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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Modeling Entry Systems to Explore Our Solar System

Exploration of our Solar System is a foundational element of NASA’s identity. Delivering a scientific payload through an atmosphere to the surface of a planetary body requires safely navigating the extreme temperatures and stresses generated by flying many times the speed of sound. An entry system is the outer shell of a vehicle designed to protect the payload and, as a single point of failure for a mission, reliability is paramount. Unfortunately, it is not possible to fully replicate the flight environment in ground test facilities – so how do we confidently design a vehicle that needs to work the first time, every time? Modeling and simulation are critical tools for filling gaps in ground test capability and providing traceability from ground to flight. Modeling an entry system is a truly interdisciplinary effort, requiring knowledge of fluid dynamics, high-temperature chemistry, radiation, materials science, structural dynamics, guidance and control – and, finally, the mathematical and computing capability to pull it all together. In this talk, Dr. Michael Barnhardt will discuss the latest research in modeling entry systems and how it is being used in NASA’s exploration missions.

Entry systems↗
NASA NTRS2008

Deep Space Network Scheduling Using Multi-Objective Optimization with Uncertainty

We have developed a novel technique to incorporate uncertainty modeling within an evolutionary algorithm approach to multi-objective scheduling, with the goal of identifying a Pareto frontier (tradeoff curve) that recognizes the likelihood of events that can impact the schedule outcome. Our approach is particularly applicable to the generation of multiobjective optimized robust schedules, where objectives are assigned a service level, for example that we require an objective value to be greater than or equal to X with Y% confidence. We have demonstrated that such an approach can, for example, minimize scheduling on less reliable resources, based solely on a resource reliability model and not on any ad hoc heuristics. We have also investigated an alternative method of optimizing for robustness, in which we add to the set of objectives a failure risk objective to minimize. We compare the advantages and disadvantages of these two approaches. Future plans for further developing this technology include its application to space-based observatory scheduling problems.

Johnston, Mark D.↗
DOE OSTI2024

On the Origin of Holes During Polarization Reset in Floating Body Ferroelectric FETs Towards Improving Switching Efficiency

In this article, we performed a comprehensive combined experimental and modeling study on the polarization reset mechanisms of floating body (i.e., channel) ferroelectric FETs, an important class of device with growing interests due to added functionalities and improved reliabilities. Using fully-depleted silicon-on-insulator (FDSOI) FeFET as a classical example, we demonstrate that: 1) without hole generation mechanisms, floating body FeFETs during reset is simply a capacitor divider, with negligible ferroelectric voltage drop for switching; ii) Band-to-band-tunneling (BTBT) around gate-to-S/D overlap even with zero drain bias generates holes to facilitate the reset in FDSOI FeFET, though at a slower speed and hold the reset state; iii) With scaling, S/D inner fringe field can enable fast reset, thus offering a potential efficiency boost approach; iv) a compact FDSOI FeFET model is developed that can capture the BTBT effect and reproduce the observed behaviors; v) the reset mechanism is also validated in a NAND string composed of FDSOI FeFETs, demonstrating its relevant applications. These insights show the strategies in improving reset efficiency, i.e., enhanced BTBT and inner fringe field.

42 ENGINEERING↗
DOE OSTI2024

Data Generation for Machine Learning Interatomic Potentials and Beyond

The field of data-driven chemistry is undergoing an evolution, driven by innovations in machine learning models for predicting molecular properties and behavior. Recent strides in ML-based interatomic potentials have paved the way for accurate modeling of diverse chemical and structural properties at the atomic level. The key determinant defining MLIP reliability remains the quality of the training data. A paramount challenge lies in constructing training sets that capture specific domains in the vast chemical and structural space. This Review navigates the intricate landscape of essential components and integrity of training data that ensure the extensibility and transferability of the resulting models. We delve into the details of active learning, discussing its various facets and implementations. We outline different types of uncertainty quantification applied to atomistic data acquisition and the correlations between estimated uncertainty and true error. The role of atomistic data samplers in generating diverse and informative structures is highlighted. Furthermore, we discuss data acquisition via modified and surrogate potential energy surfaces as an innovative approach to diversify training data. The Review also provides a list of publicly available data sets that cover essential domains of chemical space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1991

Thermal modeling of a Ni-H2 battery cell

The nickel-hydrogen secondary battery has many desirable features which make it attractive for satellite power systems. It can provide a significant improvement over the energy density of present spacecraft nickel-cadnium batteries, combined with longer life, tolerance to overcharge and possibility of state-of-charge indication. However, to realize these advantages, accurate thermal modeling of nickel-hydrogen cells is required in order to properly design the battery pack so that it operates within a specified temperature range during the operation. Maintenance of a low operating temperature and a uniform temperature profile within the cell will yield better reliability, improved cycle life and better charge/discharge efficiencies. This research has the objective of developing and testing a thermal model which can be used to characterize battery operation. Primarily, temperature distribution with the heat generation rates as a function of position and time will be evaluated for a Ni-H2 cell in the three operating modes: (1) charge cycle, (2) discharge cycle, and (3) overcharge condition, if applicable. Variables to be examined include charging current, discharge rates, state of charge, pressure and temperature. Once the thermal model has been developed, this resulting model will predict the actual operating temperature and temperature gradient for the specific cell geometry to be used.

Ryu, Si-Ok↗
DOE OSTI2024

A Diffusion‐Based Uncertainty Quantification Method to Advance E3SM Land Model Calibration

Abstract Calibrating land surface models and accurately quantifying their uncertainty are crucial for improving the reliability of simulations of complex environmental processes. This, in turn, advances our predictive understanding of ecosystems and supports climate‐resilient decision‐making. Traditional calibration methods, however, face challenges of high computational costs and difficulties in accurately quantifying parameter uncertainties. To address these issues, we develop a diffusion‐based uncertainty quantification (DBUQ) method. Unlike conventional generative diffusion methods, which are computationally expensive and memory‐intensive, DBUQ innovates by formulating a parameterized generative model and approximates this model through supervised learning, which enables quick generation of parameter posterior samples to quantify its uncertainty. DBUQ is effective, efficient, and general‐purpose, making it suitable for site‐specific ecosystem model calibration and broadly applicable for parameter uncertainty quantification across various earth system models. In this study, we applied DBUQ to calibrate the Energy Exascale Earth System Model land model at the Missouri Ozark AmeriFlux forest site. Results indicated that DBUQ produced accurate parameter posterior distributions similar to those from Markov Chain Monte Carlo sampling but with 30 times less computing time. This significant improvement in efficiency suggests that DBUQ can enable rapid, site‐level model calibration at a global scale, enhancing our predictive understanding of climate impacts on terrestrial ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Scalable Unit Commitment with Security Constrained AC Power Flow via ADMM and Hybrid Modeling Strategies

This research introduces a more efficient way to optimize power grid operations, breaking the problem into manageable steps and using advanced mathematical techniques to speed up calculations. By incorporating smart heuristics, improved preprocessing, and contingency analysis, the approach allows operators to make better decisions faster. These innovations enhance our understanding of how to optimize energy generation, making it possible to anticipate failures before they happen, reduce system costs, and improve overall grid performance. Ultimately, this research helps bridge the gap between theoretical models and real-world applications, paving the way for a smarter, more resilient power grid. This research directly benefits the public by making electricity more affordable, reliable, and sustainable. By improving how power grids schedule and distribute electricity, the project helps energy providers reduce operational costs, which can lead to lower electricity prices for consumers. Additionally, the ability to predict and prevent power system failures enhances grid reliability, reducing the likelihood of blackouts that can disrupt homes, businesses, and critical infrastructure such as hospitals. From an environmental perspective, optimizing power generation reduces energy waste and lowers carbon emissions, contributing to cleaner air and a more sustainable energy system. Furthermore, with extreme weather events becoming more frequent, these advancements make the power grid more resilient, ensuring communities are better prepared for emergencies and natural disasters. By strengthening the nation's energy infrastructure, this research plays a crucial role in improving economic stability, public safety, and environmental sustainability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2022

Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary Landing

Hazard detection is critical for enabling autonomous landing on planetary surfaces. Current state-of-the-art methods leverage traditional computer vision approaches to automate the identification of safe terrain from input digital elevation models (DEMs). However, performance for these methods can degrade for input DEMs with increased sensor noise. In the last decade, deep learning techniques have been developed for various applications. Nevertheless, their applicability to safety-critical space missions has often been limited due to concerns regarding their outputs’ reliability. In response to these limitations, this paper proposes an application of the Bayesian deep learning segmentation method for hazard detection. The developed approach enables reliable, safe landing site detection by i) generating simultaneously a safety prediction map and its uncertainty map via Bayesian deep learning and semantic segmentation, and ii) using the uncertainty map to filter out the uncertain pixels in the prediction map so that the safe site identification is performed only based on the certain pixels (i.e., pixels for which the model is certain about its safety prediction). Experiments are presented with simulated data based on a Mars HiRISE digital terrain model by varying uncertainty threshold and noise levels to demonstrate the performance of the proposed approach.

Kento Tomita↗
NASA NTRS2018

System Value Model of a Launch Vehicle

System value provides a mathematical representation of stakeholder’s preferences for the system. System value represents the utility that the system provides to the stakeholders. For a launch vehicle, important values are revenue generated through successful payloads, mission reliability, and the ability to accommodate payloads without modification to the payload instruments or spacecraft bus. This paper considers the value provided by a heavy lift launch vehicle to the satellite industry and to human exploration. An estimate is developed for a generic launch vehicle value in terms of impact to the Gross Domestic Product. Thermo-economics are applied in the calculation of system value. Mission reliability considers both successful delivery and on time delivery (i.e., operational availability). Payload accommodation considers mainly the diameter of the payload fairing for various types of missions.

Barth, Andrew↗
DOE OSTI2026

Correlating and Simulating Socio-Demographically Driven Residential End-Use Activity Schedules

Incorporating socio-demographic and behavioral considerations into decision-support tools is crucial for identifying gaps and addressing consumer needs to ensure reliable and affordable energy solutions. In energy simulation models, the correlation between socio-demographics and time-use behavior is not well-captured. Thus, we developed a large-scale simulation workflow to generate schedules for 10 residential activities across 24 population segments defined by age, income, and employment status. Using pre-pandemic 2015-2019 American Time Use Survey (ATUS) data, we used ANOVA to confirm the correlation between demographic factors and time use. We explored three k-modes clustering methods-backward, forward, and a new hybrid approach-to delineate the occupancy patterns based on demographics. Using the probability of cluster membership for each population segment and a time inhomogeneous Markov chain to generate activity transition probabilities for each cluster, we simulated 50,000 schedules per segment and validated them against the ATUS data. The hybrid method produced the most socio-demographically differentiated clusters while demonstrating comparable performance to other approaches, with an overall root mean square error of 0.12 for both weekday and weekend schedules. Thus, the hybrid method, where each cluster is dominated by certain demographic segments and occupancy patterns, offers more modeling versatility in terms of scenario analysis. The new workflow improves the socio demographic differentiation of energy consumption by considering differences in time use. This approach enables future research on demographically segmented time of use (TOU) energy consumption, including impacts of TOU utility bills and rate analysis, long-run marginal emissions, and energy retrofits.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

mzPeak: Designing a Scalable, Interoperable, and Future-Ready Mass Spectrometry Data Format

Advances in mass spectrometry (MS) instrumentation, such as higher resolution, faster scan speeds, and improved sensitivity, have significantly increased the volume and complexity of data. The growing adoption of imaging and ion mobility further amplifies these challenges across MS-based omics fields, including proteomics, metabolomics, and lipidomics. While these technologies unlock new possibilities, they also present significant challenges in data management, storage, and accessibility. Existing open formats, such as the XML-based community standards mzML and imzML, struggle to meet the demands of modern MS workflows due to their large file sizes, slow data access, and limited metadata support. Vendor-specific formats, while optimized for proprietary instruments, lack interoperability, comprehensive metadata support and long-term archival reliability. This white paper lays the groundwork for mzPeak, a next-generation community data format designed to address these challenges and support high-throughput, multi-dimensional MS workflows. By adopting a hybrid model that combines efficient binary storage for numerical data and both human and machine-readable metadata storage, mzPeak will reduce file sizes, accelerate data access, and offer a scalable, adaptable solution for evolving MS technologies. For researchers, mzPeak will enable enhanced interoperability across platforms, seamless support for complex workflows including ion mobility and MS imaging, and faster data access compared to existing community formats such as mzML. Its design will ensure data is managed in compliance with regulatory standards, essential for applications such as precision medicine and chemical safety, where long-term data integrity and accessibility are critical. For vendors, mzPeak provides a streamlined, open alternative to proprietary formats, reducing the burden of regulatory compliance while aligning with the industry's push for transparency and standardization. By offering a high-performance, interoperable solution, mzPeak positions vendors to meet customer demands for sustainable data management tools which will be able to handle emerging and future data types and workflows. mzPeak aspires to become the cornerstone of MS data management, empowering researchers, vendors, and developers to innovate and collaborate more effectively.

data formats↗
DOE OSTI2026

Interface and Thermophysical Properties of R 32 Refrigerant

Driven by the urgent demand for efficient cooling in microelectronics and advanced thermal management systems, difluoromethane (R32/CH 2 F 2 ) has emerged as a promising candidate owing to its favorable thermophysical properties, including high heat transfer efficiency and low viscosity. While bulk properties such as density, viscosity, and thermal conductivity have been widely studied, interfacial properties, including surface tension and interfacial thickness, remain comparatively underexplored, despite their importance in phase-transition dynamics. Here, we perform molecular dynamics (MD) simulations from 180 to 300 K using an optimized transferable force field for fluoropropenes with enhanced electrostatics to assess both bulk and interfacial behavior of R32. Simulations reproduced density within ±2.1%, viscosity within 3.05%, and thermal conductivity within 7.41% of NIST reference data. Heat capacities (C p and C v ) were predicted within 5%. For interfacial properties, surface tension trends were reproduced within 13.58% deviation, and the vapor–liquid coexistence curve closely matched reference data, yielding a critical temperature of 345.7 K (1.6% deviation) and a critical density of 0.397 g/cm 3 (6.4% deviation). Importantly, the vapor–liquid interface exhibited pronounced temperature-dependent broadening across the 180–290 K range. This behavior correlates with increasing molecular kinetic energy, reduction in intermolecular cohesive interactions, and a progressive loss of preferential dipole alignment, which collectively enhance thermal fluctuation amplitudes at elevated temperatures. These validated results provide predictive molecular-level insights, particularly for interfacial properties that remain less characterized. By reducing property prediction errors in key parameters such as critical temperature, this work provides reliable inputs for heat-exchanger and system models. Such correlations can support optimized component sizing, improved performance, and reduced refrigerant charge. Beyond R32, the methodology offers a transferable framework for blended and next-generation low-GWP refrigerants, contributing to sustainable thermal management aligned with the 2027 EU F-Gas regulation and 2030 Kigali Amendment.

Fluids↗
NASA NTRS1982

Developments in the design, analysis, and fabrication of advanced technology transmission elements

Over the last decade, the presently reported proprietary development program for the reduction of helicopter drive system weight and cost and the enhancement of reliability and survivability has produced high speed roller bearings, resin-matrix composite rotor shafts and transmission housings, gear/bearing/shaft system integrations, photoelastic investigation methods for gear tooth strength, and the automatic generation of complex FEM models for gear/shaft systems. After describing the design features and performance capabilities of the hardware developed, attention is given to the prospective benefits to be derived from application of these technologies, with emphasis on the relationship between helicopter drive system performance and cost.

Drago, R. J.↗
NASA NTRS1986

Multiple incidence angle SIR-B experiment over Argentina Generation of secondary image products

Original radar images may be geometrically and radio metrically distorted. This may be a particular problem when multiple angle imagery is analyzed and there is topographic relief in the area of interest. This paper describes a set of techniques designed to combine a multiple angle radar data set with a digital terrain elevation model, to generate a set of new images called secondary image products. These new images are geometrically rectified radar ortho-images radiometrically rectified images, and stereo ortho-images. These secondary images can then reliably be used for thematic interpretation.

Domik, G.↗
NASA NTRS2015

Investigation of HZETRN 2010 as a Tool for Single Event Effect Qualification of Avionics Systems - Part II

An accurate prediction of spacecraft avionics single event effect (SEE) radiation susceptibility is key to ensuring a safe and reliable vehicle. This is particularly important for long-duration deep space missions for human exploration where there is little or no chance for a quick emergency return to Earth. Monte Carlo nuclear reaction and transport codes such as FLUKA can be used to generate very accurate models of the expected in-flight radiation environment for SEE analyses. A major downside to using a Monte Carlo-based code is that the run times can be very long (on the order of days). A more popular choice for SEE calculations is the CREME96 deterministic code, which offers significantly shorter run times (on the order of seconds). However, CREME96, though fast and easy to use, has not been updated in several years and underestimates secondary particle shower effects in spacecraft structural shielding mass. Another modeling option to consider is the deterministic code HZETRN 20104, which includes updates to address secondary particle shower effects more accurately. This paper builds on previous work by Rojdev, et al. to compare the use of HZETRN 2010 against CREME96 as a tool to verify spacecraft avionics system reliability in a space flight SEE environment. This paper will discuss modifications made to HZETRN 2010 to improve its performance for calculating SEE rates and compare results with both in-flight SEE rates and other calculation methods.

Rojdev, Kristina↗
NASA NTRS2011

Dynamic Loads Generation for Multi-Point Vibration Excitation Problems

A random-force method has been developed to predict dynamic loads produced by rocket-engine random vibrations for new rocket-engine designs. The method develops random forces at multiple excitation points based on random vibration environments scaled from accelerometer data obtained during hot-fire tests of existing rocket engines. This random-force method applies random forces to the model and creates expected dynamic response in a manner that simulates the way the operating engine applies self-generated random vibration forces (random pressure acting on an area) with the resulting responses that we measure with accelerometers. This innovation includes the methodology (implementation sequence), the computer code, two methods to generate the random-force vibration spectra, and two methods to reduce some of the inherent conservatism in the dynamic loads. This methodology would be implemented to generate the random-force spectra at excitation nodes without requiring the use of artificial boundary conditions in a finite element model. More accurate random dynamic loads than those predicted by current industry methods can then be generated using the random force spectra. The scaling method used to develop the initial power spectral density (PSD) environments for deriving the random forces for the rocket engine case is based on the Barrett Criteria developed at Marshall Space Flight Center in 1963. This invention approach can be applied in the aerospace, automotive, and other industries to obtain reliable dynamic loads and responses from a finite element model for any structure subject to multipoint random vibration excitations.

Shen, Lawrence↗
DOE OSTI2025

Predicting Initial Trans-Membrane Pressure for Optimized Operations in UF Unit Using Random Forest

With the growing scarcity of freshwater, innovative process design mechanisms like Ultra-filtration(UF) units are increasingly gaining attention among water treatment utilities to address the rising demand. Ensuring reliable water production necessitates efficient resource utilization, minimizing downtime in UF systems. Recent advancements in machine learning (ML) have enabled the development of accurate data-driven models for Model Predictive Control (MPC), often requiring minimal prior knowledge of underlying physical processes. In this study, we present predictive regression models based on Random Forest (RF) and Auto-Regressive (AR) approaches to forecast the initial Trans-Membrane Pressure (TMP) for each filtration cycle in data generated by Direct Potable Reuse (DPR) systems. The proposed RF-based model demonstrates superior performance compared to baseline methods, including historical mean, Last Observation Carried Forward (LOCF), and naïve AR models, across various forecasting horizons in terms of root mean square (RMSE) metric. Accurate prediction of initial TMP is critical for optimizing CCRO operations, as it enables the development of robust modelling frameworks that enhance process efficiency and reliability. The demonstrated efficacy of the RF-based approach highlights its potential as a tool for real-time decision-making in water treatment systems, paving the way for advanced process optimization and sustainable water resource management.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗
DOE OSTI2025

Influence of Control and Limiter Schemes on Sequence-Domain Fault Models of Grid-Forming Inverter-Interfaced Distributed Generators

Unlike synchronous generators, the fault response of grid-forming (GFM) inverter-interfaced distributed generators (IIDGs) is notably governed by the selection of control and current limiting strategies rather than inherent physical traits. While recent research has focused on the sequence domain fault model of GFM IIDGs, a research gap exists in elucidating the influence of control and current limiting schemes on this model's characteristics. This article aims to fill this void by examining how different control and current limiting schemes influence the positive and negative sequence impedances in the phasor-domain fault model of GFM IIDGs. This investigation encompasses droop-based, virtual synchronous machine-based, and virtual oscillator-based reference generation controls alongside rotating and stationary reference-frame-based voltage controls. Furthermore, saturation-based, latching-based, circular and virtual impedance-based current limiting schemes are analyzed. To achieve this goal, a thorough numerical simulation study is conducted. Findings indicate that outer reference generation controls exhibit minimal impact. Conversely, the choice of voltage control and various current limiting schemes emerge as the predominant factors shaping the sequence models of GFM IIDGs. These analyses and results are instrumental in devising reliable protection strategies within inverter-based grids, as a comprehensive understanding of electrical elements in the sequence domain is imperative for effective protective measures.

current limiters↗