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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Power System Redundancy Design Trends for All-Electric eVTOL Quadrotors

Electrical vertical takeoff and landing (eVTOL) vehicles promise to enable a variety of new local and intraregional air transport missions. However, there are significant challenges that must be solved to enable their use. One challenge is the relatively low reliability of power components (e.g. motors, power electronic devices, and batteries). No clear development path to create reliable eVTOL power systems has been articulated in the literature, though it is generally assumed redundancy is part of the solution. This work proposes an analysis process to examine the redundancy design space and select mass optimal designs for eVTOL power systems. The process is applied to a six passenger quadrotor vehicle, and results are used to highlight redundancy design trends for this class of vehicle. This process and associated data can help vehicle designers more effectively develop eVTOLs that meet reliability requirements.

Electric vertical takeoff and landing↗
NASA NTRS2023

An Ensemble Neural Network Model for Predicting Rare-Earth Oxide and Silicate Heat Capacities at High Temperature

In this work, a neural network model was developed to predict the constant pressure heat capacity for materials in the rare-earth oxide—silica material space. Several model architectures were trained and tested on heat capacity data generated from first-principles density functional theory calculations. Hyperparameter optimization was performed, and the optimal model was selected for heat capacity predictions. The optimal model architecture was found to have a root-mean-squared error of 5.12 ± 3.37 J/mol-K. The optimal model architecture was then used in a bagging ensemble model trained using the leave-one-group-out method to provide error estimates for model predictions. The out-of-bag score for the ensemble model was 0.997. The predicted heat capacities agree well with the DFT and experimental results and were computed orders of magnitude faster than DFT simulations. Machine learning shows the potential to provide a suitable surrogate model for thermochemical property predictions for candidate environmental barrier coating materials but refining of input material features and model architectures could further improve accuracy for these models.

environmental barrier coatings↗
NASA NTRSDate not supplied

Cloud-Based Demodulation and Data Distribution of a Satellite Downlink

Ground station networks connected to the cloud allow space missions to have global communications coverage without operating their own infrastructure. In this work, we describe the communications architecture for the TechEdSat-13 mission, which performed the first in-space characterization of a neuromorphic processor. The mission utilizes a commercial provider for S-band downlinks. A suite of cloud services and open-source software such as GNU Radio are leveraged to demodulate signals received by an AWS ground station during passes with TechEdSat-13 and store recovered data. Once a pass is scheduled, the entire process takes place without human intervention. On-orbit results the past year of operations are presented, demonstrating the advantages of this approach over traditional operator-owned ground stations. Use of software-defined radio makes possible custom signal processing. The homogeneity of apertures and their interfaces to the cloud simplifies scaling across many sites. This abundance of candidate links lays the groundwork for intelligent scheduling agents to optimize pass selection across several factors, automatically recover from failed contacts, and gather metrics to learn from past performance.

cloud demodulation↗
NASA NTRSDate not supplied

A Monte Carlo Based Ray Tracing Tool for Evaluating Wolter-Type Prescriptions

Evaluating Wolter-like X-ray mirror prescriptions via ray tracing is useful for selecting and optimizing the right mirror prescriptions for a specified application. Moreover, incorporating real metrology data into a ray trace and simulating point spread functions (PSF) allows for performance predictions representative of real manufacturing errors and tolerances. In fulfillment of an internship project, an X-ray ray trace routine using a Monte-Carlo method has been developed to examine different Wolter-like prescriptions and characterize their theoretical performances over a specified field of view. This routine includes the ability to use real metrology data to evaluate the impact of figure error on imaging performance. As a test case, the Marshall Grazing Incidence X-ray Spectrometer (MaGIXS) Wolter-I mirror prescription and an equivalent Wolter-Schwarzschild prescription were traced and imaging performance of a specified field of view were mapped. Here we present the approach used in this routine, showcase example results, and discuss future goals for expanding the routine to address azimuthally varying figure errors and surface roughness.

X-ray optics↗
DOE OSTI2018

Search for heavy resonances decaying into two Higgs bosons or into a Higgs boson and a W or Z boson in proton-proton collisions at 13 TeV

A search is presented for massive narrow resonances decaying either into two Higgs bosons, or into a Higgs boson and a W or Z boson. The decay channels considered are HH$\to \mathrm{b\overline{b}}\tau^{+}\tau^{-}$ and VH$ \to \mathrm{q\overline{q}}\tau^{+}\tau^{-}$, where H denotes the Higgs boson, and V denotes the W or Z boson. This analysis is based on a data sample of proton-proton collisions collected at a center-of-mass energy of 13 TeV by the CMS Collaboration, corresponding to an integrated luminosity of 35.9 fb$^{-1}$. For the TeV-scale mass resonances considered, substructure techniques provide ways to differentiate among the hadronization products from vector boson decays to quarks, Higgs boson decays to bottom quarks, and quark- or gluon-induced jets. Reconstruction techniques are used that have been specifically optimized to select events in which the tau lepton pair is highly boosted. The observed data are consistent with standard model expectations and upper limits are set at 95% confidence level on the product of cross section and branching fraction for resonance masses between 0.9 and 4.0 TeV. Exclusion limits are set in the context of bulk radion and graviton models: spin-0 radion resonances are excluded below a mass of 2.7 TeV at 95% confidence level. In the spin-1 heavy vector triplet framework, mass-degenerate W' and Z' resonances with dominant couplings to the standard model gauge bosons are excluded below a mass of 2.8 TeV at 95% confidence level. There are the first limits for these decay channels at $\sqrt{s}=$ 13 TeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

PERFORMANCE MODELING OF A NOVEL “SMART” MAGNETIC PARTICLE-EMBEDDED PCM LAYER FOR THERMAL MANAGEMENT SYSTEMS

Developing stable and environmentally friendly phase change materials (PCM) has been an active area of research due to many applications, such as industrial energy storage system, cooling/heating applications in buildings, and thermal management for electronics equipment as well as for batteries and photovoltaic modules. However, previous studies confirmed that most PCMs suffer a serious disadvantage of low thermal conductivity. A novel design of PCM layer is investigated here for thermal management application. The novel PCM layer is comprised of a PCM and magnetic particles coated on the shell. When a magnetic field is applied in the thermal management system, due to the magnetic of the particles, PCM layer are attached to the heat source to absorb heat, which significantly improves the heat transfer between PCM and the heat source. The melting temperature of the PCM and Curie temperature of the magnetic particles are carefully selected to optimize the performance and to ensure materials compatibility. A numerical study is conducted to reveal the heat transfer and performance improvement of the PCM layer comparing to the conventional PCMs.

Zhang, Mingkan↗
DOE OSTI2018

CRITERIA FOR COMPARATIVE EVALUATIONS OF TREATY VERIFICATION MONITORING SYSTEMS

In 2017, a team of subject matter experts from across the U.S. Department of Energy (DOE) national laboratory developed a framework to assess and compare monitoring systems implemented to support treaty verification. Drawing on principles and techniques from multi-criteria decision making, the project team developed a set of evaluation criteria to serve as a decision support tool to understand and evaluate competing options. These criteria, which focus on monitoring systems designed to maintain chain of custody, were developed to be unambiguous, independent from each other, collectively exhaustive, measurable (whether this is quantitative or by a systematic subjective evaluation) and to help identify and prioritize factors of importance considering the perspectives of both a Host and Monitor. When used in the evaluation of options, criteria help to create a consistent framework that can assist in selecting the optimal solution among a set of competing alternatives.

decision making, Arms Control, decision analysis, ↗
DOE OSTI2018

Performance of Wavelength-Shifting Fibers for the Mu2e Cosmic Ray Veto Detector

The cosmic-ray-veto detector (CRV) for the Mu2e experiment consists of four layers of plastic scintillating counters read out by silicon photo-multipliers (SiPM) through wavelength-shifting fibers. This paper reports the light properties of several wavelength-shifting fiber samples with diameters of 1.0 mm, 1.4 mm, and 1.8 mm that were considered for the CRV system. A fiber diameter of 1.4 mm was selected as optimal for the CRV, and measurements of the prototype and production fiber of this diameter are presented. Fiber performance was found to exceed the CRV requirements for >99% of the spools. The measurements are performed using a scanner designed to ensure the fiber quality for the CRV.

Dukes, E. C.↗
DOE OSTI2023

Control of matrix converters using machine learning

A method of controlling a matrix converter system is provided. The method includes receiving an operating condition and consulting a trained Q-data structure for reward values associated with respective switching states of the switching matrix for an operating state that corresponds to the operating condition. The Q-data structure is trained using Q-learning to map a reward value predicted for respective switching states to respective discrete operating states. The method further includes sorting the reward values predicted for the respective switching states mapped to the operating state that corresponds to the operating condition, selecting a subset of the set of the mappings as a function of a result of sorting the reward values associated with the switching states of the operating state, evaluating each switching state included in the subset, and selecting an optimal switching state for the operating condition based on a result of evaluating the switching states of the subset.

Chamie, Mahmoud El↗
DOE OSTI2020

Development of an In-Situ Leaching Technology for Extracting Residual Uranium from Remediated Soil - 20099

Site-specific chemical solutions for extracting uranium were developed and evaluated for treating previously remediated soil at a former thorium mill tailings site, known as the Former Rare Earths Facility (REF), in West Chicago, Illinois. Prior remediation efforts had resulted in residual sand and gravel aggregate that met the soil cleanup standards. The target soil contains residual levels of uranium, which continues to leach into the site groundwater at levels exceeding groundwater protection standards (GWPS). A series of bench-scale tests were conducted to evaluate the efficacy and viability of in-situ leaching (ISL) technology to treat the target soil for the purposes of attaining uranium GWPS at the REF. Specific objectives of the bench-scale tests entailed identification and selection of optimal leaching, quantification of the extent and rate of uranium released as a function of chemical composition, chemical concentrations, chemical residence times, and obtaining critical data to estimate treatment costs. A combination of sodium bicarbonate and sodium carbonate reagent was the most effective leaching solution. In general, leaching solutions containing a higher concentration of sodium carbonate demonstrated superior uranium leaching capabilities. Removal of 50% of the uranium mass from the soil matrix was adequate for achieving compliance with the site-specific GWPS. After six to seven pore volume flushes (PVFs), the uranium leaching effectiveness of all leaching solutions was similar. The amount of initial uranium removed with each subsequent PVF was minimal, suggesting limited solubility of the remaining mass of uranium. The effects of leaching duration were significant and persisted through at least the first five consecutive PVFs. Leaching of uranium also resulted in leaching of several non-uranium constituents, many of which exceeded applicable National Pollutant Discharge Elimination System (NPDES) standards. Given historical operations and waste disposal practices at the site, leaching of non-uranium constituents was expected. Hydrochloric acid was used to stabilize treated soil. Savings of approximately $148 million in direct and indirect remediation costs are expected with an ISL alternative compared to the excavation and landfill disposal remediation option. (authors)

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

Operational performance risk assessment in support of a supervisory control system - 251

A supervisory control system is being developed for multiunit advanced small modular reactors to minimize human interventions during normal and abnormal operations. In the supervisory control system, control action decisions are made based on a probabilistic risk assessment that employs event trees and fault trees. Although traditional probabilistic risk assessment tools are implemented, their scope is extended to normal operations, and the application is reversed to assess the success of non-safety related systems and to enable continued operation of the plant. This extended probabilistic risk assessment approach is called operational performance risk assessment (OPRA). OPRA helps to identify available paths, combine control actions for maintaining plant conditions within operational limits, and to quantify the likelihood of success of these operational trajectories to optimize the selection of alternative actions without activating reactor protection system. In this paper, a case study of OPRA in a supervisory control system is demonstrated for the Advanced Liquid Metal Reactor (ALMR) Power Reactor Inherently Safe Module (PRISM) design, specifically the power conversion system. The scenario investigated involved a condition in which the feedwater control valve that was observed to be drifting to the closed position. Alternative plant configurations that would allow the plant to continue to operate at full or reduced power were identified using OPRA. Dynamic analyses were performed with a thermal-hydraulic model of the ALMR PRISM system using Modelica to evaluate the magnitude of safety margins. Successful recovery paths for the selected scenario were identified and quantified using the supervisory control system. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Recycling Rare Earth Elements from End-of-Life Electric and Hybrid Electric Vehicle Motors

In this paper, we propose a superstructure-based approach to finding the optimal pathways for recovering rare earth elements in their commercialized rare earth oxide form from end-of-life EV and HEV motors. The proposed superstructure was modeled as a MILP optimization problem, selecting the net present value as the objective function. Whenever possible, costing data taken from the literature was used to inform this mode. However, due to the novelty of this research area data were often not available thus requiring the generation of flowsheets that were implemented in Aspen Plus.

Laliwala, Chris↗
DOE OSTI2024

Exploring gauge-fixing conditions with gradient-based optimization

Lattice gauge fixing is required to compute gauge-variant quantities, for example those used in RI-MOM renormalization schemes or as objects of comparison for model calculations. Recently, gauge-variant quantities have also been found to be more amenable to signal-to-noise optimization using contour deformations. These applications motivate systematic parameterization and exploration of gauge-fixing schemes. This work introduces a differentiable parameterization of gauge fixing which is broad enough to cover Landau gauge, Coulomb gauge, and maximal tree gauges. The adjoint state method allows gradient-based optimization to select gauge-fixing schemes that minimize an arbitrary target loss function.

Detmold, William↗
DOE OSTI2026

Dark Energy Survey Year 6 Results: MagLim++ Lens Sample Selection and Measurements of Galaxy Clustering

Galaxy clustering is a sensitive probe of the expansion history and growth of structure of the universe, and key degeneracies can be broken by combining these data with measurements of cosmic shear and galaxy-galaxy lensing (a so-called 3$\times$2pt analysis). The largest and least biased statistical samples of galaxies for use in clustering analyses can be collected photometrically through large imaging surveys. However, selecting clean photometric subsamples for cosmology are crucial for avoiding contamination that can bias cosmological constraints. Here we present the MagLim++ galaxy sample, selected to optimize for cosmological constraining power and incorporating an array of novel quality cuts to identify and remove residual contamination. This sample comes from the full six years of observations from the Dark Energy Survey. We present measurements of the two-point angular clustering ($w(θ)$) of 9,186,205 galaxies distributed over 4031 sq. degrees and in six tomographic redshift bins centered at $\bar{z}\approx$ [0.31, 0.44, 0.62, 0.78, 0.90, 1.01]. These measurements are used as part of the 3$\times$2pt and other DES Y6 legacy cosmological analyses in companion works. We describe the battery of null tests and mitigation schemes implemented to address observational, astrophysical, and methodological systematics in the analysis. The resulting $w(θ)$ measurements have a S/N = 149 (90.2 for linear scales only), which we use to place galaxy-clustering-only constraints on the matter density of the Universe, $Ω_m=0.311^{+0.023}_{-0.035}$, and amplitude of galaxy clustering in each redshift bin, $b_iσ_8=[1.16^{+0.04}_{-0.06},\ 1.40^{+0.04}_{-0.06},\ 1.57^{+0.04}_{-0.06},\ 1.59^{+0.04}_{-0.05},\ 1.50^{+0.04}_{-0.05},\ 1.74^{+0.06}_{-0.08}]$.

Weaverdyck, N. [BCCP, Berkeley; UC, Berkeley; LBL,↗
DOE OSTI2025

Contrasting Time-Frequency Representations for Unknown Waveform Detection

In real-world applications like spectrum management and interference detection, dealing with unseen electromagnetic waveforms is critical. Although some methods attempt to simulate open set data using generator models, they face challenges in generating synthetic samples for open set while simultaneously selecting an optimal discriminator for accurate classification. This results in difficulties capturing distinctive features across classes, especially in dynamic scenarios where new classes emerge. To detect unseen waveforms, we propose combining time and frequency domain features with cosine similarity loss to enhance feature distinctiveness and enabling more accurate predictions. This approach efficiently captures more comprehensive information than single-domain representations or approaches without cosine loss. Additionally, our model avoids generic feature vectors by extracting class-specific features during training, resulting in improved class representation. The experiment results show that this combined feature approach with cosine loss outperforms single-domain models and improves accuracy by 10\% over models without cosine loss.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Contrasting Time-Frequency Representations for Unknown Waveform Detection

Identifying unseen electromagnetic waveforms is critical for many applications, like interference management, electronic warfare and spectrum management. Traditionally this is done using statistical methods for anomaly detection, which has evolved to deep learning models for identifying the unseen data, formally termed as open set recognition. Some prior methods use a generative model to emulate open set data, which face challenges in generating synthetic samples for open set while simultaneously selecting an optimal discriminator for accurate classification. To alleviate this issue, we propose a discriminative model that effectively combines time and frequency domain features of communication signals for accurate predictions. We further introduce a cosine similarity loss that makes the domain specific features unique to enhance the prediction rate. Additionally, our model avoids generic feature vectors by extracting class-specific features during training, resulting in improved class representation. The experiment results show that this combined feature approach with cosine loss outperforms single-domain models and improves accuracy by 10% over models without cosine loss.

99 - GENERAL AND MISCELLANEOUS↗
NASA NTRSDate not supplied

Linear Regression Model for Predictive Service Provider Selection

The increasing number of satellites in orbit has led to a growing reliance on third-party service providers for data transfer between Earth and space. Traditional approaches to managing satellite communications require human intervention, which becomes more burdensome with the escalating number of satellites. This research addresses the need for an efficient and automated system to optimize service provider selection for NASA space communication. Previous research has utilized human-operated approaches for service provider management. Our study fills a gap by developing a cognitive algorithm that automates and optimizes the selection process based on various parameters, such as data volume, priority, quality of service and cost. This novel solution reduces user burden, facilitates service management, and contributes to the development of cognitive spaceflight missions, ultimately supporting NASA’s research into Cognitive Communications technology. The algorithm design consists of three major steps: modeling data, developing a Link Selection Algorithm (LSA) based on a grading system, and applying machine learning using linear regression. The LSA evaluates providers based on user-defined constraints, considering factors such as delivery time, cost, and quality of service. We define a suitability metric which allows our algorithm to make a recommendation to a user regarding which commercial service providers to select. The addition of Linear Regression predicts the future suitability value. Our main findings demonstrate that the resulting algorithm can autonomously manage connections between satellites and providers, maximizing communication channel efficiency. This research has significant implications, as it not only addresses a pressing issue in satellite communication management but also advances the field of cognitive spaceflight missions.

Linear regression↗
NASA NTRS2015

Distributed Space Mission Design for Earth Observation Using Model-Based Performance Evaluation

Distributed Space Missions (DSMs) are gaining momentum in their application to earth observation missions owing to their unique ability to increase observation sampling in multiple dimensions. DSM design is a complex problem with many design variables, multiple objectives determining performance and cost and emergent, often unexpected, behaviors. There are very few open-access tools available to explore the tradespace of variables, minimize cost and maximize performance for pre-defined science goals, and therefore select the most optimal design. This paper presents a software tool that can multiple DSM architectures based on pre-defined design variable ranges and size those architectures in terms of predefined science and cost metrics. The tool will help a user select Pareto optimal DSM designs based on design of experiments techniques. The tool will be applied to some earth observation examples to demonstrate its applicability in making some key decisions between different performance metrics and cost metrics early in the design lifecycle.

Constellation↗