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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Route Optimization for Energy Efficient Airport Shuttle Operations - A Case Study from Dallas Fort Worth International Airport

Air travel and requisite surface traffic supporting passenger arrival/departure constitutes a significant portion of travel and emissions in cities with large airports. An airport trip can segment into three parts namely: i) travel from a location in the city to the airport; ii) travel from a parking lot or rental car center to the terminal (i.e., within the airport premises), and iii) travel inside the terminal. Depending on the airport access mode all or a part of these legs comprise a traveler’s journey to the airport. The priority of airport ground transport management teams is to provide passengers with a seamless travel experience within the airport, so it is understandable that within airport shuttle routes might not be optimized for minimizing energy consumption. Solutions that meet the dual objective of reducing energy consumption from airport shuttle operations without compromising on passenger travel experience are key to improving system efficiency. There is currently a dearth of research and tools that can inform airports in making such decisions. Addressing this need, this research effort puts forth an optimization model that generates optimal shuttle routes for a given set of constraints, and a discrete-event simulator that evaluates the optimal solutions in a stochastic environment to understand the tradeoffs between passenger wait times, and within airport shuttle energy consumption. The proposed set of tools are tested in the context of optimizing airport shuttles routes within the Dallas Fort Worth International Airport (DFW). In addition to shuttle spatial positioning, and passenger demand information, high-fidelity vehicle data was collected using data loggers installed on DFW shuttles. Results show that 20% energy reduction in shuttle operations is possible with a modest two-minute increase in average passenger wait times. The tools developed in this research effort are designed to be generalizable and can help optimize shuttle operations planning at any major airport.

air travel↗
DOE OSTI2021

The effect of building retrofit measures on CO2 emission reduction – A case study with U.S. Medium office buildings

Building retrofit has great potential to reduce CO2 emissions since buildings are responsible for 36% of emissions in the United States. Several existing studies have examined the effect of building retrofit measures on CO2 emission reduction. However, these studies oversimplified emission factors of electricity by adopting constant annual emission factors. This study uses hourly emission factors of electricity to analyze the effect of building retrofit measures on emission reduction using U.S. medium office buildings as an example. We analyzed CO2 emission reduction effects of eight building retrofit measures that related to envelop and mechanical system in five locations: Tampa, San Diego, Denver, Great Falls, and International Falls. The main findings are: (1) estimating CO2 emission reduction with constant emission factors overestimates the emission reduction for most measures in San Diego, while it underestimates the emission reduction for most measures in Denver and International Falls; (2) The same retrofit measure may have different effects in CO2 emission reduction depending on the climates. For instance, improving lighting efficiency and improving equipment efficiency have less impacts in emission reduction in cold climates than hot climates; and (3) The most energy efficient measure may not be the most emission efficient measure. For example, in Great Falls, the most energy efficient measure is improving equipment efficiency, but the most emission efficient measure is improving heating efficiency.

Lou, Yingli↗
DOE OSTI2022

Optimizing Catalysis by Combining Molecular Scaling Relationships: Iron Porphyrin-Catalyzed Electrochemical Oxygen Reduction as a Case Study

Here, the present work demonstrates the generality of molecular scaling relationships toward achieving faster catalysis at lower overpotentials. In iron porphyrin-catalyzed oxygen reduction, independent tuning of the catalyst E 1/2 and buffer pK a resulted in improvement of both the maximum turnover frequency (TOF max ) and the effective overpotential (η eff ). Interestingly, the most optimal system is achieved with the most reducing catalyst and a weak buffered acid—a perhaps counterintuitive conclusion. More generally, this study illustrates a path to optimization of catalytic systems through their differing responses to changes in the intrinsic features of the system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Predicting wind farm operations with machine learning and the P2D‐RANS model: A case study for an AWAKEN site

Abstract The power performance and the wind velocity field of an onshore wind farm are predicted with machine learning models and the pseudo‐2D RANS model, then assessed against SCADA data. The wind farm under investigation is one of the sites involved with the American WAKE experimeNt (AWAKEN). The performed simulations enable predictions of the power capture at the farm and turbine levels while providing insights into the effects on power capture associated with wake interactions that operating upstream turbines induce, as well as the variability caused by atmospheric stability. The machine learning models show improved accuracy compared to the pseudo‐2D RANS model in the predictions of turbine power capture and farm power capture with roughly half the normalized error. The machine learning models also entail lower computational costs upon training. Further, the machine learning models provide predictions of the wind turbulence intensity at the turbine level for different wind and atmospheric conditions with very good accuracy, which is difficult to achieve through RANS modeling. Additionally, farm‐to‐farm interactions are noted, with adverse impacts on power predictions from both models.

17 WIND ENERGY↗
DOE OSTI2024

Comparison of Real-Time Pressure Rail Selection Algorithms for the Hybrid Hydraulic Electric Architecture: Case Study on a Track Loader

Abstract The hybrid hydraulic electric architecture (HHEA) seeks to combine the high power/torque/force density of hydraulics with the efficiency of electric machines. A set of common pressure rails is used to provide a majority of the power and this power is modulated by small electric machines to provide precise control for the operator. The HHEA has been studied in previous work using off-line dynamic programming optimization to determine energy efficient pressure rail selections, but this approach requires drive cycle information apriori. A Lagrange multiplier method has also been investigated where a set of gains (Lagrange multipliers) are optimized off-line with the idea the these gains, once determined, could be used for real-time operation. In this work, three new real-time pressure rail selection algorithms that do not require future drive cycle information are investigated; greedy, torque minimizing, and thresholding. The greedy control is found to only use 1% more energy than the globally optimal dynamic programming solution; but a model of energy loss is required.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1976

A numerical comparison of discrete Kalman filtering algorithms: An orbit determination case study

The numerical stability and accuracy of various Kalman filter algorithms are thoroughly studied. Numerical results and conclusions are based on a realistic planetary approach orbit determination study. The case study results of this report highlight the numerical instability of the conventional and stabilized Kalman algorithms. Numerical errors associated with these algorithms can be so large as to obscure important mismodeling effects and thus give misleading estimates of filter accuracy. The positive result of this study is that the Bierman-Thornton U-D covariance factorization algorithm is computationally efficient, with CPU costs that differ negligibly from the conventional Kalman costs. In addition, accuracy of the U-D filter using single-precision arithmetic consistently matches the double-precision reference results. Numerical stability of the U-D filter is further demonstrated by its insensitivity of variations in the a priori statistics.

Thornton, C. L.↗
NASA NTRS1977

Numerical comparison of Kalman filter algorithms - Orbit determination case study

Numerical characteristics of various Kalman filter algorithms are illustrated with a realistic orbit determination study. The case study of this paper highlights the numerical deficiencies of the conventional and stabilized Kalman algorithms. Computational errors associated with these algorithms are found to be so large as to obscure important mismodeling effects and thus cause misleading estimates of filter accuracy. The positive result of this study is that the U-D covariance factorization algorithm has excellent numerical properties and is computationally efficient, having CPU costs that differ negligibly from the conventional Kalman costs. Accuracies of the U-D filter using single precision arithmetic consistently match the double precision reference results. Numerical stability of the U-D filter is further demonstrated by its insensitivity to variations in the a priori statistics.

Bierman, G. J.↗
NASA NTRS1976

Numerical comparison of discrete Kalman filter algorithms - Orbit determination case study

Numerical characteristics of various Kalman filter algorithms are illustrated with a realistic orbit determination study. The case study of this paper highlights the numerical deficiencies of the conventional and stabilized Kalman algorithms. Computational errors associated with these algorithms are found to be so large as to obscure important mismodeling effects and thus cause misleading estimates of filter accuracy. The positive result of this study is that the U-D covariance factorization algorithm has excellent numerical properties and is computationally efficient, having CPU costs that differ negligibly from the conventional Kalman costs. Accuracies of the U-D filter using single precision arithmetic consistently match the double precision reference results. Numerical stability of the U-D filter is further demonstrated by its insensitivity to variations in the a priori statistics.

Bierman, G. J.↗
DOE OSTI2024

Beyond conventional batteries: a review on semi-solid and redox targeting flow batteries-LiFePO{sub 4} as a case study.

Clean and sustainable energy is becoming increasingly crucial to tackle the current energy crisis. However, the intermittent nature of renewable energy sources presents a challenge for their effective implementation. Redox flow batteries (RFBs) have emerged as a promising solution to this problem, as they can help enhance the stability of grid networks and promote the use of renewable energy sources. RFBs are highly modular and scalable systems that can be customized to meet the power and energy requirements of different renewable energy plants. Moreover, they offer several advantages over conventional battery technologies, including cost and safety concerns. However, conventional RFBs have limited energy densities due to the low solubility of their active species in electrolyte. To overcome this limitation, semi-solid (SSRFBs) and redox targeting (RTFBs) flow batteries have been proposed. These systems feature high concentrations of active species and impressive energy densities, making them highly attractive for renewable energy applications. LiFePO4 (LFP) is a highly promising active material for semi-solid and targeting flow batteries. One of the key advantages of LFP is its low raw materials cost, as it is composed of Earth-abundant elements such as iron and phosphorus. This makes it an attractive option for large-scale battery production. The recent developments in SSRFBs and RTFBs using LFP as catholyte hold great promise for the future of sustainable energy storage. The combination of LFP's low cost, safety, durability, and high energy density with the modularity and scalability of flow battery systems make for a compelling solution to the challenges of intermittent renewable energy sources. Ongoing research and development in this area will likely yield even further improvements in the performance and efficiency of LFP-based flow batteries, opening exciting new possibilities for sustainable energy storage.

El Halya, Nabil↗
DOE OSTI2026

Data for Spatial Analysis of Cell Patterning to Aid Genetic and Phenotypic Understanding of Grass Stomatal Density: A Case Study in Maize

Biological processes involve complex hierarchies where composite traits result from multiple component traits. However, holistically understanding of how sets of component traits interact to underpin genotype-to-phenotype relationships is generally lacking. Stomatal density (SD) is a tractable model system for exploring how high-throughput phenotyping (HTP) data could be exploited by a new spatial analysis approach to better understand a developmentally and functionally important trait. SD is a composite trait, resulting from various components related to cell identity and size, which are themselves governed by a series of spatio-developmental processes. Data from 192 recombinant inbred lines of maize [Zea mays (L.)] were analyzed by a new stomatal patterning phenotype (SPP) to (1) describe the average spatial probability distribution of the nearest neighboring stomata; (2) derive a core set of component traits related to cell size, cell packing, and positional probabilities; (3) build a structural equation model of component traits underlying SD; and (4) identify stomatal patterning quantitative trait loci (QTL). The core set of SPP-derived traits explained 74% of the variation in SD. Analyzing SPP component traits allowed some loci previously identified as generic SD QTL to be recognized as specific to lateral versus longitudinal elements of stomatal patterning. Therefore, this study highlights how novel insights can be gained by decomposing a composite trait (e.g., SD) into a set of component traits that were present in HTP data but not previously exploited.

AI/ML↗
NASA NTRS1993

Making intelligent systems team players: Additional case studies

Observations from a case study of intelligent systems are reported as part of a multi-year interdisciplinary effort to provide guidance and assistance for designers of intelligent systems and their user interfaces. A series of studies were conducted to investigate issues in designing intelligent fault management systems in aerospace applications for effective human-computer interaction. The results of the initial study are documented in two NASA technical memoranda: TM 104738 Making Intelligent Systems Team Players: Case Studies and Design Issues, Volumes 1 and 2; and TM 104751, Making Intelligent Systems Team Players: Overview for Designers. The objective of this additional study was to broaden the investigation of human-computer interaction design issues beyond the focus on monitoring and fault detection in the initial study. The results of this second study are documented which is intended as a supplement to the original design guidance documents. These results should be of interest to designers of intelligent systems for use in real-time operations, and to researchers in the areas of human-computer interaction and artificial intelligence.

Malin, Jane T.↗
DOE OSTI2021

Nutrient and stress tolerance traits linked to fungal responses to global change: Four case studies

In this case study analysis, we identified fungal traits that were associated with the responses of taxa to 4 global change factors: elevated CO2, warming and drying, increased precipitation, and nitrogen (N) enrichment. We developed a trait-based framework predicting that as global change increases limitation of a given nutrient, fungal taxa with traits that target that nutrient will represent a larger proportion of the community (and vice versa). In addition, we expected that warming and drying and N enrichment would generate environmental stress for fungi and may select for stress tolerance traits. We tested the framework by analyzing fungal community data from previously published field manipulations and linking taxa to functional gene traits from the MycoCosm Fungal Portal. Altogether, fungal genera tended to respond similarly to 3 elements of global change: increased precipitation, N enrichment, and warming and drying. The genera that proliferated under these changes also tended to possess functional genes for stress tolerance, which suggests that these global changes—even increases in precipitation—could have caused environmental stress that selected for certain taxa. In addition, these genera did not exhibit a strong capacity for C breakdown or P acquisition, so soil C turnover may slow down or remain unchanged following shifts in fungal community composition under global change. Since we did not find strong evidence that changes in nutrient limitation select for taxa with traits that target the more limiting nutrient, we revised our trait-based framework. The new framework sorts fungal taxa into Stress Tolerating versus C and P Targeting groups, with the global change elements of increased precipitation, warming and drying, and N enrichment selecting for the stress tolerators.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Adding Efficiency to Renovations - Case Study: Bank

This case study describes the implementation of the Tenant Fit-Out Integrated Systems Package (ISP) at a retail bank branch, including project details, key takeaways, and data comparisons before and after the renovations.

integrated systems package, ISP, bank, retail, off↗