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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Fort Erie On-Demand Transit Case Study

Rural and smaller-sized communities in North America face unique mobility challenges due to their low population density, lower public transit spending per capita compared to major cities, and a high reliance on private vehicles. In recent years, communities such as Fort Erie, Ontario have restructured or advanced their public transit systems using on-demand services. Fort Erie is a relatively sparsely populated region of 32,901 residents, spread across a land area of 166 square kilometers (64 square miles), for an average population density of 193 residents per square kilometer (500 per square mile). In October 2021, the town implemented a mobility-on-demand system integrated with smartphone software to replace its fixed-route community bus system, which consisted of four buses with three routes, each with a roughly one-hour, one way loop. The new service utilizes a fleet of six minivans, two of which are retrofitted with wheelchair-accessible ramps. The system may require that a passenger requesting a standard van walk up to 400 meters (a quarter mile) to their pickup location to optimize vehicle routing while providing origin-to-destination service. The on-demand system proved effective in providing service, eclipsing pre-pandemic ridership by 40%, decreasing greenhouse gas emissions per ride by 63%, and decreasing the cost per ride to the town by 29%. This report documents both the previous system and the new system in terms of routes, ridership, costs, fuel, and other notable system parameters. This work is part of an ongoing series of case studies on providing small communities with on-demand, right-sized vehicle service coupled with a smartphone application.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

FLASH: FPGA-Accelerated Smart Switches with GCN Case Study

Some communication switches, e.g., the Mellanox SHArP and those in the IBM BlueGene clusters, are augmented to process packets at the application level with fixed-function collectives. This approach, however, lacks flexibility, which limits their applicability in diverse and dynamic workloads. Recently, a new type of programmable packet processor, which uses high-level languages, e.g., P4, has emerged as possible candidates. P4-based switches, however, fall short in certain applications, including machine learning, where capabilities not currently supported by P4 are needed. These include more complex calculation, such as sparse computation and fused multiply-accumulate, data-intensive floating point operations, data reuse, and significant memory. The problem addressed here is that such a switch augmentation needs to support: a large amount of state, significant flexible compute capability, and ease of programming, all while maintaining full functionality, including ensuring high throughput, and demonstrating utility. In this work, we propose a programmable look-aside-type accelerator that can be embedded into, or attached to, existing communication switch pipelines and that is capable of processing packets at line-rate. The proposed in-switch accelerator is based on mixing an ISA (subset of RISC-V instructions) with dataflow graphs (found in CGRAs). To augment performance, vector instructions are also supported. To facilitate usability, we have developed a complete toolchain to compile user-provided C/C++ codes to appropriate back-end instructions for configuring the accelerator. While this approach is flexible enough to support various workloads, in this paper, we consider Graph Convolutional Networks (GCNs) as a case study. Experimental results show that this approach considerably improves the performance of distributed GCN applications.

Haghi, Pouya↗
NASA NTRS2006

Projecting Future Urbanization with Prescott College's Spatial Growth Model to Promote Environmental Sustainability and Smart Growth, A Case Study in Atlanta, Georgia

Planning is an integral element of good management and necessary to anticipate events not merely respond to them. Projecting the quantity and spatial distribution of urban growth is essential to effectively plan for the delivery of city services and to evaluate potential environmental impacts. The major drivers of growth in large urban areas are increasing population, employment opportunities, and quality of life attractors such as a favorable climate and recreation opportunities. The spatial distribution of urban growth is dictated by the amount and location of developable land, topography, energy and water resources, transportation network, climate change, and the existing land use configuration. The Atlanta region is growing very rapidly both in population and the consumption of forestland or low-density residential development. Air pollution and water availability are significant ongoing environmental issues. The Prescott Spatial Growth Model (SGM) was used to make growth projections for the metropolitan Atlanta region to 2010,2020 and 2030 and results used for environmental assessment in both business as usual and smart growth scenarios. The Prescott SGM is a tool that uses an ESRI ArcView extension and can be applied at the parcel level or more coarse spatial scales and can accommodate a wide range of user inputs to develop any number of growth rules each of which can be weighted depending on growth assumptions. These projections were used in conjunction with meteorological and air quality models to evaluate future environmental impacts. This presentation will focus on the application of the SGM to the 13-County Atlanta Regional Commission planning jurisdiction as a case study. The SGM will be described, including how rule sets are developed and the decision process for allocation of future development to available land use categories. Data inputs required to effectively run the model will be discussed. Spatial growth projections for ten, twenty, and thirty year planning horizons will be presented and results discussed, including regional climate and air quality impacts.

Estes, Maurice G., Jr.↗
NASA NTRS2004

Variable Step Integration Coupled with the Method of Characteristics Solution for Water-Hammer Analysis, A Case Study

One-dimensional water-hammer modeling involves the solution of two coupled non-linear hyperbolic partial differential equations (PDEs). These equations result from applying the principles of conservation of mass and momentum to flow through a pipe, and usually the assumption that the speed at which pressure waves propagate through the pipe is constant. In order to solve these equations for the interested quantities (i.e. pressures and flow rates), they must first be converted to a system of ordinary differential equations (ODEs) by either approximating the spatial derivative terms with numerical techniques or using the Method of Characteristics (MOC). The MOC approach is ideal in that no numerical approximation errors are introduced in converting the original system of PDEs into an equivalent system of ODEs. Unfortunately this resulting system of ODEs is bound by a time step constraint so that when integrating the equations the solution can only be obtained at fixed time intervals. If the fluid system to be modeled also contains dynamic components (i.e. components that are best modeled by a system of ODEs), it may be necessary to take extremely small time steps during certain points of the model simulation in order to achieve stability and/or accuracy in the solution. Coupled together, the fixed time step constraint invoked by the MOC, and the occasional need for extremely small time steps in order to obtain stability and/or accuracy, can greatly increase simulation run times. As one solution to this problem, a method for combining variable step integration (VSI) algorithms with the MOC was developed for modeling water-hammer in systems with highly dynamic components. A case study is presented in which reverse flow through a dual-flapper check valve introduces a water-hammer event. The predicted pressure responses upstream of the check-valve are compared with test data.

Turpin, Jason B.↗
DOE OSTI2023

Fort Erie Case Study - Transition from Fixed-Route to On-Demand Transit

Rural and smaller-sized communities in North America face unique mobility challenges due to their low population density, lower public transit spending per capita compared to major cities, and a high reliance on private vehicles. In recent years, communities such as Fort Erie, Ontario, have restructured or advanced their public transit systems using on-demand services. Fort Erie is a relatively sparsely populated region of 32,901 residents, spread across a land area of 166 square kilometers (64 square miles), for an average population density of 193 residents per square kilometer (500 per square mile). In October 2021, the town implemented a mobility-on-demand system integrated with smartphone software to replace its fixed-route community bus system, which consisted of four buses with three routes, each with a roughly 1-hour, one-way loop. The new service utilizes a fleet of six minivans, two of which are retrofitted with wheelchair-accessible ramps. The system may require that a passenger requesting a standard van walk up to 400 meters (a quarter mile) to their pickup location to optimize vehicle routing while providing origin-to-destination service. The on-demand system proved effective in providing service, eclipsing pre-pandemic ridership by 40%, decreasing greenhouse gas emissions per ride by 63%, and decreasing the cost to the town per ride by 29%. This report documents both the previous system and the new system in terms of routes, ridership, costs, fuel, and other notable system parameters. This work is part of an ongoing series of case studies on providing small communities with on-demand, right-sized vehicle service coupled with a smartphone application.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
NASA NTRS1994

A search for upstream pressure pulses associated with flux transfer events: An AMPTE/ISEE case study

On September 19, 1984, the Active Magnetospheric Particle Tracers Explorers (AMPTE) United Kingdom Satellite (UKS) and Ion Release Module (IRM) and International Sun Earth Explorers (ISEE) 1 and 2 spacecraft passed outbound through the dayside magnetopause at about the same time. The AMPTE spacecraft pair crossed first and were in the near-subsolar magnetosheath for more than an hour. Meanwhile the ISEE pair, about 5 R(sub E) to the south, observed flux transfer event (FTE) signatures. We use the AMPTE UKS and IRM plasma and field observations of magnetosheath conditions directly upstream of the subsolar magnetopause to check whether pressure pulses are responsible for the FTE signatures seen at ISEE. Pulses in both the ion thermal pressure and the dynamic pressure are observed in the magnetosheath early on when IRM and UKS are close to the magnetopause, but not later. These large pulses appear to be related to reconnection going on at the magnetopause nearby. AMPTE magnetosheath data far from the magnetopause do not show a pressure pulse correlation with FTEs at ISEE. Moreover, the magnetic pressure and tension effects seen in the ISEE FTEs are much larger than any pressure effects seen in the magnetosheath. A superposed epoch analysis based on small-amplitude peaks in the AMPTE magnetosheath total static pressure (nkT + B(exp 2)/2 mu(sub 0)) hint at some boundary effects, less than 5 nT peak-to-peak variations in the ISEE 1 and 2 B(sub N) signature starting about 1 min after the pressure peak epoch. However, these variations are much smaller than the standard deviations of the B(sub N) field component. Thus the evidence from this case study suggests that upstream magnetosheath pressure pulses do not give rise to FTEs, but may produce very small amplitude signatures in the magnetic field at the magnetopause.

Elphic, R. C.↗
NASA NTRSDate not supplied

Formation of Tropopause Cirrus clouds by Typhoon-induced gravity waves during the Asian Summer Monsoon: A Case Study from the BATAL 2017 Campaign

Cirrus clouds in the Tropical Tropopause Layer (TTL) have a net warming impact on Earth’s climate and they regulate the amount of water vapor entering the lower stratosphere through dehydration process near the cold-point tropopause. During the Boreal summer, Asian Summer Monsoon (ASM) is an important source of cirrus clouds and humidity in the TTL owing to frequent deep convection. However, better representation of convection and TTL cirrus clouds in global climate models is needed for accurate assessment of their response to changing climate. In this study, we investigate the mechanisms involved in the formation of a tropopause cirrus cloud layer observed during the Balloon measurement campaigns of the Asian Tropopause Aerosol Layer (BATAL) over Hyderabad (17.47 °N, 78.58 °E), India on 23 August 2017. A subvisible cirrus cloud layer (optical thickness ~0.025) was detected by a backscatter sonde (COBALD) onboard a balloon near the cold-point tropopause (CPT, temperature~ -86.4 ° C, altitude~17.9 km) which was later confirmed by the CATS lidar on the ISS. Simultaneous measurements from an optical particle counter (Boulder Counter) revealed the presence of ice crystals smaller than 50 microns in this layer. The formation mechanism responsible for this tropopause cirrus is investigated using a technique combining three-dimensional back-trajectories, satellite observations, and ERA5 reanalysis data. Satellite observations revealed that the overshooting convection associated with a category-3 typhoon Hato, which hit Macau and Hong Kong on 23 August 2017 injected ice into the lower stratosphere. This caused a hydration patch that followed the ASM anticyclone subsequently moving towards Hyderabad. The presence of tropopause cirrus cloud layers in the cold temperature anomalies and updrafts along the back-trajectories indicated towards the role of typhoon-induced gravity waves in their formation. This case study highlights the role of typhoons in influencing the formation of tropopause cirrus clouds through stratospheric hydration and gravity waves in the ASM anticyclone.

Amit Kumar Pandit↗
DOE OSTI2020

DPSIR-ESA Vulnerability Assessment (DEVA) Framework: Synthesis, Foundational Overview, and Expert Case Studies

Land resources are central to understanding the relationship between humans and their environment. We broadly define a land resource to include all the ecological resources of climate, water, soil, landforms, flora, and fauna, and all the socioeconomic systems that interact with agriculture, forestry, and other land uses within some system boundary. Understanding the vulnerability of land resources to changes in land management or climate forcing is critical to developing sustainable land management strategies. Vulnerability assessments are complex given the multiple uses of the assessments, the multi-disciplinary nature of the problem, limited understanding, the dynamic structure of vulnerability, scale issues, and problems with identifying effective vulnerability indicators. Here, we propose a novel conceptual framework for vulnerability assessments of land resources that combines the driver– pressure–state–impact–response (DPSIR) framework adopted by the European Environment Agency to describe interactions between society and the environment, and the exposure-sensitivity-adaptive capacity (ESA) framework used by the Intergovernmental Panel on Climate Change to assess impacts of climate change. The DPSIR-ESA Vulnerability Assessment (DEVA) framework operationalizes the process of assessing the vulnerability of a target system to external stressors. The DEVA framework includes the following elements: 1) Definition of the target system (Land resource), 2) Description of internal characteristics of the target system (State), 3) Description of target system vulnerability indicators (Adaptive capacity, Sensitivity), 4) Description of stressor characteristics (Drivers, Pressures), 5) Description of stressor vulnerability indicators (Exposure), 6) Description of target system response to stressors (Impacts), and 7) Description of modifications to target systems or stressors (Responses). In stating that they have “applied the DEVA framework”, analysts acknowledge that they have (a) considered the full breadth of each DEVA element, (b) have made conscious decisions to limit the scope and complexity of certain elements, and (c) can communicate both the rationale for these decisions and the impact of these decisions on the vulnerability assessment results and recommendations. The DEVA framework was refined during invited presentations and follow-up discussions from a series of Special Sessions with leading experts at two successive ASABE Annual International Meetings. Six case studies drawn from the sessions elaborate upon the DEVA framework and provide concrete examples of the key concepts. The DEVA approach gives engineers, planners, and analysts a new, flexible framework to apply a broad array of useful tools toward assessment of land resource system vulnerability.

Anandhi, Aauvadi↗
DOE OSTI2024

Improved detection of low-frequency within-host variants from deep sequencing: A case study with human papillomavirus

Abstract High-coverage sequencing allows the study of variants occurring at low frequencies within samples, but is susceptible to false-positives caused by sequencing error. Ion Torrent has a very low single nucleotide variant (SNV) error rate and has been employed for the majority of human papillomavirus (HPV) whole genome sequences. However, benchmarking of intrahost SNVs (iSNVs) has been challenging, partly due to limitations imposed by the HPV life cycle. We address this problem by deep sequencing three replicates for each of 31 samples of HPV type 18 (HPV18). Errors, defined as iSNVs observed in only one of three replicates, are dominated by C→T (G→A) changes, independently of trinucleotide context. True iSNVs, defined as those observed in all three replicates, instead show a more diverse SNV type distribution, with particularly elevated C→T rates in CCG context (CCG→CTG; CGG→CAG) and C→A rates in ACG context (ACG→AAG; CGT→CTT). Characterization of true iSNVs allowed us to develop two methods for detecting true variants: (1) VCFgenie, a dynamic binomial filtering tool which uses each variant’s allele count and coverage instead of fixed frequency cut-offs; and (2) a machine learning binary classifier which trains eXtreme Gradient Boosting models on variant features such as quality and trinucleotide context. Each approach outperforms fixed-cut-off filtering of iSNVs, and performance is enhanced when both are used together. Our results provide improved methods for identifying true iSNVs in within-host applications across sequencing platforms, specifically using HPV18 as a case study.

Virology↗
DOE OSTI2023

Economic Evaluation of a Coupled Nuclear Power Plant and Hydrogen Production Facility: A Case Study

This study optimized the design sizes and operation of a power-to-hydrogen-to-power integrated energy system to allow a baseload power plant to operate flexibly in the energy market. In collaboration with a utility industry partner, the system, consisting of an electrolyzer, compressors, storage tank, and fuel cell, was optimized under conditions specific to the proposed project at the site of a nuclear power plant. The Design Integration and Synthesis Platform to Advance Tightly Coupled Hybrid Energy Systems (DISPATCHES) maximized net present value by optimizing sizing of components and dispatch decisions. Revenues included sale of electricity, capacity payments typical of the New York Independent System Operator, and the section 45V hydrogen production tax credit of the Inflation Reduction Act of 2022 (the tax credit was assumed to be available to legacy plants in the absence of clear guidance at present). Under default assumptions which excluded many capital expenditures, the base case optimized solution had a net present value of $\$$1.4 million over a 30 year lifetime, with a 0.365 MW fuel cell operating nearly continuously and 85% of revenues supplied by the hydrogen production tax credit (which was counted as a revenue regardless of profit, thus assuming credit monetization or offset of taxes within the larger firm was possible in all years). Beyond the base case, a sensitivity study elucidated drivers of the economics as capacity payment rate and hydrogen production tax credit rate vary. Additional sensitivity studies also extended results to variation of other, previously fixed parameters, including the fuel cell capital cost, and to imposition of further constraints. Optimization was also repeated for the default assumptions but recognizing tax credits upon use of hydrogen rather than upon its production, producing no change in the optimal solution. Most notably, capacity payments above $\$$15/kW-month drove optimal fuel cells multiple times larger than those with the default estimated capacity payment of $\$$2.5/kW-month (approaching 11 vs. 0.365 MW), and these larger fuel cells operated rarely (capacity factors of ~0.03). Furthermore, when the hydrogen production tax credit was provided for only 10 years, under the specific assumptions of this study (e.g., neither site preparation costs nor electrolyzer capital cost counted), the optimal solution avoided economic loss by ceasing system operation after the 10th year. Viewed broadly, this study demonstrated the capabilities of DISPATCHES, which can be user-adapted to serve other industrial case studies.

08 HYDROGEN↗
DOE OSTI2020

Dynamic hosting capacity analysis for distributed photovoltaic resources—Framework and case study

Distributed photovoltaic systems can cause adverse distribution system impacts, including voltage violations at customer locations and thermal overload of lines, transformers, and other equipment resulting from high current. The installed capacity at which violations first occur and above which would require system upgrades is called the hosting capacity. Current static methods for determining hosting capacity tend to either consider infrequent worst-case snapshots in time and/or capture coarse time and spatial resolution. Because the duration of violations cannot be captured with these traditional methods, the metric thresholds used in these studies conservatively use the strictest constraints given in operating standards, even though both worse voltage performance and higher overloads may be temporarily acceptable. However, assessing the full details requires accurately capturing time-dependence, voltage-regulating equipment operations, and performance of advanced controls-based mitigation techniques. In this paper, we propose a dynamic distributed photovoltaic hosting capacity methodology to address these issues by conducting power flow analysis for a full year. A key contribution is the formulation of time aware metrics to take these annual results and identify the hosting capacity. Through a case study, we show that this approach can more fully capture grid impacts of distributed photovoltaic than traditional methods and the dynamic hosting capacity was 60%–200% higher than the static hosting capacity in this case study.

14 SOLAR ENERGY↗
DOE OSTI2022

A machine learning approach to water quality forecasts and sensor network expansion: Case study in the Wabash River Basin, United States

Abstract Midwestern cities require forecasts of surface nitrate loads to bring additional treatment processes online or activate alternative water supplies. Concurrently, networks of nitrate monitoring stations are being deployed in river basins, co‐locating water quality observations with established stream gauges. However, tools to evaluate the future value of expanded networks to improve water quality forecasts remains challenging. Here, we construct a synthetic data set of stream discharge and nitrate for the Wabash River Basin—one of the United States’ most nutrient polluted basins—using the established Agro‐IBIS and THMB models. Synthetic data enables rapid, unbiased and low‐cost assessment of potential sensor placements to support management objectives, such as near‐term forecasting. Using the synthetic data, we established baseline 1‐day forecasts for surface water nitrate at 12 cities in the basin using support vector machine regression (SVMR; RMSE 0.48–3.3 ppm). Next, we used the SVMRs to evaluate the improvement in forecast performance associated with deployment of additional nitrate sensors. We identified the optimal sensor placement to improve forecasts at each city, and the relative value of sensors at each candidate location. Finally, we assessed the co‐benefit realized by other cities when a sensor is deployed to optimize a forecast at one city, finding significant positive externalities in all cases. Ultimately, our study explores the potential for machine learning to make near‐term predictions and critically evaluate the improvement realized by expanding a monitoring network. While we use nitrate pollution in the Wabash River Basin as a case study, this approach could be readily applied to any problem where the future value of sensors and network design are being evaluated.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

Application of MODIS Products to Infer Possible Relationships Between Basin Land Cover and Coastal Waters Turbidity Using the Magdalena River, Colombia, as a Case Study

Basin development and consequent change in basin land cover have been often associated with an increased turbidity in coastal waters because of sediment yield and nutrients loading. The later leads to phytoplankton abundance further exacerbating water turbidity. This subsequently affects biological and physical processes in coastal estuaries by interfering with sun light penetration to coral reefs and sea grass, and even affecting public health. Therefore, consistent estimation of land cover changes and turbidity trend lines is crucial to design environmental and restoration management plans, to predict fate of possible pollutants, and to estimate sedimentary fluxes into the ocean. Ground solely methods to estimate land cover change would be unpractical and traditional methods of monitoring in situ water turbidity can be very expensive and time consuming. Accurate monitoring on the status and trends of basin land cover as well as the water quality of the receiving water bodies are required for analysis of relationships between the two variables. Use of remote sensing (RS) technology provides a great benefit for both fields of study, facilitating monitoring of changes in a timely and cost effective manner and covering wide areas with long term measurements. In this study, the Magdalena River basin and fixed geographical locations in the estuarine waters of its delta are used as a case to study the temporal trend lines of both, land cover change and the reflectance of the water turbidity using satellite technology. Land cover data from a combined product between sensors Terra and Aqua (MCD12Q1) from MODIS will be adapted to the conditions in the Magdalena basin to estimate changes in land cover since year 2000 to 2009. Surface reflectance data from a MODIS, Terra (MOD09GQ), band 1, will be used in lieu of in situ water turbidity for the time period between 2000 and present. Results will be compared with available existing data.

Madrinan, Max Jacobo Moreno↗