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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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334 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Effect of High Temperature Storage on AC Characteristics of Polymer Tantalum Capacitors

Replacement of MnO2 with conductive polymers as cathode materials in chip tantalum capacitors allows for a substantial reduction of the equivalent series resistance (ESR), improvement of frequency characteristics, and elimination of the possibility of ignition during failures. One of the drawbacks of chip polymer tantalum capacitors (CPTCs) is a relatively poor long-term stability at high temperatures. In this work, variations of capacitance, dissipation factor, and ESR in different types of capacitors including automotive grade parts from three manufacturers have been monitored during storage at temperatures from 100 ºC to 175 ºC for up to 18,000 hours. Results show that ESR is the most and capacitance the least sensitive parameter to degradation. Times to parametric failures have been simulated using a Weibull-Arrhenius model that allowed for assessments of activation energies of the degradation and prediction of times to failure at the use temperature. Degradation of CPTCs was explained by thermo-oxidative processes in conductive polymers that result in exponential increase of the resistivity with time of aging. This process starts after a certain incubation period that depends on packaging materials and design and corresponds to the time that is necessary to form delamination between the encapsulating molding compound and lead frame. The effectiveness of the existing qualification procedures to assure stable operation of CPTCs is discussed.

high temperature storage (HTS)↗
NASA NTRS2022

Winds at the Mars 2020 Landing Site. 2. Wind Variability and Turbulence

Wind speeds measured by the Mars 2020 Perseverance rover in Jezero crater were fitted as a Weibull distribution. InSight wind data acquired in Elysium Planitia were also used to contextualize observations. Jezero winds were found to be much calmer on average than in previous landing sites, despite the intense aeolian activity observed. However, a great influence of turbulence and wave activity was observed in the wind speed variations, thus driving the probability of reaching the highest wind speeds at Jezero, instead of sustained winds driven by local, regional, or large-scale circulation. The power spectral density of wind speed fluctuations follows a power-law, whose slope deviates depending on the time of day from that predicted considering homogeneous and isotropic turbulence. Daytime wave activity is related to convection cells and smaller eddies in the boundary layer, advected over the crater. The signature of convection cells was also found during dust storm conditions, when prevailing winds were consistent with a tidal drive. Nighttime fluctuations were also intense, suggesting strong mechanical turbulence. Convective vortices were usually involved in rapid wind fluctuations and extreme winds, with variations peaking at 9.2 times the background winds. Transient high wind events by vortex-passages, turbulence, and wave activity could be driving aeolian activity at Jezero. We report the detection of a strong dust cloud of 0.75–1.5 km in length passing over the rover. The observed aeolian activity had major implications for instrumentation, with the wind sensor suffering damage throughout the mission, probably due to flying debris advected by winds.

D. Viúdez-moreiras↗
NASA NTRSDate not supplied

Evaluating the Feasibility of Data Simulation for Radiation Research: Estimating RBE Values for HZE Radiation

As the drive for deep space exploration intensifies, a comprehensive understanding of the health effects of radiation exposure becomes paramount to the future of human space flight. However, epidemiological data for radiation exposure, particularly to high-energy (HZE) ions, is limited, partially due to the financial and logistical costs of radiation studies. As an alternative, this study aims to assess the viability of data simulation strategies to accurately model potential study parameters prior to utilizing laboratory conditions. This study estimates a relative biological effectiveness (RBE) factor based on the solid tumor data for outbred mice provided by Edmundson et al. 2020. Excess relative risk (ERR) models for HZE-Fe ions and gamma radiation were estimated using Poisson regression with Weibull models to represent the background solid tumor hazard without radiation. RBE values were calculated from the ratio of the heavy ion linear slope to the gamma linear slope. The parameters from these models were then applied to simulate iterations of 300 datasets across HZE-Fe doses of 0.05, 0.2, 0.4, and 0.75 Gy; gamma radiation doses of 0.75, 2, and 3 Gy, as well as an unirradiated control group. Sample size per dose varied from 100-500 mice across simulations (800-4000 mice total per dataset). 1500 datasets were generated total (300 each for sample sizes 800, 1600, 3200, and 4000). For each dataset, ERR per radiation type and RBE for HZE-Fe were calculated. The RBE from the Edmondson data was calculated to be 5.55. The RBEs from the simulated data converged around this value across the increasing sample sizes. For n = 4000, the mean RBE across the 300 datasets was 5.71 (95% CI: 5.47-5.95). These results suggest that RBEs calculated from simulated data are credible. Based on this exploration, data simulation is a viable method of testing radiation studies. It provides a method of testing study assumptions and refining research questions prior to embarking on costly laboratory experiments.

Sneha Mehta↗
NASA NTRS2023

Predicting Patterns of Solar Energy Buildout to Identify Opportunities for Biodiversity Conservation

The construction of solar energy facilities can have positive or negative impacts on biodiversity depending on siting and associated land use transitions. We identified drivers of solar siting and quantified patterns of buildout in states surrounding the Chesapeake Bay watershed – a biodiversity hotspot with numerous ecosystem services. Using a convolutional neural network, we mapped the footprints of ground-mounted solar arrays present in satellite imagery annually from 2017 to 2021 in Delaware, Maryland, Pennsylvania, New York, Virginia, and West Virginia. As of 2021, we identified 958 solar arrays covering 52.3 km2 built primarily on previously cultivated land, while avoiding natural landcover. We fit a binomial-Weibull model to these solar timeseries data in a hierarchical, Bayesian framework to quantify the relationship between geospatial covariates and rate of solar development. Solar array construction rate increased in cultivated areas, areas of lower agricultural suitability, lower slope, lower forest cover, lower biodiversity protection, and greater distances from roads. We also estimated changes in the rate of solar construction over time and found differences among states: acceleration in Virginia and deceleration in New York. We used parameter estimates to map the relative likelihood of future solar development across the study area. This methodology can be used to anticipate where solar is likely to be built in different landscapes and how these patterns align with conservation goals. Around the Chesapeake Bay watershed, the selection of lower quality agricultural areas for solar energy minimizes removal of important habitat and provides opportunities for native plant and pollinator restoration.

Artificial Intelligence↗
NASA NTRSDate not supplied

Microscale Constitutive Model Sensitivity on Multiscale Modeling of Fiber Reinforced Composites

Fiber reinforced composites are desirable in applications where low weight and high strength are needed, but are susceptible to microscale variability during manufacturing, making failure predictions difficult. The impact microscale variability has on macroscale mechanical response is difficult to predict due to the computational efficiency needed to simulate many, large, high fidelity, microscale models. In this study, a multiscale approach was taken to model 3-point bend, 4-point bend, and tensile experiments of a unidirectional composite from only having microstructure scans of these samples and constituent properties from literature. These scans were sampled with different sized windows, and statistically equivalent microstructures were generated, then simulated for stiffness, strength, and fracture toughness using an efficient micromechanical. Mesoscale models were created where element sizes equaled microstructure size, and properties were assigned through sampling of microscale simulation results. First, this study showed the effect of using Weibull scaling on constituent matrix strength on macroscale response. Then, a comparison was made between different element sizes and experiments. Finally, model dimensions were fixed, and the effect of randomly distributed local properties alone was examined. Results showed that the scatter of strength and stiffness in the experiments could be predicted well using images of the microscale fiber morphologies and that using stochastic properties produced a 3% coefficient of variation of strength for all experiments.

statistical microstructure↗
NASA NTRS2002

SIP qualification and PRA approaches

System in a package (SIP) development is a response to an increasing demand for integration of different functions into one unit to reduce size and cost and improve functionality. Qualification of the newly developed multifunctional electronics are becoming complex at the package level and even more at the second and system levels.

probability risk assessment weibull lognormal ball↗
NASA NTRS2002

Basis for the thermal-vacuum hot dwell test duration

This work suggests that the hot dwell duration for each test should be determined in two steps: a minimum hot duration of 80 hours be required and then followed by specific tailoring be conducted to arrive at the proper test duration.

early test failure distribution Weibull analysis r↗