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Results for “Capacity estimation”

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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
NASA NTRS1980

Evaluation of solar thermal power plants using economic and performance simulations

An energy cost analysis is presented for central receiver power plants with thermal storage and point focusing power plants with electrical storage. The present approach is based on optimizing the size of the plant to give the minimum energy cost (in mills/kWe hr) of an annual plant energy production. The optimization is done by considering the trade-off between the collector field size and the storage capacity for a given engine size. The energy cost is determined by the plant cost and performance. The performance is estimated by simulating the behavior of the plant under typical weather conditions. Plant capital and operational costs are estimated based on the size and performance of different components. This methodology is translated into computer programs for automatic and consistent evaluation.

El-Gabawali, N.↗
NASA NTRS1920

Effect of altitude on radiator performance

As an airplane rises to high altitudes the decrease in the density and the temperature of the air have important effects on the performance of the radiator. This report gives the results of a study of the effect of reduced pressure and temperature upon the capacity of airplane radiators. A method is presented by which the performance of a radiator at an altitude may be estimated for a particular speed of the airplane at a particular altitude.

James, W S↗
DOE OSTI2025

Impedance-Based State-of-Health Estimation for Lithium-Ion Battery Management Systems

This paper discusses an impedance-based state-of-health (SOH) estimation for lithium-ion battery management systems. Features obtained from the Nyquist plot of the electrochemical impedance spectroscopy (EIS) are utilized to estimate the SOH. Two commercial lithium-ion batteries were aged (cycled) in the laboratory to collect aging and impedance data. An algorithm for SOH estimation is presented and evaluated. The performance evaluation results show that the SOH features extracted from the Nyquist plot can be utilized for SOH estimation. Estimated SOH values can be then utilized in various battery management systems (BMS) functions such as for calibrating the available capacity, adjusting charging/discharging strategies, and protection.

Al-Smadi, Mohammad↗
DOE OSTI2025

Regional Inertia Estimation Using Actual Event Measurements: Florida Case

As inverter-based resources’ integration in power grids increases, their uneven distribution across the electrical network leads to the formation of local regions that are weakly coupled to the larger interconnection. This signifies the necessity of regional frequency dynamics investigation and analyzing various inertia metrics. This paper presents a practical methodology for estimating regional rate-of-change of frequency (RoCoF) using actual event recordings, which is then used to evaluate regional inertia. Florida (FL) is selected as the region of interest due to its distinct regional frequency dynamics and the rising levels of solar generation. We identify and analyze confirmed events from 2017 to 2024 that occurred in FL. The results indicate that FL contributes about 14% to the total inertia of the US Eastern Interconnection, which approximately matches its share of generation capacity. Results also highlight seasonal fluctuations in energy generation, which play a significant role in influencing inertia and thus the RoCoF levels. This emphasizes the importance of estimating regional inertia to enhance grid operations for a future that focuses on distributed generation.

Dulal, Saurav [University of Tennessee, Knoxville ↗
DOE OSTI2016

Impact of Market Behavior, Fleet Composition, and Ancillary Services on Revenue Sufficiency

Revenue insufficiency, or the missing money problem, occurs when the revenues that generators earn from the market are not sufficient to cover both fixed and variable costs to remain in the market and/or justify investments in new capacity, which may be needed for reliability. The near-zero marginal cost of variable renewable generators further exacerbates these revenue challenges. Estimating the extent of the missing money problem in current electricity markets is an important, nontrivial task that requires representing both how the power system operates and how market participants behave. This paper explores the missing money problem using a production cost model that represented a simplified version of the Electric Reliability Council of Texas (ERCOT) energy-only market for the years 2012-2014. We evaluate how various market structures -- including market behavior, ancillary services, and changing fleet compositions -- affect net revenues in this ERCOT-like system. In most production cost modeling exercises, resources are assumed to offer their marginal capabilities at marginal costs. Although this assumption is reasonable for feasibility studies and long-term planning, it does not adequately consider the market behaviors that impact revenue sufficiency. In this work, we simulate a limited set of market participant strategic bidding behaviors by means of different sets of markups; these markups are applied to the true production costs of all gas generators, which are the most prominent generators in ERCOT. Results show that markups can help generators increase their net revenues overall, although net revenues may increase or decrease depending on the technology and the year under study. Results also confirm that conventional, variable-cost-based production cost simulations do not capture prices accurately, and this particular feature calls for proxies for strategic behaviors (e.g., markups) and more accurate representations of how electricity markets work. The analysis also shows that generators face revenue sufficiency challenges in this ERCOT-like energy-only market model; net revenues provided by the market in all base markup cases and sensitivity scenarios (except when a large fraction of the existing coal fleet is retired) are not sufficient to justify investments in new capacity for thermal and nuclear power units. Overall, the work described in this paper points to the need for improved behavioral models of electricity markets to more accurately study current and potential market design issues that could arise in systems with high penetrations of renewable generation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Network-Scale Ubiquitous Volume Estimation Using Tree-Based Ensemble Learning Methods

Currently ubiquitous volume data for roadway networks remains the key missing dimension in traffic operations. Most volume data are average annual daily traffic (AADT) measures derived from the Highway Performance Monitoring System (HPMS). Although methods to factor the AADT to hourly averages for typical day of week exist, actual volume data is limited to a sparse collection of locations in which volumes are continuously recorded. This paper/poster explores the use of state-of-art machine learning techniques to estimate accurate volume measures that span the highway network providing ubiquitous coverage in space, and point-in-time measures for a specific date and time. Three tree-based ensemble learning models, random forest (RF), gradient boost machine (GBM), and extreme gradient boost (XGBoost), were tested for volume estimation by learning from combined dataset of commercial probe data provided by TomTom, the FHWA's Travel Monitoring Analysis System (TMAS) data, and other infrastructure attributes such as number of lanes, speed limit, and weather. The methods were tested on major corridors and freeways in the metropolitan area of Denver. All three machine learning methods were able to provide hourly volume estimates 24 hours a day, 7 days a week, and 365 days a year with around 18% mean absolute error to true volume and about 5% of error with respect to roadway capacity. The low error measures allow the potential application by transportation agencies.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1983

Expected cycle life vs. depth of discharge relationships of well-behaved single cells and cell strings

The present investigation is concerned with the factors which might influence the cycle life vs. depth of discharge relationship, taking into account the rate of loss of cell capacity, the amount of excess capacity built into the cells, and the penalty in capacity loss resulting from the use of deep depths of discharge. 'First principles' are used to develop a cell life model for somewhat arbitrary conditions. This model is then used to estimate the cycle life vs. depth of discharge relationships for 'well behaved' cells. The stochastic variations associated with groupings of single cells are then introduced to the battery pack cycle life model. The term 'well behaved' cell is used to describe a single cell which does not suffer any abrupt failure mode during the course of its operation. It gradually loses capacity for any number of the usual reasons at a rate which is the product of the fractional depth of discharge and a factor which is characteristic of the cell under consideration.

Thaller, L. H.↗
NASA NTRS1977

Evaluation of an infrared horizon scanner bias determination algorithm for earth-oriented spacecraft

An algorithm for estimating infrared horizon scanner biases for earth-oriented spacecraft is presented. A mathematical description of the proposed algorithm is given, and the algorithm is evaluated for use by two earth-oriented spacecraft: the Applications Explorer Missions-A/Heat Capacity Mapping Mission (AEM-A/HCMM) and Seasat-A. The results of this study indicate that scanner alignment and calibration errors appear as nearly constant biases in the scanner pitch and roll data and that these constant biases can be estimated to within 0.05 degree for AEM-A and 0.03 degree for Seasat-A.

Hotovy, S. G.↗
NASA NTRS2016

Cooling Rates of Lunar Volcanic Glass Beads

It is widely accepted that the Apollo 15 green and Apollo 17 orange glass beads are of volcanic origin. The diffusion profiles of volatiles in these glass beads are believed to be due to degassing during eruption (Saal et al., 2008). The degree of degassing depends on the initial temperature and cooling rate. Therefore, the estimations of volatiles in parental magmas of lunar pyroclastic deposits depend on melt cooling rates. Furthermore, lunar glass beads may have cooled in volcanic environments on the moon. Therefore, the cooling rates may be used to assess the atmospheric condition in an early moon, when volcanic activities were common. The cooling rates of glasses can be inferred from direct heat capacity measurements on the glasses themselves (Wilding et al., 1995, 1996a,b). This method does not require knowledge of glass cooling environments and has been applied to calculate the cooling rates of natural silicate glasses formed in different terrestrial environments. We have carried out heat capacity measurements on hand-picked lunar glass beads using a Netzsch DSC 404C Pegasus differential scanning calorimeter at University of Munich. Our preliminary results suggest that the cooling rate of Apollo 17 orange glass beads may be ~12 K/min, based on the correlation between temperature of the heat capacity curve peak in the glass transition range and glass cooling rate. The results imply that the parental magmas of lunar pyroclastic deposits may have contained more water initially than the early estimations (Saal et al., 2008), which used higher cooling rates, 60-180 K/min in the modeling. Furthermore, lunar volcanic glass beads could have been cooled in a hot gaseous medium released from volcanic eruptions, not during free flight. Therefore, our results may shed light on atmospheric condition in an early moon.

Hui, Hejiu↗
DOE OSTI2018

Network Scale Ubiquitous Volume Estimation Using Tree-Based Ensemble Learning Methods

Currently real-time, ubiquitous volume data for roadway networks remains the key missing dimension in traffic operations. Most volume data are average annual daily traffic (AADT) measures derived from the Highway Performance Monitoring System (HPMS). Although methods to factor the AADT to hourly averages for typical day of week exist, actual volume data is limited to a sparse collection of locations in which volumes are continuously recorded. This paper explores the use of state-of-art machine learning techniques to estimate accurate real-time volume measures that span the highway network providing ubiquitous coverage in space, and point-in-time measures for a specific date and time. Three tree-based ensemble learning models, random forest (RF), gradient boost machine (GBM), and extreme gradient boost (XGBoost), were tested for volume estimation by learning from combined dataset of commercial probe data provided by TomTom, the FHWA's Travel Monitoring Analysis System (TMAS) data, and other infrastructure attributes such as number of lanes, speed limit, and weather. The methods were tested on major corridors and freeways in the metropolitan area of Denver. All three machine learning methods were able to provide hourly volume estimates 24 hours a day, 7 days a week, and 365 days a year with around 18% mean absolute error to true volume and about 5% of error with respect to roadway capacity. The low error measures allow the potential application by transportation agencies.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

A Bayesian statistical approach to inferring particle dynamics from in-situ pump POC and chloropigment data from the Mediterranean Sea

Concentrations of chloropigments and particulate organic carbon (POC) in large-volume in-situ pump samples from the Mediterranean Sea were used to estimate rate constants of processes that control the fate of particles, and specifically chloropigments, in the water column. In this paper we introduce a Bayesian statistical inversion method that combines the data with a new box model and has the capacity to infer rate constants for POC respiration/dissolution, chlorophyll and pheopigment degradation, and particle aggregation and disaggregation. We use first-order kinetics to model disaggregation, and use both first-order and second-order kinetics to model aggregation. Using these methods, the estimated small-particle (1–70 μm) POC respiration rate constant was 2.44 -1.00 +1.69 yr -1 (0.41 yr). The estimated disaggregation and second-order aggregation rate constants were 85.6 -36.4 +63.4 yr -1 (1.17×10 -2 yr) and 2.78 -1.17 +2.01 μM -1 yr -1 , respectively. Using the optimal rate constants and the corresponding particle concentrations, disaggregation is ~4.2 times faster than the small-size POC dissolution rate, which indicates that disaggregation is a dominant processes at the time of sampling. More importantly, by comparing our results with those of previous studies, we conclude that sampling methods have less influence than tracers themselves on inferring particle dynamic rate constants. We previously introduced a somewhat similar approach to modeling SV sediment trap data, but large volume pumps are a much more common sample collection method in oceanographic surveys than SV sediment traps, and thus our new model should have a wider applicability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

The Water Use of Sweet Sorghum and Development of Crop Coefficients

Abstract Reliable estimates of crop water use are essential to economic production of sweet sorghum under irrigated conditions. In Tucson, Arizona, USA, two planting dates, May 5th and June 15th, and four sweet sorghum varieties (M81E, ST, SM and A4) were evaluated to estimate crop coefficients (kc) based on the heat unit accumulation concept (GDD). One irrigation level at 50% depletion of available water capacity prior to irrigation and its effect on juice, biomass and ethanol yields was evaluated. Results show that it was possible to develop heat unit based kc´s for two day‐neutral varieties (ST and SM) and that they required 25% less water than the photo‐sensitive varieties. Peak crop coefficients were determined as 1.13, 1.05, 1.16 and 1.22 for M81E, ST, SM and A4. Results also showed that growing degree days accumulated to flowering were 2226 and 1759 GDD for ST and SM respectively. At a P ‐value of 5% there were differences in crop water use, juice yields and bagasse production among treatments. For the May planting there was a slight effect on ethanol yields but no effect for the June planting, showing that regardless of the planting date and variety they are more likely to be similar. Copyright © 2015 John Wiley & Sons, Ltd.

Martínez‐Cruz, Tania E.↗
DOE OSTI2023

Identifying High-Performance Metal–Organic Frameworks for Low-Temperature Oxygen Recovery from Helium by Computational Screening

Metal–organic frameworks (MOFs) are an important class of porous crystalline materials for applications ranging from gas adsorption and separation to catalysis. There are thousands of potential MOFs available for separation applications. Here, we developed a computational approach to screen MOFs for the separation of oxygen–helium mixtures at low temperatures (100–200 K), conditions that were motivated by issues associated with propulsion in space-based settings. We used detailed molecular simulations for a small number of MOFs to develop screening methods that were then used to estimate the optimum temperatures for separations using pressure swing adsorption for 2932 MOFs from the CoRE MOF database and the swing capacity and oxygen–helium selectivity at these temperatures. We used the stability of the best-performing structures in the presence of moisture as a means to provide a short list of high-performance materials. In addition to identifying specific materials for oxygen–helium separations, this approach could prove useful for selecting adsorbents for other gas separations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Nonlinear magnons and exchange Hamiltonians of the delafossite proximate quantum spin liquid candidates KYbSe 2 and NaYbSe 2

Quantum spin liquids (QSLs) are theoretical states of matter with long-range entanglement and exotic quasiparticles. However, they generally elude quantitative theory, rendering their underlying phases mysterious and hampering efforts to identify experimental QSL states. Here we study triangular-lattice resonating-valence-bond QSL candidate materials KYbSe 2 and NaYbSe 2 . We measure the magnon modes in their 1/3 plateau phase, where quantitative theory is tractable, using inelastic neutron scattering and fit them using nonlinear spin wave theory. We also fit the KYbSe 2 heat capacity using high-temperature series expansion. Both KYbSe 2 fits yield the same magnetic Hamiltonian to within uncertainty, confirming previous estimates and showing the Heisenberg ratio J 2 / J 1 to be an accurate model for these materials. Most importantly, comparing KYbSe 2 and NaYbSe 2 shows that the smaller A -site Na + ion has a larger J 2 / J 1 ratio. However, hydrostatic pressure applied to KYbSe 2 increases the ordering temperature (a result consistent with density functional theory calculations), indicating that pressure decreases J 2 / J 1 . In conclusion, these results show how the periodic table and hydrostatic pressure can tune the A YbSe 2 materials in a controlled way.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Modeling and Analysis of a Fast Charging Station and Evaluation of Service Quality for Electric Vehicles

The deployment of public charging infrastructure networks has been a major factor in enabling electric vehicle (EV) technology transition and must continue to support the adoption of this technology. DC fast charging (DCFC) increases customer convenience by lowering charging time, enables long-distance EV travel, and could allow the electrification of high-mileage fleets. Yet, high capital costs and uneven power demand have been major challenges to the widespread deployment of DCFC stations. There is a need to better understand DCFC stations' loading and customer service quality. Furthermore, the relationship between the initial investment decision on building certain number of ports and customer satisfaction should be quantified. This paper aims to analyze these aspects using one million vehicle days of travel data within the Columbus, OH, USA, region. Monte Carlo analysis is carried out in three types of areas - urban, suburban, and rural - to quantify the effect of uncertain parameters on DCFC station loading and service quality. Additional simulations based on a homogeneous vehicle population are carried out, and closed-form equations are derived therefrom to estimate charging duration and waiting time in the queue. Optimization of DCFC station design is also addressed through the number and capacity of ports.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

National Dataset of EV Charging Stations With Estimates of Load and Vehicle Throughput

Current data from the AFDC provide locations and many details about EV charging stations, but not estimates of their peak loads or the number of vehicles they can accommodate. This dataset will augment the AFDC charging station locations with estimates of transmission load and vehicle throughput based on engineering specifications of the chargers, charging patterns based on vehicle types, battery capacities, and user behavior.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Radioisotope production at the Spallation Neutron Source: Design concept of isotope production target

Upon completion of the Second Target Station (STS) Project in the mid 2030s, the Spallation Neutron Source accelerator at Oak Ridge National Laboratory will deliver a 2.7 MW proton beam to the neutron production targets. In the post-STS phase, the accelerator will have a reserve beam power capacity of at least 100 kW beyond what the two neutron production targets will receive, which could potentially be ramped up to 300 kW. In this paper, a design concept for a radioisotope production target that could utilize 250 kW of the reserve beam power capacity is presented. The target consists of thorium discs encapsulated in 316L austenitic steel shells that are cooled by water. The estimated post-irradiation activity of Ac-225 and Ra-225, critical medical radioisotopes used in targeted alpha therapy cancer treatment, is calculated at the end of bombardment after a 14 day long irradiation time. Thermal and structural analyses are performed on the basis of calculated nuclear heating data. The technical feasibility of a high-power target under a 250-kW beam load with an extremely low duty factor of $3.5\cdot 10^{-6}$ is presented from thermal, structural and fatigue lifetime perspectives.

Lee, Yong Joong [ORNL] (ORCID:0000000298381723)↗