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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1977

Launch processing system transition from development to operation

The Launch Processing System has been under development at Kennedy Space Center since 1973. A prototype system was developed and delivered to Marshall Space Flight Center for Solid Rocket Booster checkout in July 1976. The first production hardware arrived in late 1976. The System uses a distributed computer network for command and monitoring and is supported by a dual large scale computer system for 'off line' processing. A high level of automation is anticipated for Shuttle and Payload testing and launch operations to gain the advantages of short turnaround capability, repeatability of operations, and minimization of operations and maintenance (O&M) manpower. Learning how to efficiently apply the system is our current problem. We are searching for more effective ways to convey LPS system performance characteristics from the designer to a large number of users. Once we have done this, we can realize the advantages of LPS system design.

Paul, H. C.↗
NASA NTRS1992

Hi-alpha forebody design. Part 1: Methodology base and initial parametrics

The use of Computational Fluid Dynamics (CFD) has been investigated for the analysis and design of aircraft forebodies at high angle of attack combined with sideslip. The results of the investigation show that CFD has reached a level of development where computational methods can be used for high angle of attack aerodynamic design. The classic wind tunnel experiment for the F-5A forebody directional stability has been reproduced computationally over an angle of attack range from 10 degrees to 45 degrees, and good agreement with experimental data was obtained. Computations have also been made at combined angle of attack and sideslip over a chine forebody, demonstrating the qualitative features of the flow, although not producing good agreement with measured experimental pressure distributions. The computations were performed using the code known as cfl3D for both the Euler equations and the Reynolds equations using a form of the Baldwin-Lomax turbulence model. To study the relation between forebody shape and directional stability characteristics, a generic parametric forebody model has been defined which provides a simple analytic math model with flexibility to capture the key shape characteristics of the entire range of forebodies of interest, including chines.

Mason, William H.↗
NASA NTRS1990

A real time neural net estimator of fatigue life

A neural network architecture is proposed to estimate, in real-time, the fatigue life of mechanical components, as part of the intelligent Control System for Reusable Rocket Engines. Arbitrary component loading values were used as input to train a two hidden-layer feedforward neural net to estimate component fatigue damage. The ability of the net to learn, based on a local strain approach, the mapping between load sequence and fatigue damage has been demonstrated for a uniaxial specimen. Because of its demonstrated performance, the neural computation may be extended to complex cases where the loads are biaxial or triaxial, and the geometry of the component is complex (e.g., turbopumps blades). The generality of the approach is such that load/damage mappings can be directly extracted from experimental data without requiring any knowledge of the stress/strain profile of the component. In addition, the parallel network architecture allows real-time life calculations even for high-frequency vibrations. Owing to its distributed nature, the neural implementation will be robust and reliable, enabling its use in hostile environments such as rocket engines.

Troudet, T.↗
NASA NTRS1994

Influence of Two-Phase Thermocapillary Flow on Cryogenic Liquid Retention in Microscopic Pores

Previous experiments indicate that the bubble point pressure of spacecraft liquid hydrogen acquisition devices is reduced substantially when the ullage is pressurized with heated hydrogen vapor. The objective is to determine whether the two-phase thermocapillary convection arising from thermodynamic non-equilibrium along the porous surfaces of such devices could lead to this observed degradation in retention performance. We also examine why retention capability appears to be unaffected by pressurization with heated helium or direct heating through the porous structure. Computational assessments based on coupled solution of the flowfield and liquid free surface indicate that for highly wetting fluids in small pores, dynamic pressure and vapor recoil dictate surface morphology and drive meniscus deformation. With superheating, the two terms exert the same influence on curvature and promote mechanical equilibrium, but with subcooling, the pressure distribution produces a suction about the pore center-line that degrades retention. This result points to thermocapillary-induced deformation arising from condensation as the cause for retention loss. It also indicates that increasing the level of non-equilibrium by reducing accommodation coefficient restricts deformation and explains why retention failure does not occur with direct screen heating or helium pressurization.

Schmidt, G. R.↗
DOE OSTI2023

Predicting Wind Loading and Instability in Solar Tracking PV Arrays

Wind loading and the fluctuating pressure loads it creates on PV panel surfaces are associated with multiple degradation mechanisms and failures. Modest wind speeds create reversing loads that can initiate cell cracks and weather cracked cells. Stronger wind speeds and extreme weather events can lead to larger scale forces and the aerodynamic instability known as torsional galloping. All these effects are dependent on the complex coupling between wind speed, panel orientation, and a myriad of other hardware and site-specific factors. In this work, we present the latest developments from our work to build an open-source, high-performance computing (HPC) fluid dynamics solver to predict and mitigate these effects. This simulation package allows users to easily specify different array layouts, solar-tracking angles, panel geometries, and weather conditions before automatically generating a refined computational mesh and solving for the unsteady loading on each panel surface. Small domains (e.g., a single panel row in isolation) can be solved on a modern laptop, while larger domains or very high-fidelity studies can be solved on distributed or HPC resources with minimal modifications to the underlying problem specification. We present preliminary case studies obtained using this simulation package and highlight how increased wind speeds combined with sub-optimal tracking angles can exacerbate degradation drivers.

aerodynamics↗
DOE OSTI2022

Profiling the BLAST bioinformatics application for load balancing on high-performance computing clusters

Abstract Background The Basic Local Alignment Search Tool (BLAST) is a suite of commonly used algorithms for identifying matches between biological sequences. The user supplies a database file and query file of sequences for BLAST to find identical sequences between the two. The typical millions of database and query sequences make BLAST computationally challenging but also well suited for parallelization on high-performance computing clusters. The efficacy of parallelization depends on the data partitioning, where the optimal data partitioning relies on an accurate performance model. In previous studies, a BLAST job was sped up by 27 times by partitioning the database and query among thousands of processor nodes. However, the optimality of the partitioning method was not studied. Unlike BLAST performance models proposed in the literature that usually have problem size and hardware configuration as the only variables, the execution time of a BLAST job is a function of database size, query size, and hardware capability. In this work, the nucleotide BLAST application BLASTN was profiled using three methods: shell-level profiling with the Unix “time” command, code-level profiling with the built-in “profiler” module, and system-level profiling with the Unix “gprof” program. The runtimes were measured for six node types, using six different database files and 15 query files, on a heterogeneous HPC cluster with 500+ nodes. The empirical measurement data were fitted with quadratic functions to develop performance models that were used to guide the data parallelization for BLASTN jobs. Results Profiling results showed that BLASTN contains more than 34,500 different functions, but a single function, RunMTBySplitDB, takes 99.12% of the total runtime. Among its 53 child functions, five core functions were identified to make up 92.12% of the overall BLASTN runtime. Based on the performance models, static load balancing algorithms can be applied to the BLASTN input data to minimize the runtime of the longest job on an HPC cluster. Four test cases being run on homogeneous and heterogeneous clusters were tested. Experiment results showed that the runtime can be reduced by 81% on a homogeneous cluster and by 20% on a heterogeneous cluster by re-distributing the workload. Discussion Optimal data partitioning can improve BLASTN’s overall runtime 5.4-fold in comparison with dividing the database and query into the same number of fragments. The proposed methodology can be used in the other applications in the BLAST+ suite or any other application as long as source code is available.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2011

High Precision Thermal, Structural and Optical Analysis of an External Occulter Using a Common Model and the General Purpose Multi-Physics Analysis Tool Cielo

The efficient simulation of multidisciplinary thermo-opto-mechanical effects in precision deployable systems has for years been limited by numerical toolsets that do not necessarily share the same finite element basis, level of mesh discretization, data formats, or compute platforms. Cielo, a general purpose integrated modeling tool funded by the Jet Propulsion Laboratory and the Exoplanet Exploration Program, addresses shortcomings in the current state of the art via features that enable the use of a single, common model for thermal, structural and optical aberration analysis, producing results of greater accuracy, without the need for results interpolation or mapping. This paper will highlight some of these advances, and will demonstrate them within the context of detailed external occulter analyses, focusing on in-plane deformations of the petal edges for both steady-state and transient conditions, with subsequent optical performance metrics including intensity distributions at the pupil and image plane.

integrated modeling↗
DOE OSTI2018

HyperSpace: Distributed Bayesian Hyperparameter Optimization

As machine learning models continue to increase in complexity, so does the potential number of free model parameters commonly known as hyperparameters. While there has been considerable progress toward finding optimal configurations of these hyperparameters, many optimization procedures are treated as black boxes. We believe optimization methods should not only return a set of optimized hyperparameters, but also give insight into the effects of model hyperparameter settings. To this end, we present HyperSpace, a parallel implementation of Bayesian sequential model-based optimization. HyperSpace leverages high performance computing (HPC) resources to better understand unknown, potentially non-convex hyperparameter search spaces. We show that it is possible to learn the dependencies between model hyperparameters through the optimization process. By partitioning large search spaces and running many optimization procedures in parallel, we also show that it is possible to discover families of good hyperparameter settings over a variety of models including unsupervised clustering, regression, and classification tasks.

Computer Science↗
DOE OSTI2026

Illuminating the Material World: Autonomous Microscopy to Understand Order, Disorder, and Everything In Between

Artificial intelligence (AI) holds immense promise for revolutionizing microscopy, yet its widespread adoption has been hindered by challenges ranging from user inexperience to limited model transferability and difficulties in operationalizing machine learning. This presentation showcases our approach to developing practical autonomy for materials discovery, aiming to accelerate the integration of AI into everyday microscopy workflows. As shown in Fig. 1, I will focus on three key areas: understanding order-disorder transitions, quantifying point defects, and achieving truly device-scale microscopy. First, I will demonstrate the power of multi-modal knowledge graphs for integrating diverse microscopy data. By combining imaging, spectroscopy, and diffraction data, these graphs provide a holistic view of material behavior, capturing the intricate relationships between different modalities [1,2]. I will present a case study on how these models illuminate the structural and chemical changes associated with irradiation in oxide thin films, revealing critical insights for designing materials for extreme environments like spaceflight and nuclear energy. Specifically, I will show how multi-modal analysis clarifies the evolution of order-disorder transitions under irradiation, a key factor influencing material performance in these applications. Next, I will address the challenge of quantifying point defects in 2D materials. We demonstrate the application of computer vision and transfer learning to accurately identify and classify various defect types, such as vacancies and substitutional atoms, and to quantify their concentrations. This information is crucial for understanding and tailoring the properties of 2D materials for applications in electronics, optoelectronics, and catalysis. For example, I will show how our models can characterize the topological distribution of point defects in MXene transition metal carbides, providing valuable insights for optimizing their performance in energy storage and separation science. Finally, I will discuss our progress toward autonomous device-scale microscopy [3,4]. We are fundamentally redesigning electron microscopes around the principles of machine reasoning, enabling automation beyond basic tasks like sample navigation and data acquisition to include sophisticated experimental design. This approach paves the way for truly reproducible and massively scaled analysis campaigns. I will emphasize the importance of autonomous microscopy platforms for high-throughput materials discovery and characterization, facilitating the rapid screening of materials for a broad range of applications and accelerating the development of next-generation technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Margin to onset of nucleate boiling and flow instability studies for preliminary MITR design-demonstration element thermal-hydraulics

This study covers the onset of flow instability (OFI) preliminary results obtained from leveraging correlations, in addition to the preliminary thermal hydraulics results such as pressure, flow velocity, temperature, and oxide layer over the design demonstration experiment (DDE) for the Massachusetts Institute of Technology Reactor (MITR). Current computational fluid dynamics (CFD) models in fluid structure interaction (FSI) have added the capability of assessing margins to onset of nucleate boiling (ONB). This study initiates the capability to model the margin to OFI and ONB presented for the MITR. Such study is supportive of the United States High Performance Research Reactor (USHPRR) program. Previous studies provided preliminary thermal-hydraulic and mechanical analyses of the hydrodynamic effects in the MITR DDE under conservative approximations for plate power distribution. In conclusion, this study focuses on providing insights into the OFI future research direction optimizing the transport of thermal energy, mass-flow rates, flow-channel geometries, and boundary conditions.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Excitation of whistler and slow-X waves by runaway electrons in a collisional plasma

Runaway electrons are known to provide robust ideal or collisionless kinetic drive for plasma wave instabilities in both the whistler and slow-X branches, via the anomalous Doppler-shifted cyclotron resonances. In a cold and dense post-thermal-quench plasma, collisional damping of the plasma waves can compete with the collisionless drive. Previous studies have found that, due to their higher wavelength and frequency, slow-X waves suffer stronger collisional damping than the whistlers, while the ideal growth rate of slow-X modes is higher. Here, we study runaway avalanche distributions that maintain the same eigen distribution and increase only in magnitude over time. The distributions are computed from the relativistic Fokker–Planck–Boltzmann solver, upon which a linear dispersion analysis is performed to search for the most unstable or least damped slow-X and whistler modes. Taking into account the effect of plasma density, plasma temperature, and effective charge number, we find that the slow-X modes tend to be excited before the whistlers in a runaway current ramp-up. Furthermore, even when the runaway current density is sufficiently high that both branches are excited, the most unstable slow-X mode has a much higher growth rate than the most unstable whistler mode. The qualitative and quantitative trends uncovered in the current study indicate that even though past experiments and modeling efforts have concentrated on whistler modes, there is a compelling case that slow-X modes should also be a key area of focus in the runaway self-mediation through wave instabilities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Topics in Proton Structure: BSM answers to its radius puzzle and latticesubtleties within its momentum distribution

This thesis covers two subjects, brie y outlined below, of hadronic physics. The proton radius puzzle and the muon anomalous magnetic moment discrepancy point to possible signs of lepton-universality violation. We introduce the context and background necessary to understand these two problems. And we analyze two suitable beyond-the-standard-model solutions, one vector-based and one scalar-based, which simultaneously solves both issues. Furthermore, we demonstrate that the parameter space of the respective solutions can be chosen so as to not be completely forbidden by considered experimental constraints. Specifically, we show that certain violations, analyzed by other authors in the context of similar solutions to those proposed, are ameliorated if one adds to the proposed solutions particular criteria required for renormalizability. We also explore the field of lattice perturbation theory in the context of deep inelastic scattering. In particular, we study the connection between lattice-based Euclidean parton distributions and their experiment-based Minkowskian equivalents. These parton distributions are extracted from deep inelastic scattering processes in which leptons are scattered of hadronic targets at energy levels high enough to resolve the internal hadronic structure. To synchronize experiment with theory, one must find the proper matching criteria to transform approximations of parton distributions, known as quasidistributions, as they are resolved on a computer (i.e. the lattice), back to the full parton distributions extracted from experiment. We provide the necessary background to understand context of the above and show the procedures necessary to match experiment with the lattice. We demonstrate how one must tread carefully to properly perform such a matching.

Freid, Michael↗
NASA NTRS1983

Study of relationships of material properties and high efficiency solar cell performance on material composition

The performance improvements obtainable from extending the traditionally thin back-surface-field (BSF) layer deep into the base of silicon solar cells under terrestrial solar illumination (AM1) are analyzed. This extended BSF cell is also known as the back-drift-field cell. About 100 silicon cells were analyzed, each with a different emitter or base dopant impurity distribution whose selection was based on physically anticipated improvements. The four principal performance parameters (the open-circuit voltage, the short-circuit current, the fill factor, and the maximum efficiency) are computed using a FORTRAN program, called Circuit Technique for Semiconductor-device Analysis, CTSA, which numerically solves the six Shockley Equations under AM1 solar illumination at 88.92 mW/cm, at an optimum cell thickness of 50 um. The results show that very significant performance improvements can be realized by extending the BSF layer thickness from 2 um (18% efficiency) to 40 um (20% efficiency).

Sah, C. T.↗
NASA NTRS1992

Design of a hybrid laminar flow control nacelle

Consideration is given to the potential application of hybrid-laminar-flow control to the external surface of a modern, high-bypass-ratio (HBR) turbofan engine nacelle. With the advent of advanced ultra-HBR fans (with bypass ratios of 10-15), the wetted areas of these nacelles approach 10 percent of the total wetted area of future commercial transports. A hybrid-laminar-flow-control pressure distribution is specified and the corresponding nacelle geometry is computed utilizing a predictor/corrector design method. Linear stability calculations are conducted to provide predictions of the extent of the laminar boundary layer. Performance studies on an advanced twin-engine transport configuration are presented to determine potential benefits in terms of reduced fuel consumption.

Wie, Yong-Sun↗
NASA NTRSDate not supplied

A Comparative Study of Contrail Frequency Indices and GOES-16 Contrail Data Set

Contrail formations have been shown to contribute to the greenhouse effect: they are practically transparent to incoming solar radiation and do little to reflect heat away from Earth but are highly effective at trapping heat within Earth’s atmosphere. To understand the impact contrails have on climate change, contrail frequency indices (CFIs) can be used as a method to quantify aircraft-induced persistent contrails. These indices are capable of tracking long-term contrail formation and identify regions of airspace with the highest contrail formation rates. In this aper, an algorithm is proposed which is capable of using NASA Sherlock and Global Forecast System (GFS) datasets and computing CFIs over large geographic regions and long temporal intervals using NASA Ames’ High-End Computing Capability (HECC) supercomputing system. CFIs are computed using nowcast weather data and previously flown flight tracks. This paper calculated the CFIs of all twenty Air Route Traffic Control Centers in the National Airspace System on October 28th, 2019 and compared the distribution of non-zero CFIs with observed contrail data collected from GOES-16 Satellite data in order to assess the accuracy of the CFI system as a contrail prediction model. It was ultimately determined that the computed CFIs were broadly distributed in the same way as the GOES-16 contrail data and that the individual CFIs computed at the latitude/longitude points at which GOES-16 contrail masks were available had high precision and recall (at 0.75 and 0.86 respectively). While these validation results bode well for the accuracy of the CFI method, the number of provided GOES-16 masks was quite small. Future work should aim to increase the size of the GOES-16 dataset in order to perform a more comprehensive comparison between these two datasets.

Contrails↗
NASA NTRSDate not supplied

A Comparative Study of Contrail Frequency Indices and GOES-16 Contrail Data Set

Contrail formations have been shown to contribute to the greenhouse effect: they are practically transparent to incoming solar radiation and do little to reflect heat away from Earth but are highly effective at trapping heat within Earth’s atmosphere. To understand the impact contrails have on climate change, contrail frequency indices (CFIs) can be used as a method to quantify aircraft-induced persistent contrails. These indices are capable of tracking long-term contrail formation and identify regions of airspace with the highest contrail formation rates. In this aper, an algorithm is proposed which is capable of using NASA Sherlock and Global Forecast System (GFS) datasets and computing CFIs over large geographic regions and long temporal intervals using NASA Ames’ High-End Computing Capability (HECC) supercomputing system. CFIs are computed using nowcast weather data and previously flown flight tracks. This paper calculated the CFIs of all twenty Air Route Traffic Control Centers in the National Airspace System on October 28th, 2019 and compared the distribution of non-zero CFIs with observed contrail data collected from GOES-16 Satellite data in order to assess the accuracy of the CFI system as a contrail prediction model. It was ultimately determined that the computed CFIs were broadly distributed in the same way as the GOES-16 contrail data and that the individual CFIs computed at the latitude/longitude points at which GOES-16 contrail masks were available had high precision and recall (at 0.75 and 0.86 respectively). While these validation results bode well for the accuracy of the CFI method, the number of provided GOES-16 masks was quite small. Future work should aim to increase the size of the GOES-16 dataset in order to perform a more comprehensive comparison between these two datasets.

Contrails↗
DOE OSTI2022

Accelerated coupled Monte Carlo-Thermal hydraulic calculations using a hybrid GTF-diffusion-based prediction block: first results

Accurate predictions of spatial power and temperature distributions require the coupling of a neutron transport solver with a thermal-hydraulic (TH) feedback. Nowadays, Monte Carlo (MC) codes are widely coupled to TH solvers, typically via a Picard iteration (PI) method, due to the higher fidelity that such frameworks can produce. To speed up a PI, a prediction step can produce an improved initial guess for a source distribution and feed it to the MC code. Recent work investigated a prediction step that uses generalized transfer functions (GTFs) to predict the macroscopic cross sections' variations following a perturbation in TH properties, such as coolant density. The previous method also relied on first order perturbation (FOP) theory to predict perturbed power profiles, rather than using an expensive MC iterate. The implemented FOP method relied on generating a fission matrix from which the forward and adjoint Eigenmodes were extracted and later used to for power calculations. The generation of the fission matrix can introduce a significant computational overhead, therefore undermining the performance of the proposed hybrid technique when applied to high-dimensional problems, e.g., full core calculations. This work attempts to improve the GTF-FOP prediction step by replacing the FOP solver with a nodal diffusion solver, thus eliminating the need to calculate a fission matrix. The GTF-diffusion step was tested for various moderator density perturbations. In each case, the predicted power distribution showed good agreement with the reference case. The latter is attributed to the generally good prediction of most spatially distributed macroscopic cross sections, except the transport cross section, which will become the focus of future work. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗