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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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197 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

High School Citizen Scientists Use AI/ML to Predict Intra-Ocular Pressure From Gene Expression Data for Spaceflown Mice

Artificial Intelligence (AI) and Machine Learning (ML) have increasingly become pivotal in biological and biomedical research, largely due to the culture of open data sharing and its associated benefits. The methodologies inherent in AI/ML are particularly adept at identifying and forecasting biological phenotypes from the vast amounts of data generated by next-generation sequencing technologies. These techniques offer substantial promise for advancing research in space biosciences and for the development of automated systems for monitoring space health. Nevertheless, there are crucial aspects to consider when training, validating, and testing machine learning models in both biological research and clinical contexts. It is essential that Open Science principles, including data sharing and the availability of open-source code, are complemented by high-quality, publicly accessible training resources. These resources should focus on best practices and include modules based on real-world scientific cases and data to ensure that future AI/ML practitioners gain practical experience with genuine problems. Addressing this knowledge gap, we have designed, developed, and delivered both interactive and self-paced training programs for citizen scientists worldwide, enabling them to utilize AI/ML for space biology research. This initiative was made possible through generous funding from a Transformation to Open Science Training grant. The interactive training sessions, conducted this summer, utilized AI/ML techniques to analyze data from the Open Science Data Repository, specifically targeting the effects of spaceflight on ocular structure and function. The dataset OSD-583, from the Rodent Research 9 mission, provides experimental data detailing the ocular responses of mice subjected to a 35-day spaceflight, compared with ground control counterparts. Using OSD-583 as observational data, our summer training participants applied AI/ML methods to predict intraocular pressure from RNA-seq data and identify the genes most predictive of the observed responses. Further analysis through pathway enrichment and gene set enrichment revealed that these genes are involved in molecular and cellular processes contributing to retinal degeneration.

James Casaletto↗
NASA NTRSDate not supplied

Experimental Study of Aero-Propulsive Interactions for Electric Ducted Fan Arrays in Multiple Positions on a Wing

The Adaptable Distributed Electric Propulsion Testbed (ADEPT) Wing was tested in the NASA Langley 12-Foot Low-Speed Tunnel for the SUbsonic Single Aft eNgine (SUSAN) Electrofan 25% scale flight research vehicle, which aims to reduce emissions for commercial transport aircraft with electric propulsion technologies. The purpose of the test was to study the aero-propulsive effects across angle of attack and fan speed variations for several propulsor placements and array sizes, and to investigate how DEP can be leveraged for augmented lift and vehicle control. Six electric ducted fan (EDF) array placements were tested by positioning the fans along the leading and trailing edges on the top and bottom surfaces, as well as centered on the chord line in front of and behind the wing. Each fan placement configuration was tested with an array of one, three, and five EDFs to characterize an aero-propulsive extrapolation effect, and the bare wing was tested separately to provide baseline aerodynamic measurements. The results compare the longitudinal forces and moments for each configuration by analyzing the combined aero-propulsive interaction effects, and offer valuable insight into considerations for fan placement along a wing to enable the benefits of DEP integration.

Rose Weinstein↗
NASA NTRS2026

Experimental Study of Aeropropulsive Interactions for Electric Ducted Fans on a Wing

The Adaptable Distributed Electric Propulsion Testbed (ADEPT)Wing was tested in the NASA Langley 12-Foot Low-Speed Tunnel to investigate electric propulsion technologies for commercial transport aircraft. The purpose of the test was to study the aeropropulsive effects across angle of attack and fan speed variations for several propulsor placements and array sizes, and to investigate how distributed electric propulsion (DEP) can be leveraged for augmented lift and vehicle control. Six electric ducted fan (EDF) array placements were tested by positioning the fans along the leading and trailing edges on the top and bottom surfaces, as well as centered on the chord line in front of and behind the wing. Each fan placement configuration was tested with an array of one, three, and five EDFs to characterize an aeropropulsive extrapolation effect, and the bare wing was tested separately to provide baseline aerodynamic measurements. The results compare the longitudinal forces and moments for each configuration by analyzing the combined aeropropulsive interaction effects, and offer valuable insight into considerations for fan placement along a wing to enable the benefits of DEP integration.

Rose Weinstein↗
NASA NTRSDate not supplied

Thermomechanical Modeling of Woven Materials With Particle-Based, Explicit-Fiber Simulations

Fiber-based materials are extensively used to protect spacecraft during entry. Insulative fibers, often in a fiber network or woven, provide rigidity, strength, and control of material anisotropy and density. Woven thermal protection materials, such as ADEPT (Adaptable, Deployable Entry and Placement Technology), 3D-MAT (3-Dimensional Multifunctional Ablative Thermal Protection), and 3MDCP (3D Woven Mid-Density Carbon Phenolic), enable missions with stronger and denser materials for entry profiles with high shear and heat flux. Vulnerabilities to woven thermal protection materials include manufacturing-induced material property variation, and impact from micrometeoroids. Simulating woven materials under these conditions require models that can resolve hierarchal structures, thermomechanical behavior, and failure. To address this, we simulate weave thermal conduction and mechanical deformation. We simulate the full weave with a coarse-grained yarn model is presented. The model combines a validated, high-resolution single 3MDCP yarn model and phenolic resin model. Instead of modeling every fiber, each yarn ply with order 10, instead of order 1000, fibers. The discrete element bonded particle model (DEM-BPM) of fibers captures the thermal and mechanical behavior within and between fibers. We study the proportion of heat transfer and stress via the contact network, fiber bonds, and overall weave geometry.

bonded particle↗
DOE OSTI2017

Pure JavaScript Storyline Layout Algorithm

This is a JavaScript library for a storyline layout algorithm. Storylines are adept at communicating complex change by encoding time on the x-axis and using the proximity of lines in the y direction to represent interaction between entities. The library in this disclosure takes as input a list of objects containing an id, time, and state. The output is a data structure that can be used to conveniently render a storyline visualization. Most importantly, the library computes the y-coordinate of the entities over time that decreases layout artifacts including crossings, wiggles, and whitespace. This is accomplished through multi-objective, multi-stage optimization problem, where the output of one stage produces input and constraints for the next stage.

Source record↗
DOE OSTI2018

Progress Toward a Deep-Learning-Based Search for Low-Energy Electron Neutrinos in MicroBooNE

MicroBooNE is a neutrino experiment based at Fermilab which consists of a liquid argon time-projection chamber in the Booster Neutrino Beam (BNB). The experiment aims to investigate the excess of electron neutrino-like events seen by the MiniBooNE experiment, also located in the BNB. I will describe the status of our deep-learning-based search for low-energy electron neutrino interactions within the MicroBooNE detector and the muon neutrino interactions by which we will constrain them. This analysis features a novel hybrid approach of traditional reconstruction methods combined with the use of convolutional neural networks, a type of deep learning algorithm highly adept at pattern recognition. I will present work on ongoing studies that will characterize and quantify the systematic uncertainties associated with this analysis.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

FAST - A Unique Experiment and Simplified Approach to Accelerated Integral Fuel Testing

Idaho National Laboratory has a long and internationally recognized history of performing irradiation testing of advanced fast reactor fuels in the Experimental Breeder Reactor II (EBR-II) and the Advanced Test Reactor (ATR). Since EBR-II’s decommissioning in the early 1990’s, irradiation testing has been exclusively performed in ATR. The testing of fast reactor fuels in ATR is challenging given its thermal neutron spectrum and coolant temperatures far below those prototypic of fast reactors. Adept researchers have overcome these obstacles through the use of Cadmium shrouds which attenuate all thermalized neutrons and an experiment design that fosters fuel and cladding temperatures prototypic of fast neutron reactors. Although this design has successfully demonstrated temperatures and temperature gradients that closely mimic those seen in fast reactors, irradiation performance is highly sensitive to fabrication tolerances and the time requirements necessary for high fuel burnup, where irradiation performance data of fast fuel is lacking, are significant. This work summarizes two novel experiment designs and their associated physics analyses for testing fast reactor fuels in ATR; an accelerated irradiation experiment and a fast spectrum experiment. The accelerated irradiation experiment does not utilize a Cadmium shroud, has been shown to triple the burnup rate, yet yields temperatures and radial power profiles prototypic of fast reactor fuels. The fast spectrum experiment utilizes a booster fuel element that that hardens the incident neutron spectrum and also yields temperatures and radial power profiles prototypic of fast reactor fuels. These experiment designs efficiently reduce irradiation time requirements, are more economical, and significantly increase ATR throughput. Fast reactor experiments that capitalize on these designs expedite irradiation performance data for fast reactor fuels and provide information invaluable to fuel performance modeling and simulation validation over a wide range of parameters.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

High Performance Teams Enable Mission Success for SRNL - 16363

The accomplishment of the DOE EM mission of cleaning up the radioactive waste generated as a byproduct of the development and maintenance of nuclear materials over the last 60 years is a daunting task. SRNL specializes in working directly with the 'owners' of the cleanup challenges to identify the desired outcome and develop a solution that can be put into practice. 'We put science to work' is not just a 'tag line' rather it is a concise statement of our values and approach to applying science and engineering to the cleanup objectives. The management of radioactive waste poses multi-decade challenges to DOE EM and the Nation. The ability to harness the depth of knowledge, experience, expertise and passion for safely and effectively solving the technical challenges has reached a critical junction. Perhaps too late to be a natural and organic transition the industry finds itself critically devoid of resources to effectively and efficiently keep focused on the highest order task: cleanup. When project managers plan the resources necessary to perform the planned scope they typically are focused on financial, human, and industrial resources that would be beneficial to project execution. The critical shortage we will be evaluating is experienced and skilled leaders, mentors and coaches in the workforce. The production of educated scientists and engineers is a primary focus at all levels of educational institutions with Science Technology Engineering and Mathematics (STEM) focused curricula beginning in elementary school. The need for leaders who are skilled in inspiring organizational creativity, adept in harnessing and focusing individual energy while effectively communicating to the work force and the clientele equally compelling messages, has never been greater, or their absence in the workforce more ominous. Managers trained in the classical management theory (insert references here) are aging out of the workforce. In many cases these managers have not retained close connection to advancements in their primary field of study but rather have succumbed to the administrivia burdens that typify highly regulated work. The current technically trained workforce while interested in the 'perks' of management (i.e. visibility, pay increases, advancing status) are driven to keep close connection to their technical 'roots' as a fall-back plan should they need to change their career path either by choice or necessity. The challenge is how to achieve the productive hum of a finely tuned organizational model that blends executive vision and drive with technical leadership and excellence in execution. If any of these three tenets of a high performance team falters the ability to recover erodes the performance of the team and begins to weaken the cultural foundation essential to sustaining high performance work teams. At SRNL, recognition of the need to adapt the management dynamics and the business model and to harness the energy of the workforce to sustain - and in fact increase - the level of performance is a driving force in the transformation of the organization. Examples of changes in organizations structure, business systems and employee engagement are discussed as they relate to the impact on accomplishing the cleanup mission and fostering the next generation of great leaders and innovators. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Lessons Learned From Public Outreach: Effective Incorporation of the Stakeholder Voice - 18447

The licensing process for any facility associated with nuclear power or aspects of the nuclear fuel cycle can be expected to encounter intense stakeholder interest, and often significant challenges. Over the last decade, staff of the Center for Nuclear Waste Regulatory Analyses (CNWRA{sup R}) at Southwest Research Institute{sup R} (SwRI{sup R}) provided comprehensive support to the U.S. Nuclear Regulatory Commission (NRC) for public outreach efforts across a wide variety of nuclear activities. Many of these projects included public involvement and consultation activities for compliance with the National Environmental Policy Act (NEPA), including scoping, public meetings, responding to public comments on draft documents, and holding small meetings with affected local, state, and tribal governments. In addition, we have supported NRC public outreach related to nuclear facility licensing activities (including the proposed repository at Yucca Mountain), rulemaking, and regulatory guidance development. This experience has offered us a broad view of the challenges faced by industry, regulators, and stakeholders. In the process, we have observed some best practices that lead to effective stakeholder communication. Communication about complex technical issues is challenging, particularly for societally controversial topics, such as civilian use of nuclear power and management of radioactive waste. Most commonly, we have observed a lack of mutual trust and respect among industry, regulators, and stakeholders that creates an atmosphere ripe for suspicion and misunderstanding. Stakeholders have, in public comments, criticized the lack of appropriate venues to raise concerns in which they feel they can get meaningful responses from the nuclear industry and regulators. Conversely, industry faces difficulty in reaching compromise with stakeholders, who sometimes misinterpret or misrepresent factual information. Within this contentious environment, regulators are constrained by their statutory framework, even as they are challenged to be more responsive, transparent, and fair to both stakeholders and industry. In spite of these obstacles, there are examples where feedback from stakeholders was heard and utilized. Because of successful collection of appropriate feedback, regulators became more adept at understanding the full range of perspectives from both industry and stakeholders, and subsequently did a better job at effectively incorporating feedback and communicating their decisions and findings in a clear and understandable way. Ultimately, the success of these examples was based on a measure of mutual respect, which was reached through effective communication and implementation of some best practices. These best practices include (i) outreach planning that is abundantly mindful of the various stakeholder points of view, site history, regional characteristics, and probable future and related actions, combined with advance identification of key stakeholders; (ii) development and distribution of information that facilitates public participation; and (iii) use of avenues by which stakeholders can provide meaningful and effective comments. Illustrative examples include (i) how concerns raised by stakeholders were heard by an applicant and resulted in a revised project proposal; (ii) use of a variety of interaction formats that incorporated input across a broad spectrum of stakeholders; (iii) utilizing stakeholder feedback received during scoping; and (iv) how a facility underestimated the impact of 'outsider' stakeholder voices that were in contrast to the known local community. By moving past the viewpoint that public outreach is a 'necessary evil' and engaging only to check a procedural box, each participant can benefit from mutually satisfactory and improved outcomes. (author)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

14 Steps to Creating Value in Cost and Schedule Resources for Nuclear Waste Management - 20188

While Waste Management budgets are shrinking, DOE required cost and schedule controls are becoming increasingly stringent. At the same time, there is a scarcity of adept, qualified, and competent projects controls staff who both understand nuclear waste management and are capable of addressing today's cost and schedule challenges. In response, we have developed a systematic approach to training and mentoring project controls staff. The approach is based on the observations and lessons learned on projects, both successful and unsuccessful, over the past 20 years. During that time, we evaluated staff development and used that experience to design a program that progresses the maturity, knowledge and experience of the individual. The program is based on the unique needs of cost and schedule specialists while incorporating the best leadership and managerial applications. The system is not limited to developing individual software skills, which is a primary focus of many project controls training programs. Instead, our system develops an individual's soft and hard skill sets. The system develops the user's skill, talent, attitude, and drive to do the best job possible. It focuses on all aspects of becoming a more productive technical team while accepting responsibility for individual growth. It also enhances the project controls team by setting up checks and balances that everyone is aware of and participates in. The system is based on 14 steps of increasing complexity in knowledge and application. The system clarifies roles, responsibilities and expectations. It helps eliminate uncertainty and reduces stress on staff and managers. It contains everyday routine actions and is simple to implement. It allows the development of a consistent rhythm to staff growth and development. Mastering the steps will allow the user to achieve excellence in cost and schedule control. The 14 steps are: 1 - Key Performance Objectives, 2 - Check Lists, 3 - Desktop Guides, 4 - System Description, 5 - Procedures, 6 - Software and On the Job Training (OTJ), 7 - Traceability Checks, 8 - Surveillances, 9 - 32 Criteria, 10 - The Contract, 11 - Customer/Government Guidance, 12 - Training and Mentoring Others, 13 - Master your weekly and daily schedules, 14 - Improving your interpersonal skills. Application and mastery of this system will produce better cost and schedule control results. Your project controls staff will write better reports, analyze data better, make better use of their time, and be more valuable to decision makers. If followed rigorously, our system will produce a great project controls team, which is invaluable to the success of each project. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Solving Inverse Transport Problems with Neutron Multiplication Measurements and Improved Differential Evolution

In the problem of inverse radiation transport, measurements of particle leakages from radioactive source/shield systems are used to infer unknown parameters within the systems. This reconstruction can be accomplished by finding the physical parameters of the unknown system that minimize the difference between calculated detector responses and measured detector responses. Recently, the differential evolution (DE) method was shown to be highly adept at solving inverse transport problems in which the measured quantities were leakages of unscattered gamma-ray lines. In this work, we present an extension of the DE method to problems in which both gamma-ray lines and neutron multiplication are measured. The neutron multiplication measurement is demonstrated to be an important addition when few gamma-ray lines are available. We also discuss the implementation of a variant of DE that significantly reduces the number of forward transport computations required to solve the inverse problem.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Linac_Gen: integrating machine learning and particle-in-cell methods for enhanced beam dynamics at Fermilab

Here, we introduce Linac_Gen, a tool developed at Fermilab, which combines machine learning algorithms with Particle-in-Cell methods to advance beam dynamics in linacs. Linac_Gen employs techniques such as Random Forest, Genetic Algorithms, Support Vector Machines, and Neural Networks, achieving a tenfold increase in speed for phase-space matching in linacs over traditional methods through the use of genetic algorithms. Crucially, Linac_Gen's adept handling of 3D field maps elevates the precision and realism in simulating beam instabilities and resonances, marking a key advancement in the field. Benchmarked against established codes, Linac_Gen demonstrates not only improved efficiency and precision in beam dynamics studies but also in the design and optimization of linac systems, as evidenced in its application to Fermilab's PIP-II linac project. This work represents a notable advancement in accelerator physics, marrying ML with PIC methods to set new standards for efficiency and accuracy in accelerator design and research. Linac_Gen exemplifies a novel approach in accelerator technology, offering substantial improvements in both theoretical and practical aspects of beam dynamics.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Bidding Curve Design for Hybrid Power Plants with Uncertain Solar Forecast: Preprint

This paper presents a novel bidding curve design algorithm tailored for hybrid power plants (HPPs) to participate in the wholesale electricity market. Utilizing forecasts for photovoltaic (PV) generation and available battery power, our algorithm strategically computes the bidding curve to maximize HPP profit while adeptly managing the inherent uncertainty associated with PV power generation. In addition, the introduction of the penalty cost in HPP bidding curves provides the system operator a tool to effectively manage the system-level uncertainty that caused by HPPs. Numerical analysis through Monte Carlo simulations confirms that our bidding curve methodology outperforms the benchmark across various scenarios.

bidding curve↗
DOE OSTI2025

MOOSE ProbML: Parallelizable Probabilistic Machine Learning and Uncertainty Quantification Capabilities

The Multiphysics Object Oriented Simulation Environment (MOOSE) is a widely used open- source finite element software for performing multiphysics multiscale simulations in a massively parallel fashion. Recently, the computational team at Idaho National Laboratory (INL) has implemented Probabilistic Machine Learning (ProbML) capabilities in MOOSE—in a parallelized fashion—and enable active learning with large-scale computational models for tasks such as surrogate model development, scale bridging, forward/inverse uncertainty quantification (UQ), Bayesian optimization, etc. This presentation summarizes these developments in MOOSE along with demonstrations on several real applications relevant to nuclear energy. At the fundamental level, samplers like Monte Carlo/Latin Hypercube, variance reduction, parallelized Markov Chain Monte Carlo (MCMC) support uncertainty propagation in both forward and inverse settings. These samplers can be integrated with the Gaussian processes (GP) suite in MOOSE, which offer several variants like scalar GPs, multi-output GPs, and deep GPs, to enable active learning. These GPs can be tuned using gradient-based optimization methods like Adam and its variants or gradient-free methods like the elliptical slice sampler (a variant of MCMC adept under Gaussian settings) for more complex covariance kernels or likelihoods whose gradient computations can be cumbersome. A variety of batch acquisition functions permit parallelized evaluation of the computational model and support different learning objectives with high efficiency like Bayesian inference, global surrogate development, optimization, etc. Furthermore, libtorch integration supports training, evaluation, and re-training of neural networks and other complex machine learning models in active learning settings. The impacts of these developments are shown on several real applications: (1) nuclear fuel inverse UQ and model inadequacy assessment using the Kennedy O’Hagan framework; (2) uncertainty aware surrogate modeling for additive manufacturing to predict field quantities; (3) nuclear reactor rare events analysis; and (4) complex fluid flow prediction using a global surrogate with quantified prediction uncertainty. Finally, the outlook of MOOSE ProbML is discussed for both outer-loop and inner-loop computations in the broad view to accelerate fuels and materials qualification, address gaps in knowledge and data, and assess new reactor/fuel systems.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2018

Code TS: Entry Systems and Technology Division

The TS division (Entry Systems and Technology Division) includes people who 1) Help design spacecraft for different exploration missions; 2) Figure out how hot the environments around a spacecraft will get; 3) Invent new materials that can protect the spacecraft; 4) Figure out how those materials will behave on a spacecraft and how thick they need to be; 5) Plan and perform tests on those materials and spacecraft designs to prove they will fly successfully; and 6) Help get those spacecraft ready to launch. This presentation will describe a little bit about all 6 areas.

Code TS↗
NASA NTRSDate not supplied

Comparison of Pre- and Post-Flight Estimations of Pitch Damping Coefficients for Unguided Entry Vehicles

Pre-flight predictions of pitch damping coefficient (C M q + C m ȧ ) curves are used in entry simulations to determine flight readiness and if any dynamic remediation strategies are required. Accurate characterization of these pitch damping coefficient curves is crucial for mission success. Historically, pre-flight estimations of these curves are derived experimentally either via ballistic range testing or with 1-degree-of-freedom (DOF) free-or forced-oscillation wind tunnel testing. With post-flight best-estimated trajectories, a fitting procedure as described in Karlgaard, et. al. is used to compare pre-and post-flight pitch damping coefficients. This technique relies on an assumed analytical form of the trajectory, a limited 3-DOF representation described in Schoenenberger and Queen, which results in an Euler-Cauchy solution form. This solution assumes constant density, thus limited amplitude multi-peak windows of the trajectory are considered to populate the pitch damping-amplitudes pace. The estimated post-flight pitch damping approximations and the pre-flight predictions shown are effective representations of pitch damping, or the integrated effect of the pitch damping coefficient over one pitch cycle at the corresponding cycle’s peak amplitude. Effective pitch damping coefficient curves provide more intuitive insights about where the vehicle is stable and permits readily processed comparisons between data-sets. It is important to note that most pitch damping curves, such as those reported in aero databases and utilized in flight mechanics trajectory simulations, are represented in instantaneous angle-of-attack space distinct from what is presented in the paper.

pitch damping coefficient↗