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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Targeted Chemical Looping Materials Discovery by an Inverse Design

Chemical looping with oxygen uncoupling (CLOU) materials is actively sought for combustion of carbonaceous materials to achieve complete conversion and capture of carbon dioxide. These materials may play a vital role in reducing atmospheric carbon via negative carbon output. However, there is no one‐size‐fits‐all approach as different operating conditions and feedstocks may require different CLOU materials. As a result, the exploration and discovery of high‐performance CLOU materials can be a slow process. To address this challenge, a high‐throughput inverse machine learning workflow that identifies optimum materials from perovskite oxides for a given set of targets is developed—temperature and Gibbs free energy of oxygen formation. The model is trained on high‐throughput density functional theory calculations of CLOU materials and inverts the materials design process using a genetic algorithm to produce realistic substituted SrFeO 3‐δ compositions as output. Using the inverse model, it is able to identify several interesting new families of CLOU materials: Sr 1‐ x A x Fe 1‐ y B y O 3‐δ (e.g., A = Ca or K; B = Mg, Bi, Mn, Ni, Co, Cu, or Zn). These materials have shown promising properties, and some of them even outperform the benchmark material in terms of oxygen release kinetics under relevant CLOU operating conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Raman spectrum and polarizability of liquid water from deep neural networks

We introduce a scheme based on machine learning and deep neural networks to model the environmental dependence of the electronic polarizability in insulating materials. Application to liquid water shows that training the network with a relatively small number of molecular configurations is sufficient to predict the polarizability of arbitrary liquid configurations in close agreement with ab initio density functional theory calculations. In combination with a neural network representation of the interatomic potential energy surface, the scheme allows us to calculate the Raman spectra along 2-nanosecond classical trajectories at different temperatures for H 2 O and D 2 O. The vast gains in efficiency provided by the machine learning approach enable longer trajectories and larger system sizes relative to ab initio methods, reducing the statistical error and improving the resolution of the low-frequency Raman spectra. Decomposing the spectra into intramolecular and intermolecular contributions elucidates the mechanisms behind the temperature dependence of the low-frequency and stretch modes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Classification of computed thermal tomography images with deep learning convolutional neural network

Thermal tomography (TT) is a computational method for the reconstruction of depth profile of the internal material defects from Pulsed Infrared Thermography (PIT) nondestructive evaluation. Here, the PIT method consists of recording material surface temperature transients with a fast frame infrared camera, following thermal pulse deposition on the material surface with a flashlamp and heat diffusion into material bulk. TT algorithm obtains depth reconstructions of thermal effusivity, which has been shown to provide visualization of the subsurface internal defects in metals. In many applications, one needs to determine the defect shape and orientation from reconstructed effusivity images. Interpretation of TT images is non-trivial because of blurring, which increases with depth due to the heat diffusion-based nature of image formation. We have developed a deep learning convolutional neural network (CNN) to classify the size and orientation of subsurface material defects in TT images. CNN was trained with TT images produced with computer simulations of 2D metallic structures (thin plates) containing elliptical subsurface voids. The performance of CNN was investigated using test TT images developed with computer simulations of plates containing elliptical defects, and defects with shapes imported from scanning electron microscopy images. CNN demonstrated the ability to classify radii and angular orientation of elliptical defects in previously unseen test TT images. We have also demonstrated that CNN trained on the TT images of elliptical defects is capable of classifying the shape and orientation of irregular defects.

42 ENGINEERING↗
DOE OSTI2025

Implementation of stacked ensemble machine learning for the detection of surrogate plutonium contamination in soil via LIBS

Supervised machine learning methods have demonstrated increased utility for the quantification of lanthanide and actinide elements in atomic spectroscopy applications. This study implements laser-induced breakdown spectroscopy (LIBS) for the identification of plutonium surrogate material (CeO 2 ) in soil matrices by training supervised machine learning methods on the recorded spectral data. A bagged ensemble using Random Forest yields the highest sensitivity predictions with a detection limit of 0.015 wt.% CeO 2 . However, high precision in Ce content prediction required the use of a stacked ensemble regression, which provided the superlative Ce quantification model with an error of 0.107% and a detection limit of 0.022 wt.%. Furthermore, the high performance of the stacked ensemble demonstrates its potential to enhance the accuracy and sensitivity of nuclear contaminant detection using field-deployable spectroscopic analyzers in real-world scenarios.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Data From Experiments on Bubbling Fluidization of Zeolite in a Rectangular Bubbling Fluidized Bed

Fluidization experiments were conducted in a lab-scale rectangular bubbling fluidized bed with the objective of generating a high-quality dataset for model validation and artificial intelligence/machine learning (AI/ML) training. Zeolite was chosen as the bed material, and the fluidizing medium was air as supplied by a compressor. Three different flow rates at the inlet were chosen such that the particles were fluidized but not elutriated from the system. The test matrix involved randomization and replicates to provide uncertainty estimates as well as four different batches of zeolite as the bed material. The quantities of interest obtained from this study were statistics of differential pressures, interface heights, and particle velocities. Considering all the components of the elaborate test plan, the results obtained were consistent and reproducible. Characterization tests were performed to estimate particle properties including size, density, coefficient of friction, coefficient of restitution, and minimum fluidization velocity. In addition, the angle of repose from granular discharge experiments has been reported to account for rolling friction, though its effect on the overall process is expected to be negligible.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

SNM Radiation Signature Classification Using Different Semi-Supervised Machine Learning Models

The timely detection of special nuclear material (SNM) transfers between nuclear facilities is an important monitoring objective in nuclear nonproliferation. Persistent monitoring enabled by successful detection and characterization of radiological material movements could greatly enhance the nuclear nonproliferation mission in a range of applications. Supervised machine learning can be used to signal detections when material is present if a model is trained on sufficient volumes of labeled measurements. However, the nuclear monitoring data needed to train robust machine learning models can be costly to label since radiation spectra may require strict scrutiny for characterization. Therefore, this work investigates the application of semi-supervised learning to utilize both labeled and unlabeled data. As a demonstration experiment, radiation measurements from sodium iodide (NaI) detectors are provided by the Multi-Informatics for Nuclear Operating Scenarios (MINOS) venture at Oak Ridge National Laboratory (ORNL) as sample data. Anomalous measurements are identified using a method of statistical hypothesis testing. After background estimation, an energy-dependent spectroscopic analysis is used to characterize an anomaly based on its radiation signatures. In the absence of ground-truth information, a labeling heuristic provides data necessary for training and testing machine learning models. Supervised logistic regression serves as a baseline to compare three semi-supervised machine learning models: co-training, label propagation, and a convolutional neural network (CNN). In each case, the semi-supervised models outperform logistic regression, suggesting that unlabeled data can be valuable when training and demonstrating value in semi-supervised nonproliferation implementations.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
NASA NTRS1983

Research and technology at the Kennedy Space Center

Cryogenic engineering, hypergolic engineering, hazardous warning, structures and mechanics, computer sciences, communications, meteorology, technology applications, safety engineering, materials analysis, biomedicine, and engineering management and training aids research are reviewed.

Source record↗
NASA NTRS1984

Research and technology, 1984 report

Research and technology projects in the following areas are described: cryogenic engineering, hypergolic engineering, hazardous warning instrumentation, structures and mechanics, sensors and controls, computer sciences, communications, material analysis, biomedicine, meteorology, engineering management, logistics, training and maintenance aids, and technology applications.

Source record↗
NASA NTRS1992

Integration and use of Microgravity Research Facility: Lessons learned by the crystals by vapor transport experiment and Space Experiments Facility programs

The Crystals by Vapor Transport Experiment (CVTE) and Space Experiments Facility (SEF) are materials processing facilities designed and built for use on the Space Shuttle mid deck. The CVTE was built as a commercial facility owned by the Boeing Company. The SEF was built under contract to the UAH Center for Commercial Development of Space (CCDS). Both facilities include up to three furnaces capable of reaching 850 C minimum, stand-alone electronics and software, and independent cooling control. In addition, the CVTE includes a dedicated stowage locker for cameras, a laptop computer, and other ancillary equipment. Both systems are designed to fly in a Middeck Accommodations Rack (MAR), though the SEF is currently being integrated into a Spacehab rack. The CVTE hardware includes two transparent furnaces capable of achieving temperatures in the 850 to 870 C range. The transparent feature allows scientists/astronauts to directly observe and affect crystal growth both on the ground and in space. Cameras mounted to the rack provide photodocumentation of the crystal growth. The basic design of the furnace allows for modification to accommodate techniques other than vapor crystal growth. Early in the CVTE program, the decision was made to assign a principal scientist to develop the experiment plan, affect the hardware/software design, run the ground and flight research effort, and interface with the scientific community. The principal scientist is responsible to the program manager and is a critical member of the engineering development team. As a result of this decision, the hardware/experiment requirements were established in such a way as to balance the engineering and science demands on the equipment. Program schedules for hardware development, experiment definition and material selection, flight operations development and crew training, both ground support and astronauts, were all planned and carried out with the understanding that the success of the program science was as important as the hardware functionality. How the CVTE payload was designed and what it is capable of, the philosophy of including the scientists in design and operations decisions, and the lessons learned during the integration process are descussed.

Heizer, Barbara L.↗
NASA NTRSDate not supplied

Operating and Maintaining the JWST Cleanroom attached to JSC's Chamber A during preparations for the OTIS Cryogenic Thermal Vacuum Test

In order to maintain cleanliness during preparations for JWST’s OTIS (Optical Telescope Element-Integrated Science Instrument Module) Cryogenic Thermal Vacuum Test, a cleanroom was built that attached directly to the 60-year-old Chamber A. The cleanroom and chamber were outfitted with independent environmental control systems each providing ISO Class 71 air cleanliness. To maintain balanced, positive pressure in both the cleanroom and chamber volumes, a special control protocol was developed and successfully implemented. Dual back-up environmental control units (one each for the chamber and cleanroom) were installed just outside the building to provide environmental control redundancy due to a single source chilled water supply and weather threats. In addition, lack of a dedicated cleanroom airlock facilitating clean ingress and egress made it necessary to perform additional cleaning and packaging, as well as augment the uncontrolled truck lock space with small clean tents for pre-cleaning. Special procedures were developed to allow ingress of extra-large support equipment required for load testing of the cleanroom crane, installation of optical equipment in Chamber A and accommodation of the OTIS shipping container. A thorough bake-out and cleaning of Chamber A was also necessary to reduce volatiles from the shroud’s black thermal paint and to reduce particle fallout. Acrylic adhesive fracture discovered during early cryo-testing represented a significant challenge that was successfully mitigated prior to OTIS testing. A dedicated team of Contamination Control (CC) Technicians was specifically trained to clean support equipment and screen materials entering the cleanroom and chamber to ensure cleanliness and vacuum compatibility.

Optics↗
NASA NTRSDate not supplied

Operating and Maintaining the JWST Cleanroom attached to JSC's Chamber A during preparations for the OTIS Cryogenic Thermal Vacuum Test

In order to maintain cleanliness during preparations for JWST’s OTIS (Optical Telescope Element-Integrated Science Instrument Module) Cryogenic Thermal Vacuum Test, a cleanroom was built that attached directly to the 60-year-old Chamber A. The cleanroom and chamber were outfitted with independent environmental control systems each providing ISO Class 71 air cleanliness. To maintain balanced, positive pressure in both the cleanroom and chamber volumes, a special control protocol was developed and successfully implemented. Dual back-up environmental control units (one each for the chamber and cleanroom) were installed just outside the building to provide environmental control redundancy due to a single source chilled water supply and weather threats. In addition, lack of a dedicated cleanroom airlock facilitating clean ingress and egress made it necessary to perform additional cleaning and packaging, as well as augment the uncontrolled truck lock space with small clean tents for pre-cleaning. Special procedures were developed to allow ingress of extra-large support equipment required for load testing of the cleanroom crane, installation of optical equipment in Chamber A and accommodation of the OTIS shipping container. A thorough bake-out and cleaning of Chamber A was also necessary to reduce volatiles from the shroud’s black thermal paint and to reduce particle fallout. Acrylic adhesive fracture discovered during early cryo-testing represented a significant challenge that was successfully mitigated prior to OTIS testing. A dedicated team of Contamination Control (CC) Technicians was specifically trained to clean support equipment and screen materials entering the cleanroom and chamber to ensure cleanliness and vacuum compatibility.

James Webb Space Telescope↗
NASA NTRSDate not supplied

Impact Ice Microstructure Segmentation Using Transfer Learned Model

A process of using machine learning to segment impact ice microstructure is presented and analyzed. The segmentation was conducted with the goal of obtaining average grain size estimations. The model was trained on a set of micrographs of impact ice grown at NASA Glenn’s Icing Research Tunnel. The model leveraged a model pre-trained on a large set of micrographs of various materials as a starting point. Post-processing of the segmented images was done to connect broken boundaries. An automatic method of determining grain size following an ASTM standard was implemented. Segmentation results using different training sets as well as different encoder and decoder pairs are presented. Calculated sizes are compared to manual grain size measurement methods. Results show promise in accuracy as well as a possible improvement in repeatability and consistency. Next steps for improving the model are suggested.

Machine learning↗
DOE OSTI2026

Conditional Latent Diffusion for High-Resolution Prediction of Electrochemical Surface Morphology

A conditionally guided generative latent diffusion process that is trained on a set of experimental processing parameters and their associated resulting electron microscope images of the electrodeposition process is able to interpolate between processing parameters in a physically consistent way. Electrodeposition of rhenium with pulse and pulse-reverse waveforms is used as a model system, and the process is adaptable to other electrodeposition, electropolishing, or corrosion processes. The method is able to extrapolate, predicting estimates of material morphologies for experimental setups unseen in the training data. The results are demonstrated with experimental data.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Subcritical Assembly for Training and Education Use at the Oak Ridge National Laboratory [Slides]

Sufficient AGN-201M fuel plate material exists at Y-12 to support the development of a new, inherently safe, subcritical assembly for use in NCS training courses at ORNL. Four experiments with the ORNL subcritical assembly are possible and feasible. Future work includes the final design of the core and reflector and split table component design specifications. The ORNL Material Demonstration Facility (MDF) may be used to 3D print ORSA stand/table components and graphite reflector. Researchers will consider fuel coatings for contamination control, rather than an Al can. A visit to Y-12 will be made to determine the location of AGN fuel plates and remaining fuel transportation considerations. Final facility location will be considered

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

On the Generalizability of Time-of-Flight Convolutional Neural Networks for Noninvasive Acoustic Measurements

Bulk wave acoustic time-of-flight (ToF) measurements in pipes and closed containers can be hindered by guided waves with similar arrival times propagating in the container wall, especially when a low excitation frequency is used to mitigate sound attenuation from the material. Convolutional neural networks (CNNs) have emerged as a new paradigm for obtaining accurate ToF in non-destructive evaluation (NDE) and have been demonstrated for such complicated conditions. However, the generalizability of ToF-CNNs has not been investigated. In this work, we analyze the generalizability of the ToF-CNN for broader applications, given limited training data. We first investigate the CNN performance with respect to training dataset size and different training data and test data parameters (container dimensions and material properties). Furthermore, we perform a series of tests to understand the distribution of data parameters that need to be incorporated in training for enhanced model generalizability. This is investigated by training the model on a set of small- and large-container datasets regardless of the test data. We observe that the quantity of data partitioned for training must be of a good representation of the entire sets and sufficient to span through the input space. The result of the network also shows that the learning model with the training data on small containers delivers a sufficiently stable result on different feature interactions compared to the learning model with the training data on large containers. To check the robustness of the model, we tested the trained model to predict the ToF of different sound speed mediums, which shows excellent accuracy. Furthermore, to mimic real experimental scenarios, data are augmented by adding noise. We envision that the proposed approach will extend the applications of CNNs for ToF prediction in a broader range.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Cyberguardians and STEM Warriors (Final Technical Report (FTR))

In the past decade, solar power, with and without energy storage has become the fastest growing source of energy generation in the world. In the U.S., solar employment more than doubled from 105,145 jobs in 2011 to 255,037 jobs in 2021, four times faster than the U.S. job growth rate overall. These factors, combined with technology advancements, creates a skills gap that puts tremendous stress on society to deliver the workers to fill the open job requisitions. The Cyberguardians and STEM Warriors project (Cyberguardians) was designed to address the trained-worker shortage in the energy industry in three ways: 1) by developing educational curriculum that addresses DER technology changes; 2) by delivering curriculum to prospective workers, including military veterans and their families, via universities, community colleges, and vocational training outlets; and 3) introducing individuals who have completed training to employers that can hire them. Cyberguardians exceeded its curriculum goals by producing 27 academic units of university-accredited material (12 total courses) covering energy fundamentals, smart inverters, Distributed Energy Resource (DER) data communication, cybersecurity, standardization, certification, data analytics, and IEEE 1547 standard topics. The North American Board of Certified Energy Practitioners (NABCEP) also accredited the material for use in their credential program. Seven instructors were recruited and trained, and six academic institutions (University of California San Diego, State University of New York, North Carolina State University, Harper Community College, Green Village Academy, and the SunSpec Alliance) were enlisted, meeting program goals. All course material was published under the Creative Commons license and made available royalty free, thus providing a long-lasting public benefit. The program’s outreach program vastly exceeded program goals and incorporated the efforts of 13 outreach partners (11 of which are veteran focused), an advisory board representing 15 companies, webinars and 10’s of thousands of email messages sent to prospective students and hiring managers. Despite these efforts, the global pandemic depressed anticipated program participation by about a third. Still, a total of 396 students enrolled and 289 completed the courses and were accredited. The job applicant task achieved similar results (111 realized vs a 174 goal) but reported job placement was weaker at (9 realized vs. a 51 goal). The Cyberguardians program fills a critical void for cost-effective, royalty-free curriculum and training pertaining to DER technologies and cybersecurity that prospective energy workers must possess to be effective in the 21 st century. On this basis alone, the investment of taxpayer funds will pay dividends for years to come.

14 SOLAR ENERGY↗