Search NASA⌕ Search

SEARCH · Search NASA

Results for “temperature algorithm”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

A Method for Obtaining High Time and Spatial Resolution Convective Cloud Top Data for the TTL

A method for obtaining high time and spatial resolution convective cloud top data for the TTL Leonhard Pfister, Eric Jensen, Rei Ueyama, Eliot Atlas, and Maria Navarro Convective systems in the tropics have a maximum in the cloud top altitude distribution of about 13.5 km. However, there is a significant tail to this distribution -- a few percent reach the cold point tropopause (CPT) at 16.5 km, and there has been clear evidence of convective mass deposited as high as 19 km in the tropics. The region between 13.5 km and the cold point tropopause is transitional, between the free tropical troposphere where convective mixing dominates, and the stratosphere where slow upward ascent dominates. In this region (the Tropical Tropopause Layer), convective injection, slow ascent, and mixing from midlatitudes all have similar time scales. So, even though only a few percent of convective systems reach the CPT, convection is important. Space Based Lidar and cloud radar measurements have yielded information about long term average statistical distributions of cloud altitude as a function of location. However, we also need time-dependent cloud top altitude and cloud top potential temperature information, primarily to understand the water vapor and TTL cloud distributions. This is because the effect of convection depends on the local temperature, and on the subsequent temperature history. Time dependent cloud top information is also needed to understand short-lived tracers because cross-isentropic flow is time and space dependent. This paper presents a method of obtaining time and space dependent convective cloud top theta (and altitude) information using 3-hourly geostationary brightness temperature data, coupled with global 3 -hourly rainfall estimates and temperature analyses. We explore different mixing algorithms to obtain the most reasonable agreement with near-simultaneous observations by cloudsat and calipso. Observations of short-lived tracers from ATTREX, coupled with short-term trajectories are used to test the method's accuracy. An important caveat is the ambiguity of evaluating convective cloud top altitudes under from combined cloudsat and calipso measurements.

cold point tropopause (CPT)↗
NASA NTRS2021

Error Propagation in Microwave Soil Moisture and Vegetation Optical Depth Retrievals

Satellite soil moisture and vegetation optical depth (VOD; related to the total vegetation water mass per unit area) are increasingly being used to study water relations in the soil-plant continuum across the globe. However, soil moisture and VOD are typically jointly estimated, where errors in the optimization approach can cause compensation between both variables and confound such studies. It is thus critical to quantify how satellite microwave measurement errors propagate into soil moisture and VOD. Such a study is especially important for VOD given limited investigations of whether VOD reflects in-situ plant physiology. Furthermore, despite new approaches that constrain (or regularize) VOD dynamics to reduce soil moisture errors, there is limited study of whether regularization reduces VOD errors without obscuring true vegetation temporal dynamics. Here, we find that, across the globe, VOD is less robust to measurement error (more difficult for optimization methods to find the true solution) than soil moisture in their joint estimation. However, a moderate degree of regularization (via time-constrained VOD) reduces errors in VOD to a greater degree than soil moisture and reduces spurious soil moisture-VOD coupling. Furthermore, despite constraining VOD time dynamics, regularized VOD variations on sub-weekly scales are both closer to simulated true VOD time series and have global VOD post-rainfall responses with reduced error signatures compared to VOD retrievals without regularization. Ultimately, we recommend moderately regularized VOD for use in large scale studies of soil-plant water relations because it suppresses noise and spurious soil moisture-VOD coupling without removing the physical signal.

Vegetation optical depth↗
NASA NTRSDate not supplied

A Principal-Component-Based Radiative Transfer Model (PCRTM) for Hyperspectral Shortwave and Longwave Satellite Sensors and Its Applications

The radiative transfer model (RTM) or forward model is an essential component in satellite remote sensing. For modern hyperspectral remote sensors, fast and accurate RTMs are needed due to a large number of spectral dimensions and high spatial resolutions. We will describe a Principal Component-based radiative transfer model (PCRTM) which can simulate the top-of-atmosphere (TOA) radiance or reflectance spectra 250 nm to 2000 micrometers quickly and accurately. The PCRTM has been demonstrated to be extremely accurate, compared to the line-by-line RTM benchmarks, and the former is several orders more computationally efficient than the latter. We will demonstrate how the PCRTM and the associated inversion algorithms are used to infer atmospheric temperature, moisture, and trace gas profiles, as well as cloud and surface properties from hyperspectral IR sounders such as Atomspheric Infrared Souder (AIRS) and Cross-track Infrared Sounder (CrIS). High-quality climate records for a 20-year duration have been derived from these IR hyperspectral data. Finally, we will show some examples of using PCRTM to retrieve cloud properties from Earth Surface Mineral Dust Source Investigation (EMIT) and its applicability of PCRTM to future missions such as the CLARREO (Climate Absolute Radiance and Refractivity Observatory) Pathfinder (CPF) CPF and the Surface Biology and Geology (SBG).

Xu Liu↗
DOE OSTI2022

Machine Learning-based Prediction of Departure from Nucleate Boiling Power for the PSBT Benchmark

Machine Learning (ML) has seen an exponential growth in its applications due to its advanced data driven prediction capabilities. The study presents a data-driven approach as a preliminary attempt to predict the power at which departure from nucleate boiling (DNB) occurs in pressurized water reactors (PWRs) by constructing an advanced ML algorithm that takes outlet pressure, inlet temperature and inlet mass flux as the input features. DNB is a critical heat flux (CHF) phenomenon seen in PWRs. The experimental data from the PWR subchannel and bundle tests (PSBT) benchmark is first used to train an artificial neural network (ANN) to predict the DNB power, which produces a root mean square error (RMSE) of 6.89 kW/m when tested on a blind subset of the PSBT data. Since the PSBT dataset is relatively small to train an accurate ANN, a data augmentation methodology based on generative adversarial networks (GANs) is used to expand the training dataset. By assuming that the real data follows a certain distribution, GANs try to learn that underlying distribution to generate similar synthetic data to augment the database and to improve the predictive capabilities of the ANN. The data generated from GANs are validated using 1-nearest neighbor and kernel maximum mean discrepancy. To further ensure data from GAN is similar to PSBT, the data is tested and filtered out using the sub-channel thermal-hydraulic code CTF. The results indicate that with the addition of 120 data points from GAN the RMSE reduces to 4.84 kW/m showing promising results for future developments.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2008

A Physical Model to Estimate Snowfall over Land using AMSU-B Observations

In this study, we present an improved physical model to retrieve snowfall rate over land using brightness temperature observations from the National Oceanic and Atmospheric Administration's (NOAA) Advanced Microwave Sounder Unit-B (AMSU-B) at 89 GHz, 150 GHz, 183.3 +/- 1 GHz, 183.3 +/- 3 GHz, and 183.3 +/- 7 GHz. The retrieval model is applied to the New England blizzard of March 5, 2001 which deposited about 75 cm of snow over much of Vermont, New Hampshire, and northern New York. In this improved physical model, prior retrieval assumptions about snowflake shape, particle size distributions, environmental conditions, and optimization methodology have been updated. Here, single scattering parameters for snow particles are calculated with the Discrete-Dipole Approximation (DDA) method instead of assuming spherical shapes. Five different snow particle models (hexagonal columns, hexagonal plates, and three different kinds of aggregates) are considered. Snow particle size distributions are assumed to vary with air temperature and to follow aircraft measurements described by previous studies. Brightness temperatures at AMSU-B frequencies for the New England blizzard are calculated using these DDA calculated single scattering parameters and particle size distributions. The vertical profiles of pressure, temperature, relative humidity and hydrometeors are provided by MM5 model simulations. These profiles are treated as the a priori data base in the Bayesian retrieval algorithm. In algorithm applications to the blizzard data, calculated brightness temperatures associated with selected database profiles agree with AMSU-B observations to within about +/- 5 K at all five frequencies. Retrieved snowfall rates compare favorably with the near-concurrent National Weather Service (NWS) radar reflectivity measurements. The relationships between the NWS radar measured reflectivities Z(sub e) and retrieved snowfall rate R for a given snow particle model are derived by a histogram matching technique. All of these Z(sub e)-R relationships fall in the range of previously established Z(sub e)-R relationships for snowfall. This suggests that the current physical model developed in this study can reliably estimate the snowfall rate over land using the AMSU-B measured brightness temperatures.

Kim, Min-Jeong↗
DOE OSTI2025

Experimental demonstration of real-time electron temperature profile control in DIII-D

Future tokamak reactor operation will require the ability to maintain a given plasma scenario for extended periods of time. This will necessitate the capability to react to changes in the plasma state and return the plasma to the target scenario; the principal method to achieve this is through feedback control. Thus, it is necessary to develop and test feedback controllers for the plasma profiles that define a target scenario. In this work, a feedback controller for the electron temperature (Te) profile is tested experimentally in DIII-D. This experiment relied on the ability to ascertain the electron temperature profile in real time, which was achieved using an observer algorithm. The observer relies on both diagnostic data and a predictive model of the electron temperature profile evolution; this predictive model includes contributions from neural network surrogate models. Because of these dependencies, a number of capabilities needed to be added to the real-time PCS for DIII-D in order to support the Te profile control experiment. The neural network surrogates needed to be integrated into the PCS to be called in real time. An observer algorithm for the Te profile needed to be added and connected to the Thomson scattering system to allow access to the current state of the profile in real time. When tested, the observer was shown to produce Te profiles that are consistent with the shape of the Thomson scattering data while rejecting much of the noise in the diagnostic data. Finally, the controller itself was tested in real time. This experiment showed that the controller is capable of tracking the electron temperature target at locations across the spatial profile.

Morosohk, Shira [Oak Ridge Associated Universities↗
DOE OSTI2024

Mesoscale Convective Systems Represented in High Resolution E3SMv2 and Impact of New Cloud and Convection Parameterizations

Mesoscale convective systems (MCSs) play an important role in modulating the global hydrological cycle, general circulation, and radiative energy budget. In this study, we evaluate MCS simulations in the second version of U.S. Department of Energy (DOE) Energy Exascale Earth System Model (E3SMv2). E3SMv2 atmosphere model (EAMv2) is run at the uniform 0.25? horizontal resolution. We track MCSs consistently in the model and observations using the PyFLEXTRKR algorithm, which defines MCS based on both cloud-top brightness temperature (Tb) and surface precipitation. Results from using Tb only to define MCS, commonly used in previous studies, are also discussed. Furthermore, sensitivity experiments are performed to examine the impact of new cloud and convection parameterizations developed for EAMv3 on simulated MCSs. Our results show that EAMv2 simulated MCS precipitation is largely underestimated in the tropics and contiguous United States. This is mainly attributed to the underestimated precipitation intensity in EAMv2. In contrast, the simulated MCS frequency becomes more comparable to observations if MCSs are defined only based on cloud-top Tb. The Tb-based MCS tracking method, however, includes many cloud systems with very weak precipitation which conflicts with the MCS definition. This result illustrates the importance of accounting for precipitation in evaluating simulated MCSs. We also find that the new physics parameterizations help increase the relative contribution of convective precipitation to total precipitation in the tropics, but the simulated MCS properties are generally not improved. This suggests that simulating MCSs will remain a challenge for the next version of E3SM.

Zhang, Meng↗
NASA NTRS2023

Assessing High Resolution Thermal Monitoring of Complex Intertidal Environments From Space: the Case of ECOSTRESS at Rias Baixas, NW Iberia

Currently, thermal sensors on satellites and other orbital platforms with high spatial resolution designed to monitor the oceans are often insufficient to assess the surface temperature of small bodies of water or within the coastal boundary layer. As the accuracy and sensitivity of remote sensing satellites improve, the demand for more accurate and up-to-date basic data sets for calibration increases. The quality of the thermal data collected by ECOSTRESS on the International Space Station allows the characterization of thermal stress levels in coastal ecosystems with a high spatial resolution of 70 m and a return time from hours to 5 days. This study focused on the calibration of ECOSTRESS estimates with in situ surface temperature data from sensors installed at 3 cm depth in the sediment on the intertidal muddy sandflats of three of the Rías Baixas in Galicia, NW Iberian Peninsula, from 2019 to 2021. A final number of 45 ECOSTRESS temperature images were analyzed. From these, 20% contained substantial georeferencing errors which had to be corrected manually with GIS software tools. We applied the Fourier's law of Heat Conduction to derive surface estimates from loggers sub-surface measurements that could be directly compared with ECOSTRESS data. Overall, a good calibration which explained more than 80% of ECOSTRESS temperature estimates for the whole dataset was obtained, but with an intrinsic cold bias around 1.39 °C. When temperatures at a depth of 3 cm were used, the linear fit became worse and the negative bias increased to 1.49 °C. Closer inspection revealed that night measurements were responsible for this larger deviation, as ECOSTRESS estimates became much colder compared to within sediment measurements because of the combined effect of the instrument intrinsic bias and nocturnal surface cooling. The best calibration was obtained when surface estimates were calculated just for the nighttime, as the cold bias decreased to 0.93 °C. More importantly, during hot daytime conditions in emersion above 20 °C, ECOSTRESS data matched surface temperature estimates, probably because of a better performance of ECOSTRESS algorithm at dry surfaces with lower emissivity. Thus, during the most ecologically relevant periods when high temperatures could drive thermal stress in many commercially-important bivalve species, ECOSTRESS provides accurate surface estimates that can be used to derive sub surface temperatures at the depths at which the different burrowing organisms live. We thus conclude that this instrument constitutes an important global tool to examine thermal stress at an unprecedented spatial scale for complex sea-land boundary systems.

ECOSTRESS↗
NASA NTRS2019

Assessment of Satellite Sea Surface Salinity Products Using a Coupled ENSO Prediction Model

Much work has gone into revising and updating algorithms for converting satellite-measured radiances to useful ocean variables like sea surface salinity (e.g. SMOS - Boutin et al., 2017, SMAP - Fore et al., 2016 and Aquarius - Meissner et al., 2018). As part of our Ocean Salinity Science Team work, we utilize an intermediate-complexity air/sea coupled model as a transfer function to test if more mature satellite SSS model algorithms actually improve ENSO forecast skill. For initialization of the coupled forecast, we demonstrate that the positive impact of SSS assimilation is brought about by surface freshening near the eastern edge of the western Pacific warm pool and density changes that lead to shallower mixed layer between 10S-5N. In addition, salting near the ITCZ leads to a deepening of the mixed layer and thermocline near 8N. These patterns together provide the background state to amplify equatorial Kelvin waves and improve ENSO hindcasts (Hackert et al., 2019). Here we extend this work to compare the impact of various pairs of original and improved satellite SSS algorithms. For instance we compare SMAP V4.1 with the latest, SMAP V4.2, to see what impact algorithm improvements may have on ENSO forecasts. SSS observations are tested on seasonal to interannual variability of tropical Indo-Pacific Ocean dynamics as well as on dynamical ENSO forecasts by initializing twelve-month forecasts for each month of available data. All experiments assimilate satellite sea level (SL), sea surface temperature (SST), and in situ subsurface temperature and salinity observations (Tz, Sz). Additionally various satellite, blended, and in-situ SSS products are assimilated. We find that including satellite SSS significantly improves Niño3.4 sea surface temperature anomaly validation, more mature SSS model algorithms are generally improving ENSO forecasts over time, and more satellite SSS data coverage helps to extend useful forecasts.

Ocean↗
DOE OSTI2023

Internal Variability Increased Arctic Amplification During 1980–2022

Abstract Since 1980, the Arctic surface has warmed four times faster than the global mean. Enhanced Arctic warming relative to the global average warming is referred to as Arctic Amplification (AA). While AA is a robust feature in climate change simulations, models rarely reproduce the observed magnitude of AA, leading to concerns that models may not accurately capture the response of the Arctic to greenhouse gas emissions. Here, we use CMIP6 data to train a machine learning algorithm to quantify the influence of internal variability in surface air temperature trends over both the Arctic and global domains. Application of this machine learning algorithm to observations reveals that internal variability increases the Arctic warming but slows global warming in recent decades, inflating AA since 1980 by 38% relative to the externally forced AA. Accounting for the role of internal variability reconciles the discrepancy between simulated and observed AA.

58 GEOSCIENCES↗
NASA NTRS1998

Numerical Investigation of Hot Gas Ingestion by STOVL Aircraft

This report compiles the various research activities conducted under the auspices of the NASA Grant NAG3-1026, "Numerical Investigation of Hot Gas Ingestion by STOVL Aircraft" during the period of April 1989 to April 1994. The effort involved the development of multigrid based algorithms and computer programs for the calculation of the flow and temperature fields generated by Short Take-off and Vertical Landing (STOVL) aircraft, while hovering in ground proximity. Of particular importance has been the interaction of the exhaust jets with the head wind which gives rise to the hot gas ingestion process. The objective of new STOVL designs to reduce the temperature of the gases ingested into the engine. The present work describes a solution algorithm for the multi-dimensional elliptic partial-differential equations governing fluid flow and heat transfer in general curvilinear coordinates. The solution algorithm is based on the multigrid technique which obtains rapid convergence of the iterative numerical procedure for the discrete equations. Initial efforts were concerned with the solution of the Cartesian form of the equations. This algorithm was applied to a simulated STOVL configuration in rectangular coordinates. In the next phase of the work, a computer code for general curvilinear coordinates was constructed. This was applied to model STOVL geometries on curvilinear grids. The code was also validated in model problems. In all these efforts, the standard k-Epsilon model was used.

Vanka, S. P.↗
NASA NTRS2008

A Temperature Sensor using a Silicon-on-Insulator (SOI) Timer for Very Wide Temperature Measurement

A temperature sensor based on a commercial-off-the-shelf (COTS) Silicon-on-Insulator (SOI) Timer was designed for extreme temperature applications. The sensor can operate under a wide temperature range from hot jet engine compartments to cryogenic space exploration missions. For example, in Jet Engine Distributed Control Architecture, the sensor must be able to operate at temperatures exceeding 150 C. For space missions, extremely low cryogenic temperatures need to be measured. The output of the sensor, which consisted of a stream of digitized pulses whose period was proportional to the sensed temperature, can be interfaced with a controller or a computer. The data acquisition system would then give a direct readout of the temperature through the use of a look-up table, a built-in algorithm, or a mathematical model. Because of the wide range of temperature measurement and because the sensor is made of carefully selected COTS parts, this work is directly applicable to the NASA Fundamental Aeronautics/Subsonic Fixed Wing Program--Jet Engine Distributed Engine Control Task and to the NASA Electronic Parts and Packaging (NEPP) Program. In the past, a temperature sensor was designed and built using an SOI operational amplifier, and a report was issued. This work used an SOI 555 timer as its core and is completely new work.

Patterson, Richard L.↗
DOE OSTI2021

How much information on precipitation is contained in satellite infrared imagery?

Satellite infrared (IR) brightness temperature providing information on the characteristics of cloud tops is one of the primary input data used for estimating precipitation from satellites. Thus, the natural questions to ask are: Does IR imagery contain more information about precipitation in some regions more than others? What are the factors that lead to such differences? Should we expect the amount of information provided by IR imagery to change with respect to temporal or spatial aggregation? Comprehensive analysis of the accuracy of satellite-based precipitation estimates obtained from any given algorithm may provide qualitative answers to the above questions. However, such an approach only offers conclusions within the limited context of the algorithm used to obtain precipitation estimates; that is, patterns caused by the inherent information content of IR imagery are inextricable from those induced by assumptions embedded in the precipitation estimation algorithm. Here, we address the abovementioned questions from a different perspective using an information-theoretic measure, free of structural assumptions and general to a wide range of relationships, to characterize the average and seasonal dependence between IR imagery provided by satellite sensors in the spectral range (10.7–11.5 μm) and precipitation rates in the contiguous United States (CONUS) across distinct temporal and spatial scales. We analyze a total of more than 1.3 billion pairs of IR and precipitation observations over CONUS, and we observe interesting patterns. First, we show that there is a strong inverse relationship (ρ = - 0.73) between the information content of IR data and the number of no-rain observations; however, its robustness varies regionally and seasonally with less significant correlation during the warm season. Second, we demonstrate that the intuitive relationship of increasing dependence between IR and precipitation as a result of temporal or spatial aggregation exhibits a diminishing returns behavior. For instance, temporal aggregation from 1 to 3 h increases the dependence approximately 7 times as much as temporal aggregation from 12 to 24 h. Furthermore, we quantitatively examine and re-confirm several statements previously reported in the literature such as the strong association between IR and precipitation in convective storms and the low association in orographic rainfall. Finally, we conclude by illustrating the potential of the analysis in diagnosis of operational algorithms for estimating precipitation from IR brightness temperature. The findings of this study pinpoint spatial domains and time scales at which IR is not an adequate proxy for estimating precipitation. Thus, they can potentially guide the development of operational algorithms that utilize satellite Infrared imagery for estimating, downscaling and data fusion of precipitation. Finally, the analysis presented here opens up the possibility of developing new methodologies for diagnosis of satellite-based precipitation estimation algorithms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1988

Nimbus-7 global cloud climatology. I - Algorithms and validation

An improved version of the Nimbus-7 cloud retrieval algorithm was validated using data from Nimbus-7 Temperature Humidity Infrared Radiometer and Total Ozone Mapping Spectrometer to determine cloudiness parameters for the globe. Quantitative validation of total cloud amount was performed by comparing the algorithm results with estimates derived from GOES images and auxiliary meteorological data. The systematic errors of the Nimbus-7 total cloud-amount algorithm, relative to the GOES-derived estimates, were found to be less than 10 percent. The random errors of daily estimates ranged between 7 and 16 percent, day or night.

Stowe, L. L.↗
NASA NTRS1994

Observation of melt onset on multiyear Arctic sea ice using the ERS 1 synthetic aperture radar

We present nearly coincident observations of backscattering from the Earth Remote-Sensing Satellite (ERS) 1 synthetic aperture radar (SAR) and of near-surface temperature from six drifting buoys in the Beaufort Sea, showing that the onset of melting in snow on multiyear sea ice is clearly detectable in the SAR data. Melt onset is marked by a clean, steep decrease in the backscattering cross section of multiyear ice at 5.3 GHz and VV polarization. We investigate the scattering physics responsible for the signature change and find that the cross section decrease is due solely to the appearance of liquid water in the snow cover overlying the ice. A thin layer of moist snow is sufficient to cause the observed decrease. We present a prototype algorithm to estimate the date of melt onset using the ERS 1 SAR and apply the algorithm first to the SAR data for which we have corresponding buoy temperatures. The melt onset dates estimated by the SAR algorithm agree with those obtained independently from the temperature data to within 4 days or less, with the exception of one case in which temperatures oscillated about 0 C for several weeks. Lastly, we apply the algorithm to the entire ERS 1 SAR data record acquired by the Alaska SAR Facility for the Beaufort Sea north of 73 deg N during the spring of 1992, to produce a map of the dates of melt onset over an area roughly 1000 km on a side. The progression of melt onset is primarily poleward but shows a weak meridional dependence at latitudes of approximately 76 deg-77 deg N. Melting begins in the southern part of the study region on June 13 and by June 20 has progressed to the northermost part of the region.

Winebrenner, D. P.↗
NASA NTRS1999

Improved Stratospheric Temperature Retrievals for Climate Reanalysis

The Data Assimilation Office (DAO) is embarking on plans to generate a twenty year reanalysis data set of climatic atmospheric variables. One of the focus points will be in the evaluation of the dynamics of the stratosphere. The Stratospheric Sounding Unit (SSU), flown as part of the TIROS Operational Vertical Sounder (TOVS), is one of the primary stratospheric temperature sensors flown consistently throughout the reanalysis period. Seven unique sensors made the measurements over time, with individual instrument characteristics that need to be addressed. The stratospheric temperatures being assimilated across satellite platforms will profoundly impact the reanalysis dynamical fields. To attempt to quantify aspects of instrument and retrieval bias we are carefully collecting and analyzing all available information on the sensors, their instrument anomalies, forward model errors and retrieval biases. For the retrieval of stratospheric temperatures, we adapted the minimum variance approach of Jazwinski (1970) and Rodgers (1976) and applied it to the SSU soundings. In our algorithm, the state vector contains an initial guess of temperature from a model six hour forecast provided by the Goddard EOS Data Assimilation System (GEOS/DAS). This is combined with an a priori covariance matrix, a forward model parameterization, and specifications of instrument noise characteristics. A quasi-Newtonian iteration is used to obtain convergence of the retrieved state to the measurement vector. This algorithm also enables us to analyze and address the systematic errors associated with the unique characteristics of the cell pressures on the individual SSU instruments and the resolving power of the instruments to vertical gradients in the stratosphere. The preliminary results of the improved retrievals and their assimilation as well as baseline calculations of bias and rms error between the NESDIS operational product and col-located ground measurements will be presented.

Rokke, L.↗
DOE OSTI2024

A Thermo‐Flow‐Mechanics‐Fracture Model Coupling a Phase‐Field Interface Approach and Thermo‐Fluid‐Structure Interaction

This work proposes a novel approach for coupling non-isothermal fluid dynamics with fracture mechanics to capture thermal effects within fluid-filled fractures accurately. This method addresses critical aspects of calculating fracture width in enhanced geothermal systems, where the temperature effects of fractures are crucial. The proposed algorithm features an iterative coupling between an interface-capturing phase-field fracture method and interface-tracking thermo-fluid-structure interaction using arbitrary Lagrangian–Eulerian coordinates. We use a phase-field approach to represent fractures and reconstruct the geometry to frame a thermo-fluid-structure interaction problem, resulting in pressure and temperature fields that drive fracture propagation. We developed a novel phase-field interface model accounting for thermal effects, enabling the coupling of quantities specific to the fluid-filled fracture with the phase-field model through the interface between the fracture and the intact solid domain. We provide several numerical examples to demonstrate the capabilities of the proposed algorithm. In particular, we analyze mesh convergence of our phase-field interface model, investigate the effects of temperature on crack width and volume in a static regime, and highlight the method's potential for modeling slowly propagating fractures.

fracture↗
NASA NTRS1991

An evaluation of analog and numerical techniques for unsteady heat transfer measurement with thin-film gauges in transient facilities

The importance of frequency response considerations in the use of thin-film gages for unsteady heat transfer measurements in transient facilities is considered, and methods for evaluating it are proposed. A departure frequency response function is introduced and illustrated by an existing analog circuit. A Fresnel integral temperature which possesses the essential features of the film temperature in transient facilities is introduced and is used to evaluate two numerical algorithms. Finally, criteria are proposed for the use of finite-difference algorithms for the calculation of the unsteady heat flux from a sampled temperature signal.

George, William K.↗