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At least 55 records · Page 3

Assembling, maintaining and servicing Space Station

The assembly, maintenance, and servicing of the Space Station and its facilities are discussed. The tools and facilities required for the assembly, maintenance, and servicing of the Station are described; the ground and transportation infrastructures needed for the Space Station are examined. The roles of automation and robotics in reducing the EVAs of the crew, minimizing disturbances to the Space Station environment, and enhancing user friendliness are investigated. Servicing/maintenance tasks are categorized based on: (1) urgency, (2) location of servicing/maintenance, (3) environmental control, (4) dexterity, (5) transportation, (6) crew interactions, (7) equipment interactions, and (8) Space Station servicing architecture. An example of a servicing mission by the Space Station for the Hubble Space Telescope is presented.

Doetsch, K. H.

Advanced propulsion for LEO-Moon transport. 3: Transportation model

A simplified computational model of low Earth orbit-Moon transportation system has been developed to provide insight into the benefits of new transportation technologies. A reference transportation infrastructure, based upon near-term technology developments, is used as a departure point for assessing other, more advanced alternatives. Comparison of the benefits of technology application, measured in terms of a mass payback ratio, suggests that several of the advanced technology alternatives could substantially improve the efficiency of low Earth orbit-Moon transportation.

Henley, Mark W.

Update on Parallel Process Execution in the Next Generation System Analysis Model

As of the end of 2021, 88,880 metric tons of heavy metal (MTHM) (44,741 MTHM in dry storage; 44,139 MTHM in wet storage) of spent nuclear fuel (SNF) were stored at various reactor sites across the United States [1]. The Office of Storage and Transportation in the Department of Energy is planning for the transportation, storage, and eventual disposal of SNF and high-level radioactive waste (HLW). To aid in this effort and inform decision-makers about the backend of the spent fuel cycle, systems analysis tools capable of analyzing the various options with respect to SNF and HLW management are being used as well as continuously improved to meet the evolving needs of the program. System analysts typically use these tools to vary underlying assumptions (shipping rates, allocation priority, available facilities, start dates, etc.) and study the implications of these changes on site clearance schedules, campaign costs, transportation infrastructure acquisition, etc.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS

Lunar transportation system

The University Space Research Association (USRA) requested the University of Minnesota Spacecraft Design Team to design a lunar transportation infrastructure. This task was a year long design effort culminating in a complete conceptual design and presentation at Johnson Space Center. The mission objective of the design group was to design a system of vehicles to bring a habitation module, cargo, and crew to the lunar surface from LEO and return either or both crew and cargo safely to LEO while emphasizing component commonality, reusability, and cost effectiveness. During the course of the design, the lunar transportation system (LTS) has taken on many forms. The final design of the system is composed of two vehicles, a lunar transfer vehicle (LTV) and a lunar excursion vehicle (LEV). The LTV serves as an efficient orbital transfer vehicle between the earth and the moon while the LEV carries crew and cargo to the lunar surface. Presented in the report are the mission analysis, systems layout, orbital mechanics, propulsion systems, structural and thermal analysis, and crew systems, avionics, and power systems for this lunar transportation concept.

Source record

Automated Design of Noise-Minimal, Safe Rotorcraft Trajectories

NASA and the international community are investing in the development of a commercial transportation infrastructure that includes the increased use of rotorcraft, specifically helicopters and aircraft such as a 40-passenger civil tilt rotors. Rotorcraft have a number of advantages over fixed wing aircraft, primarily in not requiring direct access to the primary fixed wing runways. As such they can operate at an airport without directly interfering with major air carrier and commuter aircraft operations. However, there is significant concern over the impact of noise on the communities surrounding the transportation facilities. In this paper we propose to address the rotorcraft noise problem by exploiting powerful search techniques coming from artificial intelligence, coupled with simulation and field tests, to design trajectories that are expected to improve on the amount of ground noise generated. This paper investigates the use of simulation based on predictive physical models to facilitate the search for low-noise trajectories using a class of automated search algorithms called local search. A novel feature of this approach is the ability to incorporate constraints into the problem formulation that addresses passenger safety and comfort.

Morris, Robert A.

Southeast Regional CO 2 Utilization and Storage Acceleration Partnership (SECARB-USA): Regional Commercialization Plan (Final): Work Products 4.4.b (Regional Commercialization Plan) & 4.3 (Socioeconomic Impacts of CCUS and Workforce Readiness)

Commercialization of carbon capture and storage (CCS) within the SECARB-USA region is scaling up and will continue as governments and private companies expand on current decarbonization efforts. The Inflation Reduction Act (2022) enhanced 45Q tax credits beyond the 2018 Bipartisan Budget Act levels by incentivizing previously uneconomic projects to develop CCS plans and infrastructure. Additionally, the continued interest and support for CCS mitigates the relative risk of commercial-scale projects that are seeking long-term viability to develop capture facilities to accommodate their emissions reduction goals. This multifaceted Commercialization Plan provides an overview of the current status and potential for CCS in the Southeast and the pathways to achieve emissions reduction goals in the region by 2050. Specifically, the study addresses: • The current status of CCS activities in the SECARB region, • Assessment of buildout pathways for CCS projects and carbon transport infrastructure development opportunities, • Estimation of CCS job creation and economic impacts, and • The social justice considerations and public benefits associated with CCS activities in the SECARB-USA region.

42 ENGINEERING

Is bigger always better? An economic study of U.S. ETO vehicle architectures

This analysis, performed as part of NASA's Space Transportation Infrastructure Study, compares the cost efficiency of two candidate launch vehicle families, used in conjunction with existing expendable launch vehicles and the Shuttle, to accomplish the nation's future space missions through 2030. Missions analyzed include those identified in NASA's Civil Needs Data Base (CNDB) and Space Exploration Initiative (SEI) Option E program, as well as estimates of commercial and U.S. Department of Defense flights. A family of Shuttle-derived In-line vehicles is compared against a family designed around a vehicle optimized for large (250t) payloads. The analysis is performed within the context of an entire space transportation architecture. The goal is to determine which architecture best captures the required missions most cost-effectively. Results indicate that, for the particular mission model analyzed, neither option is clearly superior across the entire payload range. The Shuttle-derived family displays a better evolutionary path for supporting the total mission model (CNDB plus SEI missions). It also reduces earth-to-orbit (ETO) peak funding by 20 percent. The architecture incorporating the Heavy Lift ETO family minimizes SEI program cost and on-orbit operations but is subject to greater schedule risk because of the need for concurrent ETO core and engine development.

Eimers, Dan R.

Improving SMAP freeze-thaw retrievals for pavements using effective soil temperature from GEOS-5: Evaluation against in situ road temperature data over the U.S

Seasonal freeze-thaw (FT) affects over half the northern hemisphere and impacts many key processes of the Earth System such as energy exchange, hydrology and vegetation. Nearly all past studies using spaceborne FT retrievals have focused on characterizing FT specifically for natural environments. FT in the built environment is also routinely studied and a topic of great interest, especially with regards to transportation infrastructure. Whereas natural FT process are frequently investigated using spaceborne observations, FT studies of roads are often limited to local scales, using in situ or nearby weather station data only. Comparisons between FT retrievals obtained from NASA's Soil Moisture Active Passive (SMAP) satellite and roads in Alaska (AK) and the Contiguous United States (CONUS) showed that spaceborne FT retrievals had good agreement with road data. But those results also indicated that NASA FT retrievals in CONUS were relatively too warm compared to road data. If SMAP FT retrievals were to be used for identifying FT transition timing for applications by the transportation community, it is also important for frozen conditions to be identified more accurately. This work is primarily concerned with improving frozen retrievals made in CONUS by calculating new Normalized Polarization Ratio (NPR) thresholds as compared to those currently used in SMAP FT. We found that focusing on a temporal subset of October through May for comparisons greatly improved the correlation between NPR and effective soil temperature (Teff, one of SMAP's ancillary datasets), often from about zero to 0.6. We then applied linear regression between NPR and Teff to obtain new NPR thresholds resulting in the FT-Roads (FT-R) product. NASA FT and FT-R were evaluated against road data at about 1000 locations in CONUS and a battery of different tests indicated that FT-R performed better under nearly all conditions compared to NASA FT. Overall, NASA FT accuracies were 69% and 80% for 6 am and 6 pm SMAP retrievals, while FT-R achieved accuracies of 79% and 82%. We also investigated the potential for using Teff for road FT (6 am, only) and found that those comparisons were even more accurate (84%). We've also quantified inter- and intraregional differences of SMAP FT performance and found that accuracy metrics vary over twice as much between geographic subdivisions (9%) as compared to between the states within a subdivision (4%). Most importantly, the main goal of improving the detection of in situ frozen conditions in CONUS was realized, with FT-R accurately detecting frozen conditions >50% more frequently than NASA FT.

Passive microwave

Data Fusion for the Development of a Multimodal Freight Transload Facilities Dataset in the U.S.

To withstand the growing demand of commodity volume and its strain on the transportation infrastructure, it is necessary to identify the flow of commodities by route and mode. However, a national multimodal freight routing model does not exist for the U.S. The development of such model requires multiple building blocks, such as virtual representations of roadway, railway, and waterway networks, transload facilities (TFs), and access/egress links. Most of these blocks have a robust database in the U.S., except for the TFs. Here, this paper presents the fusion of dispersed and heterogeneous representations of multimodal TFs into a single, comprehensive, geospatial freight TF dataset. The TF dataset is derived from several sources, including the U.S. Army Corps of Engineers Master Docks Plus, the National Transportation Atlas Database, the Intermodal Association of North America, industry publications, and other public information. First, individual datasets were queried and reconciled. A geocoding/reverse geocoding process was applied to get the best street address and latitude/longitude location for each terminal. Then, duplicate terminals were identified by a fuzzy match algorithm based on terminal name and location, and removed. Validation was performed by visual inspection of random facilities. The main contributions of this work are: a publicly available version of the TF dataset, including facility location and multimodal transfer capability of 9,003 facilities, and an enterprise-version with the same facilities but including commodity handling capabilities. The main purpose of developing the TF dataset is to inform multimodal routing algorithms. The proposed TF dataset allows for credibly modeling the multimodal transfer of commodities within shipment routes.

Commodity Routing

Carbon Capture and Storage for Small-to-Medium Biorefineries: Promising Carbon Removal Solution with Economic Challenges

Carbon capture and storage (CCS) integrated with biomass-based fuel production can provide cost-effective biomass carbon removal and storage (BiCRS) and produce high-value bioproducts, such as sustainable aviation fuels. To accelerate BiCRS deployment, it is crucial to quantify the costs of CCS integration, particularly for small- and medium-scale biorefineries that are representative of early-stage deployment. Existing studies tend to focus on plant sizes that are orders-of-magnitude larger than early-stage installations, possibly underestimating CCS costs for small-to-medium biorefineries. We show that the capture, transport, and storage costs to maximize CO 2 removal from a 526 dry tonne per day (tpd) biomass gasification plant (largest existing size) could be 13–47% higher than costs for a typical modeled plant size (2000 dry tpd). The higher cost estimates are driven by less favorable economies of scale and realistic assumptions about the availability of affordable CO 2 transport infrastructure with both drivers broadly applicable to other BiCRS technologies. Compliance and voluntary carbon markets could incentivize biorefinery CCS, but both carry a high degree of uncertainty. In conclusion, these findings highlight that sufficient and reliable financial mechanisms would be essential to unlocking the full CO 2 removal potential of biorefineries and facilitating BiCRS scale-up.

Biological transport

Mission and Implementation of an Affordable Lunar Return

We present an architecture that establishes the infrastructure for routine space travel by taking advantage of the Moon's resources, proximity and accessibility. We use robotic assets on the Moon that are teleoperated from Earth to prospect, test, demonstrate and produce water from lunar resources before human arrival. This plan is affordable, flexible and not tied to any specific launch vehicle solution. Individual surface pieces are small, permitting them to be deployed separately on small launchers or combined together on single large launchers. Schedule is our free variable; even under highly constrained budgets, the architecture permits this program to be continuously pursued using small, incremental, cumulative steps. The end stage is a fully functional, human-tended lunar outpost capable of producing 150 metric tonnes of water per year enough to export water from the Moon and create a transportation system that allows routine access to all of cislunar space. This cost-effective lunar architecture advances technology and builds a sustainable transportation infrastructure. By eliminating the need to launch everything from the surface of the Earth, we fundamentally change the paradigm of spaceflight.

Spudis, Paul

Enriching OpenStreetMap network data for transportation applications: Insights into the impact of urban congestion on accessibility

OpenStreetMap (OSM) data is a valuable open-source resource for various transportation, traffic, and planning applications. However, OSM network data lack operating traffic speed information, which is critical for transport planning and operations. Addressing this shortcoming, this study leverages commercial vendor data (to serve as ground truth) with exogenous, open-source variables characterizing local transport infrastructure, land use, and demographic information to predict average congested traffic speeds on OSM networks. Three machine-learning models were tested and estimated for OSM links with and without speed limit information in the Denver metropolitan region. Among these, XGBoost performed best, with mean absolute errors of 3.27 and 3.62 mph for links with and without speed limits, respectively. The developed models accurately predicted traffic speeds for different hours and days of the week compared to ground truth data. Using these predicted speeds, drive accessibility scores were computed for the Denver region for different time periods using the Mobility Energy Productivity (MEP) metric to understand the impact of congestion on energy-efficient accessibility. Results show that congestion-adjusted drive accessibility can be significantly lower compared to accessibility calculated using free flow speeds. Specifically, weekday evening hours saw a 42 % drop in accessibility due to reduced speeds, particularly around downtown Denver. Across the Denver metro region, approximately half as many opportunities and jobs are accessible in under 20 min by car during the evening peak period relative to free flow conditions. These findings underscore the importance of using congestion-adjusted operating speeds rather than speed limits in accessibility calculations, as reliance on speed limits can substantially overestimate energy-efficient drive accessibility in large, car-centric cities susceptible to significant congestion. In conclusion, the methodology presented here could further enrich OSM network data, making them useful for an even broader range of transportation applications.

29 ENERGY PLANNING, POLICY, AND ECONOMY

NASA's Ares I and Ares V Launch Vehicles -- Effective Space Operations Through Efficient Ground Operations

The United States (U.S.) plans to return to the Moon by 2020, with the development of a new human-rated space transportation system to replace the Space Shuttle, which is due for retirement in 2010 after it completes its missions of building the International Space Station and servicing the Hubble Space Telescope. Powering the future of space-based scientific exploration will be the Ares I Crew Launch Vehicle, which will transport the Orion Crew Exploration Vehicle to orbit where it will rendezvous with the Lunar Lander. which will be delivered by the Ares V Cargo Launch Vehicle. This new transportation infrastructure, developed by the National Aeronautics and Space Administration (NASA), will allow astronauts to leave low-Earth orbit for extended lunar exploration and preparation for the first footprint on Mars. All space-based operations begin and are controlled from Earth. NASA's philosophy is to deliver safe, reliable, and cost-effective solutions to sustain a multi-billion-dollar program across several decades. Leveraging 50 years of lessons learned, NASA is partnering with private industry, while building on proven hardware experience. This paper will discuss how the Engineering Directorate at NASA's Marshall Space Flight Center is working with the Ares Projects Office to streamline ground operations concepts and reduce costs. Currently, NASA's budget is around $17 billion, which is less than 1 percent of the U.S. Federal budget. Of this amount, NASA invests approximately $4.5 billion each year in Space Shuttle operations, regardless of whether the spacecraft is flying or not. The affordability requirement is for the Ares I to reduce this expense by 50 percent, in order to allow NASA to invest more in space-based scientific operations. Focusing on this metric, the Engineering Directorate provides several solutions-oriented approaches, including Lean/Six Sigma practices and streamlined hardware testing and integration, such as assembling major hardware elements before shipping to the Kennedy Space Center for launch operations. This paper provides top-level details for several cost saving initiatives, including both process and product improvements that will result in space transportation systems that are designed with operations efficiencies in mind. The Engineering Directorate provides both the intellectual capital embodied in an experienced workforce and unique facilities in which to validate the information technology tools that allow a nationwide team to collaboratively connect across miles that separate them and the engineering disciplines that integrate various piece parts into a whole system. As NASA transforms ground-based operations, it also is transitioning its workforce from an era of intense hands-on labor to a new one of mechanized conveniences and robust hardware with simpler interfaces. Ensuring that space exploration is on sound footing requires that operations efficiencies be designed into the transportation system and implemented in the development stage. Applying experience gained through decades of ground and space op'erations, while using value-added processes and modern business and engineering tools, is the philosophy upon which a new era of exploration will be built to solve some of the most pressing exploration challenges today -- namely, safety, reliability, and affordability.

Dumbacher, Daniel L.

Analyzing School Bus Electrification in Richmond, Virginia

School buses are an essential component of the transportation infrastructure, serving as a lifeline for students across the globe. However, the widespread use of diesel school buses has raised concerns about the health impact on millions of students exposed to harmful emissions daily. Recognizing this issue, school districts worldwide are urgently seeking cleaner energy alternatives. Electric school buses emerge as an environmentally friendly and sustainable option, fostering a healthier environment for both students and communities. However, school bus electrification faces the challenges of high upfront cost, cumbersome charging management, and constraints from power grids. To help school bus operators address those challenges, this study presents a data-driven analysis for school bus electrification. This study considered a real-world school bus system in Richmond, VA, and developed a mathematical programming model to analyze the system design, charging strategies, and charging load profiles for the electrification scenario. The study evaluated different charging strategies based on model outcomes, aiming to optimize efficiency and effectiveness. Ultimately, this research generated electric school bus charging demand profiles under various scenarios, shedding light on the feasibility and implications of transitioning to electric-powered school buses.

29 ENERGY PLANNING, POLICY, AND ECONOMY

Macroscopic Traffic Modeling Using Probe Vehicle Data: A Machine Learning Approach

Abstract The macroscopic fundamental diagram (MFD) captures an orderly relationship among traffic flow, density, and speed at the network level. It is a simple yet powerful tool for modeling traffic dynamics in large urban networks with broad application in traffic control and management. However, empirically derived MFDs in urban regions require high-resolution traffic data from the network. Having the network flow and vehicular density estimated at the (granular) census tract level using vehicle probe data, we apply machine learning methods to predict the MFDs across U.S. urban areas and capture the impacts of location-specific input features on the network flow–density relationships at a large scale. The results show that, among the four tested machine learning approaches (Random Forest, XGBoost, Support Vector Machine, and Neural Network), XGBoost delivers the best performance in predicting network traffic flow based on vehicular density and location attributes. Using interaction Shapley Additive explanation (SHAP) values and partial correlation analysis, we examine the factors influencing MFD shapes across different locations. Our empirical findings reveal that across U.S. urban areas, network topology, transportation infrastructure, and land use are primary factors shaping MFD curves, while demand and trip-related factors play a lesser role. Specifically, higher ranking roads, centrality, and development levels correlate positively with network capacity and critical density, whereas negative associations are observed for network connectivity, mixed-use development, and road roughness levels.

Jin, Ling

Analyzing inference workloads for spatiotemporal modeling

Ensuring power grid resiliency, forecasting climate conditions, and optimization of transportation infrastructure are some of the many application areas where data is collected in both space and time. Spatiotemporal modeling is about modeling those patterns for forecasting future trends and carrying out critical decision-making by leveraging machine learning/deep learning. Once trained offline, field deployment of trained models for near real-time inference could be challenging because performance can vary significantly depending on the environment, available compute resources and tolerance to ambiguity in results. Users deploying spatiotemporal models for solving complex problems can benefit from analytical studies considering a plethora of system adaptations to understand the associated performance-quality trade-offs. To facilitate the co-design of next-generation hardware architectures for field deployment of trained models, it is critical to characterize the workloads of these deep learning (DL) applications during inference and assess their computational patterns at different levels of the execution stack. In this paper, we develop several variants of deep learning applications that use spatiotemporal data from dynamical systems. We study the associated computational patterns for inference workloads at different levels, considering relevant models (Long short-term Memory, Convolutional Neural Network and Spatio-Temporal Graph Convolution Network), DL frameworks (Tensorflow and PyTorch), precision (FP16, FP32, AMP, INT16 and INT8), inference runtime (ONNX and AI Template), post-training quantization (TensorRT) and platforms (Nvidia DGX A100 and Sambanova SN10 RDU). Overall, our findings indicate that although there is potential in mixed-precision models and post-training quantization for spatiotemporal modeling, extracting efficiency from contemporary GPU systems might be challenging. Instead, co-designing custom accelerators by leveraging optimized High Level Synthesis frameworks (such as SODA High-Level Synthesizer for customized FPGA/ASIC targets) can make workload-specific adjustments to enhance the efficiency.

97 MATHEMATICS AND COMPUTING

Enhancing traffic safety analysis with digital twin technology: integrating vehicle dynamics and environmental factors into microscopic traffic simulation

Traffic safety is a critical concern in transportation engineering and urban planning. Traditional traffic safety analysis requires trained observers to collect data in the field, which is time-consuming, labor-intensive, and sometimes inaccurate. In recent years, microscopic traffic simulation, which simulates individual vehicles’ movements within a transportation network, have been utilized to study traffic safety. However, microscopic traffic simulation only focuses on traffic-related factors, such as traffic volume, traffic signals, and lane configurations, neglecting vehicle dynamics and environment-related factors like weather and lighting conditions, which can significantly impact traffic safety. In light of this, this paper explores the application of digital twin technology in traffic safety analysis, integrating vehicle simulators, which consider vehicle dynamics and environmental factors, and microscopic traffic simulators, which simulate the operations of traffic flow, for enhanced safety evaluations. Various scenarios, including different weather conditions and visibility levels, are simulated using a digital twin of a road segment in Tuscaloosa, Alabama. The simulations employ Surrogate Safety Measures (SSMs) like Time to Collision (TTC) and Deceleration Rate to Avoid a Crash (DRAC) to assess safety under varying conditions. The results demonstrate that traffic digital twin can identify potential safety issues that traditional microscopic simulation cannot, providing insights for improving traffic control strategies and transportation infrastructure to enhance traffic safety.

Civil engineering

Geologic hydrogen: a review of resource potential, subsurface dynamics, exploration, production, transportation, and research opportunities

Hydrogen is a versatile resource with critical roles in decarbonization, industrial manufacturing, and energy integration. However, most hydrogen today is produced from fossil fuels, resulting in high emissions and energy consumption. Although low-carbon hydrogen production methods, such as steam methane reforming with carbon capture and renewable-powered electrolysis, are advancing, their high costs hinder large-scale deployment. Identifying alternative pathways for producing low-cost, low-emission hydrogen is therefore essential. Geologic hydrogen, referring to natural and stimulated hydrogen generated in the Earth's subsurface, has attracted growing attention as a potential source of sustainable, economically viable, and environmentally favorable hydrogen. This paper provides a comprehensive review of geologic hydrogen, covering its resource potential, origins, migration and trapping mechanisms, exploration techniques, production strategies, and pipeline transportation. It also identifies key knowledge gaps and proposes a roadmap for future research. The review indicates that geologic hydrogen has vast resource potential and can leverage existing subsurface technologies and geophysical exploration methods. However, major challenges persist, including uncertain hydrogen generation rates, limited understanding and control of serpentinization processes, costly transportation infrastructure, the lack of validated techno-economic analysis, and potential social and environmental issues. As the field is still in its early stages, progress will require interdisciplinary collaboration spanning geoscience, engineering, economics, environmental science, and policy and regulation.

42 ENGINEERING