Impacts of vehicle electrification on large-scale transportation and charging infrastructure: A dynamic network modeling approach
Not Available
Zou, Qiling (ORCID:000000032146558X)↗
SEARCH · Search NASA
Results for “transport infrastructure”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Unified SimCCS Platform for Decision-Making in Carbon Capture, Transport, and Storage Infrastructure
Explore the source record for details and available documents.
Earth Sciences↗
Utilizing Probabilistic Analyses to Explore Performance Margins of Natural Gas Infrastructure for the Transport and Delivery of Hydrogen and Hydrogen Blends
Explore the source record for details and available documents.
Schroeder, Benjamin B↗
Economic consequences of commercial space operations
The potential economic benefits generated from increased industry involvement and investment in space activities and the subsequent cost implications are discussed. A historical overview of commercial industry involvement in space is given and sources of new economic growth in space are discussed. These include communications satellites, small satellites, positioning and navigation services, space transportation and infrastructure, remote sensing, and materials processing in space such as the manufacturing of protein crystals and zeolites. Macroeconomic trends and principles such as limits on technology trade, eased restrictions on international joint ventures, foreign investments in U.S. firms, and increased foreign competition are discussed. Earth observations and mapping are considered. Opportunities for private sector involvement in building space infrastructure and space transportation are highlighted.
Stone, Barbara A.↗
Electric Transportation and Distribution Infrastructure: A Heavy-Duty Vehicle Deep Dive
This presentation is a deep dive into charging behavior and grid impacts of short-haul electric Class 8 semi trucks.
ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
Count Every Trip: Finding the Uncertainty in Energy Estimates Made from Inferred Travel Modes
To properly inform transport policy and infrastructure changes, transportation related metrics need both measured values and uncertainties of those values. Travel monitoring smartphone apps can record people's travel behavior, but trip data quality is limited by sensor errors, user labeling rates and the accuracy of inference algorithms used for travel diary creation. We discuss the use of phone app recorded travel diary data to estimate energy consumption, and propose the use of propagation of variance to find error bars for such estimates. We define energy consumption for one trip as trip length times the energy intensity per distance unit of the travel mode used. We characterize trip length errors with relative error and inferred trip mode errors with confusion matrix columns. The resulting variances of each measurement are then propagated to the final calculated energy consumption. We tested our uncertainty methods on a dataset that used phone app data combined with prompted recall, consisting of 92,234 labeled trips for over 500,000 miles. Accounting for uncertainty using expected energy intensities and variance propagation gives a dataset-wide aggregate energy consumption percent error of about 9%, within one standard deviation from the truth. Future work could involve applying similar methods to other travel diary based metrics.
ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗
Count Every Trip: Finding the Uncertainty in Energy Estimates Made from Inferred Travel Modes
To properly inform transport policy and infrastructure changes, transportation related metrics need both measured values and uncertainties of those values. Travel monitoring smartphone apps can record people's travel behavior, but trip data quality is limited by sensor errors, user labeling rates and the accuracy of inference algorithms used for travel diary creation. We discuss the use of phone app recorded travel diary data to estimate energy consumption, and propose the use of propagation of variance to find error bars for such estimates. We define energy consumption for one trip as trip length times the energy intensity per distance unit of the travel mode used. We characterize trip length errors with relative error and inferred trip mode errors with confusion matrix columns. The resulting variances of each measurement are then propagated to the final calculated energy consumption. We tested our uncertainty methods on a dataset that used phone app data combined with prompted recall, consisting of 92,234 labeled trips for over 500,000 miles. Accounting for uncertainty using expected energy intensities and variance propagation gives a dataset-wide aggregate energy consumption percent error of about 9%, within one standard deviation from the truth. Future work could involve applying similar methods to other travel diary based metrics.
ADVANCED PROPULSION SYSTEMS↗
Estimating Travel Energy Consumption Uncertainty Based on Inferred Travel Mode and Sensed Travel Length
To properly inform transport policy and infrastructure changes, transportation related metrics need both measured values and uncertainties of those values. Travel monitoring smartphone apps can record people's travel behavior, but trip data quality is limited by sensor errors, user labeling rates and the accuracy of inference algorithms used for travel diary creation. We discuss the use of phone app recorded travel diary data to estimate energy consumption, and propose the use of propagation of variance to find error bars for such estimates. We define energy consumption for one trip as trip length times the energy intensity per distance unit of the travel mode used. We characterize trip length errors with relative error and inferred trip mode errors with confusion matrix columns. The resulting variances of each measurement are then propagated to the final calculated energy consumption. We tested our uncertainty methods on a dataset that used phone app data combined with prompted recall, consisting of 92,234 labeled trips for over 500,000 miles. Accounting for uncertainty using expected energy intensities and variance propagation gives a dataset-wide aggregate energy consumption percent error of about 8%, within one standard deviation from the truth. Future work could involve applying similar methods to other travel diary based metrics.
ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
Utilizing Probabilistic Analyses to Explore Performance Margins of Natural Gas Infrastructure for the Transportation and Delivery of Hydrogen and Hydrogen Blends
Explore the source record for details and available documents.
Schroeder, Benjamin B.↗
SimCCS 3.0 User GUIDE
Carbon Capture and Storage (CCS) is an important technology aimed at reducing CO 2 emissions and contributing to the mitigation strategies of climate change. The process involves three main steps: CO 2 capture, transport, and storage. Specifically, CO 2 is captured from industrial facilities, such as coal-fired power plants and natural gas processing plants. It is then transported through infrastructure networks, such as pipelines, rails, and ships, and injected into underground geological formations, such as depleted oil and gas reservoirs or deep saline aquifers, for permanent storage. The entire CCS value chain encompasses multiple components, including CO 2 sources, transport networks, and storage sites. The proper design of CCS transport infrastructure is an essential step for the deployment of CCS technology. However, the design is not a trivial process, as it presents many challenges and requires consideration of numerous economic, social, and environmental factors.
42 ENGINEERING↗
International Space Station: Introduction and ISS Familiarization
This viewgraph presentation provides an overview of the latest complete set of International Space Station (ISS) cargo transportation requirements, and the infrastructure supporting transportation, such as cargo bags, and flight support equipment. The aim of this presentation is also to give industry representatives the requirements for commercial support of the cargo handling operations for the ISS, and give the industry representatives an opportunity to exchange ideas.
Hatafield, Caris↗
2020 Transportation Research Board Workshop: Infrastructure Spatial Sensing at Intersections: The Potential to Enhance Safety and Enable Automation
The sensing technology that is enabling vehicle automation also can revolutionize traffic intersection safety, control, and efficiency. Infrastructure spatial sensing technology refers to the deployment of sensors that can detect and track all objects in the field of view - for example, mounting lidar sensors, radar, and video imaging at an intersection and fusing the data to produce a three-dimensional dynamic operational awareness. Combining such sensing capability with fail-safe vehicle communications can facilitate applications such as eco-approach and departure, optimizing efficient vehicle-signal coordination, and reducing traffic accidents through increasing safety. Furthermore, evidence is mounting suggesting that infrastructure-based sensing is needed in order to safely deploy automated public mobility alongside the normal vehicle and pedestrian traffic in our cities. Six speakers were invited to present their work from various aspects (safety, efficiency, and automation) addressing infrastructure-based sensing at the workshop on Infrastructure Spatial Sensing hosted in Washington DC as part of the Transportation Research Board Annual Meeting in January 12, 2019. A summary of the presentation are included herein.
47 OTHER INSTRUMENTATION↗
Shape-shifting Elephants: Multi-modal Transport for Integrated Research Infrastructure
Data Acquisition (DAQ) workloads form an important class of scientific network traffic that by its nature (1) flows across different research infrastructure, including remote instruments and supercomputer clusters, (2) has ever-increasing throughput demands, and (3) has ever-increasing integration demands---for example, observations at one instrument could trigger a reconfiguration of another instrument. Today's DAQ transfers rely on UDP and (heavily tuned) TCP, but this is driven by convenience rather than suitability. The mismatch between Internet transport protocols and scientific workloads becomes more stark with the steady increase in link capacities, data generation, and integration across research infrastructure.This position paper argues the importance of developing specialized transport protocols for DAQ workloads. It proposes a new transport feature for this kind of elephant flow: multi-modality involves the network actively configuring the transport protocol to change how DAQ flows are processed across different underlying networks that connect scientific research infrastructure. Multi-modality is a layering violation that is proposed as a pragmatic technique for DAQ transport protocol design. It takes advantage of programmable network hardware that is increasingly being deployed in scientific research infrastructure. The paper presents an initial evaluation through a pilot study that includes a Tofino2 switch and Alveo FPGA cards, and using data from a particle detector.
97 MATHEMATICS AND COMPUTING↗
Roles of airships in economic development
It is proposed that airships of known and tested technology could, in some cases, perform routine transport missions more economically than conventional transport modes. If infrastructure for direct surface transport is already in place or if such infrastructure can be justified by the size of the market and there are no unusual impediments to constructing it, then the airships of tested technology cannot normally compete. If, however, the surface routes would be unusually expensive or circuitous, or if they involve several transhipments, or if the market size is too small to spread infrastructure costs of conventional transport, the airships of tested technology present a workable alternative. A series of special cases are considered. The cases, though unusual, are not unique; there are several similar possible applications which, in total, would provide a reasonably large market for airships.
Beier, G. J.↗
Establishing the infrastructure - An integrated Space Transportation System
A discussion is presented concerning the various transfer vehicle/propulsion systems that are currently under consideration within the egis of the NASA Space Exploration Initiative for the establishment of an infrastructure for lunar base construction and Mars exploration. Cryogenically fueled and nuclear propulsion alternatives are considered, as well as such advanced concepts as that employing nuclear-electric propulsion and artificial gravity and a solar-electric propulsion zero-gravity concept. Mars lander and earth-to-LEO launcher concepts are also noted.
Huber, W. G.↗
Status of Mercury and Imp: Two Monte Carlo Transport Codes Developed Using Shared Infrastructure at Lawrence Livermore National Laboratory
The Monte Carlo Transport Project at Lawrence Livermore National Laboratory develops two Monte Carlo transport codes used in production by a sizable internal user community. Mercury is a Monte Carlo particle transport code used to model the interaction of neutrons, gammas, and light ions with a material. Imp is an implicit Monte Carlo thermal x-ray photon transport code used to model the interaction of x-ray photons with a material. This paper describes the two codes and highlights recent developments.
Pozulp, Michael↗
Agent-based modeling for multimodal transportation of CO 2 for carbon capture, utilization, and storage: CCUS-agent
Here, to understand the system-level interactions between the entities in Carbon Capture, Utilization, and Storage (CCUS), an agent-based foundational modeling tool, CCUS-Agent, is developed for a large-scale study of transportation flows and infrastructure in the United States. Key features of the tool include (i) modular design, (ii) multiple transportation modes, (iii) capabilities for extension, and (iv) testing against various system components and networks of small and large sizes. Five matching algorithms for CO 2 supply agents (e.g., powerplants and industrial facilities) and demand agents (e.g., storage and utilization sites) are explored: Most Profitable First Year (MPFY), Most Profitable All Years (MPAY), Shortest Total Distance First Year (SDFY), Shortest Total Distance All Years (SDAY), and Shortest distance to long-haul transport All Years (ACAY). Before matching, the supply agent, demand agent, and route must be available, and the connection must be profitable. A profitable connection means the supply agent portion of revenue from the 45Q tax credit must cover the supply agent costs and all transportation costs, while the demand agent revenue portion must cover all demand agent costs. A case study employing over 5500 supply and demand agents and multimodal CCUS transportation infrastructure in the contiguous United States is conducted. The results suggest that it is possible to capture over 9 billion tonnes (GT) of CO 2 from 2025 to 2043, which will increase significantly to 22 GT if the capture costs are reduced by 40 %. The MPFY and SDFY algorithms capture more CO 2 earlier in the time horizon, while the MPAY and SDAY algorithms capture more later in the time horizon.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Improved Diagnosis of Precipitation Type with LightGBM Machine Learning
Abstract Existing precipitation-type algorithms have difficulty discerning the occurrence of freezing rain and ice pellets. These inherent biases are not only problematic in operational forecasting but also complicate the development of model-based precipitation-type climatologies. To address these issues, this paper introduces a novel light gradient-boosting machine (LightGBM)-based machine learning precipitation-type algorithm that utilizes reanalysis and surface observations. By comparing it with the Bourgouin precipitation-type algorithm as a baseline, we demonstrate that our algorithm improves the critical success index (CSI) for all examined precipitation types. Moreover, when compared with the precipitation-type diagnosis in reanalysis, our algorithm exhibits increased F1 scores for snow, freezing rain, and ice pellets. Subsequently, we utilize the algorithm to compute a freezing-rain climatology over the eastern United States. The resulting climatology pattern aligns well with observations; however, a significant mean bias is observed. We interpret this bias to be influenced by both the algorithm itself and assumptions regarding precipitation processes, which include biases associated with freezing drizzle, precipitation occurrence, and regional synoptic weather patterns. To mitigate the overall bias, we propose increasing the precipitation cutoff from 0.04 to 0.25 mm h −1 , as it better reflects the precision of precipitation observations. This adjustment yields a substantial reduction in the overall bias. Finally, given the strong performance of LightGBM in predicting mixed precipitation episodes, we anticipate that the algorithm can be effectively utilized in operational settings and for diagnosing precipitation types in climate model outputs. Significance Statement Freezing rain can have significant impacts on transportation and infrastructure, making accurate prediction of precipitation types crucial. In this study, we use a machine learning method known as LightGBM to predict precipitation types. We show that the new algorithm performs better than the existing methods for all precipitation types examined. Additionally, we compute a freezing-rain climatology over the eastern United States. Although the resulting climatology pattern corresponds well to observations, the algorithm overpredicts freezing-rain occurrence. We argue that this bias can be substantially reduced by increasing the precipitation cutoff from 0.04 to 0.25 mm h −1 . Overall, this work highlights the potential of the LightGBM algorithm for both weather forecasting and diagnosing precipitation types in climate models.
Meteorology & Atmospheric Sciences↗