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At least 1,765 records · Page 98
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Utilizing Reinforcement Learning to Continuously Improve a Primitive-Based Motion Planner

We report in this paper describes how the performance of motion primitive-based planning algorithms can be improved using reinforcement learning. Specifically, we describe and evaluate a framework that autonomously improves the performance of a primitive-based motion planner. The improvement process consists of three phases: exploration, extraction, and reward updates. This process can be iterated continuously to provide successive improvement. The exploration step generates new trajectories, and the extraction step identifies new primitives from these trajectories. These primitives are then used to update rewards for continued exploration. This framework required novel shaping rewards, development of a primitive extraction algorithm, and modification of the Hybrid A* algorithm. The framework is tested on a navigation task using a nonlinear F-16 model. The framework autonomously added 91 motion primitives to the primitive library and reduced average path cost by 21.6 seconds, or 35.75% of the original cost. The learned primitives are applied to an obstacle field navigation task, which was not used in training, and reduced path cost by 16.3 seconds, or 24.1%. Additionally, two heuristics for the modified Hybrid A* algorithm are designed to improve effective branching factor.

42 ENGINEERING↗
DOE OSTI2023

Review of multi-fidelity models

Multi-fidelity models provide a framework for integrating computational models of varying complexity, allowing for accurate predictions while optimizing computational resources. These models are especially beneficial when acquiring high-accuracy data is costly or computationally intensive. This review offers a comprehensive analysis of multi-fidelity models, focusing on their applications in scientific and engineering fields, particularly in optimization and uncertainty quantification. It classifies publications on multi-fidelity modeling according to several criteria, including application area, surrogate model selection, types of fidelity, combination methods and year of publication. The study investigates techniques for combining different fidelity levels, with an emphasis on multi-fidelity surrogate models. Here this work discusses reproducibility, open-sourcing methodologies and benchmarking procedures to promote transparency. The manuscript also includes educational toy problems to enhance understanding. Additionally, this paper outlines best practices for presenting multi-fidelity-related savings in a standardized, succinct and yet thorough manner. The review concludes by examining current trends in multi-fidelity modeling, including emerging techniques, recent advancements, and promising research directions.

42 ENGINEERING↗
DOE OSTI2026

A Co-Simulation Framework for Steady-State Analyses of Multiple Droop-based MTdc Grids in Continental-Scale Systems

The growing scale and complexity of planning continental hybrid ac and multi-terminal dc (MTdc) systems require scalable steady-state modeling and analysis approaches not currently available in commercial tools. This paper presents a comprehensive multi-fidelity model-conversion framework that enables the efficient transition of MTdc grid models from production cost modeling (PCM) and approximated ac power flow to detailed ac–MTdc power flow for large-scale planning studies. The core of this framework is a scalable co-simulation approach that, for the first time, enables power flow analysis in continental-scale ac–MTdc systems. It seamlessly couples commercial ac solvers with a detailed MTdc grid model that incorporates droop-based control and current-limiting strategies of multiple meshed MTdc grids. Leveraging this capability, an evaluation framework to systematically assess and compare different MTdc power redispatch strategies under ac and dc contingencies is introduced. The proposed framework and algorithm are evaluated using a combined Western and Eastern Interconnection system with 11 MTdc grids of various sizes, showing a coherent transition from PCM to detailed ac–MTdc power flow and improved system performance in voltage regulation and line overload mitigation following typical contingencies.

Nguyen, Quan H.↗
DOE OSTI2021

Radar-Based Bayesian Estimation of Ice Crystal Growth Parameters within a Microphysical Model

The potential for polarimetric Doppler radar measurements to improve predictions of ice microphysical processes within an idealized model–observational framework is examined. In an effort to more rigorously constrain ice growth processes (e.g., vapor deposition) with observations of natural clouds, a novel framework is developed to compare simulated and observed radar measurements, coupling a bulk adaptive-habit model of vapor growth to a polarimetric radar forward model. Bayesian inference on key microphysical model parameters is then used, via a Markov chain Monte Carlo sampler, to estimate the probability distribution of the model parameters. The statistical formalism of this method allows for robust estimates of the optimal parameter values, along with (non-Gaussian) estimates of their uncertainty. To demonstrate this framework, observations from Department of Energy radars in the Arctic during a case of pristine ice precipitation are used to constrain vapor deposition parameters in the adaptive habit model. The resulting parameter probability distributions provide physically plausible changes in ice particle density and aspect ratio during growth. A lack of direct constraint on the number concentration produces a range of possible mean particle sizes, with the mean size inversely correlated to number concentration. Consistency is found between the estimated inherent growth ratio and independent laboratory measurements, increasing confidence in the parameter PDFs and demonstrating the effectiveness of the radar measurements in constraining the parameters. Furthermore, the combined Doppler and polarimetric observations produce the highest-confidence estimates of the parameter PDFs, with the Doppler measurements providing a stronger constraint for this case.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Trial Implementation of the High-Impact, Low-Frequency Power Grid Event Risk Framework to Support Informed Decision-Making

In 2015, Pacific Northwest National Laboratory (PNNL) developed a multi-hazard risk-assessment framework for modeling high-impact, low-frequency (HILF) power grid events to support risk-informed decisions for power grid management and emergency planning. The identified framework elements are based on a systematic and comprehensive characterization of hazards to the level of detail required for modeling risk. In 2016, PNNL performed a trial implementation of the framework for a single hazard category and a set of key power grid asset types as a way to test the framework. A specific realization of a HILF event is referred to as an initiating event, or initiator. An initiating event is the manifestation of a hazard, and can be, for example, an earthquake of specified magnitude at a specified epicenter. Other examples of HILF events include geomagnetic disturbances and pandemics. For this test implementation, grid assets of a benchmark power system transmission network were assigned proxy geographical coordinates associated with Washington State locations; and real-world seismic hazard information was used for those locations. Based on the fragility of the grid assets to events of different severities, the set of possible failure scenarios (i.e., combinations of different asset failures) and their associated probabilities were determined, and recovery of grid assets and restoration of the network were considered. These combinations of failures and their subsequent recovery constitute a HILF event sequence that results in a given level of consequence, such as loss of power over a given geographic area for a given duration. The concurrent quantitative consideration of event probabilities and event consequences characterizes a risk model. For HILF event sequences, risk was expressed as expected unserved energy per year. This implementation demonstrates the feasibility and value of using the HILF event risk framework despite some of the identified challenges. This report discusses this trial implementation and presents risk results and insights from the implementation.

Veeramany, Arun↗
DOE OSTI2013

Flavor violating Higgs decays

We study a class of nonstandard interactions of the newly discovered 125 GeV Higgs-like resonance that are especially interesting probes of new physics: flavor violating Higgs couplings to leptons and quarks. These interaction can arise in many frameworks of new physics at the electroweak scale such as two Higgs doublet models, extra dimensions, or models of compositeness. We rederive constraints on flavor violating Higgs couplings using data on rare decays, electric and magnetic dipole moments, and meson oscillations. We confirm that flavor violating Higgs boson decays to leptons can be sizeable with, e.g., h → τμ and h → τe branching ratios of (10%) perfectly allowed by low energy constraints. We estimate the current LHC limits on h → τμ and h → τe decays by recasting existing searches for the SM Higgs in the ττ channel and find that these bounds are already stronger than those from rare tau decays. We also show that these limits can be improved significantly with dedicated searches and we outline a possible search strategy. Flavor violating Higgs decays therefore present an opportunity for discovery of new physics which in some cases may be easier to access experimentally than flavor conserving deviations from the Standard Model Higgs framework.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2014

A Model-Based Approach to Scintillator/Photomultiplier System Characterization

Scintillator/photomultiplier systems are prevalent in many instruments to measure a variety on nuclear events. When gamma energy from a nuclear event irradiates the scintillator, event radiation interacts with the scintillator material generating photons which are detected by the photomultiplier tube (PMT). The PMT photoelectrons or photocathode current is amplified in the PMT by a number of dynode stages producing a total charge out for a given radiation flux into scintillator. The resulting current is converted to a voltage when passed through a typical load impedance. This voltage is attenuated and provided as input to a digitizer through a variety of signal conditioners (filters, amplifiers, receivers) before analog-to-digital conversion. In this report, we concentrate on the development of analytic models for the scintillator/photomultiplier system and develop both a (1) statistical simulator; and (2) a model-based signal processor to extract the desired signal information while rejecting the noise and uncertainty. We develop the model analytically, transform it to the model-based framework (state-space), and then use actual experimental data to fit the model and apply it to our processing problem.

42 ENGINEERING↗
DOE OSTI2025

Performance Analysis and Simulaion of the Hydraulic Scram System in TREAT Reactor

The Transient Reactor Test Facility (TREAT) at Idaho National Laboratory (INL) serves a vital role in nuclear fuel safety research, enabling transient experiments that simulate reactivity excursions and accident scenarios. Central to these operations is the transient control rod drive system (TCRDS), which drives rapid motion of the transient control rods such that TREAT can simulate rapid power changes typical of reactor accidents. The reliability and performance of this system are critical for protecting both fuel specimens and reactor infrastructure. This study presents the initial phase of a two-year investigation into the dynamics and reliability of the TREAT hydraulic TCRDS. Conducted in collaboration with INL, the research employs a combined computational and experimental approach to analyze the system's response time, pressure transients, and potential failure modes. Emphasis is placed on understanding how fluid characteristics influence the TCRDS’s ability to achieve both rapid power changes and mechanical stability. The TRDS and the skid that powers it will be analyzed throughout this investigation. Computational modeling using computational fluid dynamics (CFD) will simulate the hydraulic response under varying conditions. In parallel, experimental testing planned at INL will validate these models and capture key performance metrics. This paper outlines the system design, analytical framework, and modeling strategies that form the foundation for later testing. Ultimately, this work aims to support improvements to the TCRDS’s design and reliability, contributing to the broader goal of enhancing nuclear fuel safety and sustaining TREAT’s mission as a premier nuclear fuel test facility.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2022

Modeling Monthly Nitrate Concentration in a Karst Spring with and without Discrete Conduit Flow

Understanding the coupled continuum pipe-flow framework for modeling contaminant transport in karst systems is critical for protecting water resources therein. This study simulated point and non-point source nitrate concentration in a karst spring and investigated the results generated from the flow and transport model with and without discrete conduit flow. CFPv2 and CMT3D models were integrated to address the changes in nitrate concentration at a monthly scale in a karst spring, and the results were compared with that from an equivalent porous media (EPM) model with high-hydraulic conductivity (K) zones set in the main karstified area to represent conduits. The results show that the CFPv2+CMT3D model is able to describe well the recession of nitrate concentration in spring discharge, and the relatively larger deviation (slower nitrate recession) from the observed trend for the EPM model is probably a result of the limitation of utilizing high hydraulic conductivity cells to represent conduit. Moreover, simulated hydraulic heads in poorly karstified areas from the two models both show slight differences from the observations (the head RMSE values of calibration/validation for CFPv2 and MODFLOW models are 0.16 m/0.25 m and 0.26 m/0.17 m, respectively), indicating the inclusion of conduits may not affect the simulation considerably, and the lower the proportion of karstic area, the slight effects brought from the inclusion of conduits in the model. For highly karstified areas, the CFPv2+CMT3D model could provide more accurate results (head RMSE of calibration/validation for CFPv2 and MODFLOW are 0.22 m/0.06 and 0.52 m/0.47 m, respectively), showing the coupled continuum pipe-flow framework may be more appropriate for applying to highly and maturely karstified areas where the variations in the behavior of flow and contaminant transport are more affected by turbulent flow regime.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A phase-field fracture formulation for generalized standard materials: The interplay between thermomechanics and damage

Accurately modeling fracture of ductile materials poses open challenges in the field of computational mechanics due to the multiphysics nature of their failure processes. Integrating the interplay between thermodynamics and damage into ductile fracture models is vital for predicting critical failure modes. Here, in this paper, we develop a versatile phase-field (PF) framework for modeling ductile fracture, taking into account finite-strain elasto-plasticity. The framework stems from a variational formulation of constitutive relations for generalized standard materials (GSMs), whose response is described by a Helmholtz free energy and a dissipation pseudo-potential. Its variational structure is based on a minimum principle for a functional that expresses the sum of power densities for reversible and irreversible processes. By minimizing this functional with a constraint on a von Mises yield function, we derive the evolution equation for the equivalent plastic strain and an associative flow rule. This constrained optimization problem is analytically solved for a wide class of thermo-viscoplasticity models. The key innovations of the current work include (i) a cubic plastic degradation function that accounts for a non-vanishing damage-dependent yield stress, (ii) closed-form expressions of the Helmholtz free energy and dissipation pseudo-potential for three thermo-viscoplasticity models, (iii) an extended Johnson–Cook plasticity model with a nonlinear hardening law, and (iv) a plastic work heat source that depends on the plastic degradation function and a variable Taylor–Quinney (TQ) coefficient. The capabilities of the proposed framework are tested with the aid of four ductile fracture problems, including the Sandia Fracture Challenge. In each of these problems, we examine the evolution of relevant field variables such as the PF order parameter, the equivalent plastic strain, the temperature, and the internal power dissipation density, in addition to the overall structural response quantified by the force–displacement curve. These numerical studies demonstrate that the proposed framework effectively represents ductile fracture, yielding computational results that exhibit good agreement with experimental data.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Simulation of Chilled-water Injection at EGS Collab Testbed 2 using the GEOS simulation framework

This work seeks to model the chilled-water injection that occurred at EGS Collab Testbed 2 as part of experiment 3, using the open-source GEOS simulation framework. Modeling of this process, previously conducted using STOMP-GT, confirmed that thermal breakthrough was expected to occur during the chill water injection and may have been masked by strong Joule-Thompsons effects induced by large pressure drops between the fractures and the production wells. Building a GEOS numerical model of this process will allow to include additional physics. In fact, GEOS can model fully coupled thermo-poromechanical processes in fractured porous media and can be employed to model the process of artificially enhancing rock permeability by hydraulically fracturing the rocks. For example, the GEOS model could help understanding the dynamic nature of the EGS collab experiment 3 flow system. Since the limited time and resources do not allow for an extensive study, the focus is on building a baseline model of this process. As such, the main outcomes of this modelling efforts are listed below.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Ambient and Initial Temperature Effects on Energy Consumption Rate Modeled in FASTSim

Ambient and initial temperatures significantly impact the energy consumption rate (ECR) of battery electric vehicles (BEVs) due to auxiliary loads and the temperature dependence of battery efficiency. This study introduces a streamlined, physics-based thermal modeling approach within the FASTSim tool that bridges the gap between oversimplified constant-load models and computationally expensive high-fidelity simulations. By employing a lumped thermal mass framework, the model captures fundamental energy balances and critical non-linear energy penalties while maintaining the computational efficiency required for expansive sensitivity studies. The simulations evaluated a compact BEV hatchback with a resistive heater over city (UDDS) and highway (HWFET) test cycles. Compared to a 22 degrees Celsius initial and ambient temperature baseline, a -7 degrees Celsius initial/ambient temperature resulted in a 221% increase in the ECR for the city cycle and a 100% increase for the highway cycle. Conversely, a 45 degrees Celsius initial / 40 degrees Celsius ambient temperature resulted in a 40% increase for UDDS and an 18% increase for HWFET. These results demonstrate that while cold conditions impose the most severe energy penalties due to resistive heating, the impact is consistently more pronounced in city driving where auxiliary loads represent a larger proportion of total energy. This lightweight yet robust framework enables researchers to rapidly quantify BEV thermal sensitivity across diverse climates without the need for high-overhead simulation environments.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2016

Thermoplasticity and strain localization in transversely isotropic materials based on anisotropic critical state plasticity

Geomaterials including soils and rocks are inherently anisotropic and sensitive to temperature changes caused by various internal and external processes. They are also susceptible to strain localization in the form of shear bands when subjected to critical loads. In this paper, we present a thermo-plastic framework for modeling coupled thermo-mechanical response and for predicting the inception of a shear band in a transversely isotropic material using the general framework of critical state plasticity and the specific framework of an anisotropic modified Cam-Clay model. The formulation incorporates anisotropy in both elastic and plastic responses under the assumption of infinitesimal deformation. The model is first calibrated using experimental data from triaxial tests to demonstrate its capability in capturing anisotropy in the mechanical response. Subsequently, stress-point simulations of strain localization are carried out under two different conditions, namely, isothermal localization and adiabatic localization. The adiabatic formulation investigates the effect of temperature on localization via thermo-mechanical coupling. Numerical simulations are presented to demonstrate the important role of anisotropy, hardening, and thermal softening on strain localization inception and orientation.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Revealing EDL-driven reduction mechanisms in binary, ternary, and quaternary fluorinated electrolytes via an integrated MD–DFT–ML framework

Accurately predicting solid electrolyte interphase (SEI) formation requires explicitly resolving the electric double layer (EDL) structure, which deviates significantly from that of the bulk electrolyte. Although an established molecular dynamics (MD) and Density Functional Theory (DFT) framework can model SEI formation by evaluating reduction reactions of local clusters in the EDL, it suffers from a combinatorial computational bottleneck. To overcome this limitation, we introduce a machine-learning-accelerated simulation workflow (MD–DFT–ML), integrating a gradient-boosted regression model trained on EDL composition data to efficiently predict reduction potentials. We apply this framework to seven fluorinated electrolytes comprising fluorinated anions, a fluorinated ester solvent, two types of diluent (ion-solvating ester vs. non-solvating ether), and an FEC additive. The analysis shows that the EDL selectively accumulates cation-binding species; consequently, the non–cation-binding ether diluent rarely enters the EDL and makes minimal contributions to SEI formation. DFT calculations on statistically representative EDL clusters provide reduction potentials and fluorine-release pathways, while the ML model, which substantially reduces the DFT workload, predicts cluster reduction energies with a mean absolute error of 0.1 eV. The combined MD–DFT–ML approach also quantifies contributions from different sources to LiF formation in the SEI. This methodology establishes a generalizable route for multiscale modeling electrolyte and interphase design for next-generation electrochemical energy-storage systems.

DFT-MD-ML workflow↗
DOE OSTI2019

Behavioral modeling of on-demand mobility services: general framework and application to sustainable travel incentives

This paper presents a systematic way of understanding and modeling traveler behavior in response to on-demand mobility services. We explicitly consider the sequential and yet inter-connected decision-making stages specific to on-demand service usage. The framework includes a hybrid choice model for service subscription, and three logit mixture models with inter-consumer heterogeneity for the service access, menu product choice and opt-out choice. Different models are connected by feeding logsums. The proposed modeling framework is essential for accounting the impacts of real-time on-demand system's dynamics on traveler behaviors and capturing consumer heterogeneity, thus being greatly relevant for integrations in multi-modal dynamic simulators. The methodology is applied to a case study of an innovative personalized on-demand real-time system which incentivizes travelers to select more sustainable travel options. The data for model estimation is collected through a smartphone-based context-aware stated preference survey. Through model estimation, lower values of time are observed when the respondents opt to use the reward system. The perception of incentives and schedule delay by different population segments are quantified. These results are fundamental in setting the ground for different behavioral scenarios of such a new on-demand system. Here, the proposed methodology is flexible to be applied to model other on-demand mobility services such as ride-hailing services and the emerging mobility as a service.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Model Predictive Control of Discrete-Continuous Energy Systems via Generalized Disjunctive Programming

Generalized Disjunctive Programming (GDP) provides an alternative framework to model optimization problems with both discrete and continuous variables. The key idea behind GDP involves the use of logical disjunctions to represent discrete decisions in the continuous space, and logical propositions to denote algebraic constraints in the discrete space. Compared to traditional mixed-integer programming (MIP), the inherent logic structure in GDP yields tighter relaxations that are exploited by global branch and bound algorithms to improve solution quality. In this paper, we present a general GDP model for optimal control of hybrid systems that exhibit both discrete and continuous dynamics. Specifically, we use GDP to formulate a model predictive control (MPC) model for piecewise-affine systems with implicit switching logic. As an example, the GDP-based MPC approach is used as a supervisory control to improve energy efficiency in residential buildings with binary on/off, relay-based thermostats. A simulation study is used to demonstrate the validity of the proposed approach, and the improved solution quality compared to existing MIPbased control approaches.

Bhattacharya, Arnab↗
DOE OSTI2023

An approach to grid-to-rod fretting wear modeling using dynamic mortar contact

This article presents the development of a grid-to-rod fretting (GTRF) modeling methodology that utilizes dynamic mortar contact. Here, we leverage a recently developed computational framework for modeling thermomechanical contact in dynamic simulations from the nuclear fuel performance code Bison. Usage of mortar contact ensures a smooth distribution of normal and frictional contact forces, displacements, and velocities on the contact interface, thus facilitating an accurate computation of wear. The integration within an advanced nuclear fuel performance code enables analysis of complex interactions between the fuel, cladding, and spacer grid. Such interactions include swelling, creep, fuel fragmentation, burnup, plenum pressure, and fission gas release. Our methodology is demonstrated via the modeling of two spacer grid geometries and the influence of fuel-cladding mechanical contact on the generation of fretting wear.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Reservoirs Modify River Thermal Regime Sensitivity to Climate Change: A Case Study in the Southeastern United States

Seasonal thermal stratification in reservoirs changes the thermal regime of regulated river systems as well as stream temperature responses to climate change. Cold releases from reservoir hypolimnion can depress downstream river temperature during warm seasons. Recent large-scale climate change studies on stream temperature have largely ignored reservoir thermal stratification. In this study, we used established models to develop a framework which considers water demand and reservoir regulation with thermal stratification, and applied this model framework to the southeastern US. About half of all 271 reservoirs in our study area retain strong thermal stratification by the 2080s (2070-2099) under RCP8.5 even as median residence times decrease to 60 days from 69 days in the historic period (1979-2010). Reservoir impacts on downstream temperatures become slightly weaker in the future because of higher air temperature and stronger solar radiation. We defined a “cooling potential” to quantify the thermal energy that a water body can absorb before exceeding a water temperature threshold. In the future, higher river temperatures will reduce the cooling potential for all river segments, but more so for river segments minimally impacted by thermal stratification. Reservoir impacts on cooling potential remain strong for river segments downstream of reservoirs with strong thermal stratification. We conducted a sensitivity analysis to evaluate the robustness of our findings to errors in the hydrological simulations. While river segments subject to reservoir regulation are more sensitive to errors in hydrology than those without regulation impacts, our overall findings do not materially change due to these errors.

54 ENVIRONMENTAL SCIENCES↗