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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Performance of Closed Loop Venturi Aspirated Aeration System: Experimental Study and Numerical Analysis with Discrete Bubble Model

In wastewater treatment plants, aeration plays a significant role as it encourages aerobic respiration of microbes, which are necessary to break down carbonaceous matter in the waste stream. This process can account for the majority of energy use in wastewater treatment plants. The aeration process is also necessary in odor control in lagoons and in the aquaculture industry. Generally, the aeration process is accomplished with compressors or blowers which can be of low efficiency due to ideal gas laws. This study introduces the idea of increasing aeration efficiency by looping water from a reservoir through a piping network which includes a venturi aspirator at its inlet. For this purpose, an experimental study has been conducted in a laboratory setup with a pump which pulls water from a tank, passes it through a Venturi aspirator and a helical piping network intended to increase bubble residence time, before depositing it back into the bulk fluid tank. This same process is modeled computationally, using a discrete bubble method (DBM), with good agreement with experiments. The overall purpose here is to determine the optimal configurations for standard aeration efficiency (SAE) and the standard oxygen transfer rate (SOTR). A parametric study has been implemented using the DBM based on different hydraulic and flow parameters. The model is also used to predict the SAE of a hypothetical aeration system. Results indicate that it is possible to achieve SAE values in the range of surface aerators or submersed jet aerators using the proposed aeration system with less complex components, thereby decreasing overall costs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Economic and environmental feasibility of recycling flexible plastic packaging from single stream collection

As demand for plastic increases, there is an urgent need to ramp up its collection and recycling rate. This study reports results of a pilot study in the United States to recycle flexible plastic packaging (FPP) from single stream curbside collection focusing on both the economic feasibility and carbon footprint. To explore the marketability of recycled FPP, four down-stream market pathways were studied, including roof coverboard, plastic pellets, pallets, and film. In this work, results indicated that (1) the cleaning and pelletizing process at reclaimers contributed the most to the total greenhouse gas (GHG) emissions of recycled FPP products, and (2) the GHG emissions of recycled FPP products in all four pathways are lower than the comparator products. Therefore, using a higher percentage of recycled FPP substituted in a product can result in greater GHG emission reductions. All the recycled FPP products also showed favorable economics compared to their direct competitors.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Regional Variation in Light-Duty Plug-in Electric Vehicle Emissions

Automobile electrification is viewed as one of the key requirements for deep decarbonization of transportation. However, the exact magnitude of decarbonization from switching from a combustion of petroleum-based fuels to grid-derived electricity is a complex issue. Variations in local grid mixes lead to differing carbon intensities of electricity in different regions. This complexity is compounded by geographical differences in vehicle characteristics. This analysis considers these geographic differences to produce a historical assessment of the fuel-cycle carbon emissions of plug-in electric vehicles (PEVs) in the United States from 2011 to 2021. We find that PEVs in the United States decreased in electricity-derived carbon intensity, from 187 grams per mile to 110 grams per mile from 2011 to 2021 due to improvements in the electric grid and vehicle efficiency. Through 2021, PEVs emitted a total of 13.6 million metric tons of greenhouse gas (GHG) emissions in the United States, including both emissions from electricity and gasoline, while driving a total of 100 billion miles. Of this total, 8.39 million metric tons was due to electricity consumption. Over the same distance, comparable gasoline vehicles would have emitted between 29 and 41 million metric tons of GHG, leading to a reduction of 15 to 27 million metric tons. Relative to national-level assessments that do not factor in this regional difference, these electric vehicles resulted in 13% greater reductions in GHG emissions than previously calculated. In the future, announced goals to further decarbonize the electricity sector will continue to reduce the emissions of PEVs, including reducing emissions rates for vehicles already on the road, further improving the benefits relative to the unchanging emissions of contemporaneous gasoline vehicles.

02 PETROLEUM↗
DOE OSTI2020

Techno-economic analysis of cathode material production using flame-assisted spray pyrolysis

The cost of cathode materials contributes approximately 32.7% of the total cell construction cost of lithium-ion batteries, significantly affecting the price of battery packs. To reduce the cathode material manufacturing cost, a flame-assisted spray pyrolysis (FSP) method has been developed to utilize a sustainable solvent of glycerol to manufacture the LiNi 1/3 Mn 1/3 Co 1/3 O 2 (NMC333) cathode materials. The purpose of this study is to evaluate the minimum cathode material selling price (MCSP) of the FSP processes compared with a traditional carbonate co-precipitation pathway. Results show that the MCSP of the FSP is $\$$19.1/kg that is 17% lower than the traditional carbonate co-precipitation pathway as a result of lower fixed operating cost and variable overhead. Sensitivity analysis shows that when the new process is integrated with in-situ sintering and processing, the MCSP can be as low as $\$$15.6/kg. When all the material prices are decreased by 20%, the FSP process can synthesize NMC333 at a price of $\$$2.3/kg lower. According to the simulation result, LiNi 0·8 Mn 0·1 Co 0·1 O 2 (NMC811) has the best potential to meet the U.S. Department of Energy battery price target of $\$$125/kWh, demonstrating that the FSP process is an attractive manufacturing technology for NMC cathode powder material production.

25 ENERGY STORAGE↗
DOE OSTI2020

Predicting Biological Cleanliness: An Empirical Bayes Approach for Spacecraft Bioburden Accounting

To comply with the international planetary protection policy set forth by the Committee on Space Research and NASA Agency level requirements, spacecraft destined to biologically sensitive planetary bodies have to minimize terrestrial biological contamination. Analysis, testing and inspection are the standard forward verification activities that are used to demonstrate compliance with the biological contamination requirements. For testing of spacecraft surface areas, a swab or wipe sample is collected from surfaces prior to last access and subsequently processed in the lab using NASA Approved Planetary Protection Methods for Culture Based Assays. Raw data resulting from this assay is then statistically treated employing a mathematical paradigm stemming from the 1970’s Viking Lander Project to generate the bioburden density and total microbial bioburden present. This standard approach arbitrarily accounts for error and provides an upper conservative bound as it reports the maximum number of spores estimated to be present on flight hardware surfaces. A bioburden density estimate factors in the following variables: the observed bioburden count, representative volume processed, sampling efficiencies. Notably, to account for error in the approach, a 0 observed count is arbitrarily changed to a count of 1 for each hardware grouping. The data generated by spacecraft bioburden verification campaigns in the past have resulted in <80% of wipes and <90% of swabs containing a bioburden count of 0. As such, having a robust and well documented statistical approach for dealing with the probability of low incident rates is necessary to be able to estimate spacecraft bioburden. Being able to statistically describe the bioburden distribution and associated confidence level is a gamechanger for the development of bioburden allocations during mission design and will allow for tighter management of risk throughout spacecraft build. Thus, Empirical Bayes statistical approach was evaluated to estimate the microbial bioburden on spacecraft to mitigate the aforementioned mathematical concerns and provide a probabilistic bioburden distribution of the flight hardware surface. For application of this approach to performing bioburden calculations, a range of non-informative prior assumptions on hardware surfaces are explored for Bayesian analyses while informative priors using posterior distributions from prior assays are utilized for Empirical Bayes analyses. Several non-informative priors are currently under investigation to assess fitness including use of these priors to serve as a foundation to build off of NASA specification values or a basis of risk to account for unknowns during the integration and testing process. Informative priors under consideration are generated using sampled bioburden values from hardware originating within like processing environments (e.g. vendor cleaning process or similar assembly process), temporal spacecraft status events as a prediction for hardware cleanliness of future samples, and heritage system bioburden actuals to predict allocation for subsequent missions. Informative priors and probabilistic bioburden distributions are then validated using data sets from the Mars Exploration Rover, Mars Science Laboratory, and InSight missions. Using Empirical Bayes approach to generate a probabilistic bioburden distribution as demonstrated through mission use cases provides a valid approach for use in the end-to-end requirements verification process.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Bayesian Framework for Bioburden Density Estimation in Planetary Protection

To comply with the international planetary protection policy set forth by the Committee on Space Research and NASA Agency level requirements, spacecraft destined to biologically sensitive planetary bodies have to minimize terrestrial biological contamination. Analysis, testing and inspection are the standard forward verification activities that are used to demonstrate compliance with the biological contamination requirements. For testing of spacecraft surface areas, a swab or wipe sample is collected from surfaces prior to last access and subsequently processed in the lab using NASA Approved Planetary Protection Methods for Culture Based Assays. Raw data resulting from this assay is then statistically treated employing a mathematical paradigm stemming from the 1970’s Viking Lander Project to generate the bioburden density and total microbial bioburden present. This standard approach arbitrarily accounts for error and provides an upper conservative bound as it reports the maximum number of spores estimated to be present on flight hardware surfaces. A bioburden density estimate factors in the following variables: the observed bioburden count, representative volume processed, sampling efficiencies. Notably, to account for error in the approach, a 0 observed count is arbitrarily changed to a count of 1 for each hardware grouping. The data generated by spacecraft bioburden verification campaigns in the past have resulted in <80% of wipes and <90% of swabs containing a bioburden count of 0. As such, having a robust and well documented statistical approach for dealing with the probability of low incident rates is necessary to be able to estimate spacecraft bioburden. Being able to statistically describe the bioburden distribution and associated confidence level is a gamechanger for the development of bioburden allocations during mission design and will allow for tighter management of risk throughout spacecraft build. Thus, Empirical Bayes statistical approach was evaluated to estimate the microbial bioburden on spacecraft to mitigate the aforementioned mathematical concerns and provide a probabilistic bioburden distribution of the flight hardware surface. For application of this approach to performing bioburden calculations, a range of non-informative prior assumptions on hardware surfaces are explored for Bayesian analyses while informative priors using posterior distributions from prior assays are utilized for Empirical Bayes analyses. Several non-informative priors are currently under investigation to assess fitness including use of these priors to serve as a foundation to build off of NASA specification values or a basis of risk to account for unknowns during the integration and testing process. Informative priors under consideration are generated using sampled bioburden values from hardware originating within like processing environments (e.g. vendor cleaning process or similar assembly process), temporal spacecraft status events as a prediction for hardware cleanliness of future samples, and heritage system bioburden actuals to predict allocation for subsequent missions. Informative priors and probabilistic bioburden distributions are then validated using data sets from the Mars Exploration Rover, Mars Science Laboratory, and InSight missions. Using Empirical Bayes approach to generate a probabilistic bioburden distribution as demonstrated through mission use cases provides a valid approach for use in the end-to-end requirements verification process.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Neural Network-Based Electric Vehicle Range Prediction for Smart Charging Optimization

Range prediction is a standard feature in most modern road vehicles, allowing drivers to make informed decisions about when to refuel. Most vehicles make range predictions through data- or model-driven means, monitoring the average fuel consumption rate or using a tuned vehicle model to predict fuel consumption. The uncertainty of future driving conditions makes the range prediction problem challenging, particularly for less pervasive battery electric vehicles (BEV). Most contemporary machine learning-based methods attempt to forecast the battery SOC discharge profile to predict vehicle range. In this work, we propose a novel approach using two recurrent neural networks (RNNs) to predict the remaining range of BEVs and the minimum charge required to safely complete a trip. Each RNN has two outputs that can be used for statistical analysis to account for uncertainties; the first loss function leads to mean and variance estimation (MVE), while the second results in bounded interval estimation (BIE). These outputs of the proposed RNNs are then used to predict the probability of a vehicle completing a given trip without charging, or if charging is needed, the remaining range and minimum charging required to finish the trip with high probability. Training data was generated using a low-order physics model to estimate vehicle energy consumption from historical drive cycle data collected from medium-duty last-mile delivery vehicles. Here, the proposed method demonstrated high accuracy in the presence of day-to-day route variability, with the root-mean-square error (RMSE) below 6% for both RNN models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Unraveling the CO Oxidation Mechanism over Highly Dispersed Pt Single Atom on Anatase TiO 2 (101)

Catalysts with noble metals deposited as single atoms on metal oxide supports have recently been studied extensively due to their maximized metal utilization and potential for performing difficult chemical conversions owing to their unique electronic properties. Understanding of the reaction mechanisms on supported single-metal atoms is still limited but is highly important for designing more efficient catalysts. In this study, we report the complexity of the CO oxidation reaction mechanism on Pt single atoms supported on anatase TiO 2 (Pt SA /a-TiO 2 ) by coupling density functional theory (DFT) calculations and microkinetic analysis with kinetic measurements, in situ/operando infrared, and X-ray absorption spectroscopies. Starting from the adsorbed Pt SA occupying an O vacancy induced by reductive pretreatment, we show that CO oxidation follows a complex mechanism consisting of initiation steps to reorganize the active site and multibranch reactive cycles, with the Pt SA /a-TiO 2 catalyst not returning to its initial configuration. The initiation step consists of CO and O 2 adsorption healing the O vacancy, followed by CO oxidation using gas-phase CO to form Pt(CO). The reactive cycle alternates O 2 adsorption and dissociation to oxidize the catalyst to Pt(O)(O)(CO) and branching pathways of competing Langmuir–Hinshelwood (LH)- or Eley–Rideal (ER)-type CO oxidation steps to reduce it again to Pt(CO). In situ/operando infrared experiments, including cryogenic CO adsorption and isotopic CO exchange, confirm the combined involvement of strongly adsorbed CO and gas-phase CO in an Eley–Rideal step along the reaction cycle. Microkinetic modeling shows that Pt single atoms are present in a mixture of Pt(CO), Pt(CO)(O 2 ), Pt(O)(CO)(O 2 ), and Pt(CO)(CO 3 ) structures as the main intermediates during steady-state CO oxidation, all having the C–O vibrational stretch close to the experimentally observed value of 2115 cm –1 . Microkinetic modeling also shows that the fractional orders of CO and O 2 measured experimentally originate from multiple steps with a high degree of rate control and not from a simple competitive adsorption. The results demonstrate the complex reaction pathways that even CO oxidation on a simple single-atom system can follow, providing mechanistic insights for designing efficient Pt-based single-atom catalysts. Here, we further show that microkinetic modeling results are sensitive to changes in energies of intermediate and transition states within errors of density functional theory, which can ultimately lead to incorrect conclusions regarding the reaction pathways and most abundant reaction intermediates if not accounted for by experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A Detailed Vehicle Simulation Process to Support CAFE and CO 2 Standards (MY 2021–2026 Final Rule Analysis)

In 1975, Congress passed the Energy Policy and Conservation Act (EPCA), requiring standards for corporate average fuel economy (CAFE), and charging the U.S. Department of Transportation (DOT) with the establishment and enforcement of these standards. The Secretary of Transportation has delegated these responsibilities to the National Highway Traffic Safety Administration (NHTSA). NHTSA has contracted the DOT Volpe National Transportation Systems Center (Volpe Center) to provide analytical support for NHTSA’s regulatory and analytical activities related to fuel economy standards. Unlike long-standing safety and criteria pollutant emissions standards, fuel economy standards apply to manufacturers’ overall fleets rather than to individual vehicle models. In developing the standards, NHTSA made use of the CAFE Compliance and Effects Modeling System (the “CAFE model”), which was developed by DOT’s Volpe Center for the 2005-2007 CAFE rulemaking and has been continually updated since. The model is the primary tool used by the agency to evaluate potential CAFE stringency levels by applying technologies incrementally to each manufacturer’s fleet until the requirements under consideration are met. The CAFE model relies on numerous technology-related and economic inputs such as market forecasts and technology cost and effectiveness estimates; these inputs are categorized by vehicle classification, technology synergies, phase-in rates, cost learning curve adjustments, and technology “decision trees.” The Volpe Center assists NHTSA in the development of the engineering and economic inputs to the CAFE model by analyzing the application of potential technologies to the current automotive industry vehicle fleet to determine the feasibility of future CAFE standards, the associated costs, and the benefits of the standards.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Expanding market opportunities: cogeneration strategies for integrated PWR and thermal energy storage systems

We assess the economic viability of nuclear cogeneration by investigating three different modes—fixed dispatch, fully flexible dispatch, and flexible dispatch with minimum heat supply requirements. The analysis focuses on an existing pressurized water reactor (PWR) integrated with thermal energy storage (TES). Heat production costs are estimated under these modes for two U.S. electricity markets: the Electric Reliability Council of Texas (ERCOT) and the Pennsylvania–New Jersey–Maryland Interconnection (PJM). A sensitivity analysis examines profitability at varying heat market prices. Results indicate that fixed heat dispatch inflates heat production costs, often rendering projects economically feasible only at higher heat price levels. Fully-flexible dispatch lowers heat production costs by an average of 43 % compared to fixed dispatch. However, the current 30 % thermal dispatch limit may be insufficient to serve high baseline industrial demands cost‐effectively; higher maximum dispatch rates could enhance project economics. Markets with higher and more volatile electricity prices (e.g., ERCOT) offer greater total energy sales potential (i.e., heat and electricity), but also increase opportunity costs when heat production scheduling restrictions are imposed. In contrast, lower-price, less volatile markets (e.g., PJM) experience smaller impacts from such constraints and provide greater flexibility in accommodating varying cogeneration modes. In conclusion, these findings provide a framework to guide nuclear plant operators in aligning cogeneration strategies with industrial process requirements and electricity market conditions.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Charge Reduction and Performance Analysis of a Heat Pump Water Heater Using R290 as a Refrigerant—A Field Study

Heat pump water heaters (HPWHs) are a proven technology for water heating that has been commercialized. The adoption of HPWHs for domestic and commercial water heating is growing rapidly because of their superior performance compared with alternative water heating methods. Whereas most existing systems use R-134a as a working refrigerant, R290 has gained major attention owing to its superior thermodynamic properties. The goal of the current study is to assess the performance of residential HPWH with R290 as a direct refrigerant replacement for R134a. Two units of a 50 gal HPWH were used in this experimental study. A baseline unit contained R134a refrigerant, and a prototype unit contained R290 refrigerant. The prototype unit was developed through the modification of a commercially available HPWH unit to achieve a low charge of R290 refrigerant. Another major modification was the replacement of the baseline compressor with a compressor designed for R290. Tests were conducted in a field environment (a research and demonstration house) using programmed drawn profiles daily. The prototype that reduced the charge by 43–47% provided displayed performance comparable to the baseline unit regarding first-hour rating (FHR) and the uniform energy factor (UEF).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Computational Modeling of Graphite Degradation due to Molten Salt Infiltration and Wear

Molten-salt reactors (MSRs) represent a promising next-generation reactor design, with graphite serving as a moderator and/or reflector in several designs. However, due to limited experimental data and operational experience, a technical understanding of the structural integrity of graphite in molten salt environments remains incomplete. This report presents a modeling-based evaluation of graphite degradation in MSR environments, focusing on the effects of salt infiltration in fuel salt-based designs and surface wear in pebble bed reactor designs. The objective of this study is to enhance understanding of the structural integrity challenges posed by these degradation mechanisms and to provide a framework for assessing graphite behavior in MSRs. The first part of the report investigates the phenomenon of molten salt infiltration into graphite. This infiltration occurs when molten salt permeates the interconnected pore structure of the graphite moderator, driven by factors such as pressure differentials and the physical properties of both the salt and graphite. The infiltration process is influenced by characteristics of the pore structure, viscosity of the molten salt, and the interfacial energies between the graphite, salt, and the atmosphere within the graphite pore. Utilizing a coupled multiphysics modeling approach with Grizzly software, the study evaluates the stress induced by internal heat sources due to infiltration, which can lead to structural concerns. This evaluation is crucial for understanding how infiltration affects the mechanical integrity of graphite components in MSRs. The study considers the Molten-Salt Reactor Experiment (MSRE) graphite stringer geometry due to the availability of relevant data. Through detailed finite element analysis, the study examines stress distributions at varying infiltration percentages, revealing that stress levels increase with higher amounts of infiltration. Rare-event simulations, using the parallel subset simulation (PSS) framework, further quantify the failure probabilities under input uncertainties, with a user-specified failure metric. The PSS framework also identifies critical input parameters that significantly affect the stress values, including infiltration amount, thermal conductivity, and power density. Additionally, considering realistic reactor scenarios, the analysis was performed to account for the combined effects of radiation and infiltration, and modeling strategies on how to analyze new reactor designs or new graphite grades are discussed. The second part of the report focuses on wear mechanisms in pebble bed-based MSRs. As graphite fuel pebbles interact with the graphite reflector block, wear can result in material loss and the formation of surface defects, which may act as stress concentrators. A similar multiphysics modeling framework is employed to assess the impact of wear on the structural integrity of graphite components. This study considers a generic fluoride-cooled high-temperature reactor (gFHR) design due to the availability of comprehensive data. Worst-case scenario dimensions of the reflector blocks were analyzed under thermal and radiation conditions. Subsequently, wear in the form of idealized pits and grooves is modeled on the inner surface of the graphite block, with the maximum stress from previous simulations. The simulations show that groove-type defects are more detrimental than pits, leading to higher stress concentrations. Considering worst-case simulation scenarios and experimental wear rates, it was determined that the formation of a surface defect critical enough to affect the stress may not be possible in a gFHR design. Overall, the findings of this research contribute to the development of robust modeling tools for predicting graphite behavior under various operational conditions in MSRs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

The Relationship of Hydrology in the Green River to Colorado Pikeminnow Populations

The Federally listed Colorado Pikeminnow (CP) (Ptychocheilus Lucius) has been declining in the Green River Basin over the last 20 years. Muth et al. (2000) recommended flows and temperatures to benefit endangered fish species downstream of Flaming Gorge Dam on the Green River. The recommendations identified a range of flows during the base flow period (flows that occur after the annual snowmelt runoff period, usually from early summer to the following spring) that were intended to benefit CP. The recommendations indicated that the mean flow for the summer–winter period should be established each year on the basis of anticipated hydrologic conditions, but adjustments could be made if hydrologic conditions change. Flow should gradually decline from peak flow to base flow, with the base flow reached by early to middle summer (depending on hydrologic conditions) and maintained through February. The rate of decline should depend on rates of decline in Yampa River flows and the recommended rates of decline for dam releases. No specific recommendations were made for base flow variation in Reach 1, but variation around the annual mean base flow should be restricted to achieve recommended levels of variation in Reach 2 (middle Green River) (Table 5.5 in Muth et al. 2000). Implementation of the flow and temperature recommendations served as the proposed action for an EIS on Flaming Gorge Dam operations. A Biological Assessment of the impacts on endangered species of implementing the recommendations was prepared and a Biological Opinion that called for development of a study plan to examine the effects of implementation. Projects to address this evaluation were identified in the Green River Study Plan and have been conducted since then. The purpose of the Study Plan was to identify and recommend to the Recovery Program those monitoring or research projects necessary for implementation and evaluation of the flow and temperature recommendations. Those projects included studies to evaluate the anticipated effects of implementing the recommendations (including potential adverse effects identified in the 2006 Biological Opinion) and studies to examine recognized uncertainties of the recommendations. A new recovery program summer base flow study plan was approved in September of 2020 to evaluate stable and elevated summer base flows below Flaming Gorge Dam intended to benefit CP spawning and rearing success. Anecdotal observations suggest CP production may have been higher in years prior to flow restrictions for endangered fish. Based on long-term CP monitoring data in the Green River, Bestgen and Hill (2016) concluded that CP would benefit from higher summer baseflows (1,700-3,000 ft 3 /sec) with flows above or below associated with lower abundance of age-0 CP. Therefore, flexibility in the ROD is currently being utilized to establish higher stable summer base flows in an attempt create more advantageous conditions for spawning and rearing CP. These fish recovery flows are restrictive to WAPA in the summer months, therefore it is necessary to conduct an analysis of historic river data relationships to CP abundance to better understand if the recommended fish flows (elevated and stable base flows) are necessary for fish recovery.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

WEC-Sim Modeling of Laminar Scientific's Patented Seesaw Wave Energy Converter: Preprint

Laminar Scientific's patented seesaw wave energy converter was modeled in WEC-Sim to predict performance. The device operates by utilizing ocean surface waves to rotate a truss in pitch about a pivot. The pivot is located at the top of two pylons, which are embedded in the seafloor. The seesaw has a float on either end, and the buoyancy forces from each float cause the system to rise or fall with passing waves. Device performance relies upon seesaw length and ocean wavelength creating an antiphase effect. The seesaw truss has an adjustable length intended to achieve this effect. The operation method enforces a narrow band of wavelengths which induce the largest rotational motion from the device. The hydrodynamic analysis of the device was performed using Capytaine, and the results confirmed that the device operates best in a narrow frequency band. Four float-to-float spacing cases and three pylon radii were examined. The hydrodynamic results indicate a match between the model and the physical expectations for the device, and that varying the pylon radii by 0.1-m increments for three instances creates minimal changes in hydrodynamic properties. Power matrices for three float spacing cases of the device were simulated with Joint North Sea Wave Project spectra waves and optimal power take-off damping in WEC-Sim. The maximum average power production for the 15-m spacing case was 14.1 kW with a 5.0-s peak wave period and 4-m significant wave heights. Plots of capture-width ratios indicated that the device performance was linear and confirmed that the device is optimal in a narrow frequency band. The maximum percentage of the available wave power produced by the 15-m device was approximately 16%. Simulations of the device in regular waves were used to produce plots of average power compared to a ratio of float spacing to wavelength. These plots indicate that the power production is maximized at a ratio of 0.5, and further confirm that the device has a narrow frequency response. The device was simulated at an example field location, where the device produced an annual average power rating of 1.6 kW given an average omnidirectional wave climate of 10.3 kW m-1 and an optimal, linearized power take-off model. While the maximum predicted device performance is reliant upon a narrow band of wave frequencies, the conducted analysis provides an opportunity to improve device design prior to prototyping and testing. Modifying the design to respond to a broader frequency range would improve device performance.

numerical modeling↗
DOE OSTI2026

Theoretical analysis of a refrigerant injection-enhanced air source heat pump with thermal energy storage for extended cold-climate and defrosting operations

This study investigates a conceptual refrigerant injection-enhanced air source heat pump with integrated thermal energy storage (ITES-ASHP) that combines two-phase flash tank injection with phase change material (PCM) thermal energy storage (TES) to extend cold-climate operation and maintain indoor heating during defrost. A steady-state, mode-based thermodynamic cycle model is developed to evaluate heating and defrosting modes with and without refrigerant injection and TES integration over an outdoor temperature range of 8 to −25 °C. The framework is applied to refrigerants R-290, R-454B, R-410 A, and R-32. A parametric study is conducted for an 18-kW system rated for an outdoor temperature of 8 °C, with the compressor frequency scheduled to 150 Hz. The optimal PCM phase-change temperature is identified as 20 °C for R-290, R-454B, and R-410 A, and 15 °C for R-32. At the design condition of −25 °C outdoor temperature, the proposed concept achieves a combined COP of 2.22 for R-290, corresponding to a 60% increase relative to the theoretical single-stage baseline for combined heating and defrosting operation. Safety screening based on compressor discharge temperature (≤ 120 °C) and injection quality (≥ 0.65), together with performance evaluation, indicates that R-290 is the most suitable candidate, followed by R-410 A and R-454B. Under the assumptions and constraints used here, R-32 is found to be less suitable due to excessive discharge temperatures and a higher risk of wet compression.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Integrated Optimization and Control of a Hybrid Gas Turbine/sCO 2 Power System

During phase-I, the project team led by Echogen Power Systems (EPS) had two primary objectives based on investigating the application of gas turbines with supercritical carbon dioxide (sCO 2 ) power cycles. The first objective was to improve the overall efficiency and performance of a hybrid gas turbine/sCO 2 power system through a joint optimization of the two subsystems (gas turbine and sCO 2 power cycle) using non-linear optimization techniques that simultaneously evaluate thermal performance of the combined cycle. The hybrid power system included several points of interaction, including (but not limited to) gas turbine exhaust, fuel heating, inlet chilling and turbine cooling. The second objective was to establish a baseline transient response model of the hybrid power system and a notional microgrid and begin steps to integrate the control systems of the three major elements (gas turbine, sCO 2 cycle and grid controller). The project team established a baseline performance for a combined cycle power plant using a production gas turbine and scaled sCO 2 power cycle only utilizing exhaust heat recovery. Echogen’s non-linear techno-economic optimization code was extended by adding gas turbine component models derived from a in-house developed gas turbine design code. With the two cycles coupled by the gas turbine exhaust, design parameters of both cycles were allowed to vary simultaneously to determine performance opportunity versus isolated designs. Returning to the baseline gas turbine/sCO 2 power cycle transient models: Echogen had in-house developed sCO 2 cycle transient model in GT-Suite system simulation software, and had partnered with Siemens Finspång for gas turbine transient model, and Siemens PTI group to provide micro-grid load profile as well as hybrid power cycle generated load (power and frequency) analysis. The transient model for the SGT-750 Siemens gas turbine was a “black-box” functional mock-up interface (FMI) model developed by Siemens Industrial Turbomachinery in Finspång, Sweden. The SGT-750 is a twin-shaft gas turbine that produces 40 MW electricity with an efficiency of about 40% at ISO conditions. At 100% gas turbine throttle (load), the SGT-750 has average exhaust conditions of 114.6 kg/s and 469.8°C. The transient model for sCO 2 power cycle was developed by Echogen in GT-SUITE 1D system simulation software platform. The basic CO 2 flow circuit has single-shaft turbomachinery with net 11.5 MW electrical power output at design conditions. The power turbine has a double-ended shaft with one end connected to synchronous generator through a fixed-ratio gearbox. The other end of power turbine is connected to the compressor through a continuously variable transmission. The major components of the sCO 2 power cycle modeled include air cooled condenser/cooler, CO 2 compressor, recuperator, two waste heat exchanger coils, power turbine, continuous variable transmission, gearbox and generator. Integration of SGT-750 transient model and sCO 2 power cycle transient model was done in Matlab Simulink. In the integrated model, the gas turbine and sCO 2 power cycle interacted at two points, first one being the gas turbine exhaust gas flow rate and temperature, which were inputs to sCO 2 power cycle model. The second point was the distribution of micro-grid load demand signal between the SGT-750 generator and sCO 2 cycle generator. For a given combined-cycle load demand, the gas turbine load demand was equal to the total demand minus the sCO 2 cycle power generated. In the present study the integrated model was simulated for two cases of grid load demand: (i) for a step change, both positive-step and negative-step, in grid load demand (ii) for a micro-grid load demand curve provided by Siemens PTI group. Finally, the time series plots representing load demand versus integrated system response were presented including the sCO 2 power cycle control system performance plots. The actual generated power and frequency of both the generators, gas turbine and sCO 2 power cycle, was supplied to Siemens PTI group for dynamic grid assessment, results of which are provided in appendices.

03 NATURAL GAS↗
DOE OSTI2021

How New Mexico Leveraged a COVID-19 Case Forecasting Model to Preemptively Address the Healthcare Needs of the State: Quantitative Analysis

Prior to the COVID-19 pandemic, US hospitals relied on static projections of future trends for long-term planning and were only beginning to consider forecasting methods for short-term planning of staffing and other resources. With the overwhelming burden imposed by COVID-19 on the health care system, an emergent need exists to accurately forecast hospitalization needs within an actionable timeframe. Our goal was to leverage an existing COVID-19 case and death forecasting tool to generate the expected number of concurrent hospitalizations, occupied intensive care unit (ICU) beds, and in-use ventilators 1 day to 4 weeks in the future for New Mexico and each of its five health regions. We developed a probabilistic model that took as input the number of new COVID-19 cases for New Mexico from Los Alamos National Laboratory’s COVID-19 Forecasts Using Fast Evaluations and Estimation tool, and we used the model to estimate the number of new daily hospital admissions 4 weeks into the future based on current statewide hospitalization rates. The model estimated the number of new admissions that would require an ICU bed or use of a ventilator and then projected the individual lengths of hospital stays based on the resource need. By tracking the lengths of stay through time, we captured the projected simultaneous need for inpatient beds, ICU beds, and ventilators. We used a postprocessing method to adjust the forecasts based on the differences between prior forecasts and the subsequent observed data. Thus, we ensured that our forecasts could reflect a dynamically changing situation on the ground. Forecasts made between September 1 and December 9, 2020, showed variable accuracy across time, health care resource needs, and forecast horizon. Forecasts made in October, when new COVID-19 cases were steadily increasing, had an average accuracy error of 20.0%, while the error in forecasts made in September, a month with low COVID-19 activity, was 39.7%. Across health care use categories, state-level forecasts were more accurate than those at the regional level. Although the accuracy declined as the forecast was projected further into the future, the stated uncertainty of the prediction improved. Forecasts were within 5% of their stated uncertainty at the 50% and 90% prediction intervals at the 3- to 4-week forecast horizon for state-level inpatient and ICU needs. However, uncertainty intervals were too narrow for forecasts of state-level ventilator need and all regional health care resource needs. Real-time forecasting of the burden imposed by a spreading infectious disease is a crucial component of decision support during a public health emergency. Our proposed methodology demonstrated utility in providing near-term forecasts, particularly at the state level. This tool can aid other stakeholders as they face COVID-19 population impacts now and in the future.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Integrated Methane Monitoring Platform Extension, Volume I: Final Technical Report

The IMMPE project, DE-FE0032284, was to enhance methane monitoring technologies and their applications across various natural gas asset classes. The scope included deploying advanced methane detection and monitoring technologies to identify and mitigate fugitive methane emissions, measuring emission rates, and assessing impacts. The findings included the successful mitigation of identified emissions and quantification of emission rates. A key outcome was the development of a comprehensive template and summary of recommendations for methane emissions monitoring, which is replicable for both upstream and downstream applications. Furthermore, the project emphasized the importance of education by providing training opportunities for technicians and regulators, thereby fostering awareness and promoting the adoption of cost-effective methane emissions monitoring and management techniques.

02 PETROLEUM↗