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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

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NASA NTRS2017

MATRIX-VBS (v1.0): Implementing an Evolving Organic Aerosol Volatility in an Aerosol Microphysics Model

The gas-particle partitioning and chemical aging of semi-volatile organic aerosol are presented in a newly developed box model scheme, where its effect on the growth, composition, and mixing state of particles is examined. The volatility-basis set (VBS) framework is implemented into the aerosol microphysical scheme MATRIX (Multiconfiguration Aerosol TRacker of mIXing state), which resolves mass and number aerosol concentrations and in multiple mixing-state classes. The new scheme, MATRIX-VBS, has the potential to significantly advance the representation of organic aerosols in Earth system models by improving upon the conventional representation as non-volatile particulate organic matter, often also with an assumed fixed size distribution. We present results from idealized cases representing Beijing, Mexico City, a Finnish forest, and a southeastern US forest, and investigate the evolution of mass concentrations and volatility distributions for organic species across the gas and particle phases, as well as assessing their mixing state among aerosol populations. Emitted semi-volatile primary organic aerosols evaporate almost completely in the intermediate-volatility range, while they remain in the particle phase in the low-volatility range. Their volatility distribution at any point in time depends on the applied emission factors, oxidation by OH radicals, and temperature. We also compare against parallel simulations with the original scheme, which represented only the particulate and non-volatile component of the organic aerosol, examining how differently the condensed-phase organic matter is distributed across the mixing states in the model. The results demonstrate the importance of representing organic aerosol as a semi-volatile aerosol, and explicitly calculating the partitioning of organic species between the gas and particulate phases.

volatility-basis set↗

NASA NTRS2001

Evaluation of GLAS Demonstration Model Loop Heat Pipe Thermal Vacuum Performance with Various Fluid Charges

Two loop heat pipes (LHPs) are to be used for tight thermal control of the Geoscience Laser Altimeter System (GLAS) instrument, planned for flight in late 2001. The LHPs are charged with Propylene as a working fluid. One LHP will be used to transport 110 W from a laser a radiator, the other will transport 190 W from electronic boxes to a separate radiator. The application includes a large amount of thermal mass in each LHP system and low initial startup powers. This along with some non-ideal flight design compromises, such as a less than ideal charge level for this design concept with a symmetrical secondary wick, lead to inadequate performance of the flight LHPs during the flight thermal vacuum test in October of 2000. This presentation focuses on identifying; the sources of the flight test difficulties by modifying the charge and test setup of the successfully tested Development Model Loop Heat Pipe (DM LHP). While very similar to the flight design, the DM L14P did have several significant difference in design and method of testing. These differences were evaluated for affect on performance by conforming the DM LHP to look more like the flight units. The major difference that was evaluated was the relative fill level of the working fluid within the concentrically design LHP compensation chamber. Other differences were also assessed through performance testing including starter heater size and "hot biasing" of major interior components. Performance was assessed with respect to startup, low power operation, conductance, and control heater power. The results of the testing showed that performance improves as initial charge increases, and when the starter heater is made smaller. The "hot biasing" of the major components did not appear to have a detrimental effect. As a result of test results of the DM LHP, modifications are being made to the flight units to increase the fluid charge and increase the watt-density of the starter heater.

Baker, Charles↗

DOE OSTI2021

System risk quantification and decision making support using functional modeling and dynamic Bayesian network

Risk-informed decision-making requires a probabilistic assessment of the likelihood of success of control action, given the system status. This paper presents a systematic state transition modeling approach integrating dynamic probabilistic risk assessment with a decision-making process using a dynamic Bayesian network (DBN) coupled with functional modeling. A functional model designed with multilevel flow modeling (MFM) technique was used to build a system state structure inferred by energy, mass, and information flow so that one can verify the developed model with respect to system functionality. The MFM model represents the causal relationship among the nodes, which captures the structure of process parameters and control units. Each node may have multiple possible states, and the DBN structured by the MFM model represents the time-domain transitions among the defined states. Furthermore, the MFM-DBN integrated state transition modeling is a white-box approach that allows one to draw the system's risk profile by updating the system states and supports the decisions probabilistically with physical inference. An example of a simple heating system has been used to illustrate this process, including decision-making support based on quantitative risk profile. For demonstrating its applicability to a complex system operational decision making, a case study of station blackout accident scenario leading to the seal loss of coolant accident in a nuclear power plant is presented. The proposed approach effectively provided the risk profile along time for each option so that the operators can make the best decision, which minimizes the plant risk.

42 ENGINEERING↗

DOE OSTI2024

Projecting Large Fires in the Western US With an Interpretable and Accurate Hybrid Machine Learning Method

More frequent and widespread large fires are occurring in the western United States (US), yet reliable methods for predicting these fires, particularly with extended lead times and a high spatial resolution, remain challenging. In this study, we proposed an interpretable and accurate hybrid machine learning (ML) model, that explicitly represented the controls of fuel flammability, fuel availability, and human suppression effects on fires. The model demonstrated notable accuracy with a F 1 -score of 0.846 ± 0.012, surpassing process-driven fire danger indices and four commonly used ML models by up to 40% and 9%, respectively. More importantly, the ML model showed remarkably higher interpretability relative to other ML models. Specifically, by demystifying the “black box” of each ML model using the explainable AI techniques, we identified substantial structural differences across ML fire models, even among those with similar accuracy. The relationships between fires and their drivers, identified by our model, were aligned closer with established fire physical principles. The ML structural discrepancy led to diverse fire predictions and our model predictions exhibited greater consistency with actual fire occurrence. With the highly interpretable and accurate model, we revealed the strong compound effects from multiple climate variables related to evaporative demand, energy release component, temperature, and wind speed, on the dynamics of large fires and megafires in the western US. Our findings highlight the importance of assessing the structural integrity of models in addition to their accuracy. They also underscore the critical need to address the rise in compound climate extremes linked to large wildfires.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2016

Imparting Barely Visible Impact Damage to a Stitched Composite Large-Scale Pressure Box

The Pultruded Rod Stitched Efficient Unitized Structure (PRSEUS) is a concept that was developed by The Boeing Company to address the complex structural design aspects associated with a pressurized hybrid wing body (HWB) aircraft configuration, which has been a focus of the NASA Environmentally Responsible Aviation Project. The NASA-Boeing structural development for the HWB aircraft culminated in testing of the multi-bay box, which is an 80%-scale representation of the pressurized center-body section. This structure was tested in the NASA Langley Research Center Combined Loads Test System facility. As part of this testing, barely visible impact damage was imparted to the interior and exterior of the test article to demonstrate compliance with a condition representative of the requirements for Category 1 damaged composite structure as defined by the Federal Aviation Regulations. Interior impacts were imparted using an existing spring-loaded impactor, while the exterior impacts were imparted using a newly designed, gravity-driven impactor. This paper describes the impacts to the test article, and the design of the gravitydriven guided-weight impactor. The guided-weight impactor proved to be a very reliable method to impart barely visible impact damage in locations which are not easily accessible for a traditional drop-weight impactor, while at the same time having the capability to be highly configurable for use on other aircraft structures.

Lovejoy, Andrew E.↗

NASA NTRS2021

SHARPEN: A Scheme to Restore the Distribution of Averaged Precipitation Fields

A key strategy in obtaining complete global coverage of high-resolution precipitation is to combine observations from multiple fields, such as the intermittent passive microwave observations, precipitation propagated in time using motion vectors, and geosynchronous infrared observations. These separate precipitation fields can be combined through weighted averaging, which produces estimates that are generally superior to the individual parent fields. However, the process of averaging changes the distribution of the precipitation values, leading to an increase in precipitating area and a decrease in the values of high precipitation rates, a phenomenon observed in IMERG. To mitigate this issue, we introduce a new scheme called SHARPEN (Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood), which recovers the distribution of the averaged precipitation field based on the idea of quantile mapping applied to the local environment. When implemented in IMERG, precipitation estimates from SHARPEN exhibit a distribution that resembles that of the original instantaneous observations, with matching precipitating area and peak precipitation rates. Case studies demonstrate its improved ability in bridging between the parent precipitation fields. Evaluation against ground observations reveals a distinct improvement in precipitation detection skill, but also a slightly reduced correlation likely because of a sharper precipitation field. The increased computational demand of SHARPEN can be mitigated by striding over multiple grid boxes, which has only marginal impacts on the accuracy of the estimates. SHARPEN can be applied to any precipitation algorithm that produces an average from multiple input precipitation fields and is being considered for implementation in IMERG V07.

Jackson Tan↗

DOE OSTI2025

General field evaluation in high-order meshes on GPUs

Robust and scalable function evaluation at any arbitrary point in the finite/spectral element mesh is required for querying the partial differential equation solution at points of interest, comparison of solution between different meshes, and Lagrangian particle tracking. This is a challenging problem, particularly for high-order unstructured meshes partitioned in parallel with MPI, as it requires identifying the element that overlaps a given point and computing the corresponding reference space coordinates. Here, we present a robust and efficient technique for general field evaluation in large-scale high-order meshes with quadrilaterals and hexahedra. In the proposed method, a combination of globally partitioned and processor-local maps are used to first determine a list of candidate MPI ranks, and then locally candidate elements that could contain a given point. Next, element-wise bounding boxes further reduce the list of candidate elements. Finally, Newton’s method with trust region is used to determine the overlapping element and corresponding reference space coordinates. Since GPU-based architectures have become popular for accelerating computational analyses using meshes with tensor-product elements, specialized kernels have been developed to utilize the proposed methodology on GPUs. The method is also extended to enable general field evaluation on surface meshes. The paper concludes by demonstrating the use of the proposed method in various applications ranging from mesh-to-mesh transfer during r-adaptivity to Lagrangian particle tracking.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1981

Remote sensing research in geographic education: An alternative view

It is noted that within many geography departments remote sensing is viewed as a mere technique a student should learn in order to carry out true geographic research. This view inhibits both students and faculty from investigation of remotely sensed data as a new source of geographic knowledge that may alter our understanding of the Earth. The tendency is for geographers to accept these new data and analysis techniques from engineers and mathematicians without questioning the accompanying premises. This black-box approach hinders geographic applications of the new remotely sensed data and limits the geographer's contribution to further development of remote sensing observation systems. It is suggested that geographers contribute to the development of remote sensing through pursuit of basic research. This research can be encouraged, particularly among students, by demonstrating the links between geographic theory and remotely sensed observations, encouraging a healthy skepticism concerning the current understanding of these data.

Wilson, H.↗

NASA NTRSDate not supplied

Semantic Search with Sentence-BERT for Design Information Retrieval

Managing and referencing design knowledge is a critical activity in the design process. However, reliably retrieving useful knowledge can be a frustrating experience for users of knowledge management systems due to inherent limitations of standard keyword-based searches. In this research, we consider the task of retrieving relevant lessons learned from the NASA Lessons Learned Information System (LLIS). To this end, we apply a state-of-the-art natural language processing (NLP) technique for information retrieval: semantic search with sentence-BERT, which is a modification of a Bidirectional Encoder Representations from Transformers (BERT) model that uses siamese and triplet network architectures to obtain semantically meaningful sentence embeddings. While the pretrained sBERT model shows excellent out-of-the-box performance, we further fine-tune the model on data from the LLIS so that it learns on design engineering-relevant vocabulary. We quantify the improvement in query results using both standard sBERT and fine-tuned sBERT over the LLIS’s built-in keyword search. Additionally, we demonstrate a use case for the query system by searching for lessons learned relevant to specific requirements from a NASA project as part of a broader knowledge management and retrieval system. Results indicate that applying state-of-the-art natural language processing techniques, especially when fine-tuned using engineering data, to design information retrieval tasks shows significant promise in modernizing design knowledge management systems.

Hannah S. Walsh↗

DOE OSTI2021

Instance Segmentation for Direct Measurements of Satellites in Metal Powders and Automated Microstructural Characterization from Image Data

In this work, we propose instance segmentation as a useful tool for image analysis in materials science. Instance segmentation is an advanced technique in computer vision which generates individual segmentation masks for every object of interest that is recognized in an image. Using an out-of-the-box implementation of Mask R-CNN, instance segmentation is applied to images of metal powder particles produced through gas atomization. Leveraging transfer learning allows for the analysis to be conducted with a very small training set of labeled images. As well as providing another method for measuring the particle size distribution, we demonstrate the first direct measurements of the satellite content in powder samples. After analyzing the results for the labeled data dataset, the trained model was used to generate measurements for a much larger set of unlabeled images. The resulting particle size measurements showed reasonable agreement with laser scattering measurements. The satellite measurements were self-consistent and showed good agreement with the expected trends for different samples. Finally, we present a small case study showing how instance segmentation can be used to measure spheroidite content in the UltraHigh Carbon Steel DataBase, demonstrating the flexibility of the technique.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Graph interpolating activation improves both natural and robust accuracies in data-efficient deep learning

Improving the accuracy and robustness of deep neural nets (DNNs) and adapting them to small training data are primary tasks in deep learning (DL) research. In this paper, we replace the output activation function of DNNs, typically the data-agnostic softmax function, with a graph Laplacian-based high-dimensional interpolating function which, in the continuum limit, converges to the solution of a Laplace–Beltrami equation on a high-dimensional manifold. Furthermore, we propose end-to-end training and testing algorithms for this new architecture. The proposed DNN with graph interpolating activation integrates the advantages of both deep learning and manifold learning. Compared to the conventional DNNs with the softmax function as output activation, the new framework demonstrates the following major advantages: First, it is better applicable to data-efficient learning in which we train high capacity DNNs without using a large number of training data. Second, it remarkably improves both natural accuracy on the clean images and robust accuracy on the adversarial images crafted by both white-box and black-box adversarial attacks. Third, it is a natural choice for semi-supervised learning. This paper is a significant extension of our earlier work published in NeurIPS, 2018. For reproducibility, the code is available at https://github.com/BaoWangMath/DNN-DataDependentActivation .

Mathematics↗

NASA NTRS1981

Prediction of boundary-layer characteristics of an oscillating airfoil

The evolution of unsteady boundary layers on oscillating airfoils is investigated by solving the governing equations by the Characteristic Box scheme. The difficulties associated with computing the first profile on a given time line, and the velocity profiles with partial flow reversal are solved. A sample calculation is performed for an external velocity distribution typical of those found near the leading edge of thin airfoils. The viability of the calculation procedure is demonstrated.

Cebeci, T.↗

NASA NTRS1981

Prediction of boundary-layer characteristics of an oscillating airfoil

Cebeci, T.↗

NASA NTRS2016

The Behavior of a Stitched Composite Large-Scale Multi-Bay Pressure Box

NASA has created the Environmentally Responsible Aviation (ERA) Project to develop technologies to reduce impact of aviation on the environment. A critical aspect of this pursuit is the development of a lighter, more robust airframe to enable the introduction of unconventional aircraft configurations. NASA and The Boeing Company have worked together to develop a structural concept that is lightweight and an advancement beyond state-of-the-art composite structures. The Pultruded Rod Stitched Efficient Unitized Structure (PRSEUS) is an integrally stiffened panel design where elements are stitched together and designed to maintain residual load-carrying capabilities under a variety of damage scenarios. With the PRSEUS concept, through-the-thickness stitches are applied through dry fabric prior to resin infusion, and replace fasteners throughout each integral panel. Through-the-thickness reinforcement at discontinuities, such as along flange edges, has been shown to suppress delamination and turn cracks, which expands the design space and leads to lighter designs. The pultruded rod provides stiffening away from the more vulnerable skin surface and improves bending stiffness. A series of building block tests were evaluated to explore the fundamental assumptions related to the capability and advantages of PRSEUS panels. The final step in the building block series of tests is an 80%-scale pressure box representing a portion of the center section of a Hybrid Wing Body (HWB) transport aircraft. The testing of this test article under maneuver and internal pressure loading conditions is the subject of this paper. The experimental evaluation of this article, along with the other building block tests and the accompanying analyses, has demonstrated the viability of a PRSEUS center body for the HWB vehicle. Additionally, much of the development effort is also applicable to traditional tube-and-wing aircraft, advanced aircraft configurations, and other structures where weight and through-the-thickness strength are design considerations.

Jegley, Dawn C.↗

NASA NTRS2016

Testing of a Stitched Composite Large-Scale Multi-Bay Pressure Box

NASA has created the Environmentally Responsible Aviation (ERA) Project to develop technologies to reduce aviation's impact on the environment. A critical aspect of this pursuit is the development of a lighter, more robust airframe to enable the introduction of unconventional aircraft configurations. NASA and The Boeing Company have worked together to develop a structural concept that is lightweight and an advancement beyond state-of-the-art composite structures. The Pultruded Rod Stitched Efficient Unitized Structure (PRSEUS) is an integrally stiffened panel design where elements are stitched together. The PRSEUS concept is designed to maintain residual load carrying capabilities under a variety of damage scenarios. A series of building block tests were evaluated to explore the fundamental assumptions related to the capability and advantages of PRSEUS panels. The final step in the building block series is an 80%-scale pressure box representing a portion of the center section of a Hybrid Wing Body (HWB) transport aircraft. The testing of this article under maneuver load and internal pressure load conditions is the subject of this paper. The experimental evaluation of this article, along with the other building block tests and the accompanying analyses, has demonstrated the viability of a PRSEUS center body for the HWB vehicle. Additionally, much of the development effort is also applicable to traditional tube-and-wing aircraft, advanced aircraft configurations, and other structures where weight and through-the-thickness strength are design considerations.

Jegley, Dawn↗

NASA NTRS2018

Subsetting Satellite Level 2 Data at NASA GES DISC

Working with Level 2 satellite swath data can prove challenging for many users. The variety of metadata, high resolution, and orbit-based nature of such data makes it difficult to acquire and manage. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) has developed a general-purpose Level 2 Subsetting service that facilitates access to Level 2 data products. The Level 2 Subsetter enables users to sub-sample swath and gridded data in a variety of ways: by location (in a lat/lon box, within a circle of given radius, or at a specific point), by time (via swath segments or collections of granules), by any nonspatial dimension, or by selecting specific variables. The Level 2 Subsetting service also allows users to specify output data configuration and file format. In this presentation, we will detail and demonstrate the capabilities of this new service.

Huwe, Paul↗

NASA NTRS1976

Test data report: Low speed wind tunnel tests of a full scale, fixed geometry inlet, with engine, at high angles of attack

A full scale inlet test was to be done in the NASA-ARC 40' X 80' WT to demonstrate satisfactory inlet performance at high angles of attack. The inlet was designed to match a Hamilton-Standard 55 inch, variable pitch fan, driven by a Lycoming T55-L-11A gas generator. The test was installed in the wind tunnel on two separate occasions, but mechanical failures in the fan drive gear box early in each period terminated testing. A detailed description is included of the Model, installation, instrumentation and data reduction procedures.

Shain, W. M.↗

DOE OSTI2025

A foundation model for atomistic materials chemistry

Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early machine-learning (ML) force fields have largely been limited by (i) the substantial computational and human effort required to develop and validate potentials for each particular system of interest and (ii) a general lack of transferability from one chemical system to the next. Here, we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model-and its qualitative and at times quantitative accuracy-on a diverse set of problems in the physical sciences, including properties of solids, liquids, gases, chemical reactions, interfaces, and even the dynamics of a small protein. The model can be applied out of the box as a starting or "foundation" model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users obtain reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step toward democratizing the revolution in atomic-scale modeling that has been brought about by ML force fields.

Batatia, Ilyes↗