SEARCH · Search NASA
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.
In this paper we focus on the network routing problem, and survey swarm intelligent approaches for its efficient solution, after a brief overview of power-aware routing schemes, which are important in the network examples outlined above.
Computational self-sufficiency - the making of communication decisions on the basis of locally available information that is already in place, rather than on the basis of information residing at other entities - is a fundamental principle of Delay-Tolerant Networking. Contact Graph Routing is an attempt to apply this principle to the problem of dynamic routing in an interplanetary DTN. Testing continues, but preliminary results are promising.
Dynamic Weather Routes (DWR) is a weather-avoidance system for airline dispatchers and FAA traffic managers that continually searches for and advises the user of more efficient routes around convective weather. NASA and American Airlines (AA) have been conducting an operational trial of DWR since July 17, 2012. The objective of this evaluation is to assess DWR from a traffic management coordinator (TMC) perspective, using recently retired TMCs and actual DWR reroutes advisories that were rated acceptable by AA during the operational trial. Results from the evaluation showed that the primary reasons for a TMC to modify or reject airline reroute requests were related to airspace configuration. Approximately 80 percent of the reroutes evaluated required some coordination before implementation. Analysis showed TMCs approved 62 percent of the requested DWR reroutes, resulting in 57 percent of the total requested DWR time savings.
This paper discusses a machine learning-based approach to routing for delay tolerant networks (DTNs) [1]. DTNs are networks which experience frequent disconnections between nodes, uncertainty of an end-to-end path, long one-way trip times, and may have high error rates and asymmetric links. Such networks exist in deep space satellite networks, very rural environments, disaster areas and underwater environments. In this work, we use machine learning classifiers to predict a set of neighboring nodes which are the most likely to deliver a message to a desired location based on message history delivery information.We use the Common Open Research Emulator (CORE) [2] to emulate the DTN environment based on real-world location traces and collect network traffic statistics from the Bundle Protocol implementation IBR-DTN [3]. The software architecture for classification-based routing, analysis and preparation of the network history data and prediction results are discussed.
Development of Detect-and-avoid (DAA) systems’ maneuver guidance requirements at RTCA has centered on tactical maneuvers away from the intruder. Recapturing the flight-plan route the initial maneuver is not directly taken into account by most DAA maneuver guidance algorithms. This work demonstrates potential challenges and inefficiencies that can arise in recapturing the route after resolving a conflict. Horizontal resolution trajectories for a test matrix of encounters with varying aircraft speeds and geometries are computed by minimizing either flight time or deviation to serve as a baseline. Turn directions computed from a reference DAA algorithm coupled with a pilot selection model and a second algorithm called Autoresolver (AR) are compared to these baseline resolution trajectories. Baseline results show that turning into the intruder yields favorable cost for most encounters. Additional analysis of pilot response data shows that only three-fourths of pilots’ horizontal maneuvers turn into intruders, a percentage much lower than the baseline results. Improvement to a DAA guidance algorithm based on findings in this work is discussed.
Development of Detect-and-avoid (DAA) systems’ maneuver guidance requirements at RTCA has centered on tactical maneuvers away from the intruder. Recapturing the flight-plan route the initial maneuver is not directly taken into account by most DAA maneuver guidance algorithms. This work demonstrates potential challenges and inefficiencies that can arise in recapturing the route after resolving a conflict. Horizontal resolution trajectories for a test matrix of encounters with varying aircraft speeds and geometries are computed by minimizing either flight time or deviation to serve as a baseline. Turn directions computed from a reference DAA algorithm coupled with a pilot selection model and a second algorithm called Autoresolver (AR) are compared to these baseline resolution trajectories. Baseline results show that turning into the intruder yields favorable cost for most encounters. Additional analysis of pilot response data shows that only three-fourths of pilots’ horizontal maneuvers turn into intruders, a percentage much lower than the baseline results. Improvement to a DAA guidance algorithm based on findings in this work is discussed.
A data-informed model to predict energy use for a proposed vehicle trip has been developed in this paper. The methodology leverages nearly 1 million miles of real-world driving data to generate the estimation model. Driving is categorized at the sub-trip level by average speed, road gradient, and road network geometry, then aggregated by category. An average energy consumption rate is determined for each category, creating an energy rates look-up table. Proposed vehicle trips are then categorized in the same manner, and estimated energy rates are appended from the look-up table. The methodology is robust and applicable to almost any type of driving data. The model has been trained on vehicle global positioning system data from the Transportation Secure Data Center at the National Renewable Energy Laboratory and validated against on-road fuel consumption data from testing in Phoenix, Arizona. The estimation model has demonstrated an error range of 8.6% to 13.8%. The model results can be used to inform control strategies in routing tools, such as change in departure time, alternate routing, and alternate destinations to reduce energy consumption. This work provides a highly extensible framework that allows the model to be tuned to a specific driver or vehicle type.
There is a growing interest in reducing the amount of vehicle miles traveled (VMT), both in public and private sector organizations, in an effort to reduce overall carbon emissions. The Oak Ridge National Laboratory (ORNL), located in Oak Ridge, Tennessee, and located within close proximity to Knoxville, Tennessee, has more than 4,500 employees, most of which live in and around Knoxville. ORNL is currently developing a pilot commuting program for all employees, which incorporates the use of park-and-ride facilities. This study outlines the methodology behind the preliminary geoanalysis and routing used in developing a lab-wide commuting program. Due to privacy concerns, the data used for the study included numbers of employee residences per zip code rather than actual residential addresses. Commuting configurations by clustered zip code area and vehicle type were developed based on number of residences in a limited area, and the desire to minimize VMT. Central locations were selected using ArcGIS software. Satellite imagery was used to locate actual, suitable parking facilities to accommodate the specified number of residents involved in each commuting configuration. Optimized routing, and estimations of travel times, were performed using TransCAD GIS software. Energy estimates in kilowatt-hours (kwh) and gallons of gasoline, and gallons of gasoline equivalent, were all determined based on the scenarios. Standard petroleum-fueled vehicles were used in the initial estimates. Standard electric vehicles were also used in alternative scenarios to estimate potential additional energy and fuel savings. Furthermore the initial findings from this work will be used to develop a pilot program for ORNL.
A widely used land surface model, the Variable Infiltration Capacity (VIC) model, is coupled with a newly developed hierarchical dominant river tracing-based runoff-routing model to form the Dominant river tracing-Routing Integrated with VIC Environment (DRIVE) model, which serves as the new core of the real-time Global Flood Monitoring System (GFMS). The GFMS uses real-time satellite-based precipitation to derive flood monitoring parameters for the latitude band 50°N–50°S at relatively high spatial (~12 km) and temporal (3 hourly) resolution. Examples of model results for recent flood events are computed using the real-time GFMS (http://flood.umd.edu). To evaluate the accuracy of the new GFMS, the DRIVE model is run retrospectively for 15 years using both research-quality and real-time satellite precipitation products. Evaluation results are slightly better for the research-quality input and significantly better for longer duration events (3 day events versus 1 day events). Basins with fewer dams tend to provide lower false alarm ratios. For events longer than three days in areas with few dams, the probability of detection is ~0.9 and the false alarm ratio is ~0.6. In general, these statistical results are better than those of the previous system. Streamflow was evaluated at 1121 river gauges across the quasi-global domain. Validation using real-time precipitation across the tropics (30°S–30°N) gives positive daily Nash-Sutcliffe Coefficients for 107 out of 375 (28%) stations with a mean of 0.19 and 51% of the same gauges at monthly scale with a mean of 0.33. Finally, there were poorer results in higher latitudes, probably due to larger errors in the satellite precipitation input.
Efficient CO 2 utilization is key to limit global climate change. Carbon monoxide, which is a crucial feedstock for chemical synthesis, can be produced by splitting CO 2 . However, existing thermochemical routes are energy intensive requiring high operating temperatures. A hybrid redox process (HRP) involving CO 2 –to–CO conversion using a lattice oxygen–deprived redox catalyst at relatively low temperatures (<700 °C) is reported. The lattice oxygen of the redox catalyst, restored during CO 2 –splitting, is subsequently used to convert methane to syngas. Operated at temperatures significantly lower than a number of industrial waste heat sources, this cyclic redox process allows for efficient waste heat–utilization to convert CO 2 . To enable the low temperature operation, lanthanum modified ceria (1:1 Ce:La) promoted by rhodium (0.5 wt%) is reported as an effective redox catalyst. Near–complete CO 2 conversion with a syngas yield of up to 83% at low temperatures is achieved using Rh–promoted LaCeO 4–x . While La improves low–temperature bulk redox properties of ceria, Rh considerably enhances the surface catalytic properties for methane activation. Density functional theory calculations further illustrate the underlying functions of La–substitution. In conclusion, the highly effective redox catalyst and HRP scheme provide a potentially attractive route for chemical production using CO 2 , industrial waste heat, and methane, with appreciably lowered CO 2 emissions.
Geometric isomerism in polyacetylene is a basic concept in chemistry textbooks. Polymerization to cis-isomer is kinetically preferred at low temperature, not only in the classic catalytic reaction in solution but also, unexpectedly, in the crystalline phase when it is driven by external pressure without a catalyst. Until now, no perfect reaction route has been proposed for this pressure-induced polymerization. Using in situ neutron diffraction and meta-dynamic simulation, we discovered that under high pressure, acetylene molecules react along a specific crystallographic direction that is perpendicular to those previously proposed. Moreover, following this route produces a pure cis-isomer and more surprisingly, predicts that graphane is the final product. Experimentally, polycyclic polymers with a layered structure were identified in the recovered product by solid-state nuclear magnetic resonance and neutron pair distribution functions, which indicates the possibility of synthesizing graphane under high pressure.
Barium Y-type hexaferrite Ba{sub 2}Co{sub 2}Fe{sub 12}O{sub 22} nanopowders have been purposefully tailored using two different chemical wet methods, namely sol-gel auto-combustion (SGA) and co-precipitation (CP) routes. The impact of the pre-annealing and the annealing temperature of the formed precursors synthesized using two strategies on the structural, microstructure, and magnetic properties was investigated using x-ray diffraction (XRD), scanning electron microscopic (SEM), Fourier transform infrared spectroscopy (FT-IR), and vibrating sample magnetometer (VSM). For instance, well-defined Ba{sub 2}Co{sub 2}Fe{sub 12}O{sub 22} hexagonal plate-like particles was observed using SGA and CP routes for the precursors pre-annealed at 600 {sup ∘}C for 4 h and then annealed at low temperature 1000 {sup ∘}C for 2 h. The magnetic properties measured at room temperature evinced that hexaferrite Co{sub 2}Y particles have ferromagnetic characteristics. Good magnetic properties involving saturation magnetization (M{sub s}) of 23.64 and 24.37 emu/g and the coercivity (H{sub c}) of 417.76 and 280.92 Oe were accomplished for the samples pre-annealed at 600 {sup ∘}C for 4 h and then annealed at 1000 {sup ∘}C for 2 h for SGA and CP methods, respectively.
Nanocrystalline materials are defined by their fine grain size, but details of the grain boundary character distribution should also be important. Grain boundary character distributions are reported for ball-milled, sputter-deposited, and electrodeposited Ni and Ni-based alloys, all with average grain sizes of ~20 nm, to study the influence of processing route. The two deposited materials had nearly identical grain boundary character distributions, both marked by a Σ3 length percentage of 23 to 25 pct. In contrast, the ball-milled material had only 3 pct Σ3-type grain boundaries and a large fraction of low-angle boundaries (16 pct), with the remainder being predominantly random high angle (73 pct). Furthermore, these grain boundary character measurements are connected to the physical events that control their respective processing routes. Consequences for material properties are also discussed with a focus on nanocrystalline corrosion. As a whole, the results presented here show that grain boundary character distribution, which has often been overlooked in nanocrystalline metals, can vary significantly and influence material properties in profound ways.
AlSb is a low-cost, high-band-gap semiconducting material, yet largely limited in its potential due to difficulty in the synthesis of single-phase AlSb. The present work compares different processing routes in their potency to produce bulk and nanocrystalline AlSb. Vacuum arc melting has been successfully employed, for the first time, to synthesize single-phase bulk AlSb. Owing to high vapor pressure of Sb, an optimum amount of excess Sb (3%) needs to be added to achieve single-phase line compound AlSb. Two methods, viz. mechanical alloying of elemental powders and mechanical milling of cast alloy, have been used to synthesize nanocrystalline AlSb. Nanocrystalline AlSb could be produced after ball milling the cast alloy; however, prolonged milling results in the appearance of Sb along with a non-stoichiometric Al-Sb-O oxide. Mechanical alloying of Al and Sb does not lead to appearance of single-phase AlSb, and the mixture largely consists of starting material and amorphous products. Therefore, the effectiveness of a synthesis route in producing single-phase AlSb is governed by the tendency of oxygen pickup during the processing.
Purpose of Review: Cardiac regenerative medicine is a field bridging together biotechnology and surgical science. In this review, we present the explored surgical roads to cell delivery and the known effects of each delivery method on cell therapy efficiency. We also list the more recent clinical trials, exploring the safety and efficacy of delivery routes used for cardiac cell therapy approaches. Recent Findings: There is no consensus in defining which way is the most suitable for the delivery of the different therapeutic cell types to the damaged heart tissue. In addition, it emerged that the “delivery issue” has not been systematically addressed in each clinical trial and for each and every cell type capable of cardiac repair. Summary: Cardiac damage occurring after an ischemic insult triggers a cascade of cellular events, eventually leading to heart failure through fibrosis and maladaptive remodelling. None of the pharmacological or medical interventions approved so far can rescue or reverse this phenomenon, and cardiovascular diseases are still the leading cause of death in the western world. Therefore, for nearly 20 years, regenerative medicine approaches have focused on cell therapy as a promising road to pursue, with numerous preclinical and clinical testing of cell-based therapies being studied and developed. Nonetheless, consistent clinical results are still missing to reach consensus on the most effective strategy for ischemic cardiomyopathy, based on patient selection, diagnosis and stage of the disease, therapeutic cell type, and delivery route.
This study presents a novel approach to solve the vehicle routing problem by focusing on greenhouse gas emissions and fuel consumption aiming to mitigate adverse environmental effects of transportation. A time-dependent model with time windows is developed to incorporate speed and schedule in transportation planning. The model considers speed limits for different times of the day in a realistic delivery context. Due to the complexity of solving the model, a graph transformation approach is proposed to reduce the complexity of the problem. By means of several steps, the problem is transformed into a vehicle routing problem without time windows. In this way, we can reduce the complexity of the problem. Our method can be used in practice to decrease fuel consumption and greenhouse gas emissions, while total cost is also controlled to some extent. Finally, future research directions and conclusion remarks are provided.
To ensure reliable and predictable service in the electrical grid between distributed renewable distributed energy resources (DERs) it is important to gauge the level of trust present within critical components and DER aggregators (DERAs). Although trust throughout a smart grid is temporal and dynamically varies according to measured states, it is possible to accurately formulate communications and service level strategies based on such trust measurements. Utilizing an effective set of machine learning and statistical methods, it is shown that establishment of trust levels between DERAs using behavioral pattern analysis is possible. Further, it is also shown that the establishment of such trust can facilitate simple secure communications routing between DERAs. Providing secure routing between DERAs enables a grid operator to maintain service level agreements to its customers, reduce the attack surface and increase operational resiliency.
Highlights: • First evidences prove that Amazonian wildlife consume petroleum-contaminated soil. • Geophagy may emerge as an important exposure route to petrogenic pollution. • It is urgent to assess the health risks for populations relying on subsistence hunting. • There are substantial gaps in our knowledge of the health impacts of oil extraction. Videos recorded with infrared camera traps placed in petroleum contaminated areas of the Peruvian Amazon have shown that four wildlife species, the most important for indigenous peoples’ diet (lowland tapir, paca, red-brocket deer and collared peccary), consume oil-contaminated soils and water. Further research is needed to clarify whether Amazonian wildlife's geophagy can be a route of exposure to petrogenic contamination for populations living in the vicinity of oil extraction areas and relying on subsistence hunting.