SEARCH · Engineering Papers

Results for “temperature data”

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.

SourceResearch connectionFrom yearThrough year

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

At least 91 records · Page 5

All sources NASA NTRS DOE OSTI

DOE OSTI2012

Utility of Bromide and Heat Tracers for Aquifer Characterization Affected by Highly Transient Flow Conditions

A tracer test using both bromide and heat tracers conducted at the Integrated Field Research Challenge site in Hanford 300 Area (300A), Washington, provided an instrument for evaluating the utility of bromide and heat tracers for aquifer characterization. The bromide tracer data were critical to improving the calibration of the flow model complicated by the highly dynamic nature of the flow field. However, most bromide concentrations were obtained from fully screened observation wells, lacking depth-specific resolution for vertical characterization. On the other hand, depth-specific temperature data were relatively simple and inexpensive to acquire. However, temperature-driven fluid density effects influenced heat plume movement. Moreover, the temperature data contained “noise” caused by heating during fluid injection and sampling events. Using the hydraulic conductivity distribution obtained from the calibration of the bromide transport model, the temperature depth profiles and arrival times of temperature peaks simulated by the heat transport model were in reasonable agreement with observations. This suggested that heat can be used as a cost-effective proxy for solute tracers for calibration of the hydraulic conductivity distribution, especially in the vertical direction. However, a heat tracer test must be carefully designed and executed to minimize fluid density effects and sources of noise in temperature data. A sensitivity analysis also revealed that heat transport was most sensitive to hydraulic conductivity and porosity, less sensitive to thermal distribution factor, and least sensitive to thermal dispersion and heat conduction. This indicated that the hydraulic conductivity remains the primary calibration parameter for heat transport.

Ma, Rui↗

DOE OSTI2022

Low-Power, Flexible Sensor Arrays with Solderless Board-to-Board Connectors for Monitoring Soil Deformation and Temperature: Supporting Data

This dataset was used to assess the potential of a Soil Deformation and Temperature Monitoring System developed and presented in the article named "Low-Power, Flexible Sensor Arrays with Solderless Board-to-Board Connectors for Monitoring Soil Deformation and Temperature" and published in Sensors. There are 21 comma-delimited data files (.csv). 14 files contain current measurements performed in a lab setting, enabling the electrical evaluation of the sensor probe. These files are generated by the Keithley DMM6500 multimeter, and list the measured supply current (first column) as a function of time (3rd column). Another set of 6 files is also acquired in lab experiments, but contain soil temperature an deformation measurements, enabling an assessment of the developed device's accuracy. In these files, the first column contains the time (UTC), followed by the sensor's battery voltage and temperature and acceleration values (X, Y, Z) in subsequent columns. The measurements were acquired every 5 seconds. Another .csv file contains field data in a similar format (time, temperature, acceleration) collected at the Teller road (mile 27) site near Nome, Alaska with one probe. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Using Bayesian Inference Framework towards Identifying Gas Species and Concentration from High Temperature Resistive Sensor Array Data

High temperature gas sensors have been highly demanded for combustion process optimization and toxic emissions control, which usually suffer from poor selectivity. In order to solve this selectivity issue and identify unknown reducing gas species (CO, CH 4 , and CH 8 ) and concentrations, a high temperature resistive sensor array data set was built in this study based on 5 reported sensors. As each sensor showed specific responses towards different types of reducing gas with certain concentrations, based on which calibration curves were fitted, providing benchmark sensor array response database, then Bayesian inference framework was utilized to process the sensor array data and build a sample selection program to simultaneously identify gas species and concentration, by formulating proper likelihood between input measured sensor array response pattern of an unknown gas and each sampled sensor array response pattern in benchmark database. This algorithm shows good robustness which can accurately identify gas species and predict gas concentration with a small error of less than 10% based on limited amount of experiment data. These features indicate that Bayesian probabilistic approach is a simple and efficient way to process sensor array data, which can significantly reduce the required computational overhead and training data.

Liu, Yixin↗

DOE OSTI2019

Comparison of Mesoscale Model Setups for Offshore Wind Resource Assessment: A New Jersey Case Study

As the offshore wind energy industry begins to emerge on the U.S. northeast coast, there is an urgent need for an accurate characterization of the offshore wind resource. The offshore regime features various resource characteristics not seen onshore, including frequent coastal low-level jets, non-steady surface conditions (e.g. waves), complex air-sea and land-sea interactions, and coastal mesoscale circulations (e.g. sea breeze). An accurate characterization of these phenomena in mesoscale models is critical to support accurate energy production estimates and forecasting for future offshore wind farms. However, a robust validation of mesoscale model performance in the offshore U.S. has been limited due to sparse data and difficulty obtaining offshore measurements. Consequently, it is unclear how mesoscale models perform offshore and which suite of parameterizations and input data are best suited for accurate resource characterization. To begin addressing this knowledge gap, the National Renewable Energy Laboratory, in collaboration with the Rutgers Center for Ocean Observing Leadership, conducted a mesoscale model validation campaign in the offshore environment. The Weather Research and Forecasting (WRF) model was assessed and the New Jersey offshore coastal area served as the validation domain. An ensemble of WRF simulations conducted over a one-year period sampled from a range of model setups, including different reanalysis and forecast data inputs, sea surface temperature data inputs, planetary boundary layer parameterizations, and WRF versions. The performance of each ensemble member was validated against a network of observations including offshore floating lidars, coastal met towers, offshore platform measurements, and offshore buoys. Model performance was assessed on annual, seasonal, and diurnal scales with particular emphasis on the vertical wind speed profile and temperature stratification. Preliminary results from this validation work will be presented here. The key WRF inputs and parameters driving changes in the modeled wind resource will be discussed and preliminary recommendations on WRF setups for offshore wind resource modeling will be proposed. The application of this validation exercise to other proposed offshore wind energy areas in the U.S. will also be discussed. mesoscale circulations (e.g. sea breeze). An accurate characterization of these phenomena in mesoscale models is critical to support accurate energy production estimates and forecasting for future offshore wind farms. However, a robust validation of mesoscale model performance in the offshore U.S. has been limited due to sparse data and difficulty obtaining offshore measurements. Consequently, it is unclear how mesoscale models perform offshore and which suite of parameterizations and input data are best suited for accurate resource characterization. To begin addressing this knowledge gap, the National Renewable Energy Laboratory, in collaboration with the Rutgers Center for Ocean Observing Leadership, conducted a mesoscale model validation campaign in the offshore environment. The Weather Research and Forecasting (WRF) model was assessed and the New Jersey offshore coastal area served as the validation domain. An ensemble of WRF simulations conducted over a one-year period sampled from a range of model setups, including different reanalysis and forecast data inputs, sea surface temperature data inputs, planetary boundary layer parameterizations, and WRF versions. The performance of each ensemble member was validated against a network of observations including offshore floating lidars, coastal met towers, offshore platform measurements, and offshore buoys. Model performance was assessed on annual, seasonal, and diurnal scales with particular emphasis on the vertical wind speed profile and temperature stratification. Preliminary results from this validation work will be presented here. The key WRF inputs and parameters driving changes in the modeled wind resource will be discussed and preliminary recommendations on WRF setups for offshore wind resource modeling will be proposed. The application of this validation exercise to other proposed offshore wind energy areas in the U.S. will also be discussed.

17 WIND ENERGY↗

DOE OSTI2017

A 2500 deg 2 CMB Lensing Map from Combined South Pole Telescope and Planck Data

We present a cosmic microwave background (CMB) lensing map produced from a linear combination of South Pole Telescope (SPT) and \emph{Planck} temperature data. The 150 GHz temperature data from the $2500\ {\rm deg}^{2}$ SPT-SZ survey is combined with the \emph{Planck} 143 GHz data in harmonic space, to obtain a temperature map that has a broader $\ell$ coverage and less noise than either individual map. Using a quadratic estimator technique on this combined temperature map, we produce a map of the gravitational lensing potential projected along the line of sight. We measure the auto-spectrum of the lensing potential $C_{L}^{\phi\phi}$, and compare it to the theoretical prediction for a $\Lambda$CDM cosmology consistent with the \emph{Planck} 2015 data set, finding a best-fit amplitude of $0.95_{-0.06}^{+0.06}({\rm Stat.})\! _{-0.01}^{+0.01}({\rm Sys.})$. The null hypothesis of no lensing is rejected at a significance of $24\,\sigma$. One important use of such a lensing potential map is in cross-correlations with other dark matter tracers. We demonstrate this cross-correlation in practice by calculating the cross-spectrum, $C_{L}^{\phi G}$, between the SPT+\emph{Planck} lensing map and Wide-field Infrared Survey Explorer (\emph{WISE}) galaxies. We fit $C_{L}^{\phi G}$ to a power law of the form $p_{L}=a(L/L_{0})^{-b}$ with $a=2.15 \times 10^{-8}$, $b=1.35$, $L_{0}=490$, and find $\eta^{\phi G}=0.94^{+0.04}_{-0.04}$, which is marginally lower, but in good agreement with $\eta^{\phi G}=1.00^{+0.02}_{-0.01}$, the best-fit amplitude for the cross-correlation of \emph{Planck}-2015 CMB lensing and \emph{WISE} galaxies over $\sim67\%$ of the sky. The lensing potential map presented here will be used for cross-correlation studies with the Dark Energy Survey (DES), whose footprint nearly completely covers the SPT $2500\ {\rm deg}^2$ field.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Hemispherical variance anomaly and reionization optical depth

ABSTRACT Cosmic microwave background (CMB) full-sky temperature data show a hemispherical asymmetry in power nearly aligned with the Ecliptic, with the Northern hemisphere displaying an anomalously low variance, while the Southern hemisphere appears consistent with expectations from the best-fitting theory, Lambda Cold Dark Matter (ΛCDM). The low signal-to-noise ratio in current polarization data prevents a similar comparison. Polarization realizations constrained by temperature data show that in ΛCDM the lack of variance is not expected to be present in polarization data. Therefore, a natural way of testing whether the temperature result is a fluke is to measure the variance of CMB polarization components. In anticipation of future CMB experiments that will allow for high-precision large-scale polarization measurements, we study how the variance of polarization depends on ΛCDM-parameter uncertainties by forecasting polarization maps with Planck’s Markov chain Monte Carlo chains. We show that polarization variance is sensitive to present uncertainties in cosmological parameters, mainly due to current poor constraints on the reionization optical depth τ, which drives variance at low multipoles. We demonstrate how the improvement in the τ measurement seen between Planck’s two latest data releases results in a tighter constraint on polarization variance expectations. Finally, we consider even smaller uncertainties on τ and how more precise measurements of τ can drive the expectation for polarization variance in a hemisphere close to that of the cosmic-variance-limited distribution.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Contribution of urbanization to the increase of extreme heat events in an urban agglomeration in east China

Abstract The urban agglomeration of Yangtze River Delta (YRD) is emblematic of China's rapid urbanization during the past decades. Based on homogenized daily maximum and minimum temperature data, the contributions of urbanization to trends of summer extreme temperature indices (ETIs) in YRD are evaluated. Dynamically classifying the observational stations into urban and rural, this study presents unexplored changes in temperature extremes during the past four decades in YRD and quantifies the amplification of the positive trends in ETIs by the urban heat island effect. Overall, urbanization contributes to more than one third of the increase of intensity of extreme heat events in the region, which is comparable to the contribution of greenhouse gases. Compared to rural stations, more notable shifts to the right in the probability distribution of temperature and ETIs are found in urban stations. The rapid urbanization in YRD has resulted in large increases in the risk of heat extremes.

Yang, Xuchao↗

DOE OSTI2011

Accurate temperature model for absorptance determination of optical components with laser calorimetry

In the international standard (International Organization for Standardization 11551) for measuring the absorptance of optical components (i.e., laser calorimetry), the absorptance is obtained by fitting the temporal behavior of laser irradiation-induced temperature rise to a homogeneous temperature model in which the infinite thermal conductivity of the sample is assumed. In this paper, an accurate temperature model, in which both the finite thermal conductivity and size of the sample are taken into account, is developed to fit the experimental temperature data for a more precise determination of the absorptance. The difference and repeatability of the results fitted with the two theoretical models for the same experimental data are compared. The optimum detection position when the homogeneous model is employed in the data-fitting procedure is also analyzed with the accurate temperature model. The results show that the optimum detection location optimized for a wide thermal conductivity range of 0.2-50W/m{center_dot}K moves toward the center of the sample as the sample thickness increases and deviates from the center as the radius and irradiation time increase. However, if the detection position is optimized for an individual sample with known sample size and thermal conductivity by applying the accurate temperature model, the influence of the finite thermal conductivity and sample size on the absorptance determination can be fully compensated for by fitting the temperature data recorded at the optimum detection position to the homogeneous temperature model.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Sparse thermal data for cellular automata modeling of grain structure in additive manufacturing

Grain growth in the wake of the melt pool formed during alloy-based additive manufacturing (AM) is complex and multifaceted, depending on parameters governing heat transport, fluid flow, and solidification itself. Cellular automata (CA) models have proven effective in providing computationally efficient and physically sound predictions of grain structure for several AM problems, but their efficiency is tied to the performance of heat transport models. CA models use only a small portion of the problem's temperature data (near the moving melt pool boundary), and much of the CA calculations do not affect the final result due to re-melting of material. Coupling of and communication between heat transport and solidification models, and eliminating operations irrelevant towards final grain structure prediction, will be necessary for using these methods for efficient simulation of large parts. In this work, we introduce a procedure of decoupling the CA from temperature field simulation, using files of relevant temperature data written by the heat transport model. This approach is validated against the standard coupling approach using data obtained through the computational fluid dynamics software OpenFOAM. Negligible differences are seen in grain size, volume, and texture distributions for multilayer simulation of test problems, while the quantity of temperature data for these test problems was reduced by four orders of magnitude (from 100s of GB to 10s of MB) and the code performance sped up by a factor of around 50. Furthermore, variability in microstructure as a function of cell size, substrate, time step, and nucleation parameters is studied, and it is found that cell sizes less than or equal to 1.67 μm and sufficiently small time steps yield Αstatistically equivalent microstructures. Finally, a potential use case for this CA approach—the layer-wise convergence in grain structure starting from extremes in initial grain size—is examined. This approach's ability to simulate expected trends in nucleation and epitaxial grain growth for large regions of microstructure, simulated independently of heat transport models themselves, should prove useful for investigation of various microstructure uncertainties and prediction of part-scale experimental results.

36 MATERIALS SCIENCE↗

DOE OSTI2022

A numerical evaluation of the ambient air temperature in the Electron-Ion Collider tunnel

The Electron-Ion Collider (EIC) is a next-generation collider-accelerator that may require consistent operating temperature conditions for the beams within the accelerator tunnels to maintain stable operation. Variations in ambient temperature within the tunnel can cause thermal expansion of beampipe and component supports and can negatively affect the tunnel equipment, impacting the stability of the beamline. Modifications will be made to the Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Laboratory (BNL) to create the EIC, which necessitates a temperature model that addresses these modifications. To approach this problem, the consistency of temperature changes in different tunnel sections was first evaluated by plotting RHIC tunnel temperature data at various times of the day and year. From this data, a tunnel section was selected and a 2D temperature model was created for RHIC, EIC, and EIC with added cooling configurations. Soil temperature data was analyzed to determine the maximum, average, and mode soil temperatures, which were used as boundary conditions in different temperature scenarios. Computational fluid dynamics modeling was used to create 2D temperature profiles for the configurations. From this model, the predicted temperatures indicate that further analysis is required to validate the boundary conditions and benchmark the current conditions to allow the prediction of the tunnel ambient conditions at EIC. This research can be used as a preliminary model to create an EIC tunnel cooling system that will increase the operational stability of the EIC. As a result of my work this summer, I have become familiar with computational fluid dynamics, including creating fluid dynamic simulations using ANSYS Fluent and related software. I have also learned about the project process required for planning large-scale engineering projects.

43 PARTICLE ACCELERATORS↗

DOE OSTI2013

The topology and size of the universe from CMB temperature and polarization data

We analyze seven year and nine year WMAP temperature maps for signatures of three finite flat topologies M{sub 0} = T{sup 3}, M{sub 1} = T{sup 2} × R{sup 1}, and M{sub 2} = S{sup 1} × R{sup 2}. We use Monte-Carlo simulations with the Feldman-Cousins method to obtain confidence intervals for the size of the topologies considered. We analyze the V, W, and Q frequency bands along with the ILC map and find no significant difference in the results. The 95.5% confidence level lower bound on the size of the topology is 1.5L{sub 0} for M{sub 0}, 1.4L{sub 0} for M{sub 1}, and 1.1L{sub 0} for M{sub 2}, where L{sub 0} is the radius of the last scattering surface. Our results agree very well with the recently released results from the Planck temperature data. We show that the likelihood function is not Gaussian in the size, and therefore simulations are important for obtaining accurate bounds on the size. We then introduce the formalism for including polarization data in the analysis. The improvement that we find from WMAP polarization maps is small because of the high level of instrumental noise, but our forecast for Planck maps shows a much better improvement on the lower bound for L. For the M{sub 0} topology we expect an improvement on the lower bound of L from 1.7L{sub 0} to 1.9L{sub 0} at 95.5% confidence level. Using both polarization and temperature data is important because it tests the hypothesis that deviations in the TT spectrum at small l originate in the primordial perturbation spectrum.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Projecting the Thermal Response in a HTGR-Type System during Conduction Cooldown Using Graph-Laplacian Based Machine Learning

Accurate prediction of an off-normal event in a nuclear reactor is dependent upon the availability of sensory data, reactor core physical condition, and understanding of the underlying phenomenon. This work presents a method to project the data from some discrete sensory locations to the overall reactor domain during conduction cooldown scenarios similar to High Temperature Gas-cooled Reactors (HTGRs). The existing models for conductive cooldown in a heterogeneous multi-body system, such as an assembly of prismatic blocks or pebble beds relies on knowledge of the thermal contact conductance, requiring significant knowledge of local thermal contacts and heat transport possibilities across those contacts. With a priori knowledge of bulk geometry features and some discrete sensors, a machine learning approach was devised. The presented work uses an experimental facility to mimic conduction cooldown with an assembly of 68 cylindrical rods initially heated to 1200 K. High-fidelity temperature data were collected using an infrared (IR) camera to provide training data to the model and validate the predicted temperature data. The machine learning approach used here first converts the macroscopic bulk geometry information into Graph-Laplacian, and then uses the eigenvectors of the Graph-Laplacian to develop Kernel functions. Support vector regression (SVR) was implemented on the obtained Kernels and used to predict the thermal response in a packed rod assembly during a conduction cooldown experiment. The usage of SVR modeling differs from most models today because of its representation of thermal coupling between rods in the core. When trained with thermographic data, the average normalized error is less than 2% over 400 s, during which temperatures of the assembly have dropped by more than 500 K. The rod temperature prediction performance was significantly better for rods in the interior of the assembly compared to those near the exterior, likely due to the model simplification of the surroundings.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2018

Surface Temperature Mapping Models for STAR and CTF

This milestone presents a demonstration of a surface mapping model to map the surface temperature for single-phase STAR-CCM+ to Cobra-TF using average temperature data. This model can be used to generate high-resolution surface temperature data. This can be accomplished with linear equations or with an alternative non-linear model. Improvements and a path forward for the surface mapping model to be applied to two-phase temperature mappings is also laid out in this milestone report.

42 ENGINEERING↗

DOE OSTI2016

Quantifying impacts of heat waves on power grid operation

Climate change is projected to cause an increase in the severity and frequency of extreme weather events such as heat waves and droughts. Such changes present planning and operating challenges and risks to many economic sectors. In the electricity sector, statistics of extreme events in the past have been used to help plan for future peak loads, determine associated infrastructure requirements, and evaluate operational risks, but industry-standard planning tools have yet to be coupled with or informed by temperature models to explore the impacts of the "new normal" on planning studies. For example, high ambient temperatures during heat waves reduce the output capacity and efficiency of gas fired combustion turbines just when they are needed most to meet peak demands. This paper describes the development and application of a production cost and unit commitment model coupled to high resolution, hourly temperature data and a temperature dependent load model. The coupled system has the ability to represent the impacts of hourly temperatures on load conditions and available capacity and efficiency of combustion turbines, and therefore capture the potential impacts on system reliability and production cost. Ongoing work expands this capability to address the impacts of water availability and temperature on power grid operation.

Climate change↗

DOE OSTI2020

Ion beam irradiation of ABO4 compounds with the fergusonite, monazite, scheelite, and zircon structures

The effects of irradiation on CaWO4, SrWO4, BaWO4, YVO4, LaVO4, YNbO4, and LaNbO4 were investigated on thin crystals using 1.0 MeV Kr ions at 50-1000 K. All of the ABO4 compounds can be amorphized with calculated damage cross sections (σa = 1/Fc0) in the range of ~0.30-1.09 × 10-14 cm2 ion-1 at zero Kelvin. The analysis of fluence-temperature data returned critical temperatures for amorphization (Tc) of 311 ± 1, 358 ± 90, 325 ± 19, 415 ± 17, 541 ± 6, 636 ± 26, and 1012 ± 1 K, respectively, for the compounds listed above. Compared with previous in situ irradiation of ABO4 orthophosphate samples using 0.8 MeV Kr ions, the Tc values of LaVO4 and YVO4 are higher than those of LaPO4 and YPO4 by 82 K and 124 K, respectively. The Tc values of the three scheelite structures, CaWO4, SrWO4, and BaWO4, indicate that they are the most radiation tolerant compounds under these conditions. The A-B cation anti-site energies, EfAB, determined by DFT range from 2.48 to 10.58 eV and are highly correlated with the A-B cation ionic radius ratio, rA/rB, but are not correlated with Tc across the different structure types, suggesting that the formation and migration energies of Frenkel defects play a more important role in damage recovery in these compounds. We also discuss the role of cation and anion charge/iconicity as determined by DFT. ABO4 compounds with the zircon structure and B = P or V have a distinct advantage over those with B = Si as the damaged regions do not appear to be significantly affected by polymerization of (PO4) 3- or (VO4) 3- groups which might stabilize the amorphous fraction and ultimately lead to phase separation as observed in zircon (ZrSiO4).

amorphization↗

DOE OSTI2023

Method and apparatus for providing wear leveling

Exemplary embodiments provide wear spreading among die regions (i.e., one or more circuits) in an integrated circuit or among dies by using operating condition data in addition to or instead of environmental data such as temperature data, from each of a plurality of die regions. Control logic produces a cumulative amount of time each of the plurality of die regions has spent at an operating condition based on operating condition data wherein the operating condition data is based on at least one of the following operating characteristics: frequency of operation of the plurality of die regions, an operating voltage of the plurality of die regions, an activity level of the plurality of die regions, a timing margin of the plurality of die regions, and a number of detected faults of the plurality of die regions. The method and apparatus spreads wear among the plurality of same type of die regions by controlling task execution among the plurality of die regions using the die wear-out data.

Raasch, Steven↗

DOE OSTI2026

Report priority gaps in high temperature thermodynamic data (Interim Progress Report)

This interim progress report (Level 4 Milestone Number M4SF-26LL010203023) summarizes research conducted at Lawrence Livermore National Laboratory (LLNL) within the Argillite Host Rock Properties & Processes SF-26LL01020302. Our focus is to assess gaps in data availability and understanding for radionuclide thermodynamics within the context of a “hot repository” concept and expand SUPCRT-NE database development to address higher temperatures needed for a DPC DGR disposal concept. The database is intended to inform the argillite GDSA baseline model. The leading European thermochemical database (Thermochimie) is only applicable to temperatures below 80°C. Thus, a US effort to integrate and expand upon other international thermodynamics database efforts is needed, particular if a “hot repository” concept moves forward.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2013

Geologic Map and Cross Sections of the McGinness Hills Geothermal Area - GIS Data

Geologic map data in shapefile format that includes faults, unit contacts, unit polygons, attitudes of strata and faults, and surficial geothermal features. 5 cross-sections in Adobe Illustrator format. Comprehensive catalogue of drill-hole data in spreadsheet, shapefile, and Geosoft database formats. Includes XYZ locations of well heads, year drilled, type of well, operator, total depths, well path data (deviations), lithology logs, and temperature data. 3D model constructed with EarthVision using geologic map data, cross-sections, drill-hole data, and geophysics.

15 GEOTHERMAL ENERGY↗