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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Mechanical, Electrochemical & Thermal Models - Training (CRADA CRD-19-00798 Final Report)

Under the proposed effort in partnership with Hyundai Motor Company (HMC), the National Renewable Energy Laboratory (NLR) will host Dr. Jaeyoung Lim from HMC for a period of one year to jointly develop mathematical models for battery cells and modules subject to mechanical crush. NLR will assist with the development of mathematical models that Dr. Lim will incorporate into his research effort on new concepts of mobility with electric vehicles.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

MODELING AND METHOD FOR OPTICAL PERFORMANCE OF A RECEIVER COATING AND ITS DEGRADATION DUE TO OPERATION

The optical properties of the coating used on a concentrated solar power (CSP)’s receiver directly impact the performance of the power plant. Receiver coatings are designed to absorb as much heat as possible and transfer it to the circulating fluid. Over time, the absorptivity of the receiver coating degrades due to environmental and operational conditions. This project aims to (1) create a test apparatus that allows coatings to undergo accelerated lifetime testing under conditions that mimic what the coating will experience on-tower; and (2) develop a mathematical model that based on the test data predicts absorptivity over time. To create the mathematical model, samples of BrightSource Energy’s Gen1 and Gen2 coatings were tested first looking at individual failure modes (such as high temperature cycling) and then for combined failure modes in the newly developed apparatus. In addition to taking optical measurements, the samples were destroyed to allow for metalogical evaluation to understand what was happening on a microscopic level. Pre-existing models from the literature that describe accelerated testing as well as individual phenomena observed in the coating were then combined to create the model. Additionally, the Gen2 and Gen3 coatings are being testing at the Plataforma Solar de Almería (PSA) facility in Almeria, Spain to confirm that the apparatus test results are similar to those received on-sun. (PSA’s facility closely mimics on-tower conditions). The goal is to enable industry players to test coatings using the new apparatus, gather absorptivity measurements (without needing to destroy samples), and input the information into the model to determine how the coating will perform over time, thus allowing the optical performance of coatings to be compared even before they are used on-tower. The model will be publicly available by the end of the project (currently projected as December 2024).

14 SOLAR ENERGY↗
DOE OSTI2024

Designing Particle Morphologies for Materials with Solid Transport Limitations: A Case Study of Lithium and Manganese Rich Cathode Oxides

A lithium and manganese rich nickel-manganese-cobalt oxide (LMR-NMC) cathode is a promising candidate for next-generation batteries due to its high specific capacity, low cost, and low cobalt content. However, the material suffers from poor rate capability due to the diffusion limitations of lithium in the cathode particles. Understanding the material performance requires careful control of the morphology of the cathode particles, taking into account the primary and agglomerated diffusion pathways and the presence of pores, some of which could be closed from electrolyte infiltration. Here, in this study, we use a microstructure-based mathematical model combined with experimental data to understand the role of the complex cathode particle morphology in the rate performance of the material. Scanning electron microscopy images of cathodes made under different synthesis conditions, which results in different agglomerate morphologies, serve as the input into the mathematical model. The model is then compared to rate data to understand the controlling parameters. The presence of intra-agglomerate closed pores results in a large agglomerate diffusion length in comparison to the ideal condition, where the primary particles are agglomerated in an open and dispersed manner such that the entire interfacial area is available for electrochemical reaction. Smaller primary and agglomerate diffusion lengths result in better electrochemical performance. This points us toward designing the morphology of the cathode particles to compensate for the diffusion limitation of LMR-NMC while maximizing the density.

Tewari, Deepti↗
DOE OSTI2024

High-Fidelity Modeling of a Type-5 Wind Turbine Gearbox (Intern Poster) [Poster]

Type-5 wind turbines are unique in their use of a permanent magnet synchronous generator, as well as their use of a hydraulic torque converter. This architecture presents an opportunity to provide steady and grid-ready energy without the need for a power converter. With infrastructure continuity and reliability being an important topic amongst renewable energies, researchers have been prompted to further investigate the benefits of type-5 turbines’ unique electromechanical configuration on stable electricity generation. Researchers involved in the WindSG project, SG standing for synchronous generator, are aiming to model a type-5 turbine using Real Time Digital Simulation (RTDS) to evaluate its efficacy in the grid. RSCAD, the software run on the RTDS, comes pre-loaded with electrical and electromechanical components to help simulate electrical generation and grid conditions. However, within this repertoire there is a lack of a component to represent a gearbox with high-fidelity. Within RSCAD’s case studies, the gearbox is often represented simply by a gear ratio value. This presented the task of developing a high-fidelity gearbox model in RSCAD for use in the larger RTDS type-5 wind turbine model. This poster describes a method of developing a lumped parameter mathematical model to represent a planetary-parallel-parallel gearbox in RSCAD for use in RTDS.

17 WIND ENERGY↗
DOE OSTI2025

Adopting Code Verification Methodology Based on Model Form

Code verification is an essential part of credibility analysis for computational models. It assesses whether the mathematical model is implemented correctly into the code and whether the numerical methods behave consistently, and is done before solution verification and validation. Robust guidance for code verification exists in the literature. However, there is no known, concise guide for selecting the approach based on the model form that also presents an overview of the common elements. This document was written to address this gap as an accessible reference for beginning a code-verification effort.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Adapting Code Verification Methodology to Model Form

Code verification is an essential part of credibility analysis for computational models. It assesses whether the mathematical model is implemented correctly into the code and whether the numerical methods behave consistently, and is done before solution verification and validation. Robust guidance for code verification exists in the literature. However, there is no known, concise guide for selecting the approach based on the model form that also presents an overview of the common elements. This document was written to address this gap as an accessible reference for beginning a code-verification effort.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

The kinetics of SARS-CoV-2 infection based on a human challenge study

Studying the early events that occur after viral infection in humans is difficult unless one intentionally infects volunteers in a human challenge study. Here, we use data about severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in such a study in combination with mathematical modeling to gain insights into the relationship between the amount of virus in the upper respiratory tract and the immune response it generates. We propose a set of dynamic models of increasing complexity to dissect the roles of target cell limitation, innate immunity, and adaptive immunity in determining the observed viral kinetics. We introduce an approach for modeling the effect of humoral immunity that describes a decline in infectious virus after immune activation. We fit our models to viral load and infectious titer data from all the untreated infected participants in the study simultaneously. We found that a power-law with a power h < 1 describes the relationship between infectious virus and viral load. Viral replication at the early stage of infection is rapid, with a doubling time of ~2 h for viral RNA and ~3 h for infectious virus. We estimate that adaptive immunity is initiated ~7 to 10 d postinfection and appears to contribute to a multiphasic viral decline experienced by some participants; the viral rebound experienced by other participants is consistent with a decline in the interferon response. Altogether, we quantified the kinetics of SARS-CoV-2 infection, shedding light on the early dynamics of the virus and the potential role of innate and adaptive immunity in promoting viral decline during infection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

High-Fidelity Modeling of a Type-5 Wind Turbine Gearbox (Intern Technical Presentation) (Poster)

Type-5 wind turbines are unique in their use of a permanent magnet synchronous generator, as well as their use of a hydraulic torque converter. This architecture presents an opportunity to provide steady and grid-ready energy without the need for a power converter. With infrastructure continuity and reliability being an important topic amongst renewable energies, researchers have been prompted to further investigate the benefits of type-5 turbines’ unique electromechanical configuration on stable electricity generation. Researchers involved in the WindSG project, SG standing for synchronous generator, are aiming to model a type-5 turbine using Real Time Digital Simulation (RTDS) to evaluate its efficacy in the grid. RSCAD, the software run on the RTDS, comes pre-loaded with electrical and electromechanical components to help simulate electrical generation and grid conditions. However, within this repertoire there is a lack of a component to represent a gearbox with high-fidelity. Within RSCAD’s case studies, the gearbox is often represented simply by a gear ratio value. This presented the task of developing a high-fidelity gearbox model in RSCAD for use in the larger RTDS type-5 wind turbine model. This presentation describes a method of developing a lumped parameter mathematical model to represent a planetary-parallel-parallel gearbox in RSCAD for use in RTDS.

17 WIND ENERGY↗
DOE OSTI2025

Ethical considerations in infectious disease modelling for public health policy: the case of school closures

Mathematical models of infectious diseases are frequently used as a tool to support public health policy and decisions around the implementation of interventions such as school closures. However, most publications on policy-relevant modelling lack an ethical framework and do not explicitly consider the ethical implications of the work. This creates a risk that the unintended consequences of interventions are overlooked or that models are used to justify decisions that are inconsistent with public health ethics. In this article, we focus on the case study of school closures as a commonly modelled intervention against pandemic influenza, COVID-19 and other infectious disease threats. We briefly review some of the key concepts in public health ethics and describe approaches to modelling the effects of school closures. We then identify a series of ethical considerations involved in modelling school closures. These include accounting for population heterogeneity and inequalities; including a diversity of viewpoints and expertise in model design; considering the distribution of benefits and harms; and model transparency and contextualization. Furthermore, we conclude with some recommendations to ensure that policy-relevant modelling is consistent with some key ethics values.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Ergodic Lagrangian dynamics in a superhero universe

We present a fictional scenario that, while undeniably whimsical, provides the foundation for a unique exercise in extended problem solving, physics analysis, and quantitative model development. Starting with the foundational premise of the Wild Cards shared-world superhero universe, we demonstrate how a variety of concepts appropriate to the advanced undergraduate level—ergodicity, functional analysis, Lagrangian mechanics, and the ever-important simplifying approximation—can be combined into a rich, coherent mathematical model. The goal of this case study is to develop a useful pedagogical exercise in exploring an open-ended research question that presents, at first glance, no clear path forward. Being both eclectic and lengthy, this exercise offers a unique way for students to apply their core physics and mathematics education. It is perhaps best used within a senior honors seminar or within a brief (e.g., January term) elective class.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Modeling suggests SARS-CoV-2 rebound after nirmatrelvir-ritonavir treatment is driven by target cell preservation coupled with incomplete viral clearance

In a subset of SARS-CoV-2-infected individuals treated with the antiviral nirmatrelvir-ritonavir, the virus rebounds following treatment. The mechanisms driving this rebound are not well understood. We used a mathematical model to describe the longitudinal viral load dynamics of 51 individuals treated with nirmatrelvir-ritonavir, 20 of whom rebounded. Target cell preservation, either by a robust innate immune response or initiation of N-R near the time of symptom onset, coupled with incomplete viral clearance, appears to be the main factor leading to viral rebound. Moreover, the occurrence of viral rebound is likely influenced by the time of treatment initiation relative to the progression of the infection, with earlier treatments leading to a higher chance of rebound. A comparison with an untreated cohort suggests that early treatments with nirmatrelvir-ritonavir may be associated with a delay in the onset of an adaptive immune response. Nevertheless, our model demonstrates that extending the course of nirmatrelvir-ritonavir treatment to a 10-day regimen may greatly diminish the chance of rebound in people with mild-to-moderate COVID-19 and who are at high risk of progression to severe disease. Altogether, our results suggest that in some individuals, a standard 5-day course of nirmatrelvir-ritonavir starting around the time of symptom onset may not completely eliminate the virus. Thus, after treatment ends, the virus can rebound if an effective adaptive immune response has not fully developed. These findings on the role of target cell preservation and incomplete viral clearance also offer a possible explanation for viral rebounds following other antiviral treatments for SARS-CoV-2.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

A multiscale model of the action of a capsid assembly modulator for the treatment of chronic hepatitis B

Chronic hepatitis B virus (HBV) infection is strongly associated with increased risk of liver cancer and cirrhosis. While existing treatments effectively inhibit the HBV life cycle, viral rebound frequently occurs following treatment interruption. Consequently, functional cure rates of chronic HBV infection remain low and there is increased interest in a novel treatment modality, capsid assembly modulators (CAMs). Here, we develop a multiscale mathematical model of CAM treatment in chronic HBV infection. By fitting the model to participant data from a phase I trial of the first-generation CAM vebicorvir, we estimate the drug’s dose-dependent effectiveness and identify the physiological mechanisms that drive the observed biphasic decline in HBV DNA and RNA, and mechanistic differences between HBeAg-positive and negative infection. Finally, we demonstrate analytically and numerically that the relative change of HBV RNA more accurately reflects the antiviral effectiveness of a CAM than the relative change in HBV DNA.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Quantifying the basic reproduction number and underestimated fraction of Mpox cases worldwide at the onset of the outbreak

In 2022, there was a global resurgence of mpox, with different clinical-epidemiological features compared with previous outbreaks. Sexual contact was hypothesized as the primary transmission route, and the community of men having sex with men (MSM) was disproportionately affected. Because of the stigma associated with sexually transmitted infections, the real burden of mpox could be masked. We quantified the basic reproduction number (R 0 ) and the underestimated fraction of mpox cases in 16 countries, from the onset of the outbreak until early September 2022, using Bayesian inference and a compartmentalized, risk-structured (high-/low-risk populations) and two-route (sexual/non-sexual transmission) mathematical model. Machine learning (ML) was harnessed to identify underestimation determinants. Estimated R 0 ranged between 1.37 (Canada) and 3.68 (Germany). The underestimation rates for the high- and low-risk populations varied between 25–93% and 65–85%, respectively. The estimated total number of mpox cases, relative to the reported cases, is highest in Colombia (3.60) and lowest in Canada (1.08). In the ML analysis, two clusters of countries could be identified, differing in terms of attitudes towards the 2SLGBTQIAP+ community and the importance of religion. Given the substantial mpox underestimation, surveillance should be enhanced, and country-specific campaigns against the stigmatization of MSM should be organized, leveraging community-based interventions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Continual Load Modelling

Lack of harmonic rich datasets limits the ability to have fine grained load models at grid edge. We aim to develop mathematical models for power electronic based load combinations at grid edge to help replicate current and future evolving load conditions

Vasios, Orestis↗
DOE OSTI2026

Learning thermodynamic master equations for open quantum systems

The characterization of Hamiltonians and other components of open quantum dynamical systems plays a crucial role in quantum computing and other applications. Scientific machine learning techniques have been applied to this problem in a variety of ways, including by modeling with deep neural networks. However, the majority of mathematical models describing open quantum systems are linear, and the natural nonlinearities in learnable models have not been incorporated using physical principles. We present a data-driven model for open quantum systems that includes learnable, thermodynamically consistent terms. The trained model is interpretable, as it directly estimates the system Hamiltonian and linear components of coupling to the environment. We validate the model on synthetic two and three-level data, as well as experimental two-level data collected from a quantum device at Lawrence Livermore National Laboratory.

Mathematics and Computing↗
DOE OSTI2024

Effective many-body interactions in reduced-dimensionality spaces through neural network models

Accurately describing properties of challenging problems in physical sciences often requires complex mathematical models that are unmanageable to tackle head on. Therefore, developing reduced-dimensionality representations that encapsulate complex correlation effects in many-body systems is crucial to advance the understanding of these complicated problems. However, a numerical evaluation of these predictive models can still be associated with a significant computational overhead. To address this challenge, in this paper we discuss a combined framework that integrates recent advances in the development of active-space representations of coupled cluster (CC) downfolded Hamiltonians with neural network approaches. The primary objective of this effort is to train neural networks to eliminate the computationally expensive steps required for evaluating hundreds or thousands of Hugenholtz diagrams, which correspond to multidimensional tensor contractions necessary for evaluating a many-body form of downfolded effective Hamiltonians. Using small molecular systems (the H 2 O and HF molecules) as examples, we demonstrate that training neural networks employing effective Hamiltonians for a few nuclear geometries of molecules can accurately interpolate or extrapolate their forms to other geometrical configurations characterized by different intensities of correlation effects. We also discuss differences between effective interactions that define CC downfolded Hamiltonians with those of bare Hamiltonians defined by Coulomb interactions in the active spaces. Published by the American Physical Society 2024

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Crop models: integrating systems from the molecular to global for agricultural productivity and sustainability

Mathematical models that simulate crop growth in response to environmental conditions and management practices are essential tools for exploring agriculture-based strategies to address food security and environmental sustainability challenges. Early applications of crop models focused on supporting farmers in making management decisions. Applications have since expanded to estimating future impacts on local and global food production from changing climates. Emerging applications of crop models aim to leverage how these models integrate plant processes across biological scales to identify engineering or breeding strategies that account for environmentally-responsive dynamics at field scales and for exploring solutions to improve sustainability. In this review, we highlight recent studies across these four broad application areas and highlight potential future directions for the crop modeling field.

Piao, Ximin [Univ. of Illinois at Urbana-Champaign↗
DOE OSTI2025

Theoretical and methodological challenges in hierarchical Bayesian inference for model-form uncertainty

This report describes challenges associated with the hierarchical Bayesian approach to inform model-form uncertainty (MFU) representations, which are parameterized modifications to a mathematical models’ governing equations to express uncertainty in form of the equations. To inform model-form uncertainties, hierarchical Bayesian inference is often employed. Here, the MFU parameters are distributed parametrically, and the hyperparameters of the parametric distribution are informed through Bayesian inference, with the aim of determining the MFU parameter distribution that best agrees with calibration data. In practice, however, we have found the hierarchical Bayesian approach falls short of this aim. We discuss theoretical and methodological challenges of the approach, and we present several numerical demonstrations of these challenges. To conclude, we suggest promising alternative approaches for future investigation.

97 MATHEMATICS AND COMPUTING↗