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At least 181 records · Page 10

Providing Levelized Cost and Waiting Time Inputs for HDV Hydrogen Refueling Station Planning: A Case Study of U.S. I-75 Corridor

Widespread use of diesel fuels in freight transportation leads to greenhouse gas (GHG) emissions that play an important role in air pollution. The air pollution, as well as the energy crisis, drives the transition from diesel fuel to cleaner and more energy-efficient fuels such as hydrogen fuels. With such a transition, the strategic planning and optimization of supporting refueling infrastructure along the national highway for heavy-duty vehicles (HDVs) become a necessity. Cost and service level are two essential factors to be considered in hydrogen refueling station location and capacity optimization. The service level in this study is assessed by the delay (waiting time) at fueling stations as well as the fueling demand fulfilled. To this end, this study presents a methodology to provide waiting time (delay) and levelized hydrogen fuel cost inputs under different station configurations (number of dispensers and fill rates) for hydrogen refueling station planning. Determining the station configurations based on the trade-off between the two inputs is further discussed. Particularly, this study points out that the levelized cost and waiting time are affected by hourly demand patterns and estimates the two inputs under different peak hour fueling demand scenarios. The results suggest that, with the same daily demand, the cost, as well as the waiting time, increases obviously with the peak hour demand. In the case study, the cost grows at least 30% when the peak hour visit frequency of the daily total visits increases from 5% (evenly distributed pattern) to 10% (the most common pattern for existing diesel fueling stations along I-75). Accounting for this peak hour effect in hydrogen refueling station planning is recommended. Overall, the waiting time model and cost analysis provide key inputs for optimizing hydrogen refueling station location and configurations based on anticipated refueling demand patterns. The paper is the second in a series that aims to build a comprehensive modeling plan and optimize hydrogen refueling infrastructure along the Interstate 75 (I-75) corridor for HDVs.

Liu, Yuandong↗

Democratizing life cycle assessment by developing a streamlined model of greenhouse gas emissions from US natural gas supply chains

Natural gas (NG) supply chains contribute substantially to the global energy supply and anthropogenic methane emissions, making them frequent subjects of life cycle assessments (LCAs). To better characterize central tendencies and variability, we systematically reviewed and harmonized published estimates of life cycle greenhouse gas (GHG) emissions from United States NG supply chains. Results informed a streamlined LCA model (SLiNG-GHG: streamlined LCAs of NG-GHGs) that quantifies carbon dioxide and methane from three gates: transmission, distribution, and shipping. Median estimates employing harmonized emission inputs, are 10, 11, and 21 g CO2e/MJ gas (100-year global warming potentials [GWPs]), and 20, 22, and 33 g CO2e/MJ gas (20-year GWPs), delivered to each gate, respectively. Alternatively, inputting available, independent methane measurements, SLiNG-GHG estimates varied from -23% to +316% relative to baseline. Bottom-up inventories used in LCAs tend to underestimate methane compared with measurements. Results underscore the need for open-source, streamlined LCA models that can easily incorporate rapidly evolving measurements for non-experts like investors and regulators.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

GEM-CEDAR Study of Ionospheric Energy Input and Joule Dissipation

We are studying ionospheric model performance for six events selected for the GEM-CEDAR modeling challenge. DMSP measurements of electric and magnetic fields are converted into Poynting Flux values that estimate the energy input into the ionosphere. Models generate rates of ionospheric Joule dissipation that are compared to the energy influx. Models include the ionosphere models CTIPe and Weimer and the ionospheric electrodynamic outputs of global magnetosphere models SWMF, LFM, and OpenGGCM. This study evaluates the model performance in terms of overall balance between energy influx and dissipation and tests the assumption that Joule dissipation occurs locally where electromagnetic energy flux enters the ionosphere. We present results in terms of skill scores now commonly used in metrics and validation studies and we can measure the agreement in terms of temporal and spatial distribution of dissipation (i.e, location of auroral activity) along passes of the DMSP satellite with the passes' proximity to the magnetic pole and solar wind activity level.

Rastaetter, Lutz↗

Quantifying parameters for Bayesian prior assumptions when estimating the probability of failure of software

Software reliability has become increasingly important, especially in life-critical situations. The ability to measure the results of testing and to quantify software reliability is needed. If this is accomplished, a certain minimum amount of reliability for a piece of software can be specified, and testing and/or other analysis may be done until that minimum number has been attained. There are many models for estimating software reliability. The accuracy of these models has been challenged and many revisions for the models and recalibration techniques have been devised. Of particular interest is the method of estimating the probability of failure of software when no failures have yet occurred in its current version as described by Miller. This model uses black box testing with formulae based on Bayesian estimation. The focus is on three interrelated issues: estimating the probability of failure when testing has revealed no errors; modifying this estimation when the input use distribution does not match the test distribution; and combining the results from random testing with other relevant information to obtain a possibly more accurate estimate of the probability of failure. Obtaining relevant information about the software and combining the results for a better estimate for the Miller model are discussed.

Long, Jacquelyn E.↗

Computing Fault Displacements from Surface Deformations

Simplex is a computer program that calculates locations and displacements of subterranean faults from data on Earth-surface deformations. The calculation involves inversion of a forward model (given a point source representing a fault, a forward model calculates the surface deformations) for displacements, and strains caused by a fault located in isotropic, elastic half-space. The inversion involves the use of nonlinear, multiparameter estimation techniques. The input surface-deformation data can be in multiple formats, with absolute or differential positioning. The input data can be derived from multiple sources, including interferometric synthetic-aperture radar, the Global Positioning System, and strain meters. Parameters can be constrained or free. Estimates can be calculated for single or multiple faults. Estimates of parameters are accompanied by reports of their covariances and uncertainties. Simplex has been tested extensively against forward models and against other means of inverting geodetic data and seismic observations. This work

Lyzenga, Gregory↗

Comparing Landsat-7 ETM+ and ASTER Imageries to Estimate Daily Evapotranspiration Within a Mediterranean Vineyard Watershed

We compared the capabilities of Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imageries for mapping daily evapotranspiration (ET) within a Mediterranean vineyard watershed. We used Landsat and ASTER data simultaneously collected on four dates in 2007 and 2008, along with the simplified surface energy balance index (S-SEBI) model. We used previously ground-validated good quality ASTER estimates as reference, and we analyzed the differences with Landsat retrievals in light of the instrumental factors and methodology. Although Landsat and ASTER retrievals of S-SEBI inputs were different, estimates of daily ET from the two imageries were similar. This is ascribed to the S-SEBI spatial differencing in temperature, and opens the path for using historical Landsat time series over vineyards.

Temperature measurement↗

Bayesian recursive image estimation.

A procedure for recursively estimating images that are characterized statistically by the mean and correlation functions associated with the random process representing the brightness level is proposed for the case where the images are corrupted by additive noise. First, a dynamic model is developed with a response characteristic which matches that of the scanner output (the input of the estimator is the output of a horizontal line scanner) in a statistical sense. Such models have the form of an ordinary differential or difference equation with white noise input. An insignificant approximation is introduced by using a constant-coefficient model. The appropriate model is a vector valued difference equation with the solution representing a vector Markov process. The next step is to obtain the minimum mean square estimate of the image by using a Kalman filter. Since the image estimation is an interpolation problem, two successive runs over the observation are performed in opposite directions and the resultant estimates are averaged. Examples are included for illustration.

Nahi, N. E.↗

Open-Loop Acquisition Of Frequency In BPSK

Proposed open-loop analog/digital signal-processing system would be Costas-loop error detector functioning in closed-loop manner overall. Detector estimates difference between frequency of input signal and internal reference oscillator. Estimate used to close frequency-control loop. Precise symbol timing not necessary. Performance better than systems that effect open-loop acquisition using integrators instead of low-pass filters in arms of Costas loops and in which performance varies with symbol timing.

Shah, Biren N.↗

Radium Inputs Into the Arctic Ocean From Rivers: A Basin‐Wide Estimate

Abstract Radium isotopes have been used to trace nutrient, carbon, and trace metal fluxes inputs from ocean margins. However, these approaches require a full accounting of radium sources to the coastal ocean including rivers. Here, we aim to quantify river radium inputs into the Arctic Ocean for the first time for 226 Ra and to refine the estimates for 228 Ra. Using new and existing data, we find that the estimated combined (dissolved plus desorbed) annual 226 Ra and 228 Ra fluxes to the Arctic Ocean are [7.0–9.4] × 10 14 dpm y −1 and [15–18] × 10 14 dpm y −1 , respectively. Of these totals, 44% and 60% of the river 226 Ra and 228 Ra, respectively are from suspended sediment desorption, which were estimated from laboratory incubation experiments. Using Ra isotope data from 20 major rivers around the world, we derived global annual 226 Ra and 228 Ra fluxes of [7.4–17] × 10 15 and [15–27] × 10 15 dpm y −1 , respectively. As climate change spurs rapid Arctic warming, hydrological cycles are intensifying and coastal ice cover and permafrost are diminishing. These river radium inputs to the Arctic Ocean will serve as a valuable baseline as we attempt to understand the changes that warming temperatures are having on fluxes of biogeochemically important elements to the Arctic coastal zone.

Bullock, Emma J.↗

Subsonic flight test evaluation of a propulsion system parameter estimation process for the F100 engine

An adaptive-performance-seeking control system which optimizes the quasi-steady-state performance of the F-15 propulsion system is discussed. This paper presents flight- and ground-test evaluations of the propulsion system parameter-estimation process used by the performance seeking control system. The estimator consists of a compact propulsion system model and an extended Kalman filter. The extended Kalman filter estimates five engine component deviation parameters from measured inputs. The compact model uses measurements and Kalman-filter estimates as inputs to predict unmeasured propulsion parameters such as net propulsive force and fan stall margin. The ability to track trends and estimate absolute values of propulsion system parameters was demonstrated. For example, thrust stand results show a good correlation especially in trends between the performance seeking control estimated and measured thrust.

Orme, John S.↗

Subsonic flight test evaluation of a propulsion system parameter estimation process for the F100 engine

Integrated engine-airframe optimal control technology may significantly improve aircraft performance. This technology requires a reliable and accurate parameter estimator to predict unmeasured variables. To develop this technology base, NASA Dryden Flight Research Facility (Edwards, CA), McDonnell Aircraft Company (St. Louis, MO), and Pratt & Whitney (West Palm Beach, FL) have developed and flight-tested an adaptive performance seeking control system which optimizes the quasi-steady-state performance of the F-15 propulsion system. This paper presents flight and ground test evaluations of the propulsion system parameter estimation process used by the performance seeking control system. The estimator consists of a compact propulsion system model and an extended Kalman filter. The extended Laman filter estimates five engine component deviation parameters from measured inputs. The compact model uses measurements and Kalman-filter estimates as inputs to predict unmeasured propulsion parameters such as net propulsive force and fan stall margin. The ability to track trends and estimate absolute values of propulsion system parameters was demonstrated. For example, thrust stand results show a good correlation, especially in trends, between the performance seeking control estimated and measured thrust.

Orme, John S.↗

Computational Estimation by Scientific Data Mining with Classical Methods to Automate Learning Strategies of Scientists

Experimental results are often plotted as 2-dimensional graphical plots (aka graphs) in scientific domains depicting dependent versus independent variables to aid visual analysis of processes. Repeatedly performing laboratory experiments consumes significant time and resources, motivating the need for computational estimation. The goals are to estimate the graph obtained in an experiment given its input conditions, and to estimate the conditions that would lead to a desired graph. Existing estimation approaches often do not meet accuracy and efficiency needs of targeted applications. We develop a computational estimation approach called AutoDomainMine that integrates clustering and classification over complex scientific data in a framework so as to automate classical learning methods of scientists. Knowledge discovered thereby from a database of existing experiments serves as the basis for estimation. Challenges include preserving domain semantics in clustering, finding matching strategies in classification, striking a good balance between elaboration and conciseness while displaying estimation results based on needs of targeted users, and deriving objective measures to capture subjective user interests. These and other challenges are addressed in this work. The AutoDomainMine approach is used to build a computational estimation system, rigorously evaluated with real data in Materials Science. Our evaluation confirms that AutoDomainMine provides desired accuracy and efficiency in computational estimation. It is extendable to other science and engineering domains as proved by adaptation of its sub-processes within fields such as Bioinformatics and Nanotechnology.

Computer Science↗

Towards Resilient Autonomous Navigation of Drones

Robots and particularly drones are especially useful in exploring extreme environments that pose hazards to humans. To ensure safe operations in these situations, usually perceptually degraded and without good GNSS, it is critical to have a reliable and robust state estimation solution. The main body of literature in robot state estimation focuses on developing complex algorithms favoring accuracy. Typically, these approaches rely on a strong underlying assumption: the main estimation engine will not fail during operation. In contrast, we propose an architecture that pursues robustness in state estimation by considering redundancy and heterogeneity in both sensing and estimation algorithms. The architecture is designed to expect and detect failures and adapt the behavior of the system to ensure safety. To this end, we present HeRO (Heterogeneous Redundant Odometry): a stack of estimation algorithms running in parallel supervised by a resiliency logic. This logic carries out three main functions: a) perform confidence tests both in data quality and algorithm health; b) re-initialize those algorithms that might be malfunctioning; c) generate a smooth state estimate by multiplexing the inputs based on their quality. The state and quality estimates are used by the guidance and control modules to adapt the mobility behaviors of the system. The validation and utility of the approach are shown with real experiments on a ying robot for the use case of autonomous exploration of subterranean environments, with particular results from the STIX event of the DARPA Subterranean Challenge.

Agha-mohammadi, Ali-akbar↗

Helmet mounted display flight symbology research

Screen/head stabilized MIL-STD 1295 helmet mounted display (HMD) flight symbology as integrated on the Apache helicopter was compared to world referenced/stabilized flight symbology. Simulation test results indicate that pilots perform significantly better using world-stabilized conformal attitude symbology. They were accurate to an average of 1/2 degree at estimating terrain relief and aerial target locations. World stabilized conformal symbology was preferred while performing contour flight tasks. They reported that the use of climb-dive-marker during contour flight greatly reduced pilot work load under conditions tested. Cyclic input errors occurred when using both 1295 hover symbology and test symbology indicating that a better approach for depicting hover symbology is warranted. The magnitude of cyclic input and spatial estimation errors increased as the off-axis viewing angle became larger.

Haworth, Loran A.↗

Real-Time Stability and Control Derivative Extraction From F-15 Flight Data

A real-time, frequency-domain, equation-error parameter identification (PID) technique was used to estimate stability and control derivatives from flight data. This technique is being studied to support adaptive control system concepts currently being developed by NASA (National Aeronautics and Space Administration), academia, and industry. This report describes the basic real-time algorithm used for this study and implementation issues for onboard usage as part of an indirect-adaptive control system. A confidence measures system for automated evaluation of PID results is discussed. Results calculated using flight data from a modified F-15 aircraft are presented. Test maneuvers included pilot input doublets and automated inputs at several flight conditions. Estimated derivatives are compared to aerodynamic model predictions. Data indicate that the real-time PID used for this study performs well enough to be used for onboard parameter estimation. For suitable test inputs, the parameter estimates converged rapidly to sufficient levels of accuracy. The devised confidence measures used were moderately successful.

Smith, Mark S.↗

Fertilizer management for global ammonia emission reduction

Crop production is a large source of atmospheric ammonia (NH 3 ), which poses risks to air quality, human health and ecosystems. However, estimating global NH 3 emissions from croplands is subject to uncertainties because of data limitations, thereby limiting the accurate identification of mitigation options and efficacy. In this report we develop a machine learning model for generating crop-specific and spatially explicit NH 3 emission factors globally (5-arcmin resolution) based on a compiled dataset of field observations. We show that global NH 3 emissions from rice, wheat and maize fields in 2018 were 4.3 ± 1.0 Tg N yr -1 , lower than previous estimates that did not fully consider fertilizer management practices. Furthermore, spatially optimizing fertilizer management, as guided by the machine learning model, has the potential to reduce the NH3 emissions by about 38% (1.6 ± 0.4 Tg N yr -1 ) without altering total fertilizer nitrogen inputs. Specifically, we estimate potential NH3 emissions reductions of 47% (44–56%) for rice, 27% (24–28%) for maize and 26% (20–28%) for wheat cultivation, respectively. Under future climate change scenarios, we estimate that NH3 emissions could increase by 4.0 ± 2.7% under SSP1–2.6 and 5.5 ± 5.7% under SSP5–8.5 by 2030–2060. However, targeted fertilizer management has the potential to mitigate these increases.

54 ENVIRONMENTAL SCIENCES↗

Avoiding Stair-Step Artifacts in Image Registration for GOES-R Navigation and Registration Assessment

In developing software for independent verification and validation (IVV) of the Image Navigation and Registration (INR) capability for the Geostationary Operational Environmental Satellite R Series (GOES-R) Advanced Baseline Imager (ABI), we have encountered an image registration artifact which limits the accuracy of image offset estimation at the subpixel scale using image correlation. Where the two images to be registered have the same pixel size, subpixel image registration preferentially selects registration values where the image pixel boundaries are close to lined up. Because of the shape of a curve plotting input displacement to estimated offset, we call this a stair-step artifact. When one image is at a higher resolution than the other, the stair-step artifact is minimized by correlating at the higher resolution. For validating ABI image navigation, GOES-R images are correlated with Landsat-based ground truth maps. To create the ground truth map, the Landsat image is first transformed to the perspective seen from the GOES-R satellite, and then is scaled to an appropriate pixel size. Minimizing processing time motivates choosing the map pixels to be the same size as the GOES-R pixels. At this pixel size image processing of the shift estimate is efficient, but the stair-step artifact is present. If the map pixel is very small, stair-step is not a problem, but image correlation is computation-intensive. This paper describes simulation-based selection of the scale for truth maps for registering GOES-R ABI images.

stair-step artifact↗