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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

The Galactic Cosmic Ray Simulator at the NASA Space Radiation Research Laboratory

With NASA’s new Artemis plan for a sustainable return to the moon, astronauts will once again leave Earth’s protective magnetosphere only to endure higher levels of radiation from galactic cosmic radiation (GCR). The ever penetrating GCR will continue to pose significant health risks especially as lunar missions increase in duration and as NASA sets its aspirations on Mars. The primary risks of concern include carcinogenesis, central nervous system (CNS) effects resulting in potential in-mission cognitive or behavioral impairment and/or late neurological disorders, and degenerative tissue effects including circulatory and heart disease. Characterization and mitigation of these risks requires a significant reduction in the large biological uncertainties of chronic (low-dose rate) heavy ion exposures and the validation of countermeasures in a relevant space environment. Historically, most research on understanding space radiation-induced health risks has been performed using acute exposures of monoenergetic single-ion beams. However, the space radiation environment consists of a wide variety of ion species over a broad energy range. Using the fast beam switching and controls systems technology recently developed at the NASA Space Radiation Laboratory at Brookhaven National Laboratory, a new era in radiobiological research is possible. NASA has developed the “GCR simulator” to generate a spectrum of ion beams that approximates the primary and secondary GCR field experienced at human organ locations within a deep-space vehicle.

NASA Space Radiation Laboratory↗
NASA NTRSDate not supplied

NASA Space Radiation Laboratory Galactic Cosmic Ray Simulator

With exciting new Agency plans for a sustainable return to the moon, astronauts will once again leave earth’s protective magnetosphere only to endure higher levels of radiation from galactic cosmic rays (GCR) and the possibility of a large solar particle event (SPE). Gateway, lunar landers, and surface habitats will be designed to protect crew against SPEs with vehicle optimization, storm shelter concepts, and/or active dosimetry; however, the ever-penetrating GCR will continue to pose the most significant health risks especially as lunar missions increase in duration and as NASA sets its aspirations on Mars. The primary risks of concern include carcinogenesis, central nervous system effects resulting in potential in-mission cognitive or behavioral impairment and/or late neurological disorders, degenerative tissue effects including cataracts, circulatory and heart disease, as well as, potential immune system decrements impacting multiple aspects of crew health. Characterization and mitigation of these risks requires a significant reduction in the large biological uncertainties of chronic (low-dose rate) heavy ion exposures and the validation of countermeasures in a relevant space environment. NASA has developed the “GCR Simulator” at Brookhaven National Laboratory to generate a spectrum of ion beams that approximates the primary and secondary GCR field experienced at human organ locations within a deep-space vehicle. The majority of the dose is delivered from protons (~65-75%) and helium ions (~10-20%) with heavier ions (Z>3) contributing the remainder. The “GCR Simulator” exposes state-of-the art cellular and animal model systems to 33 sequential beams including 4 proton energies plus degrader, 4 helium energies plus degrader, and the five heavy ions of C, O, Si, Ti, and Fe. A polyethylene degrader is used with the 100 MeV/n H and He beams to provide a nearly continuous distribution of low energy particles. A 500 mGy exposure, delivering doses from each of the 33 beams, requires 75-90 minutes. To more closely simulate the low dose rates found in space, sequential field exposures can be divided into daily fractions over 2-6 weeks, with individual beam fractions as low as 0.1-0.2 mGy. In the large beam configuration (60 x 60 cm2), 54 special housing cages can accommodate 2-3 mice each for a 70-75 min duration or ~15 individually housed rats. Emerging research results from our 2018 runs utilizing mixed heavy ion fields and protracted space exposures are forthcoming and will deepen our understanding of the numerous health risks faced by astronauts. This talk discusses NASA’s innovative technology solution for a ground-based GCR simulator at the NASA Space Radiation Laboratory to enable future exploration missions.

galactic cosmic ray simulator↗
DOE OSTI2026

Influence of Shear Strength Assumptions on BISON Debonding Simulations

Accurately predicting the thermomechanical response of buffer–IPyC debonding in TRISO fuel particles requires reliable mechanical property inputs for each coating layer, particularly the normal and shear strengths that influence interlayer delamination and stress concentrations. Micro tensile testing of AGR-2 fuel particles provided experimentally measured normal strengths for the buffer, IPyC, and buffer–IPyC interface; however, shear strength was not measured. As a result, BISON simulations of interface debonding must rely on assumed shear strength values, typically estimated as 20–40% of the measured ultimate tensile strength. This study evaluates how these assumed shear strength values influence cohesive zone model (CZM) predictions of buffer–IPyC separation in AGR 2 TRISO particles. Using micro tensile data from three AGR 2 compacts (2 1 3, 5 1 3, and 6 3 3), BISON simulations were performed with multiple shear strength assumptions to quantify their effect on radial and tangential stress evolution, debonding, and gap propagation. The results show that shear strength is a high sensitivity parameter: increasing the assumed shear strength significantly alters the stress distribution at the buffer–IPyC junction, shifts the predicted debonding location, and changes the extent of partial gap formation. While normal strength controls the initiation of interface separation, shear strength strongly influences the mode mixity of the failure process and the resulting stress concentrations transmitted to the IPyC and SiC layers. These findings highlight a critical gap in current TRISO mechanical characterization. Without experimentally measured shear strength, BISON simulations must rely on approximations that introduce uncertainty into predictions of coating layer integrity and fission product barrier performance. Future fuel qualification campaigns should therefore consider measurement of shear strength at the interlayer interfaces to reduce model uncertainty and improve the fidelity of TRISO fuel performance simulations.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Network-Cognizant Time-Coupled Aggregate Flexibility of Distribution Systems Under Uncertainties

Increasing integration of distributed energy resources (DERs) within distribution feeders provides unprecedented flexibility at the distribution-transmission interconnection. To exploit this flexibility and to use the capacity potential of aggregate DERs, feasible substation power injection trajectories need to be efficiently characterized. This paper provides an ellipsoidal inner approximation of the set of feasible power injection trajectories at the substation such that for any point in the set, there exists a feasible disaggregation strategy of DERs for any load uncertainty realization. The problem is formulated as one of finding the robust maximum volume ellipsoid inside the flexibility region under uncertainty. Though the problem is NP-hard even in the deterministic case, this paper derives novel approximations of the resulting adaptive robust optimization problem based on optimal second-stage policies. The proposed approach yields less conservative flexibility characterization than existing flexibility region approximation formulations. The efficacy of the proposed method is demonstrated on a realistic distribution feeder.

aggregates↗
NASA NTRSDate not supplied

Uncertainty Analysis of the Pitch Damping Coefficient of Blunt Bodies, Measured from Magnetic Suspension Wind Tunnel Tests

Blunt body aerodynamics in re-entry conditions are characterized by oscillatory behavior that is often encapsulated by the pitch damping coefficient (Cmq). Magnetic Suspension Wind Tunnel (MSWT) testing with a Magnetic Suspension Balance System (MSBS) aims to improve blunt body wind tunnel testing by removing the sometimes unknown aerodynamic effects of traditional sting mounts, as well as generate more constrained outputs than vertical spin tunnel testing. Efforts to calculate the uncertainty of the pitch damping coefficient with a proposed Earth Entry Vehicle (EEV) capsule model have been made by fitting an analytic prediction of the attitude time history to video data of a free-to-oscillate MSBS test.

Magnetic suspension, dynamic stability, uncertaint↗
DOE OSTI2022

A Bayesian Deep Learning Approach to Near-Term Climate Prediction

Since model bias and associated initialization shock are serious shortcomings that reduce prediction skills in state-of-the-art decadal climate prediction efforts, we pursue a complementary machine-learning-based approach to climate prediction. The example problem setting we consider consists of predicting natural variability of the North Atlantic sea surface temperature on the interannual timescale in the pre-industrial control simulation of the Community Earth System Model. While previous works have considered the use of recurrent networks such as convolutional LSTMs and reservoir computing networks in this and other similar problem settings, we currently focus on the use of feedforward convolutional networks. In particular, we find that a feedforward convolutional network with a Densenet architecture is able to outperform a convolutional LSTM in terms of predictive skill. Next, we go on to consider a probabilistic formulation of the same network based on Stein variational gradient descent and find that in addition to providing useful measures of predictive uncertainty, the probabilistic (Bayesian) version improves on its deterministic counterpart in terms of predictive skill. Finally, we characterize the reliability of the ensemble of machine learning models obtained in the probabilistic setting by using analysis tools developed in the context of ensemble numerical weather prediction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Very small-scale, segregating-fluidized-bed experiments: A dataset for CFD-DEM validation and uncertainty quantification

IWe report fluidization experiments were conducted on a small scale and with a rapid response (short duration) to enable corresponding simulations at low-computational cost. Rise times are reported for four or fewer polyethylene particles (intruders) in an air-fluidized bed of ~5000 group D glass beads. Experimental inputs were completely characterized—particle properties, system dimensions and operating conditions—which is necessary for validating computational fluid mechanics (CFD)-discrete element method (DEM) including a comprehensive uncertainty quantification (UQ) analysis. Input uncertainties are reported as bounds or cumulative distribution functions of measured values. The staggering number of simulations required to complete a UQ analysis (~O[10 4 ] simulations corresponding to ~5 uncertain inputs) motivates this study. These segregating-bed experiments are designed to permit analogous CFD-DEM simulations to complete in less than a day on a single (~2.5 GHz) computational processor unit (CPU). Segregation times are reported for several operating conditions, intruder sizes, and initial configurations, providing a rich dataset for numerical model testing, validation and UQ.

42 ENGINEERING↗
NASA NTRS2018

Results from the Deep Convective Clouds-Based Response Versus Scan-Angle Characterization for the MODIS Reflective Solar Bands

The Terra and Aqua Moderate-Resolution Imaging Spectroradiometer (MODIS) scan mirror reflectance is a function of the angle of incidence (AOI) and was characterized prior to launch by the instrument vendor. The relative change of the prelaunch response versus scan angle (RVS) is tracked and linearly scaled on-orbit using observations at two AOIs of 11.2° and 50.2° corresponding to the moon view and solar diffuser, respectively. As the missions continue to operate well beyond their design life of six years, the assumption of linear scaling between the two AOIs is known to be inadequate in accurately characterizing the RVS, particularly at short wavelengths. Consequently, an enhanced approach of supplementing the on-board measurements with response trends from desert pseudoinvariant calibration sites (PICS) was formulated in MODIS Collection 6 (C6). An underlying assumption for the continued effectiveness of this approach is the long-term (multiyear) and short-term (month to month) stability of the PICS. Previous work has shown that the deep convective clouds (DCC) can also be used to monitor the onorbit RVS performance with less trend uncertainties compared with desert sites. In this paper, the raw sensor response to the DCC is used to characterize the on-orbit RVS on a band and mirror-side basis. These DCC-based RVS results are compared with those of C6 PICS-based RVS, showing an agreement within 2% observed in most cases. The pros and cons of using a DCC-based RVS approach are also discussed in this paper. Although this reaffirms the efficacy of the C6 PICS-based RVS, the DCC-based RVS approach presents itself as an effective alternative for future considerations. Potential applications of this approach to other instruments, such as Suomi National Polarorbiting Partnership, Joint Polar Satellite Systems, and Visible Infrared Imaging Radiometer Suite, are also discussed.

Angal, Amit↗
NASA NTRS2017

Boundary Condition Study for the Juncture Flow Experiment in the NASA Langley 14x22-Foot Subsonic Wind Tunnel

Because future wind tunnel tests associated with the NASA Juncture Flow project are being designed for the purpose of CFD validation, considerable effort is going into the characterization of the wind tunnel boundary conditions, particularly at inflow. This is important not only because wind tunnel flowfield nonuniformities can play a role in integrated testing uncertainties, but also because the better the boundary conditions are known, the better CFD can accurately represent the experiment. This paper describes recent investigative wind tunnel tests involving two methods to measure and characterize the oncoming flow in the NASA Langley 14- by 22-Foot Subsonic Tunnel. The features of each method, as well as some of their pros and cons, are highlighted. Boundary conditions and modeling tactics currently used by CFD for empty-tunnel simulations are also described, and some results using three different CFD codes are shown. Preliminary CFD parametric studies associated with the Juncture Flow model are summarized, to determine sensitivities of the flow near the wing-body juncture region of the model to a variety of modeling decisions.

Rumsey, C. L.↗
NASA NTRSDate not supplied

Multiscale Modeling of Woven Ablative Thermal Protection System Materials

The NASA Entry Systems Modeling project maintains a portfolio of computational model and tool development activities focused on reducing performance uncertainties in ablative Thermal Protection System (TPS) materials for NASA missions. The development activities span material scale and strive to allow microstructural characterization of material structure and properties, mesoscale analyses of damage, and macroscale evaluation of heatshield performance and recession in a given aerothermodynamic environment. This talk will detail the application of developed capabilities at all three scales to the woven TPS material that the Agency has selected as the heatshield for the Mars Sample Return Earth Entry System (MSR-EES) mission – 3D Mid-Density Carbon Phenolic (3MDCP). Each of the applications focuses on driving down uncertainties in material performance and thus risk for MSR-EES and other future missions that may leverage woven TPS. At the microscale, machine learning techniques are used to characterize images from destructive microscopy and inform structural variability. At the mesoscale, Lagrangian techniques are used to simulate ballistic impact and interpret damage modes noted in experiments. At the macroscale, coupled flow-material response techniques are validated by Arc Jet testing to enable heatshield design for missions with massive ablation.

Justin B Haskins↗
DOE OSTI2021

Free-moving Quantitative Gamma-ray Imaging

The ability to map and estimate the activity of radiological source distributions in unknown three-dimensional environments has applications in the prevention and response to radiological accidents or threats as well as the enforcement and verification of international nuclear non-proliferation agreements. Such a capability requires well-characterized detector response functions, accurate time-dependent detector position and orientation data, a digitized representation of the surrounding 3D environment, and appropriate image reconstruction and uncertainty quantification methods. We have previously demonstrated 3D mapping of gamma-ray emitters with free-moving detector systems on a relative intensity scale using a technique called Scene Data Fusion (SDF). Here we characterize the detector response of a multi-element gamma-ray imaging system using experimentally benchmarked Monte Carlo simulations and perform 3D mapping on an absolute intensity scale. We present experimental reconstruction results from hand-carried and airborne measurements with point-like and distributed sources in known configurations, demonstrating quantitative SDF in complex 3D environments.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Network-Cognizant Time-Coupled Aggregate Flexibility of Distribution Systems Under Uncertainties

Increasing integration of distributed energy resources (DERs) within distribution feeders provides unprecedented flexibility at the distribution-transmission interconnection. To exploit this flexibility and to use the capacity potential of aggregate DERs, feasible substation power injection trajectories need to be efficiently characterized. This paper provides an ellipsoidal inner approximation of the set of feasible power injection trajectories at the substation such that for any point in the set, there exists a feasible disaggregation strategy of DERs for any load uncertainty realization. The problem is formulated as one of finding the robust maximum volume ellipsoid inside the flexibility region under uncertainty. Though the problem is NP-hard even in the deterministic case, this paper derives novel approximations of the resulting adaptive robust optimization problem based on optimal second-stage policies. The proposed approach yields less conservative flexibility characterization than existing flexibility region approximation formulations. The efficacy of the proposed method is demonstrated on a realistic distribution feeder.

adaptive robust optimization↗
DOE OSTI2022

Measurements of differential cross-sections in top-quark pair events with a high transverse momentum top quark and limits on beyond the Standard Model contributions to top-quark pair production with the ATLAS detector at $ \sqrt{s} $ = 13 TeV

Cross-section measurements of top-quark pair production where the hadronically decaying top quark has transverse momentum greater than 355 GeV and the other top quark decays into ℓνb are presented using 139 fb –1 of data collected by the ATLAS experiment during proton-proton collisions at the LHC. The fiducial cross-section at √s = 13 TeV is measured to be σ = 1.267 ± 0.005 ± 0.053 pb, where the uncertainties reflect the limited number of data events and the systematic uncertainties, giving a total uncertainty of 4.2%. The cross-section is measured differentially as a function of variables characterizing the tt¯ system and additional radiation in the events. The results are compared with various Monte Carlo generators, including comparisons where the generators are reweighted to match a parton-level calculation at next-to-next-to-leading order. The reweighting improves the agreement between data and theory. The measured distribution of the top-quark transverse momentum is used to search for new physics in the context of the effective field theory framework. No significant deviation from the Standard Model is observed and limits are set on the Wilson coefficients of the dimension-six operators O tG and $O$ $^{(8)}_{tq}$, where the limits on the latter are the most stringent to date.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Development of a versatile plate spectrometer for electron beam characterization

A new instrument for electron beam characterization has been developed, based on measuring the range of beam electrons in different thicknesses of aluminum, to determine both the current and energy of an electron beam traveling in open air. This instrument is referred to as a ‘plate spectrometer’ as the thickness of the spectrometer is adjusted by adding or removing aluminum plates. This spectrometer was assembled and then characterized at the Idaho Accelerator Center (IAC) with three different beam energies and with one beam energy at the University of Maryland Radiation Facilities. The plate spectrometer was able to match the median energies and provide a reasonable constraint to the beam distributions seen in the IAC magnetic spectrometer. Finally, in a separate experiment, the plate spectrometer was also seen to agree with a dosimetry characterization of the beam at the University of Maryland Radiation Facilities and improved upon the uncertainties of this measurement.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Combining synchrotron X-ray diffraction, mechanistic modeling and machine learning for in situ subsurface temperature quantification during laser melting

Laser melting, such as that encountered during additive manufacturing, produces extreme gradients of temperature in both space and time, which in turn influence microstructural development in the material. Qualification and model validation of the process itself and the resulting material necessitate the ability to characterize these temperature fields. However, well established means to directly probe the material temperature below the surface of an alloy while it is being processed are limited. To address this gap in characterization capabilities, a novel means is presented to extract subsurface temperature-distribution metrics, with uncertainty, from in situ synchrotron X-ray diffraction measurements to provide quantitative temperature evolution data during laser melting. Temperature-distribution metrics are determined using Gaussian process regression supervised machine-learning surrogate models trained with a combination of mechanistic modeling (heat transfer and fluid flow) and X-ray diffraction simulation. The trained surrogate model uncertainties are found to range from 5 to 15% depending on the metric and current temperature. The surrogate models are then applied to experimental data to extract temperature metrics from an Inconel 625 nickel superalloy wall specimen during laser melting. The maximum temperatures of the solid phase in the diffraction volume through melting and cooling are found to reach the solidus temperature as expected, with the mean and minimum temperatures found to be several hundred degrees less. The extracted temperature metrics near melting are determined to be more accurate because of the lower relative levels of mechanical elastic strains. However, uncertainties for temperature metrics during cooling are increased due to the effects of thermomechanical stress.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Investigation of onshore wind farm wake recovery with in situ aircraft measurements during AWAKEN

The share of wind power for electricity supply is increasing worldwide. This highly variable resource requires the improved prediction of power output for network stability. The interaction between wind farm wakes and the atmospheric boundary layer (ABL) introduces uncertainties in power production that warrant detailed investigation. The flow downwind of wind farms is characterized by a reduction in wind speed and an increase in turbulence, which both vary with atmospheric conditions. During the American WAKE experimeNt (AWAKEN), the Technische Universität Braunschweig conducted measurement flights with a research aircraft upwind and downwind of onshore wind farms in the southern Great Plains in Oklahoma in the USA. This study utilizes data from 20 flights conducted at approximately hub height in September 2023 to investigate the wind field variability downwind of the wind farms and vertical profiles to observe atmospheric stratification. The flights were aligned perpendicular to the main wind direction downwind of the King Plains and Armadillo Flats wind farms. Additionally, lidar data from both upwind and downwind ground-based measurement sites and sonic anemometer data were used for comprehensive analysis. Results indicate that under stable ABL conditions, the wake persists at greater downwind distances with a higher velocity deficit in the wake relative to the undisturbed flow compared to unstable stratification. In homogeneous terrain under stable conditions, wake recovery to 95 % occurs between a distance of 4.5 and 9 km downwind of the wind farm. In the semi-complex terrain characterized by shallow hills, slopes, and valleys, the wake exhibits a higher velocity deficit compared to homogeneous terrain, while in some cases the wake was amplified by the terrain resulting in higher velocity deficit 10 km downwind of the wind farm compared to the measurements closer to the wind farm. The turbulent kinetic energy (TKE) and “TKE difference” was found to be a valuable measure in understanding wakes in a semi-complex terrain, showing a clear wake recovery and formation depending on the stratification of the ABL.

17 WIND ENERGY↗
NASA NTRS1980

Voyager 1 Saturn targeting strategy

A trajectory targeting strategy for the Voyager 1 Saturn encounter has been designed to accomodate predicted uncertainties in Titan's ephemeris while maximizing spacecraft safety and science return. The encounter is characterized by a close Titan flyby 18 hours prior to Saturn periapse. Retargeting of the nominal trajectory to account for late updates in Titan's estimated position can disperse the ascending node location, which is nominally situated at a radius of low expected particle density in Saturn's ring plane. The strategy utilizes a floating Titan impact vector magnitude to minimize this dispersion. Encounter trajectory characteristics and optimal tradeoffs are presented.

Cesarone, R. J.↗
NASA NTRS1988

Robustness with real parametric and structured complex uncertainty

The problem of robust stability and performance in feedback control systems with n real (possibly related) uncertain parameters and structured unmodeled dynamics (the latter taking the form of m complex blocks dynamic uncertainties) is considered. A robustness margin rm is defined with respect to such model uncertainty structure and an algorithm is developed to compute it. Robust stability and performance are characterized in terms of r sub m or the structured singular value mu. An iterative procedure using the above algorithm is given to compute tight bounds on k sub m and mu. These bounds are exact for the case m = 3 or less.

Pena, Ricardo S. Sanchez↗