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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Wind turbine power production and annual energy production depend on atmospheric stability and turbulence

Using detailed upwind and nacelle-based measurements from a General Electric (GE) 1.5sle model with a 77 m rotor diameter, we calculate power curves and annual energy production (AEP) and explore their sensitivity to different atmospheric parameters to provide guidelines for the use of stability and turbulence filters in segregating power curves. The wind measurements upwind of the turbine include anemometers mounted on a 135 m meteorological tower as well as profiles from a lidar. We calculate power curves for different regimes based on turbulence parameters such as turbulence intensity (TI) as well as atmospheric stability parameters such as the bulk Richardson number ( R B ). We also calculate AEP with and without these atmospheric filters and highlight differences between the results of these calculations. The power curves for different TI regimes reveal that increased TI undermines power production at wind speeds near rated, but TI increases power production at lower wind speeds at this site, the US Department of Energy (DOE) National Wind Technology Center (NWTC). Similarly, power curves for different R B regimes reveal that periods of stable conditions produce more power at wind speeds near rated and periods of unstable conditions produce more power at lower wind speeds. AEP results suggest that calculations without filtering for these atmospheric regimes may overestimate the AEP. Because of statistically significant differences between power curves and AEP calculated with these turbulence and stability filters for this turbine at this site, we suggest implementing an additional step in analyzing power performance data to incorporate effects of atmospheric stability and turbulence across the rotor disk.

17 WIND ENERGY↗
DOE OSTI2021

An overview of wind-energy-production prediction bias, losses, and uncertainties

Abstract. The financing of a wind farm directly relates to the preconstruction energy yield assessments which estimate the annual energy production for the farm. The accuracy and the precision of the preconstruction energy estimates can dictate the profitability of the wind project. Historically, the wind industry tended to overpredict the annual energy production of wind farms. Experts have been dedicated to eliminating such prediction errors in the past decade, and recently the reported average energy prediction bias is declining. Herein, we present a literature review of the energy yield assessment errors across the global wind energy industry. We identify a long-term trend of reduction in the overprediction bias, whereas the uncertainty associated with the prediction error is prominent. We also summarize the recent advancements of the wind resource assessment process that justify the bias reduction, including improvements in modeling and measurement techniques. Additionally, because the energy losses and uncertainties substantially influence the prediction error, we document and examine the estimated and observed loss and uncertainty values from the literature, according to the proposed framework in the International Electrotechnical Commission 61400-15 wind resource assessment standard. From our findings, we highlight opportunities for the industry to move forward, such as the validation and reduction of prediction uncertainty and the prevention of energy losses caused by wake effect and environmental events. Overall, this study provides a summary of how the wind energy industry has been quantifying and reducing prediction errors, energy losses, and production uncertainties. Finally, for this work to be as reproducible as possible, we include all of the data used in the analysis in appendices to the article.

17 WIND ENERGY↗
DOE OSTI2026

Biases in preconstruction estimates of wind plant annual energy production

Estimating the energy yield of a wind plant during the preconstruction phase is a historically difficult task, even with industry improvements in these estimations. We build on prior research comparing the realized energy production of wind plants and their estimated annual energy production P50 values (median energy production), using owner-provided energy production and losses. We produced similar results to prior studies but with a slightly increasing bias of overestimating median energy production (a bias between realized and estimated energy production of −7.4 % to −6.6 %, depending on the scenario, as opposed to −6.7 % to −5.5 % from earlier studies). In addition to assessing annual energy production P50 bias, we compared both the 1-year and the long-term annual energy production P90 and uncertainty energy yield assessment estimates to the observed long-term-corrected energy production. We found that neither the energy yield assessment uncertainty nor the P90 is conservative enough compared to the observed distribution of prediction errors, suggesting significant room for improvement in the energy yield assessment process.

17 WIND ENERGY↗
DOE OSTI2016

Accelerator Reactor Coupling for Energy Production in Advanced Nuclear Fuel Cycles

This article is a review of several accelerator–reactor interface issues and nuclear fuel cycle applications of acceleratordriven subcritical systems. The systems considered here have the primary goal of energy production, but that goal is accomplished via a specific application in various proposed nuclear fuel cycles, such as breed-and-burn of fertile material or burning of transuranic material. Several basic principles are reviewed, starting from the proton beam window including the target, blanket, reactor core, and up to the fuel cycle. We focus on issues of interest, such as the impact of the energy required to run the accelerator and associated systems on the potential electricity delivered to the grid. Accelerator-driven systems feature many of the constraints and issues associated with critical reactors, with the added challenges of subcritical operation and coupling to an accelerator. Reliable accelerator operation and avoidance of beam trips are critically important. One interesting challenge is measurement of blanket subcriticality level during operation. We also review the potential benefits of accelerator-driven systems in various nuclear fuel cycle applications. Ultimately, accelerator-driven subcritical systems with the goal of transmutation of transuranic material have lower 100,000-year radioactivity than a critical fast reactor with recycling of uranium and plutonium.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2015

Communication: State-to-state dynamics of the Cl + H{sub 2}O → HCl + OH reaction: Energy flow into reaction coordinate and transition-state control of product energy disposal

Quantum state-to-state dynamics of a prototypical four-atom reaction, namely, Cl + H{sub 2}O → HCl + OH, is investigated for the first time in full dimensionality using a transition-state wave packet method. The state-to-state reactivity and its dependence on the reactant internal excitations are analyzed and found to share many similarities both energetically and dynamically with the H + H{sub 2}O → H{sub 2} + OH reaction. The strong enhancement of reactivity by the H{sub 2}O stretching vibrational excitations in both reactions is attributed to the favorable energy flow into the reaction coordinate near the transition state. On the other hand, the insensitivity of the product state distributions with regard to reactant internal excitation stems apparently from the transition-state control of product energy disposal.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Impacts of Voltage-Based Grid Support Functions on Energy Production of PV Customers

This paper presents the impact of inverter grid- support functions (GSFs) on photovoltaic (PV) customer energy production on a real distribution feeder in Oahu, HI. These autonomous GSFs based on local voltage measurements are good alternatives to increase PV hosting capacity. However, these functions can result in PV energy curtailment to the customer, and this study addresses the concerns about the impact of inverter GSFs through detailed quasi-static time series (QSTS) simulations. It proposes four metrics: maximum and average GSF curtailment, average increased generation and average net generation change, to assess the impact of a given control on PV systems located on customer sites. It was found that curtailment of PV production is negligible for customers where peak voltage is within ANSI C84.1 range. A reliable relationship between curtailment and peak customer voltage is demonstrated, suggesting that peak voltage could be used as an indicator of customer curtailment.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

A Novel and Practical Method to Quantify the Quality of Mobility: The Mobility Energy Productivity Metric: Preprint

Recent technology innovations are enabling fundamental improvements in mobility systems, including options for new travel modes, methods, and opportunities to connect people with goods, services, and employment. A desire to quantify and compare both existing and emerging transportation options motivated development of the mobility energy productivity (MEP) metric described herein. The MEP metric fundamentally measures the potential of a city's transportation system to connect a person to a variety of services and activities that define a high-quality of life, relative to the convenience, cost and energy needed to provide these connections. Fundamentally derived from accessibility theory, the MEP advances practice by using readily available travel time data (either from web-based application programming interfaces (APIs) or outputs from an urban transportation model) combined with established parameters that reflect the energy intensity and cost of various travel modes, and relative frequency of activity engagement. The construction of the MEP metric allows for aggregation and disaggregation to the appropriate spatial, modal, and trip purpose resolution, as analysis needs dictate. The MEP could be used to compare alternative futures related to technology, infrastructure investment, or policy, providing a much-needed tool for planners, researchers, and analysts.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Predictive Data-driven Platform for Subsurface Energy Production

Subsurface energy activities such as unconventional resource recovery, enhanced geothermal energy systems, and geologic carbon storage require fast and reliable methods to account for complex, multiphysical processes in heterogeneous fractured and porous media. Although reservoir simulation is considered the industry standard for simulating these subsurface systems with injection and/or extraction operations, reservoir simulation requires spatio-temporal “Big Data” into the simulation model, which is typically a major challenge during model development and computational phase. In this work, we developed and applied various deep neural network-based approaches to (1) process multiscale image segmentation, (2) generate ensemble members of drainage networks, flow channels, and porous media using deep convolutional generative adversarial network, (3) construct multiple hybrid neural networks such as convolutional LSTM and convolutional neural network-LSTM to develop fast and accurate reduced order models for shale gas extraction, and (4) physics-informed neural network and deep Q-learning for flow and energy production. We hypothesized that physicsbased machine learning/deep learning can overcome the shortcomings of traditional machine learning methods where data-driven models have faltered beyond the data and physical conditions used for training and validation. We improved and developed novel approaches to demonstrate that physics-based ML can allow us to incorporate physical constraints (e.g., scientific domain knowledge) into ML framework. Outcomes of this project will be readily applicable for many energy and national security problems that are particularly defined by multiscale features and network systems.

58 GEOSCIENCES↗
DOE OSTI2015

Energy Productivity of the High Velocity Algae Raceway Integrated Design (ARID-HV)

The ARID raceway is an effective method to maintain temperature in the optimal growing range. However, the energy input is excessive. Thus, the ARID-HV raceway was developed in order to reduce energy input requirements. This was accomplished by improving pumping efficiency and using a serpentine flow pattern in which the water flows through channels instead of over barriers. A prototype ARID-HV raceway was installed in Tucson, Arizona in order to evaluate the flow and energy requirements of the raceway. Preliminary results show continued high energy usage, but this paper explores the possible energy reductions with the new ARID-HV design with efficient propeller pumps and properly engineered channel lengths. Channel lengths are evaluated with Manning’s equation in order to determine the maximum channel length that could be implemented with the current pumping configuration

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

CERF – A Geospatial Model for Assessing Future Energy Production Technology Expansion Feasibility

Electricity system expansion planning faces significant challenges not only because of commonly modeled uncertainties such as future demand growth, fuel prices, and technology cost and performance, but also because of challenges such as climate change and carbon policies that can affect supply and demand. Changes in energy technology, natural resource availability, and population migration and growth will affect the feasibility of power plant siting, potentially hindering the achievement of policy goals. New modeling approaches are needed that couple future projections of energy system expansion with “on the ground” feasibility modeling—where both are informed by a consistent set of assumptions about future climate trends, resource availability, climate policy, socioeconomics, and environmental regulations. This research describes the development and evaluation of the Capacity Expansion Regional Feasibility (CERF) model, a geospatial tool for scenario analysis of electricity system expansions.

grid expansion, water-energy nexus, power plant si↗
DOE OSTI2017

U.S. Industrial Sector Energy Productivity Improvement Pathways

Economy-wide energy systems forecasting models often propose deep emission reduction pathways as a course of action to achieve environmental goals such as de-carbonization. These pathways typically assume that industries adopt all possible energy efficiency measures and replace their remaining energy demand with low e missions resources, e.g. , renewable or nuclear energy. However, given the significant diversity of energy sources and energy-consuming processes, this over-simplifies the real-world technology, process, and co st constraints common to the industrial sector and is a weakness in existing pathways. Moreover, these pathways do not adequately consider the importance of manufacturing to economic prosperity and for producing the technologies that achieve economy-wide environmental goals. This paper reviews historical industrial energy productivity and introduces an analytic framework for developing industrial sector energy productivity pathways that are driven by the goal to transform both the business as usual trends within the industrial sector, as well as the downstream impacts attributable to manufactured products. The framework includes (1) metrics for quantifying energy productivity growth in term s of physical units, value-added, and service; and (2) concurrent approaches for decoupling energy use an d emissions from manufacturing value-added. The framework presented here highlights that emission reduction pathways need to consider the industrial sector not in the aggregate, but as comprised of diverse manufacturing and non-manufacturing industries that affect the energy productivity of other economic sectors: electric power, transportation, and commercial sectors. Such pathways are better equipped to focus on improving the energy productivity of the US economy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Prospects for the Production of Silicon and Solar Energy Products in the Republic of Uzbekistan

The paper presents the results of a comparative analysis of the technical and economic parameters of the trichlorosilane and monosilane processes of silicon production. It is shown that Russia’s available gangue quartz, quartz sands, natural gas, and new methods of smelting technical silicon, as well as new technologies for its conversion into monosilane, makes it possible to organize environmentally friendly industrial production of raw silicon and competitive, highly efficient photovoltaic energy products based on monosilane technology.

14 SOLAR ENERGY↗
DOE OSTI2017

Additively Manufactured, Net Shape Powder Metallurgy Cans for Valves Used in Energy Production

Additive manufacturing (AM) has the potential to enable a paradigm shift in fabricating complex net shape components with yields over 90%, significantly increasing productivity, decreasing energy consumption during manufacturing and improving downstream energy efficiency. ORNL has developed a technology for producing large-scale additively manufactured polymer matrix composite components at deposition rates on the order of 100 lbs. per hour. This project utilized tools, dies and molds made from this technology to enable the net shape forming of cans for the powder metallurgy (PM) manufacturing of valves used in energy production plants. This approach has the potential to develop affordable valves with superior performance to castings. It will also lead to increased strength values, decreased wall thickness size, and the potential for higher yields (less machining). This project is critical for evaluating new methodologies for the fabrication of additively manufactured tooling, molds and dies that could be used across multiple industries. In the first phase of this project, a new methodology of creating a Powder Metallurgy and Hot Isostatic Pressing (PM-HIP) container by hydroforming sheet metal over additively manufactured (AM) dies was evaluated. The goal in this stage of development was to evaluate some of the new polymer matrix composite materials designed for deposition on large scale additive manufacturing systems as candidates for hydroforming steel and aluminum sheet to make thin gauge net shape components. In order to meet this objective, most research was performed on subscale tools in order to evaluate the processing pressures, material options, and gauge thicknesses most relevant to this approach. In addition, full scale AM tools (30% over final component geometry) were produced to demonstrate scalability, but were not used in testing. The first phase of this project has proven this concept has merit, but future studies are required to address minor tool failures that occurred in phase one due to geometry, cost of the process over other techniques, and demonstration of a full-scale tool that could then be used in (HIP). Lastly, this project provides the PM community a new approach in using hydroforming for the manufacturing of cans, a process that was not highly utilized for this purpose previously.

42 ENGINEERING↗
DOE OSTI2023

A multiscale recurrent neural network model for predicting energy production from geothermal reservoirs

Optimization of energy production from geothermal reservoirs requires reliable prediction of energy production performance under alternative operation and development scenarios. Traditionally, reservoir simulation models are used for the evaluation and screening of alternative production and development plans. However, simulation models require extensive data collection and modeling efforts and are time-consuming to build, run, and update. Data-driven predictive models, on the other hand, can serve as efficient prediction tools that can be used for decision support and management of daily operations and surveillance activities. Data-driven models become particularly attractive when a reservoir simulation model for a field does not exist and/or is difficult to build. Machine learning (ML)-based data-driven models that have recently become popular in several fields exploit statistical patterns and relations in training data to generate predictions. As such, they tend to perform better in interpolation problems (that is, prediction within the training data range) than when they are used to extrapolate beyond the training data. Production data from geothermal reservoirs tend to exhibit short-term variabilities as well as long-term trends, such as monotonically declining production temperatures. Capturing both short-term features and long-term trends with ML-based models is not trivial. We evaluate the use of recurrent neural networks (RNN) for the prediction of energy production from geothermal reservoirs. RNN is a class of ML architectures that are used to represent and predict sequential/dynamic data. Thus, it can be challenging to apply RNN to problems where long-term trends must be captured and extrapolation beyond the training data range is needed. We introduce the multiscale RNN architecture to extend the application of RNN to detect and predict both short-term variabilities and long-term trends in geothermal data. The developed architecture consists of a long-term component to only capture low-frequency data patterns, and a short-term component to detect features with higher frequency and more nonlinearity. The final prediction is obtained by combining the long-term and short-term predictions. Both synthetic and field data are used to evaluate the presented multiscale RNN model. The prediction performance of the multiscale RNN is compared against those obtained from the regular RNN and the autoregressive (AR) model. The results suggest that the multiscale architecture improves the long-term prediction performance of the regular RNN and enhances its robustness against noise.

15 GEOTHERMAL ENERGY↗
DOE OSTI2016

Multifluid geo-energy systems: Using geologic CO 2 storage for geothermal energy production and grid-scale energy storage in sedimentary basins

We present an approach that uses the huge fluid and thermal storage capacity of the subsurface, together with geologic carbon dioxide (CO 2 ) storage, to harvest, store, and dispatch energy from subsurface (geothermal) and surface (solar, nuclear, fossil) thermal resources, as well as excess energy on electric grids. Captured CO 2 is injected into saline aquifers to store pressure, generate artesian flow of brine, and provide a supplemental working fluid for efficient heat extraction and power conversion. Concentric rings of injection and production wells create a hydraulic mound to store pressure, CO 2 , and thermal energy. This energy storage can take excess power from the grid and excess/waste thermal energy, and dispatch that energy when it is demanded and thus enable higher penetration of variable renewable energy technologies (e.g., wind, solar). CO 2 stored in the subsurface functions as a cushion gas to provide enormous pressure-storage capacity and displace large quantities of brine, some of which can be treated for a variety of beneficial uses. Geothermal power and energy-storage applications may generate enough revenues to compensate for CO 2 capture costs. While our approach can use nitrogen (N 2 ), in addition to CO 2 , as a supplemental fluid, and store thermal energy, this study focuses using CO 2 for geothermal energy production and grid-scale energy storage. We conduct a techno-economic assessment to determine the levelized cost of electricity of using this approach to generate geothermal power. We present a reservoir pressure-management strategy that diverts a small portion of the produced brine for beneficial consumptive use to reduce the pumping cost of fluid recirculation, while reducing the risk of seismicity, caprock fracture, and CO 2 leakage.

15 GEOTHERMAL ENERGY↗