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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 OSTI2022

A reliable workflow for improving nanoscale X-ray fluorescence tomographic analysis on nanoparticle-treated HeLa cells

Abstract Scanning X-ray fluorescence (XRF) tomography provides powerful characterization capabilities in evaluating elemental distribution and differentiating their inter- and intra-cellular interactions in a three-dimensional (3D) space. Scanning XRF tomography encounters practical challenges from the sample itself, where the range of rotation angles is limited by geometric constraints, involving sample substrates or nearby features either blocking or converging into the field of view. This study aims to develop a reliable and efficient workflow that can (1) expand the experimental window for nanoscale tomographic analysis of local areas of interest within a laterally extended specimen, and (2) bridge 3D analysis at micrometer and nanoscales on the same specimen. We demonstrate the workflow using a specimen of HeLa cells exposed to iron oxide core and titanium dioxide shell (Fe3O4/TiO2) nanocomposites. The workflow utilizes iterative and multiscale XRF data collection with intermediate sample processing by focused ion beam (FIB) sample preparation between measurements at different length scales. Initial assessment combined with precise sample manipulation via FIB allows direct removal of sample regions that are obstacles to both incident X-ray beam and outgoing XRF signals, which considerably improves the subsequent nanoscale tomography analysis. This multiscale analysis workflow has advanced bio-nanotechnology studies by providing deep insights into the interaction between nanocomposites and single cells at a subcellular level as well as statistical assessments from measuring a population of cells.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

On Distributed Model-Free Reinforcement Learning Control with Stability Guarantee

Distributed learning can enable scalable and effective decision making in numerous complex cyber-physical systems such as smart transportation, robotics swarm, power systems, etc. However, the stability of the system is usually not guaranteed in most existing learning paradigms; and this limitation can hinder the wide deployment of machine learning in decision making of safety-critical systems. This paper presents a stability guaranteed distributed reinforcement learning (SGDRL) framework for interconnected linear subsystems, without knowing the subsystem models. While the learning process requires data from a peer-to-peer (p2p) communication architecture, the control implementation of each subsystem is only based on its local state. The stability of the interconnected subsystems will be ensured by a diagonally dominant eigenvalue condition, which will then be used in a model-free RL algorithm to learn the feedback control gains. The RL algorithm structure follows an off-policy iterative framework, with interleaved policy evaluation and policy update steps. We numerically validate our theoretical results by performing simulations on four interconnected sub-systems.

Mukherjee, Sayak↗
NASA NTRS1997

Visualization of Unsteady Computational Fluid Dynamics

The current compute environment that most researchers are using for the calculation of 3D unsteady Computational Fluid Dynamic (CFD) results is a super-computer class machine. The Massively Parallel Processors (MPP's) such as the 160 node IBM SP2 at NAS and clusters of workstations acting as a single MPP (like NAS's SGI Power-Challenge array and the J90 cluster) provide the required computation bandwidth for CFD calculations of transient problems. If we follow the traditional computational analysis steps for CFD (and we wish to construct an interactive visualizer) we need to be aware of the following: (1) Disk space requirements. A single snap-shot must contain at least the values (primitive variables) stored at the appropriate locations within the mesh. For most simple 3D Euler solvers that means 5 floating point words. Navier-Stokes solutions with turbulence models may contain 7 state-variables. (2) Disk speed vs. Computational speeds. The time required to read the complete solution of a saved time frame from disk is now longer than the compute time for a set number of iterations from an explicit solver. Depending, on the hardware and solver an iteration of an implicit code may also take less time than reading the solution from disk. If one examines the performance improvements in the last decade or two, it is easy to see that depending on disk performance (vs. CPU improvement) may not be the best method for enhancing interactivity. (3) Cluster and Parallel Machine I/O problems. Disk access time is much worse within current parallel machines and cluster of workstations that are acting in concert to solve a single problem. In this case we are not trying to read the volume of data, but are running the solver and the solver outputs the solution. These traditional network interfaces must be used for the file system. (4) Numerics of particle traces. Most visualization tools can work upon a single snap shot of the data but some visualization tools for transient problems require dealing with time.

Haimes, Robert↗
DOE OSTI2022

Quantification of the importance of the modeling fidelity in the Best Estimate Plus Uncertainty analysis

In this work, the multi-physics coupling of CTF-PARCS for both steady-state and transient simulations is evaluated focusing on the quantification of the importance of the modeling fidelity when applied to the Best Estimate Plus Uncertainty safety analysis. For the steady-state, a Picard iteration approach is adopted in the coupled code while for the transient an explicit approach has been developed as a first step. The CTF-PARCS coupling is applied to the REA at HFP BOC scenario proposed in the exercise I-2c of LWR-UAM benchmark Phase III. Two different modeling choices are investigated for the nominal calculations. The first consists of including the 3D burnup information in CTF, and the second of modeling the spacers grids in both PARCS and CTF. It is observed that due to the degradation of the fuel thermal conductivity, the 3D burnup inclusion in CTF leads to significant higher centerline temperatures than the base case, with a difference up to 300 K. The spacer grids modeling shows the expected power and temperature decrease in the axial positions of the spacer grids. For the uncertainty quantification study, the multi-physics modeling including the burnup information in CTF is used. The results show that the inserted reactivity has a relative standard deviation of 13.6% mainly attributed to the nuclear data. The maximum linear power has a relative standard deviation of 16.2% due to the nuclear data as well, while the maximum centerline temperature has a relative standard deviation of 3.5% primarily caused by the fuel thermal conductivity. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS2016

NASA's Space Launch System: A Transformative Capability for Exploration

Currently making rapid progress toward first launch in 2018, NASA's exploration-class Space Launch System (SLS) represents a game-changing new spaceflight capability, enabling mission profiles that are currently impossible. Designed to launch human deep-space missions farther into space than ever before, the initial configuration of SLS will be able to deliver more than 70 metric tons of payload to low Earth orbit (LEO), and will send NASA's new Orion crew vehicle into lunar orbit. Plans call for the rocket to evolve on its second flight, via a new upper stage, to a more powerful configuration capable of lofting 105 tons to LEO or co-manifesting additional systems with Orion on launches to the lunar vicinity. Ultimately, SLS will evolve to a configuration capable of delivering more than 130 tons to LEO. SLS is a foundational asset for NASA's Journey to Mars, and has been recognized by the International Space Exploration Coordination Group as a key element for cooperative missions beyond LEO. In order to enable human deep-space exploration, SLS provides unrivaled mass, volume, and departure energy for payloads, offering numerous benefits for a variety of other missions. For robotic science probes to the outer solar system, for example, SLS can cut transit times to less than half that of currently available vehicles, producing earlier data return, enhancing iterative exploration, and reducing mission cost and risk. In the field of astrophysics, SLS' high payload volume, in the form of payload fairings with a diameter of up to 10 meters, creates the opportunity for launch of large-aperture telescopes providing an unprecedented look at our universe, and offers the ability to conduct crewed servicing missions to observatories stationed at locations beyond low Earth orbit. At the other end of the spectrum, SLS opens access to deep space for low-cost missions in the form of smallsats. The first launch of SLS will deliver beyond LEO 13 6-unit smallsat payloads, representing multiple disciplines, including three spacecraft competitively chosen through NASA's Centennial Challenges competition. Private organizations have also identified benefits of SLS for unique public-private partnerships. This paper will give an overview of SLS' capabilities and its current status, and discuss the vehicle's potential for human exploration of deep space and other game-changing utilization opportunities.

Robinson, Kimberly F.↗
DOE OSTI2022

Advanced Reactor Control and Operations (ARCO): A University Research Facility for Developing Optimized Digital Control Rooms

The Advanced Reactor Control and Operations (ARCO) facility was constructed in January 2018 to serve as a test bed for advanced reactor control rooms and operator support systems. Since then, it has supported human-machine interface user experience research, fault detection and mitigation technology development, control room concept of operations development, and remote operations research. ARCO serves as the control room for the Compact Integral Effects Test (CIET) facility, which replicates the primary-side flow paths and thermal-hydraulic behavior of a fluoride-salt-cooled high-temperature reactor (FHR) using simulant fluids and scaling principles. New reactor designs feature different operating conditions and scenarios than those in existing reactors. ARCO supports the research and development of digital tools for operator communications, intuitive real-time data analysis, online health monitoring and prognostics, and control room cybersecurity. By integrating these different technologies, ARCO acts as a prototypical control system to iteratively develop methods and tools of operation in advanced small modular nuclear reactors. This paper describes the features of and challenges to operating advanced small modular reactors underlying the design basis for ARCO and its operator support systems.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS2016

Design and Development Comparison of Rapid Cycle Amine 1.0, 2.0, and 3.0

The development of the Rapid Cycle Amine (RCA) swing-bed technology for carbon dioxide (CO2) removal has been in progress since favorable results were published in 1996. Shortly thereafter, a prototype was designed, developed, and tested successfully and delivered to Johnson Space Center in 1999. An improved prototype was delivered to NASA in 2006 and was notated as RCA 1.0 and sized for the extravehicular activity (EVA). The new RCA swing-bed technology is a regenerative system which employs two alternating solid-amine sorbent beds to remove CO2 and water. The two- bed design employs a chemisorption process whereby the beds alternate between adsorbtion and desorbsion. This process provides for an efficient operation of the RCA so that while one bed is in adsorb (uptake) mode, the other is in the desorb (regeneration) mode. The RCA has now progressed through several iterations of technology readiness levels. Test articles have now been designed, developed, and tested for the advanced space suit portable life support system (PLSS) including RCA 1.0, RCA 2.0, and RCA 3.0. The RCA 3.0 was the most recent RCA fabrication and was delivered to NASA-JSC in June 2015. The RCA 1.0 test article was designed with a pneumatically actuated linear motion spool valve. The RCA 2.0 and 3.0 test articles were designed with a valve assembly which allows for switching between uptake and regeneration modes while minimizing gas volume losses to the vacuum source. RCA 2.0 and 3.0 also include an embedded controller design to control RCA operation and provide the capability of interfacing with various sensors and other ventilation loop components. The RCA technology is low power, small, and has fulfilled all test requirements levied upon the technology during development testing thus far. This paper will provide an overreview of the design and development of RCA 1.0, 2.0 and 3.0 including detail differences between the design specifications of each.

Chullen, Cinda↗
NASA NTRS2016

Design and Development Comparison of Rapid Cycle Amine 1.0, 2.0, and 3.0

The development of the Rapid Cycle Amine (RCA) swing-bed technology for carbon dioxide (CO2) removal has been in progress since favorable results were published in 1996. Shortly thereafter, a prototype was designed, developed, and tested successfully and delivered to Johnson Space Center in 1999. An improved prototype (RCA 1.0) was delivered to NASA in 2006 and sized for the extravehicular activity (EVA). The RCA swing-bed technology is a regenerative system which employs two alternating solid-amine sorbent beds to remove CO2 and water. The two-bed design employs a chemisorption process whereby the beds alternate between adsorption and desorption. This process provides for an efficient RCA operation that enables one bed to be in adsorb (uptake) mode, while the other is in the desorb (regeneration) mode. The RCA has progressed through several iterations of technology readiness levels. Test articles have now been designed, developed, and tested for the advanced space suit portable life support system (PLSS) including RCA 1.0, RCA 2.0, and RCA 3.0. The RCA 3.0 was the most recent RCA fabrication and was delivered to NASA-JSC in June 2015. The RCA 1.0 test article was designed with a pneumatically actuated linear motion spool valve. The RCA 2.0 and 3.0 test articles were designed with a valve assembly which allows for switching between uptake and regeneration modes while minimizing gas volume losses to the vacuum source. RCA 2.0 and 3.0 also include an embedded controller design to control RCA operation and provide the capability of interfacing with various sensors and other ventilation loop components. The RCA technology is low power, small, and has fulfilled all test requirements levied upon the technology during development testing thus far. This paper will provide an overview of the design and development of RCA 1.0, 2.0 and 3.0 including detail differences between the design specifications of each. Nomenclature.

Chullen, Cinda↗
NASA NTRS2019

Development of a Maxwell X-57 High Lift Motor Reference Design

NASA's all-electric X-57 airplane will utilize 14 electric motors, of which 12 are exclusively for lift augmentation during takeoff and landing. This report covers the design and development process taken to create an open reference model representative of the 12 lift augmenting motors. A combined worst case scenario was used as the design point, which represents the simultaneously occurring worst case aspects of thermal, static stress, electromagnetic, and rotor dynamic conditions. This work also highlights the tightly coupled nature of aerospace electric motor design, requiring constant iteration between all disciplines involved. Further adding to the uniqueness is the cooling method, which is limited to nacelle skin forced convection cooling only, no internal air flow is permitted. The stator outer diameter limit of 156.45 mm greatly impacts the degree of coupling between the electromagnetic design with the thermal analysis. The permanent magnet synchronous motor developed here operates between 385 V and 538 V, at a peak current of 50 A. Detailed electromagnetic, thermal, static load, and rotordynamic analysis was completed for this electric motor; all of which are required for a full design. The rotordynamic analysis took into consideration the motor housing which is designed specifically for this motor. The final electric motor has a mass of 2.34 kg, produces 24.1 Nm of torque with a specific power of 5.56 kW/kg, and has an efficiency of 96.61% at the combined worst case design point.

Electric Aircraft↗
DOE OSTI2026

SODAs: sparse optimization for the discovery of differential and algebraic equations

Differential-algebraic equations (DAEs) integrate ordinary differential equations (ODEs) with algebraic constraints, providing a fundamental framework for developing models of dynamical systems characterized by time-scale separation, conservation laws and physical constraints. While sparse optimization has revolutionized model development by allowing data-driven discovery of parsimonious models from a library of possible equations, existing approaches for dynamical systems assume DAEs can be reduced to ODEs by eliminating variables before model discovery. This assumption limits the applicability of such methods for DAE systems with unknown constraints and time scales. We introduce sparse optimization for differential-algebraic systems (SODAs), a data-driven method for the identification of DAEs in their explicit form. By discovering the algebraic and dynamic components sequentially without prior identification of the algebraic variables, this approach leads to a sequence of convex optimization problems. It has the advantage of discovering interpretable models that preserve the structure of the underlying physical system. To this end, SODAs improves since SODAs is singular numerical stability when handling high correlations between library terms, caused by near-perfect algebraic relationships, by iteratively refining the conditioning of the candidate library. We demonstrate the performance of our method on biological, mechanical and electrical systems, showcasing its robustness to noise in both simulated time series and real-time experimental data.

DAE↗
NASA NTRS2003

Sub-problem Optimization With Regression and Neural Network Approximators

Design optimization of large systems can be attempted through a sub-problem strategy. In this strategy, the original problem is divided into a number of smaller problems that are clustered together to obtain a sequence of sub-problems. Solution to the large problem is attempted iteratively through repeated solutions to the modest sub-problems. This strategy is applicable to structures and to multidisciplinary systems. For structures, clustering the substructures generates the sequence of sub-problems. For a multidisciplinary system, individual disciplines, accounting for coupling, can be considered as sub-problems. A sub-problem, if required, can be further broken down to accommodate sub-disciplines. The sub-problem strategy is being implemented into the NASA design optimization test bed, referred to as "CometBoards." Neural network and regression approximators are employed for reanalysis and sensitivity analysis calculations at the sub-problem level. The strategy has been implemented in sequential as well as parallel computational environments. This strategy, which attempts to alleviate algorithmic and reanalysis deficiencies, has the potential to become a powerful design tool. However, several issues have to be addressed before its full potential can be harnessed. This paper illustrates the strategy and addresses some issues.

Guptill, James D.↗
DOE OSTI2021

Reconstructing magnetic deflections from sets of proton images using differential evolution

Proton imaging is a powerful technique for imaging electromagnetic fields within an experimental volume, in which spatial variations in proton fluence are a result of deflections to proton trajectories due to interaction with the fields. When deflections are large, proton trajectories can overlap, and this nonlinearity creates regions of greatly increased proton fluence on the image, known as caustics. The formation of caustics has been a persistent barrier to reconstructing the underlying fields from proton images. We have developed a new method for reconstructing the path-integrated magnetic fields, which begins to address the problem posed by caustics. Our method uses multiple proton images of the same object, each image at a different energy, to fill in the information gaps and provide some uniqueness when reconstructing caustic features. We use a differential evolution algorithm to iteratively estimate the underlying deflection function, which accurately reproduces the observed proton fluence at multiple proton energies simultaneously. We test this reconstruction method using synthetic proton images generated for three different, cylindrically symmetric field geometries at various field amplitudes and levels of proton statistics and present reconstruction results from a set of experimental images. Here, the method we propose requires no assumption of deflection linearity and can reliably solve for fields underlying linear, nonlinear, and caustic proton image features for the selected geometries and is shown to be fairly robust to noise in the input proton intensity.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Core-edge integrated predictive studies of ST40 and NSTX plasmas with the scrape-off layer box model

The ability to model the interplay between the core and edge of tokamak plasmas is crucial to designing both the plasma operating scenario of a fusion pilot plant and the design of the tokamak itself. Scrape-off-layer (SOL) models that are tailored to integrated scenario modeling need to have fast turn-around time and minimal computational burden to enable wide parameter-space coverage for design scoping. The SOL 0-D Box model is a reduced SOL model based on global power and particle balance that captures the essential physics of SOL transport with little computational cost. The usage of the 0-D Box model in core-edge coupled simulations has been demonstrated in both interpretive and predictive modes on a variety of devices. This paper presents a sensitivity study of the 0-D Box model to the input SOL heat-flux width for an ST40 plasma. This study demonstrates that accurate prediction of this width is crucial to predicting global performance parameters of a plasma scenario, such as energy confinement time and flux consumption. We also present an extension of the Box model to 1-D to allow for parallel variation of plasma parameters along the magnetic field lines. The 1-D Box model is then compared with SOLPS-ITER simulations of an NSTX plasma. Advantages and limitations of the Box model are discussed, and future directions are outlined.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1991

Mission and system optimization of nuclear electric propulsion vehicles for lunar and Mars missions

The detailed mission and system optimization of low thrust electric propulsion missions is a complex, iterative process involving interaction between orbital mechanics and system performance. Through the use of appropriate approximations, initial system optimization and analysis can be performed for a range of missions. The intent of these calculations is to provide system and mission designers with simple methods to assess system design without requiring access or detailed knowledge of numerical calculus of variations optimizations codes and methods. Approximations for the mission/system optimization of Earth orbital transfer and Mars mission have been derived. Analyses include the variation of thruster efficiency with specific impulse. Optimum specific impulse, payload fraction, and power/payload ratios are calculated. The accuracy of these methods is tested and found to be reasonable for initial scoping studies. Results of optimization for Space Exploration Initiative lunar cargo and Mars missions are presented for a range of power system and thruster options.

Gilland, James H.↗
DOE OSTI2020

AI-enabled high-resolution scanning coherent diffraction imaging

Ptychographic imaging is a powerful means of imaging beyond the resolution limits of typical x-ray optics. Capturing recovered images from raw ptychographic data, however, requires the solution of an inverse problem, namely, phase retrieval. Phase retrieval algorithms are computationally expensive, which precludes real-time imaging. In this work, we propose PtychoNN, an approach to solve the ptychography data inversion problem based on a deep convolutional neural network. We demonstrate how the proposed method can be used to predict real-space structure and phase at each scan point solely from the corresponding far-field diffraction data. Our results demonstrate the practical application of machine learning to recover high fidelity amplitude and phase contrast images of a real sample hundreds of times faster than current ptychography reconstruction packages. Furthermore, by overcoming the constraints of iterative model-based methods, we can significantly relax sampling constraints on data acquisition while still producing an excellent image of the sample. Besides drastically accelerating acquisition and analysis, this capability has profound implications for the imaging of dose sensitive, dynamic, and extremely voluminous samples.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Mind the gap: the discrepancy between simulation and reality drives interpretations of the Galactic Center Excess

The Galactic Center Excess (GCE) in GeV gamma rays has been debated for over a decade, with the possibility that it might be due to dark matter annihilation or undetected point sources such as millisecond pulsars (MSPs). This study investigates how the gamma-ray emission model (γEM) used in Galactic center analyses affects the interpretation of the GCE's nature. To address this issue, we construct an ultra-fast and powerful inference pipeline based on convolutional Deep Ensemble Networks. We explore the two main competing hypotheses for the GCE using a set of γEMs with increasing parametric freedom. We calculate the fractional contribution (f src ) of a dim population of MSPs to the total luminosity of the GCE and analyze its dependence on the complexity of the γEM. For the simplest γEM, we obtain fsrc = 0.10 ± 0.07, while the most complex model yields f src = 0.79 ± 0.24. In conclusion, we find that the statement about the nature of the GCE (dark matter or not) strongly depends on the assumed γEM. The quoted results for f src do not account for the additional uncertainty arising from the fact that the observed gamma-ray sky is out-of-distribution concerning the investigated γEM iterations. We quantify the reality gap between our γEMs using deep-learning-based One-Class Deep Support Vector Data Description networks, revealing that all employed γEMs have gaps to reality. Finally, our study casts doubt on the validity of previous conclusions regarding the GCE and dark matter, and underscores the urgent need to account for the reality gap and consider previously overlooked "out of domain" uncertainties in future interpretations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

MultiLoad-GAN: A GAN-Based Synthetic Load Group Generation Method Considering Spatial-Temporal Correlations

This paper presents a deep-learning framework, Multi-load Generative Adversarial Network (MultiLoad-GAN), for generating a group of synthetic load profiles (SLPs) simultaneously. The main contribution of MultiLoad-GAN is the capture of spatial-temporal correlations among a group of loads that are served by the same distribution transformer. This enables the generation of a large amount of correlated SLPs required for microgrid and distribution system studies. Here, the novelty and uniqueness of the MultiLoad-GAN framework are three-fold. First, to the best of our knowledge, this is the first method for generating a group of load profiles bearing realistic spatial- temporal correlations simultaneously. Second, two complementary realisticness metrics for evaluating generated load profiles are developed: computing statistics based on domain knowledge and comparing high-level features via a deep-learning classifier. Third, to tackle data scarcity, a novel iterative data augmentation mechanism is developed to generate training samples for enhancing the training of both the classifier and the MultiLoad-GAN model. Simulation results show that MultiLoad- GAN can generate more realistic load profiles than existing approaches, especially in group level characteristics. With little finetuning, MultiLoad-GAN can be readily extended to generate a group of load or PV profiles for a feeder or a service area.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Evaluation of 1/100-Scale Mooring Systems for Wave Energy Converters

This study evaluates the feasibility, accuracy, and limitations of using 1/100th scale physical mooring systems to represent full-scale mooring behavior for wave energy converters (WECs) during small-scale tank testing. The work focuses on an RM3-style point absorber deployed in conditions representative of the PacWave South test site and examines whether small-scale physical testing can reliably inform numerical modeling, design decisions, and future prototype development. Overall, this study concludes that small-scale (1/100th) physical mooring models can provide valuable qualitative insights, including relative comparisons between mooring types and trends in device behavior, but cannot reliably replicate full-scale mooring loads or dynamic response without significant scaling distortion. Physical tests at this scale are most appropriate for motion characterization and model validation within known limitations, not for deriving absolute mooring loads or final engineering design values. Key lessons learned emphasize the importance of improved instrumentation strategies, iterative wave tank tuning, more precisely manufactured scaled mooring components, and enhanced anchoring systems to reduce experimental uncertainty.

16 TIDAL AND WAVE POWER↗