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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 OSTI2014

Utilization of the St. Peter Sandstone in the Illinois Basin for CO2 Sequestration

This project is part of a larger project co-funded by the United States Department of Energy (US DOE) under cooperative agreement DE-FE0002068 from 12/08/2009 through 9/31/2014. The study is to evaluate the potential of formations within the Cambro-Ordovician strata above the Mt. Simon Sandstone as potential targets for carbon dioxide (CO2) sequestration in the Illinois and Michigan Basins. This report evaluates the potential injectivity of the Ordovician St. Peter Sandstone. The evaluation of this formation was accomplished using wireline data, core data, pressure data, and seismic data acquired through funding in this project as well as existing data from two additional, separately funded projects: the US DOE funded Illinois Basin – Decatur Project (IBDP) being conducted by the Midwest Geological Sequestration Consortium (MGSC) in Macon County, Illinois, and the Illinois Industrial Carbon Capture and Sequestration (ICCS) Project funded through the American Recovery and Reinvestment Act (ARRA), which received a phase two award from DOE. This study addresses the question of whether or not the St. Peter Sandstone may serve as a suitable target for CO2 sequestration at locations within the Illinois Basin where it lies at greater depths (below the underground source of drinking water (USDW)) than at the IBDP site. The work performed included numerous improvements to the existing St. Peter reservoir model created in 2010. Model size and spatial resolution were increased resulting in a 3 fold increase in the number of model cells. Seismic data was utilized to inform spatial porosity distribution and an extensive core database was used to develop porosity-permeability relationships. The analysis involved a Base Model representative of the St. Peter at “in-situ” conditions, followed by the creation of two hypothetical models at in-situ + 1,000 feet (ft.) (300 m) and in-situ + 2,000 ft. (600 m) depths through systematic depthdependent adjustment of the Base Model parameters. Properties for the depth shifted models were based on porosity versus depth relationship extracted from the core database followed by application of the porosity-permeability relationship. Each of the three resulting models were used as input to dynamic simulations with the single well injection target of 3.2 million tons per annum (MTPA) for 30 years using an appropriate fracture gradient based bottom hole pressure limit for each injection level. Modeling results are presented in terms of well bottomhole pressure (BHP), injection rate profiles, and three-dimensional (3D) saturation and differential pressure volumes at selected simulation times. Results suggest that the target CO2 injection rate of 3.2 MTPA may be achieved in the St. Peter Sandstone at in-situ conditions and at the in-situ +1,000 ft. (300 m) depth using a single injector well. In the latter case the target injection rate is achieved after a ramp up period which is caused by multi-phase flow effects and thus subject to increased modeling uncertainty. Results confirm that the target rate may not be achieved at the in-situ +2,000 ft. (600 m) level even with multiple wells. These new modeling results for the in-situ case are more optimistic than previous modeling results. This difference is attributed to the difference in methods and data used to develop model permeability distributions. Recommendations for further work include restriction of modeling activity to the in-situ +1,000 ft. (300 m) and shallower depth interval, sensitivity and uncertainty analysis, and refinement of porosity and permeability estimates through depth and area selective querying of the available core database. It is also suggested that further modeling efforts include scope for evaluating project performance in terms of metrics directly related to the Environmental Protection Agency (EPA) Class VI permit requirements for the area of review (AoR) definition and post injection site closure monitoring.

Will, Robert↗
DOE OSTI2025

Constrained or unconstrained? Neural-network-based equation discovery from data

Throughout many fields, practitioners often rely on differential equations to model systems. Yet, for many applications, the theoretical derivation of such equations and/or the accurate resolution of their solutions may be intractable. Instead, recently developed methods, including those based on parameter estimation, operator subset selection, and neural networks, allow for the data-driven discovery of both ordinary and partial differential equations (PDEs), on a spectrum of interpretability. The success of these strategies is often contingent upon the correct identification of representative equations from noisy observations of state variables and, as importantly and intertwined with that, the mathematical strategies utilized to enforce those equations. Specifically, the latter has been commonly addressed via unconstrained optimization strategies. Representing the PDE as a neural network, we propose to discover the PDE (or the associated operator) by solving a constrained optimization problem and using an intermediate state representation similar to a physics-informed neural network (PINN). The objective function of this constrained optimization problem promotes matching the data, while the constraints require that the discovered PDE is satisfied at a number of spatial collocation points. We present a penalty method and a widely used trust-region barrier method to solve this constrained optimization problem, and we compare these methods on numerical examples. Our results on several example problems demonstrate that the latter constrained method outperforms the penalty method, particularly for higher noise levels or fewer collocation points. This work motivates further exploration into using sophisticated constrained optimization methods in scientific machine learning, as opposed to their commonly used, penalty-method or unconstrained counterparts. For both of these methods, we solve these discovered neural network PDEs with classical methods, such as finite difference methods, as opposed to PINNs-type methods relying on automatic differentiation. Here, we briefly highlight how simultaneously fitting the data while discovering the PDE improves the robustness to noise and other small, yet crucial, implementation details.

Data-driven discovery↗
DOE OSTI2026

Hierarchical semi-Markov models with duration-aware dynamics for activity sequences

Residential electricity demand at granular scales is driven by what people do and for how long. Accurately forecasting this demand for applications like microgrid management and demand response therefore requires generative models for activities that can produce realistic daily activity sequences, capturing both the timing and duration of human behavior. This paper develops a generative model of human activity sequences using nationally representative time-use diaries at a 10-min resolution. We use this model to quantify which demographic factors are most critical for improving predictive performance. We propose a hierarchical semi-Markov framework that addresses two key modeling challenges. First, a time-inhomogeneous Markov router learns the patterns of “which activity comes next.” Second, a semi-Markov hazard component explicitly models activity durations, capturing “how long” activities realistically last. To ensure statistical stability when data are sparse, the model pools information across related demographic groups and time blocks. The entire framework is trained and evaluated using survey design weights to ensure our findings are representative of the U.S. population. On a held-out test set, we demonstrate that explicitly modeling durations with the hazard component provides a substantial and statistically significant improvement over purely Markovian models. Furthermore, our analysis reveals a clear hierarchy of demographic factors: Sex, Day-Type, and Household Size provide the largest predictive gains, while Region and Season, though important for energy calculations, contribute little to predicting the activity sequence itself. The result is an interpretable and robust generator of synthetic activity traces, providing a high-fidelity foundation for downstream energy systems modeling.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

The Potosi Reservoir Model 2013

As a part of a larger project co-funded by the United States Department of Energy (US DOE) to evaluate the potential of formations within the Cambro-Ordovician strata above the Mt. Simon as potential targets for carbon sequestration in the Illinois and Michigan Basins, the Illinois Clean Coal Institute (ICCI) requested Schlumberger to evaluate the potential injectivity and carbon dioxide (CO2) plume size of the Cambrian Potosi Formation. The evaluation of this formation was accomplished using wireline data, core data, pressure data, and seismic data from the US DOE-funded Illinois Basin–Decatur Project (IBDP) being conducted by the Midwest Geological Sequestration Consortium in Macon County, Illinois. In 2010, technical performance evaluations on the Cambrian Potosi Formation were performed through reservoir modeling. The data included formation tops from mud logs, well logs from the VW1 and the CCS1 wells, structural and stratigraphic formation from three dimensional (3D) seismic data, and field data from several waste water injection wells for Potosi Formation. Intention was for two million tons per annum (MTPA) of CO2 to be injected for 20 years. In the preceding, the 2010 Potosi heterogeneous model (referred to as the "Potosi Dynamic Model 2010" in this topical report) was re-run using a new injection scenario; 3.2 MTPA for 30 years. The extent of the Potosi Dynamic Model 2010, however, appeared too small for the new injection target. It was not sufficiently large enough to accommodate the evolution of the plume. The new model, Potosi Dynamic Model 2013a, was built by extending the Potosi Dynamic Model 2010 grid to 30 miles x 30 miles (48.3km x48.3km), while preserving all property modeling workflows and layering. This model was retained as the base case of Potosi Dynamic Model 2013a. The Potosi reservoir model was updated to take into account the new data from the verification well VW2 which was drilled in 2012. The new porosity and permeability modeling was performed to take into account the log data from the new well. Revisions of the 2010 modeling assumptions were also done on relative permeability, capillary pressures, formation water salinity, and the maximum allowable well bottomhole pressure. Dynamic simulations were run using the injection target of 3.2 MTPA for 30 years. This new dynamic model was named Potosi Dynamic Model 2013b. Due to the major uncertainties on the vugs permeability, two models were built; the Pessimistic and Optimistic Cases. The Optimistic Case assumes vugs permeability of 9,000 mD, which is analog to the vugs permeability identified in the pressure fall off test of a waste water injector in the Tuscola site, approx. 40 miles (64.4km) away from the IBDP area. The Pessimistic Case assumes that the vugs permeability is equal to the log data, which does not take into account the permeability from secondary porosity. The probability of such case is deemed low and could be treated as the worst case scenario, since the contribution of secondary porosity to the permeability is neglected and the loss circulation events might correspond to a much higher permeability. It is considered important, however, to identify the range of possible reservoir performance since there are no rigorous data available for the vugs permeability. The Optimistic Case gives an average CO2 injection rate of 0.8 MTPA and cumulative injection of 26 MT in 30 years, which corresponds to 27% of the injection target. The injection rate is approx. 3.2 MTPA in the first year as the well is injecting into the surrounding vugs, and declines rapidly to 0.8 MTPA in year 4 once the surrounding vugs are full and the CO2 start to reach the matrix. This implies that according to this preliminary model, a minimum of four (4) wells could be required to achieve the injection target. This result is lower than the injectivity estimated in the Potosi Dynamic Model 2013a (43 MT in 30 years), since the permeability model applied in the Potosi Dynamic Model 2013b is more conservative. This revision was deemed necessary to treat the uncertainty in a more appropriate manner. As the CO2 follows the paths where vugs interconnection exists, a reasonably large and irregular plume extent was created. For the Optimistic Case, the plume extends 17 miles (27.4km) in E-W and 14 miles (22.5km) in N-S directions after 30 years. After injection is completed, the plume continues to migrate laterally, mainly driven by the remaining pressure gradient. After 100 years post injection, the plume extends 20 miles (32.2km) in E-W and 15.5 miles (24.9km) in N-S directions. Should the targeted cumulative injection of 96 MT be achieved; a much larger plume extent could be expected. For the Optimistic Case, the increase of reservoir pressure at the end of injection is approximately 1200 psia (8,274 kPa) around the injector and gradually decreases away from the well. The reservoir pressure increase is less than 30 psia (206.8 kPa) beyond 14 miles (22.5km) away from injector. Should the targeted cumulative injection of 96 MT be achieved; a much larger areal pressure increase could be expected. The initial reservoir pressure is nearly restored after approximately 100 years post injection. The presence of matrix slows down the pressure dissipations. The Pessimistic Case gives an average CO2 injection rate of 0.2 MTPA and cumulative injection of 7 MT in 30 years, which corresponds to 7% of the injection target. This implies that in the worst case scenario, a minimum of sixteen (16) wells could be required to achieve the injection target. The present evaluation is mainly associated with uncertainty on the vugs permeability, distribution, and interconnectivity. The different results indicated by the Optimistic and Pessimistic Cases signify the importance of vugs permeability characterization. Therefore, injection test and pressure interference test among the wells could be considered to evaluate the local vugs permeability, extent, and interconnectivity. Porosity mapping derived from the seismic inversion could also be used in the succeeding task to characterize the lateral porosity distribution within the reservoir. With or without seismic inversion porosity mapping, it is worth exploring whether increased lateral heterogeneity plays a significant role in Potosi injectivity. Investigations on vugular, dolomitic outcrops suggest that there may be significantly greater lateral heterogeneity than what has been modeled here. Facies modeling within the Potosi has yet to be thoroughly addressed. The carbonates during the time of deposition are believed to be regionally extensive. However, it may be worth delineating the reservoir with other regional wells or modern day analogues to understand the extent of the Potosi. More specifically, the model could incorporate lateral changes or trends if deemed necessary to represent facies transition. Data acquisitions to characterize the fracture pressure gradient, the formation water properties, the relative permeability, and the capillary pressure could also be considered in order to allow a more rigorous evaluation of the Potosi storage performance. A simulation using several injectors could also be considered to determine the required number of wells to achieve the injection target while taking into account the pressure interference.

Adushita, Yasmin↗
DOE OSTI2014

Development of Automated Software Program for the Analysis of Alzheimer's Disease Scans: Evaluation of PET imaging with PBR-111 in HV and AD subjects

Study goal: A Phase 1 evaluation of the kinetics, clearance and cerebral distribution of one novel peripheral benzodiazepine receptors(PBR)positron emission tomography (PET) imaging agent, 18F-PBR-111 following intravenous administration in healthy volunteers and Alzheimer's disease (AD) patients. Short title: Evaluation of PET imaging with PBR-111 in HV and AD subjects Proof of Mechanism. Primary Objective: To evaluate the cerebral distribution of PBR-111 positron emission tomography (PET) for detection/exclusion of microglial activation in patients with Alzheimer's disease subjects compared to healthy volunteers. Secondary objectives: - To assess the dynamic uptake and washout of [18F]PBR-111, a potential imaging bio-marker for inflammatory changes in brain, using positron emission tomography in subjects with Alzheimer's disease (AD) and healthy volunteers (HV). - To perform blood metabolite characterization of [18F]PBR-111 in subjects with AD and HV to determine the nature of metabolites in assessment of [18F]-PBR-111 as a PET brain imaging agent. Name of radioactive drug substance: PBR-111 Dose(s): The applied PBR-111 radioactive dose will be up to 5.0 mCi, diluted in a maximum of 10 ml of saline. The radioligand will be administered as a slow intravenous bolus injection (i.e., 6 sec/ml) into a large vein (e.g., antecubital vein). Route of administration: Intravenous injection Duration of treatment: Single administration of a diagnostic agent Indication: PBR-111 positron emission tomography (PET) imaging has the potential to detect microglial activation. In the presence of PBR-111 uptake (representative of microglial activation), inflammation in the brain can be detected. Diagnosis and main criteria for inclusion: Study participants will be HVs and patients diagnosed with probable AD. HVs must be 18 years of age (at least four subjects 50 years of age) and have no evidence of cognitive impairment or other neurologic disease by medical history. The lack of cognitive impairment will also be based on a Clinical Dementia Rating (CDR) of 0. Patients with probable AD must be 50 years of age and must fulfill the National Institute of Neurological and Communicative Disorders and Stroke, Alzheimer's Disease and Related Disorders Association [NINCDS-ADRDA] criteria for probable AD. The CDR score must be 1.0 and 2.0 and have a modified Hachinski of 4. All HVs and all patients with probable AD must be able to comply with all study procedures. Study design: This is a Phase 1, open-label, single-center, non-randomized single dose study to assess the kinetics, clearance and cerebral distribution of PBR-111 PET imaging in detecting microglial activation in the brain in patients with probable AD compared to HVs. All aspects related to image acquisition, processing, and visual as well as quantitative evaluation will be developed, optimized, and validated (where required). Each subject will be required to visit the study center during the screening phase and on the PBR-111 PET imaging day (baseline). A telephone follow-up visit will be performed 7 days (± 3 days) after PBR-111 PET administration. At the screening visit, each subject (or caregiver in the case of AD subjects) will be asked to provide written informed consent or assent. During the screening phase (maximum duration of 60 days) subject medical, neurological, and surgical history, clinical assessments, and a neuro-psychiatric evaluation will be performed on all eligible subjects. Subjects will be allowed to leave the center after all evaluations have been completed. During this period an MRI of the brain will be performed during the screening period. If an MRI of the brain has been performed within six months of the imaging visit using the methods described in the protocol, and there has been no medically significant events in the interim, the previous MRI may be used. During the PBR-111 PET imaging day, all subjects will receive a single intravenous injection of PBR-111 and scanning will be performed over a 3.5 hour period. Each subject will have a telephone follow-up 7 days (± 3 days) thereafter to assess for adverse events. Methodology: - Assessments to provide clinical characterization of the AD subjects will be performed. - After administration of PBR-111, images will be generated with state-of-the-art PET imaging. Images will be assessed quantitatively for the presence of microglial activation by a nuclear physician blinded to clinical data. - Total radioactivity and estimation of the fraction of radioactivity associated to the un-metabolized tracer will be determined. In addition, the metabolite patterns of PBR-111 are determined in venous plasma and arterial samples based on high-performance liquid chromatography (HPLC) analyses. - Arterial sampling will be acquired in the initial two AD and two HV subjects and modeling will be assessed to determine if additional arterial sampling is necessary.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Scaling Input Distributions for Probabilistic Models - 19472

It is important to address scaling issues when building models to support risk-based decisions for radioactive waste disposal sites. Currently, probabilistic risk assessment and probabilistic performance assessment models are run for multiple realizations where each realization uses one value from each input distribution over the entire modeled time. The input distributions represent the current state of knowledge in the mean of the input variable over the spatial region of interest and over the time period of the model run. This paper focuses on the time factor in input distribution scaling because it presents more serious conceptual and distributional challenges than the spatial factor. One of the challenges in temporal scaling is the effect of non-linearity on propagation of distribution uncertainty through to uncertainty in results. Radioactive waste disposal models tend to have both linear and non-linear components in their models. Dose assessment calculations are typically represented by linear models, while contaminant transport models have linear and non-linear components. In principle, temporal scaling of input parameter distributions for a linear model is straightforward, but scaling of input distributions for non-linear models is not. This paper uses examples to investigate the effects of input distribution temporal scaling on simple linear and non-linear models. Different scaling scenarios were investigated by comparing three analysis tools: analytical mathematical representations, statistical software programming, and GoldSim Monte Carlo simulation software. R statistical software is an open source statistical programming language often used for statistical analyses and graphical representation of data. GoldSim is a robust probabilistic dynamic modeling platform used in performance assessment modeling of radioactive waste sites. A simple linear model, a quadratic model and a multiplicative model were the cases explored for each of these tools. For the three analysis tools considered and with each of the scaling cases, 1,000 realizations were implemented from distributions of the random variables of interest. This is a sufficient number of realizations for simple models to capture the model space. An annual temporal resolution of a 100-year simulation period was chosen for all models and analysis tools. The effects of temporal scaling were demonstrated by investigating two different options for time scaling. Option 1 was set up so that the input distributions represented the expected value on an annual time scale, and a new value from that distribution was chosen every year (at every time step which equaled a year) for 100 years. In Option 2, the input distributions were scaled to represent the expected mean value of the input variable over the 100 years of simulation, and one value from that distribution was chosen for the entire elapsed time. Results show agreement across the three analysis tools. As expected, the linear models have identical results for the two different scaling Options, and the non-linear models diverge in the results for different time scale implementation. The implications of this work point to the need for careful consideration of input distribution temporal scaling. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

A Public-Private-Acadmic Partnership to Advance Solar Power Forecasting

The National Center for Atmospheric Research (NCAR) is pleased to have led a partnership to advance the state-of-the-science of solar power forecasting by designing, developing, building, deploying, testing, and assessing the SunCast™ Solar Power Forecasting System. The project has included cutting edge research, testing in several geographically- and climatologically-diverse high penetration solar utilities and Independent System Operators (ISOs), and wide dissemination of the research results to raise the bar on solar power forecasting technology. The partners include three other national laboratories, six universities, and industry partners. This public-private-academic team has worked in concert to perform use-inspired research to advance solar power forecasting through cutting-edge research to advance both the necessary forecasting technologies and the metrics for evaluating them. The project has culminated in a year-long, full-scale demonstration of provide irradiance and power forecasts to utilities and ISOs to use in their operations. The project focused on providing elements of a value chain, beginning with the weather that causes a deviation from clear sky irradiance and progresses through monitoring and observations, modeling, forecasting, dissemination and communication of the forecasts, interpretation of the forecast, and through decision-making, which produces outcomes that have an economic value. The system has been evaluated using metrics developed specifically for this project, which has provided rich information on model and system performance. Research was accomplished on the very short range (0-6 hours) Nowcasting system as well as on the longer term (6-72 hour) forecasting system. The shortest range forecasts are based on observations in the field. The shortest range system, built by Brookhaven National Laboratory (BNL) and based on Total Sky Imagers (TSIs) is TSICast, which operates on the shortest time scale with a latency of only a few minutes and forecasts that currently go out to about 15 min. This project has facilitated research in improving the hardware and software so that the new high definition cameras deployed at multiple nearby locations allow discernment of the clouds at varying levels and advection according to the winds observed at those levels. Improvements over “smart persistence” are about 29% for even these very short forecasts. StatCast is based on pyranometer data measured at the site as well as concurrent meteorological observations and forecasts. StatCast is based on regime-dependent artificial intelligence forecasting techniques and has been shown to improve on “smart persistence” forecasts by 15-50%. A second category of short-range forecasting systems employ satellite imagery and use that information to discern clouds and their motion, allowing them to project the clouds, and the resulting blockage of irradiance, in time. CIRACast (the system produced by the Cooperative Institute for Atmospheric Research [CIRA] at Colorado State University) was already one of the more advanced cloud motion systems, which is the reason that team was brought to this project. During the project timeframe, the CIRA team was able to advance cloud shadowing, parallax removal, and implementation of better advecting winds at different altitudes. CIRACast shows generally a 25-40% improvement over Smart Persistence between sunrise and approximately 1600 UTC (Coordinated Universal Time) . A second satellite-based system, MADCast (Multi-sensor Advective Diffusive foreCast system), assimilates data from multiple satellite imagers and profilers to assimilate a fully three-dimensional picture of the cloud into the dynamic core of WRF. During 2015, MADCast (provided at least 70% improvement over Smart Persistence, with most of that skill being derived during partly cloudy conditions. That allows advection of the clouds via the Weather Research and Forecasting (WRF) model dynamics directly. After WRF-Solar™ showed initial success, it was also deployed in nowcasting mode with coarser runs out to 6 hours made hourly. It provided improvements on the order of 50-60% over Smart Persistence for forecasts up to 1600 UTC. The advantages of WRF-Solar-Nowcasting and MADCast were then blended to develop the new MAD-WRF model that incorporates the most important features of each of those models, both assimilating satellite cloud fields and using WRF-So far physics to develop and dissipate clouds. MAE improvements for MAD-WRF for forecasts from 3-6 hours are improved over WRF-Solar-Now by 20%. While all the Nowcasting system components by themselves provide improvement over Smart Persistence, the largest benefit is derived when they are smartly blended together by the Nowcasting Integrator to produce an integrated forecast. The development of WRF-Solar™ under this project has provided the first numerical weather prediction (NWP) model specifically designed to meet the needs of irradiance forecasting. The first augmentation improved the solar tracking algorithm to account for deviations associated with the eccentricity of the Earth’s orbit and the obliquity of the Earth. Second, WRF-Solar™ added the direct normal irradiance (DNI) and diffuse (DIF) components from the radiation parameterization to the model output. Third, efficient parameterizations were implemented to either interpolate the irradiance in between calls to the expensive radiative transfer parameterization, or to use a fast radiative transfer code that avoids computing three-dimensional heating rates but provides the surface irradiance. Fourth, a new parameterization was developed to improve the representation of absorption and scattering of radiation by aerosols (aerosol direct effect). A fifth advance is that the aerosols now interact with the cloud microphysics, altering the cloud evolution and radiative properties, an effect that has been traditionally only implemented in atmospheric computationally costly chemistry models. A sixth development accounts for the feedbacks that sub-grid scale clouds produce in shortwave irradiance as implemented in a shallow cumulus parameterization Finally, WRF-Solar™ also allows assimilation of infrared irradiances from satellites to determine the three dimensional cloud field, allowing for an improved initialization of the cloud field that increases the performance of short-range forecasts. We find that WRF-Solar™ can improve clear sky irradiance prediction by 15-80% over a standard version of WRF, depending on location and cloud conditions. In a formal comparison to the NAM baseline, WRF-Solar™ showed improvements in the Day-Ahead forecast of 22-42%. The SunCast™ system requires substantial software engineering to blend all of the new model components as well as existing publically available NWP model runs. To do this we use an expert system for the Nowcasting blender and the Dynamic Integrated foreCast (DICast®) system for the NWP models. These two systems are then blended, we use an empirical power conversion method to convert the irradiance predictions to power, then apply an analog ensemble (AnEn) approach to further tune the forecast as well as to estimate its uncertainty. The AnEn module decreased RMSE (root mean squared error) by 17% over the blended SunCast™ power forecasts and provided skill in the probabilistic forecast with a Brier Skill Score of 0.55. In addition, we have also developed a Gridded Atmospheric Forecast System (GRAFS) in parallel, leveraging cost share funds. An economic evaluation based on Production Cost Modeling in the Public Service Company of Colorado showed that the observed 50% improvement in forecast accuracy will save their customers $819,200 with the projected MW deployment for 2024. Using econometrics, NCAR has scaled this savings to a national level and shown that an annual expected savings for this 50% forecast error reduction ranges from $11M in 2015 to $43M expected in 2040 with increased solar deployment. This amounts to a $455M discounted savings over the 26 year period of analysis.

14 SOLAR ENERGY↗
DOE OSTI2024

Innovating the next generation of commercial smart building software

Nearly 30% of commercial building energy use is wasted due to equipment faults and HVAC controls problems. The result is increased emissions, compromised comfort and productivity, and less reliable coordination of building power needs with a clean grid. The energy impact alone represents $17 billion in potential savings. Today’s smart building software provides a robust solution to address these operational deficiencies. Energy management and information systems (EMIS) are saving up to 9% on average, with two-year paybacks. They are being incorporated into energy management processes, commissioning services, and utility programs. As effective as they are, two barriers prevent even deeper benefits; limited personnel to fix problems once they are identified, and the expense and time to manually implement changes in control systems. In partnership with the research community, the EMIS industry is developing new capabilities to overcome these barriers. Moving beyond siloed products for either fault detection and diagnostics, or optimal control, these new capabilities empower users to not only automatically identify faults, but also to push corrective action, and control improvements to their buildings. In this paper, several areas for enhancements are documented: ‘one-time’ correction of faults such as setpoints, schedules, and economizer lockouts; short-term active testing for automated proportional integral derivative (PID) loop tuning and functional testing; and continuous supervisory control for demand flexibility and year-round efficiency. Results are presented from a pair of partner implementations out of a dozen providers integrating these enhancements into their products, including field tests from across the country, and insights into operator acceptance and integration into operations and maintenance practices.

Casillas, Armando↗
DOE OSTI2025

Design Basis Document / Owner’s Technical Specification for Nitrate Salt Systems in CSP Projects (Final Technical Report)

The number of commercial coal, gas, and nuclear projects built over the past 100 years number in the thousands. As such, there is a large database, available to a wide range of commercial engineering contractors, on proven designs. In essence, unsuccessful designs have, through generations of iterations, been identified and then deleted from further consideration. In contrast, the number of commercial parabolic trough projects using nitrate salt for the thermal storage media is perhaps 60. Further, the number of commercial central receiver projects using nitrate salt as the working fluid is on the order of 20, including estimates for China. Given the relative immaturity of salt technology, and commercial pressures to successfully bid new solar projects into a mature electricity market, solar projects often promise more than has been delivered. The Design Basis Document / Owner’s Technical Specification is a first step in the iteration process. The report describes the successful features of commercial projects, outlines a range equipment and system failures in projects that didn’t operate as intended, and provides a draft set of design changes intended to correct the known problems. The product of the study is 3 volumes of technical material; one volume is on parabolic trough technologies; a second is on central receiver technologies, and the third is on potential design changes to parabolic trough and central receiver projects. The 3 volumes, which total some 590 pages, can be found at https://www.solardynllc.com/csp-plant-technologies. One of the principal topics in the report is the use of functional or prescriptive specifications. Functional specifications describe what the equipment needs to do, consistent with the minimum legal requirements of the local jurisdictions. The details of how this is to be accomplished is developed by the engineering contractor. Prescriptive specifications, which are developed by the Owner, prescribe to the engineering contractor how the functional requirements are to be met. This arrangement ensures that the favorable experience from a previous project is repeated. One example is the design code for the hot salt tank in central receiver projects. The closest design basis is API Standard 650 Welded Steel Tanks for Oil Storage. However, the maximum design temperature in API 650 is 260 °C. As such, solar projects have typically adopted a hybrid Code approach, in which allowable material stresses are taken from ASME Section II Materials. Further, since the tanks experience daily changes in temperature and in (static) pressure, and since portions of the tank can operate at stresses beyond the elastic range, the low cycle fatigue life of the tank is conducted using the rules of Section VIII Division 2. However, in a recent study by NREL, the principal damage mechanism was identified as creep rather than fatigue. Further, design stresses permitted under Section VIII Division 2, corresponding to a fatigue life of 30 years, result in projected creep lifetimes of only 2 to 5 years. An alternate design approach, prescribed by the Owner, would be based on Code sections intended for high temperature service in the creep regime. A candidate is Section III Division 5. Granted, this is a nuclear code section, and it’s use would not likely be mandated by local jurisdictions. However, the effects of creep have been deemed to be of sufficient importance that one nuclear project developer, and one central receiver project developer, have stipulated in the tank design specification that the equipment be designed to the requirements of Section III Division 5.

14 SOLAR ENERGY↗
DOE OSTI2020

Physics and technology considerations for the deuterium–tritium fuel cycle and conditions for tritium fuel self sufficiency

The tritium aspects of the DT fuel cycle embody some of the most challenging feasibility and attractiveness issues in the development of fusion systems. The review and analyses in this paper provide important information to understand and quantify these challenges and to define the phase space of plasma physics and fusion technology parameters and features that must guide a serious R&D in the world fusion program. We focus in particular on components, issues and R&D necessary to satisfy three 'principal requirements': (1) achieving tritium self-sufficiency within the fusion system, (2) providing a tritium inventory for the initial start-up of a fusion facility, and (3) managing the safety and biological hazards of tritium. A primary conclusion is that the physics and technology state-of-the-art will not enable DEMO and future power plants to satisfy these principal requirements. We quantify goals and define specific areas and ideas for physics and technology R&D to meet these requirements. A powerful fuel cycle dynamics model was developed to calculate time-dependent tritium inventories and flow rates in all parts and components of the fuel cycle for different ranges of parameters and physics and technology conditions. Dynamics modeling analyses show that the key parameters affecting tritium inventories, tritium start-up inventory, and tritium self-sufficiency are the tritium burn fraction in the plasma (f b ), fueling efficiency (η f ), processing time of plasma exhaust in the inner fuel cycle (t p ), reactor availability factor (AF), reserve time (tr) which determines the reserve tritium inventory needed in the storage system in order to keep the plant operational for time t r in case of any malfunction of any part of the tritium processing system, and the doubling time (t d ). Results show that η f f b > 2% and processing time of 1–4 h are required to achieve tritium self-sufficiency with reasonable confidence. For η f f b = 2% and processing time of 4 h, the tritium start-up inventory required for a 3 GW fusion reactor is ~11 kg, while it is <5 kg if η f f b = 5% and the processing time is 1 h. To achieve these stringent requirements, a serious R&D program in physics and technology is necessary. The EU-DEMO direct internal recycling concept that carries fuel directly from the plasma exhaust gas to the fueling systems without going through the isotope separation system reduces the overall processing time and tritium inventories and has positive effects on the required tritium breeding ratio (TBR R ). A significant finding is the strong dependence of tritium self-sufficiency on the reactor availability factor. Simulations show that tritium self-sufficiency is: impossible if AF < 10% for any η f f b , possible if AF > 30% and 1% ≤ η f f b ≤ 2%, and achievable with reasonable confidence if AF > 50% and η f f b > 2%. These results are of particular concern in light of the low availability factor predicted for the near-term plasma-based experimental facilities (e.g. FNSF, VNS, CTF), and can have repercussions on tritium economy in DEMO reactors as well, unless significant advancements in RAMI are made. There is a linear dependency between the tritium start-up inventory and the fusion power. The required tritium start-up inventory for a fusion facility of 100 MW fusion power is as small as 1 kg. Since fusion power plants will have large powers for better economics, it is important to maintain a 'reserve' tritium inventory in the tritium storage system to continue to fuel the plasma and avoid plant shutdown in case of malfunctions of some parts of the tritium processing lines. But our results show that a reserve time as short as 24 h leads to unacceptable reserve and start-up inventory requirements. Therefore, high reliability and fast maintainability of all components in the fuel cycle are necessary in order to avoid the need for storing reserve tritium inventory sufficient for continued fusion facility operation for more than a few hours. The physics aspects of plasma fueling, tritium burn fraction, and particle and power exhaust are highly interrelated and complex, and predictions for DEMO and power reactors are highly uncertain because of lack of experiments with burning plasma. Fueling by pellet injection on the high field side of tokamak has evolved to be the preferred method to fuel a burning plasma. Extrapolation from the DIII-D penetration scaling shows fueling efficiency expected in DEMO to be <25%, but such extrapolations are highly uncertain. The fueling efficiency of gas in a reactor relevant regime is expected to be extremely poor and not very useful for getting tritium into the core plasma efficiently. Gas fueling will nonetheless be useful for feedback control of the divertor operating parameters. Extensive modeling has been carried out to predict burn fraction, fueling requirements, and fueling efficiency for ITER, DEMO, and beyond. The fueling rate required to operate Q = 10 ITER plasmas in order to provide the required core fueling, helium exhaust and radiative divertor plasma conditions for acceptable divertor power loads was calculated. If this fueling is performed with a 50–50 DT mix, the tritium burn fraction in ITER would be ~0.36%, which is too low to satisfy the self-sufficiency conditions derived from the dynamics modeling for fusion reactors. Extrapolation to DEMO using this approach would also yield similarly low burn fraction. Extensive analysis presented shows that specific features of edge neutral dynamics in ITER and fusion reactors, which are different from present experiments, open possibilities for optimization of tritium fueling and thus to improve the burn fraction. Using only tritium in pellet fueling of the plasma core, and only deuterium for edge density, divertor power load and ELM control results in significant increase of the burn fraction to 1.8–3.6%. These estimates are performed with physics models whose results cannot be fully validated for ITER and DEMO plasma conditions since these cannot be achieved in present tokamak experiments. Thus, several uncertainties remain regarding particle transport and scenario requirements in ITER and DEMO. The safety standard requirements for protection of the public and release guidelines for tritium have been reviewed. General safety approaches including minimizing tritium inventories, reducing tritium permeation through materials, and decontaminating material for waste disposal have been suggested.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

The hierarchical growth of bright central galaxies and intracluster light as traced by the magnitude gap

Using a sample of 2800 galaxy clusters identified in the Dark Energy Survey across the redshift range 0.20 < z < 0.60, we characterize the hierarchical assembly of bright central galaxies (BCGs) and the surrounding intracluster light (ICL). To quantify hierarchical formation we use the stellar mass–halo mass (SMHM) relation, comparing the halo mass, estimated via the mass–richness relation, to the stellar mass within the BCG + ICL system. Moreover, we incorporate the magnitude gap (M14), the difference in brightness between the BCG (measured within 30 kpc) and fourth brightest cluster member galaxy within 0.5 $R_{200,c}$, as a third parameter in this linear relation. The inclusion of M14, which traces BCG hierarchical growth, increases the slope and decreases the intrinsic scatter, highlighting that it is a latent variable within the BCG + ICL SMHM relation. Moreover, the correlation with M14 decreases at large radii. However, the stellar light within the BCG + ICL transition region (30 –80 kpc) most strongly correlates with halo mass and has a statistically significant correlation with M14. Since the transition region and M14 are independent measurements, the transition region may grow due to the BCG’s hierarchical formation. Additionally, as M14 and ICL result from hierarchical growth, we use a stacked sample and find that clusters with large M14 values are characterized by larger ICL and BCG + ICL fractions, which illustrates that the merger processes that build the BCG stellar mass also grow the ICL. Furthermore, this may suggest that M14 combined with the ICL fraction can identify dynamically relaxed clusters.

79 ASTRONOMY AND ASTROPHYSICS↗