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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 1,477 records · Page 82
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Modeling a generic TRISO-fueled heat pipe microreactor using SCALE: Depletion, transportation criticality, and shielding

This paper demonstrates the applicability of the SCALE code system to tristructural-isotropic (TRISO)-fueled heat pipe microreactors through depletion, transportation criticality, and shielding analyses of a generic reference design. The study conducted supports US Nuclear Regulatory Commission code readiness efforts for advanced non–light-water reactor technologies and is intended as a code capability demonstration rather than as an optimization of a specific microreactor design. The modeled reactor employs high-assay low-enriched uranium (HALEU) uranium oxycarbide (UCO) TRISO fuel and beryllium oxide (BeO) reflectors and operates at 7.5 MWth with a nominal lifetime of about 3 effective full power years. Representative cases for fresh and irradiated cores were selected to exercise SCALE methods relevant to reactor operation and post-irradiation transport. The discharged-core decay heat is approximately 6% of operating power immediately after shutdown. Transportation criticality calculations show that internal water ingress is the dominant reactivity effect, with fully flooded fresh core and irradiated core configurations remain above the subcriticality criterion, even with the available control mechanisms. Shielding calculations for a simplified transportation package indicate that normal-condition dose rates are governed mainly by shielding thickness and cooling time, whereas the breached hypothetical accident case is governed primarily by cooling time. Overall, the study shows that SCALE supports depletion, transportation criticality, and shielding evaluations efficiently for TRISO-fueled heat pipe microreactors within a single code system.

Criticality↗
DOE OSTI2026

Assessment of multi-stage telescopic control rods in a pebble bed reactor. Part II: corrosion and wear performance

To support High-Temperature Gas-Cooled Reactor designs, the effects of corrosion and wear on a SS316L telescopic control rod prototype were investigated in helium at 775 °C with up to 5 ppm oxygen for durations up to 500 h. Three deployment conditions were tested to assess their impact on wear and oxide formation: severe cycles, daily cycles, and gravity-driven deployments. Here, a chromium-rich oxide layer developed on regions exposed to the helium flow, reaching up to 2 µm in the most affected areas. No carburization or decarburization was observed. Oxide breakthrough occurred mainly at grooves and contact points during severe cycling of the telescopic rod, while in the other tests it was limited to contact points. Friction forces increased due to oxide growth and thermal expansion, the latter being the dominant factor, but never enough to hinder rod deployment significantly. Coaxial misalignment of stages was identified as the main potential risk to reliable operation under normal conditions.

Control rod↗
DOE OSTI2020

Superconductivity in dilute SrTiO 3 : A review

Doped SrTiO 3 , one of the most dilute bulk systems to display superconductivity, is perhaps the first example of an unconventional superconductor, as it does not fit into the standard BCS paradigm. More than five decades of research has revealed a rich temperature-carrier concentration phase diagram that showcases a superconducting dome, proximity to a putative quantum critical point, Lifshitz transitions, a multi-gap pairing state and unusual normal-state transport properties. Research has also extended beyond bulk SrTiO 3 , ushering the new field of SrTiO 3 -based heterostructures. Because many of these themes are also featured in other quantum materials of contemporary interest, recent years have seen renewed interest in SrTiO 3 . Here, we review the challenges and recent progress in elucidating the superconducting state of this model system. At the same time that its extreme dilution requires to revisit several of the approximations that constitute the successful Migdal–Eliashberg description of electron–phonon superconductivity, including the suppression of the Coulomb repulsion via the Tolmachev–Anderson–Morel mechanism, it opens interesting routes for alternative pairing mechanisms whose applicability remains under debate. For instance, pairing mechanisms involving longitudinal optical phonons have to overcome the hurdles created by the anti-adiabatic nature of the pairing interaction, whereas mechanisms that rely on the soft transverse optical phonons associated with incipient ferroelectricity face challenges related to the nature of the electron–phonon coupling. Proposals in which pairing is mediated by plasmons or promoted locally by defects are also discussed. In conclusion, we finish by surveying the existing evidence for multi-band superconductivity and outlining promising directions that can potentially shed new light on the rich problem of superconductivity in SrTiO 3 .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Eliashberg theory of phonon-mediated superconductivity — When it is valid and how it breaks down

We analyze the validity of Eliashberg theory of phonon-mediated superconductivity in 2D systems in light of recent extensive Monte-Carlo studies of the Holstein model. Conventional wisdom says that Eliashberg theory is applicable as long as vertex corrections remain small. For small ratio of the phonon energy Ω 0 and the Fermi energy E F , this condition is supposed to hold even when the dimensionless electron–phonon coupling λ is larger than one, i.e., in the strong coupling regime. A comparison between various quantities computed in the Migdal approximation and those computed by Quantum Monte Carlo prove that this belief is wrong, and we identify analytically some of the ways in which this breakdown occurs for various ‘‘normal state’’ properties at λ = λ cr , where λ cr = O(1). Here, the breakdown occurs at temperatures high enough that neither superconducting nor charge–density wave correlations extend over any significant range of distances, so it cannot be associated with the onset of an instability toward any of the relevant ordered ground-states — rather it is associated with the local physics of classical bipolaron formation. Still, we show that certain properties, including the superconducting Tc and the superconducting gap structure below T c , can be accurately inferred from the strong-coupling limit of Eliashberg theory at λ ≤ λ cr .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Unusual water-assisted NO adsorption over Pd/FER calcined at high temperatures: The effect of cation migration

Moisture contained in vehicle exhaust gas normally degrades the capacity and efficiency of Pd ion-exchanged zeolites as NO x adsorbents by competitive adsorption on active sites. Here, we report a counterexample to this general proposition, in which moisture facilitates the storage of NO as a nitrosyl complex on hydrated Pd ions in high temperature calcined FER-type zeolites. The divalent Pd 2+ cations upon elevated temperature (>800 °C) calcination occupy cationic position that render them fully coordinated by oxygen ions of the zeolite framework, and become inactive for the adsorption of probe molecules such as NO or CO. These ‘hidden’ Pd ions, however, are accessible by NO when the zeolite is hydrated, but readily release NO at around 200°C as dehydration proceeds. Herein, by combining systematic in situ infra-red data with X-ray diffraction Rietveld analyses, we revealed that the high temperature-induced relocation of Pd ions to more stable cationic positions located near 6-membered ring of the ferrierite cage is responsible for this anomalous behavior. This discovery constitutes a notable advance in understanding coordination chemistry of cations in zeolites.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Economic analysis of a nuclear hybrid energy system in a stochastic environment including wind turbines in an electricity grid

The electricity market in the U.S. is moving toward an energy mix including more variable renewable energy (VRE) sources like wind and solar. This increasing VRE penetration is altering the profile of the net electricity demand. Normally, flexible generators (e.g. gas turbines) are used to absorb fluctuations in the electricity demand. Therefore, an increase in VRE also leads to an increase in installed capacity for such flexible generators which, in turn, may increase the price of electricity. Nuclear-Renewable Hybrid Energy Systems (N-R HES) are seen as a solution to this problem. The N-R HES contains a nuclear power plant electrically and/or thermally linked to an industrial process (IP). This paper demonstrates a new methodology and presents the corresponding RAVEN/Modelica-based software framework to evaluate the financial performance of N-R HES. The novelty of the method is that it explicitly incorporates the stochastic nature of N-R HES inputs such as electricity demand and wind speeds. The method is applied to a generic (rather than region-specific) N-R HES. The analysis shows that under the right market conditions, economic benefits can be achieved by adding a suitable IP to the base-load generator. In particular, there exists an optimal relationship between the IP capacity and the demand. Furthermore, global trends indicate VRE penetration raises the cost of electricity due to the added volatility, an issue only mitigated when operating with high profit margins for the IP.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A Novel Simulation-Based Framework for Sensor Error Impact Analysis in Smart Building Systems: A Case Study for a Demand-Controlled Ventilation System

Sensors are one of the fundamental components for sensor-rich controls in buildings but are prone to different errors. Existing studies show that sensor errors hold a place among top-priority faults in building systems. Before we take countermeasures to mitigate the sensor errors, it is vital to prioritize key sensors and quantify the collective impacts of concurrent sensor errors. In response to this, a simulation-based methodology is introduced to conduct a comprehensive sensor error impact analysis in building systems, which adds a stochastic sensor prioritization through a sensitivity analysis on top of a commonly used deterministic sensor error quantification. The synergies of these two parts help better interpret the sensor error impacts on building energy consumption, ventilation performance, thermal comfort, etc. A sensor-rich CO2-based Demand-Controlled Ventilation system is used as a case study to demonstrate the viability of the methodology as a proof-of-the-concept. The results show that the energy savings potential and ventilation performance are mostly influenced by the accuracy of the AHU outdoor airflow sensors. The accuracy of zone level airflow sensors has a negligible impact on both energy savings and ventilation performance. The accuracy of zone CO2 sensors has more influence on the ventilation performance compared with the accuracy of zone airflow sensors. Compared with the baseline case with zero errors, the largest deviation percentages could reach 16.90% and 94.32%, respectively, in terms of the Heating, Ventilation, and Air-Conditioning (HVAC) annual energy consumption and the Outdoor Air Ratio (OAR) when multiple key sensors suffer from normal error intensities simultaneously.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

A robust offering strategy for wind producers considering uncertainties of demand response and wind power

This paper proposes a risk-constrained decision-making approach for a wind power producer participating in the day-ahead market. In the developed model, a flexible demand response trading scheme between the wind power producer and different customers is employed. Through the proposed demand response mechanism, the wind power producer is able to trade demand response resource internally with different customers, and then trade energy externally with the market to increase the expected profit and the wind energy utilization. The uncertainties in the wind power and demand response are modeled by using the information gap decision theory approach from risk averse (robust) and risk-seeking (opportunistic) perspectives. The objective of the robust model is to maximize the robust level while satisfying the desired profit, whereas the opportunistic model aims to evaluate the possibility of achieving windfall profits with favorable uncertainties. The overall offering strategy problem is modeled as a bi-objective mixed integer nonlinear programming, which is linearized by proper techniques and solved efficiently by using the normal boundary intersection technique. In this work, simulation results show that utilizing demand response resource to mitigate wind power deviations can increase a wind power producer's profit and reduce potential risks. In addition, the results demonstrate that the proposed bi-objective optimization approach enables the wind power producer to select appropriate offering decisions with respect to uncertainties.

17 WIND ENERGY↗
DOE OSTI2020

Reinforcement learning based automated history matching for improved hydrocarbon production forecast

History matching aims to find a numerical reservoir model that can be used to predict the reservoir performance. An engineer and model calibration (data inversion) method are required to adjust various parameters/properties of the numerical model in order to match the reservoir production history. In this study, we develop deep neural networks within the reinforcement learning framework to achieve automated history matching that will reduce engineers’ efforts, human bias, automatically and intelligently explore the parameter space, and remove the need of large set of labeled training data. To that end, a fast-marching-based reservoir simulator is encapsulated as an environment for the proposed reinforcement learning. The deep neural-network-based learning agent interacts with the reservoir simulator within reinforcement learning framework to achieve the automated history matching. Reinforcement learning techniques, such as discrete Deep Q Network and continuous Deep Deterministic Policy Gradients, are used toth, used to train the learning agents. The continuous actions enable the Deep Deterministic Policy Gradients to explore more states at each iteration in a a learning episode; consequently, a better history matching is achieved using this algorithm as compared to Deep Q Network. For simplified dual-target composite reservoir models, the best history-matching performances of the discrete and continuous learning methods in terms of normalized root mean square errors are 0.0447 and 0.0038, respectively. Furthermore, our study shows that continuous action space achieved by the deep deterministic policy gradient drastically outperforms deep Q network.

42 ENGINEERING↗
DOE OSTI2022

Black Swan Event Small-Sample Transfer Learning (BEST-L) and its Case Study on Electrical Power Prediction in COVID-19

The black swan event will usually cause a great impact on the normal operation of society. The scarcity of such events leads to a lack of relevant data and challenges in dealing with related problems. Different situations also make the traditional methods invalid. In this paper, a transfer learning framework and a convolutional neuron network are proposed to deal with the black swan small-sample events (BEST-L). Taking the COVID-19 as a typical black swan event, the BEST-L is utilized to achieve accurate mid-term load forecasting using the relationship between economy and electricity consumption. The experiment results show that the transfer learning model can effectively learn the basic knowledge about the relationship between the adopted input and output data and use a relatively small amount of data during the black swan event to improve the target areas' generalization. The approach and results can provide an effective approach to respond and react to sudden changes quickly and effectively in similar open problems.

black swan event↗
DOE OSTI2022

Optimizing the physical design and layout of a resilient wind, solar, and storage hybrid power plant

We report as wind and solar technologies improve and their costs decrease, the share of power produced by these sources will increase. As the market penetration increases, these power sources will need to provide grid services, such as dispatchability, in addition to providing energy. One way to reduce variability, provide higher quality power to the grid, and address local grid stability issues is through colocating wind and solar power plants. In addition to operating reliably during normal operating conditions, in scenarios with high penetrations of renewable generation, it is important that these hybrid plants can withstand production disruptions and continue to supply power despite prolonged resource reduction, extreme weather events, or other disruptions. In this paper, we present a methodology to optimize a wind-solar-battery hybrid power plant down to the component level that is resilient against production disruptions and that can continually produce some minimum required power. We introduce the models and assumptions we used to simulate a hybrid power plant as well as the design variable parameterization and specific methods we used to optimize the plant. We demonstrate the performance of our method by comparing a plant optimized for different objectives, generation outage durations, minimum power requirements, and power purchase agreements. Although the plant design is sensitive to model parameters and various other assumptions, our results demonstrate some of the optimal designs that occur in different scenarios and what one should expect when designing a hybrid wind-solar-storage power plant.

14 SOLAR ENERGY↗
DOE OSTI2022

A comparison of building system parameters between affordable and market-rate housing in New York City

Low-income households in the United States experience higher than average energy burdens (defined as the proportion of household income spent on energy utilities), and many of these households struggle to simultaneously pay for rent, energy, and basic household necessities. The analysis presented here in this study examines whether the underlying characteristics of buildings and their energy systems could contribute to this disparity for affordable housing residents in New York City. It combines an energy audit dataset of 7,328 multifamily buildings with a database of properties receiving local, state, or federal housing subsidies. The results of this analysis indicate that the building-level installed equipment in large (greater than 50,000 square feet) affordable housing buildings in New York City is more efficient than that in market-rate buildings, but this trend largely disappears when considering overall building characteristics, such as location, size, or age. Significant differences in the types of systems installed in affordable and market-rate housing are also observed, as well as the types of energy efficiency recommendations made by energy auditors. However, these latter data were not normalized by building system characteristics, as that analysis is much more difficult to interpret for categorical data such as heating system type. These findings indicate that retrofit policies and building performance standards focused on affordable housing will likely need to account for underlying differences in building characteristics between affordable and market-rate housing to achieve intended impacts.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Dispatch optimization of a concentrating solar power system under uncertain solar irradiance and energy prices

The integration of thermal energy storage into a concentrating solar power system allows for mitigating some of the risk associated with uncertain solar irradiance and uncertain energy prices. We solve a 48 h dispatch optimization model with continually updated conditional point forecasts of both direct normal irradiance (DNI) and electricity prices with a rolling-horizon scheme at hourly resolution over the course of a year. Joint, conditional forecasts for DNI and prices are formed using an autoregressive moving-average time series model with exogenous weather predictors. We guide dispatch using a mixed-integer programming model, but in order to evaluate performance we use the System Advisor Model (SAM) of the National Renewable Energy Laboratory. SAM is a techno-economic simulation model that accounts for plant thermodynamics with higher fidelity. Our conditional DNI forecasts improve annual revenue by 4%–12% over using historical forecasts based on data from previous years. Conditional price forecasts improve annual revenue by 6%–19% in the real-time market over analogous historical forecasts. Updating these forecasts every six hours, rather than every 24 h, further improves annual revenue by 5%–6%. Here, we also investigate a method that values terminal inventory in our dispatch optimization model, again when used in a rolling-horizon scheme.

14 SOLAR ENERGY↗
DOE OSTI2022

Using discrete Bayesian networks for diagnosing and isolating cross-level faults in HVAC systems

Fault detection and diagnosis (FDD) technologies are critical to ensure satisfactory building performance, such as reducing energy wastes and negative impacts on occupant comfort and productivity. Existing FDD technologies mainly focus on component-level FDD solutions, which could lead to mis-diagnosis of cross-level faults in heating, ventilating, and air-conditioning (HVAC) systems. Cross-level faults are those faults that occur in one component or subsystem, but cause operational abnormalities in other components or subsystems, and result in a building level performance degradation. How to effectively diagnose the root cause of a cross-level fault is the focus of this study. Here, this paper presents a novel discrete Bayesian Network (DisBN)-based method for diagnosing cross-level faults in an HVAC system commonly used in commercial buildings. A two-level DisBN structure model is developed in this study. The parameters used in the DisBN model are obtained either from expert knowledge or through machine-learning strategies from normal system operation data. Meanwhile, the probability parameters are discretized to incorporate the uncertainties associated with typical expert knowledge. Thus, the developed DisBN method addresses the challenges many other BN based FDD methods face, i.e., the lack of fault data for BN parameter training. The developed DisBN represents causal relationships between a fault and its cross-level system impacts (i.e., fault symptoms or fault indicators) by considering how fault impacts propagate across different levels in an HVAC system. A weather and schedule information-based Pattern Matching (WPM) method is employed to automatically create WPM baseline data sets for each incoming real time snapshot data from the building systems. Consequently, BN inference and real-time diagnostics are achieved by comparing incoming snapshot data and the WPM baseline data set. The proposed method is evaluated using experimental fault data collected in a campus building. Fault diagnosis results demonstrate that the WPM-DisBN method is effective at locating the root causes of cross-level faults in an HVAC system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Seasonality and trade in hydro-heavy electricity markets: A case study with the West Africa Power Pool (WAPP)

Hydroelectric power plants account for 25% of West Africa's total installed capacity. In general, electricity generated by these plants is seasonal and intra-annual fluctuation has a considerable impact on electricity supply and cross-border electricity trade. We soft-linked a global hydrologic model to a multi-region capacity expansion and planning model for the West Africa Power Pool (WAPP) to examine the effects of seasonality in hydropower electricity generation on electricity trade, as well as the economic benefits of unconstrained cross-border electricity trade. We found that transitioning from rainy to dry season decreases hydroelectricity generation by an average of 40% in a normal year across the region. Between 2018 and 2050, satisfying the region's electricity demand will require significant capital investment; nevertheless, extending from current bilateral electricity trade agreements to a fully competitive cross-border electricity trade will result in net annual savings of $3 billion in 2015 USD. To achieve year-round energy security, countries should plan ahead and diversify their energy sources to ensure that supply reliability can be maintained in the case of lower supply during the dry season.

13 HYDRO ENERGY↗
DOE OSTI2022

Coupled loads analysis of a novel shared-mooring floating wind farm

Shared mooring lines are a method to reduce the stationkeeping cost of floating offshore wind farms; however, their impacts on floating array dynamics are not yet well understood. Here this article presents advancements to floating wind farm simulation capabilities that allow the dynamic modeling of shared-mooring floating wind arrays - specifically, platform coupling through shared lines and proper phasing of wave loads across the array. These capabilities are demonstrated on a 10-turbine shared-mooring array featuring two staggered rows of wind turbines. A baseline array design with individually moored turbines is used as a basis for comparison. Coupled analysis using FAST.Farm shows that the shared-mooring design has smaller fluctuations in surge when compared to the baseline design. The mean and maximum platform offsets are highest in normal operating conditions, but the maximum mooring line tensions are highest in 50-year storm conditions. Power spectral density analysis of platform surge and sway motions shows that the shared mooring configuration does not introduce any significant inter-platform resonances. The tower-base bending moment statistics and the line tension damage equivalent loads indicate no consequential differences in turbine loading or mooring fatigue life. Anchor load analysis shows that the total required anchor capacity for the shared-mooring array is decreased by approximately 25% when shared anchors are used. Dynamic simulations of the shared mooring system under line failures show that remaining line tensions stay within bounds and that offsets are significantly smaller when compared to the baseline design. Overall, the results show that shared moorings do not introduce any dynamic response concerns in this floating wind array design.

17 WIND ENERGY↗
DOE OSTI2023

Temporal performance indicators for an integrated pilot-scale membrane distillation-concentrated solar power/photovoltaic system

Management of concentrate streams in inland applications has uncertain long-term environmental impacts. This study investigates an intensified solar-energy capture desalination system that integrates membrane distillation (MD) with a hybrid concentrated solar power (CSP)/photovoltaic (PV) collector to realize self-sustained zero-waste discharge for effective management of concentrate streams in inland and off-grid applications. The demonstration-scale CSP/PV system can produce up to 178 kWh of thermal energy and 4 kWh of electrical energy per day. The thermal and electrical energy from the CSP/PV system is directly supplied to the air gap MD (AGMD) pilot-scale system producing up to 288 L of distilled water per day. Experiments were performed on the hybrid AGMD-CSP/PV system to evaluate system performance under various operating conditions including AGMD and CSP flow rates, CSP system pre-heating, and AGMD vacuum pressure. Experimental results indicate that doubling the AGMD flow rate results in a 119% increase in thermal energy utilization and a 71% increase in distillate production. Compared to the winter months, operating the system in summer months when direct normal irradiance (DNI) is highest results in nearly double the distillate production (88 L in winter and 168 L in summer) and nearly three times the amount of thermal energy consumption (15 kWh in winter and 43 kWh in summer). Operating with vacuum resulted in a 34% increase in distillate production and allowing the thermal storage reservoir to preheat in the winter resulted in a 61% increase in distillate production. Altogether, experimental results highlight the tradeoff between distillate production and thermal and electrical energy production and consumption under various environmental conditions and the potential for AGMD-CSP/PV to be a stand-alone desalination system.

14 SOLAR ENERGY↗