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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

An Experimental Study of Design and Performance for the Water-Based Reactor Cavity Cooling System

The document summarizes the test activity conducted at the Texas A&M University water-cooled Reactor Cavity Cooling System experimental facility. Flow and temperature characterization of the risers' panel is conducted using advanced measurement techniques. Steady-state and transient scenarios are studied, analyzed, and discussed in the report. The experimental work is complemented with modeling and simulations using system level codes.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

System Model Calculations for the Active Trenches of the Low-Level Burial Grounds, Hanford Site, Washington

Two lined trenches (Trench 31 and 34) located within the 200 West Low-Level Burial Ground (LLBG) area of the U.S. Department of Energy’s (DOE) Hanford Site are designated for permanent disposal of low-level radioactive waste (LLW) and mixed low-level radioactive waste (MLLW). In accordance with DOE O 435.1, Radioactive Waste Management, radioactive waste shall be managed and disposed in a manner that is protective of worker, public health and safety, and the environment. DOE O 435.1 requires a site-specific radiological performance assessment (PA) that includes calculations of potential releases and subsequent doses to members of the public for a period of 1,000 years after closure of a low-level waste (LLW) disposal facility. The purpose of this environmental calculation file (ECF) is to document the various dose calculations performed to support DOE’s LLBG PA. The dose assessments are performed to evaluate the potential exposure of an all-pathway representative person to radionuclide contaminants of potential concern (COPCs) that may be released from the LLBG Trenches 31 and 34 to the point of calculation (POC) located at the outer edge of a 100 m buffer zone surrounding the trench boundary. This ECF uses inputs from other ECFs and their associated model package reports (MPRs) supporting the LLBG PA. The five major objectives of this ECF are to present the results of the following: (1) Deterministic dose assessments for both groundwater and atmospheric exposure scenarios; (2) Radon-222 (Rn-222) flux emanating at the surface from the disposed wastes; (3) Uncertainty analyses of the groundwater pathway annual dose due to uncertainty in input parameters; (4) Sensitivity analyses of the input parameters that could potentially impact the fate and transport of the contaminants and doses; (5) Doses arising from inadvertent intruder exposure scenarios.

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

Oklo Sponsored Testing using PELICAN: System, Subchannel, and High-Fidelity Software Validation (Final CRADA Report)

Argonne National Laboratory (the Contractor), located in Lemont IL, and Oklo, Inc., (the Participant), headquartered in Santa Clara, CA, propose to enter into a Cooperative Research and Development Agreement (CRADA) to perform a gap analysis of thermal hydraulic data, perform prototypical fuel assembly pressure drop and cavitation model validation, generate the experimental data as well as the corresponding uncertainties for this matrix and, finally, develop the validation models with the Argonne system level code SAS4A/SASSYS-1, the Argonne subchannel analysis code DASSH, the Argonne high fidelity code Nek5000, and/or the Idaho National Laboratory code Pronghorn Subchannel. The work outlined below will significantly improve the experimental and validation database currently available for liquid metal fast reactors, thus making it a viable part of a comprehensive reactor design and licensing suite to be used by the participant.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Two-Phase Turbulence Statistics from High Fidelity Dispersed Droplet Flow Simulations in a Pressurized Water Reactor (PWR) Sub-Channel with Mixing Vanes

In the dispersed flow film boiling regime (DFFB), which exists under post-LOCA (loss-of-coolant accident) conditions in pressurized water reactors (PWRs), there is a complex interplay between droplet dynamics and turbulence in the surrounding steam. Experiments have accredited particular significance to droplet collision with the spacer-grids and mixing vane structures and their consequent positive feedback to the heat transfer recorded in the immediate downstream vicinity. Enabled by high-performance computing (HPC) systems and a massively parallel finite element-based flow solver—PHASTA (Parallel Hierarchic Adaptive Stabilized Transient Analysis)—this work presents high fidelity interface capturing, two-phase, adiabatic simulations in a PWR sub-channel with spacer grids and mixing vanes. Selected flow conditions for the simulations are informed by the experimental data found in the literature, including the steam Reynolds number and collision Weber number (Wec={40,80}), and are characteristic of the DFFB regime. Data were collected from the simulations at an unprecedented resolution, which provides detailed insights into the continuous phase turbulence statistics, highlighting the effects of the presence of droplets and the comparative effect of different Weber numbers on turbulence in the surrounding steam. Further, axial evolution of droplet dynamics was analyzed through cross-sectionally averaged quantities, including droplet volume, surface area and Sauter mean diameter (SMD). The downstream SMD values agree well with the existing empirical correlations for the selected range of Wec. The high-resolution data repository from the simulations herein is expected to be of significance to guide model development for system-level thermal hydraulic codes.

Saini, Nadish↗
DOE OSTI2021

Suitable low Global Warming Potential (GWP) refrigerants for two-speed Heat Pumps for residential applications based on speed Heat Pumps for residential applications based on simulated performance simulated performance

Cold climate Heat Pumps (CCHPs) using single-speed compressors in tandem have been successfully built and tested to reach its performance metrics with outdoor temperatures as low as -25°C. CCHPs must be designed to alleviate the problems of too high discharge temperatures, low suction pressure and high-pressure ratio at low ambient conditions and insufficient heating capacity relative to the heating capacity at 8.3°C. In this paper we recognize those concerns. Low-GWP refrigerants are screened based on the shape of their T-S saturation boundary. Simulations of the performance of low-GWP refrigerants (R32, R454B, R466A, and R452B) relative to R410A is done using the DOE/ORNL Heat Pump Design Model at the component level. Systemic inefficiency, the root cause of lost work, is addressed by component-level exergy analysis to refine design options. CCHPs using low-GWP refrigerants addresses issues of reducing energy consumption, lowering carbon footprint, and enabling environmentally sustainability. The CCHP was designed to meet a HSPF > 11.0 and SEER > 15.0 having adequate heat pump heating capacity down to -25°C. It is estimated that at least 2.6 million U.S homes using electric furnaces and heat pumps in cold or very old climates with an annual energy consumption of 0.17EJ would benefit by CCHPs.

Ally, Moonis↗
DOE OSTI2021

U.S. Solar Photovoltaic BESS System Cost Benchmark Q1 2020 Report

NREL has been modeling U.S. solar photovoltaic (PV) system costs since 2009. This year, our report benchmarks costs of U.S. PV for residential, commercial, and utility-scale systems, with and without storage, built in the first quarter of 2020 (Q1 2020). The methodology includes bottom-up accounting for all system and project-development costs incurred when installing residential, commercial, and utility-scale systems and it models the capital costs and levelized cost of energy for such systems.

14 SOLAR ENERGY↗
DOE OSTI2026

Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling as Catalysts for Next-Generation Breakthroughs

The Presidential Symposium (PRES) at the 2025 Fall Meeting, hosted by the President’s Office and Energy and Fuels Division, American Chemical Society (ACS) in Washington, DC, brought together a diverse group of chemists, engineers, and materials scientists working in battery materials & systems, automation and artificial intelligence from academia, industry, and national laboratories. The accelerating demand for high-performance, scalable, and sustainable energy storage has catalyzed a paradigm shift in how materials are dis-covered, devices are engineered, and systems are optimized. This Presidential Symposium, entitled “Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling Driving Next-Gen Breakthroughs”, brings together global leaders to unveil transformative strategies anchored in the AAA framework: Artificial Intelligence, Automation, and Advanced Modeling. Artificial Intelligence is redefining the frontiers of energy storage by enabling predictive design, real-time optimization, and intelligent control across diverse chemistries and architectures. Automation is streamlining the synthesis, characterization, and testing of battery materials, dramatically accelerating innovation cycles and unlocking scalable solutions for grid and mobility applications. Advanced Modeling, spanning atomic to system-level scales, provides unprecedented insight into electrochemical dynamics, degradation pathways, and thermal behavior, particularly when coupled with physics-informed machine learning and digital twin technologies. Digital twins, in turn, leverage the AAA framework by integrating real-time data, physics-based models, and AI predictions into dynamic virtual replicas, enabling proactive diagnostics, optimization, and system resilience. Together, these synergistic pillars are not only re-shaping the scientific landscape but also forging a new era of reproducible, data-driven, and resilient energy storage innovation. In conclusion, this symposium marks a pivotal moment in the convergence of computational intelligence and experimental rigor, charting the course for next-generation breakthroughs in lithium-ion, solid-state, and flow battery technologies.

Artificial Intelligence (AI)↗
DOE OSTI2022

Network Uncertainty Quantification for Analysis of Multi-Component Systems

To impact physical mechanical system design decisions and realize the full promise of high-fidelity computational tools, simulation results must be integrated at the earliest stages of the design process. This is particularly challenging when dealing with uncertainty and optimizing for system-level performance metrics, as full-system models (often notoriously expensive and time-consuming to develop) are generally required to propagate uncertainties to system-level quantities of interest. Methods for propagating parameter and boundary condition uncertainty in networks of interconnected components hold promise for enabling design under uncertainty in real-world applications. These methods avoid the need for time consuming mesh generation of full-system geometries when changes are made to components or subassemblies. Additionally, they explicitly tie full-system model predictions to component/subassembly validation data which is valuable for qualification. These methods work by leveraging the fact that many engineered systems are inherently modular, being comprised of a hierarchy of components and subassemblies that are individually modified or replaced to define new system designs. By doing so, these methods enable rapid model development and the incorporation of uncertainty quantification earlier in the design process. The resulting formulation of the uncertainty propagation problem is iterative. We express the system model as a network of interconnected component models, which exchange solution information at component boundaries. We present a pair of approaches for propagating uncertainty in this type of decomposed system and provide implementations in the form of an open-source software library. We demonstrate these tools on a variety of applications and demonstrate the impact of problem-specific details on the performance and accuracy of the resulting UQ analysis. This work represents the most comprehensive investigation of these network uncertainty propagation methods to date.

42 ENGINEERING↗
DOE OSTI2021

Progress on continuum modeling of lithium–sulfur batteries

While lithium–sulfur batteries are a promising next-generation chemistry devices due to their high theoretical energy density, commercialization has been slow due to low coulombic efficiency and poor cycle life. This review explores the ways in which continuum modeling contributes to the understanding of lithium–sulfur (LiS) battery mechanisms and cell-level performance through the lens of micro- and macroscale phenomena. We examine different approaches to modeling important physical phenomena such as reaction mechanisms, cathode microstructure, shuttling, nucleation and precipitation, and transport limitations. This paper also emphasizes the significance and challenge of connecting typical modeling parameters and assumptions to systems-level metrics of a standard state-of-art high performing lithium–sulfur cell. Particularly important, the considerations for high energy density cells and the areas where continuum models can facilitate better collaboration are discussed. Here, we also summarize a few selected works to highlight experimentally-driven modeling, use of electroanalytical techniques, and parameter identification approaches to enable model-based design and advanced battery management systems.

25 ENERGY STORAGE↗
DOE OSTI2021

A fast reduced model for a shell-and-tube based latent heat thermal energy storage heat exchanger and its application for cost optimal design by nonlinear programming

Numerical simulation of latent heat thermal energy storage (LHTES) systems plays a fundamental role in studying the physical process and guiding the engineering design. Discretization of the PDEs describing the nonlinear solidification/melting process of phase change materials (PCMs) leads to a large-scale complex dynamics system, where the system behavior depends on a set of parameters. In a design setting, repeated model evaluations are required over the set of parameters results in significant computational burden. In this paper, an explicit analytic solution was built for the propagation of the solidification front in a cylindrical coordinate. The analytic solution approach is further employed to develop a low computational reduced model (RM) as a module for a shell-and-tube based LHTES heat exchanger. The levelized Cost of Energy (LCOE) is used as a design metric and the RM model is used to apply system-level constraints in the nonlinear programming formulation that facilitates efficient global optimal design of the PCM properties, flow conditions and tube geometries. The use of LCOE as the design metric prevents over design of the heat transfer rate and also establishes a fair ground for evaluation of different thermal storage technologies and their integrated applications with other systems. Optimal results showed that a higher effectiveness results in a higher LCOE; the velocity of the HTF and the length of the channel are highly correlated with each other; both larger PCM latent energy and conductivity result in lower LCOE.

25 ENERGY STORAGE↗
DOE OSTI2020

Simulations of the High Temperature Test Facility using SAM

Under the support from the U.S. Department of Energy Office of Nuclear Energy’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, an effort is being pursued to support the modeling and simulation needs of high-temperature gas-cooled reactor (HTGR) technology development. There is a particular need for advanced modeling and simulation methods and tools to predict thermal-fluid behavior in the nuclear reactor primary system during safety-related transients. This report focuses on one such activity related to HTGR: developing a model of the High Temperature Test Facility (HTTF) at Oregon State University using the system-level code SAM, and using the model to understand thermal response behavior in the facility. Note this activity is coordinated with the DOE-NE’s Advanced Reactor Technology Gas-Cooled Reactor Program

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Tidal frequencies and quasiperiodic subsurface water level variations dominate redox dynamics in a salt marsh system

Salt marshes are hotspots of nutrient processing en route to sensitive coastal environments. Whilst our understanding of these systems has improved over the years, we still have limited knowledge of the spatiotemporal variability of critical biogeochemical drivers within salt marshes. Sea-level rise will continue to force change on salt marsh functioning, highlighting the urgency of filling this knowledge gap. Our study was conducted in a central California estuary experiencing extensive marsh drowning and relative sea-level rise, making it a model system for such an investigation. Here we instrumented three marsh positions subjected to different degrees of tidal inundation (6.7%, 8.9%, and 11.2% of the time for the upper, middle, and lower marsh positions, respectively), providing locations with varied biogeochemical characteristics and hydrological interactions at the site. We continuously monitored redox potential (Eh) at depths of 0.1, 0.3, and 0.5 m, subsurface water levels (WL), and temperature at 0.7 m depth at each marsh position. To understand how drivers of subsurface biogeochemical processes fluctuate across tidal cycles, we used wavelet analyses to explain the interactions between Eh and WL. We found that tidal forcing significantly affects key drivers of biogeochemical processes by imparting controls on Eh variability, likely driving subsurface hydro-biogeochemistry of the salt marsh. Wavelet coherence showed that the Eh-WL relationship is nonlinear, and their lead–lag relationship is variable. We found that precipitation events perturb Eh at depth over timescales of hours, even though WL shows relatively minimal change during events. Furthermore, this work highlights the importance of high frequency in situ measurements, such as Eh, to help explain factors that govern subsurface biogeochemistry and hydrological processes in salt marshes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Multi-Model and Multi-Scale Global Sensitivity Analysis for Identifying Controlling Processes of Complex Systems

An environmental model consists of multiple process level sub-models, and each sub-model represents a process that is key to the operation of the simulated system. Global sensitivity analysis methods have been widely used to identify important processes for system model development and improvement. The existing methods of global sensitivity analysis only consider parametric uncertainty, and are not capable of handling model uncertainty caused by multiple process models that arise from competing hypotheses about one or more processes. To address this problem, this project develops a new method to probe model output sensitivity to competing process models by integrating model averaging methods with variance-based global sensitivity analysis to address uncertainty in process models and parameters. The new method yields three process sensitivity indices. The first one is called first-order process sensitivity index, and it is derived as a single summary measure of relative process importance. Evaluating the index is computationally expensive, because it relies in a Monte Carlo scheme that requires thousands and even millions of model executions. To reduce computational cost, this project develops a computationally efficient, quasi Monte Carlo method, and this method is presented in Chapter 2 of this report with and a numerical example for demonstration. The numerical example shows that the results of the quasi Monte Carlo method are substantially close to those of the full Monte Carlo method, but the computational cost of the quasi Monte Carlo method is only 0.7% of that of the full Monte Carlo method. The second index is called total-effect process sensitivity index, and it measures interactions between different processes. Therefore, this sensitivity index includes the first-order process sensitivity index, and can be used to identify influential processes. On the other hand, the total-effect process sensitivity index can also be used to screen non-influential processes. This is demonstrated by two numerical examples using the Sobol-G* functions and groundwater flow models that consider recharge process, geological process, and snowmelt process. The numerical examples shows that the total-effect process sensitivity index is more informative than the first-order process sensitivity. The derivation of the process sensitivity index and the numerical examples are discussed in Chapter 3. Chapter 4 presents two computationally efficient methods for screening non-influential processes to exclude them from further investigation. The two methods are the multi-model difference-based sensitivity (MMDS) analysis method, which can be implemented using the Latin Hypercube Sampling. The second one is the implementation of MMDS method using a binning method. The numerical example for the Sobol-G* function indicates the two methods are capable of identifying non-influential models, and the numerical examples for the groundwater flow and reactive transport show that the two methods are effective for groundwater problems. However, it should be noted that the two methods are numerical approximations, and they can only be used for screening non-influential processes, not for ranking importance of system processes. All the sensitivity analysis methods are implemented by developing python codes, and the codes are in a software called SAMMPY: a python package for process sensitivity analysis under multiple models. The SAMMPY design and structure are discussed in Chapter 5, and the package is released to the public for free download.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

M4SF-20LL010301082-Sorption Database and Model for Generic Disposal System Assessment

This progress report (Level 4 Milestone Number M4SF-20LL010301082) summarizes research conducted at Lawrence Livermore National Laboratory (LLNL) within the Argillite International Collaborations Activity Number Activity SF-20LL010301081. The activity is focused on our long-term commitment to engaging our partners in international nuclear waste repository research. The focus of this milestone is surface complexation model international collaborations. Specifically, we are developing a database framework for Spent Fuel and Waste and Science Technology (SFWST) that is aligned with the Helmholtz Zentrum Dresden Rossendorf (HZDR) sorption database development group in support of the database needs of the SFWST program.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

SBML Level 3: an extensible format for the exchange and reuse of biological models

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Machine-Learning Assisted Identification of Battery Life Models

Predictive battery life models are commonly utilized to extrapolate degradation trends observed during accelerated aging tests for simulation of degradation in real-world applications. Thus, fitting accelerated aging data as accurately as possible and with low uncertainty is crucial for making believable projections of battery lifetime, but it is challenging to identify algebraic expressions that accurately fit multivariate degradation trends. A review of models published in literature reveal some common expressions for fitting calendar aging data, which is only dependent on temperature and state-of-charge, but no consistency across many models for fitting cycle aging data, indicating the need for a statistically rigorous data driven approach for developing empirical models. This talk will describe a machine-learning assisted method for identification of predictive battery life models utilizing bilevel optimization and symbolic regression. Bilevel optimization with cross-validation is used to statistically determine cell- and stress-dependent model parameters, while symbolic regression identifies both linear and multiplicative candidate expressions to predict stress-dependent degradation rates by selecting low-order subsets of features from a generated feature library. Because model expressions are identified empirically, it is crucial to ensure resulting models behave according to physical expectations, so the stability of models for interpolation or extrapolation is interrogated qualitatively through simulation and quantitatively through cross-validation and uncertainty quantification via bootstrap resampling. This model identification approach substantially improves upon models identified purely using expert judgement in terms of both accuracy and uncertainty. Model simulation and validation is then conducted by deriving a state-equation form of the predictive model, enabling simulation of battery aging under dynamic stresses. This enables validation of the predictive battery model on lab-based tests with varying conditions or on drive-cycle or application-cycle testing protocols. Parameter uncertainty can be carried forward into model simulation, giving lifetime estimates and confidence windows for cell- or system-level lifetime. The financial impact of battery model uncertainty can be estimated by incorporating uncertainty into a technoeconomic model.

battery↗
DOE OSTI2022

Techno-Economic Analysis and Optimization of Integrated NGCC-MEA System at High CO2 Capture Levels

This poster contains preliminary results for an ongoing analysis of modeling and optimization of an NGCC plant with a solvent-based CO2 capture system with high levels of CO2 capture – up to and beyond net-zero emissions. The work was performed to understand the incremental cost of high capture in order to compare with direct-air capture and other net-negative technologies, as well as to understand optimal operation and design of the CCS unit to achieve high capture with minimal increase in cost.

Deshpande, Anuja↗
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↗