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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Cross Section Generation Capability in Griffin

The Griffin code is a Multiphysics Object-Oriented Simulation Environment (MOOSE) based reactor multiphysics analysis application jointly developed by Idaho National Laboratory and Argonne National Laboratory. The code includes a variety of steady-state solvers for fixed-source, k-eigenvalue, adjoint, and subcritical multiplication, as well as transient solvers for point-kinetics, improved quasi-static, and spatial dynamics. The code reads multigroup cross sections in the ISOXML format generated from external deterministic or Monte Carlo cross section generation codes. The implementation of the cross section generation capability in Griffin was initiated last year by plugging in the cross section application programming interface (CSAPI) and reviewing the methodologies for treating particulate fuels. The focus this year was on improving the CSAPI integration and implementing advanced self-shielding methods for applications to advanced reactor problems with TRISO fuels. First, the process for cross section library generation was updated to accurately and rigorously produce isotopic cross section data. Second, the equivalent Dancoff factor cell method performing slowing down calculations on the fly for the resonance treatment was implemented in CSAPI to improve the accuracy of effective multigroup cross sections in the resonance energy range. Third, the iterative local spatial self-shielding method was implemented under the calculation framework of the equivalent Dancoff factor cell method to accurately deal with the double heterogeneity effect of particulate fuel. The updated CSAPI with the advanced self-shielding methods, together with the cross section libraries generated based on the improved process, were tested for pin-cell, unit-cell, and fuel assembly problems with various resonance self-shielding conditions based on very high temperature reactor, high temperature test reactor, and Empire benchmark cores, indicating that the updated CSAPI in Griffin is able to produce multigroup cross sections accurately and efficiently. We also showed that the methodology worked well for pebble bed fuel from HTR-10, but the capability still needs to be fully integrated into CSAPI. In the future, further benchmark tests will be performed for various thermal reactor core problems, including particulate fuel-based pebble bed reactors.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRSDate not supplied

Variance Decomposition of MEDLI2 Reconstructed Heating Using Neural Networks

The Mars Entry, Descent, and Landing Instrumentation (MEDLI2) sensor suite collected data during entry of the Mars 2020 Perseverance rover into Mars’ atmosphere. This suite included a network of MEDLI2 Instrumented Sensor Plugs (MISPs). Each MISP was comprised of a cylinder made of Thermal Protection System (TPS) material with 1-3 embedded thermocouples (TCs), and it was flush mounted into the heatshield or backshell. Data from these in-depth TCs were used to reconstruct the aeroheating environment of the vehicle throughout entry. Surface heating was posed as an inverse problem, with the goal of estimating the surface heating by minimizing an objective function of the difference between MISP temperature measurements during flight and the temperature predictions derived from the Fully Implicit Ablation and Thermal response (FIAT) program. Given an aerothermal environment, FIAT calculates the material response and provides in-depth temperatures throughout the TPS material. To achieve the reverse, an internal tool called FIAT_Opt runs through multiple different environments until the output temperature at the TC depth closely matches the flight data. 95% confidence intervals on the reconstructed surface heating were obtained using Monte Carlo analysis, in which uncertainties in the thermocouple depth and the TPS material properties (e.g., density, thermal conductivity, heat capacity, emissivity) based on flight-lot material testing were included. A variance decomposition method using Sobol indices was employed to assess the sensitivity of the reconstructed peak heating to the TC placement and material property uncertainties. Variance decomposition was found to require tens of thousands of FIAT_Opt runs in order for the Sobol indices to converge. With a single FIAT_Opt run taking on the order of 40 minutes, the required number of computations would take months to complete, even if using multiple CPUs. To mitigate this problem, three machine learning models (ridge regression with cross-validation, random forest regression, and a deep neural network) were trained and tested using the 2000 Monte Carlo runs that were already completed. A subset of 1600 runs were used to train the model (i.e., training set), while the remaining 400 runs were used as the test set. The predictions from the deep neural network (DNN) on the test set showed nearly perfect agreement to the actual values computed with FIAT_Opt (R2 > 0.99). Using the DNN as a surrogate model, the variance decomposition using 50,000 runs was completed within minutes. The resulting Sobol indices showed that the reconstructed peak surface heating was most sensitive to the uncertainties in the thermal conductivity (ST = 0.37) and heat capacity (ST = 0.26). This method can be leveraged to provide requirements for material property measurements needed to improve the accuracy of surface heating prediction and ultimately lead to the reduction of design margins in the future. This presentation will include background on the MEDLI2 suite; the method used for inverse heating estimation; the way that material property uncertainties were accounted for using Monte Carlo analysis; a brief background on variance decomposition; the motivation for using machine learning in this context; how a neural network was trained on the data to enable variance decomposition in a fraction of the time; and the variance decomposition results for one of the MISPs.

Hannah Alpert↗
DOE OSTI2021

Cross Section Generation Capability in Griffin

The Griffin code is a Multiphysics Object-Oriented Simulation Environment (MOOSE) based reactor multiphysics analysis application jointly developed by Idaho National Laboratory and Argonne National Laboratory. The code includes a variety of steady-state solvers for fixed-source, k-eigenvalue, adjoint, and subcritical multiplication, as well as transient solvers for point-kinetics, improved quasi-static, and spatial dynamics. The code reads multigroup cross sections in the ISOXML format generated from external deterministic or Monte Carlo cross section generation codes. The implementation of the cross section generation capability in Griffin was initiated last year by plugging in the cross section application programming interface (CSAPI) and reviewing the methodologies for treating particulate fuels. The focus this year was on improving the CSAPI integration and implementing advanced self-shielding methods for applications to advanced reactor problems with TRISO fuels. First, the process for cross section library generation was updated to accurately and rigorously produce isotopic cross section data. Second, the on-the-fly slowing down method for the resonance treatment was implemented in CSAPI to improve the accuracy of effective multigroup cross sections in the resonance energy range. Among various on-the-fly slowing down methods, the equivalent Dancoff factor cell method was employed. Third, the iterative local spatial self-shielding method was implemented under the calculation framework of the equivalent Dancoff factor cell method to accurately deal with the double heterogeneity effect of particulate fuel. The updated CSAPI with the advanced self-shielding methods, together with the cross section libraries generated based on the improved process, were tested for the very high temperature reactor (VHTR), high temperature test reactor (HTTR), and Empire benchmark problems with various resonance self-shielding conditions, indicating that the updated CSAPI in Griffin is able to produce multigroup cross sections accurately and efficiently. We also show that the methodology works well for pebble bed fuel from HTR-10, but the capability still needs to be fully integrated into CSAPI. In the future, further benchmark tests will be performed for various thermal reactor core problems, including particulate fuel-based pebble bed reactors.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Core-flood Effluent and Shale Surface Chemistries in Predicting Interaction between Shale, Brine, and Reactive Fluid

We report field and laboratory observations to date indicate that the efficiency of hydraulic fracturing, as it relates to hydrocarbon recovery, depends significantly on geochemical alterations to rock surfaces that diminish accessibility by partial or total plugging of the pore and fracture networks. This is caused by mineral scale deposition such as coating of fracture surfaces with precipitates, particle migration, and/or crack closure due to dissolution under stress. In reactive flow-through experiments, mineral reactions in response to acidic fluid injection significantly reduced system porosity and core permeability. The present study focuses on changes to fluid chemistry and shale surfaces (inlet and fracture walls) resulting from shale-fluid interactions and integrating these findings for an improved estimate of transport-related consequences. The reacted shale surfaces were examined by spatially-resolved scanning electron microscopy - energy dispersive spectroscopy (SEM-EDS) analysis. Importantly, inductively coupled plasma - mass spectrometry/optical emission spectroscopy (ICP-MS/OES) was utilized to probe the chemical evolution of the core-flood effluents. The three study cores selected from the Marcellus formation represent different mineralogies and structural features. In flow-through experiments, lab-generated brine and HCl-based fracture fluid (pH=2) were injected sequentially under effective stress (up to 500 psi) at reservoir temperature (80°C). SEM-EDS results confirmed by the ICP concentration trends showed significant Fe hydroxide precipitates in clay- and pyrite-rich outcrop samples due to partial oxidation of Fe-bearing phases in the case of intrusion of low salinity water-based fluids. Porosity reduction in the MSEEL (Marcellus Shale Energy and Environmental Laboratory) carbonate-rich sample is related to compaction of cores under stress due to matrix softening with substantial dissolution, and pore-filling by hydroxides, as well as barite and salts. Despite the same fluid compositions and experimental conditions used for both MSEEL samples, barite precipitation was much more intense in the MSEEL clay-rich sample due to its greater sorption capacity and additional sulfate source as well as fissile nature with multiple lengthwise cracks. ICP tests revealed time-resolved concentration trends in produced brine and reactive fluids that in turn complemented the pre-/post-reaction SEM-EDS observations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2003

Flexible Cryogenic Temperature and Liquid-Level Probes

Lightweight, flexible probes have been developed for measuring temperatures at multiple locations in tanks that contain possibly pressurized cryogenic fluids. If the fluid in a given tank is subcritical (that is, if it consists of a liquid and its vapor), then in one of two modes of operation, the temperature measurements made by a probe of this type can be used to deduce the approximate level of the liquid. The temperature sensors are silicon diodes located at intervals along a probe. If the probe is to be used to measure a temperature gradient along a given axis in the tank, then the probe must be mounted along that axis. In the non-liquid-level-sensing temperature-measurement mode, a constant small electric current is applied to each diode and the voltage across the diode . a known function of the current and temperature . is measured as an indication of its temperature. For the purpose of this measurement, "small electric current" signifies a current that is not large enough to cause a significant increase in the measured temperature. More specifically, the probe design calls for a current of 10 A, which, in the cryogenic temperature range of interest, generates heat at a rate of only about 0.01 mW per diode. In the liquid-level-sensing mode, one applies a larger current (30 mA) to each diode so as to heat each diode appreciably (with a power of about 36 mW in the temperature range of interest). Because the liquid cools the diode faster than does the vapor, the temperature of the diode is less when diode is immersed in the liquid than when it is above the surface of the liquid. Thus, the temperature (voltage) reading from each diode can be used to determine whether the liquid level is above or below the diode, and one can deduce that the liquid level lies between two adjacent diodes, the lower one of which reads a significantly lower temperature. The aforementioned techniques for measuring temperature and deducing liquid level are not new. What is new here are the designs of the probes and of associated external electronic circuitry. In each probe, the diodes and the lead wires are embedded in a strong, lightweight, flexible polyimide strip. Each probe is constructed as an integral unit that includes a multipin input/output plug or socket for solderless connection of the lead wires to the external circuitry. The polyimide strip includes mounting tabs with holes that can accommodate rivets, screws, or other fasteners. Alternatively, a probe can be mounted by use of an epoxy. A probe can be manufactured to almost any length or width, and the diodes can be embedded at almost any desired locations along and across the polyimide strip. In designing a probe for a specific application, one seeks a compromise between (1) minimizing the number of diodes in order to minimize the complexity of input/output connections and external electronic circuitry while (2) using enough diodes to obtain the required precision. Optionally, to minimize spurious heating of the cryogenic fluid, the external circuitry can be designed to apply power to the probe only during brief measurement intervals. Assuming that the external circuitry is maintained at a steady temperature, a power-on interval of only a few seconds is sufficient to obtain accurate data on temperatures and/or the height of the liquid/vapor interface.

Haberbusch, Mark↗
NASA NTRS2005

Flexible Cryogenic Temperature and Liquid-Level Probes

Lightweight, flexible probes have been developed for measuring temperatures at multiple locations in tanks that contain possibly pressurized cryogenic fluids. If the fluid in a given tank is subcritical (that is, if it consists of a liquid and its vapor), then in one of two modes of operation, the temperature measurements made by a probe of this type can be used to deduce the approximate level of the liquid. The temperature sensors are silicon diodes located at intervals along a probe. If the probe is to be used to measure a temperature gradient along a given axis in the tank, then the probe must be mounted along that axis. In the temperature-measurement mode, a constant small electric current is applied to each diode and the voltage across the diode a known function of the current and temperature is measured as an indication of its temperature. For the purpose of this measurement, small electric current signifies a current that is not large enough to cause a significant increase in the measured temperature. More specifically, the probe design calls for a current of 10 A, which, in the cryogenic temperature range of interest, generates heat at a rate of only about 0.01 mW per diode. In the liquid-level-sensing mode, one applies a larger current (30 mA) to each diode so as to heat each diode appreciably (with a power of about 36 mW in the temperature range of interest). Because the liquid cools the diode faster than does the vapor, the temperature of the diode is less when the diode is immersed in the liquid than when it is above the surface of the liquid. Thus, the temperature (voltage) reading from each diode can be used to determine whether the liquid level is above or below the diode, and one can deduce that the liquid level lies between two adjacent diodes, the lower one of which reads a significantly lower temperature. The aforementioned techniques for measuring temperature and deducing liquid level are not new. What is new here are the designs of the probes and of associated external electronic circuitry. In each probe, the diodes and the lead wires are embedded in a strong, lightweight, flexible polyimide strip. Each probe is constructed as an integral unit that includes a multipin input/output plug or socket for solderless connection of the lead wires to the external circuitry. The polyimide strip includes mounting tabs with holes that can accommodate rivets, screws, or other fasteners. Alternatively, a probe can be mounted by use of an epoxy. A probe can be manufactured to almost any length or width, and the diodes can be embedded at almost any desired location along and across the polyimide strip. In designing a probe for a specific application, one seeks a compromise between (1) minimizing the number of diodes in order to minimize the complexity of input/output connections and external electronic circuitry while (2) using enough diodes to obtain the required precision. Optionally, to minimize spurious heating of the cryogenic fluid, the external circuitry can be designed to apply power to the probe only during brief measurement intervals. Assuming that the external circuitry is maintained at a steady temperature, a power-on interval of only a few seconds is sufficient to obtain accurate data on temperatures and/or the height of the liquid/vapor interface.

Haberbusch, Mark↗
NASA NTRS1998

Mechanistic Models of Friction Stir Welding

Friction stir welding is a welding process developed at The Welding Institute (TWI) in England. The method uses very large strain plastic deformation of the material to join two pieces of metal together. The material is deformed using a tool which is forced between the two pieces which rotates causing a bond. Beyond this, very little is actually known although many people working in the field are willing to speculate on the detailed mechanisms involved. Some measurements made using sacrificial thermocouples at the weld joint indicate that the maximum temperature during the weld process is on the order of 370C - well below the melting temperature of the material. However, at this temperature, the material properties are highly temperature dependent, and the yield stress is approximately an order of magnitude less at this temperature than it is at room temperature. As expected, there are many interpretations of the physical mechanisms occurring during the weld process. Although there is very little published concerned with FSW, some of the anecdotal theories will be described. One describes the primary mechanism as frictional heating at the front of the tool caused by slip between the tool and the material. At elevated temperatures, the weld material becomes soft and deforms around the tool but not essentially altered by the tool rotation, similar to an extrusion. As the material meets again at the rear of the tool, the temperatures and pressures are sufficient to cause the material to bond. All other structures seen are secondary and unimportant. Another theory examined last summer at NASA's Marshall Space Flight Center (MSFC) was that there was no slip between the tool and the material resulting in a rotating mass of plastic weld material traveling at a variety of angular velocities - the greatest at the tool surface diminishing to zero at the outer edge of the plastic mass surrounding the tool. This conceptual model was followed by simplified calculations which showed that the balance of moments through the weld plug was not possible under steady state conditions and realistic temperature profiles. This led to some consideration of a quasi-steady oscillating process. Later when force measurements became available some models were modified and new ones were proposed.

Stewart, Michael B.↗
NASA NTRS2019

Laboratory-Based Thermal Shock Investigation of Heat Flux Sensors for the Mars 2020 Backshell

In 2012 during the entry, descent, and landing of the Mars Science Laboratory (MSL), the MSL Entry, Descent, and Landing Instrumentation (MEDLI) sensor suite was collecting in-flight heatshield pressure and temperature data. The data collected by the MEDLI instruments has since been used for reconstruction of vehicle aerodynamics, atmospheric conditions, aerothermal heating, and Thermal Protection System (TPS) performance as well as material response model validation and refinement. The Mars Entry, Descent, and Landing Instrumentation 2 (MEDLI2) sensor suite for the Mars 2020 heatshield and backshell is being designed to expand on the measurements and knowledge gained from MEDLI. Similar to MEDLI, MEDLI2 will measure the pressure and temperature of the heatshield. MEDLI2 will additionally measure the temperature, pressure, total heat flux, and radiative heat flux on the backshell.Since the backshell instrumentation is new to MEDLI2, Do No Harm (DNH) testing was conducted on instrumented backshell TPS (SLA-561V) panels. The panels consisted of four pressure port holes, one Mars Entry Atmospheric Data System (MEADS) pressure port plug, one MEDLI2 Integrated Sensor Plug (MISP) thermal plug, and one heat flux sensor. DNH testing was conducted to ensure the performance of the TPS was not degraded due to sensor integration and to characterize any TPS performance changes. The testing consisted of environmental testing— vibration, shock, thermal vacuum (TVAC) cycling— and bounding aerothermal (arc jet) testing. During arc jet testing, the heat flux sensors embedded in the SLA-561V panels exhibited an unexpected temporary reduction in the heat flux sensor temperature and response. After review of the test results, it was determined that this unexpected response was confined to the two heat flux sensors that experienced the greatest thermal shock condition. This condition consisted of a liquid nitrogen (LN2) bath that induced temperatures of approximately -190°C, and then a transition (thermal shock) to an arc jet test at a heat rate of approximately 21 W/cm2. Both heat flux sensors that were exposed to this thermal shock experienced a blister in the thermal coating during the arc jet test.Two heat flux sensor thermal shock test series were performed to investigate the cause of the blistering and subsequent energy release. In these tests, the heat flux sensor was first cold soaked in either a dry ice or LN2 bath to induce temperatures of approximately -78°C or -190°C, respectively. Then the sensors were thermally shocked using two propane torches with a heat rate of either approximately 8 W/cm2 or 21 W/cm2. The key findings indicated that there is a correlation between thermal shock and the blistering observed in the DNH test series, and that the cause appeared to be rooted in the heat flux sensor epoxy that encapsulates the sensor thermopile.Since the heat flux sensors are required to measure heat fluxes up to 15 W/cm2 during the Mars 2020 entry, a third test series was designed to determine if blistering is an issue at this maximum expected flight heat flux. Results from all three thermal shock test series and a discussion about whether or not blistering of the heat flux sensor thermal coating could be an issue for the Mars 2020 mission will be presented.

Miller, R. A.↗