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Results for “Scale Spalling Models”

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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1,158 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2026

Thermoplastic Matrix Composite Design for Cryotanks Using Multiscale Modeling and Bayesian Optimization

Designing lightweight, robust cryogenic storage tanks is critical for future launch vehicles, in-space propellant storage, and hydrogen powered aircraft. This work presents a multiscale modeling and Bayesian optimization framework for the design of thermoplastic matrix composite cryotanks. Molecular dynamics simulations are first used to determine temperature-dependent constituent properties for candidate thermoplastic matrices, which are homogenized to the lamina scale using NASA’s Multiscale Analysis Tool (NASMAT). These lamina properties, in combination with laminate family generation rules, are evaluated in HyperX structural optimization software to identify stacking sequences that meet all cryogenic load requirements. A Bayesian optimization framework is applied, with HyperX in the loop (via the HyperX API) to efficiently search across material and laminate design variables, yielding an optimized cryotank configuration with significant reductions in design cycle time compared to exhaustive search approaches.

thermoplastics↗
DOE OSTI2026

SODAs: sparse optimization for the discovery of differential and algebraic equations

Differential-algebraic equations (DAEs) integrate ordinary differential equations (ODEs) with algebraic constraints, providing a fundamental framework for developing models of dynamical systems characterized by time-scale separation, conservation laws and physical constraints. While sparse optimization has revolutionized model development by allowing data-driven discovery of parsimonious models from a library of possible equations, existing approaches for dynamical systems assume DAEs can be reduced to ODEs by eliminating variables before model discovery. This assumption limits the applicability of such methods for DAE systems with unknown constraints and time scales. We introduce sparse optimization for differential-algebraic systems (SODAs), a data-driven method for the identification of DAEs in their explicit form. By discovering the algebraic and dynamic components sequentially without prior identification of the algebraic variables, this approach leads to a sequence of convex optimization problems. It has the advantage of discovering interpretable models that preserve the structure of the underlying physical system. To this end, SODAs improves since SODAs is singular numerical stability when handling high correlations between library terms, caused by near-perfect algebraic relationships, by iteratively refining the conditioning of the candidate library. We demonstrate the performance of our method on biological, mechanical and electrical systems, showcasing its robustness to noise in both simulated time series and real-time experimental data.

DAE↗
DOE OSTI2026

Design and Modeling of the Charge Readout of a SiMOS Quantum Dot with a Single Electron Transistor and CryoCMOS

Single electron spin qubits trapped in SiMOS quantum dots are a promising technology for scaling to thousand- or million-qubit systems due to their compatibility with mature CMOS manufacturing processes. A readout system that combines a single electron transistor with a custom cryogenic CMOS amplification and digitization chain offers key advantages by avoiding the use of bulky RF components or room-temperature interconnects. We present design techniques and simulation results for an optimized qubit-SET cryoCMOS interface, culminating in the design of the QNDR1 ASIC, the first cryogenic readout ASIC designed under the Quandarum project, which targets the development of a many-channel spin qubit based detector for use in high energy physics.

Quinn, Adam [Fermilab] (ORCID:0009000605056371)↗
NASA NTRS2014

A Predictive Bubble Point Pressure Model for Porous Liquid Acquisition Device Screens

This article presents a simplified model for porous screen channel liquid acquisition devices based on a maximum bubble point pressure method from Adamson and Gast (1997). To validate the model, three 304 stainless steel (325 × 2300, 450 × 2750, and 510 × 3600) mesh samples were tested in methanol, acetone, isopropyl alcohol, and water. Screen pores are estimated based on analysis from scanning electron microscopy, historical data, and current test data. Results show that the bubble point pressure is proportional to the surface tension of the fluid only when accounting for nonzero contact angles. The previous assumption that bubble point pressure scales inversely with effective pore diameter is shown to be invalid, as the second finest 450 × 2750 produced the highest bubble point of the three screens. The simplified bubble point model can be used to make predictions for any pure fluid when pore diameters are based on bubble point tests and not SEM analysis.

Liquid Acquisition Device↗
DOE OSTI2024

Learning Nonlinear Reduced Models from Data with Operator Inference

This review discusses Operator Inference, a nonintrusive reduced modeling approach that incorporates physical governing equations by defining a structured polynomial form for the reduced model, and then learns the corresponding reduced operators from simulated training data. The polynomial model form of Operator Inference is sufficiently expressive to cover a wide range of nonlinear dynamics found in fluid mechanics and other fields of science and engineering, while still providing efficient reduced model computations. The learning steps of Operator Inference are rooted in classical projection-based model reduction; thus, some of the rich theory of model reduction can be applied to models learned with Operator Inference. This connection to projection-based model reduction theory offers a pathway toward deriving error estimates and gaining insights to improve predictions. Furthermore, through formulations of Operator Inference that preserve Hamiltonian and other structures, important physical properties such as energy conservation can be guaranteed in the predictions of the reduced model beyond the training horizon. This review illustrates key computational steps of Operator Inference through a large-scale combustion example.

Mechanics↗
NASA NTRS1985

Observational Requirements for the Regional Scale

Observational requirements are provided for the 'regional scale' (10 2 to 10 3 km space scale; 3 to 24 h time scale). Given this range, the regional scale represents a spatial and temporal domain in which important scale-interactive processes occur that act to concentrate large vertical wind shears, significant horizontal thermal gradients, and vertical motion patterns into narrow regions. A short review of the mass and momentum adjustments associated with jet streak-induced circulations is discussed. Evidence for the need to specify the wind field in the upper troposphere to accurately simulate forcing for the transverse circulations is also presented. The importance of specifying temperature tendency to resolve the lower tropospheric portion of the transverse circulations is highlighted. The observational requirements are then discussed, along with possible approaches for meeting the requirements on the regional scale.

Louis W Uccellini↗
DOE OSTI2026

Future Projection of Tropical Upper-Tropospheric Troughs and Implications for Tropical Cyclone Activity

Summertime tropical upper-tropospheric troughs (TUTTs) provide a unified framework to better understand how extratropical and tropical forcings jointly modulate basin-scale tropical cyclone (TC) activity. In this study, we examine future changes in TUTTs and their implications for TC activity. Multimodel ensemble-mean projections from 45 Coupled Model Intercomparison Project phase 6 (CMIP6) models suggest a contraction of the Pacific TUTT and an expansion of the Atlantic TUTT as the climate warms. Consistently, future changes in environment-based TC indices indicate that the large-scale conditions will become more favorable for TC genesis and intensification over the central North Pacific but less favorable over the tropical North Atlantic and Gulf of Mexico. Utilizing a TC-permitting large-ensemble dataset [i.e., the Database for Policy Decision-Making for Future Climate Change (d4PDF)] that adequately captures the observed interannual TUTT–TC relationships, we further confirm the impacts of projected TUTT changes on the TC activity in a warmer climate. In contrast, the TUTT–TC relationship is poorly represented in most CMIP6 High-Resolution Model Intercomparison Project (HighResMIP) models; such deficiencies call for caution when assessing future TC risk based on explicitly tracked TCs in these models. Additionally, CMIP6 projections show large intermodel spread in TUTT changes, implying uncertainty in projected TC activity, especially over the central-to-eastern Pacific and the North Atlantic. This intermodel spread is associated with interhemispheric sea surface temperature warming asymmetry, which leads to a meridional shift of the intertropical convergence zone (ITCZ) and the simultaneous weakening or strengthening of TUTTs in the North Pacific and North Atlantic. The potential contributions of anthropogenic aerosol forcing and oceanic circulation to this interhemispheric warming asymmetry are briefly examined.

Climate Change↗
DOE OSTI2026

Challenges for Megawatt-Scale Artificial Intelligence Rack Infrastructure

As artificial intelligence (AI) computing densities continue to increase, industry is pursuing megawatt-scale rack architectures that require tightly coordinated advances in electrical power delivery, thermal management, operations, and infrastructure integration. This document summarizes the primary technical challenges for achieving this target.

Nawaz, Kashif [ORNL] (ORCID:0000000251612491)↗
DOE OSTI2026

Powering Large Loads: Solutions Across Transmission, Utility, and Facility Scales

This report synthesizes current strategies for accommodating large load growth through a review of publicly available technical literature and media insights around the United States (U.S.). Solutions are organized across three implementation scales (transmission, utility, and facility) and categorized according to common implementation pathways (including structural expansion, operations and efficiency, upgrades, and planning and policy).

Valdez, Raquel Lynn [Sandia National Laboratories ↗
DOE OSTI2025

Micrometer: Micromechanics transformer for predicting full field mechanical responses of heterogeneous materials

Predicting mechanical responses of heterogeneous materials across scales remains a significant challenge. Traditional computational methods often struggle with complex and multiscale nature of these materials, limiting their effectiveness in real-world applications. Here, in this paper, we introduce Micrometer, a vision transformer based deep learning model designed to predict full field mechanical responses of heterogeneous materials, bridging the gap between computer vision and solid mechanics problems. We show that Micrometer, trained on a large-scale high-resolution dataset of 2D fiber-reinforced composites, can achieve state-of-the-art performance in predicting microscale strain fields across a wide range of material properties and loading conditions. Our model demonstrates accuracy and computational efficiency in applications such as computational homogenization and multiscale modeling, reducing computational time by up to two orders of magnitude compared to conventional numerical solvers while maintaining less than 1 % errors in predicting macroscale stress fields. Furthermore, we showcase Micrometer’s adaptability through transfer learning experiments on new materials with limited data, highlighting its potential to tackle diverse scenarios in computational solid mechanics. These results represent a significant step towards AI-driven innovation in materials science, addressing the limitations of traditional numerical methods and paving the way for more efficient simulations of heterogeneous materials across various industrial applications.

Composite materials↗
NASA NTRS1962

Experimental Effects of Scaling on the Performance of Ion Rockets Employing Electron-Bombardment Ion Sources

A scaling program was undertaken to establish the relations between performance parameters and the size of the electron bombardment ion source. The experimental results of this investigation are the subject of this paper. Two geometrically similar sources, a 5- and a 20-cm-diameter beam source, were scaled from a 10-cm-diameter source to allow a performance comparison to be made. The three ion sources are compared for ion chamber characteristics and overall engine efficiency. The results of the chamber investigations are compared with scaling variations indicated by simple plasma theory. The effects of size on operating limits are also discussed. Mercury was used as the propellant in this investigation.

Power Supply↗
NASA NTRSDate not supplied

Manufacturing and Scale-Up of Natural Fiber Composite eVTOL Propeller Skin and C-Channel

This presentation discusses the manufacturing and scale-up of natural fiber composites for aerospace applications, conducted within NASA’s Convergent Aeronautics Solutions portfolio to advance aircraft capabilities and efficiency. The effort focused on the development and demonstration of two proof-of concept articles, an electric vertical takeoff and landing (eVTOL) propeller skin and a C-channel subcomponent. Best practices and guidance on addressing key challenges with bio-based composite materials and manufacturing are provided.

Composites↗