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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
DOE OSTI2024

Development of the tangent linear and adjoint models of the global online chemical transport model MPAS-CO 2 v7.3

We describe the development of the tangent linear (TL) and adjoint models of the Model for Prediction Across Scales (MPAS)-CO 2 transport model, which is a global online chemical transport model developed upon the non-hydrostatic Model for Prediction Across Scales – Atmosphere (MPAS-A). The primary goal is to make the model system a valuable research tool for investigating atmospheric carbon transport and inverse modeling. First, we develop the TL code, encompassing all CO 2 transport processes within the MPAS-CO 2 forward model. Then, we construct the adjoint model using a combined strategy involving re-calculation and storage of the essential meteorological variables needed for CO 2 transport. This strategy allows the adjoint model to undertake a long-period integration with moderate memory demands. To ensure accuracy, the TL and adjoint models undergo vigorous verifications through a series of standard tests. The adjoint model, through backward-in-time integration, calculates the sensitivity of atmospheric CO 2 observations to surface CO 2 fluxes and the initial atmospheric CO 2 mixing ratio. To demonstrate the utility of the newly developed adjoint model, we conduct simulations for two types of atmospheric CO 2 observations, namely the tower-based in situ CO 2 mixing ratio and satellite-derived column-averaged CO 2 mixing ratio (X CO 2 ). A comparison between the sensitivity to surface flux calculated by the MPAS-CO 2 adjoint model with its counterpart from CarbonTracker–Lagrange (CT-L) reveals a spatial agreement but notable magnitude differences. These differences, particularly evident for X CO 2 , might be attributed to the two model systems' differences in the simulation configuration, spatial resolution, and treatment of vertical mixing processes. Moreover, this comparison highlights the substantial loss of information in the atmospheric CO 2 observations due to CT-L's spatial domain limitation. Furthermore, the adjoint sensitivity analysis demonstrates that the sensitivities to both surface flux and initial CO 2 conditions spread out throughout the entire Northern Hemisphere within a month. MPAS-CO 2 forward, TL, and adjoint models stand out for their calculation efficiency and variable-resolution capability, making them competitive in computational cost. In conclusion, the successful development of the MPAS-CO 2 TL and adjoint models, and their integration into the MPAS-CO 2 system, establish the possibility of using MPAS's unique features in atmospheric CO 2 transport sensitivity studies and in inverse modeling with advanced methods such as variational data assimilation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2018

Importance and Incorporation of User Feedback in Data Stewardship

Since August 1994, The National Aeronautics and Space Administration's (NASA's) Earth Observation System Data and Information System (EOSDIS) has been serving a global community of over 3 million users with Earth science data in a variety of disciplines. During the entire life of EOSDIS, various mechanisms for user feedback have been extremely important and valuable to its evolution and proven performance. Some inputs from user groups have resulted in fundamental changes in the architecture, design and operations of EOSDIS, while others have provided ideas for incremental changes. The purpose of this paper is to share this experience and the benefits that have resulted from the user feedback.In early to mid-1990s, the EOSDIS Advisory Panel (a.k.a. Data Panel) provided significant inputs for the architecture and design of EOSDIS resulting in NASA's establishment of a set of Distributed Active Archive Centers (DAACs) and development of a "working prototype with operating elements" called Version 0 EOSDIS, which went into operation in August 1994. The Data Panel also participated in many of the requirement and design reviews and influenced the design of EOSDIS through their comments.In 1995, the U.S. National Research Council's Committee on Global Change Research conducted a review of the U.S. Global Change Research Program and NASA's Mission to Planet Earth/EOS, including the plans for EOSDIS. One of this committee's recommendations was that the "Responsibility for product generation and publication and for user services should be transferred to a federation of partners selected through a competitive process open to all". In response, NASA initiated an experiment with a "self-governing" federation called the Working Prototype ESIP (WP-ESIP) Federation. This federation, with support from NASA, NOAA and USGS, has now grown into the Earth Science Information Partners (ESIP) with over 140 member organizations.The EOSDIS DAACs' User Working Groups (UWGs) represent broad user communities served by the respective DAACs. As regular users of the DAACs and experts in their scientific disciplines, the UWG members provide valuable inputs for planning and prioritizing the services as well as addition of new datasets for the benefit of the community.

Research Integrity↗
DOE OSTI2020

Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models

Software flaw detection using multimodal deep learning models has been demonstrated as a very competitive approach on benchmark problems. In this work, we demonstrate that even better performance can be achieved using neural architecture search (NAS) combined with multimodal learning models. We adapt a NAS framework aimed at investigating image classification to the problem of software flaw detection and demonstrate improved results on the Juliet Test Suite, a popular benchmarking data set for measuring performance of machine learning models in this problem domain.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Using Neural Architecture Search for Improving Software Flaw Detection in Multimodal Deep Learning Models

Software flaw detection using multimodal deep learning models has been demonstrated as a very competitive approach on benchmark problems. In this work, we demonstrate that even better performance can be achieved using neural architecture search (NAS) combined with multimodal learning models. We adapt a NAS framework aimed at investigating image classification to the problem of software flaw detection and demonstrate improved results on the Juliet Test Suite, a popular benchmarking data set for measuring performance of machine learning models in this problem domain.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1977

Industrial ion source technology

Plasma probe surveys were conducted in a 30-cm source to verify that the uniformity in the ion beam is the result of a corresponding uniformity in the discharge-chamber plasma. A 15 cm permanent magnet multipole ion source was designed, fabricated, and demonstrated. Procedures were investigated for texturing a variety of seed and surface materials for controlling secondary electron emission, increasing electron absorption of light, and improved attachment of biological tissue for medical implants using argon and tetrafluoromethane as the working gases. The cross section for argon-argon elastic collisions in the ion-beam energy range was calculated from interaction potentials and permits calculation of beam interaction effects that can determine system pumping requirements. The data also indicate that different optimizations of ion-beam machines will be advantageous for long and short runs, with 1 mA-hr/cm being the rough dividing line for run length. The capacity to simultaneously optimize components in an ion-beam machine for a single application, a capacity that is not evident in competitive approaches such as diode sputtering is emphasized.

Kaufman, H. R.↗
DOE OSTI2024

MatRIS: Addressing the Challenges for Portability and Heterogeneity Using Tasking for Matrix Decomposition (Cholesky)

The ubiquitous in-node heterogeneity of HPC and cloud computing platforms makes software portability and performance optimization extremely challenging. Described here, the MatRIS multilevel math library abstraction framework employs tasking to alleviate these difficulties. MatRIS includes the IRIS task-based runtime on the bottom level and exposes different layers of abstraction to render algorithms architecturally agnostic. MatRIS ensures the decomposition and creation of tasks that represent the necessary encapsulation of the optimized kernels from both vendor and open-source math libraries. Once built, MatRIS can select different combinations of accelerators at runtime, making it portable even on diverse heterogeneous architectures. By leveraging the IRIS runtime’s features for managing heterogeneity, MatRIS deploys algorithms that remove the need to specify orchestration and data transfer. This study describes how the serial task abstraction of a tiled Cholesky factorization is made portable and scalable in the case of multi-device and multi-vendor heterogeneity on a node with NVIDIA and AMD GPUs by using MatRIS. First, we demonstrate that Cholesky in MatRIS provides multi-GPU scalability that offers competitive performance versus cuSolverMG. Then, we present the challenges and opportunities for heterogeneous execution.

Monil, M. A. H.↗
DOE OSTI2021

Uranium Oxide Synthetic Pathway Discernment through Unsupervised Morphological Analysis

We present a novel unsupervised machine learning method for quantitative representation of scanning electron micrographs and its applications and performance for nuclear forensic analysis of uranium ore concentrates. The method uses a vector quantizing variational autoencoder followed by a histogram operation to encode a micrograph into a single dimensional representation, called the latent vector. The method requires no extant labeling of the data and can be applied over large datasets of micrographs with minimal human interaction. The representations generated are broadly descriptive of each micrograph and the microstructure of the material imaged. In the case of uranium ore concentrate analysis, the representations were amenable to processing reagent and ore concentrate species classification with accuracy of 81:8%, which is competitive with state-of-the-art supervised networks. The representations were also used to classify previously unseen processing routes, were able to classify imaging parameters such as magnification (to 76:0% accuracy), were able to classify fine grained process parameters such as calcining temperature (to 74:4% accuracy), and their informatic properties indicate that they are generally descriptive of the image represented. This method can be applied across microstructure analysis fields to perform quantitative analysis without the need for labor intensive and possibly biased human analysis.

Scanning Electron Microscopy, Vector Quantizing Va↗
DOE OSTI2024

Species interactions amplify functional group responses to elevated CO 2 and N enrichment in a 24‐year grassland experiment

Abstract Plant functional groups (FGs) differ in their response to global changes, although species within those groups also vary in such responses. Both species and FG responses to global change are likely influenced by species interactions such as inter‐specific competition and facilitation, which are prevalent in species mixtures but not monocultures. As most studies focus on responses of plants growing in either monocultures or mixtures, but rarely both, it remains unclear how interspecific interactions in diverse ecological communities, especially among species in different FGs, modify FG responses to global changes. To address these issues, we leveraged data from a 16‐species, 24‐year perennial grassland experiment to examine plant FG biomass responses to atmospheric CO 2 , and N inputs at different planted diversity. FGs differed in their responses to N and CO 2 treatments in monocultures. Such differences were amplified in mixtures, where N enrichment strongly increased C3 grass success at ambient CO 2 and C4 grass success at elevated CO 2 . Legumes declined with N enrichment in mixtures at both CO 2 levels and increased with elevated CO 2 in the initial years of the experiment. Our results suggest that previous studies that considered responses to global changes in monocultures may underestimate biomass changes in diverse communities where interspecific interactions can amplify responses. Such effects of interspecific interactions on responses of FGs to global change may impact community composition over time and consequently influence ecosystem functions.

Mohanbabu, Neha↗
DOE OSTI2023

Toward extracting $$\gamma $$ from $$B\rightarrow DK$$ without binning

Abstract $$B^\pm \rightarrow DK^\pm $$ B ± → D K ± transitions are known to provide theoretically clean information about the CKM angle $$\gamma $$ γ , with the most precise available methods exploiting the cascade decay of the neutral D into CP self-conjugate states. Such analyses currently require binning in the D decay Dalitz plot, while a recently proposed method replaces this binning with the truncation of a Fourier series expansion. In this paper, we present a proof of principle of a novel alternative to these two methods, in which no approximations at the level of the data representation are required. In particular, our new strategy makes no assumptions about the amplitude and strong phase variation over the Dalitz plot. This comes at the cost of a degree of ambiguity in the choice of test statistic quantifying the compatibility of the data with a given value of $$\gamma $$ γ , with improved choices of test statistic yielding higher sensitivity. While our current proof-of-principle implementation does not demonstrate optimal sensitivity to $$\gamma $$ γ , its conceptually novel approach opens the door to new strategies for $$\gamma $$ γ extraction. More studies are required to see if these can be competitive with the existing methods.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

A framework for materials informatics education through workshops

The burgeoning field of materials informatics necessitates a focus on educating the next generation of materials scientists in the concepts of data science, artificial intelligence (AI), and machine learning (ML). In addition to incorporating these topics in undergraduate and graduate curricula, regular hands-on workshops present the most effective medium to initiate researchers to informatics and have them start applying the best AI/ML tools to their own research. With the help of the Materials Research Society (MRS), members of the MRS AI Staging Committee, and a dedicated team of instructors, we successfully conducted workshops covering the essential concepts of AI/ML as applied to materials data, at both the Spring and Fall Meetings in 2022, with plans to make this a regular feature in future meetings. Here, in this article, we discuss the importance of materials informatics education via the lens of these workshops, including details such as learning and implementing specific algorithms, the crucial nuts and bolts of ML, and using competitions to increase interest and participation.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Onboarding in a Virtual Environment

A need for a standardized Division 10000 onboarding program for virtual hires was identified by management to formalize the way employees and interns are onboarded and trained into Division 10000. This white paper provides effective short and long-term suggestions in the efforts of improving virtual onboarding. Data suggests that remote work is going to become the forefront of many industry practices, which indicates the need of a standardized virtual onboarding practices. With our research, gap assessments, benchmarking, and conducting interviews both internally and externally, we found that clarity, culture, and connection proved to be the strongest solutions in order to maintain Sandia’s competitive edge and sustain workers both remote and in-person.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS1998

Construction and Operation of 1-5 Micron Array Camera (Minne-Cam) for the University of Minnesota Observatories

The 1-5 Micron Imaging Infrared (IR) Camera (MinneCam) for the University of Minnesota (UM) observatories has been constructed. It is presently housed at the Mt. Lemmon Observing Facility (MLOF) in Arizona for its inaugural observing run. MinneCam is testing and comparing various observational approaches, and will produce data sets that can be used to define and test data analysis algorithms. The camera is being used in support of studies to define strategies for using NASA's Space Infrared Telescope Facility (SIRTF) for observations of comets and other targets of opportunity. A long term goal is to modify the instrument to accommodate imaging polarimetry. This instrument is presently putting the University of Minnesota observatories in competition with other major IR observatories and is allowing us to develop state-of-the-art instrumentation for the U of M's proposed 2.4 meter telescope proposed jointly with NOAO and the University of Colorado. The camera has provided important research opportunities for the Minnesota IR group, including other faculty and graduate students in the Department of Astronomy.

Gehrz, Robert D.↗
NASA NTRS2018

NASA Guidelines for Promoting Scientific and Research Integrity

The National Aeronautics and Space Administration (NASA) seeks to explore and expand human knowledge of the Earth, solar system, and universe and to enable the development of aeronautical and space exploration systems. NASA's ability to achieve these purposes depends on the integrity of the research and technology activities the agency conducts and supports. The NASA workforce - as well as all external entities who review proposals for or receive NASA funding - must maintain the highest standards of scientific integrity. These responsibilities include selecting the most meritorious research activities through open and fair competition, peer review and other appropriate merit review processes, and avoidance of actual and perceived conflicts of interest; avoiding fabrication, falsification, or plagiarism in proposing, performing, or reviewing research, or in reporting research results; openly sharing results and methods not subject to classification or privacy standards; disclosing assumptions and biases in sharing and applying scientific information and data; and acting honestly and transparently in using and serving on advisory committees and in engaging in professional development activities.

Guidelines↗
NASA NTRS2014

Humic Acid Complexation of Th, Hf and Zr in Ligand Competition Experiments: Metal Loading and Ph Effects

The mobility of metals in soils and subsurface aquifers is strongly affected by sorption and complexation with dissolved organic matter, oxyhydroxides, clay minerals, and inorganic ligands. Humic substances (HS) are organic macromolecules with functional groups that have a strong affinity for binding metals, such as actinides. Thorium, often studied as an analog for tetravalent actinides, has also been shown to strongly associate with dissolved and colloidal HS in natural waters. The effects of HS on the mobilization dynamics of actinides are of particular interest in risk assessment of nuclear waste repositories. Here, we present conditional equilibrium binding constants (Kc, MHA) of thorium, hafnium, and zirconium-humic acid complexes from ligand competition experiments using capillary electrophoresis coupled with ICP-MS (CE- ICP-MS). Equilibrium dialysis ligand exchange (EDLE) experiments using size exclusion via a 1000 Damembrane were also performed to validate the CE-ICP-MS analysis. Experiments were performed at pH 3.5-7 with solutions containing one tetravalent metal (Th, Hf, or Zr), Elliot soil humic acid (EHA) or Pahokee peat humic acid (PHA), and EDTA. CE-ICP-MS and EDLE experiments yielded nearly identical binding constants for the metal- humic acid complexes, indicating that both methods are appropriate for examining metal speciation at conditions lower than neutral pH. We find that tetravalent metals form strong complexes with humic acids, with Kc, MHA several orders of magnitude above REE-humic complexes. Experiments were conducted at a range of dissolved HA concentrations to examine the effect of [HA]/[Th] molar ratio on Kc, MHA. At low metal loading conditions (i.e. elevated [HA]/[Th] ratios) the ThHA binding constant reached values that were not affected by the relative abundance of humic acid and thorium. The importance of [HA]/[Th] molar ratios on constraining the equilibrium of MHA complexation is apparent when our estimated Kc, MHA values attained at very low metal loading conditions are compared to existing literature data. Overall, experimental data suggest that the tetravalent transition metal/-actinide-humic acid complexation is important over a wide range of pH values, including mildly acidic conditions, and thus, these complexes should be included in speciation models.

humic acid↗
DOE OSTI2021

Enabling Load Following Capability in the Transatomic Power MSR

This project is dedicated towards designing a fuel processing system that enables liquid-fueled molten salt reactors (MSR) to load follow by removing the dissolved xenon in the fuel salt. As one of the Gen-IV nuclear reactor concepts, the molten salt reactor receives increasing development interests in the recent years. One distinguishing feature of the liquid-fueled molten salt reactor is its improved ability to operate in a load-following mode by including the unique online fission product removal system. Load-following means that the reactor changes its power output based on the demand on the grid. Most of the current operating nuclear reactors have limited load-following ability and operate as the base load on the grid. Due to the rapid increase of solar energy, the requirement on load-following capacity is significantly increased because of the varying power output of the solar panels, yet the traditional load-following capacity is expected to decrease as the decarbonization of the grid continues. Therefore, the ability to perform load-following operation for the nuclear reactors will greatly enhance the resilience of the grid and make nuclear energy more economically competitive. This load-following feature is included in many commercial molten salt reactor designs, such as the designs by Transatomic Power, Terrestrial Energy, and Flibe Energy. Unfortunately, detailed analysis of the fuel processing system for commercial scale MSRs is still lacking, as well as how the fuel processing quantitively impacts the load-following operation. Moreover, experimental data for many of the underlying physics of fuel processing is limited. This project aims to pave the way for the fuel processing technology to advance to the commercial stage by performing combined experimental and simulation research. During the project period, four interconnected aspects of the development of the fuel processing system in liquid-fueled molten salt reactors are investigated. These aspects are the simulation and analysis of the fission product removal system, the fuel cycle simulation, the coupled reactor neutronics and thermal hydraulics transient simulation, and the gaseous fission product removal experiment. Multiphase CFD simulations are performed for components of the processing systems, and simplified air-water experiments are carried out to provide validation data. It is concluded that the CFD simulation can satisfactorily predict the system level performance of the components, and engineering models are constructed based on this success. Fuel cycle analysis is performed for two representative MSR design, the MSBR and the Transatomic Power MSR. Open-source code SaltProc is developed to incorporate the unique fuel processing system of the MSRs. It is concluded that the removal of xenon is essential for load-following operation in thermal spectrum MSR and Molten Salt Breeder Reactor. For the Transatomic Power MSR, the xenon poisoning effect is negligible due to its relatively fast neutron spectrum, though the overall fuel cycle economics still benefits from the removal of xenon. Coupled reactor neutronics and thermal hydraulics transient simulation is performed specifically for the Transatomic Power MSR. It is concluded that the reactor core design could perform power ramping fast enough to satisfy load-following operation. Combining the findings from each aspect, it is concluded that the load-following operation of a thermal neutron MSR is dependent upon the removal of xenon, which could be achieved for a commercial sized reactor using continuous inert gas sparging in a separate system with reasonable dimensions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Approximating Nash Equilibrium in Day-ahead Electricity Market Bidding with Multi-agent Deep Reinforcement Learning

In this paper, a day-ahead electricity market bidding problem with multiple strategic generation company (GEN-CO) bidders is studied. The problem is formulated as a Markov game model, where GENCO bidders interact with each other todevelop their optimal day-ahead bidding strategies. Considering unobservable information in the problem, a model-free and data-driven approach, known as multi-agent deep deterministic policy gradient (MADDPG), is applied for approximating the Nash equilibrium (NE) in the above Markov game. The MADDPG algorithm has the advantage of generalization due to the automatic feature extraction ability of the deep neural networks. The algorithm is tested on an IEEE 30-bus system with three competitive GENCO bidders in both an uncongested caseand a congested case. Comparisons with a truthful bidding strategy and state-of-the-art deep reinforcement learning methods including deep Q network and deep deterministic policy gradient (DDPG) demonstrate that the applied MADDPG algorithm can find a superior bidding strategy for all the market participants with increased profit gains. In addition, the comparison with a conventional model-based method shows that the MADDPG algorithm has higher computational efficiency, which is feasible for real-world applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRSDate not supplied

High Flying Interns: NASA's Student Airborne Research Program

The NASA Student Airborne Research Program is an annual summer internship for upper-level undergraduate STEM majors. Each summer since 2009, we have competitively selected ~30 undergraduates from colleges and universities across the United States for this unique airborne research experience. In all past summers, students flew onboard a NASA research aircraft where they assisted in the operation of remote sensing and in situ instrumentation to study the Earth, ocean, and atmosphere. Students also participated in field trips where they acquired data to ground-truth and complement the airborne data. After their flights and field trips, students then spent the rest of summer developing individual research projects using the data they collected as well as data from previous year SARP flights, other NASA airborne campaigns, and NASA satellite data. This summer, we adapted the program to be entirely online. Each of the 28 undergraduate students still completed an individual research project using data from previous SARP flights as well as publicly available data from other airborne campaigns, satellites, and/or ground stations. Students were mentored remotely by five university faculty members, five graduate students, and several additional scientists and engineers from NASA. In order to preserve some aspects of the hands-on research experience, we also shipped each student a box of twenty-four Whole Air Sampling canisters identical to what they would have used to collect air samples onboard the NASA aircraft. Instead, each student collected ground samples near their home starting in mid-April through July. The goal of this sampling was to attempt to characterize the impacts of pandemic-related changes in emissions with time across the United States. The results of that ground sampling are being presented in scientific sessions in this meeting. In addition, students also measured PM2.5 and aerosol optical depth from June through August using sensors provided by the Citizen-Enabled Aerosol Measurements for Satellites (CEAMS) at Colorado State University. We will discuss strategies we employed to conduct research online, the unexpected opportunities that arose, and lessons learned.

STEM disciplines↗
NASA NTRSDate not supplied

The Development and Deployment of Machine Learning Models for Aircraft Engine Concept Assessment

In today's competitive landscape, the effective development and utilization of machine-learning (ML) applications have become imperative across various sectors. This study presents an outline of the procedure involved in creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models leverage supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. The main areas of focus encompass crucial engine parameters like thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While the creation of ML models is fundamental for their utilization, ensuring their seamless deployment holds equal significance. To address this aspect, a conversational AI chatbot is constructed, utilizing natural language processing (NLP) techniques, to facilitate the deployment of these ML models. The comprehensive workflow encompasses several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine assessment.

Development↗