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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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Heat transfer is best understood through theory and application of principles in thermal analysis. Modern thermal analysis leverages the power of computers and numerical methods to simulate heat transfer in networks representing a physical system. This lesson is an introduction to numerical methods in heat transfer.
This code solves a steady state heat equation problem on a wire with random walks implemented using a spiking neural algorithm. SAND2020-12194 M
Accurate knowledge of thermophysical properties of rock is vital to develop meaningful models of high level nuclear waste emplacement scenarios. The Israel Atomic Energy Commission is considering storing high level nuclear waste in the Ghareb formation, a porous kerogen bearing chalk. Sandia is supporting this effort with an evolving lab- based geomechanics testing program. We have completed measurements of thermal properties up to 275C and room temperature hydrostatic compaction measurements. We report thermal conductivity, thermal diffusivity, specific heat, and mass loss from our thermal measurements, and we report bulk moduli and porosity loss from our compaction measurements. These values are crucial for the numerical models to simulate heat transfer and formation compressibility around a heat generating repository.
A commercial Zr-V-Fe alloy (i.e., SAES Getters trade name alloy St-172) has been assessed as reversible hydrogen storage material for use in actuators of gas gap heat switches. Two prototype actuators containing the SAES St-172 material were built and operated for several thousand cycles to evaluate performance of the metal hydride system under conditions simulating heat switch operation.
Land-atmosphere (L-A) interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface heat and moisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. Recent efforts to quantify the strength of L-A coupling in prediction models have produced diagnostics that integrate across both the land and PBL components of the system. In this study, we examine the impact of improved specification of land surface states, anomalies, and fluxes on coupled WRF forecasts during the summers of extreme dry and wet land surface conditions in the U.S. Southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through calibration of the Noah land surface model using the new optimization and uncertainty estimation subsystem in NASA's Land Information System (LIS-OPT/UE). The impact of the calibration on the a) spinup of the land surface used as initial conditions, and b) the simulated heat and moisture states and fluxes of the coupled WRF simulations is then assessed. Changes in ambient weather and land-atmosphere coupling are evaluated along with measures of uncertainty propagation into the forecasts. In addition, the sensitivity of this approach to the period of calibration (dry, wet, average) is investigated. Results indicate that the offline calibration leads to systematic improvements in land-PBL fluxes and near-surface temperature and humidity, and in the process provide guidance on the questions of what, how, and when to calibrate land surface models for coupled model prediction.
Fire tests were conducted in a 737 airplane fuselage at NASA-JSC to characterize jet fuel fires in open steel pans (simulating post-crash fire sources and a ruptured airplane fuselage) and to characterize fires in some common combustibles (simulating in-flight fire sources). Design post-crash and in-flight fire source selections were based on these data. Large panels of airplane interior materials were exposed to closely-controlled large scale heating simulations of the two design fire sources in a Boeing fire test facility utilizing a surplused 707 fuselage section. Small samples of the same airplane materials were tested by several laboratory fire test methods. Large scale and laboratory scale data were examined for correlative factors. Published data for dangerous hazard levels in a fire environment were used as the basis for developing a method to select the most desirable material where trade-offs in heat, smoke and gaseous toxicant evolution must be considered.
Forced convection heat transfer data for fluid nitrogen flowing through a vertical tube were acquired and assumed to simulate the heat transfer characteristics of LOX. Comparisons between LN2 and LOX data over the pressure range for T sub r 1 using a modified form of Petukhov's correlation factor was shown, qualitatively, to substantiate the simulation. Other parameters (buoyancy, Stanton number, and relative velocity), were found to group those data; assessment of these parametric interrelations must be made before the simulation can be termed quantitative.
The Rice Convection Model-Equilibrium (RCM-E) and SuperThermal Electron Transport (STET) are combined to investigate electron heat flux formation in the region of the diffuse aurora for the geomagnetic storms of 17 March 2013 and 17 March 2015. The primary electron precipitation into the atmosphere resulting from wave particle scattering in the magnetosphere are simulated by the magnetically and electrically RCM-E during these two geomagnetic storms. The primary precipitating electron fluxes are modified by the STET model by taking into account atmospheric backscatter processes. The modified electron energy fluxes and their mean energies are coupled to the STET code to calculate electron thermal fluxes associated with diffuse aurora on a global scale. We use the simulated heat flux to estimate electron temperatures at the upper ionospheric altitudes and compare them with corresponding observations from the Defense Meteorological Satellite Program satellite.
Simulated-fuel containment characteristics in heated and unheated vortex flows for nuclear light bulb engines
Building energy models are used to simulate heat and mass transfer and estimate end-use load in buildings. With the proliferation of solar photovoltaics on residential and commercial buildings, increasingly, buildings are expected to provide grid services, for which accurate and computationally efficient building energy simulations and end-use load prediction are imperative. Existing building energy simulation tools, however, have significant computational overhead that make them less practical in real-time deployment for optimization, design, uncertainty quantification and control in building energy management systems. Here this article presents a data-driven machine learning model based on light gradient boosting method (LightGBM) as a surrogate for a physics-based simulator for residential buildings to predict end-use load. The machine learning based surrogate model accounts for time-series related variables, seasonality and trend component of end-use load, and history of end-use load. The accuracy of the surrogate model is assessed on the prediction of the load profiles of 100 different houses in Cook County, Illinois, USA. The LightGBM surrogate model is shown to reduce the root-mean-squared error by 53% relative to a reference decision tree (DT) based model reported previously in the literature. Moreover, the model predicts the load spikes and high-ramp rate events throughout the year which are often the Achilles heel of other models in the literature. The machine learning based surrogate model is demonstrated to be computationally efficient, with a ten-fold reduction in the computational time compared to a physics-based building energy simulation, and suitable for uncertainty analysis and real-time control of building characteristics in response to uncertainty.
Heat & impact test on insulated & uninsulated fuse mechanism of ballistic missile in reentry aerodynamic heating
This UNT Senior design team was tasked by NASA to develop a variable conductance thermal radiator prototype for CO2 deposition for deep space transit. NASA selects university teams every year to partake in the X-HAB Academic Innovation Challenge, with this year’s number of teams being six. Air Revitalization is a crucial system for any space travel, be it for Low Earth Orbit, such as the International Space Station, or for deep space transit. Current systems, such as the Carbon Dioxide Removal Apparatus aboard the ISS, require upkeep and maintenance, which cannot be done on long distance space missions. For the past several years, NASA has done research on Cryogenic systems for Carbon Dioxide removal. These systems operate on the fact that Carbon Dioxide freezes at a higher temperature than Oxygen and Nitrogen, so Carbon Dioxide can be frozen out of the cabin atmosphere without the use of filters, which degrade over time. To cool the cabin air down to a temperature where Carbon Dioxide freezes, Stirling cryocoolers have been used, which have shown promise in the hope of Carbon Dioxide deposition for Cabin Air Revitalization. Cryogenic systems are much more reliable but require significant energy input to operate. Physical systems, such as radiators, have generally not been used for this task, as there is a need to be able to “turn off” the rejection of heat to allow the frozen carbon dioxide to be collected. However, with working fluids pumped through a physical radiator, that aspect of operation can be achieved. The goal of this challenge is to determine the effectiveness of a variable conductance thermal radiator that can reject heat to deep space, without the use of a dedicated cryocooler to remove energy from the cabin air. The proposed design uses piping, hot and cold working fluids, and non-condensable gas to absorbl heat from the cabin air on one side of the radiator and reject the heat to deep space by means of thermal radiation. As well, the system will allow for the recovery of deposited Carbon Dioxide. The UNT X-HAB 2021 team will create a model radiator and test its performance with simulated heat sources and sinks and extrapolate those data points to analyze for real world conditions.
Progressing toward a future increasingly reliant on renewable energy sources, the development of effective, durable energy storage solutions becomes essential to balance supply and demand fluctuations. Borehole Thermal Energy Storage (BTES) is a long-duration thermal energy storage technology that captures excess heat generated from renewable energy sources and stores it underground for later use, enabling the efficient utilization of sustainable energy. This approach is particularly valuable in district energy networks when integrated with Ground Source Heat Pumps (GSHP) to provide stable heating and cooling. However, traditional three-dimensional (3D) numerical models of BTES systems demand extensive computational resources, limiting their practicality for real-time and large-scale applications. This study introduces a novel two-dimensional (2D) modeling approach that reduces computational costs while maintaining high accuracy. By employing a radial ring-based discretization method, the model simulates heat injection, retention, and retrieval dynamics over seasonal cycles. A new thermal-mass weighted-average temperature parameter is introduced to evaluate the performance of BTES systems. Model validation against FEFLOW simulations demonstrates a 17-fold improvement in computational speed compared to traditional Computational Fluid Dynamics (CFD) models while achieving a mean absolute percentage error (MAPE) of 2 % during charging and 4 % during discharging. Additionally, a trade-off analysis between computational efficiency and accuracy is conducted, ensuring the model's applicability for real-world scenarios. The findings of this research contribute to the development of computationally efficient BTES models, facilitating better optimization, control, and integration into renewable energy systems. This work provides a foundation for further studies in techno-economic analysis, multi-year performance evaluation, and real-time operational strategies for BTES applications, supporting a more sustainable energy future.
This paper puts forth the case for the need for improved injector design tools to meet NASA s Vision for Space Exploration goals. Requirements for this improved tool are outlined and discussed. The potential for Computational Fluid Dynamics (CFD) to meet these requirements is noted along with its current shortcomings, especially relative to demonstrated solution accuracy. The concept of verification and validation is introduced as the primary process for building and quantifying the confidence necessary for CFD to be useful as an injector design tool. The verification and validation process is considered in the context of the Marshall Space Flight Center (MSFC) Combustion Devices CFD Simulation Capability Roadmap via the Simulation Readiness Level (SRL) concept. The portion of the validation process which demonstrates the ability of a CFD code to simulate heat fluxes to a rocket engine combustor wall is the focus of the current effort. The FDNS and Loci-CHEM codes are used to simulate a shear coaxial single element G02/GH2 injector experiment. The experiment was conducted a t a chamber pressure of 750 psia using hot propellants from preburners. A measured wall temperature profile is used as a boundary condition to facilitate the calculations. Converged solutions, obtained from both codes by using wall functions with the K-E turbulence model and integrating to the wall using Mentor s baseline turbulence model, are compared to the experimental data. The initial solutions from both codes revealed significant issues with the wall function implementation associated with the recirculation zone between the shear coaxial jet and the chamber wall. The FDNS solution with a corrected implementation shows marked improvement in overall character and level of comparison to the data. With the FDNS code, integrating to the wall with Mentor s baseline turbulence model actually produce a degraded solution when compared to the wall function solution with the K--E model. The Loci-CHEM solution, produced by integrating to the wall with Mentor s baseline turbulence model, matches both the heat flux rise rate in the near injector region and the peak heat flux level very well. However, it moderately over predicts the heat fluxes downstream of the reattachment point. The Loci-CHEM solution achieved by integrating to the wall with Mentor s baseline turbulence model was clearly superior to the other solutions produced in this effort.
Provided in this study are predicted in-depth temperature and pyrolysis gas pressure distributions for carbon phenolic materials that are externally heated with a laser source. Governing equations, numerical techniques and comparisons to measured temperature data are also presented. Surface thermochemical conditions were determined using the Aerotherm Chemical Equilibrium (ACE) program. Surface heating simulation used facility calibrated radiative and convective flux levels. Temperatures and pyrolysis gas pressures are predicted using an upgraded form of the SINDA/CMA program that was developed by NASA during the Solid Propulsion Integrity Program (SPIP). Multispecie mass balance, tracking of condensable vapors, high heat rate kinetics, real gas compressibility and reduced mixture viscosity's have been added to the algorithm. In general, surface and in-depth temperature comparisons are very good. Specie partial pressures calculations show that a saturated water-vapor mixture is the main contributor to peak in-depth total pressure. Further, for most of the cases studied, the water-vapor mixture is driven near the critical point and is believed to significantly increase the local heat capacity of the composite material. This phenomenon if not accounted for in analysis models may lead to an over prediction in temperature response in charring regions of the material.
Abstract Experimental data on electromagnetic fluctuations in DIII-D, made available by the Faraday-effect radial interferometer-polarimeter (RIP) diagnostic Chen (2016 Rev. Sci. Instrum. 87 11E108), is examined in comparison with detailed gyrokinetic simulations using gyrokinetic electromagnetic numerical experiment (GENE). The diagnostic has the unique capability of making internal measurements of fluctuating magnetic fields ∫ n e δ B r d R ∫ n e d R . Local linear simulations identify microtearing modes (MTMs) over a substantial range of toroidal mode numbers (peaking at n = 15) with frequencies in good agreement with the experimental data. Local nonlinear simulations reinforce this result by producing a magnetic frequency spectrum in good agreement with that diagnosed by RIP. Simulated heat fluxes are in the range of experimental expectations. However, magnetic fluctuation amplitudes are substantially lower than the experimental expectations. Possible sources of this discrepancy are discussed, notably the fact that the diagnostics are localized at the mid-plane—the poloidal location where the simulations predict the fluctuation amplitudes to be smallest. Despite some discrepancies, several connections between simulations and experiments, combined with general criteria discriminating between potential pedestal instabilities, strongly point to MTMs as the source of the observed magnetic fluctuations.
Electron heat flux that comes from the magnetosphere to the upper ionospheric altitudes controls the value of electron temperature in the core plasma, and, as a result, the total electron density content that is required for different kinds of space weather applications. Knowing the thermal electron heat flux at the upper ionospheric boundaries is the Achilles' heel of all ionospheric models. Such a thermal heat flux setting is especially difficult to justify in the region of the diffuse aurora that is connected to a large energy reservoir of electrons with energies of a few kiloelectron volts, the Earth's plasma sheet, where magnetosphere-ionosphere coupling processes are strongly interconnected. We use the simulated heat flux provided by SuperThermal Electron Transport (STET) code to estimate electron temperatures at the upper ionospheric altitudes and compare these results with corresponding observations from the Defense Meteorological Satellite Program satellite during Saint Patrick's Days 2013 and 2015 Geomagnetic Storms.
Liquid pumping requires large quantities of electrical energy, including about 7% of the energy of building heating, ventilation, and air conditioning (HVAC) systems. To replace pumped condenser-cooling tower water loops with a passive alternative system, we implemented a commercial-scale two-phase loop thermosyphon (TPLT). The unit consists of a 13 m riser integrated with a commercially available cooling tower and circulation water heater that simulates heat loads up to 25 kW. In addition to providing passive cooling capabilities, the cooling tower unit is also maintenance free, reliable, and can operate in both dry and wet modes. This study characterizes the performance (temperature difference between the evaporator and condenser) and the two-phase flow behavior of the loop under various refrigerant charges. Sight glasses installed throughout the loop are used to identify the operating flow regimes in the riser and downcomer. Over the range of operating conditions, we identified that there is an optimal refrigerant charge range for a specific heat load at which near-isothermal operation can be achieved. We further developed a model to predict the operating flow rate and gravitational height in the downcomer and compared it with the experimental data. The results show that the model agrees with the experimental data, in particular the threshold gravitational head height at which there will be subcooled liquid at the condenser exit, indicating that it can be used as a predictive tool for identifying the optimal loop charge for a given heat load.