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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

GetAway Tether Experiment (GATE) for the Tether Dynamics Explorer (TDE) series

Designs for the GetAway Tether Experiment (GATE) can easily be adapted and applied to the Tether Dynamics Explorer (TDE) series. The GATE development schedule coincides with the planned first flight of the TDE. GATE technology has centered on the development of miniature deployers/actuators, sensors, control laws and simulation capability. The sensors currently under development are a tension sensor and a video based tether tracker. Both sensors are currently undergoing laboratory testing and development. The actuators currently being investigated are a small reel/deployed for active control of the tether and a small tether crawler to damp vibrations of the tether. Laboratory results are presented and the designs reviewed and discussed.

Greene, Michael↗
DOE OSTI2020

Characterizing the Performance of Executing Many-tasks on Summit

Many scientific workloads are comprised of many tasks, where each task is an independent simulation or analysis of data. The execution of millions of tasks on heterogeneous HPC platforms requires scalable dynamic resource management and multi-level scheduling. RADICAL-Pilot (RP) -- an implementation of the Pilot abstraction, addresses these challenges and serves as an effective runtime system to execute workloads comprised of many tasks. In this paper, we characterize the performance of executing many tasks using RP when interfaced with JSM and PRRTE on Summit: RP is responsible for resource management and task scheduling on acquired resource; JSM or PRRTE enact the placement of launching of scheduled tasks. Our experiments provide lower bounds on the performance of RP when integrated with JSM and PRRTE. Specifically, for workloads comprised of homogeneous single-core, 15 minutes-long tasks we find that: PRRTE scales better than JSM for > O(1000) tasks; PRRTE overheads are negligible; and PRRTE supports optimizations that lower the impact of overheads and enable resource utilization of 63% when executing O(16K), 1-core tasks over 404 compute nodes.

Matteo, Turilli↗
NASA NTRS2007

Quasi-Linear Parameter Varying Representation of General Aircraft Dynamics Over Non-Trim Region

For applying linear parameter varying (LPV) control synthesis and analysis to a nonlinear system, it is required that a nonlinear system be represented in the form of an LPV model. In this paper, a new representation method is developed to construct an LPV model from a nonlinear mathematical model without the restriction that an operating point must be in the neighborhood of equilibrium points. An LPV model constructed by the new method preserves local stabilities of the original nonlinear system at "frozen" scheduling parameters and also represents the original nonlinear dynamics of a system over a non-trim region. An LPV model of the motion of FASER (Free-flying Aircraft for Subscale Experimental Research) is constructed by the new method.

Shin, Jong-Yeob↗
DOE OSTI2023

Topology, criticality, and dynamically generated qubits in a stochastic measurement-only Kitaev model

Here we consider a paradigmatic solvable model of topological order in two dimensions, Kitaev's honeycomb Hamiltonian, and turn it into a measurement-only dynamics consisting of stochastic measurements of two-qubit bond operators. We find an entanglement phase diagram that resembles that of the Hamiltonian problem in some ways, while being qualitatively different in others. When one type of bond is dominantly measured, we find area-law entangled phases that protect two topological qubits (on a torus) for a time exponential in system size. This generalizes the recently proposed idea of Floquet codes, where logical qubits are dynamically generated by a time-periodic measurement schedule, to a stochastic setting. When all types of bonds are measured with comparable frequency, we find a critical phase with a logarithmic violation of the area law, which sharply distinguishes it from its Hamiltonian counterpart. The critical phase has the same set of topological qubits, as diagnosed by the tripartite mutual information, but protects them only for a time polynomial in system size. Furthermore, we observe an unusual behavior for the dynamical purification of mixed states, characterized at late times by the dynamical exponent z = $\frac{1}{2}$ a superballistic dynamics made possible by measurements.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

An Agent-Based Modeling Approach for Spatiotemporal Optimization of Electric Vehicle Fast-Charging Station Demand

With increasing electric vehicle (EV) adoption, managing public fast-charging demand effectively is crucial to avoid grid strain. This study investigates the potential of using dynamic pricing schemes to address this challenge. Presented in this study is a scalable agent-based simulation framework, which is applied to a case study in Richmond, Virginia, that assumes a 50% EV adoption rate in 2040. Two pricing schemes are compared: (1) a dynamic-pricing scheme based on station utilization and (2) a dynamic-pricing scheme based on peak power at the station. These schemes are compared to two baseline scenarios: (1) unscheduled first-come, first-served and (2) scheduled with constant price. The study’s results suggest that dynamic pricing has the potential to influence EV charging behavior, inducing both spatial and temporal shifts, but does so at the cost of inducing inconvenience to EV drivers. The results suggest the peak-power dynamic pricing scheme has the potential to mitigate peak demand pressures on the grid with minimal inconvenience, offering a promising approach for sustainable EV charging infrastructure expansion.

33 - ADVANCED PROPULSION SYSTEMS↗
NASA NTRSDate not supplied

Integrated Control Design for A Partially Turboelectric Aircraft Propulsion System

Electrified Aircraft Propulsion (EAP) holds great potential for reducing aviation emissions and fuel burn. A variety of EAP architectures have been proposed including partially-turboelectric configurations that combine turbofan engines with motor-driven propulsors. Such architectures exhibit coupling between subsystems and thus require an integrated control solution. To address this need, this paper presents an integrated control design strategy for a commercial single-aisle partially-turboelectric aircraft concept consisting of two wing-mounted turbofan engines and an electric motor driven tailfan propulsor. Within this architecture the turbofans serve the dual purpose of generating thrust and supplying mechanical offtake power used to generate electricity for the tailfan motor. The propulsion control system is tasked with coordinating turbofan and tailfan operation under both steady-state and transient scenarios. The paper introduces a linear state-space representation of the architecture reflecting the coupling between the turbofan and tailfan subsystems along with loop transfer functions reflecting open- and closed-loop system dynamics. Also discussed is an applied strategy for scheduling the tailfan setpoint command based on the average sensed fan speed of the two turbofans. This approach ensures synchronized operation of the turbofan and tailfan subsystems while also allowing the turbofan fuel control design to be simplified. Performance of the integrated control design is assessed through a real-time hardware-in-the-loop test conducted at the NASA Electric Aircraft Testbed. During this test a scaled version of the electrical system and turbomachinery shaft dynamics were implemented in electrical machine hardware and evaluated under closed-loop control. Results from this facility test are presented to illustrate the efficacy of the applied integrated control design approach under steady-state and transient scenarios including a full-flight mission profile.

Electrified Aircraft Propulsion↗
NASA NTRSDate not supplied

Integrated Control Design for A Partially Turboelectric Aircraft Propulsion System

Electrified Aircraft Propulsion (EAP) holds great potential for reducing aviation emissions and fuel burn. A variety of EAP architectures have been proposed including partially-turboelectric configurations that combine turbofan engines with motor-driven propulsors. Such architectures exhibit coupling between subsystems and thus require an integrated control solution. To address this need, this paper presents an integrated control design strategy for a commercial single-aisle partially-turboelectric aircraft concept consisting of two wing-mounted turbofan engines and an electric motor driven tailfan propulsor. Within this architecture the turbofans serve the dual purpose of generating thrust and supplying mechanical offtake power used to generate electricity for the tailfan motor. The propulsion control system is tasked with coordinating turbofan and tailfan operation under both steady-state and transient scenarios. The paper introduces a linear state-space representation of the architecture reflecting the coupling between the turbofan and tailfan subsystems along with loop transfer functions reflecting open- and closed-loop system dynamics. Also discussed is an applied strategy for scheduling the tailfan setpoint command based on the average sensed fan speed of the two turbofans. This approach ensures synchronized operation of the turbofan and tailfan subsystems while also allowing the turbofan fuel control design to be simplified. Performance of the integrated control design is assessed through a real-time hardware-in-the-loop test conducted at the NASA Electric Aircraft Testbed. During this test a scaled version of the electrical system and turbomachinery shaft dynamics were implemented in electrical machine hardware and evaluated under closed-loop control. Results from this facility test are presented to illustrate the efficacy of the applied integrated control design approach under steady-state and transient scenarios including a full-flight mission profile.

Electrified Aircraft Propulsion↗
NASA NTRS2023

Integrated Control Design for A Partially Turboelectric Aircraft Propulsion System

Electrified Aircraft Propulsion (EAP) holds great potential for reducing aviation emissions and fuel burn. A variety of EAP architectures have been proposed including partially-turboelectric configurations that combine turbofan engines with motor-driven propulsors. Such architectures exhibit coupling between subsystems and thus require an integrated control solution. To address this need, this paper presents an integrated control design strategy for a commercial single-aisle partially-turboelectric aircraft concept consisting of two wing-mounted turbofan engines and an electric motor driven tailfan propulsor. Within this architecture the turbofans serve the dual purpose of generating thrust and supplying mechanical offtake power used to generate electricity for the tailfan motor. The propulsion control system is tasked with coordinating turbofan and tailfan operation under both steady-state and transient scenarios. The paper introduces a linear state-space representation of the architecture reflecting the coupling between the turbofan and tailfan subsystems along with loop transfer functions reflecting open- and closed-loop system dynamics. Also discussed is an applied strategy for scheduling the tailfan setpoint command based on the average sensed fan speed of the two turbofans. This approach ensures synchronized operation of the turbofan and tailfan subsystems while also allowing the turbofan fuel control design to be simplified. Performance of the integrated control design is assessed through a real-time hardware-in-the-loop test conducted at the NASA Electric Aircraft Testbed. During this test a scaled version of the electrical system and turbomachinery shaft dynamics were implemented in electrical machine hardware and evaluated under closed-loop control. Results from this facility test are presented to illustrate the efficacy of the applied integrated control design approach under steady-state and transient scenarios including a full-flight mission profile.

Electrified Aircraft Propulsion↗
NASA NTRS1993

Miniature linear-to-rotary motion actuator

A miniature hydraulic actuation system capable of converting linear actuator motion to control surface rotary motion has been designed for application to active controls on dynamic wind tunnel models. Due to space constraints and the torque requirements of an oscillating control surface at frequencies up to 50 Hertz, a new actuation system was developed to meet research objectives. This new actuation system was designed and developed to overcome the output torque limitations and fluid loss/sealing difficulties associated with an existing vane type actuator. Static control surface deflections and dynamic control surface oscillations through a given angle are provided by the actuation system. The actuator design has been incorporated into a transonic flutter model with an active trailing edge flap and two active spoilers. The model is scheduled for testing in the LaRC 16 Foot Transonic Dynamics Tunnel during Summer 1993. This paper will discuss the actuation system, its design, development difficulties, test results, and application to aerospace vehicles.

Sorokach, Michael R., Jr.↗
NASA NTRS2004

Autonomous Multi-Sensor Coordination: The Science Goal Monitor

Many dramatic earth phenomena are dynamic and coupled. In order to fully understand them, we need to obtain timely coordinated multi-sensor observations from widely dispersed instruments. Such a dynamic observing system must include the ability to Schedule flexibly and react autonomously to sciencehser driven events; Understand higher-level goals of a sciencehser defined campaign; Coordinate various space-based and ground-based resources/sensors effectively and efficiently to achieve goals. In order to capture transient events, such a 'sensor web' system must have an automated reactive capability built into its scientific operations. To do this, we must overcome a number of challenges inherent in infusing autonomy. The Science Goal Monitor (SGM) is a prototype software tool being developed to explore the nature of automation necessary to enable dynamic observing. The tools being developed in SGM improve our ability to autonomously monitor multiple independent sensors and coordinate reactions to better observe dynamic phenomena. The SGM system enables users to specify what to look for and how to react in descriptive rather than technical terms. The system monitors streams of data to identify occurrences of the key events previously specified by the scientisther. When an event occurs, the system autonomously coordinates the execution of the users' desired reactions between different sensors. The information can be used to rapidly respond to a variety of fast temporal events. Investigators will no longer have to rely on after-the-fact data analysis to determine what happened. Our paper describes a series of prototype demonstrations that we have developed using SGM and NASA's Earth Observing-1 (EO-1) satellite and Earth Observing Systems' Aqua/Terra spacecrafts' MODIS instrument. Our demonstrations show the promise of coordinating data from different sources, analyzing the data for a relevant event, autonomously updating and rapidly obtaining a follow-on relevant image. SGM was used to investigate forest fires, floods and volcanic eruptions. We are now identifying new Earth science scenarios that will have more complex SGM reasoning. By developing and testing a prototype in an operational environment, we are also establishing and gathering metrics to gauge the success of automating science campaigns.

Koratkar, Anuradha↗
DOE OSTI2023

HCPerf: Driving Performance-Directed Hierarchical Coordination for Autonomous Vehicles

The rapid development of autonomous driving poses new research challenges to the on-vehicle computing system. In particular, the execution time of autonomous driving tasks highly depends on the specific driving environment. For instance, the execution time of configurable sensor fusion increases significantly as the scene becomes complex, which leads to end-to-end deadline misses from sensing to control and may cause accidents. Thus, a framework that can effectively utilize the system resources to guarantee the end-to-end deadlines of autonomous driving tasks as well as effectively prioritize the responsiveness and throughput of the control commands is crucial for autonomous driving. In this paper, we propose HCPerf, a performance-directed hierarchical coordination framework that intelligently coordinates the autonomous driving tasks with high execution time variation and complex dependencies according to the driving performance in real-time. Specifically, HCPerf mainly consists of two coordinators. The internal coordinator intelligently schedules the tasks according to the driving performance of the vehicle in order to help them meet the end-to-end deadlines while well prioritizing the responsiveness and throughput of the control commands. At the same time, the external coordinator dynamically tunes the rates of tasks according to the schedulability in order to efficiently utilize the system resource. We conduct extensive experiments on both simulation and hardware testbeds with the representative autonomous driving application. The results show that HCPerf can effectively improve the driving performance by 7.69%-45.94% in different driving scenarios.

Ma, Jialiang↗
DOE OSTI2024

Circular Economy for Photovoltaics in Service of Energy Transition

The challenge of energy transition is immediate and immense; current projections target 75 TW of photovoltaics (PV) capacity by 2050. While any transition to renewable energy technology is preferable to the current fossil-based system, it is ideal to improve the sustainability of PV to minimize negative environmental and social impacts. Circular economy (CE) has been proposed as a method to improve the sustainability of PV, especially for emerging materials like perovskites. CE is a set of actions, principles, and systems which aim to design out waste and keep products and materials in use, to reduce environmental impacts and enable sustainable development. At the most basic level, CE is "reduce, reuse, recycle", the R-actions, in ranked order. CE of a PV technology can be metricized in a variety of ways, such as the Material Circularity Indicator (Smith and Jones, Ellen MacArthur Foundation, 2019) or recycling rates. Unfortunately, standard CE metrics have several shortcomings for measuring renewable energy technologies in the context of deployment for energy transition (Figge 2018, Saidani 2019): 1) Only measure mass flows; 2) De-prioritization of the use phase in favor of mass circularity when scoring; and 3) Tight focus on a single product scale The use phase and energy flows of PV are key to energy transition, and therefore need to be quantified. Additionally, correlating product-scale to system-scale is necessary for quantifying the environmental impacts of energy transition. Life Cycle Assessment (LCA) can address some of these concerns, but also focuses on a single product scale and has trouble capturing the dynamics of system-scale energy transition, such as the interaction of module lifetime with manufacturing demands for energy transition deployment schedules. Therefore, we developed an open-source Python-based system dynamics model to quantify the mass, energy and carbon impacts of CE R-actions for PV technologies in the energy transition; PV in the CE (PV ICE) (Ovaitt & Mirletz 2021). The tool captures supply chains from material extraction through end of life, incorporating 5 circular end of life pathways. PV ICE takes in any evolving bill of materials, module properties and deployment schedule to support researchers and decision makers with data-backed insights. In this work, we quantify and compare proposed CE sustainable PV module designs and lifecycle management strategies, spanning currently commercialized technologies, government and industry technology targets, and several low Technology Readiness Level (TRL) emerging PV technologies, including perovskites. Our analyses capture the projected evolutions of lifetime, efficiency and material circularity of these PV technologies, as well as their material supply chains. Our analyses emphasize the importance of examining a suite of metrics to identify priorities and tradeoffs, and inform design or lifecycle management decisions holistically. Previous analyses have demonstrated the central importance of PV module lifetime to support energy transition while minimizing impacts. High levels of material circularity (>90%) enable minimizing lifecycle wastes, can reduce virgin material demands if paired with improving efficiency, but demonstrate tradeoffs in energy return on investment. In the fervor of new material and technology development, it is important to remember that CE is not the end goal; decarbonization and energy transition are the end goal. CE should be used in service to improve the sustainability of PV, and R-actions evaluated for their usefulness and efficacy to this end.

carbon↗
DOE OSTI2022

MARS: Malleable Actor-Critic Reinforcement Learning Scheduler

In this paper, we introduce MARS, a new scheduling system for HPC-cloud infrastructures based on a cost-aware, flexible reinforcement learning approach, which serves as an intermediate layer for next generation HPC-cloud resource manager. MARS ensembles the pre-trained models from heuristic workloads and decides on the most cost-effective strategy for optimization. A whole workflow application would be split into several optimizable dependent sub-tasks, then based on the pre- defined resource management plan, a reward will be generated after executing a scheduled task. Lastly, MARS updates the Deep Neural Network (DNN) model based on the reward. MARS is designed to optimize the existing models through reinforcement mechanisms. MARS adapts to the dynamics of workflow applications, selects the most cost-effective scheduling solution among pre-built scheduling strategies (backfilling, SJF, etc.) and self- learning deep neural network model at run-time. We evaluate MARS with different real-world workflow traces. MARS can achieve 5%-60% increased performance compare to state-of-the- art approaches.

Baheri, Betis↗
DOE OSTI2016

Predictive Scheduling for Electric Vehicles Considering Uncertainty of Load and User Behaviors

Un-coordinated Electric Vehicle (EV) charging can create unexpected load in local distribution grid, which may degrade the power quality and system reliability. The uncertainty of EV load, user behaviors and other baseload in distribution grid, is one of challenges that impedes optimal control for EV charging problem. Previous researches did not fully solve this problem due to lack of real-world EV charging data and proper stochastic model to describe these behaviors. In this paper, we propose a new predictive EV scheduling algorithm (PESA) inspired by Model Predictive Control (MPC), which includes a dynamic load estimation module and a predictive optimization module. The user-related EV load and base load are dynamically estimated based on the historical data. At each time interval, the predictive optimization program will be computed for optimal schedules given the estimated parameters. Only the first element from the algorithm outputs will be implemented according to MPC paradigm. Current-multiplexing function in each Electric Vehicle Supply Equipment (EVSE) is considered and accordingly a virtual load is modeled to handle the uncertainties of future EV energy demands. This system is validated by the real-world EV charging data collected on UCLA campus and the experimental results indicate that our proposed model not only reduces load variation up to 40% but also maintains a high level of robustness. Finally, IEC 61850 standard is utilized to standardize the data models involved, which brings significance to more reliable and large-scale implementation.

Wang, Bin↗
NASA NTRSDate not supplied

Floquet Codes on Non-Trivalent Lattices

Floquet codes are a novel form of quantum error correcting codes, where the logical degrees of freedom appear in a dynamical form. The first example of such a code was constructed by Hastings and Haah on a honeycomb lattice. Specifically, they showed how to Floquetify the Kitaev honeycomb model, which when viewed as a subsystem code, encodes no logical qubits. However, by choosing a particular measurement schedule of the check operators, this model leads to dynamically encoded logical qubits. Since this initial work, several examples of Floquet codes have been constructed, mainly on trivalent lattices. Here, we report progress on understanding when such codes can be implemented on lattices of different connectivities.

Muhammad Sohaib Alam↗
DOE OSTI2023

Energy-efficient cooperative resource allocation and task scheduling for Internet of Things environments

Offloading Internet of Things (IoT) tasks to the cloud for further processing might not always lead to an optimal execution time, particularly in situations such as resource contention, under-provisioning, over-provisioning, and fragmentation. In addition, dynamically optimizing the number of Virtual Machines (VMs) for resource scheduling in order to meet application requirements remains a major research challenge. Further, existing resource scheduling algorithms focus primarily on minimizing operational costs while maximizing resource sharing and utilization. Considering energy utilization as part of the resource allocation and scheduling process as an optimization objective for maintaining load balancing has often been neglected. To address these challenges and more, we propose a cooperative energy-aware resource allocation and scheduling strategy based on a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria decision-making method. Here we used the Grid Workloads Archive dataset to evaluate our proposed approach named TOPREAL. Experimental results with respect to the allocation of VM resources when considering processing a large segment of tasks indicate that TOPREAL outperforms existing algorithms in terms of energy savings, with an average improvement of 40.25%, while maintaining an average improvement of 16.21% when it comes to execution time. Results also demonstrate that our method can save an average of 78.06 processing hours and 63,215kJ of energy when compared to existing scheduling algorithms. These results demonstrate the effectiveness of our proposed model and the viability of using multi-criteria decision-making techniques such as TOPSIS to solve the resource allocation and scheduling problem in edge environments.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Adaptive Runtime Features For Distributed Graph Algorithms

Performance of distributed graph algorithms can benefit greatly by forming rapport between algorithmic abstraction and the underlying runtime system that is responsible for scheduling work and exchanging messages. However, due to their dynamic and irregular nature of computation, distributed graph algorithms written in different programming models impose varying degree of workload pressure on the runtime. To cope with such vastly different workload characteristics, a runtime has to make several trade-offs. One such trade-off arises, for example, when the runtime scheduler has to choose among alternatives such as whether to execute algorithmic work, or progress the network by probing network buffers, or throttle sending messages (termed flow control). This tradeoff decides between optimizing the throughput of a runtime scheduler by increasing the rate of execution of algorithmic work, and reducing the latency of the network messages. Another trade-off exists when a decision has to be made about when to send aggregated messages in buffers (message coalescing). This decision chooses between trading off latency for network bandwidth and vice versa. At any instant, such trade-offs emphasize either on improving the quantity of work being executed (by maximizing the scheduler throughput) or on improving the quality of work (by prioritizing better work). However, encoding static policies for different runtime features (such as flow control, coalescing) can prevent graph algorithms from achieving their full potential, thus can undermine the actual performance of a distributed graph algorithm . In this paper, we investigate runtime support for distributed graph algorithms in the context of two paradigms: variants of wellknown Bulk-Synchronous Parallel model and asynchronous programming model. We explore generic runtime features such as message coalescing (aggregation) and flow control and show that execution policies of these features need to be adjusted over time to make a positive impact on the execution time of a distributed graph algorithm. Since synchronous and asynchronous graph algorithms have different workload characteristics, not all of such runtime features may be good candidates for adaptation. Each of these algorithmic paradigms may require different set of features to be adapted over time. We demonstrate which set of feature(s) can be useful in each case to achieve the right balance of work in the runtime layer. Existing implementation of different graph algorithms can benefit from adapting dynamic policies in the underlying runtime.

Firoz, Jesun S.↗