior is one of the critical factors in traffic accidents. Thus, advanced driver state detection system has become the focus in the field of intelligent vehicle. However, in practical applications, insufficient samples of driving distraction behaviors bring great challenges to training a personalized behavior distraction detection model for a specific driver. To this end, a novel transformer model based on a transfer learning strategy is proposed in this paper to accurately recognize driver distraction behavior. Inspired by the effect of the transformer network in visual recognition, we firstly present a transformer behavior distraction detection system to identify the behavior categories that cause driver distraction. Then, for the specific driving dataset in practical application scenarios, the transfer learning strategy is introduced into the driver distraction detection model to further train the general transformer network. The effectiveness of the transformer based on the transfer learning strategy is validated compared with other traditional deep learning methods. The results show that the proposed detection method has better generalization ability and higher accuracy.

Fang, Zhenwu↗

DOE OSTI2024

Assessing the behavioral realism of energy system models in light of the consumer adoption literature

Effective policymaking to achieve net zero greenhouse gas emissions demands an understanding of the complex drivers of, and barriers to, consumer adoption behavior via behaviorally realistic energy system models. Existing models tend to oversimplify by focusing on homogenized financial factors while neglecting consumer heterogeneity and non-monetary influences. This study develops and applies a comprehensive framework for evaluating the behavioral realism of consumer adoption models, informed by the adoption literature. It introduces a typology for factors influencing low-carbon technology adoption decisions: monetary and non-monetary factors relating to household characteristics, psychology, technological attributes, and contextual conditions. Next, reviews of the consumer adoption and decision-making literature identify the most influential adoption factor categories for distributed solar photovoltaics, electric vehicles, and air-source heat pumps. Finally, the extent to which a selection of energy system models accounts for these adoption factors is assessed. Existing models predominantly emphasize the economic aspects of technology, which are generally identified as the most important factors. Where the models fall short — in considering moderately important factor categories — sector-specific and agent-based models can offer more behaviorally realistic insights. This study sheds light on which types of factors are most important for consumer adoption decisions and investigates how well current models rise to the challenge of behavioral realism. The end-to-end analysis presented enables internally consistent comparisons across models and energy technologies. This research advances timely conversations on consumer adoption. It could inform more behaviorally realistic energy system modeling, and thereby more effective decarbonization policymaking.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Modeling household-level party composition behavior for multiparty activities: a random parameter nested logit modeling approach

This study presents findings of a household-level party composition model for multiparty activities. It exploits data from a comprehensive Household Travel Survey conducted by Chicago Metropolitan Agency of Planning. The study estimates a random parameter nested logit model to capture households’ unobserved preference heterogeneity and non-proportional substitution patterns in terms of activity party composition for multiparty activities. A wide variety of household demographics, activity attributes and residential neighborhood characteristics are examined in this paper. The magnitude of the impacts of the determinants are tested in this study by analyzing the elasticity of the variables, which suggests that household demographics and attributes of the multiparty activities have significant effects on the household-level activity party composition. Residential neighborhood characteristics, although somewhat less impactful, still play a meaningful role. This model will be implemented within the POLARIS transportation systems simulator to improve the activity generation modeling workflow, and the prediction accuracy of various activity-travel components.

activity party composition↗

DOE OSTI2023

Ensemble Spread Behavior in Coupled Climate Models: Insights From the Energy Exascale Earth System Model Version 1 Large Ensemble

AbstractAssessing uncertainty in future climate projections requires understanding both internal climate variability and external forcing. For this reason, single‐model initial condition large ensembles (SMILEs) run with Earth System Models (ESMs) have recently become popular. Here we present a new 20‐member SMILE with the Energy Exascale Earth System Model version 1 (E3SMv1‐LE), which uses a “macro” initialization strategy choosing coupled atmosphere/ocean states based on inter‐basin contrasts in ocean heat content (OHC). The E3SMv1‐LE simulates tropical climate variability well, albeit with a muted warming trend over the twentieth century due to overly strong aerosol forcing. The E3SMv1‐LE's initial climate spread is comparable to other (larger) SMILEs, suggesting that maximizing inter‐basin ocean heat contrasts may be an efficient method of generating ensemble spread. We also compare different ensemble spread across multiple SMILEs, using surface air temperature and OHC. The Community Earth system Model version 1, the only ensemble which utilizes a “micro” initialization approach perturbing only atmospheric initial conditions, yields lower spread in the first ∼30 years. The E3SMv1‐LE exhibits a relatively large spread, with some evidence for anthropogenic forcing influencing spread in the late twentieth century. However, systematic effects of differing “macro” initialization strategies are difficult to detect, possibly resulting from differing model physics or responses to external forcing. Notably, the method of standardizing results affects ensemble spread: control simulations for most models have either large background trends or multi‐centennial variability in OHC. This spurious disequlibrium behavior is a substantial roadblock to understanding both internal climate variability and its response to forcing.

Stevenson, Samantha↗

DOE OSTI2024

DEPLOYING FAST CHARGING INFRASTRUCTURE FOR ELECTRIC VEHICLES IN URBAN NETWORKS: AN ACTIVITY-BASED APPROACH

This paper explores an important problem under the domain of network modeling, the optimal configuration of charging infrastructure for electric vehicles (EVs) in urban networks considering EV users' daily activities and charging behavior. This study proposes a charging behavior simulation model considering different initial state of charge (SOC), travel distance, availability of home chargers, and the daily schedule of trips for each traveler. The proposed charging behavior simulation model examines the complete chain of trips for EV users as well as the interdependency of trips traveled by each driver. The problem of finding the optimum charging configuration is then formulated as a mixed-integer nonlinear programming problem that considers the dynamics of travel time and travel distance, the interdependency of trips made by each driver, limited range of EVs, remaining battery capacity for recharging, waiting time in queue, and detour to access a charging station. This problem is solved using a metaheuristic approach for a large-scale case network. A series of examples are presented to demonstrate the model efficacy and explore the impact of energy consumption on the final SOC and the optimum charging infrastructure.

Chain of Trips↗

DOE OSTI2023

Improved Compaction Experiments and Modeling of Waste Isolation Pilot Plant Standard, Non-degraded, Waste Containers

A credible simulation of disposal room porosity at the Waste Isolation Pilot Plant (WIPP) requires a tenable compaction model for the 55-gallon waste containers within the room. A review of the legacy waste material model, however, revealed several out-of-date and untested assumptions that could affect the model’s compaction behavior. For example, the legacy model predicted non-physical tensile out-of-plane stresses under plane strain compression. (Plane strain compression is similar to waste compaction in the middle of a long drift.) Consequently, a suite of new compaction experiments were performed on containers filled with surrogate, non-degraded, waste. The new experiments involved uniaxial, triaxial, and hydrostatic compaction tests on quarter-scale and full-scale containers. Special effort was made to measure the volume strain during uniaxial and triaxial tests, so that the lateral strain could be inferred from the axial and volume strain. These experimental measurements were then used to calibrate a pressure dependent, viscoplastic, constitutive model for the homogenized compaction behavior of the waste containers. This new waste material model’s predictions agreed far better with the experimental measurements than the legacy model’s predictions, especially under triaxial and hydrostatic conditions. Under plane strain compression, the new model predicted reasonable compressive out-of-plane stresses, instead of tensile stresses. Moreover, the new model’s plane strain behavior was substantially weaker for the same strain, yet substantially stronger for the same porosity, than the legacy model’s behavior. Although room for improvement exists, the new model appears ready for prudent engineering use.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Deformation behavior of CP-titanium under strain path changes: Experiment and crystal plasticity modeling

The deformation behavior of commercially pure rolled titanium subjected to strain path changes is studied using experiments and a crystal plasticity model. Four different loading combinations are performed at room temperature to study the activation of slip, twinning, de-twinning and double-twinning in hexagonal closed packed titanium. The strain paths considered are: rolling direction compression (RDC) followed by normal direction compression (NDC), RDC followed by transverse direction compression (TDC), NDC followed by RDC, and NDC followed by TDC. An EBSD-based analysis of the distribution of boundary misorientation angles before and after reload was developed to analyze the evolution of {$10\bar{1}2$} tensile and {$11\bar{2}2$} compression twins. This analysis supports the model results concerning the treatment of twin reorientation. A de-twinning and double-twinning model accounting for back stress effects, an important feature of strain path changes, is implemented within the framework of the visco-plastic self-consistent (VPSC) model along with a dislocation density (DD) based hardening scheme. In the model, plasticity is accommodated by prismatic $\langle$a$\rangle$, basal $\langle$a$\rangle$ and pyramidal $\langle$c+a$\rangle$ slip modes, and {$10\bar{1}2$} tensile and {$11\bar{2}2$} compression twinning modes. The VPSC model predicts the evolution of twinning, de-twinning and double-twinning processes for both tensile and compression twinning modes under strain path change. The model predicts macroscopic stress-strain response, texture evolution, and twin volume fraction that are in agreement with experimental observations. The evolution of texture is investigated in detail by separately analyzing the twinned domains, rather than the evolution of the global texture.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Exploring Social Dynamics of Hard-Disk Drives Circularity with an Agent-Based Approach

By 2025, it is estimated that installed data storage in the U.S. will be 2.2 Zettabytes, generating about 50 million units of end-of-life hard-disk drives (HDDs) per year. The circular economy (CE) tackles waste issues by maximizing value retention in the economy, for instance, through reuse and recycling. However, the reuse of hard disk drives is hindered by the lack of trust organizations have toward other means of data removal than physically destroying HDDs. Here, an agent-based approach explores how organizations' decisions to adopt other data removal means affect HDDs' circularity. The model applies the theory of planned behavior to model the decisions of HDDs end-users. Results demonstrate that the attitude (which is affected by trust) of end-users toward data-wiping technologies acts as a barrier to reuse. Moreover, social pressure can play a significant role as organizations that adopt CE behaviors can set an example for others.

28 EE - Advanced Manufacturing Office (EE-5A)↗

DOE OSTI2025

Comparative post-irradiation examination of high burnup U-19Pu-10Zr: Assessing steady-state irradiation behavior against historical and modeled fuel performance

Here, the development of next-generation sodium-cooled fast reactors necessitates comprehensive research on metallic fuels to maximize economic performance while ensuring safe operation. In this study, we investigated the steady-state irradiation behavior of two high burnup U-19Pu-10Zr fuel pins, DP-36 and DP-40, in preparation for planned safety testing. Post-irradiation examination (PIE) was performed to quantify fuel column elongation, regions of low-density at the top of the fuel column, pin deformation, fission product distribution, fractional fission gas release, microstructural evolution, and fuel constituent redistribution. Benchmarking against existing PIE data from U-19Pu-10Zr fuel pins irradiated in EBR-II revealed consistent patterns in fuel column elongation and cladding diametral strain. However, both pins exhibited longer low-density structures, and destructive examination of DP-36 revealed more complex constituent redistribution patterns compared to previously reported data for ternary fuel pins. The steady-state irradiation of both pins was also modeled using BISON. Comparisons of PIE results with modeled predictions showed overall agreement in fractional fission gas release but consistent overestimation of axial and radial swelling due to gaseous and solid swelling models. These findings underscore the critical importance of pre-test characterization on test and sibling pins to accurately capture steady-state fuel behavior ahead of transient testing, thus establishing a baseline for post-test comparison. Additionally, these analyses identified key data gaps that warrant further investigation to improve the understanding and prediction of fuel swelling, thereby enhancing the synergy between modeling and experimental efforts in supporting accident testing.

BISON↗

DOE OSTI2024

Multiobjective Constrained Symbolic Regression for Predictive Modeling of Material Creep Behavior

When creep testing is repeated on samples of the same alloy under the same parametric conditions (i.e., stress and temperature), the resulting strain/time curves can vary from each other considerably as shown in Figure 1 [1]. The time required to creep test a material to rupture can extend to the order of years. Because of this, a numerical model that can quickly analyze the incomplete results of an ongoing experiment to predict 1) the incomplete portion of the strain/time curve leading up to the rupture point and 2) the rupture point itself would be of great utility to the materials community. Such a model has the potential to save 1) the time required to finish running the experiment to rupture 2) the associated monetary cost of finishing said experiment. Furthermore, it would be advantageous if the predictive model could give a parametric function modeling strain/time curves for material scientists to investigate the impact of the temperature and stress parameters on the resulting creep behavior. This work introduces a piecewise symbolic regression algorithm to predict the remainder of the strain/time curve. Preliminary results show good model performance.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Development of a Control Co-Design Modeling Tool for Marine Hydrokinetic Turbines: Preprint

This report describes the ongoing and planned development of the software package CT-Opt (Current/Tidal Optimization), a control co-design modeling tool for marine hydrokinetic turbines. The commercialization of these turbines has faced significant challenges due to the complex, multidisciplinary nature of their design and the extreme environmental conditions of their operation. This project aims to create a modeling tool that will enable the efficient design of robust, cost-competitive hydrokinetic turbine systems. Rather than using traditional optimization methods, CT-Opt combines multiple models across a range of fidelities to enable coupled optimization of the system design and system controller via a control co-design approach. With this method, the parameters that affect system performance are considered more comprehensively at every stage of the design process. The lowest-fidelity, frequency-domain model called by CT-Opt is RAFT (Response Amplitudes of Floating Turbines), which was originally developed by the National Renewable Energy Laboratory (NREL) to model response amplitudes of floating offshore wind turbines. The highest-fidelity, time-domain model is OpenFAST, which was developed by NREL for land-based and offshore wind turbines. As part of the CT-Opt project, new functionalities will be added to RAFT and OpenFAST to enable the accurate simulation of fixed and floating marine hydrokinetic turbines. In addition to expanding the capabilities of RAFT and OpenFAST, new midfidelity models will be developed. These models will be based on RAFT and OpenFAST and will consist of linearized, state-space models derived from the fully coupled, nonlinear OpenFAST equations and derivative function surrogate models that approximate the nonlinear system behavior. Each model will be coupled with controllers to allow control co-design methods to be applied both within models and across fidelity levels, enabling efficient system optimization.

control co-design↗

DOE OSTI2025

Enhancing occupant behavior representation for interoperability between building information modeling and building energy modeling

Building Performance Simulation (BPS) has been adopted as an essential tool for designing, operating, and retrofitting buildings to optimize energy efficiency throughout the building life cycle. The Green Building XML (gbXML) schema facilitates seamless data exchange between Building Information Modeling (BIM) and Building Energy Modeling (BEM) software tools. However, limited occupant behavior (OB) representation in BIM often leads to inconsistent and inaccurate energy simulation in BEM software. This paper presents 154 systematic enhancements to the existing occupant behavior XML (obXML) schema v1.3.4, initially developed for standardizing OB representation for BEM, to address existing limitations and improve interoperability with BIM models. The enhancements encompass improved integration with BIM models through extended building representations and system operations, expanded support for advanced OB models with additional environmental parameters and mathematical capabilities, and implementation of a standardized model documentation framework. To facilitate seamless data transformation between gbXML and obXML schemas, we developed a publicly available gb-obXML Schema Converter. Three case studies demonstrate the enhanced schema’s capabilities: representation of building information using a two-story office building model, documentation of a window operation behavior model, and validation of the schema converter’s functionality. The enhanced obXML schema v1.4 enables sophisticated modeling of occupant-building interactions while maintaining consistency with industry-standard BIM schemas. The standardized documentation framework facilitates reproducibility and knowledge sharing in the OB research community, while the schema converter automates the integration of building information into OB simulation workflows. These enhancements establish a foundation for more accurate building performance simulation by supporting sophisticated representation of occupant behavior within the BIM-to-BEM simulation workflows.

Chung, Jihoon↗

DOE OSTI2020

Modeling of surface mechanical behaviors of soft elastic solids: theory and examples

Surfaces of soft solids can have significant surface stress, extensional modulus and bending stiffness. Previous theoretical studies have usually examined cases in which both the surface stress and bending stiffness are constant, assuming small deformation. In this work we consider a general formulation in which the surface can support large deformation and carry both surface stresses and surface bending moments. We show that the large deformation theory can be reduced to the classical linear theory (Shuttleworth equation). We obtain exact solutions for problems of an inflated cylindrical shell and bending of a plate with a finite thickness. Our analysis illustrates the different manners in which surface stiffening and surface bending stabilize these structures. We discuss how the complex surface constitutive behaviors affect the stress field of the bulk. Our calculation provides insights into effects of strain-dependent surface stress and surface bending in the large deformation regime, and can be used as a model to implement surface finite elements to study large deformation of complex structures.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Minimal one-dimensional model of bad metal behavior from fast particle-hole scattering

A strongly interacting plasma of linearly dispersing electron and hole excitations in two spatial dimensions (2D), also known as a Dirac fluid, can be captured by relativistic hydrodynamics and shares many universal features with other quantum critical systems. Here, we propose a one-dimensional (1D) model to capture key aspects of the 2D Dirac fluid while including lattice effects and being amenable to nonperturbative computation. When interactions are added to the Dirac-like 1D dispersion without opening a gap, we show that this kind of irrelevant interaction is able to preserve Fermi-liquid-like quasiparticle features while relaxing a zero-momentum charge current via collisions between particle-hole excitations, leading to resistivity that is linear in temperature via a mechanism previously discussed for large-diameter metallic carbon nanotubes. We further provide a microscopic lattice model and obtain numerical results via density-matrix renormalization group simulations, which support the above physical picture. The limits on such fast relaxation at strong coupling are of considerable interest because of the ubiquity of bad metals in experiments.

1-dimensional systems↗

DOE OSTI2026

Performing Numerical Analysis of Cybersecurity Options Using Dynamic Risk Analysis Tool EMRALD

Cyberattacks can have many different attack paths, durations, and goals. There are also many different mitigation options involving hardware, software, and/or humans. Considering a cyber threat should involve defense-in-depth methods and a quantitative or numerical evaluation of overall effectiveness against dynamic, time-dependent attacks to make cost and risk-informed decisions. Typical cyberattack modeling methods only provide a qualitative evaluation. The main areas of cybersecurity are confidentiality, integrity, and availability. For companies with cyber-physical systems such as advanced nuclear reactors, cyber-related safety is a requirement set by North American Electric Reliability and the U.S. Nuclear Regulatory Commission. They are also concerned about availability or reliability as a business case. As cyber threats are evolving to a business-for-hire structure, more attacks may focus on disrupting business success and reliability, causing financial and economic stability risk. Companies want to know business reliability and recovery from those threats, and that requires modeling physical behavior of the targets. Dynamic-state-based and Markov-based modeling provides a method for better cyber scenario modeling with different tools having issues such as state-base explosion. Dynamic modeling enables time and conditional features not found in other numerical evaluation methods. EMRALD (Event Modeling Risk Assessment using Lined Diagrams) is a dynamic risk analysis modeling and simulation tool and has features that reduce modeling issues. It has been used to model different time-dependent events including plant behavior and operator procedures. As a general modeling tool, EMRALD can also be used to model cyberattack scenarios with varying mitigation options and quantify effectiveness, producing numerical data for risk-informed decisions. This paper uses EMRALD to demonstrate that dynamic numerical risk analysis can be used for cyber threat modeling to provide insights for design decision-making and optimize defense strategies. Keywords: cyber modeling; cyber-physical systems; numerical cyber modeling

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2020

A thermodynamics-based damage model for the non-linear mechanical behavior of SiC/SiC ceramic matrix composites in irradiation and thermal environments

A damage model is developed and validated with experimental data for the non-linear mechanical behavior of SiC/SiC composite materials in nuclear applications. Cyclic thermal and mechanical loading associated with neutron irradiation effects of these composites leads to wide-spread and progressive micro-cracking that leads to loss of thermal conductivity and further enhancement of thermo-mechanical damage. A physics-based model of wide-spread micro-cracking is developed within the thermodynamic framework of continuum damage mechanics. Evolution equations for damage parameters that describe the growth of continuum damage are developed, where the material variables are obtained from experiments. The model novelty is in coupling mechanical, thermal, and irradiation damage through a consistent thermodynamic framework, including loss of thermal conductivity due to the evolution of mechanically induced micro-cracks. A number of thermo-mechanical experiments were conducted to confirm model assumptions. The model is shown to be validated with out-of-pile experiments, and then implemented using commercial finite element code COMSOL to the fuel cladding problem with normal and high radiation dose cases.

Materials Science↗

DOE OSTI2022

Modeling mesoscale fission gas behavior in UO2 by directly coupling the phase field method to spatially resolved cluster dynamics

Abstract Fission gas release within uranium dioxide nuclear fuel occurs as gas atoms diffuse through grains and arrive at grain boundary (GB) bubbles; these GB bubbles grow and interconnect with grain edge bubbles; and grain edge tunnels grow and connect to free surfaces. In this study, a hybrid multi-scale/multi-physics simulation approach is presented to investigate these mechanisms of fission gas release at the mesoscale. In this approach, fission gas production, diffusion, clustering to form intragranular bubbles, and re-solution within grains are included using spatially resolved cluster dynamics in the Xolotl code. GB migration and intergranular bubble growth and coalescence are included using the phase field method in the MARMOT code. This hybrid model couples Xolotl to MARMOT using the MultiApp and Transfer systems in the MOOSE framework, with Xolotl passing the arrival rate of gas atoms at GBs and intergranular bubble surfaces to MARMOT and MARMOT passing evolved GBs and bubble surface positions to Xolotl. The coupled approach performs well on the two-dimensional simulations performed in this work, producing similar results to the standard phase field model when Xolotl does not include fission gas clustering or re-solution. The hybrid model performs well computationally, with a negligible cost of coupling Xolotl and MARMOT and good parallel scalability. The hybrid model predicts that intragranular fission gas clustering and bubble formation results in up to 70% of the fission gas being trapped within grains, causing the increase in the intergranular bubble fraction to slow by a factor of six. Re-solution has a small impact on the fission gas behavior at 1800 K but it has a much larger impact at 1000 K, resulting in a twenty-times increase in the concentration of single gas atoms within grains. Due to the low diffusion rate, this increase in mobile gas atoms only results in a small acceleration in the growth of the intergranular bubble fraction. Finally, the hybrid model accounts for migrating GBs sweeping up gas atoms. This results in faster intergranular bubble growth with smaller initial grain sizes, since the additional GB migration results in more immobile gas clusters reaching GBs.

Kim, Dong-Uk↗

DOE OSTI2024

A supersymmetric SYK model with a curious low energy behavior

We consider N = 2,4 supersymmetric SYK models that have a peculiar low energy behavior, with the entropy going like S = S 0 + (constant)T a , where a ≠ 1. The large N equations for these models are a generalization of equations that have been previously studied as an unjustified truncation of the planar diagrams describing the BFSS matrix quantum mechanics or other related matrix models. Here we reanalyze these equations in order to better understand the low energy physics of these models. We find that the scalar fields develop large expectation values which explore the low energy valleys in the potential. The low energy physics is dominated by quadratic fluctuations around these values. These models were previously conjectured to have a spin glass phase. We did not find any evidence for this phase by using the usual diagnostics, such as searching for replica symmetry breaking solutions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗