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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 1,639 records · Page 91
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Large-Scale Magnetic Microcalorimeter: Modeling and Multiplexing

We will present performance predictions for a large-scale magnetic microcalorimeter detector array intended for a potential future X-ray astrophysics mission. The focal plane comprises three distinct array designs, necessitating over 100,000 pixels to meet the scientific objectives. Our presentation will describe the results of numerical modeling aimed at estimating the energy resolution and detector responses for each array. Additionally, we will discuss the application of microwave multiplexing technology for the comprehensive readout of the entire sensor array.

x-ray↗
NASA NTRSDate not supplied

Coaxial Rotor CFD Validation and ML Surrogate Model Generation

A fixed-pitch speed-controlled coaxial rotor system was tested in the NASA Langley Transonic Dynamics Tunnel in September 2022. The rotors have a diameter of 1.35 meters and an inter-rotor spacing of 25% of the diameter. Though this test was focused on the NASA New Frontiers Dragonfly mission, the resulting dataset is relevant to a wide array of applications of multirotor vehicles, especially those using fixed-pitch variable-speed rotors. Most notably beyond Dragonfly, perhaps, is the application of coaxial rotor pair configurations for eVTOL and Urban Air Mobility (UAM) aircraft. This work provides a thorough CFD validation study quantifying coaxial rotor performance estimation with accuracy on order 5-10% using an efficient hybrid BEMT-URANS flow solver over a wide range of operating conditions. This accuracy was achieved using novel approaches for the construction of both the C81 airfoil performance lookup tables and the BEMT rotor model. These novel approaches are combined with advanced scripting to further accelerate the commercial off-the-shelf CFD solver on GPU accelerated machines. Finally, the development of machine learning surrogate models is presented to produce highly efficient and accurate rotor performance predictions for fixed-pitch variable-speed multirotor aircraft over the complete flight regime including scout, cruise, climb, descent, as well as limiting cases of vortex-ring and windmill-brake states.

Coaxial↗
NASA NTRSDate not supplied

Impacts of Non-Simultaneous Global Photospheric Magnetic Measurements on Coronal and Solar Wind Modeling

The intensity and arrival time of coronal mass ejections (CMEs) can be significantly influenced by the background solar wind encountered as they propagate outward from the Sun into the interplanetary medium. In addition, solar energetic particles (SEPs) race ahead of CME shock fronts and flare regions along magnetic field lines largely determined by the background solar wind. Predicting the solar wind accurately is therefore critical for improving forecasts of CMEs, SEPs, and high-speed streams. Modeling of the corona and solar wind is challenging in general, as it is highly dependent on global photospheric magnetic field maps, which serve as the boundary conditions to all coronal models that drive solar wind models. Unfortunately, less than half of the Sun’s photospheric magnetic field is reliably measured from any given vantage point and thus it is common for the maps to have highly dated and unreliable measurements in them. While Solar Obiter (SolO) now provides for the first time the opportunity to have simultaneous measurements of nearly the entire surface magnetic field of Sun (e.g., when SolO/PHI measurements are combined with those from SDO/HMI), the required alignment to accomplish this occurs only occasionally. Further, coronal models are extremely sensitive to the strengths of the polar magnetic fields of the Sun, which remain poorly observed. Recently, efforts to mitigate this problem include using flux transport models such as the Air Force Data Assimilative Photospheric Flux Transport (ADAPT) model, which evolves the field forward in time using well known transport processes occurring on the Sun. However, it cannot account for the emergence of new magnetic flux without direct observations. The ESA VIGIL mission will eventually provide continuous observations from the L5, and the proposed SunCHASER mission will likewise do this at the L4 vantage point should it be funded. Ultimately, what is needed is a constellation of spacecraft distributed around the Sun with magnetographs that continuously measure the global surface magnetic field. In this talk, we discuss how the lack of simultaneous global measurements of the photospheric magnetic field adversely impacts the predictive performance of coronal and solar wind models.

C. Nick Arge↗
NASA NTRSDate not supplied

Applications and Development Work for Digital Image Correlation Using High Speed Cameras

NASA, Army, and industry partners are working to advance optical measurement techniques used in highly dynamic rocket engine and missile test environments. This allows for direct component data collection during test and flight environments, significantly reduced analysis time, and associated cost savings. Digital Image Correlation (DIC) uses calibrated stereo cameras to collect three-dimensional (3-D) displacements, strains, and acceleration data by referencing a stochastic speckle pattern applied to the test object. These optical measurement techniques provide significantly improved data resulting in better performance predictions, model validation, and information about components during hot-fire tests and test flights.

Rachel Paden↗
NASA NTRSDate not supplied

NASA’s Quiet Electric ENgines (QUEEN): Summary of the Acoustic Tests of the QUEEN V1

Noise produced by an electric ducted fan system was measured in tests at the NASA Glenn Research Center Acoustical Testing Laboratory. Main components of the system include a Commercial-Off-the-Shelf fan, shroud, motor, and Electronic Speed Controller, plus a custom-designed inlet bellmouth, and four experimental inlet duct acoustic liners. Fan speed and noise were measured in this static ground test of the propulsor. This propulsor prototype is one of NASA’s Quiet Electric ENgines (QUEENs) and is designated the ‘QUEEN V1.’ The Quiet Electric Engines are being developed for the 25% scale model of the Subsonic Aft Engine (SUSAN) Flight Research Vehicle and are intended to explore the potential of distributed electric propulsion for large single-aisle aircraft. Results indicated that inlet duct acoustic liners reduced tone and broadband noise as compared to a hardwall inlet duct. Predicted performance of the honeycomb liner compared well with measurements from an array of far field microphones. Inlet acoustic liners are just one method for mitigating noise for electric ducted fans for aircraft propulsion systems. Lessons learned will be used to guide development of future QUEEN prototypes. Results of a thermal test of the Electronic Speed Controller are presented in a separate report.

Aircraft propulsion and power↗
NASA NTRS2024

PALMO: An OVERFLOW Machine Learning Airfoil Performance Database

The OVERFLOW Machine Learning Airfoil Performance (PALMO) database has been created to enable robust modeling of airfoil performance in a variety of applications. The database uses OVERFLOW simulation data second-order accurate in time and fourth-order accurate in space with Spalart-Allmaras turbulence closure. The foundation of the in-development PALMO database is the airfoil base cube. Each base cube includes simulation data parametrized over a range of Mach numbers, Reynolds numbers, and angles-of-attack. This first release of the database includes the NACA 4-series airfoils, with parametrization in airfoil thickness and camber from an NACA 0006 to an NACA 4424. In total, 52,480 NACA 4-series calculations were run on the NASA High-End Compute Capability (HECC) supercomputer and the corresponding airfoil performance coefficients are embedded in the Appendix of this document for public distribution. This provides high-order-accurate simulation data covering a wide range of aerospace design applications, which enables users to develop OVERFLOW-quality airfoil performance look-up tables without additional high-performance computing. In addition to engineering design and analysis of aerospace vehicles, PALMO is well suited to be a benchmark dataset for the development and testing of machine learning methods in aerospace engineering. Downstream surrogate models enable OVERFLOW- quality airfoil performance predictions for any arbitrary combination of camber, thickness, Mach number, Reynolds number, and angle-of-attack within the bounds of the database.

Database↗
NASA NTRSDate not supplied

NASA’s Quiet Electric ENgines (QUEEN): Summary of the Acoustic Tests of the QUEEN V1

Noise produced by an electric ducted fan system was measured in tests at the NASA Glenn Research Center Acoustical Testing Laboratory. Main components of the system include a Commercial-Off-the-Shelf fan, shroud, motor, and Electronic Speed Controller, plus a custom-designed inlet bellmouth, and four experimental inlet duct acoustic liners. Fan speed and noise were measured in this static ground test of the propulsor. This propulsor prototype is one of NASA’s Quiet Electric ENgines (QUEENs) and is designated the ‘QUEEN V1.’ The Quiet Electric Engines are being developed for the 25% scale model of the Subsonic Aft Engine (SUSAN) Flight Research Vehicle and are intended to explore the potential of distributed electric propulsion for large single-aisle aircraft. Results indicated that inlet duct acoustic liners reduced tone and broadband noise as compared to a hardwall inlet duct. Predicted performance of the honeycomb liner compared well with measurements from an array of far field microphones. Inlet acoustic liners are just one method for mitigating noise for electric ducted fans for aircraft propulsion systems. Lessons learned will be used to guide development of future QUEEN prototypes. Results of a thermal test of the Electronic Speed Controller are presented in a separate report.

Aircraft propulsion and power↗
DOE OSTI2021

The Role of Interface in Additively Manufactured Interpenetrating Composites

Additively Manufactured Interpenetrating Composites (AMIPCs) are a relatively new metal-metal chain composite in development for use in high energy absorption systems. In this system, reinforcing phase of additively manufactured continuous lattice configurations 316L austenitic stainless-steel is in melt infiltrated with a matrix phase of A356 aluminum-silicon casting alloy. Measurements and observations of this material system have shown that weakly bonded or open/porous interface between the reinforcement and matrix phases exhibits dramatically different mechanical properties of AMIPCs, which is not currently well understood. In this work, Finite Element Models (FEM) are used to model the effects of interfaces between the composite phases. Mechanical tensile tests measurements of various composite volume fractions and varying degrees of casting infiltration are also examined and used to show consistency with the FEM results. The outcome provides insight into material design criteria and performance predictions for new hybrid material systems with exceptional damage tolerance.

Allen, Jason↗
DOE OSTI2022

Multimodel ensemble predictions of precipitation using bayesian neural networks

Multimodel ensembling improves predictions and considers model uncertainties. In this study, we present a Bayesian Neural Network (BNN) ensemble approach for large-scale precipitation predictions based on a set of climate models. BNN infers spatiotemporally varying model weights and biases through the calibration against observations. This ensemble scheme of BNN sufficiently leverages individual model skill for accurate predictions as well as provides interpretability about which models contribute more to the ensemble prediction at which locations and times to inform model development. Additionally, BNN accurately quantifies epistemic uncertainty to avoid overconfident projections. We demonstrate BNN’s superior prediction performance to three state-of-the-art ensemble approaches and discuss its interpretability and uncertainty quantification.

Fan, Patrick↗
DOE OSTI2022

Multimodel Ensemble Predictions of Precipitation using Bayesian Neural Networks

Multimodel ensembling improves predictions and considers model uncertainties. In this study, we present a Bayesian Neural Network (BNN) ensemble approach for large-scale precipitation predictions based on a set of climate models. BNN infers spatiotemporally varying model weights and biases through the calibration against observations. This ensemble scheme of BNN sufficiently leverages individual model skill for accurate predictions as well as provides interpretability about which models contribute more to the ensemble prediction at which locations and times to inform model development. Additionally, BNN accurately quantifies epistemic uncertainty to avoid overconfident projections. We demonstrate BNN’s superior prediction performance to three state-of-the-art ensemble approaches and discuss its interpretability and uncertainty quantification.

Fan, Patrick↗
DOE OSTI2023

PRINTED, PLANAR MICROWAVE CONNECTOR WITH MULTIPLE SIGNAL LINES

Microwave connectors with a planar geometry have been demonstrated using additive manufacturing techniques. This planar form factor is expected to enable connectors to be integrated directly with other components onto a printed circuit board. The connectors were developed with multiple signal lines that will increase the density of signal connections to PCBs, eliminating the physical constraints of single-signal COTS microwave connectors. This work developed electromagnetic models (using Ansys HFSS) of these planar connectors to predict performance and optimize connector design. Materials and printing processes were developed to fabricate the connectors based on high-temperature PEEK thermoplastics. The multiple signal connectors were designed to operate over the 1-6GHz band and are built upon the single signal printed planar geometry connector introduced in a previous paper [1].

Additive Manufacturing↗
DOE OSTI2023

Neural Network Enhanced RKPM for Electrochemical-Mechanical Coupled Damage Modeling of Energy Storage Materials

Energy storage materials undergo significant charge cycling, which makes understanding their reliability and durability fundamental in predicting performance and service life. Strong electrochemical-mechanical coupling and highly anisotropic material properties contribute to the formation and propagation of micro-cracking, largely along material interfaces and grain boundaries. For Li-ion batteries, for example, lithium moving between electrodes during charging and discharging process causes expansion and contraction of grains, and the strongly anisotropic and nonlinearly [Li]-dependent grain material properties can cause grains to expand into and contract away from each other, leading to chemo-mechanical cracking. In the first part of this work, a RKPM based computational framework for solving the coupled solid-phase lithium conservation with Fickian diffusion and the lithium concentration dependent anisotropic mechanical problem subjected to a highly nonlinear Butler-Volmer boundary condition is introduced. The choice of RKPM completeness conditions for lithium concentration and mechanical deformation fields, and the variational consistency condition for the domain integration of the coupled problem is first determined. In the second part of this work, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1] is leveraged to accurately capture damage and crack propagation throughout the material, by learning the location, orientation, and sharpness of discontinuity while allowing for a coarser nodal distribution than that is necessary for capturing sharp solution transitions using traditional mesh-based methods. NN-RKPM is used to inform how crack opening and closure in turn affect the coupled chemical equations and material microstructure.

damage modeling↗
DOE OSTI2023

Neural Network-Enhanced Reproducing Kernel Particle Method for Image-Based Multiphysics Damage Modeling of Energy Storage Materials

Energy storage materials undergo significant stresses during charge/discharge cycling, which makes understanding their reliability and durability fundamental in predicting performance and service life. Strong electrochemical-mechanical coupling and highly anisotropic material properties contribute to the formation and propagation of micro-cracking, largely along material interfaces and grain boundaries. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), image-based modeling techniques are used to represent the complex material microstructures that dictate the coupled physics of these systems. Traditional electrochemical-mechanical models rely on mesh-based finite element methods, which can lead to difficulties in capturing crack propagation due to mesh dependency. Additionally, commonly used damage models, such as the continuous damage model and the cohesive zone model, often have steep tradeoffs between discontinuous field accuracy and computational expense. In this work, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1] is leveraged to accurately capture damage and crack propagation throughout the material by learning the location, orientation, and sharpness of discontinuity while allowing for a coarser nodal distribution than that necessary for capturing sharp solution transitions using traditional mesh-based methods. NN-RKPM is used to inform how crack opening and closure in turn affect the coupled chemical equations and material microstructure. Reference: [1] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, pp 4422-4454, https://doi.org/10.1002/nme.7040, 2022.

damage modeling↗
DOE OSTI2024

Modeling for a Digital Twin-Based Remote Operation System Framework

New reactor designs and technologies are being developed to grow and advance the nuclear industry. Microreactors are one of the many new concepts for advancing the industry. Microreactors are very small reactors generally designed to have an operating power of 20 MWth or less. They are ideal for many applications in which it would not be feasible to have a large-scale reactor, such as powering remote communities, military bases, and mining sites. Many of these applications currently rely on diesel generators for power, and replacing those generators with the carbon-free energy of a microreactor is a major driving factor for microreactor development. However, most of the use cases for microreactors are in isolated locations where construction and labor costs are much higher. Microreactors will need to be comparable to other energy production methods for their deployment to be successful. Remotely operating the microreactor has a great potential to benefit the economics and make it more cost competitive. With remote operations, the operation facility location could be strategically chosen based on factors such as construction costs, workforce size, etc. The benefits of remote operations could be leveraged even more if the remote operation system is semi-autonomous. If the remote operation system is semi-autonomous, more microreactors could be operated and monitored from one remote operation facility. Additionally, with the system handling some tasks for the human operator, it could reduce the number of operating staff necessary for the microreactor. Digital twins can be used to introduce a level of automation to the remote operation system. Digital twins are capable of component monitoring, system operation and control, and predictive performance [1]. All of those features are important for a successful semi-autonomous remote operation system.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Fuel Performance Evaluation of THOR-C Experiments

The Temperature Heatsink Overpower Response Commissioning (THOR-C) and THOR-Metal (THOR-M) experiments will be performed as part of an ongoing project for testing sodium fast reactor fuels with the Japan Atomic Energy Agency (JAEA). The THOR-C experiments consist of fresh metallic fuel pins and have been analyzed using the ABAQUS, Ansys codes and the BISON fuel performance code. THOR-M-Loss of Flow-1 (THOR-M-LOF-1) is designed to test an EBR-II irradiated fuel pin under LOF conditions. Simulation of the THOR-MLOF-1 experiment required first simulating the base irradiation of the fuel pin in EBR-II. MFUEL module of SAS4A/SASSYS-1 [1] is a physics-based metallic fuel performance model applicable to the normal operation, transient scenarios and fuel failure modeling including scenarios with bulk fuel melting. The model has been validated using EBR-II normal operation, separate effect transient tests as well as TREAT M-Series transient tests [2]. In this study, MFUEL models has been utilized together with a new capsule heat transfer model developed in this project. The new heat transfer model was necessary due to (1) significant amount of heat losses that required 2D heat transfer, (2) the presence of a titanium heat sink, rejecting a significant amount of heat, and (3) stagnant coolant conditions, which are inconsistent with SAS4A/SASSYS-1 (SAS) heat transfer model. Updates to SAS4A/SASSYS-1 and MFUEL has been described below, followed by a preliminary validation effort using the results from THOR-C-2 fresh fuel capsule experiment. A previous study for THOR-C-2 analysis using BISON code is also utilized in this study to model this test [3]. [1] D. O’Grady, A. J. Brunett, L. Ibarra, A. Karahan, T. Kim, T. S. Sumner, R. Thomas, T. H. Fanning, “The SAS4A/SASSYS-2 Version 5.7 Safety Analysis Code System,” Argonne National Laboratory,ANL/NSE-SAS/5.7, (2023). [2] A. Karahan, T. Kim, T. Fanning, D. O’Grady, “Validation of MFUEL Metal Fuel Performance Models of SAS4A/SASSYS-1,” Argonne National Laboratory, ANL/NSE-23/11, (2023). [3] M. Mihelish, A. Zabriskie, K. Paaren, P. Medvedev, C. Jensen, “Fuel Performance Predictions for the TREAT THOR-C Experiments,” Idaho National Laboratory, INL/RPT-23-73397, Revision 0, (2023)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Hydride Reprecipitation in Prototypical 17x17 PWR Fuel Rods after Simulated Vacuum Drying Conditions

Light water reactor (LWR) fuel rod cladding picks up hydrogen as it oxidizes in reactor. Once the hydrogen concentration exceeds the solubility limit, zirconium hydride platelets precipitate, generally in the circumferential direction. In preparation for dry storage, elevated temperatures can dissolve some of the circumferential hydrides, which during cooling can then reprecipitate in a direction perpendicular to the hoop stress (radially). From 2019 to 2024, ORNL published a series of reports that included metallographic observations of cladding after a thermal transient to 400°C followed by slow cooling. The simulated dry storage process was performed on three full-length 17x17 PWR fuel rods at their as-discharged rod internal pressure. There were two important observations from the metallographic examinations of these heat-treated rods: (1) the propensity for hydride reorientation is strongly influenced by nearby as-discharged precipitated hydrides, (2) the radial hydrides observed near the cladding inner diameter were often associated with cracks in the pellet. Subsequent modeling using the BISON fuel performance code that considered the effects of pellet cracks and pellet-cladding bonding observed in high-burnup fuel support the conclusion that pellet cracks enhance the local hoop stress in the cladding creating preferential sites for hydride reorientation, and that the influence of the pellet cannot be ignored in accurate fuel rod performance predictions.

Montgomery, Rose [ORNL] (ORCID:0000000286038936)↗
DOE OSTI2026

Integrating Immersive Visualization in Molten-Salt Reactor Waste Management for Experimental Design and Planning

The Molten Salt Reactor (MSR) represents a significant innovation in nuclear technology, offering several operational and safety benefits over traditional solid-fuel reactors. However, MSRs face uncertainties in waste management due to their flexible designs and variable waste compositions. To address these challenges, we propose a visualization platform that illustrates solutions and performance predictions for various waste management strategies, enhancing user experience and improving strategy and communication. Immersive visualizations are widely used in the nuclear industry for training, simulation, and safety enhancement. Our project aims to develop a visualization platform incorporating virtual reality (VR) technologies to illustrate MSR characteristics immediately following reactor shutdown. This immersive simulation will allow users to interact, explore, and understand different waste management strategies. The platform will display MSR reactor characterizations, including nuclide decay, salt solidification, and corrosion, which are crucial for assessing and selecting backend management strategies. Using the Meta Quest 3 VR headset with Unity software, our platform will provide real-scale visualizations, enabling users to experience and evaluate designs and plans as if they were physically present. This user-friendly interface will make complex data accessible and understandable for non-domain experts, aiding in decision-making for MSR waste management. Our proposed visualization workflow can be applied to other nuclear reactors, assisting in the design and planning of waste management strategies.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗