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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Distributed Simulation for Space Exploration

This viewgraph presentation reviews the use of simulation and modeling in preparation for the planned exploration initiatives. The Exploration Systems Mission Directorate (EMSD) Integrated Modeling and Simulation (IM&S) team strategy encompasses a wide spectrum of simulation and modeling policies and technologies. One prominent technology is distributed simulation. The DIstributed Simulation (DIS),a collaborative simulation project with international participation (US and Japan) is reviewed as an example of distributed simulation development. The Distributed Space Exploration Simulation (DSES) is another example of distributed simulation that is described

Crues, Edwin Z.↗
DOE OSTI2025

Harnessing metastability for grain size control in multiprincipal element alloys during additive manufacturing

Abstract Controlling microstructure in fusion-based metal additive manufacturing (AM) remains a significant challenge due to the many parameters that directly impact solidification condition. Multiprincipal element alloys (MPEAs), also known as high entropy alloys, offer a vast compositional space to design for microstructural engineering due to their chemical complexity and exceptional properties. Here, we use the FeMnCoCr system as a model platform for exploring alloy design in MPEAs for AM. By exploiting the decreasing stability of the face-centered cubic phase with increasing Mn content, we achieve notable grain refinement and breakdown of epitaxial columnar grain growth. We employ a multifaceted approach encompassing thermodynamic modeling, operando synchrotron X-ray diffraction, multiscale microstructural characterization, and mechanical testing to gain insight into the solidification physics and its ramifications on the resulting microstructure of FeMnCoCr MPEAs. This work aims toward tailoring desirable grain sizes and morphology through targeted manipulation of phase stability, thereby advancing microstructure control in AM applications.

Wakai, Akane↗
DOE OSTI2026

Half-ice, half-fire-driven ultranarrow phase crossover in one-dimensional decorated 𝑞-state Potts ferrimagnets: An AI-co-led exploration

OpenAI’s reasoning model o3-mini-high was used to carry out an exact analytic study of one-dimensional ferrimagnetic site- and bond-decorated 𝑞-state Potts models. We demonstrate that the finite-temperature ultranarrow phase crossover (UNPC), driven by a hidden “half-ice, half-fire” state recently discovered in the 𝑞=2 case (Ising model), persists for 𝑞>2. Moreover, we identify unique features for 𝑞>2, including the dome structure in the field-temperature phase diagram, and for large 𝑞 a secondary high-temperature UNPC to the fully disordered paramagnetic state. As the UNPC quickly approaches a genuine transition by enhancing 𝐽, the interaction between the backbone spins, two distinct behaviors emerge: In the site-decorated Potts model, 𝑇 0 is independent of 𝐽 and thus remains unchanged (Type-I UNPC), and in the bond-decorated Potts model with 𝑞>2, 𝑇 0 depends on 𝐽 and quickly shifts toward a finite temperature as 𝐽 increases (Type-II UNPC). These results establish a versatile framework for engineering controlled fast state-flipping switches in low-dimensional systems. Our nine-dan artificial intelligence (AI)-contribution framework assigns AI the meritorious status of AI-co-led discovery in this work.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Develop a new integrated macro→micro←nano (MMN) multiscale modeling framework to optimize high strength aluminum alloys and processes for vehicle light-weighting​

Bending tests provide a means to study plane strain fracture performance of 6000 series aluminum alloys. Metrics from bending tests have been correlated with self-pierce riveting (SPR) performance of a high strength AA6111 automotive aluminum alloy in previous works. Using the ORNL HPC resources, this project developed an innovative macro→micro←nano (MMN) multiscale microstructure-based finite element (FE) code to further understanding of the relationship between microstructure and fracture properties of high-strength 6000-series alloys. This work started with microstructural characterization in both mesoscale and nanoscale and bending performance characterization of AA6111 HS2-T6 alloy at Ford, the MMN framework was applied to this alloy to simulate 3-point VDA bending. From the results of the macro-modeling of 3-point VDA bending, the critical region of fracture was identified, and the region geometry was used to construct the micro-model. The fracture criterion of micron-scale precipitates and aluminum matrix which contains submicron and nano particles (AL-SMP-NP) in the micro-model was calibrated and validated by comparing simulated and measured bending results. With the AL-SMP-NP fracture strain obtained, the fracture strain of Al-matrix containing nano particles (ALNP) will similarly be determined by a submicron scale-model using an edge-constrained FE modeling approach developed by Hu et al. With the ALNP fracture strain obtained, the fracture strain of Al-matrix containing no particles will similarly be determined by a nano scale-model using an edge-constrained FE modeling approach. After the MMN framework is built and fracture criterion calibrated, nano-model FE simulations with virtual microstructures was performed to obtain a reduced order model (ROM) of the fracture criterion of the ALNP as a function of volume fraction, size, and distribution of the nanoparticle. This nano→submicron→micro modeling part allows exploration of the influence of different material nanostructures from different process conditions on the bending properties within the multiscale bending simulation framework and the ROM of material bendability as a function of nanoparticle size and shape was established. This obtained reduced order model (ROM) could help guide the design and selection of materials to improve existing SPR process models that could replace trial-and-error rivet/die selection and help to design new rivet/die combinations capable of robustly joining new higher strength 6000 5 series alloys in automotive body structures. This would enable lightweighting of Ford vehicles leading to greater fuel efficiency and reduce manufacturing time and energy.

36 MATERIALS SCIENCE↗
NASA NTRS1990

Exploring the limits of crop productivity: A model to evaluate progress

The goal was to determine the limits of crop productivity when all environmental constraints were removed. Researchers define productivity as food output per unit of input. Researchers evaluated cultivars of wheat with reduced leaf size and number to decrease the leaf area index at high plant densities. These cultivars may also have an improved harvest index. Hydroponic studies indicate that 1 mM nitrate in solution is adequate to support maximum growth in these systems, provided iron nutrition is adequate. Wheat does not accumulate nitrate in leaves even when the solution nitrate concentration is 15 mM. Long-term photosynthetic efficiency (g mol (exp -1) of photons) and harvest index were not altered by photoperiod (16, 20, or 24 hours). Wheat does not need, nor benefit from, a diurnal dark period.

Bugbee, Bruce↗
NASA NTRS1994

Terrain modelling and motion planning for an autonomous exploration rover

To assess the feasibility of planetary exploration missions using rovers, the French national agency CNES, with a consortium of European laboratories and industrial concerns, has initiated the Eureka project, 'Illustration of an Autonomous Robot for the Exploration of Space' (IARES). IARES is a demonstrator composed of a rover and a ground station, linked by telemetry and telecommand. It is aimed at verifying, on earth, robotic concepts developed by the RISP group of French laboratories (LAAS, INRIA, CERT, LETI) to perform scientific missions such as autonomous terrain sample collecting over large areas. To cope with the actual needs of planet exploration, IARES suitability is assessed through constraints on limited bandwidth, time delay and on-board resources. This autonomy relies heavily on robust onboard trajectory generation capabilities. This paper presents the main functions of the IARES navigation sub-system and shows how they are combined to allow movement in Mars-like environments. Section 2 gives an overall description of the IARES system. Section 3 details the functions of the Navigation sub-system, and finally, section 4 illustrates with a simple example the use of these functions.

Richard, F.↗
NASA NTRSDate not supplied

NASA’s Use of MBSE and SysML Modeling to Architect the Future of Human Exploration

One of the key roles of National Aeronautics and Space Administration (NASA) is to help mitigate the risk and lower expenses associated with space exploration, science, and discovery to the point where industry and international partners are willing and able to profitably take on larger, more complex missions. To do this, the Agency must undertake a transformation to a more modern integrated Digital Engineering approach to mission definition and planning. This paper highlights NASA’s journey in understanding what this Digital Engineering Transformation means for the Agency, the benefits of this transformation to human exploration definition and planning, and the benefits to the current Artemis campaign engineering capability portfolio.

Engineering Digital Transformation↗
NASA NTRSDate not supplied

Exploring Flooded Fraction Prediction through Machine Learning Models Focusing on Medical Infrastructure in the Southeast U.S. Coastal Areas

Rising sea levels due to climate change increasingly threaten medical infrastructure through flooding. This study develops machine learning models to predict flood exposure for 11,508 medical facilities in the southeastern coastal regions of the United States by integrating datasets including meteorological, hydrological, topographic, and geological data, the Natural Risk Index, and historical flood records from NASA, HIFLD, and FEMA. Six regression models, namely Linear Regression, Support Vector Regression, Random Forest, k-Nearest Neighbors, XGBoost, and Artificial Neural Networks, are trained using 16 explanatory variables identified through literature review and correlation analysis. Data preprocessing employs the SMOGN for class imbalance and Winsorization for outliers. Model performance is evaluated using MAE, MSE, and RMSE, with Random Forest and XGBoost models achieving the highest performance (MSE of 2.58e-5 and 3.69e-5, respectively). This multifactorial approach allows the models to capture complex flood-influencing relationships, enhancing adaptability and performance across geographic regions. Future work focuses on expanding across the U.S. and developing a near real-time flood monitoring system.

Jihoon Chung↗
DOE OSTI2021

Towards data-driven constitutive modelling for granular materials via micromechanics-informed deep learning

The analytical description of path-dependent elastic-plastic responses of a granular system is highly complicated because of continuously evolving microstructures and strain localisation within the system undergoing deformation. This study offers an alternative to the current analytical paradigm by developing micromechanics-informed machine-learning based constitutive modelling approaches for granular materials. A set of critical variables associated with the constitutive behaviour of granular materials are identified through an incremental stress-strain relationship analysis. Depending on the strategy to exploit the priori micromechanical knowledge, three different training strategies are explored. The first model uses only the measurable external variables to make stress predictions; the second model utilises a directed graph to link all the external strain sequences and internal microstructural evolution variables into a single prediction model comprised of a series of sub-mappings, and the third model explicitly integrates the physically important non-temporal properties with external strain paths into training through an enhanced Gated Recurrent Unit (GRU). These three models show satisfactory agreement with unseen test specimens based on multi-directional loading cases. The basic features and potential applications of each model are explained. Lastly, the key factors for constitutive training and limitations of the current work are also discussed in detail.

36 MATERIALS SCIENCE↗
NASA NTRS1986

Regional-scale estimates of surface moisture availability and thermal inertia using remote thermal measurements

A review is presented of numerical models which were developed to interpret thermal IR data and to identify the governing parameters and surface energy fluxes recorded in the images. Analytic, predictive, diagnostic and empirical models are described. The limitations of each type of modeling approach are explored in terms of the error sources and inherent constraints due to theoretical or measurement limitations. Sample results of regional-scale soil moisture or evaporation patterns derived from the Heat Capacity Mapping Mission and GOES satellite data through application of the predictive model devised by Carlson (1981) are discussed. The analysis indicates that pattern recognition will probably be highest when data are collected over flat, arid, sparsely vegetated terrain. The soil moisture data then obtained may be accurate to within 10-20 percent.

Carlson, T. N.↗
DOE OSTI2021

Two-Color IRMPD Applied to Conformationally Complex Ions: Probing Cold Ion Structure and Hot Ion Unfolding

Two-color infrared multiphoton dissociation (2C-IRMPD) spectroscopy is a technique that mitigates spectral distortions due to nonlinear absorption that is inherent to one-color IRMPD. We use a 2C-IRMPD scheme that incorporates two independently tunable IR sources, providing considerable control over the internal energy content and type of spectrum obtained by varying the trap temperature, the time delays and fluences of the two infrared lasers, and whether the first or second laser wavelength is scanned. In this work, we describe the application of this variant of 2C-IRMPD to conformationally complex peptide ions. The 2C-IRMPD technique is used to record near-linear action spectra of both cations and anions with temperatures ranging from 10 to 300 K. In this work, we also determine the conditions under which it is possible to record IR spectra of single conformers in a conformational mixture. Furthermore, we demonstrate the capability of the technique to explore conformational unfolding by recording IR spectra with widely varying internal energy in the ion. The protonated peptide ions YGGFL (NH 3 + -Tyr-Gly-Gly-Phe-Leu, Leu-enkephalin) and YGPAA (NH 3 + -Tyr-Gly-Pro-Ala-Ala) are used as model systems for exploring the advantages and disadvantages of the method when applied to conformationally complex ions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2005

Modeling and Simulation Verification, Validation and Accreditation (VV&A): A New Undertaking for the Exploration Systems Mission Directorate

and Accreditation (VV&A) session audience, a snapshot review of the Exploration Space Mission Directorate s (ESMD) investigation into implementation of a modeling and simulation (M&S) VV&A program. The presentation provides some legacy ESMD reference material, including information on the then-current organizational structure, and M&S (Simulation Based Acquisition (SBA)) focus contained therein, to provide a context for the proposed M&S VV&A approach. This reference material briefly highlights the SBA goals and objectives, and outlines FY05 M&S development and implementation consistent with the Subjective Assessment, Constructive Assessment, Operator-in-the-Loop Assessment, Hardware-in-the-Loop Assessment, and In Service Operations Assessment M&S construct, the NASA Exploration Information Ontology Model (NExIOM) data model, and integration with the Windchill-based Integrated Collaborative Environment (ICE). The presentation then addresses the ESMD team s initial conclusions regarding an M&S VV&A program, summarizes the general VV&A implementation approach anticipated, and outlines some of the recognized VV&A program challenges, all within a broader context of the overarching Integrated Modeling and Simulation (IM&S) environment at both the ESMD and Agency (NASA) levels. The presentation concludes with a status on the current M&S organization s progress to date relative to the recommended IM&S implementation activity. The overall presentation was focused to provide, for the Verification, Validation,

Prill, Mark E.↗
DOE OSTI2022

Effect of modeled time horizon on quantifying the need for long-duration storage

Long-Duration Energy Storage (LDES) has gained interest due to its key role in attaining a decarbonized, low-cost, and stable grid driven by variable renewable electricity (VRE). Currently, there is a wide range of LDES technologies being developed to provide electricity with 8+ hours of consecutive discharge. However, current capacity expansion models used in long-term planning processes rarely consider low cost LDES as a candidate technology. If they do, the storage balancing horizon (SBH) of the model usually only considers non-consecutive 1-day periods that do not capture the potential of LDES to shift energy across multiple days or even seasons. Addressing these limitations in existing models, this work explores the ways in which the optimal energy storage changes when increasing the number of consecutive days in the SBH and how these changes will impact planners who are determining the future roles of energy storage. Our analysis uses SWITCH, an open-source capacity expansion model with a high spatial resolution for the entire Western Electricity Coordinating Council (WECC) in a zero-carbon scenario in 2050. We find that the number of consecutive days in the SBH changes both the total selected power and energy capacity of LDES when storage energy and power capacity overnight costs are $\$$13 USD/kWh (or less) and $113 USD/kW, respectively. We also find that the amount of required energy in storage to drive a future VRE-driven WECC grid ranges from 2.5 TWh to 16.0 TWh depending on the length of the SBH. The optimal storage duration (energy to power ratio) we obtain ranges from 10 h to 620 h among all the scenarios. Furthermore, depending on the storage cost assumption, we observe different charge/discharge patterns when varying the length of the SBH. Given our results, we anticipate that as more LDES technologies become commercially available, it will be critical to increase the length of the SBH to fully capture the benefits of LDES assets in long-term planning processes of high VRE-driven grids.

42 ENGINEERING↗