SEARCH · Engineering Papers

Results for “utility rate analysis”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 289 records · Page 16

All sources NASA NTRS DOE OSTI

DOE OSTI2024

From Bulk to Interface: Solvent Exchange Dynamics and Their Role in Ion Transport and the Interfacial Model of Rechargeable Magnesium Batteries

Multivalent battery chemistries have been explored in response to the increasing demand for high-energy rechargeable batteries utilizing sustainable resources. Solvation structures of working cations have been recognized as a key component in the design of electrolytes; however, most structure–property correlations of metal ions in organic electrolytes usually build upon favorable static solvation structures, often overlooking solvent exchange dynamics. We here report the ion solvation structures and solvent exchange rates of magnesium electrolytes in various solvents by using multimodal nuclear magnetic resonance (NMR) analysis and molecular dynamics/density functional theory (MD/DFT) calculations. These magnesium solvation structures and solvent exchange dynamics are correlated to the combined effects of several physicochemical properties of the solvents. Moreover, Mg 2+ transport and interfacial charge transfer efficiency are found to be closely correlated to the solvent exchange rate in the binary electrolytes where the solvent exchange is tunable by the fraction of diluent solvents. Our primary findings are (1) most battery-related solvents undergo ultraslow solvent exchange coordinating to Mg 2+ (with time scales ranging from 0.5 μs to 5 ms), (2) the cation transport mechanism is a mixture of vehicular and structural diffusion even at the ultraslow exchange limit (with faster solvent exchange leading to faster cation transport), and (3) an interfacial model wherein organic-rich regions facilitate desolvation and inorganic regions promote Mg 2+ transport is consistent with our NMR, electrochemistry, and cryogenic X-ray photoelectron spectroscopy (cryo-XPS) results. In conclusion, this observed ultraslow solvent exchange and its importance for ion transport and interfacial properties necessitate the judicious selection of solvents and informed design of electrolyte blends for multivalent electrolytes.

25 ENERGY STORAGE↗

DOE OSTI2024

Accelerating cavity fault prediction using deep learning at Jefferson Laboratory

Abstract Accelerating cavities are an integral part of the continuous electron beam accelerator facility (CEBAF) at Jefferson Laboratory. When any of the over 400 cavities in CEBAF experiences a fault, it disrupts beam delivery to experimental user halls. In this study, we propose the use of a deep learning model to predict slowly developing cavity faults. By utilizing pre-fault signals, we train a long short-term memory-convolutional neural network binary classifier to distinguish between radio-frequency (RF) signals during normal operation and RF signals indicative of impending faults. We optimize the model by adjusting the fault confidence threshold and implementing a multiple consecutive window criterion to identify fault events, ensuring a low false positive rate. Results obtained from analysis of a real dataset collected from the accelerating cavities simulating a deployed scenario demonstrate the model’s ability to identify normal signals with 99.99% accuracy and correctly predict 80% of slowly developing faults. Notably, these achievements were achieved in the context of a highly imbalanced dataset, and fault predictions were made several hundred milliseconds before the onset of the fault. Anticipating faults enables preemptive measures to improve operational efficiency by preventing or mitigating their occurrence.

43 PARTICLE ACCELERATORS↗

DOE OSTI2022

Hyperentangled Time-Bin and Polarization Quantum Key Distribution

Fiber-based quantum communication networks are currently limited without quantum repeaters. Satellite-based quantum links have been proposed to extend the network domain. Here, we develop a quantum communication system, suitable for realistic satellite-to-ground communication. With this system, we execute an entanglement-based quantum key distribution (QKD) protocol developed by Bennett, Brassard, and Mermin (BBM92), achieving quantum bit-error rates (QBERs) below 2% in all bases. More importantly, we demonstrate low-QBER execution of a higher-dimensional hyperentanglement-based QKD protocol, using photons simultaneously entangled in polarization and time bin, leading to significantly higher secure key rates, at the cost of increased technical complexity and system size. We show that our protocol is suitable for a space-to-ground link, after incorporating Doppler-shift compensation, and verify its security using a rigorous finite-key analysis. Additionally, we discuss system-engineering considerations relevant to those and other quantum communication protocols and their dependence on what photonic degrees of freedom are utilized.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

Preliminary Primary System Thermal Fluids Analysis of a Horizontal Compact HTGR

The Horizontal Compact High Temperature Gas Reactor (HC-HTGR) is being designed by a multi-disciplinary team of nuclear, mechanical, and structural engineers, under the support of a DOE-NE Advanced Reactor Demonstration Program’s Advanced Reactor Concepts-20 (ARC-20) award. The objective of this ARC-20 project is to deliver a conceptual design for the proposed HC-HTGR in 3 years and support its commercialization as a safe, low-cost HTGR. Argonne National Laboratory (Argonne) is collaborating on the thermal hydraulic design and analysis of the HC-HTGR reactor pressure vessel internals. The scope of this work includes ensuring the reactor is able to maintain maximum core temperatures below designated safety thresholds during normal operation, shutdown, and accident conditions. This report documents the preliminary thermal hydraulic analysis of the HC-HTGR core design performed with a 1D fluid-3D solid coupled model built using the System Analysis Module (SAM). This assembly level model was utilized to inform the core assembly design, predict the temperature distribution in the peak power assembly including the peak fuel temperature, coolant channel outlet temperatures, and graphite temperature gradients. A key result of this analysis was the determination that the peak fuel temperature remains below the safety threshold of 1250°C. Additionally, this model was used to assess the assembly coolant channel and bypass flow mass flow rate distribution. Following the assembly level analysis, attention turned to the development of a full core reduced order model to be used to predict the core wide coolant flow distribution and to model certain operational and accidental transients.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2020

Winners are not keepers: Characterizing household engagement, gains, and energy patterns in demand response using machine learning in the United States

Demand-response programs can help utilities manage rapidly evolving electric grids, but these programs are subject to the complexities of human behavior. This paper explores a novel method for uncovering heterogeneity in households. In this work, we use a machine-learning method known as a Conditional Inference Tree (c-tree) algorithm to categorize households based on their energy behavior characteristics collected via smart meters, and explore how this translates through into heterogeneity in their real-world response to a DR program. Using data from randomized controlled trial, we generate estimates of the changes in energy use caused by the program within each household group. Our results show that the c-tree approach differentiates households by their energy-use characteristics in a way that increases the spread in enrollment rates and critical peak reduction among household groups, compared with the spreads achieved via several conventional segmentation methods. Thus, the c-tree analysis enables the most tailored targeting of major potential energy savers and could provide the greatest increase in cost-effectiveness of household recruitment into DR programs. Our results also offer fresh insights into the relationships between household energy behavior characteristics – such as peak energy use and “structural winningness” (the ability to save money under a DR program without changing energy-use behaviors) – and household decisions about enrolling in DR programs and reducing energy use. Our research also demonstrates the potential of smart meter data, combined with machine learning and econometric methods, to provide significant value to utilities, program implementers, researchers, and other stakeholders.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Techno-economic analysis of roll-to-roll production of perovskite modules using radiation thermal processes

Perovskite solar technology can produce economically viable photovoltaic devices due to low cost of materials and scalable manufacturing. Solution phase deposition of all active layers lends itself to roll-to-roll fabrication methods which are well-established and have production rates of several hundred square meters per minute. In this techno-economic analysis, the opportunity to scale at this production throughput is considered with manufacturing rates exceeding one gigawatt per year in a single plant. Intense pulsed light is used for the post deposition process to minimize costs associated with the footprint, equipment, and operation. The analysis is limited to the operation of a roll-to-roll manufacturing facility producing a perovskite solar film that can be incorporated into photovoltaic modules. Costs associated with materials, labor, and equipment are considered along with utilities determined from the fundamental engineering calculations. Furthermore, the results show that at very large-scale operations of several gigawatts, the costs to produce the solar films range between $0.04-$0.10 per watt.

14 SOLAR ENERGY↗

DOE OSTI2023

Development of a Gas Chromatography with High-Resolution Time-of-Flight Mass Spectrometry Methodology for BDNPA/F

Analysis of thermally labile compounds such as bis(2,2-dinitropropyl) acetal/formal (BDNPA/F), an energetic plasticizer, is usually performed via liquid chromatography (LC) as opposed to gas chromatography (GC) due to thermal decomposition in the inlet or the analytical column. While LC is a powerful technique, the analysis of volatile and semivolatile compounds is best suited to GC. Herein, a method was developed for a gas chromatograph coupled to high-resolution mass spectrometer (GC-HRMS), utilizing a programmable temperature vaporizer (PTV) inlet. A subset of the native compounds and several produced by the thermal decomposition of BDNPA/F in the inlet were evaluated by using multiple PTV inlet parameters to determine the optimal ramp rate and final temperature of the inlet (60 °C/min from 60 to 325 °C). The optimized GC-HRMS method nearly reduced all thermal decomposition, allowing for an excellent separation to be obtained. Furthermore, multiple ionization methods, including electron impact (EI), negative chemical ionization (NCI), and positive chemical ionization (PCI), were used to explore the many chemical differences between the BDNPA/F samples. A preliminary investigation of the benefits of using GC-HRMS to evaluate the chemical differences between unaged and aged BDNPA/F samples for unique insight was evaluated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Applications of machine learning to a compact magnetic spectrometer for high repetition rate, laser-driven particle acceleration

Accurately and rapidly diagnosing laser–plasma interactions is often difficult due to the time-intensive nature of the analysis and will only become more so with the rise of high repetition rate lasers and the desire to implement feedback on a commensurate timescale. Diagnostic analysis employing machine learning techniques can help address this problem while maintaining a high degree of accuracy. We report on the application of machine learning to the analysis of a scintillator-based electron spectrometer for experiments on high intensity, laser–plasma interactions at the Colorado State University Advanced Lasers and Extreme Photonics facility. Our approach utilizes a neural network trained on synthetic data and tested on experiments to extract the accelerated electron temperature. By leveraging transfer learning, we demonstrate an improvement in the neural network accuracy, decreasing the network error by 50%.

Swanson, K. K. (ORCID:0000000286880568)↗

DOE OSTI2021

Anonymization of Network Traces Data through Condensation-based Differential Privacy

Network traces are considered a primary source of information to researchers, who use them to investigate research problems such as identifying user behavior, analyzing network hierarchy, maintaining network security, classifying packet flows, and much more. However, most organizations are reluctant to share their data with a third party or the public due to privacy concerns. Therefore, data anonymization prior to sharing becomes a convenient solution to both organizations and researchers. Although several anonymization algorithms are available, few of them allow sufficient privacy (organization need), acceptable data utility (researcher need), and efficient data analysis at the same time. This article introduces a condensation-based differential privacy anonymization approach that achieves an improved tradeoff between privacy and utility compared to existing techniques and produces anonymized network trace data that can be shared publicly without lowering its utility value. Our solution also does not incur extra computation overhead for the data analyzer. A prototype system has been implemented, and experiments have shown that the proposed approach preserves privacy and allows data analysis without revealing the original data even when injection attacks are launched against it. When anonymized datasets are given as input to graph-based intrusion detection techniques, they yield almost identical intrusion detection rates as the original datasets with only a negligible impact.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Understanding the Behavioral Aspects of Rate Design

Consumer demand for electricity has often been thought of as homogenous and inelastic. However, consumers may have preferences for their electricity consumption that they have been unable to realize due to the structures of electricity rates, and historic methods of regulation. As ratemaking becomes more dynamic, consumers will have a newfound ability to realize these preferences. This paper conducts a review of this issue through the lenses of utility regulation, neoclassical economics, and behavioral economics. It surveys what aspects of consumer preferences and behavior have been well established for the electricity sector and identifies gaps that could be filled by future research. This analysis has implications for stakeholders looking to understand how households will function in transactive and real time pricing environments.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Decay Dose Shielding Analysis with Hybrid Unstructured Mesh/Constructive Solid Geometry Monte Carlo Calculation and ADVANTG Acceleration

The Oak Ridge National Laboratory (ORNL) Second Target Station (STS) neutron production facility is an accelerator driven pulsed neutron source that is currently being actively developed at ORNL. The neutrons are produced by proton-induced spallation reactions. A proton beam of 700 kW power is delivered to a spallation target in short, less than 1 µs long pulses, with 15 Hz repetition rate. The spallation target of ORNL STS is a rotating water-cooled tungsten target with tantalum cladding housed in a stainless-steel shroud. It is divided into 21 segments. These segments become highly activated due to spallation reactions or nuclei transmutation by the emitted neutrons. The radioactive nuclides continue to decay after ceasing operation. The decay dose rates generated from the target segments once they are removed from their operational location within the core vessel must be accurately quantified to determine the shielding configurations of remote handling tools and transport casks and to aid in planning maintenance events. To determine the shielding configurations needed for an activated target segment after ceasing operation, both the hybrid unstructured mesh (UM)/constructive solid geometry (CSG) approach that was previously utilized for STS analyses [1] and the ADVANTG code [2] were used. Even though the ADVANTG code does not include UM capability, the utilization of its advanced variance reduction technique was crucial to accelerate the extremely difficult final photon transport calculation in this analysis. This paper also describes the procedures taken to mitigate the convergence issues that occur when ADVANTG uses a source definition that does not match the source of the final Monte Carlo (MC) calculation. These convergence issues often occur because of inconsistencies between source and transport biasing parameters.

Ibrahim, Ahmad↗

DOE OSTI2026

SAF: a promising approach to meet growing jet fuel demand

SAF provides a promising approach to aid the rising jet fuel demand from increased travel around the world and reduce the lifecycle emissions from the aviation sector. Although the feasibility of SAF pathways has been demonstrated through economic and environmental metrics quantification, the models used to quantify these variables have a high degree of variability in terms of accuracy and thereby reliability. To understand how to adopt and commercialize SAF, we need to harmonize these process models and assess metrics and technical limitations related to their production technologies. We find the production cost of SAF using hydro processed fatty acids and esters (HEFA), Fischer-Tropsch (FT), and alcohol-to-jet (ATJ) to be $\$$3-$\$$6/gallon gasoline equivalent (gge) and life cycle emissions to be lower than Jet A, except for ATJ using corn grain (≤25%). HEFA utilizing oil feedstocks has the lowest production cost (~$\$$2.9/gge) and highest jet yield (>150 gge/dry ton), while FT has the largest emission reduction (94%) compared to fossil jet. A unique contribution of this study is a comparative analysis of metrics related to SAF processes across technical, economic, and sustainability aspects. A cross-comparison of these metrics shows HEFA using fats, oils, and grease have the most favorable ratings, while HEFA using algae and ATJ using corn stover have more neutral and unfavorable ratings, respectively. These ratings can be improved by implementing the right combination of practical and technological advancements.

09 BIOMASS FUELS↗

DOE OSTI2024

RuBisCO activity assays: a simplified biochemical redox approach for in vitro quantification and an RNA sensor approach for in vivo monitoring

Abstract Background Ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) is the most abundant soluble protein in nature. Extensive studies have been conducted for improving its activity in photosynthesis through approaches like protein engineering. Concurrently, multiple biochemical and radiolabeling assays have been developed for determining its activity. Although these existing assays yield reliable results, they require addition of multiple external components, rendering them less convenient and expensive. Therefore, in this study, we have developed two relatively cheaper, convenient, and easily reproducible assays for quantitative and qualitative estimation of RuBisCO activity. Results We simplified a contemporary NADH based spectrophotometric RuBisCO assay by using cyanobacterial cell lysate as the source for Calvin cycle enzymes. We analyzed the influence of inorganic carbon substrates, CO 2 and NaHCO 3 , and varying protein concentrations on RuBisCO activity. Ribulose-1,5-bisphosphate (RuBP) consumption rates for the cultures grown under 5% CO 2 were 5–7 times higher than the ones grown with 20 mM NaHCO 3 , at different protein concentrations. The difference could be due to the impaired activity of carbonic anhydrase in the cell lysate, which is required for the conversion of HCO 3 − to CO 2 . The highest RuBisCO activity of 2.13 nmol of NAD + / µg of Chl-a/ min was observed with 50 µg of protein and 5% CO 2 . Additionally, we developed a novel RNA-sensor based fluorescence assay that is based on the principle of tracking the kinetics of ATP hydrolysis to ADP during the conversion of 3-phosphoglycerate (3-PG) to 1,3-bisphosphoglycerate (1,3-BPG) in the Calvin cycle. Under in vitro conditions, the fluorometric assay exhibited ~ 3.4-fold slower reaction rate (0.37 min −1 ) than the biochemical assay when using 5% CO 2 . We also confirmed the in vivo application of this assay, where increase in the fluorescence was observed with the recombinant strain of Synechocystis sp. PCC 6803 (SSL142) expressing the ADP-specific RNA sensor, compared to the WT. In addition, SSL142 exhibited three-fold higher fluorescence when supplemented with 20 mM NaHCO 3 as compared to the cells that were grown without NaHCO 3 supplementation. Conclusions Overall, we have developed a simplified biochemical assay for monitoring RuBisCO activity and demonstrated that it can provide reliable results as compared to the prior literature. Furthermore, the biochemical assay using 5% CO 2 (100% relative activity) provided faster RuBP consumption rate compared to the biochemical assay utilizing 20 mM NaHCO 3 (30.70% relative activity) and the in vitro fluorometric assay using 5% CO 2 (29.64% relative activity). Therefore, the absorbance-based biochemical assay using 5% CO 2 or higher would be suitable for in vitro quantification of the RuBisCO activity. On the other hand, the RNA-sensor based in vivo fluorometric assay can be applied for qualitative analysis and be used for high-throughput screening of RuBisCO variants. As RuBisCO is an enzyme shared amongst all the photoautotrophs, the assays developed in this study can easily be extended for analyzing the RuBisCO activities even in microalgae and higher plants.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Material flows and GHG emissions from housing stock evolution in US counties, 2020–60

The evolution of housing stocks determines demand for construction materials and energy, and associated emissions of greenhouse gasses (GHGs). The contribution of construction to building life-cycle emissions is growing as buildings become more energy efficient and the energy supply decarbonizes. A housing stock model is developed for counties in the United States using dynamic vacancy rates which endogenously influence stock out- and inflows. Stocks of three house types and 10 construction cohorts are projected for all contiguous US counties for the period 2020–60. Inflows and outflows of construction materials are then estimated along with GHG emissions associated with material production and construction activities in scenarios defined by stock turnover rates, population share by house type, and floor area characteristics of new houses. The results provide new insights into the drivers of construction-related emissions at local and national levels, and identify opportunities for their reduction. Demolition material flows grow in relation to construction material flows over the analysis period. Increasing the stock turnover rate increases future floor area per person, material requirements, and emissions from construction. Scenarios with reduced floor area and more multifamily homes in new construction have lower floor area growth, material requirements, and emissions from construction.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

De-Risking Large-Scale PV Systems Through Data Analytics

Large-scale photovoltaic system performance analysis is being conducted within the US Department of Energy's-sponsored PV Fleet Performance Data Initiative. This collaboration with commercial PV system owners collects and evaluates PV field performance data, and provides reports on aggregated results. Drawing on over 2200 sites across the US and over 24,000 separate PV inverters we have collected in excess of 8.3 gigawatts (GW) of performance data, representing 6-7% of the entire US installed PV capacity. A mixture of utility-scale and large commercial systems are represented, averaging 4.1 megawatts (MW) in size and 5 years in age. Initial results show average system degradation rates at -0.75% / year, which is slightly higher than historically reported module-level values of -0.5%/year. We also found that the availability of systems averaged 97.7%, which is lower than the typical 99% uptime assumed by many project economic forecasts. Given these results, we compared monthly performance with expected production values, based on satellite weather data and a simple PVWatts performance model. We found that systems were performing within 10% of monthly expectation over 90% of the time, with a fleet average vs expected monthly value of 0.994. We also evaluated the impact of extreme weather events on system performance, and found a range of short-term and longer-term performance effects ranging from grid outage, system downtime, module damage and accelerated long-term degradation rate.

analysis↗

DOE OSTI2024

A variational method for the sheath potential of hypersonic leading edges with space-charge limitations

Electron transpiration cooling for the leading edges (LE) of hypersonic aircraft utilizes thermionic emission; however, space-charge effects limit the electron emission rate, potentially diminishing the efficiency of this cooling mechanism. We develop a variational weak form of the Poisson equation that describes the sheath potential and then numerically solve it using the finite element method. This formulation has two main benefits: (1) the space-charge limit condition can be incorporated as a constraint and (2) it allows for the analysis of three-dimensional geometries with complex boundary conditions. We demonstrate that the current emitted from the surface of an LE is generally a small fraction of the Child–Langmuir limit due to space charge. We then propose several methods to enhance the emitted current from the surface and to boost the cooling effect of thermionic emission. These include increasing the plasma density, applying a negative surface potential, and using fringe fields under suitable geometric conditions. For a LaB6 emitting LE, the total emitted current is shown to be minimal and independent of the temperature of a surface with floating potential. However, when a negative potential is applied and the surface is heated, the emitted current follows the Richardson–Dushman relationship up to a critical temperature, beyond which it remains constant. At an applied surface potential of −5 V, the critical temperature is around 1700 K.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Verification of EvaluateFLux Utility Program

The EvaluateFlux program is a post-processing utility program for DIF3D, specifically DIF3D-VARIANT which handles Cartesian and hexagonal geometries. The EvaluateFlux program was developed to allow users to obtain flux and power traverses through the geometry domain, and its initial purpose was to facilitate foil analysis by evaluating the flux solution from DIF3D-VARIANT and combining it with foil cross section data. The EvaluateFlux program can calculate the neutron flux, as well as the reaction rates, at any user provided evaluation point. It does this by identifying the spatial mesh associated with the evaluation point and then evaluates the polynomial based neutron flux moments stored in the NHFLUX file at that point. The output of EvaluateFlux varies depending on the input setup. The maximum output includes the neutron flux and microscopic and macroscopic reaction rates at each evaluation point. The purpose of this work is to verify the outputs of EvaluateFlux. Simple models that have hand calculatable results are first defined and used to verify the EvaluateFlux outputs. More complex cases are then added where a duplicate program of EvaluateFlux that uses PrintTables outputs of the binary files is used to verify the EvaluateFlux outputs. In those complex cases, hand calculations of selected evaluation points were also displayed to confirm the software verification. For all the tests done, the hand calculations agreed well with those calculated by EvaluateFlux. For the larger complex problems, the duplicate program that can process hundreds of evaluation points was able to identify that zero points within some meshes have large errors. This aspect was attributed to the truncation error on the input provided to the duplicate program and is not a concern for the accuracy of the EvaluateFlux software.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Verification of the EvaluateFlux Utility Program

22 GENERAL STUDIES OF NUCLEAR REACTORS↗