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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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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Design of a Geothermal Power Plant With Solar Thermal Topping Cycle: Preprint

Geothermal power plants are a reliable source of low-carbon power generation. However, modern electricity markets comprise relatively large proportions of variable renewable energy generation that may require power plants to dispatch energy flexibly. The power output, efficiency, and dispatch flexibility of a geothermal plant can be enhanced by integrating solar thermal energy into the system, as well as possibly compensating against ambient temperature variations. Concentrating Solar Thermal (CST) can generate temperatures much higher than conventional geothermal systems. Using a solar topping cycle is one way to efficiently convert high-temperature solar heat to electricity while also adding lower-temperature heat to the geothermal power cycle, thereby increasing its power output and possibly its efficiency. A hybrid power cycle design is proposed and is simulated using SimTech IPSEpro process modelling software. The design configuration depends upon the expected temperature of the geothermal resource and the quantity of solar heat added at the design point. These design considerations are described and expected performance is calculated. The solar heat addition varies throughout the day and year, therefore off-design models are necessary to assess the impact of solar availability (and ambient temperature) on the power plant performance. Off-design models are developed and combined with hourly weather data to facilitate an evaluation of annual system performance.

concentrating solar power↗
DOE OSTI2025

Spokane Eco-District Campus Performance Under Alternative Electricity Rates: Benefits for virtual power plant participants and suppliers

Here, the respective benefits for virtual power plant participants and suppliers are revealed and compared under alternative electricity rate structures, including conventional large commercial electricity rates, large commercial electricity rates with special rates for demand-side generation, and dynamic hourly transactive prices. These three scenarios were explored using the capabilities of the Eco-District campus, a virtual power plant in Spokane, Washington, that is supplied electricity by Avista Utilities. The Eco-District Campus was modeled to host solar power generation, battery energy storage, and thermal energy resources that must be coordinated with building heating and cooling needs. First, the electricity supplier’s costs for energy, infrastructure, and energy losses were modeled. Then, the virtual power plant’s performance was modeled while presuming that its manager would minimize its costs under its electricity rate structure. The demand charges of conventional commercial electricity rates managed monthly peak, as would be expected, but hourly dynamic transactive pricing resulted in a striking alignment between the costs incurred by the supplier and the virtual power plant’s energy costs.

Electricity rates↗
NASA NTRS2003

ISRU Reactant, Fuel Cell Based Power Plant for Robotic and Human Mobile Exploration Applications

Three basic power generation system concepts are generally considered for lander, rover, and Extra-Vehicular Activity (EVA) assistant applications for robotic and human Moon and Mars exploration missions. The most common power system considered is the solar array and battery system. While relatively simple and successful, solar array/battery systems have some serious limitations for mobile applications. For typical rover applications, these limitations include relatively low total energy storage capabilities, daylight only operating times (6 to 8 hours on Mars), relatively short operating lives depending on the operating environment, and rover/lander size and surface use constraints. Radioisotope power systems are being reconsidered for long-range science missions. Unfortunately, the high cost, political controversy, and launch difficulties that are associated with nuclear-based power systems suggests that the use of radioisotope powered landers, rovers, and EVA assistants will be limited. The third power system concept now being considered are fuel cell based systems. Fuel cell power systems overcome many of the performance and surface exploration limitations of solar array/battery power systems and the prohibitive cost and other difficulties associated with nuclear power systems for mobile applications. In an effort to better understand the capabilities and limitations of fuel cell power systems for Moon and Mars exploration applications, NASA is investigating the use of in-Situ Resource Utilization (ISRU) produced reactant, fuel cell based power plants to power robotic outpost rovers, science equipment, and future human spacecraft, surface-excursion rovers, and EVA assistant rovers. This paper will briefly compare the capabilities and limitations of fuel cell power systems relative to solar array/battery and nuclear systems, discuss the unique and enhanced missions that fuel cell power systems enable, and discuss the common technology and system attributes possible for robotic and human exploration to maximize scientific return and minimize cost and risk to both. Progress made to date at the Johnson Space Center on an ISRU producible reactant, Proton Exchange Membrane (PEM) fuel cell based power plant project to demonstrate the concept in conjunction with rover applications will be presented in detail.

Baird, Russell S.↗
DOE OSTI2025

Hybrid Power Plants for Energy Resilience: A Case Study

As renewable energy technologies are increasingly adopted, they pose an opportunity to improve the sustainability and resilience of distributed grids, especially when their design and operation is coordinated as a hybrid power plant. When included in hybrid power plants, distributed wind turbines in particular have the potential to enhance the resilience of distributed grids in areas with good wind resource, due to their ability to provide more consistent generation and ancillary services as compared to photo-voltaic (PV) solar panels. Despite this benefit, U.S. distributed wind adoption is lower than other comparable renewable energy technologies. In this study, we seek to demonstrate how hybrid power plants that include distributed wind turbines can contribute to distribution grid resilience by meeting loads (especially critical loads) more consistently, increasing reserve capacity, and providing value to customers during outages. To demonstrate these contributions, we integrate three separate frameworks and apply them to a case study in a rural electric cooperative in Iowa. Through this case study, we simulate and compare hybrid power plant design and operation during two hazard events: a tornado that causes a 48-hour distribution outage and a winter weather event that causes a 6-hour generation outage. The inclusion of a hybrid power plant that leverages 1) increased battery duration and 2) advanced forecasting and dispatch strategies that reserve capacity leading up to a hazard event best reduce lost loads as well as diesel consumption that would otherwise be used to meet those loads during short- and long-duration hazard events. Depending on the hybrid power plant capacity and operation, we find that the outage mitigation value of a hybrid power plant (measured in value to customers to avoid an outage and avoided lost revenues for the utility) is significant in both hazard events; adding wind, solar, and battery assets to the existing system adds about $50-$100M in avoided lost load and at least $4-$8k in utility value in the tornado hazard event, and $570k-$2.2M in avoided lost load and at least $220-$650 in utility value in the winter hazard scenario. In both the tornado and winter hazard scenarios, optimizing the operation of the hybrid system for resilience can lend similar value as increasing battery duration by 5 MWh for the lower capacity systems considered.

17 WIND ENERGY↗
DOE OSTI2023

Coal Power Plant Reinvestment Visualization Tool

This tool, available at https://energycommunities.gov/coal-power-plant-reinvestment-visualization-tool/, serves as a public database and map for the purposes of enabling state and local economic development officials, project developers, and power plant owners to identify and pursue opportunities for plant and community reinvestment. The Coal Power Plant Reinvestment Visualization Tool focuses on coal power plants that have been closed or set-to-retire, alongside key infrastructure characteristics that are relevant for potential redevelopment reinvestment opportunities, including the opportunity to query these data based off pre-defined or user-set queries to identify opportunities for coal power facility reinvestment to support solar, wind, manufacturing, and nuclear. These Data for visualization and query include, but are not limited to: • Electric Transmission Lines • EPA Brownfield Sites (assessed with Federal funding) • Petroleum Terminals • Ports • Railroads • Others The tool will be updated periodically to provide relevant information to enable state and local economic development officials, project developers, and power plant owners to identify and pursue opportunities for economic revitalization and community reinvestment. Within this application data are focused on U.S. power plants with coal generation, where at least one on-site generator utilizes or utilized coal as a fuel source, including planned retirements through 2058 (EIA, 2024). Additional information on energy communities and revitalization opportunities can be accessed on the Interagency Working Group on Coal & Power Plant Communities & Economic Revitalization Energy Communities website (https://energycommunities.gov).

Closures↗
DOE OSTI2021

cerf: A Python package to evaluate the feasibility and costs of power plant siting for alternative futures

Long-term electric power sector planning and capacity expansion is a key area of interest to stakeholders across a wide range of organizations because it helps in making informed decisions about investments in infrastructure within the context of potential future vulnerabilities under various natural and human stressors. Future power plant siting costs will depend on a number of factors including the characteristics of the electricity capacity expansion and electricity demand (e.g., fuel mix of future electric power capacity, and the magnitude and geographic distribution of electricity demand growth) as well as the geographic location of power plants. Electricity technology capacity expansion plans modeled to represent alternate future conditions meeting a set of scenario assumptions are traditionally compared against historical trends which may not be consistent with current and future conditions. We present the `cerf` Python package (a.k.a., the Capacity Expansion Regional Feasibility model) which helps evaluate the feasibility and structure of future, scenario-driven electricity capacity expansion plans by siting power plants in areas that have been deemed the least cost option while considering dynamic future conditions. We can use `cerf` to gain insight to research topics such as: 1) under what conditions future projected electricity expansion plans from models such as GCAM-USA are possible to achieve, 2) where and which on-the-ground barriers to siting (e.g., protected areas, cooling water availability) may influence our ability to achieve certain expansions, and 3) how electricity infrastructure build-outs and value may evolve into the future when considering locational marginal pricing (LMP) based on the supply and demand of electricity from a grid operations model.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

Protecting and Defending against Autonomous Control Systems and Digital Twin Cyber Attacks: Response Strategy for Hyperparameter attacks of Digital Twin Machine Learning Models in Nuclear Power Plants (Final)

Navigating through the complex tapestry of technological advancements, "Response Strategy for Hyperparameter attacks of Digital Twin Machine Learning Model in Nuclear Power Plants" stands at the intersection of cybersecurity and nuclear power plant operations, embarking on a journey through the intricacies of securing digital twins against malicious cyber activities. As nuclear power plants progressively integrate digital twin technology and machine learning models to optimize operations and ensure system reliability, they inadvertently expose themselves to a new spectrum of vulnerabilities, notably in the realm of hyperparameter attacks. Hyperparameters, integral in machine learning model tuning and optimal performance of digital twins, have emerged as a target for adversaries aiming to destabilize the predictive capabilities and therefore, the operational accuracy of these digital entities within critical infrastructures like nuclear plants. This paper, therefore, meticulously threads the needle through the development of a robust response strategy, poised to shield these digital reflections against calculated hyperparameter manipulations, ensuring that the digital twin can effectively and securely function as a reliable proxy for its physical counterpart. The ensuing sections delve into the orchestrated maelstrom of multi-rate time-changing intelligent coordinated hyperparameter attacks and the implementation of event-triggered predictive control, laying down a structured, predictive, and responsive framework that safeguards the nexus where the digital and physical realms of nuclear power plants coalesce. The operational integrity of digital twins in nuclear power plants depends critically on the security of machine learning hyperparameters. This study makes two different contributions. First, a decision-based idea known as a multi-rate time changing intelligent coordinated hyperparameter attack is put forth. In this attack, many hyperparameters are repeatedly changed using both random and intelligent optimal techniques by the attacker. These assaults introduce varied rates at different attack steps, compromise various amounts of hyperparameters, and improve stealth and flexibility. Second, a technique is developed for event triggered predictive control to rapidly respond to potential hyperparameter attacks. This control integrates a sliding window framework, retaining a history of previous data points and employing linear regression to predict the next data point from the current dataset. The control gain K is determined using the Lyapunov-Krasovskii method, and subsequently, an action is developed. Finally, the outcome of the simulation demonstrates the viability of the proposed method for defending nuclear power plant digital twins from hyperparameter attacks.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

CERF: IM3 Projected Western US Power Plant Locations

Overview The Capacity Expansion Regional Feasibility (CERF) model is an open-source geospatial python package that provides new power plant locations at a 1km resolution. The model ingests U.S. state or regional-scale electricity system capacity expansion plans, such as those produced by the Global Change Analysis Model (GCAM-USA), and identifies feasible, site-specific locations for individual new power plants (renewable and non-renewable). CERF combines high-resolution geospatial suitability analyses with an economic algorithm that selects individual plant siting locations based on grid interconnection costs and the locational marginal value of new generation. The model incorporates a wide range of dynamic constraints and opportunities, such as protected lands, population density, existing infrastructure, and water availability. This dataset provides CERF power plant siting results for IM3 Phase 2 simulations across eight different scenarios for the Western US through 2055. The scenarios include combinations of two Shared Socioeconomic Pathways (SSP3 and SSP5) with four high-resolution climate projections specific to the United States (see, https://tgw-data.msdlive.org/). These climate projections include "hotter" and "cooler" variants for two Representative Concentration Pathways (RCP4.5 and RCP8.5). The resulting eight simulations are: rcp45cooler_ssp3 rcp45cooler_ssp5 rcp45hotter_ssp3 rcp45hotter_ssp5 rcp85cooler_ssp3 rcp85cooler_ssp5 rcp85hotter_ssp3 rcp85hotter_ssp5 CERF siting results in this dataset correspond to capacity expansion plans in the GCAM-USA IM3 Phase 2 simulation data and are available for each of the above scenarios. Data Details Temporal Range: 2015-2055 in 5-year timesteps. Note that 2015 is the experiment base year and 2020 and beyond represent model simulation years. Spatial Range: Plant locations are provided for the eleven states in the Western US including Arizona, California, Colorado, Idaho, Montana, New Mexico, Nevada, Oregon, Utah, Washington, and Wyoming. Spatial Resolution: 1 km-squared, provided in x and y coordinates Geospatial Projection: Albers Equal Area Conic (ESRI:102003) File Type: csv The dataset contains subdirectories for each of the eight scenarios described in the overview. Each scenario folder contains two subfolders with the following information: 1. Power Plant Data This directory contains a single .csv file of power plant locations for both pre-existing (non-CERF sited plants in operation in 2015) and new (CERF-sited) power plants across the temporal range along with additional CERF model output parameters for CERF-sited plants. Plant with a siting year earlier than 2020 correspond to facilities that are operational leading into the first timestep CERF simulation. For a more detailed description of CERF model output parameters, see the CERF model documentation. Note that the cerf_plant_id parameter is unique within each scenario file but not across scenario files. Parameter Descriptions scenario - Name of scenario cerf_plant_id - Unique siting identifier cerf_sited - If True, indicates that plant was sited by CERF model. If False, indicates pre-existing facility region_name - Name of region (state) tech_id - Technology ID tech_name - Full generation technology name inclusive of cooling type (if applicable) and additional characteristics tech_simple - Simplified generation technology type unit_size_mw - Power plant unit size (MW) xcoord - X coordinate in the default CRS (meters) ycoord - Y coordinate in the default CRS (meters) index - Index position in the flattend 2D array buffer_in_km - Exclusion buffer around site (km) sited_year - Year of siting retirement_year - Year of retirement lmp_zone - Locational marginal price (LMP) zone ID locational_marginal_price_usd_per_mwh - Locational marginal price ($/MWh) generation_mwh_per_year - Generation output (MWh/yr) operating_cost_usd_per_year - Cost of plant operations ($/yr) net_operational_value - Net operational value based on LMP and and operating costs ($/yr) interconnection_cost - Cost of interconnection for transmission & gas pipeline (if applicable) net_locational_cost -- Difference of interconnection cost and operating value ($/yr) capacity_factor_fraction - Capacity factor (fraction) carbon_capture_rate_fraction - Carbon capture rate (fraction) fuel_co2_content_tons_per_btu - Fuel CO2 content (tons/Btu) fuel_price_usd_per_mmbtu - Fuel price ($/MMBtu) fuel_price_esc_rate_fraction - Fuel price escalation rate (fraction) heat_rate_btu_per_kWh - Heat rate (Btu/kWh) lifetime_yrs - Technology lifetime for annuity (years) operational_life_yrs - Operational lifetime for retirement (years) variable_om_usd_per_mwh - Variable operation and maintenance costs of yearly capacity use ($/MWh) variable_om_esc_rate_fraction - Variable operation and maintenance costs escalation rate (fraction) carbon_tax_usd_per_ton - Carbon tax ($/ton) carbon_tax_esc_rate_fraction - Carbon tax escalation rate (fraction) 2. Storage Data This directory contains information on new and pre-existing energy storage facilities operational in each timestep along with various storage operational parameters. The 2015 timestep provides pre-existing energy storage data and corresponds with facilities that are operational leading into the first model simulation timestep. Note that coordinates in the storage files correspond to the interconnection point on the grid (substation location), not individual energy storage locations. Energy storage is added in a cumulative process at each given interconnection point. That is, each individual file provides the total operational storage capacity interconnected to the specified substation for the given timestep, inclusive of previously installed storage at that location and new storage installed in that timestep at that location. Parameters scenario - Name of scenario timestep - Simulation timestep name - Unique storage identifier s_typ - Type of energy storage technology (battery or pumped storage hydro) s_node - Node ID of interconnecting substation xcoord - X coordinate in the default CRS (meters) ycoord - Y coordinate in the default CRS (meters) charge_rate - Maximum charge rate (power capacity) of storage system (MW) discharge_rate - Maximum discharge rate (power capacity) of storage system (MW) duration - Duration of storage system (hours) max_SoC - Allowed maximum state of charge (energy capacity) of storage system (MWh) min_SoC -Allowed minimum state of charge (energy capacity) of storage system (MWh) charge_eff - Efficiency of charge (fraction between 0 and 1) discharge_eff - Efficiency of discharge (fraction between 0 and 1) Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program.

CERF↗
DOE OSTI2022

Wind and Solar Hybrid Power Plants for Energy Resilience

Wind-solar-storage hybrid power plants represent a significant and growing share of new proposed projects in the United States (U.S.). Their uptake is supported by increasing renewable energy market share, technical abilities for dispatch and control, and decreasing wind, solar, and battery storage costs. Simultaneously, generation and storage resources are increasingly used in distributed power systems. While concerns around the reliability of the aging, transforming U.S. electric grid are growing, diversifying energy resources through hybridization or spatial distribution provides an opportunity to enhance power system resilience compared to single-source generation. Understanding where to build hybrids for resilience value, rather than bulk power supply, has not been fully explored in previous studies. Therefore, in this study, we complete a national complementarity analysis to identify areas in the U.S. that are particularly suited for wind-solar hybrid power plant development. We show the importance of seasonal and diurnal patterns in assessing complementarity, and identify that regions in the Great Plains, Midwest, and Southeast are particularly suited for hybrid power plants. We demonstrate the resilience value of hybridization for a reference system based near Memphis, Tennessee, and show optimal sizing of wind, solar, and storage assets given 1.0 and 0.9 critical load factors. Results indicate that pairing wind and solar assets better meet constant load demand and reduce storage requirements compared to solar alone. These results enable future work integrating complementarity metrics in resilience frameworks and indicate a need for more finer resolution of local resource, demand, and hazard data.

14 SOLAR ENERGY↗
DOE OSTI2025

Grid Forming Control Tuning for a Hybrid Inverter-Based Resource Power Plant

A hybrid inverter-based resource (IBR) power plant consists of grid-following (GFL) and grid-forming inverter-based resources (GFM-IBR) connected in parallel. Here, this research focuses on how to design and tune GFM's control parameters to ensure stable operation of the hybrid power plant for weak and strong grid conditions. We consider two design cases: one where the GFL-IBR does not provide frequency support, and one where it does. It is found that the GFM's power-frequency synchronizing system can lose stability when the power-frequency droop constant is large and/or the grid is strong. Additionally, if the GFL has its frequency support enabled, oscillation stability worsens. To explain the mechanism of the interactions, we construct a feedback system for the synchronizing loop, which consists of the GFM's power-frequency droop control that generates the GFM's synchronizing angle, the GFL's phase-locked loop that measures the voltage phase angle, the GFL's frequency-power control that generates its power order, and the rest of the system. The feedback system is effective in illustrating the potential stability risks. Successful design ensures that the hybrid power plant can operate smoothly and ride through grid disturbances.

feedback systems↗
DOE OSTI2020

Dynamic optimization and economic evaluation of flexible heat integration in a hybrid concentrated solar power plant

Hybridization of concentrated solar power (CSP) plants provides flexibility in operation that can drastically improve the solar-to-electric (STE) efficiency and levelized cost of electricity (LCOE) relative to standalone CSP plants. Flexible heat integration (FHI) is a novel concept where the collection and integration of CSP within a power plant is modified relative to the amount of solar energy available. FHI improves the thermal efficiency of a hybrid solar tower steam Rankine cycle power plant but leads to increased pumping needs due to continuously elevated molten salt flow rates through the collection system, which can negatively impact STE efficiency. The present work is carried out to maximize the STE efficiency of a hybrid CSP plant utilizing FHI by employing a dynamic optimization framework where a genetic algorithm optimizes the operation of the plant over a given solar irradiance profile. The study concerns a plant hypothetically located in Salt Lake City, Utah. Here, the optimization results confirm the accuracy of a predictive heuristic where the preferred operation of the plant can be estimated relative to local peaks in the incident power generated by the heliostat collection field. The optimized FHI operation demonstrates a yearly STE efficiency of 13.8%, whereas the equivalent base-level hybrid and solar-only plants exhibit solar efficiencies of 13.4% and 11.2%, respectively. Economic analysis shows that FHI reduces yearly natural gas costs, leading to a $\$0.5$/MWh reduction in LCOE relative to the base-level hybrid configuration. Overall, the results show that hybrid FHI schemes exhibit economic benefits along with observed thermodynamic improvements.

14 SOLAR ENERGY↗
DOE OSTI2021

How different power plant types contribute to electric grid reliability, resilience, and vulnerability: a comparative analytical framework

Abstract This work explores the dependability tradeoffs provided by the most common types of central power plants in the United States. Historically, the electricity sector has lacked consensus on how reliability , resilience , and vulnerability differ and how those metrics change depending on the power plant fleet composition. We propose distinct definitions for these metrics and an analytical framework to evaluate power plant fleet dependability. Using data analysis and literature review, we identify fifteen dependability attributes across which we rank eleven power plant types relative to natural gas combined-cycle (NGCC) plants. We use NGCC as the benchmark because it is common to many locations and is of relatively recent vintage. The framework shows that each power plant type has unique dependability benefits and drawbacks. We provide examples of how researchers may use the framework to evaluate grid dependability qualitatively under different scenarios. We find that assuming all attributes that contribute to grid dependability are equally important and additive, electric grid dependability is best supported when power plant fleets include a mixture of power generation technologies. Then, we discuss scenario characteristics that could alter the prioritization and relationships of attributes. We also find that if current capacity installation trends continue to favor low- and zero-carbon power plants, US power grids may benefit from increased resilience and reduced vulnerability at the cost of decreased reliability. We conclude by recommending methods for adapting the framework and quantifying relationships between attributes in individual scenarios.

Ramirez-Meyers, K. (ORCID:0000000291216952)↗
DOE OSTI2023

Developing an AI-Powered Zero-Trust Cybersecurity Framework for Malware Prevention in Nuclear Power Plants

This study presents the development of an AI-powered Zero-Trust cybersecurity framework for malware prevention in nuclear power plants. The framework aims to enhance the security of critical systems within nuclear power plants by adopting the principles of Zero-Trust and leveraging artificial intelligence (AI) technologies. By assuming no implicit trust in any user or device and continuously authenticating and authorizing access, the framework ensures a robust defense against malware attacks. The integration of AI allows for the detection and prevention of malware through behavioral analytics, endpoint protection, network segmentation, and continuous monitoring. The paper discusses the key considerations, steps, and technologies involved in developing this framework, emphasizing the importance of regular updates, training, compliance, and auditing. The proposed framework serves as a comprehensive approach to safeguarding nuclear power plants from sophisticated malware threats and protecting the integrity and safety of critical infrastructure.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS1981

Performance calculations for 1000 MWe MHD/steam power plants

The effects of MHD generator operating conditions and constraints on the performance of MHD/steam power plants are investigated. Power plants using high temperature combustion air preheat (2500 F) and plants using intermediate temperature preheat (1100 F) with oxygen enrichment are considered. Variations of these two types of power plants are compared on the basis of fixed total electrical output (1000 MWe). Results are presented to show the effects of generator plant length and level of oxygen enrichment on the plant thermodynamic efficiency and on the required generator mass flow rate. Factors affecting the optimum levels of oxygen enrichment are analyzed. It is shown that oxygen enrichment can reduce magnet stored energy requirement.

Pian, C. C. P.↗
DOE OSTI2021

Microstructure homogenization of concrete used in nuclear power plants

Almost all nuclear power plants in the United States are operating past their intended lifetimes or are requesting lifetime extensions. Therefore, understanding changes to the concrete containment structure over time is crucial to evaluate the structure’s continued viability. Concrete materials are heterogeneous particulate composites that exhibit viscoelastic material properties, which can lead to slow deformation over time, causing stress redistribution and the potential for creep cracking. A code to generate random, three dimensional (3D) concrete microstructures has been developed and paired with finite element analysis to predict the long-term viscoelastic properties of concrete. Data from these simulations are used to develop constitutive equations for the viscoelastic behavior of the homogenized concrete. The codes in this work are used to virtualize laboratory experiments, to obtain long-term creep data in a faster, cheaper manner. To validate this work, the simulated creep behavior of concrete is compared to 800 d of experimental data that has been extended to 27 y of data using the Time-Temperature superposition (TTS) principal. Excellent agreement between the simulation results and experimental data is seen.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1981

The application of simulation modeling to the cost and performance ranking of solar thermal power plants

Small solar thermal power systems (up to 10 MWe in size) were tested. The solar thermal power plant ranking study was performed to aid in experiment activity and support decisions for the selection of the most appropriate technological approach. The cost and performance were determined for insolation conditions by utilizing the Solar Energy Simulation computer code (SESII). This model optimizes the size of the collector field and energy storage subsystem for given engine generator and energy transport characteristics. The development of the simulation tool, its operation, and the results achieved from the analysis are discussed.

Rosenberg, L. S.↗