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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Understanding the interplay between pilot fuel mixing and auto-ignition chemistry in hydrogen-enriched environment

The diesel-piloted dual-fuel compression ignition combustion strategy is well-suited to accelerate the decarbonization of transportation by adopting hydrogen as a renewable energy carrier into the existing internal combustion engine with minimal engine modifications. Despite the simplicity of engine modification, many questions remain unanswered regarding the optimal pilot injection strategy for reliable ignition with minimum pilot fuel consumption. The present study uses a single-cylinder heavy-duty optical engine to explore the phenomenology and underlying mechanisms governing the pilot fuel ignition and the subsequent combustion of a premixed hydrogen-air charge. The engine is operated in a dual-fuel mode with hydrogen premixed into the engine intake charge with a direct pilot injection of n-heptane as a diesel pilot fuel surrogate. Optical diagnostics used to visualize in-cylinder combustion phenomena include high-speed IR imaging of the pilot fuel spray evolution as well as high-speed HCHO* and OH* chemiluminescence as indicators of low-temperature and high-temperature heat release, respectively. Three pilot injection strategies are compared to explore the effects of pilot fuel mass, injection pressure, and injection duration on the probability and repeatability of successful ignition. The thermodynamic and imaging data analysis supported by zero-dimensional chemical kinetics simulations revealed a complex interplay between the physical and chemical processes governing the pilot fuel ignition process in a hydrogen containing charge. Hydrogen strongly inhibits the ignition of pilot fuel mixtures and therefore requires longer injection duration to create zones with sufficiently high pilot fuel concentration for successful ignition. Results show that ignition typically tends to rely on stochastic pockets with high pilot fuel concentration, which results in poor repeatability of combustion and frequent misfiring. In conclusion, this work has improved the understanding on how the unique chemical properties of hydrogen pose a challenge for maximization of hydrogen’s energy share in hydrogen dual-fuel engines and highlights a potential mitigation pathway.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Beyond microbial abundance: metadata integration enhances disease prediction in human microbiome studies

Multiple studies have highlighted the interaction of the human microbiome with physiological systems such as the gut, immune, liver, and skin, via key axes. Advances in sequencing technologies and high-performance computing have enabled the analysis of large-scale metagenomic data, facilitating the use of machine learning to predict disease likelihood from microbiome profiles. However, challenges such as compositionality, high dimensionality, sparsity, and limited sample sizes have hindered the development of actionable models. One strategy to improve these models is by incorporating key metadata from both the human host and sample collection/processing protocols. This remains challenging due to sparsity and inconsistency in metadata annotation and availability. In this paper, we introduce a machine learning-based pipeline for predicting human disease states by integrating host and protocol metadata with microbiome abundance profiles from 68 different studies, processed through a consistent pipeline. Our findings indicate that metadata can enhance machine learning predictions, particularly at higher taxonomic ranks like Kingdom and Phylum, though this effect diminishes at lower ranks. Our study leverages a large collection of microbiome datasets comprising 11,208 samples, therefore enhancing the robustness and statistical confidence of our findings. This work is a critical step toward utilizing microbiome and metadata for predicting diseases such as gastrointestinal infections, diabetes, cancer, and neurological disorders.

Mathematics and Computing↗
NASA NTRS2000

Object Based Numerical Zooming Between the NPSS Version 1 and a 1-Dimensional Meanline High Pressure Compressor Design Analysis Code

Within NASA's High Performance Computing and Communication (HPCC) program, NASA Glenn Research Center is developing an environment for the analysis/design of propulsion systems for aircraft and space vehicles called the Numerical Propulsion System Simulation (NPSS). The NPSS focuses on the integration of multiple disciplines such as aerodynamics, structures, and heat transfer, along with the concept of numerical zooming between 0- Dimensional to 1-, 2-, and 3-dimensional component engine codes. The vision for NPSS is to create a "numerical test cell" enabling full engine simulations overnight on cost-effective computing platforms. Current "state-of-the-art" engine simulations are 0-dimensional in that there is there is no axial, radial or circumferential resolution within a given component (e.g. a compressor or turbine has no internal station designations). In these 0-dimensional cycle simulations the individual component performance characteristics typically come from a table look-up (map) with adjustments for off-design effects such as variable geometry, Reynolds effects, and clearances. Zooming one or more of the engine components to a higher order, physics-based analysis means a higher order code is executed and the results from this analysis are used to adjust the 0-dimensional component performance characteristics within the system simulation. By drawing on the results from more predictive, physics based higher order analysis codes, "cycle" simulations are refined to closely model and predict the complex physical processes inherent to engines. As part of the overall development of the NPSS, NASA and industry began the process of defining and implementing an object class structure that enables Numerical Zooming between the NPSS Version I (0-dimension) and higher order 1-, 2- and 3-dimensional analysis codes. The NPSS Version I preserves the historical cycle engineering practices but also extends these classical practices into the area of numerical zooming for use within a companies' design system. What follows here is a description of successfully zooming I-dimensional (row-by-row) high pressure compressor results back to a NPSS engine 0-dimension simulation and a discussion of the results illustrated using an advanced data visualization tool. This type of high fidelity system-level analysis, made possible by the zooming capability of the NPSS, will greatly improve the fidelity of the engine system simulation and enable the engine system to be "pre-validated" prior to commitment to engine hardware.

Follen, G.↗
NASA NTRS2007

Discrete Data Transfer Technique for Fluid-Structure Interaction

This paper presents a general three-dimensional algorithm for data transfer between dissimilar meshes. The algorithm is suitable for applications of fluid-structure interaction and other high-fidelity multidisciplinary analysis and optimization. Because the algorithm is independent of the mesh topology, we can treat structured and unstructured meshes in the same manner. The algorithm is fast and accurate for transfer of scalar or vector fields between dissimilar surface meshes. The algorithm is also applicable for the integration of a scalar field (e.g., coefficients of pressure) on one mesh and injection of the resulting vectors (e.g., force vectors) onto another mesh. The author has implemented the algorithm in a C++ computer code. This paper contains a complete formulation of the algorithm with a few selected results.

Samareh, Jamshid A.↗
NASA NTRS2016

Dislocation Content Measured Via 3D HR-EBSD Near a Grain Boundary in an AlCu Oligocrystal

Interactions between dislocations and grain boundaries are poorly understood and crucial to mesoscale plasticity modeling. Much of our understanding of dislocation-grain boundary interaction comes from atomistic simulations and TEM studies, both of which are extremely limited in scale. High angular resolution EBSD-based continuum dislocation microscopy provides a way of measuring dislocation activity at length scales and accuracies relevant to crystal plasticity, but it is limited as a two-dimensional technique, meaning the character of the grain boundary and the complete dislocation activity is difficult to recover. However, the commercialization of plasma FIB dual-beam microscopes have made 3D EBSD studies all the more feasible. The objective of this work is to apply high angular resolution cross correlation EBSD to a 3D EBSD data set collected by serial sectioning in a FIB to characterize dislocation interaction with a grain boundary. Three dimensional high angular resolution cross correlation EBSD analysis was applied to an AlCu oligocrystal to measure dislocation densities around a grain boundary. Distortion derivatives associated with the plasma FIB serial sectioning were higher than expected, possibly due to geometric uncertainty between layers. Future work will focus on mitigating the geometric uncertainty and examining more regions of interest along the grain boundary to glean information on dislocation-grain boundary interaction.

Ruggles, Timothy↗
NASA NTRS1993

Laser tracking for vertical control

The Global Laser Tracking Network has provided LAGEOS ranging data of high accuracy since the first MERIT campaign in late 1983 and we can now resolve centimeter-level three dimensional positions of participating observatories at monthly intervals. In this analysis, the station height estimates have been considered separately from the horizontal components, and can be determined by the strongest stations with a formal standard error of 2 mm using eight years of continuous observations. The rate of change in the vertical can be resolved to a few mm/year, which is at the expected level of several geophysical effects. In comparing the behavior of the stations to that predicted by recent models of post-glacial rebound, we find no correlation in this very small effect. Particular attention must be applied to data and survey quality control when measuring the vertical component, and the survey observations are critical components of the geodynamic results. Seasonal patterns are observed in the heights of most stations, and the possibility of secular motion at the level of several millimeters per year cannot be excluded. Any such motion must be considered in the interpretation of horizontal inter-site measurements, and can help to identify mechanisms which can cause variations which occur linearly with time, seasonally, or abruptly.

Dunn, Peter↗
DOE OSTI2025

Robust Spectral Anomaly Detection in EELS Spectral Images via 3D Convolutional Variational Autoencoders

Abstract A 3D Convolutional Variational Autoencoder (3D‐CVAE) is introduced for automated anomaly detection in electron energy‐loss spectroscopy spectrum imaging (EELS‐SI) data. This approach leverages the full 3D structure of EELS‐SI data to detect subtle spectral anomalies while preserving both spatial and spectral correlations across the datacube. By employing cross‐entropy loss and training on bulk spectra, the model learns to reconstruct bulk features characteristic of the defect‐free material. In exploring methods for anomaly detection, both the 3D‐CVAE approach and principal component analysis (PCA) are evaluated, testing their performance using FeL‐edge ΔEpeak shifts designed to simulate material defects. These results show that 3D‐CVAE achieves superior anomaly detection and maintains consistent performance across various shift magnitudes. The method demonstrates clear bimodal separation between bulk and anomalous spectra, enabling reliable classification. Further analysis verifies that lower‐dimensional representations are robust to anomalies in the data. While performance advantages over PCA diminish with decreasing anomaly concentration, our method maintains high reconstruction quality even in challenging, noise‐dominated spectral regions. This approach provides a robust framework for unsupervised automated detection of spectral anomalies in EELS‐SI data, particularly valuable for analyzing complex material systems.

Chemistry↗
NASA NTRS2019

Establishing Calibration Standards for Remote Sensing Retrievals of Greenhouse Gases

While in situ greenhouse gas measurements have a concrete calibration standard, establishing a similar standard for remote sensing retrievals remains challenging. As the constellation of greenhouse gas observing satellites grows, such a standard is essential to ensure data are of the quality necessary to support scientific and policy applications. A primary goal of NASA's Atmospheric Carbon and Transport (ACT) - America aircraft campaign is to evaluate column CO2 retrievals from the Orbiting Carbon Observatory 2 (OCO-2) through coordinated underflights. The campaign also includes airborne lidar instruments that measure the amount of CO2 and CH4 below the aircraft. This presentation introduces a system that establishes calibration standards for OCO-2 and lidar retrievals based on in situ data from the ACT-America campaign. The system assimilates the in situ data into NASA's Goddard Earth Observing System (GEOS) to produce high-resolution, two-dimensional transects of CO2 along the flight path which we refer to as curtains. Excluding the ability to sample the entire atmosphere at once, any such analysis must make assumptions about the connection of measurements at different places and times to a given retrieval. We chose to use the GEOS general circulation model forced by meteorology from its data assimilation system because their scientific merits are extensively documented. Furthermore, in areas rich in data, the assimilated curtains approach a field constrained by data alone. Where data are lacking, e.g., the stratosphere, age of air and other transport diagnostics can be used to quantify the uncertainty introduced by the model. Given these uncertainties, we can determine the uncertainties of the curtains and thus our ability to evaluate remote sensing instruments. Here, we demonstrate this for several flights over North America and discuss possible applications to upcoming missions, e.g., GeoCarb.

Weir, B.↗
NASA NTRS2008

Algorithms for Spectral Decomposition with Applications to Optical Plume Anomaly Detection

The analysis of spectral signals for features that represent physical phenomenon is ubiquitous in the science and engineering communities. There are two main approaches that can be taken to extract relevant features from these high-dimensional data streams. The first set of approaches relies on extracting features using a physics-based paradigm where the underlying physical mechanism that generates the spectra is used to infer the most important features in the data stream. We focus on a complementary methodology that uses a data-driven technique that is informed by the underlying physics but also has the ability to adapt to unmodeled system attributes and dynamics. We discuss the following four algorithms: Spectral Decomposition Algorithm (SDA), Non-Negative Matrix Factorization (NMF), Independent Component Analysis (ICA) and Principal Components Analysis (PCA) and compare their performance on a spectral emulator which we use to generate artificial data with known statistical properties. This spectral emulator mimics the real-world phenomena arising from the plume of the space shuttle main engine and can be used to validate the results that arise from various spectral decomposition algorithms and is very useful for situations where real-world systems have very low probabilities of fault or failure. Our results indicate that methods like SDA and NMF provide a straightforward way of incorporating prior physical knowledge while NMF with a tuning mechanism can give superior performance on some tests. We demonstrate these algorithms to detect potential system-health issues on data from a spectral emulator with tunable health parameters.

Srivastava, Askok N.↗
NASA NTRS1994

Program to develop a performance and heat load prediction system for multistage turbines

Flows in low-aspect ratio turbines, such as the SSME fuel turbine, are three dimensional and highly unsteady due to the relative motion of adjacent airfoil rows and the circumferential and spanwise gradients in total pressure and temperature, The systems used to design these machines, however, are based on the assumption that the flow is steady. The codes utilized in these design systems are calibrated against turbine rig and engine data through the use of empirical correlations and experience factors. For high aspect ratio turbines, these codes yield reasonably accurate estimates of flow and temperature distributions. However, future design trends will see lower aspect ratio (reduced number of parts) and higher inlet temperature which will result in increased three dimensionality and flow unsteadiness in turbines. Analysis of recently acquired data indicate that temperature streaks and secondary flows generated in combustors and up-stream airfoils can have a large impact on the time-averaged temperature and angle distributions in downstream airfoil rows.

Sharma, OM↗
DOE OSTI2024

Modulated Thermomechanical Analysis of Compression-Molded High-Density Polyethylene

Thermomechanical analysis (TMA) experiments conducted on high-density polyethylene (HDPE) show both reversible and irreversible dimensional changes. To further explore these reversible and irreversible processes, modulated thermomechanical analysis (MTMA) was used. Before reliable data on compression-molded HDPE was collected, a parameter optimization was performed to obtain a suitable MTMA method. Once a suitable method was obtained, several MTMA experiments were conducted on compression-molded HDPE. This work highlights the steps taken during the MTMA parameter optimization and the results obtained from MTMA experiments conducted on pristine compression-molded HDPE samples.

36 MATERIALS SCIENCE↗
NASA NTRS2004

Pre-Hardware Optimization and Implementation Of Fast Optics Closed Control Loop Algorithms

One of the main heritage tools used in scientific and engineering data spectrum analysis is the Fourier Integral Transform and its high performance digital equivalent - the Fast Fourier Transform (FFT). The FFT is particularly useful in two-dimensional (2-D) image processing (FFT2) within optical systems control. However, timing constraints of a fast optics closed control loop would require a supercomputer to run the software implementation of the FFT2 and its inverse, as well as other image processing representative algorithm, such as numerical image folding and fringe feature extraction. A laboratory supercomputer is not always available even for ground operations and is not feasible for a night project. However, the computationally intensive algorithms still warrant alternative implementation using reconfigurable computing technologies (RC) such as Digital Signal Processors (DSP) and Field Programmable Gate Arrays (FPGA), which provide low cost compact super-computing capabilities. We present a new RC hardware implementation and utilization architecture that significantly reduces the computational complexity of a few basic image-processing algorithm, such as FFT2, image folding and phase diversity for the NASA Solar Viewing Interferometer Prototype (SVIP) using a cluster of DSPs and FPGAs. The DSP cluster utilization architecture also assures avoidance of a single point of failure, while using commercially available hardware. This, combined with the control algorithms pre-hardware optimization, or the first time allows construction of image-based 800 Hertz (Hz) optics closed control loops on-board a spacecraft, based on the SVIP ground instrument. That spacecraft is the proposed Earth Atmosphere Solar Occultation Imager (EASI) to study greenhouse gases CO2, C2H, H2O, O3, O2, N2O from Lagrange-2 point in space. This paper provides an advanced insight into a new type of science capabilities for future space exploration missions based on on-board image processing for control and for robotics missions using vision sensors. It presents a top-level description of technologies required for the design and construction of SVIP and EASI and to advance the spatial-spectral imaging and large-scale space interferometry science and engineering.

Kizhner, Semion↗
NASA NTRS1986

A high resolution spatial and temporal multiple Doppler analysis of a microburst and its application to aircraft flight simulation

Multiple Doppler radar data collected during the Joint Airport Weather Studies (JAWS) Project is used to synthesize the three-dimensional wind in the region of a microburst. The particular microburst used in this study is the strongest one to date for which three-dimensional winds have been recovered. As a diagnostic tool, a six-DOF numerical aircraft model having characteristics similar to 727 series aircraft is used to investigate jet transport aircraft response to observed microburst winds during simulated approaches and departures. Simple pilot control laws are used to adjust thrust, pitch, roll, and yaw so as to maintain given approach or departure parameters. Generally, when horizontal wind shear along the approach or departure path is 0.01/s or greater, the model is unable to maintain the desired approach path and suffers a significant reduction in climb performance during a go-around or departure. Although the mean wind shear along a path gives a good qualitative measure of the wind shear threat to a jet transport, different paths with similar mean shears can yield markedly different results, as do the same paths through the microburst at different times. These findings are a direct consequence of the fine temporal and spatial scale of microburst winds. During any given modeled aircraft traverse through the region of highest horizontal shear, time variations in the microburst wind field are shown to have an insignificant effect on the modeled flight path. This is because the traverse period is short (30 s) compared to the lifetime of a microburst (300-600 s).

Elmore, K. L.↗
NASA NTRS2019

Cloud Giovanni: Reining in Costs and Improving Performance with Analytical Data Stores Using Scalable Serverless Architecture

Giovanni is the Geospatial Interactive Online Visualization ANd aNalysis Infrastructure developed at NASA GES DISC which provides a simple and intuitive way to visualize, analyze, and access vast amounts of Earth science data. It receives large number of user requests each day for a variety of analysis and visualization services, which leads to the big data challenge of serving gradually increasing large data volumes with diverse statistical algorithms. We hereby propose a multi-dimensional accumulation method which provides fast and cost-efficient cloud analysis for diverse services including both area averaging and time averaging. This method involves the weighted volume integration over multiple variable dimensions (time and space), and is implemented in AWS using Athena providing serverless and highly scalable data analysis. Compared to the standard method, this approach dramatically reduced the computational time by order of magnitude with a minimal AWS cost incurred. For example, for a benchmark of 10-year area averaging over the 1x1 degree daily variable, the computational time was reduced from minutes to seconds, and the Athena cost is only $5 for 100,000 requests.

Zhang, Hailiang↗
DOE OSTI2025

A hybrid surrogate modeling framework for the Digital Twin of a Fluoride-salt-cooled High-temperature Reactor (FHR)

While nuclear energy is a non-greenhouse-gas emitting energy source, expensive operational costs due to the high-level of safety requirements decreases their competitiveness in the sustainable energy market. Advanced reactor concepts paired with Digital Twins aim to increase the commercialization gains of nuclear energy by reducing operational costs, increasing reactor reliability and enhancing power generation. To support Digital Twin tasks such as real-time autonomous control, proactive maintenance monitoring or optimizing power demand operations, a fast and accurate virtual representation of the Nuclear Power Plant (NPP) is required. The computational cost of high-fidelity, physics-based models are unsuitable for real-time analysis or scalability. Here, in this work, a hybrid surrogate modeling framework is developed fora Fluoride-salt-cooled High-temperature Reactor (FHR) that leverages physics-inspired models for key reactor components and uses data-driven methods for rapid system state space prediction. The Xenon reactivity feedback model is integrated to inform the surrogate model about the reactor core and the homologous pump theory model is the basis for representing pump degradation. Using a detailed, two dimensional thermal hydraulics model to generate data on the FHR, we train a network of Vectorized Autoregressive Moving-Average with eXogenous input (VARMAX) models to predict the remaining state values. The result is a surrogate model that provides a detailed reactor state representation of 41 system states and a pump degradation analysis. The framework is applied to Load Follows profiles, yielding high accuracy and a speedup that is more than 4000x faster compared to the higher- fidelity thermal hydraulics model, enabling real-time operational intelligence and applications in long horizon predictions. While the surrogate model framework is demonstrated for the particular case of FHR, the hybrid physical/data-driven modeling approach including the network of surrogates and the underlying modularity has the potential to be applied to other physical asset systems.

Digital Twins↗
NASA NTRS1975

Design of a graphite/epoxy metering truss for the Large Space Telescope

This paper presents the design development of a truss-type structure which utilizes graphite/epoxy as the primary material system. A review is given of an on-going NASA sponsored program to develop a structural assembly which is required to have a very high degree of dimensional stability. Methods of material characterization and selection are given along with property data such as strength, stiffness, outgassing and thermal expansion. Finite element (NASTRAN) analysis will be discussed and deflection plots of the lowest mode frequencies will be presented. Proposed full scale tests: static, dynamic, and thermal/vacuum will be described.

Oken, S.↗
NASA NTRS1975

High-lift aerodynamics: Trends, trades, and options

The trend toward the utilization of higher maximum lift coefficient with increased aircraft size and cruise velocities is discussed. The impact of this trend on the need for tradeoffs between cruise performance and takeoff, climb, and landing performance is examined. Theoretical methods for the analysis of the two-dimensional characteristics of flap systems are described and compared with experimental data. Four powered-lift concepts are described to outline some of the options currently being developed. Two jet-flap theories are described which provide analytical methods for estimation of the three-dimensional aerodynamic high-lift performance characteristics of powered lift systems.

Margason, R. J.↗
NASA NTRS1993

Assessment of crack growth in a space shuttle main engine first-stage, high-pressure fuel turbopump blade

A two-dimensional finite element fracture mechanics analysis of a space shuttle main engine (SSME) turbine blade firtree was performed using the MARC finite element code. The analysis was conducted under combined effects of thermal and mechanical loads at steady-state conditions. Data from a typical engine stand cycle of the SSME were used to run a heat transfer analysis and, subsequently, a thermal structural fracture mechanics analysis. Temperature and stress contours for the firtree under these operating conditions were generated. High stresses were found at the firtree lobes where crack initiation was triggered. A life assessment of the firtree was done by assuming an initial and a final crack size.

Abdul-Aziz, Ali↗