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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Optimizing Flight Departure Delay and Route Selection Under En Route Convective Weather

This paper presents a linear Integer Programming model for managing air traffic flow in the United States. The decision variables in the model are departure delays and predeparture reroutes of aircraft whose trajectories are predicted to cross weather-impacted regions of the National Airspace System. The model assigns delays to a set of flights while ensuring their trajectories are free of any conflicts with weather. In a deterministic setting, there is no airborne holding due to unexpected weather incursion in a flight s path. The model is applied to solve a large-scale traffic flow management problem with realistic weather data and flight schedules. Experimental results indicate that allowing rerouting can reduce departure delays by nearly 57%, but it is associated with an increase in total airborne time due to longer routes flown by aircraft. The computation times to solve this problem were significantly lower than those reported in the earlier studies.

Mukherjee, Avijit↗
DOE OSTI2015

Factors Affecting Route Selection and Survival of Steelhead Kelts at Snake River Dams in 2012 and 2013

In 2012 and 2013, Pacific Northwest National Laboratory (PNNL) conducted a study that summarized the passage route proportions and route-specific survival rates of steelhead kelts that passed through Federal Columbia River Power System (FCRPS) dams. To accomplish this, a total of 811 steelhead kelts were tagged with Juvenile Salmon Acoustic Telemetry System (JSATS) transmitters. Acoustic receivers, both autonomous and cabled, were deployed throughout the FCRPS to monitor the downstream movements of tagged kelts. Kelts were also tagged with passive integrated transponder tags to monitor passage through juvenile bypass systems (JBS) and detect returning fish. The current study evaluated data collected in 2012 and 2013 to identify environmental, temporal, operational, individual, and behavioral variables that were related to forebay residence time, route of passage, and survival of steelhead kelts at FCRPS dams on the Snake River. Multiple approaches, including 3-D tracking, bivariate and multivariable regression modeling, and decision tree analyses were used to identify the environmental, temporal, operational, individual, and behavioral variables that had the greatest effect on forebay residence time, route of passage, and route-specific and overall dam passage survival probabilities for tagged kelts at Lower Granite (LGR), Little Goose (LGS), and Lower Monumental (LMN) dams. In general, kelt behavior and discharge appeared to work independently to affect forebay residence times. Kelt behavior, primarily approach location, migration depth, and “searching” activities in the forebay, was found to have the greatest influence on their route of passage. The condition of kelts was the single most important factor affecting their survival. The information gathered in this study may be used by dam operators and fisheries managers to identify potential management actions to improve in-river survival of kelts or collection methods for kelt reconditioning programs to aid the recovery of Snake River steelhead populations.

13 HYDRO ENERGY↗
DOE OSTI2014

Factors Affecting Route Selection and Survival of Steelhead Kelts at Snake River Dams in 2012 and 2013

In 2012 and 2013, Pacific Northwest National Laboratory conducted a study that summarized the passage proportions and route-specific survival rates of steelhead kelts that passed through Federal Columbia River Power System (FCRPS) dams. To accomplish this, a total of 811 steelhead kelts were tagged with Juvenile Salmon Acoustic Telemetry System (JSATS) transmitters. Acoustic receivers, both autonomous and cabled, were deployed throughout the FCRPS to monitor the downstream movements of tagged-kelts. Kelts were also tagged with Passive Integrated Transponder tags to monitor passage through juvenile bypass systems and detect returning fish. The current study evaluated data collected in 2012 and 2013 to identify individual, behavioral, environmental and dam operation variables that were related to passage and survival of steelhead kelts that passed through FCRPS dams. Bayesian model averaging of multivariable logistic regression models was used to identify the environmental, temporal, operational, individual, and behavioral variables that had the highest probability of influencing the route of passage and the route-specific survival probabilities for kelts that passed Lower Granite (LGR), Little Goose (LGS), and Lower Monumental (LMN) dams in 2012 and 2013. The posterior probabilities of the best models for predicting route of passage ranged from 0.106 for traditional spill at LMN to 0.720 for turbine passage at LGS. Generally, the behavior (depth and near-dam searching activity) of kelts in the forebay appeared to have the greatest influence on their route of passage. Shallower-migrating kelts had a higher probability of passing via the weir and deeper-migrating kelts had a higher probability of passing via the JBS and turbines than other routes. Kelts that displayed a higher level of near-dam searching activity had a higher probability of passing via the spillway weir and those that did less near-dam searching had a higher probability of passing via the JBS and turbines. The side of the river in which kelts approached the dam and dam operations also affected route of passage. Dam operations and the size and condition of kelts were found to have the greatest effect on route-specific survival probabilities for fish that passed via the spillway at LGS. That is, longer kelts and those in fair condition had a lower probability of survival for fish that passed via the spillway weir. The survival of spillway weir- and deep-spill passed kelts was positively correlated with the percent of the total discharge that passed through turbine unit 4. Too few kelts passed through the traditional spill, JBS, and turbine units to evaluate survival through these routes. The information gathered in this study describes Snake River steelhead kelt passage behavior, rates, and distributions through the FCRPS as well as provide information to biologists and engineers about the dam operations and abiotic conditions that are related to passage and survival of steelhead kelts.

13 HYDRO ENERGY↗
DOE OSTI2016

Connectivity-enhanced route selection and adaptive control for the Chevrolet Volt

The National Renewable Energy Laboratory and General Motors evaluated connectivity-enabled efficiency enhancements for the Chevrolet Volt. A high-level model was developed to predict vehicle fuel and electricity consumption based on driving characteristics and vehicle state inputs. These techniques were leveraged to optimize energy efficiency via green routing and intelligent control mode scheduling, which were evaluated using prospective driving routes between tens of thousands of real-world origin/destination pairs. The overall energy savings potential of green routing and intelligent mode scheduling was estimated at 5% and 3%, respectively. Furthermore, these represent substantial opportunities considering that they only require software adjustments to implement.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2014

Connectivity-Enhanced Route Selection and Adaptive Control for the Chevrolet Volt: Preprint

The National Renewable Energy Laboratory and General Motors evaluated connectivity-enabled efficiency enhancements for the Chevrolet Volt. A high-level model was developed to predict vehicle fuel and electricity consumption based on driving characteristics and vehicle state inputs. These techniques were leveraged to optimize energy efficiency via green routing and intelligent control mode scheduling, which were evaluated using prospective driving routes between tens of thousands of real-world origin/destination pairs. The overall energy savings potential of green routing and intelligent mode scheduling was estimated at 5% and 3% respectively. These represent substantial opportunities considering that they only require software adjustments to implement.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Route selection based on buffer congestion

A switch includes a plurality of ingress ports, a plurality of egress ports, and a plurality of buffers comprising a buffer coupled to each ingress port, egress port pair. An ingress port is to determine a plurality of potential egress ports for a packet. The ingress port is to select an egress port of the plurality of potential egress ports based on congestion of the corresponding buffers coupled to the ingress port and to each of the plurality of potential egress ports. The ingress port is to place the packet into the corresponding buffer coupled to the ingress port and the selected egress port.

Olson, David↗
DOE OSTI2021

Routing packets based on congestion metric thresholds and weights

A technique includes receiving a packet at a network device, wherein the packet is to be routed in a network to a destination network device; determining a plurality of candidate routes for the packet to be routed to the destination network device; grouping the plurality of candidate routes into a first set of candidate routes and a second set of candidate routes based on hop counts associated with the plurality of candidate routes; selecting one of the first or second sets based on a congestion metric threshold; selecting a candidate route from the selected first or second set based on weight metric values associated with the candidate routes of the selected first or second set; and selecting an egress port associated with the selected candidate route.

McDonald, Nicholas George↗
NASA NTRS1992

Traffic routing for multicomputer networks with virtual cut-through capability

Consideration is given to the problem of selecting routes for interprocess communication in a network with virtual cut-through capability, while balancing the network load and minimizing the number of times that a message gets buffered. An approach is proposed that formulates the route selection problem as a minimization problem with a link cost function that depends upon the traffic through the link. The form of this cost function is derived using the probability of establishing a virtual cut-through route. The route selection problem is shown to be NP-hard, and an algorithm is developed to incrementally reduce the cost by rerouting the traffic. The performance of this algorithm is exemplified by two network topologies: the hypercube and the C-wrapped hexagonal mesh.

Kandlur, Dilip D.↗
DOE OSTI2020

RouteE: A Vehicle Energy Consumption Prediction Engine

The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts. The pre-trained RouteE models are established using NREL’s Future Automotive Systems Technology Simulator paired with approximately 1 million miles of drive cycle data from the Transportation Secure Data Center, resulting in energy consumption behavior estimates over a representative sample of driving conditions for the United States. Validations have been performed using on-road fuel consumption data for conventional and electrified vehicle powertrains. Transferring the results of the on-road validation to a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology in a green-routing application would accurately select the route that consumes the least fuel 90% of the time. The novel machine learning techniques used in RouteE make it a flexible and robust tool for a variety of transportation applications.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Breaking New Ground: MB ene Route toward Selective Vinyl Double Bond Hydrogenation in Nitroarenes

Doping, or incremental substitution of one element for another, is an effective way to tailor a compound’s structure as well as its physical and chemical properties. Herein, we replaced up to 30% of Ni with Co in members of the family of layered LiNiB compounds, stabilizing the high-temperature polymorph of LiNiB while the room-temperature polymorph does not form. By studying this layered boride with in situ high-temperature powder diffraction, we obtained a distorted variant of LiNi 0.7 Co 0.3 B featuring a perfect interlayer placement of [Ni 0.7 Co 0.3 B] layers on top of each other–a structural motif not seen before in other borides. Because of the Co doping, LiNi 0.7 Co 0.3 B can undergo a nearly complete topochemical Li deintercalation under ambient conditions, resulting in a metastable boride with the formula Li 0.04 Ni 0.7 Co 0.3 B. Heating of Li 0.04 Ni 0.7 Co 0.3 B in anaerobic conditions led to yet another metastable boride, Li 0.01 Ni 0.7 Co 0.3 B, with a CoB-type crystal structure that cannot be obtained by simple annealing of Ni, Co, and B. No significant alterations of magnetic properties were detected upon Co-doping in the temperature-independent paramagnet LiNi 0.7 Co 0.3 B or its Li-deintercalated counterparts. Finally, Li 0.01 Ni 0.7 Co 0.3 B stands out as an exceptional catalyst for the selective hydrogenation of the vinyl C=C bond in 3-nitrostyrene, even in the presence of other competing functional groups. Finally, this research showcases an innovative approach to heterogeneous catalyst design by meticulously synthesizing metastable compounds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Towards a predictive route for selection of doping elements for the thermoelectric compound PbTe from first-principles

Striving for improvements of the thermoelectric (TE) properties of the technologically important lead telluride (PbTe) compound, we investigate the influence of different doping elements on the thermal conductivity, Seebeck coefficient, and electrical conductivity applying density functional theory calculations. Our approach combines total-energy calculations yielding lattice vibrational properties with the Boltzmann transport theory to obtain electronic transport properties. We find that doping with elements from the 1st and 3rd columns of the periodic table reduces the sound velocity and, consequently, the lattice thermal conductivity, while 2nd column dopants have no such influence. Furthermore, 1.6 at. % doping with 4th and 5th column elements provides the highest reduction of lattice thermal conductivity. Out of this group, Hf doping results in maximum reduction of the sound velocity from 2030 m s{sup −1} for pure PbTe to 1370 m s{sup −1}, which is equivalent to ca. 32% reduction of lattice thermal conductivity. The highest power factor values calculated for 1.6 at. % doping range between 40 and 56 μW cm{sup −1} K{sup −2}, and are obtained for substitution with dopants having the same valence as Pb or Te, such as those located at the 2nd, 14th, and 16th columns of the periodic table. We demonstrate how this method may be generalized for dopant-selection-oriented materials design aimed at improving TE performance of other compounds.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Joint Optimization of Multimodal Transit Frequency and Shared Autonomous Vehicle Fleet Size with Hybrid Metaheuristic and Nonlinear Programming

Shared autonomous vehicles (SAVs) bring competition to traditional transit services but redesigning multimodal transit network can utilize SAVs as feeders to enhance service efficiency and coverage. This paper presents an optimization framework for the joint multimodal transit frequency and SAV fleet size problem, a variant of the transit network frequency setting problem. The objective is to maximize total transit ridership (including SAV-fed trips and subtracting boarding rejections) across multiple time periods under budget constraints, considering endogenous mode choice (transit, point-to-point SAVs, driving) and route selection, while allowing for strategic route removal by setting frequencies to zero. Due to the problem’s non-linear, non-convex nature and the computational challenges of large-scale networks, we develop a hybrid solution approach that combines a metaheuristic approach (particle swarm optimization) with nonlinear programming for local solution refinement. To ensure computational tractability, the framework integrates analytical approximation models for SAV waiting times based on fleet utilization, multimodal network assignment for route choice, and multinomial logit mode choice behavior, bypassing the need for computationally intensive simulations within the main optimization loop. Applied to the Chicago metropolitan area’s multimodal network, our method illustrates a 33.3% increase in transit ridership through optimized transit route frequencies and SAV integration, particularly enhancing off-peak service accessibility and strategically reallocating resources.

Ng, Max↗
DOE OSTI2021

RouteE: A Vehicle Energy Consumption Prediction Engine

The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts. The pre-trained RouteE models are established using NREL's Future Automotive Systems Technology Simulator paired with approximately 1 million miles of drive cycle data from the Transportation Secure Data Center, resulting in energy consumption behavior estimates over a representative sample of driving conditions for the United States. Validations have been performed using on-road fuel consumption data for conventional and electrified vehicle powertrains. Transferring the results of the on-road validation to a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology in a green-routing application would accurately select the route that consumes the least fuel 90% of the time. The novel machine learning techniques used in RouteE make it a flexible and robust tool for a variety of transportation applications.

47 OTHER INSTRUMENTATION↗
NASA NTRS2013

Enhanced Contact Graph Routing (ECGR) MACHETE Simulation Model

Contact Graph Routing (CGR) for Delay/Disruption Tolerant Networking (DTN) space-based networks makes use of the predictable nature of node contacts to make real-time routing decisions given unpredictable traffic patterns. The contact graph will have been disseminated to all nodes before the start of route computation. CGR was designed for space-based networking environments where future contact plans are known or are independently computable (e.g., using known orbital dynamics). For each data item (known as a bundle in DTN), a node independently performs route selection by examining possible paths to the destination. Route computation could conceivably run thousands of times a second, so computational load is important. This work refers to the simulation software model of Enhanced Contact Graph Routing (ECGR) for DTN Bundle Protocol in JPL's MACHETE simulation tool. The simulation model was used for performance analysis of CGR and led to several performance enhancements. The simulation model was used to demonstrate the improvements of ECGR over CGR as well as other routing methods in space network scenarios. ECGR moved to using earliest arrival time because it is a global monotonically increasing metric that guarantees the safety properties needed for the solution's correctness since route re-computation occurs at each node to accommodate unpredicted changes (e.g., traffic pattern, link quality). Furthermore, using earliest arrival time enabled the use of the standard Dijkstra algorithm for path selection. The Dijkstra algorithm for path selection has a well-known inexpensive computational cost. These enhancements have been integrated into the open source CGR implementation. The ECGR model is also useful for route metric experimentation and comparisons with other DTN routing protocols particularly when combined with MACHETE's space networking models and Delay Tolerant Link State Routing (DTLSR) model.

Segui, John S.↗
DOE OSTI2016

Security-Oriented and Load-Balancing Wireless Data Routing Game in the Integration of Advanced Metering Infrastructure Network in Smart Grid

Ensuring flexible and reliable data routing is indispensable for the integration of Advanced Metering Infrastructure (AMI) networks, we propose a secure-oriented and load-balancing wireless data routing scheme. A novel utility function is designed based on security routing scheme. Then, we model the interactive security-oriented routing strategy among meter data concentrators or smart grid meters as a mixed-strategy network formation game. Finally, such problem results in a stable probabilistic routing scheme with proposed distributed learning algorithm. One contributions is that we studied that different types of applications affect the routing selection strategy and the strategy tendency. Another contributions is that the chosen strategy of our mixed routing can adaptively to converge to a new mixed strategy Nash equilibrium (MSNE) during the learning process in the smart grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Optimizing lane reversals in transportation networks to reduce traffic congestion: A global optimization approach

This paper studies how to reduce the overall travel time of commuters in a transportation network by reversing the direction of some lanes in the network using a macroscopic network-wide perspective. Similar to the Network Design Problem, the lane reversal problem has been shown to be NP-hard given the dependence of the users’ route selection on the lane direction decision. Herein, we propose and compare three efficient methods to solve the routing and lane reversal problem jointly. First, we introduce an alternating method that decouples the routing and lane assignment problems. Second, we propose a Frank–Wolfe method that jointly takes gradient steps to adjust both the lane assignment and routing decisions. Third, we propose a convex approximation method that uses a threshold-based approach to convexify the joint routing and lane reversal objective. The convex approximation method is advantageous since it finds a global optimum solution for the approximated problem and it enables the possibility to include linear constraints. Using this method, we extend the main formulation to be able to limit a maximum number of reversed lanes, as well as to incorporate multiple origin–destination (OD) patterns. We test the proposed methods in a case study using the transportation network of Eastern Massachusetts where our results indicate an overall reduction in travel times of 4.7% by selecting the best 15 reversals. Moreover, using a small test network, we investigate the performance of the lane reversal strategies as a function of the OD demand symmetry. As expected, we observe that when the OD demand is very asymmetric (e.g., for a single OD pair, evacuations, large events), the reduction in travel times is larger than the symmetric case, reaching travel time reductions of 60%.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗