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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 127 records · Page 7

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NASA NTRS2021

Lunar Auger Dryer ISRU (LADI) Breadboard Testing and Model Validation

In 2009, the Lunar Reconnaissance Orbiter (LRO)and Lunar Crater Observation and Sensing Satellite(LCROSS) provided definitive proof of water in the Lunar’s southern permanently shadowed region (PSR)[1]. Both the 2020 NASA Technology Taxonomy[2] and the Lunar Surface Innovation Initiative (LSII) team identified capability gaps in icy regolith transfer and reactor processing in Permanently Shadowed Region(PSR) environmental conditions. A screw conveyor dryer system operating from inside the PSR can continuously process water (and volatiles) for both breathable air and propellant. NASA’s Johnson Space Center (JSC) began development of a similar sub-system for Mars operation in 2017 and fabricated a unique breadboard test stand for validating the feasibility of this concept. This testing was postponed with the redirection of NASA’s mission from Mars to Moon. A JSC led trade study[3] in FY20 formulated a plan to leverage existing hardware to test concept feasibility, developed a lunar auger dryer sizing tool, and identified that both a physical flow and thermal model is required to develop an Engineering Development Unit (EDU) for environmental testing. Beginning in FY21, the Game Changing Development Program (GCDP) funded a three-year technology development project to increase the Technology Readiness Level (TRL) of the Lunar Auger Dryer ISRU (LADI) subsystem to TRL 5.

Lunar↗

NASA NTRS2021

Lunar Auger Dryer ISRU (LADI) Breadboard Testing and Model Validation

Lunar↗

NASA NTRS2009

Predicting Pilot Performance in Off-Nominal Conditions: A Meta-Analysis and Model Validation

Pilot response to off-nominal (very rare) events represents a critical component to understanding the safety of next generation airspace technology and procedures. We describe a meta-analysis designed to integrate the existing data regarding pilot accuracy of detecting rare, unexpected events such as runway incursions in realistic flight simulations. Thirty-five studies were identified and pilot responses were categorized by expectancy, event location, and whether the pilot was flying with a highway-in-the-sky display. All three dichotomies produced large, significant effects on event miss rate. A model of human attention and noticing, N-SEEV, was then used to predict event noticing performance as a function of event salience and expectancy, and retinal eccentricity. Eccentricity is predicted from steady state scanning by the SEEV model of attention allocation. The model was used to predict miss rates for the expectancy, location and highway-in-the-sky (HITS) effects identified in the meta-analysis. The correlation between model-predicted results and data from the meta-analysis was 0.72.

Wickens, C.D.↗

NASA NTRS2018

Fast Linearized Coronagraph Optimizer (FALCO) II: Optical Model Validation and Time Savings over Other Methods

We have developed the Fast Linearized Coronagraph Optimizer (FALCO), a new software toolbox for high-contrast, coronagraphic wavefront sensing and control. FALCO rapidly calculates the linearized deformable mirror (DM) response matrices, also called control Jacobians, and can be used for the design, simulation, or testbed operation of several types of coronagraphs. In this paper, we demonstrate that the optical propagation used in FALCO is accurate and matches PROPER. In addition, we demonstrate the drastic reduction in runtime when using FALCO for DM Jacobian calculations instead of the conventional method used, for example with a model of the Wide-Field Infrared Survey Telescope (WFIRST) Coronagraph Instrument (CGI). We then compare the relative accuracy between optical models in FALCO and PROPER.

Coker, Carl↗

NASA NTRS2024

Turbulence Model Validation through Joint Experimental / Computational Studies of Separated Flow Over a Three-Dimensional Tapered Bump: Part II - Computational Investigation

This report serves as documentation of activities related to Computational Fluid Dynamics (CFD) analysis applied to the Smooth Body Separation Experiment designed to produce validation-quality experimental data from wind-tunnel tests performed at the University of Notre Dame. All CFD analysis utilized the Boeing CFD (BCFD) flow solver. BCFD was used to analyze flow over the Boeing Speed Bump with second-order accuracy in space and time. Inviscid fluxes were computed using the HLLE scheme for steady-state simulations, and the Low-Dissipation Roe scheme for time-accurate simulations. The working fluid was modeled as calorically perfect air using Sutherland's law for viscosity. Thermal diffusivity was determined by assuming a constant molecular Prandtl number of 0.72, and a turbulent Prandtl number of 0.9. Reynolds Averaged Navier-Stokes (RANS) simulations were conducted using the one-equation Spalart-Allmaras turbulence model with rotation correction3 and the quadratic constitutive relation (SA-RC-QCR), as well as Menter's two-equation, Shear-Stress Transport (SST) model. Time-accurate computations were conducted using Delayed Detached eddy Simulation (DDES) with the one-equation RANS model, and Large Eddy Stress balance (LESb) with the two-equation RANS model. Implicit dual-time stepping was used to achieve second-order temporal accuracy. The experimental investigation conducted during the course of this study is described in detail in an accompanying report.

Flow separation↗

NASA NTRS2014

Model Analysis of the Factors Regulating Trends and Variability of Methane, Carbon Monoxide and OH: 1. Model Validation

Methane (CH4) is the second most important anthropogenic greenhouse gas (GHG). Its 100-year global warming potential (GWP) is 25 times larger than that for carbon dioxide. The 100-yr integrated GWP of CH4 is sensitive to changes in OH levels. Methane's atmospheric growth rate was estimated to be more than 10 ppb yr(exp -1) in 1998 but less than zero in 2001, 2004 and 2005 (Kirschke et al., 2013). Since 2006, the CH4 is increasing again. This phenomena is yet not well understood. Oxidation of CH4 by OH is the main loss process, thus affecting the oxidizing capacity of the atmosphere and contributing to the global ozone background. Current models typically use an annual cycle of offline OH fields to simulate CH4. The implemented OH fields in these models are typically tuned so that simulated CH4 growth rates match that measured. For future and climate simulations, the OH tuning technique may not be suitable. In addition, running full chemistry, multi-decadal CH4 simulations is a serious challenge and currently, due to computational intensity, almost impossible.

OH↗

NASA NTRSDate not supplied

CFD Modeling & Validation of Single Bubble Growth During Cavitation in Zero Gravity

Cryogenic Fluid Management (CFM) in zero gravity represents a vital area of research as NASA pursues future long-duration space missions. Success of these missions relies heavily on the ability to store and transfer the cryogenic liquid fuels efficiently and safely. In the Zero Boil-Off Tank (ZBOT-1) Experiment in 2018, unexpected and intense phase change occurred at the screened Liquid Acquisition Device (LAD) during tank pressure control that was later attributed to cavitation. The cavitation ultimately resulted in pump failure. Since cryogen cavitation during storage and transfer of propellants in microgravity has been a source of serious concern for the aerospace community, a series of drop tower experiments with liquid methane were also performed at the University of Bremen to carefully study cavitation from a single bubble nucleus in an engineered micron-sized wall cavity. As the test cell is depressurized during the 10-second drop, cavitation causes bubble growth from the nucleus. Here, in a collaboration between the Case Western Reserve University and University of Bremen, we present a two-phase Computational Fluid Dynamics (CFD) model of bubble growth from a single wall cavity during depressurization in microgravity. Both 2D axisymmetric and 3D parametric simulation case studies are performed to show the evolution of the bubble growth. The model uses a Volume of Fluid (VOF) approach with and without adaptive mesh refinement and adaptive time stepping to capture the growth dynamics using an explicit formulation with algebraic reconstruction of the interface. CFD results show good agreement with corresponding experiment. Based on the velocity, temperature, and volume fraction fields generated by the validation case studies, detailed discussion and analysis of fluid flow and heat transfer during bubble growth process are presented to delineate the intricacies of the cavitation process. Strengths and shortcomings of the present numerical approach in capturing this phenomenon are also highlighted.

Two Phase Flow↗

NASA NTRS2011

Summary of the Manufacture, Testing and Model Validation of a Full-Scale Radiator for Fission Surface Power Applications

A full-scale radiator for a lunar fission surface power application was manufactured by Material innovations, Inc., for the NASA Glenn Research Center. The radiator was designed to reject 6 kWt with an inlet water temperature of 400 K and a water mass flow rate of 0.5 kg/s. While not flight hardware, the radiator incorporated many potential design features and manufacturing techniques for future flight hardware. The radiator was tested at NASA Glenn Research Center for heat rejection performance. The results showed that the radiator design was capable of rejecting over 6 kWt when operating at the design conditions. The actual performance of the radiator as a function of operational manifolds, inlet water temperature and facility sink temperature was compared to the predictive model developed by NASA Glenn Research Center. The results showed excellent agreement with the model with the actual average face sheet temperature being within 1% of the predicted value. The results will be used in the design and production of NASA s next generation fission power heat rejection systems. The NASA Glenn Research Center s Technology Demonstration Unit will be the first project to take advantage of the newly developed manufacturing techniques and analytical models.

Ellis, David L.↗

NASA NTRS2019

Air Pollution Inputs to the Mojave Desert By Fusing Surface Mobile and Airborne in Situ and Airborne and Satellite Remote Sensing: A Case Study of Interbasin Transport With Numerical Model Validation

Deserts are fragile and highly sensitive ecosystems that increasingly are affected by upwind urban areas and industrial activities. The Los Angeles Basin (LAB) contributes to poor air quality in downwind deserts including the Mojave Desert. Few studies have investigated potential air pollution inputs to the Mojave, whose fragile ecosystem includes endangered plant and animal species. Data were collected on 19 August 2015 by a mobile air quality laboratory, AMOG (AutoMObile trace Gas) Surveyor, that observed inputs can arise from the LAB as well as the San Joaquin Valley (SJV), California. The campaign used a strong methane (CH4) plume as a tracer for the downwind fate of emissions from Bakersfield area petroleum production and also measured ozone (O3). Additional in situ concurrent airborne GHG and O3 data were collected by AJAX - Alpha Jet Atmospheric eXperiment. Both AMOG and AJAX measure winds. Mojave Desert air quality was very poor (visibility ~4 km). Based on the winds, an additional source was inferred beyond the LAB and SJV Basins. Numerical transport modeling and analysis of aerosol lidar data collected the same day by the Cloud Profiling LiDAR onboard the Earth Research-2 stratospheric airplane demonstrated that fires in Northern California were responsible, with prevailing winds transporting air southwards along the eastern Sierra Nevada Range (Bishop Valley) to the Mojave. Whereas the southern and eastern Mojave are impacted by SJV and LAB outflow, the north Mojave generally avoids these inputs. This study shows it can be affected by even distant wildfires, which likely will increase in occurrence and intensity from climate change. Thus, regulatory efforts to reduce air quality impacts on the endangered Mojave ecosystem must include wildfires and also account for the significant differences between different regions of the Mojave. Currently, there is a paucity of studies, highlighting the critical need for field research.

Ira Leifer↗

NASA NTRSDate not supplied

Model Validation of Phase-Induced Amplitude Apodization Complex Mask Coronagraph for LUVOIR-A in Vacuum

The Phase-Induced Amplitude Apodization Complex Mask Coronagraph (PIAACMC) is a coronagraph architecture for the next generation of large space telescopes optimized for habitable exoplanet imaging that can achieve attractive theoretical performance with high throughput at small inner working angles (IWA). PIAACMC designs are compatible with large, on-axis, segmented apertures such as the Large UV / Optical/ Infrared A (LUVOIR-A) concept currently being considered by the decadal survey review which would greatly enhance the possibility to achieve statistically significant scientific yields and signal quality for direct imaging exoplanet surveys. PIAACMC has been recently implemented for LUVOIR-A and is currently being tested in vacuum at JPL’s High-Contrast Imaging Testbed (HCIT). In this work, we review the theoretical performance of the PIAACMC instrument designed to meet a 1e-9 raw contrast goal in 10% broadband light in a region from 2-8 L/D both before and after the wavefront control loop. We use empirical measurements from the vacuum testbed to verify the instrument model and its performance including line-of-sight errors, instrument alignment, and fabricated components. In particular, the model verification includes measured sags of the manufactured PIAA mirrors by NuTek. The CMC mask was manufactured at JPL’s Microdevices Laboratory and we include surface profile characterization measurement. We assess the impact on performance of the different manufacturing and alignment errors.

High-Contrast Imaging↗

DOE OSTI2026