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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Simulation Study for Interoperability of Urban Air Mobility Scheduling and Separation Services in Ideal Conditions

Provision of strategic scheduling and tactical separation services is vital to the safe and efficient operation of vehicles in the urban airspace. This paper describes the efforts made towards the integration of two such services in a simulation environment under ideal conditions and the subsequent studies done on evaluation of system performance. The scheduling and separation services are set up to complement each other to ensure safe separation between airborne aircraft. The utility of these services will become important as the level of traffic increases. This paper describes the simulation experiments conducted to identify cases where the system performance measured by the number of observed losses of separation degrades even with the scheduling and separation services active. From the results obtained, we identify conditions under which the required maneuvers increase and when we observe airborne conflicts even with separation service active. Results obtained will inform requirements for future advancements both in these services independently and in their joint operations.

Urban Air Mobility↗
NASA NTRSDate not supplied

Simulation Study for Interoperability of Urban Air Mobility Scheduling and Separation Services in Ideal Conditions

Provision of strategic scheduling and tactical separation services is vital to the safe and efficient operation of vehicles in the urban airspace. This paper describes the efforts made towards the integration of two such services in a simulation environment under ideal conditions and the subsequent studies done on evaluation of system performance. The scheduling and separation services are set up to complement each other to ensure safe separation between airborne aircraft. The utility of these services will become important as the level of traffic increases. This paper describes the simulation experiments conducted to identify cases where the system performance measured by the number of observed losses of separation degrades even with the scheduling and separation services active. From the results obtained, we identify conditions under which the required maneuvers increase and when we observe airborne conflicts even with separation service active. Results obtained will inform requirements for future advancements both in these services independently and in their joint operations.

Urban Air Mobility↗
NASA NTRSDate not supplied

Separation Assurance in Urban Air Mobility Systems Using Shared Scheduling Protocols

Ensuring safe separation between aircraft is a critical challenge in air traffic management, particularly in urban air mobility (UAM) environments where high traffic density and low altitudes require precise control. In these environments, conflicts often arise at the intersections of flight corridors, posing significant risks. We propose a tactical separation approach leveraging shared scheduling protocols, originally designed for Ethernet networks and operating systems, to coordinate access to these intersections. Using a decentralized Markov decision process framework, the proposed approach enables aircraft to autonomously adjust their speed and timing as they navigate these critical areas, maintaining safe separation without a central controller. We evaluate the effectiveness of this approach in simulated UAM scenarios, demonstrating its ability to reduce separation violations to zero while acknowledging trade-offs in flight times as traffic density increases. Additionally, we explore the impact of non-compliant aircraft, showing that while shared scheduling protocols can no longer guarantee safe separation, they still provide significant improvements over systems without scheduling protocols.

Separation Assurance↗
NASA NTRSDate not supplied

Separation Assurance in Urban Air Mobility Systems Using Shared Scheduling Protocols

Ensuring safe separation between aircraft is a critical challenge in air traffic management, particularly in urban air mobility (UAM) environments where high traffic density and low altitudes require precise control. In these environments, conflicts often arise at the intersections of flight corridors, posing significant risks. We propose a tactical separation approach leveraging shared scheduling protocols, originally designed for Ethernet networks and operating systems, to coordinate access to these intersections. Using a decentralized Markov decision process framework, the proposed approach enables aircraft to autonomously adjust their speed and timing as they navigate these critical areas, maintaining safe separation without a central controller. We evaluate the effectiveness of this approach in simulated UAM scenarios, demonstrating its ability to reduce separation violations to zero while acknowledging trade-offs in flight times as traffic density increases. Additionally, we explore the impact of non-compliant aircraft, showing that while shared scheduling protocols can no longer guarantee safe separation, they still provide significant improvements over systems without scheduling protocols.

system safety↗
DOE OSTI2018

A Method to Extract Soiling Loss Data from Soiling Stations with Imperfect Cleaning Schedules: Preprint

Typical PV soiling stations determine the natural soiling losses by comparing the output of a naturally soiled PV cell to that of a PV cell maintained in the clean state. Inadequate cleaning frequency or human error provides opportunity for the cleaned cell to soil, directly resulting in error in the reported soiling ratio. This work investigates an algorithm to automatically detect and correct the data stream for errors associated with soiling of the clean cell. The methodology is tested on several soiling stations with irregular cleaning schedules as well as a soiling station where both ideal and imperfect cleaning schedules are in place. The initial results show that the algorithm can reduce error associated with imperfect cleaning but also confirms the benefits of maintaining an optimal cleaning schedule.

14 SOLAR ENERGY↗
DOE OSTI2023

Outage Forecast-Based Preventative Scheduling Model for Distribution System Resilience Enhancement: Preprint

Distribution system resilience enhancement is an important topic to ensure customers have access to the power supply during extreme events. In fact, certain weather-related extreme events can be predicted ahead of time. Therefore, it is important to investigate how to predict grid outages using extreme weather forecasts, and how outage predictions can be incorporated into distribution system resilience enhancement. In this paper, a preventative scheduling model for distribution systems is proposed. The model targets at allocating resources, especially mobile responsive resources such as mobile backup generators and mobile energy storage systems, to prepare for an extreme event in the day-ahead context. To achieve efficient resource allocation and scheduling, a machine learning-based outage prediction module is developed to predict vulnerable or risky segments of the distribution system based on historical operating records and extreme weather event forecasts. By integrating the outage prediction results into the scheduling model, optimal resource allocation can be derived to help distribution systems prepare for an upcoming event and improve resilience performance. A real distribution feeder in North Carolina, U.S. is used in the case study to validate the proposed approach.

distributed energy resources↗
DOE OSTI2023

Outage Forecast-Based Preventative Scheduling Model for Distribution System Resilience Enhancement

Distribution system resilience enhancement is an important topic to ensure customers have access to power supply during extreme events. In fact, certain weather-related extreme events can be predicted ahead of time. Therefore, it is important to investigate how to predict grid outages using extreme weather forecasts, and how outage predictions can be incorporated into distribution system resilience enhancement. In this paper, a preventative scheduling model for distribution systems is proposed. The model targets at allocating resources, especially mobile responsive resources such as mobile backup generators and mobile energy storage systems, to prepare for an extreme event in the day-ahead context. To achieve efficient resource allocation and scheduling, a machine learning-based outage prediction module is developed to predict vulnerable or risky segments of the distribution system based on historical operating records and extreme weather event forecast. By integrating the outage prediction results into the scheduling model, optimal resource allocation can be derived to help distribution systems prepare for an upcoming event and improve resilience performance. A real distribution feeder in North Carolina, U.S. is used in the case study to validate the proposed approach.

distributed energy resources↗
DOE OSTI2025

Energy Scheduling-based Operating Envelopes including a Distribution System Branch Screening Algorithm

This paper presents an energy scheduling-based formulation for computing operating envelopes including a distribution branch screening algorithm, termed DBS-ES. The contribution of the paper is two-fold: firstly, it presents an innovative methodology for calculating operating envelopes using energy scheduling (baseline), and secondly, it enhances this methodology by incorporating a custom distribution branch screening algorithm (DBS-ES). The custom algorithm leverages power system knowledge to reduce both model build time and total processing time while maintaining the same scheduling results as the baseline. The effectiveness of the proposed approach is demonstrated through experiments on the IEEE13, IEEE123, and EPRI Secondary test feeders. Results highlight a 24.5% decrease in model build time and an 8.17% decrease in total processing time when using DBS-ES compared to the baseline, specifically for the IEEE123 test feeder. Additionally, the paper briefly discusses the influence of utility-controlled storage on computing operating envelopes, noting a general incre

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Enhancing capillary trapping effectiveness through proper time scheduling of injection of supercritical CO 2 in heterogeneous formations

The impact of geologic heterogeneity on capillary trapping of supercritical CO 2 (scCO 2 ) has been recognized and appraised through laboratory experimentation and modeling. However, how different injection strategies can be optimized to improve capillary trapping has not received adequate attention. Here, we present a study based on stochastic analysis to show the impact of injection scheduling on capillary trapping of scCO 2 in heterogeneous geological formations. Improvement of trapping efficiency consists of maximizing the trapped volume within a predefined secure zone of the storage formation with the goal of avoiding plume intersection with potential leakage pathways. Using knowledge acquired from physical experiments in intermediate-scale tanks with controlled heterogeneity, we conduct numerical simulations following a stochastic approach to extend the observed outcomes to a series of equally probable permeability scenarios. Our simulations involve the same combination of surrogate fluids that are used in the experiments to mimic viscosity and density contrasts between scCO 2 and brine. To account for uncertainty of formation heterogeneity, several permeability fields with three different variances and two horizontal correlation lengths are generated. The same volume of scCO 2 is emplaced using four different modes of injection scheduling. Results suggest that injection strategies aimed at enhancing capillary trapping of scCO 2 and increasing the probability of constraining the plume within a secure zone are strictly related to the specific heterogeneity of the reservoir. Finally, this preliminary theoretical finding suggests the potential for maximizing secure capillary trapping through the proper selection of injection schedule to fit the formation heterogeneity.

58 GEOSCIENCES↗
DOE OSTI2018

Experience and Practice of Batch Scheduling on Leadership Supercomputers at Argonne

The mission of the DOE Argonne Leadership Computing Facility (ALCF) is to accelerate major scientific discoveries and engineering breakthroughs for humanity by designing and providing world-leading computing facilities in partnership with the computational science community. The ALCF operates supercomputers that are generally amongst the Top 5 fastest machines in the world. Specifically, ALCF is looking for the science that is either too big to run anywhere else, or it would take so long as to be impractical (i.e., “capability jobs”). At ALCF, batch scheduling plays a critical role for achieving a set of site goals within a set of constraints. While system utilization is an important goal at ALCF, its largest mission constraint is to enable extreme scale parallel jobs to take precedence. In this paper, we will describe the specific scheduling goals and constraints, analyze the workload traces collected in 2013–2017 from the 48-rack petascale supercomputer Mira, and discuss the upcoming scheduling challenges at ALCF.

Allcock, William↗
DOE OSTI2018

A comparison of price-taker and production cost models for determining system value, revenue, and scheduling of concentrating solar power plants

Concentrating solar power (CSP) plants convert solar energy into electricity and can be coupled with low-cost thermal energy storage to provide a dispatchable renewable resource. Maximizing the economic benefits of CSP systems requires deliberate selection of the timing of thermal energy dispatch to coincide with high-price or high-value periods. The performance and value of CSP systems have traditionally been evaluated using two distinct approaches: (1) production cost models (PCMs), which optimize commitment and dispatch schedules for an entire fleet of generators to minimize the cost of satisfying electricity demand; and (2) price-taker (PT) models, which optimize dispatch of the CSP system against historical or forecasted electricity prices to maximize revenue available to the CSP operator. The PCM approach is, in principle, superior because of the ability to capture system-level details such as transmission pathways and associated constraints, and feedback between CSP dispatch schedules and operation of other generators. The computationally simpler PT technique cannot explicitly address these concerns, but the comparative reduction in solution time is attractive for sensitivity analysis and rigorous optimization of CSP plant design and operation. In this work, we directly compare the dispatch profiles, net revenue, and operational value of CSP systems using implementations of each technique to assess how closely the solutions from the PT technique can approach those from the PCM for a specific market scenario. The PT implementation produced characteristically similar dispatch profiles as the PCM for both a large solar-multiple configuration and a small solar-multiple peaking configuration, while requiring only 1% of the computational time of the PCM. In addition, dispatching the CSP plant according to the self-scheduled PT solution - as opposed to the system-optimized PCM solution - only reduced the total CSP operational value by 1-5%, with consistent trends across CSP configurations and locations. This analysis validates the ability of the PT approach to substantially capture the economic benefits of CSP systems when pricing profiles are available, and indicates that the PT approach is suitable for initial design and optimization of CSP technologies.

14 SOLAR ENERGY↗
DOE OSTI2024

QoS-aware edge AI placement and scheduling with multiple implementations in FaaS-based edge computing

Resource constraints on the computing continuum require that we make smart decisions for serving AI-based services at the network edge. AI-based services typically have multiple implementations (e.g., image classification implementations include SqueezeNet, DenseNet, and others) with varying trade-offs (e.g., latency and accuracy). The question then is how should AI-based services be placed across Function-as-a-Service (FaaS) based edge computing systems in order to maximize total Quality-of-Service (QoS). To address this question, we propose a problem that jointly aims to solve (i) edge AI service placement and (ii) request scheduling. These are done across two time-scales (one for placement and one for scheduling). Here we first cast the problem as an integer linear program. We then decompose the problem into separate placement and scheduling subproblems and prove that both are NP-hard. We then propose a novel placement algorithm that places services while considering device-to-device communication across edge clouds to offload requests to one another. Our results show that the proposed placement algorithm is able to outperform a state-of-the-art placement algorithm for AI-based services, and other baseline heuristics, with regard to maximizing total QoS. Additionally, we present a federated learning-based framework, FLIES, to predict the future incoming service requests and their QoS requirements. Our results also show that our FLIES algorithm is able to outperform a standard decentralized learning baseline for predicting incoming requests and show comparable predictive performance when compared to centralized training.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Optimal capacity planning of MG with multi-energy coordinated scheduling under uncertainties considered

Multi-objective optimisation of capacity planning for a grid-connected Microgrid (MG) with multi-energy demands is developed in this study. The optimal multi-energy coordinated scheduling, in which uncertainties of renewable energy generation and electricity demand are described by uncertainty intervals, is considered within the capacity planning model. To solve the capacity planning problem while considering the optimal scheduling with uncertainties, a two-level optimisation approach is proposed by integrating interval mixed integer linear programming for scheduling into the multi-objective particle swarm optimisation for capacity planning. The multi-objective addresses the economic issue by minimising the worst-case optimal annual costs of device investments, operation and maintenance, and pollutant emission treatment, as well as the reliability concern by minimising the worst-case optimal annual costs of energy outage. Several MG cases studies are shown to demonstrate the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Control coordination in inverter‐based microgrids using AoI‐based 5G schedulers

Abstract A coordinated set point automatic adjustment with correction enabled (C‐SPAACE) framework that uses 5G communication for real‐time control coordination between inverter‐based resources (IBR) in microgrids is proposed. Utilising slicing capability, 5G offers low‐latency communication to C‐SPAACE under normal conditions. However, given the multitude of power grid use cases, a certain 5G slice for C‐SPAACE may have access only to limited radio spectrum resources, which if not managed well, greatly undermines the communication needs of C‐SPAACE framework. Thus, optimally scheduling the available spectrum resources among IBRs in a sliced 5G network‐based C‐SPAACE framework becomes a critical problem. To address this issue, the authors utilise a novel age of information (AoI) metric and designs an AoI‐based 5G scheduler to provide low‐latency communication to C‐SPAACE. Following this, a co‐simulation environment is designed using PSCAD/EMTDC and Python to simulate a microgrid supported by 5G communication. Time‐domain simulation case studies are performed using the proposed co‐simulation environment to evaluate the performance of C‐SPAACE using 5G with both AoI‐based and other baseline (non‐AoI) schedulers.

Choudhury, Biplav↗
DOE OSTI2020

Using OpenMP for HEP framework algorithm scheduling

The OpenMP standard is the primary mechanism used at high performance computing facilities to allow intra-process parallelization. In contrast, many HEP specific software packages (such as CMSSW, GaudiHive, and ROOT) make use of Intel’s Threading Building Blocks (TBB) library to accomplish the same goal. In these proceedings we will discuss our work to compare TBB and OpenMP when used for scheduling algorithms to be run by a HEP style data processing framework. This includes both scheduling of different interdependent algorithms to be run concurrently as well as scheduling concurrent work within one algorithm. As part of the discussion we present an overview of the OpenMP threading model. We also explain how we used OpenMP when creating a simplified HEP-like processing framework. Using that simplified framework, and a similar one written using TBB, we will present performance comparisons between TBB and different compiler versions of OpenMP.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Leveraging Pre-Built Catalogs and Object-Level Scheduling to Eliminate I/O Bottlenecks in HPC Environments

Modern High-Performance Computing (HPC) environments face mounting challenges due to the shift from large to small file datasets, along with an increasing number of users and parallelized applications. As HPC systems rely on Parallel File Systems (PFS), such as Lustre for data processing, performance bottlenecks stemming from Object Storage Target (OST) contention have become a significant concern. Existing solutions, such as LADS with its object-level scheduling approach, fall short in large-scale HPC environments due to their inability to effectively address metadata I/O bottlenecks and the growing number of I/O processes. This study highlights the pressing need for a comprehensive solution that tackles both OST contention and metadata I/O challenges in diverse HPC workloads. To address these challenges, we propose SwiftLoad, an object-level I/O scheduling framework that leverages a metadata catalog to enhance the performance and efficiency of parallel HPC utilities. The adoption of the metadata catalog mitigates the metadata I/O bottlenecks that commonly occur in HPC utilities, a challenge that is particularly pronounced in object-level I/O scheduling. SwiftLoad addresses OST contention and the uneven distribution of I/O processes across different OSTs through mathematical modeling and incorporates a Loader Configuration Module to regulate the number of I/O processes. Evaluated with two representative utilities—data deduplication profiling and data augmentation—SwiftLoad achieved performance improvements of up to 5.63x and 11.0x, respectively, on a production supercomputer.

HPC↗
DOE OSTI2025

Adaptive PID Gain Scheduling Control for Hydropower Turbine Using Neural CDE and Stochastic Distribution Shaping

This paper introduces a gain-scheduling PID controller design strategy for hydroturbine frequency control mode. This scheme first uses real data to learn the nonlinear dynamics of the hydroturbine using neural controlled differential equations and then perturbs the obtained nonlinear system at different equilibrium points, based on which a static output feedback adaptive dynamic programming algorithm is then used to optimize the PID gains for each equilibrium point. Moreover, a continuous-time version of stochastic distribution control is proposed to further fine-tune the optimized PID gains. Finally, the controller is obtained by implementing linear interpolation between the optimized PID control gains. The simulation results show that the proposed gain-scheduling PID controller can control a larger range of operation points compared with the given fixed PID controller and the baseline method. Compared with the given fixed PID controller, the proposed gain-scheduling PID controller can regulate hydroturbine frequency against disturbances induced by power-load variation with over 50% less overshoot for some operation points.

13 HYDRO ENERGY↗
DOE OSTI2013

Ant Colony Optimization for Mapping, Scheduling and Placing in Reconfigurable Systems

Modern heterogeneous embedded platforms, com- posed of several digital signal, application specific and general purpose processors, also include reconfigurable devices support- ing partial dynamic reconfiguration. These devices can change the behavior of some of their parts during execution, allowing hardware acceleration of more sections of the applications. Never- theless, partial dynamic reconfiguration imposes severe overheads in terms of latency. For such systems, a critical part of the design phase is deciding on which processing elements (mapping) and when (scheduling) executing a task, but also how to place them on the reconfigurable device to guarantee the most efficient reuse of the programmable logic. In this paper we propose an algorithm based on Ant Colony Optimization (ACO) that simultaneously executes the scheduling, the mapping and the linear placing of tasks, hiding reconfiguration overheads through prefetching. Our heuristic gradually constructs solutions and then searches around the best ones, cutting out non-promising areas of the design space. We show how to consider the partial dynamic reconfiguration constraints in the scheduling, placing and mapping problems and compare our formulation to other heuristics that address the same problems. We demonstrate that our proposal is more general and robust, and finds better solutions (16.5% in average) with respect to competing solutions.

Ant Colony Optimization↗