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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Piloted Simulator Investigation of Techniques to Achieve Attitude Command Response with Limited Authority Servos

The purpose of the study was to develop generic design principles for obtaining attitude command response in moderate to aggressive maneuvers without increasing SCAS series servo authority from the existing +/- 10%. In particular, to develop a scheme that would work on the UH-60 helicopter so that it can be considered for incorporation in future upgrades. The basic math model was a UH-60A version of GENHEL. The simulation facility was the NASA-Ames Vertical Motion Simulator (VMS). Evaluation tasks were Hover, Acceleration-Deceleration, and Sidestep, as defined in ADS-33D-PRF for Degraded Visual Environment (DVE). The DVE was adjusted to provide a Usable Cue Environment (UCE) equal to two. The basic concept investigated was the extent to which the limited attitude command authority achievable by the series servo could be supplemented by a 10%/sec trim servo. The architecture used provided angular rate feedback to only the series servo, shared the attitude feedback between the series and trim servos, and when the series servo approached saturation the attitude feedback was slowly phased out. Results show that modest use of the trim servo does improve pilot ratings, especially in and around hover. This improvement can be achieved with little degradation in response predictability during moderately aggressive maneuvers.

Key, David L.↗
DOE OSTI2021

A prediction interval method for uncertainty quantification of regression models

This paper considers calculation of prediction intervals (PIs) by neural networks (NNs) for quantifying uncertainty in regression tasks, so as to provide fast, accurate and robust emulators to accelerate scientific simulations. We propose a novel method to learn lower and upper bounds of the PI using independent NNs without defining an exclusive loss. Our method requires no distributional assumption, does not introduce extra hyper-parameters, and can effectively identify out-of-distribution samples and quantify their uncertainty. We demonstrate advantages of our method using a benchmark problem and two real-world scientific applications.

Zhang, Pei↗
NASA NTRSDate not supplied

An Optimization Approach to Support Science Decision Making for Lunar Surface Exploration

Introduction: Scientific exploration is one of the three pillars of NASA’s Moon2Mars architecture, with crew surface extra vehicular activities (EVA) serving a critical enabling function. Development of surface EVA operational planning and execution, specifically integrating science and flight control teams (FCT), is currently being explored through analog scenarios. This integration, exercised, for example, through the Joint EVA and Hu-man Surface Mobility Test Team (JETT), allows for science input on EVA activities in near real-time through a Science Evaluation Room (SER), or Arte-mis science backroom, which integrates with the broader FCT through the Science Officer. The SER works within the FCT to support dynamic EVA planning in response to changes in operational constraints as well as science opportunities and re-prioritization, increasing the mission science return and accelerating the accomplishment of the Moon2Mars science objectives. The SER works within the FCT to provide recommendations to traverse execution in near real-time. One challenge is the requirement to deliver SER inputs to the FCT on operationally relevant timelines. Failure to do so may result in suboptimal execution of science exploration EVAs or even loss of key science objectives. To close this gap, we present a network optimization tool to allow the SER to provide rapid input to the FCT in response to changes in operational constraints or science opportunities. Inputs are predicated on approved science objectives, and clear rationale must be provided to the FCT for any requested change. Accordingly, this tool incorporates the Science Traceability Matrix (STM), SER prioritization scheme, and station characterization and action planning with operational constraints such as duration, traverse speed, and distance to maximize science objectives based on SER priorities, consistent with FCT operational requirements. Method: As a proof of concept, we used an existing linear programing software package used to simulate optimal routes through cellular metabolism. We built a Demonstrative Model with three STM objectives and four stations on a region of the Moon. The objectives were given an arbitrary prioritization and mapped to the stations through four possible crew actions. (Figs. 1 and 2). This station to STM mapping is consistent with the method used by the JETT5 Science Team to develop analog surface EVA science planning. We used a grid system with the landing site at the origin and the four stations placed across the positive x,y quadrant. Actions were assigned to each station and the accomplishment of those actions resulted in a numerical “reward” based on the ability of that action to achieve science objectives. The aggregate reward from each individual STM objective contributes to a global score (Science Yield), weighted by its priority. Operational constraints included a requirement to start and end at the landing site, 5 minutes each for initial station characterization and “clean up,” and variable total EVA time, traverse rate (fixed to 0.5 meters per second in our example), and time to perform each action (10, 5, 7, and 15 min for actions 1, 2, 3, and 4, respectively). Additional constraints and variables will be added in the future (e.g., sample mass, number of stations, traverse route constraints, illumination). Optimization. We converted the connections (arcs) between these stations (nodes) into a mixed integer linear programming optimization problem (arcs = constraints, nodes = variables) with the objective to maximize Science Yield. For any action, the Science Yield is equal to the relevance of that action to an STM objective [3, 2, and 1 point(s) for High, Med., and Low relevance, respectively], multiplied by the STM Objective Priority [3, 2, and 1 point(s) for High, Med., and Low priority, respectively]. This resulted in a model that computes the optimal station and action combination to maximize the Science Yield. These weightings can be adjusted by the SER as desired. Results: We explored three test cases for the Demonstrative Model. First, we set the maximum EVA duration to 120 minutes and computed the optimal route (Fig. 3A). The model suggested per-forming Actions 1 and 2 at Station P01, followed by Actions 1 and 2 at Station P02, and finally Actions 1 and 3 at Station P04 before returning to the Landing Site. Second, we adjusted the STM Objective Priori-ty order and computed the new optimal route (Fig. 3B). Under this situation, the model suggested per-forming all Actions at Station P02 followed by all Actions at Station P03. The previous test cases were relevant to SER planning activities. Next, we explored providing mid-EVA replanning input to the FCT. Scenario: While executing the Route in Fig. 3A the crew finishes at Station P01 and FCT decides that the EVA needs to finish in 45 minutes back at the Landing Site. FCT asks SER to recommend changes to the plan to accommodate this operation-al change. Using the model and incorporating these new constraints (start at Station P01, max. time of 45 min), the model suggested performing Actions 2 and 4 at Station P03 (Fig. 4), requiring 41 minutes to complete and return to the Landing Site. Interestingly, Station 3 was not part of the original route. Using the model, we determined the EVA would need 66 minutes, instead of 45, in order for the original Station P04 to yield a larger Science Yield than Station P03. The parametrization and simulation was per-formed in less than a minute, demonstrating the operational relevance of the approach. Future Efforts: The results from the Demonstrative Model suggest this tool can accelerate SER decision making on operationally relevant timelines. Use in analog activities, such as JETT5 or follow-ons, which have over a dozen stations for a crew to explore and over a dozen actions per station, will provide needed validation of the utility of this tool for planning EVAs, replanning mid-EVA, or planning follow-on EVAs based on previous results. Further integration with FCT execution monitoring tools may provide additional efficiency gains, al-lowing rapid and iterative exploration of operation-al and science decision space by the FCT and SER.

Science Operations↗
NASA NTRS1989

Kalman estimation for SEDS measurements

The first on-orbit experiment of the Small Expendable Deployer System (SEDS) for tethered satellites will collect telemetry data for tethered length, rate of deployment, and tether tension. The post-flight analysis will use this data to reconstruct the deployment history and determine payload position and tether shape. Two Kalman estimator algorithms were written, and output using simulated measurement data was compared. Both estimators exhibited the same estimated state histories, indicating that numerical instability in the traditional algorithm was not the cause of filter divergence. Estimation of acceleration biases was added, which reduced the error but did not correct the divergence. An add-a-bead estimator that adds lumped masses as the tether is deployed was written, which provides a state model that matches the BEADSIM simulation providing the true measurements and states. This twenty-one bead estimator produced state histories similar to those of the two-bead estimator, indicating that the filter divergence was not caused by a reduced-order model. The noise models used to date are relatively simple and may be the source of estimator divergence. The investigation of colored noise models, cross-correlated measurement and process covariances, and noise-adaptive filter techniques is recommended.

Carrington, Connie K.↗
DOE OSTI2025

Acceleration of Power System Dynamic Simulations Using a Deep Equilibrium Layer and Neural ODE Surrogate

The dominant paradigm for power system dynamic simulation is to build system-level simulations by combining physics-based models of individual components. The sheer size of the system along with the rapid integration of inverter-based resources exacerbates the computational burden of running time domain simulations. Here, in this paper, we propose a data-driven surrogate model based on implicit machine learningspecifically deep equilibrium layers and neural ordinary differential equationsto learn a reduced order model of a portion of the full underlying system. The data-driven surrogate achieves similar accuracy and reduction in simulation time compared to a physics-based surrogate, without the constraint of requiring detailed knowledge of the underlying dynamic models. This work also establishes key requirements needed to integrate the surrogate into existing simulation workflows; the proposed surrogate is initialized to a steady state operating point that matches the power flow solution by design.

Neural ordinary differential equations↗
NASA NTRS1994

Design for dependability: A simulation-based approach

This research addresses issues in simulation-based system level dependability analysis of fault-tolerant computer systems. The issues and difficulties of providing a general simulation-based approach for system level analysis are discussed and a methodology that address and tackle these issues is presented. The proposed methodology is designed to permit the study of a wide variety of architectures under various fault conditions. It permits detailed functional modeling of architectural features such as sparing policies, repair schemes, routing algorithms as well as other fault-tolerant mechanisms, and it allows the execution of actual application software. One key benefit of this approach is that the behavior of a system under faults does not have to be pre-defined as it is normally done. Instead, a system can be simulated in detail and injected with faults to determine its failure modes. The thesis describes how object-oriented design is used to incorporate this methodology into a general purpose design and fault injection package called DEPEND. A software model is presented that uses abstractions of application programs to study the behavior and effect of software on hardware faults in the early design stage when actual code is not available. Finally, an acceleration technique that combines hierarchical simulation, time acceleration algorithms and hybrid simulation to reduce simulation time is introduced.

Goswami, Kumar K.↗
NASA NTRS1980

G-cueing microcontroller (a microprocessor application in simulators)

A g cueing microcontroller is described which consists of a tandem pair of microprocessors, dedicated to the task of simulating pilot sensed cues caused by gravity effects. This task includes execution of a g cueing model which drives actuators that alter the configuration of the pilot's seat. The g cueing microcontroller receives acceleration commands from the aerodynamics model in the main computer and creates the stimuli that produce physical acceleration effects of the aircraft seat on the pilots anatomy. One of the two microprocessors is a fixed instruction processor that performs all control and interface functions. The other, a specially designed bipolar bit slice microprocessor, is a microprogrammable processor dedicated to all arithmetic operations. The two processors communicate with each other by a shared memory. The g cueing microcontroller contains its own dedicated I/O conversion modules for interface with the seat actuators and controls, and a DMA controller for interfacing with the simulation computer. Any application which can be microcoded within the available memory, the available real time and the available I/O channels, could be implemented in the same controller.

Horattas, C. G.↗
DOE OSTI2021

Cosmic shear cosmology beyond two-point statistics: a combined peak count and correlation function analysis of DES-Y1

ABSTRACT We constrain cosmological parameters from a joint cosmic shear analysis of peak-counts and the two-point shear correlation functions, as measured from the Dark Energy Survey (DES-Y1). We find the structure growth parameter $S_8\equiv \sigma _8\sqrt{\Omega _{\rm m}/0.3} = 0.766^{+0.033}_{-0.038}$ which, at 4.8 per cent precision, provides one of the tightest constraints on S8 from the DES-Y1 weak lensing data. In our simulation-based method we determine the expected DES-Y1 peak-count signal for a range of cosmologies sampled in four w cold dark matter parameters (Ωm, σ8, h, w0). We also determine the joint covariance matrix with over 1000 realizations at our fiducial cosmology. With mock DES-Y1 data we calibrate the impact of photometric redshift and shear calibration uncertainty on the peak-count, marginalizing over these uncertainties in our cosmological analysis. Using dedicated training samples we show that our measurements are unaffected by mass resolution limits in the simulation, and that our constraints are robust against uncertainty in the effect of baryon feedback. Accurate modelling for the impact of intrinsic alignments on the tomographic peak-count remains a challenge, currently limiting our exploitation of cross-correlated peak counts between high and low redshift bins. We demonstrate that once calibrated, a fully tomographic joint peak-count and correlation functions analysis has the potential to reach a 3 per cent precision on S8 for DES-Y1. Our methodology can be adopted to model any statistic that is sensitive to the non-Gaussian information encoded in the shear field. In order to accelerate the development of these beyond-two-point cosmic shear studies, our simulations are made available to the community upon request.

Harnois-Déraps, Joachim↗
NASA NTRS2008

Simulating Sources of Superstorm Plasmas

We evaluated the contributions to magnetospheric pressure (ring current) of the solar wind, polar wind, auroral wind, and plasmaspheric wind, with the surprising result that the main phase pressure is dominated by plasmaspheric protons. We used global simulation fields from the LFM single fluid ideal MHD model. We embedded the Comprehensive Ring Current Model within it, driven by the LFM transpolar potential, and supplied with plasmas at its boundary including solar wind protons, polar wind protons, auroral wind O+, and plasmaspheric protons. We included auroral outflows and acceleration driven by the LFM ionospheric boundary condition, including parallel ion acceleration driven by upward currents. Our plasmasphere model runs within the CRCM and is driven by it. Ionospheric sources were treated using our Global Ion Kinetics code based on full equations of motion. This treatment neglects inertial loading and pressure exerted by the ionospheric plasmas, and will be superceded by multifluid simulations that include those effects. However, these simulations provide new insights into the respective role of ionospheric sources in storm-time magnetospheric dynamics.

Fok, Mei-Ching↗
NASA NTRS1998

Simulation Accelerator

Under a NASA SBIR (Small Business Innovative Research) contract, (NAS5-30905), EAI Simulation Associates, Inc., developed a new digital simulation computer, Starlight(tm). With an architecture based on the analog model of computation, Starlight(tm) outperforms all other computers on a wide range of continuous system simulation. This system is used in a variety of applications, including aerospace, automotive, electric power and chemical reactors.

Source record↗
DOE OSTI2020

Neural-network accelerated coupled core-pedestal simulations with self-consistent transport of impurities and compatible with ITER IMAS

pedestal structure, current profile, and plasma equilibrium physics has been developed and tested against a DIII-D discharge. Here, key features of the achieved core-pedestal coupled workflow are its ability to account for the transport of impurities in the plasma self-consistently, as well as its use of machine learning accelerated models for the pedestal structure and for the turbulent transport physics. Notably, the coupled workflow is implemented within the OMFIT framework, and makes use of the ITER integrated modeling and analysis suite (IMAS) data structure for exchanging data among the physics codes that are involved in the simulations. Such technical advance has been facilitated by the development of a new numerical library named OMAS.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Accelerating discrete dislocation dynamics simulations with graph neural networks

Discrete dislocation dynamics (DDD) is a widely employed computational method to study plasticity at the mesoscale that connects the motion of dislocation lines to the macroscopic response of crystalline materials. However, the computational cost of DDD simulations remains a bottleneck that limits its range of applicability. Here, we introduce a new DDD-GNN framework in which the expensive time-integration of dislocation motion is entirely substituted by a graph neural network (GNN) model trained on DDD trajectories. As a first application, we demonstrate the feasibility and potential of our method on a simple yet relevant model of a dislocation line gliding through an array of obstacles. We show that the DDD-GNN model is stable and reproduces very well unseen ground-truth DDD simulation responses for a range of straining rates and obstacle densities, without the need to explicitly compute nodal forces or dislocation mobilities during time-integration. Our approach opens new promising avenues to accelerate DDD simulations and to incorporate more complex dislocation motion behaviors.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Collaborative Research: Enabling multi-scale studies of magnetic reconnection with interpretable data-driven models

The development of accurate reduced descriptions and improved closures for magnetic reconnection is an important and a long‐standing challenge in plasma physics. The four‐fluid approach, and associated closures, that were investigated have the potential to improve the accuracy of plasma fluid models, capturing physical effects which would otherwise require a kinetic description. If successful, this approach could have an important impact for the modeling of laboratory and space plasmas. The major goals of this project were to develop new machine learning (ML) tools based on sparse and symbolic regression techniques, and to extract interpretable and generalizable reduced models (e.g., in the form of partial differential equations - PDEs) from data generated by first principles plasma simulations. Preserving interpretability of such data‐driven models is key to addressing the long‐standing theoretical and numerical challenges. Prior proof‐of‐principle studies have demonstrated the enormous potential of this approach, by recovering the well‐established hierarchy of plasma equations (from Vlasov to MHD) from data produced by particle‐in‐cell (PIC) simulations. Our goal in this project was to extend and apply these new tools to construct better kinetic closures for magnetic reconnection; to derive better models of particle injection and acceleration by this fundamental plasma process; and to use this understanding to accelerate the development of multi‐scale plasma algorithms. While our immediate focus was on the problem of magnetic reconnection, the tools that were will developed are general and applicable to other areas of plasma physics, and more broadly to many‐body phenomena. We anticipate that the development of these multi‐scale models will have a significant impact across different areas of plasma science, from fusion to space and astrophysical plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2015

Impact Testing and Simulation of a Sinusoid Foam Sandwich Energy Absorber

A sinusoidal-shaped foam sandwich energy absorber was developed and evaluated at NASA Langley Research Center through multi-level testing and simulation performed under the Transport Rotorcraft Airframe Crash Testbed (TRACT) research project. The energy absorber, designated the "sinusoid," consisted of hybrid carbon- Kevlar® plain weave fabric face sheets, two layers for each face sheet oriented at +/-45deg with respect to the vertical or crush direction, and a closed-cell ELFOAM(TradeMark) P200 polyisocyanurate (2.0-lb/ft3) foam core. The design goal for the energy absorber was to achieve an average floor-level acceleration of between 25- and 40-g during the full-scale crash test of a retrofitted CH-46E helicopter airframe, designated TRACT 2. Variations in the design were assessed through quasi-static and dynamic crush testing of component specimens. Once the design was finalized, a 5-ft-long subfloor beam was fabricated and retrofitted into a barrel section of a CH-46E helicopter. A vertical drop test of the barrel section was conducted onto concrete to evaluate the performance of the energy absorber prior to retrofit into TRACT 2. Finite element models were developed of all test articles and simulations were performed using LSDYNA ®, a commercial nonlinear explicit transient dynamic finite element code. Test analysis results are presented for the sinusoid foam sandwich energy absorber as comparisons of load-displacement and acceleration-time-history responses, as well as predicted and experimental structural deformations and progressive damage for each evaluation level (component testing through barrel section drop testing).

Jackson, Karen E.↗
DOE OSTI2022

Interplay between beam-driven chirping modes and plasma confinement transitions in spherical tokamak ST40

Experiments on the high field spherical tokamak ST40 have led to the recent observation of interplay between beam-driven modes of sweeping frequency (chirping modes) and transitions to the enhanced global confinement regime (H-mode) and back to the low confinement regime (L-mode). The H-modes of plasma confinement are identified from decreased intensity of D α signal and from clear distinctions in the edge gradients of the visible plasma boundary (observed as a sharp plasma edge in camera images). The beam-driven chirping modes, identified as ideal magnetohydrodynamics beta-induced Alfvén acoustic eigenmodes modes, are observed in Mirnov coil signals, interferometry, and soft x-ray diagnostics. A moderate amplitude ‘primer’ chirping mode usually precedes an H–L transition. This is followed by a ‘dominant’ chirping mode with higher amplitude during the L-mode. The L–H transition back to the improved confinement occurs on a longer time scale of tens of ms, consistent with the slowing down time scale of fast beam ions. A dramatic decrease in toroidal plasma rotation is systematically observed associated with chirping modes sweeping down to zero frequency. Resonance maps built for the beam-driven chirping modes with the ASCOT (accelerated simulation of charged particle orbits in torodoial devices) code show that the resonant beam ions have orbits near the trapped-passing boundary. The ASCOT modelling assesses how losses of the resonant fast ions caused by the chirping modes with high enough amplitude modify the torque, potentially affecting the plasma rotation.

magnetic confinement fusion↗
DOE OSTI2025

Modeling Co2 Flow Through Faulted/Fractured Reservoirs Using Tedfm in Corner-Point Grids

Interest in underground CO2 storage has increased significantly over the last decade, driven by growing concern about global warming and rising levels of greenhouse gases in the atmosphere. Given that CO2 accounts for 80% of these greenhouse gases, carbon capture, utilization, and storage (CCUS) is considered one of the most direct approaches to achieving the net-zero carbon target. Although CO2 storage in deep saline aquifers and depleted gas reservoirs has been studied extensively, most studies use commercial simulators that model faults/fractures by simply modifying the transmissibility in the direction perpendicular to the fault surfaces. This work shows that this simplistic approach ignores the accelerated flow in the directions parallel to the fault plane, leading to significantly higher leakage along the fault surface. To accurately model CO2 flow in faulted reservoirs, we present the first transient embedded discrete-fracture model for corner-point grids (tEDFM-CPG). By comparing the tEDFM-CPG results with high-resolution reference solutions, we show that this approach is accurate and efficient at predicting CO2 flow in faulted/fractured reservoirs. In conclusion, this work presents the use of mixed reality (MR) to efficiently observe CO2 gas migration in the interior of these corner-point grid systems.

02 PETROLEUM↗
DOE OSTI2025

Challenge Problem 1: Preliminary Results of the Direct Numerical Simulation of Transient Flows

This report presents the first direct numerical simulations (DNS) of transient mixed convection in an idealized downcomer-like channel (Challenge Problem 1, Phase II). Using the GPU-accelerated NekRS solver, we modeled a sudden decay in driving pressure, mimicking loss-of-flow events, and tracked the resulting evolution of Reynolds number, boundary-layer structure, turbulence statistics, and heat-transfer metrics. Key findings include the systematic thickening and eventual asymmetry of velocity and thermal boundary layers under buoyant deceleration; minimal “memory” lag in Reynolds shear stress and TKE profiles when sampled at matching Re, yet clear shifts of peak locations toward the cooled wall; overshoots in transient eddy-viscosity and eddy-diffusivity (and corresponding sub-unity turbulent Prandtl numbers) on the cooled side; and a pronounced transient Nusselt-number enhancement driven by wall-temperature inertia and residual eddy mixing. These effects combined to offer a temporary cooling margin above steady-state predictions during reactor LOF transients. Future work will extend this work to a more complex “Case II” geometry (90° turn + lower plenum) and generate multi-Re/Pr datasets for data-driven turbulence closures.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1985

Three-dimensional calculation of shuttle charging in polar orbit

The charged particles environment in polar orbit can be of sufficient intensity to cause spacecraft charging. In order to gain a quantitative understanding of such effects, the Air Force is developing POLAR, a computer code which simulates in three dimensions the electrical interaction of large space vehicles with the polar ionospheric plasma. It models the physical processes of wake generation, ambient ion collection, precipitating auroral electron fluxes, and surface interactions, including secondary electron generation and backscattering, which lead to vehicle charging. These processes may be followed dynamically on a subsecond timescale so that the rapid passage through intense auroral arcs can be simulated. POLAR models the ambient plasma as isotropic Maxwellian electrons and ions (0+, H+), and allows for simultaneous precipitation of power-law, energetic Maxwellian, and accelerated Gaussian distributions of electrons. Magnetic field effects will be modeled in POLAR but are currently ignored.

Cooke, D. L.↗