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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Mission and science activity scheduling language

To support the distributed and complex operational scheduling required for future National Aeronautics and Space Administration (NASA) missions, a formal, textual language, the Scheduling Applications Interface Language (SAIL), has been developed. Increased geographic dispersion of investigators is leading to distributed mission and science activity planning, scheduling, and operations. SAIL is an innovation which supports the effective and efficient communication of scheduling information among physically dispersed applications in distributed scheduling environments. SAIL offers a clear, concise, unambiguous expression of scheduling information in a readable, hardware independent format. The language concept, syntax, and semantics incorporate language features found useful during five years of research and prototyping with scheduling languages in physically distributed environments. SAIL allows concise specification of mission and science activity plans in a format which promotes repetition and reuse.

Hull, Larry G.↗
NASA NTRS2019

A Scheduling Algorithm Compatible with a Distributed Management of Arrivals in the National Airspace System

The current system used by the FAA to schedule arrivals is the Traffic Based Flow Manager (TBFM). It is a centralized system that gives an operator (airline) no influence over scheduled times of arrival assigned to its flights. Future systems for managing arrival scheduling are proposed as distributed systems. Such a system is called upon to give operators influence to schedule and negotiate resources for their flights, and to resolve other technical challenges, such as eliminating a single point of failure. A distributed system for managing diverse air traffic will need the capability of computing a schedule for the given arriving flights in a way that complies with the operational constraints. This paper contributes an algorithm that computes such a schedule. Although developed as part of an effort toward a distributed system, the algorithm itself is neither inherently distributed nor inherently centralized and can be used in either type of system.

negotiation↗
NASA NTRS1990

SURE (Science User Resource Expert): A science planning and scheduling assistant for a resource based environment

SURE (Science User Resource Expert) is one of three components that compose the SURPASS (Science User Resource Planning and Scheduling System). This system is a planning and scheduling tool which supports distributed planning and scheduling, based on resource allocation and optimization. Currently SURE is being used within the SURPASS by the UARS (Upper Atmospheric Research Satellite) SOLSTICE instrument to build a daily science plan and activity schedule and in a prototyping effort with NASA GSFC to demonstrate distributed planning and scheduling for the SOLSTICE II instrument on the EOS platform. For the SOLSTICE application the SURE utilizes a rule-based system. Development of a rule-based program using Ada CLIPS as opposed to using conventional programming, allows for capture of the science planning and scheduling heuristics in rules and provides flexibility in inserting or removing rules as the scientific objectives and mission constraints change. The SURE system's role as a component in the SURPASS, the purpose of the SURE planning and scheduling tool, the SURE knowledge base, and the software architecture of the SURE component are described.

Thalman, Nancy E.↗
NASA NTRS2019

A Scheduling Algorithm Compatible with a Distributed Management of Arrivals in the National Airspace System

The current system used by the FAA to schedule arrivals is the Traffic Based Flow Manager (TBFM). It is a centralized system that gives an operator (airline) no influence over scheduled times of arrival assigned to its flights. Future systems for managing arrival scheduling are proposed as distributed systems. Such a system is called upon to give operators influence to schedule and negotiate resources for their flights, and to resolve other technical challenges, such as eliminating a single point of failure. A distributed system for managing diverse air traffic will need the capability of computing a schedule for the given arriving flights in a way that complies with the operational constraints. This paper contributes an algorithm that computes such a schedule. Although developed as part of an effort toward a distributed system, the algorithm itself is neither inherently distributed nor inherently centralized and can be used in either type of system.

Sadovsky, A.↗
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

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NASA NTRS2001

E-Scheduling the Deep Space Network

This paper describes an operations concept for electronic scheduling and software interface for organizations to extract required views of the schedule. Advantages include widespread accessibility to a common schedule document, virtually instantaneous distribution of new schedule releases, and the ability of missions to perfom conflict resolution off-line without time-consuming meetings.

telecommunications↗
DOE OSTI2025

Decentralized Distributed Proximal Policy Optimization (DD-PPO) for High Performance Computing Scheduling on Multi-User Systems

Resource allocation in High Performance Computing (HPC) environments presents a complex and multifaceted challenge for job scheduling algorithms. Beyond the efficient allocation of system resources, schedulers must account for and optimize multiple performance metrics, including job wait time and system throughput. Traditional heuristic-based scheduling algorithms increasingly struggle and lack the efficiency needed to meet the demands and address the complexity and scale of modern HPC systems. Consequently, recent research efforts have focused on leveraging advancements in Artificial Intelligence (AI) and Deep Learning (DL), particularly Reinforcement Learning (RL), to develop more adaptable and intelligent scheduling strategies. Previous RL-based scheduling approaches have explored a range of algorithms, from Deep Q-Networks (DQN) to Proximal Policy Optimization (PPO), and more recently, hybrid methods that integrate Graph Neural Networks (GNNs) with RL techniques. However, a common limitation across these methods is their reliance on relatively small datasets, with few methods being evaluated using large-scale, multi-million-job trace datasets representative of real-world HPC workloads. Moreover, existing RL schedulers face scalability issues due to centralized policy updates, which hinder training efficiency and performance when applied to large datasets. This study introduces a novel RL-based scheduler utilizing Decentralized Distributed Proximal Policy Optimization (DD-PPO) algorithm, which supports large-scale distributed training across multiple workers without requiring parameter synchronization at every step. By eliminating reliance on centralized updates to a shared policy, the DD-PPO scheduler enhances scalability, training efficiency, and sample utilization. Experimental validation using a large real-world dataset containing over 11.5 million job traces collected from petascale HPC systems over six years assesses the influence of dataset scale on training effectiveness and compares DD-PPO performance to traditional and advanced scheduling approaches. The experimental results demonstrate improved scheduling performance in comparison to both heuristic-based schedulers and existing RL-based scheduling algorithms.

AI↗
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 OSTI2023

Electric Vehicle Charging Management in Smart Energy Communities to Increase Renewable Energy Hosting Capacity

Abnormal climates due to global warming have emerged as a big concern in the global community. To mitigate climate change and achieve sustainability, distributed energy resources (DERs), including solar and wind, have been recently deployed in power systems. As the penetration level of DERs has increased, however, it caused a multitude of issues in the power systems, such as voltage fluctuation in the distribution network limiting renewable hosting capacity. On the other hand, the electric vehicle (EV) industry is rapidly growing to facilitate the transition to a carbon-neutral community, illuminating the potential of EVs as a flexible grid asset to mitigate some of the issues and improve grid operation, if properly exploited. To explore the potential of EVs, this paper proposes an EV scheduling strategy. By using an optimal EV charging scheduling proposed, distribution system operators (DSOs) can minimize their operating costs and stably operate the system with a high level of DERs. To validate the method, a modified IEEE 33-bus system with DERs is developed. The case study shows the proposed scheduling strategizes EV charging to reduce the cost of PV curtailment. In the study, the method outperforms the renewable-only case with curtailment by 4.97% in DSO cost. It also demonstrates its potential to increase the renewable hosting capacity by harmonizing EV charging with renewables.

ADVANCED PROPULSION SYSTEMS,POWER TRANSMISSION AND↗
NASA NTRS2004

Transformations in Air Transportation Systems For the 21st Century

Globally, our transportation systems face increasingly discomforting realities: certain of the legacy air and ground infrastructures of the 20th century will not satisfy our 21st century mobility needs. The consequence of inaction is diminished quality of life and economic opportunity for those nations unable to transform from the 20th to 21st century systems. Clearly, new thinking is required regarding business models that cater to consumers value of time, airspace architectures that enable those new business models, and technology strategies for innovating at the system-of-networks level. This lecture proposes a structured way of thinking about transformation from the legacy systems of the 20th century toward new systems for the 21st century. The comparison and contrast between the legacy systems of the 20th century and the transformed systems of the 21st century provides insights into the structure of transformation of air transportation. Where the legacy systems tend to be analog (versus digital), centralized (versus distributed), and scheduled (versus on-demand) for example, transformed 21st century systems become capable of scalability through technological, business, and policy innovations. Where air mobility in our legacy systems of the 20th century brought economic opportunity and quality of life to large service markets, transformed air mobility of the 21st century becomes more equitable available to ever-thinner and widely distributed populations. Several technological developments in the traditional aircraft disciplines as well as in communication, navigation, surveillance and information systems create new foundations for 21st thinking about air transportation. One of the technological developments of importance arises from complexity science and modern network theory. Scale-free (i.e., scalable) networks represent a promising concept space for modeling airspace system architectures, and for assessing network performance in terms of robustness, resilience, and other metrics. The lecture offers an air transportation system topology and a scale-free network linkage graphic as framework for transportation system innovation. Successful outcomes of innovation in air transportation could lay the foundations for new paradigms for aircraft and their operating capabilities, air transportation system topologies, and airspace architectures and procedural concepts. These new paradigms could support scalable alternatives for the expansion of future air mobility to more consumers in more parts of the world.

Holmes, Bruce J.↗
DOE OSTI2026

Multi-Agent Control Planes for Quantum Networks: A Scalable Architecture for Autonomous Quantum Internet Management

Quantum networks are expected to enable distributed quantum computing, secure communication, and global entanglement distribution. However, operating such networks presents significant challenges, including stochastic quantum processes, fragile entanglement resources, dynamic topology, and cross-layer control requirements. Current quantum network control architectures largely rely on centralized or hierarchical controllers inspired by classical software-defined networking (SDN). While effective for small testbeds, these approaches face scalability, latency, and reliability limitations as quantum networks grow. This paper proposes a multi-agent control plane architecture for quantum networks. In this design, intelligent software agents operate at quantum nodes, repeaters, and orchestration layers, collectively managing entanglement generation, routing, purification, and scheduling. The distributed intelligence of the agent system allows the network to adapt dynamically to quantum hardware variability and environmental noise. We argue that multi-agent systems provide significant advantages over centralized control approaches, including scalability, resilience, local autonomy, and real-time adaptation. The paper discusses architectural design principles, agent coordination mechanisms, and research challenges in deploying multi-agent control planes for the emerging quantum Internet.

Alnajjar, Anees [ORNL] (ORCID:0000000237101601)↗
DOE OSTI2023

TAMM: Tensor algebra for many-body methods

Tensor algebra operations such as contractions in computational chemistry consume a significant fraction of the computing time on large-scale computing platforms. The widespread use of tensor contractions between large multi-dimensional tensors in describing electronic structure theory has motivated the development of multiple tensor algebra frameworks targeting heterogeneous computing platforms. In this paper, we present Tensor Algebra for Many-body Methods (TAMM), a framework for productive and performance-portable development of scalable computational chemistry methods. TAMM decouples the specification of the computation from the execution of these operations on available high-performance computing systems. With this design choice, the scientific application developers (domain scientists) can focus on the algorithmic requirements using the tensor algebra interface provided by TAMM, whereas high-performance computing developers can direct their attention to various optimizations on the underlying constructs, such as efficient data distribution, optimized scheduling algorithms, and efficient use of intra-node resources (e.g., graphics processing units). The modular structure of TAMM allows it to support different hardware architectures and incorporate new algorithmic advances. We describe the TAMM framework and our approach to the sustainable development of scalable ground- and excited-state electronic structure methods. We present case studies highlighting the ease of use, including the performance and productivity gains compared to other frameworks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

CHARM-SYCL & IRIS: A Tool Chain for Performance Portability on Extremely Heterogeneous Systems

Performance portability is becoming crucial as high-performance computing systems become increasingly heterogeneous. We have many options for CPUs and accelerators (e.g., GPUs) but also for non-Von Neumann architectures such as field-programmable gate arrays. This paper presents the CHARM-SYCL unified programming environment for multiple accelerator types as a performance-portable programming environment. It uses the IRIS library developed at Oak Ridge National Laboratory as the back end accelerator runtime. IRIS has a high-performance scheduler to distribute tasks across accelerators. This design allows us to run an application from the same source on multiple systems with multiple configurations. We provide three types of portability with CHARM-SYCL: Portable Workflow, Compiler and Runtime Portability, and Application and Performance Portability. We implement a Monte Carlo simulation benchmark code on the CHARM-SYCL execution environment and demonstrate that our programming environment can accommodate extremely heterogeneous systems.

Fujita, Norihisa↗
DOE OSTI2021

Branching Dueling Q-Network Based Online Scheduling of a Microgrid With Distributed Energy Storage Systems

This letter investigates a Branching Dueling Q-Network (BDQ) based online operation strategy for a microgrid with distributed battery energy storage systems (BESSs) operating under uncertainties. Additionally, the developed deep reinforcement learning (DRL) based microgrid online optimization strategy can achieve a linear increase in the number of neural network outputs with the number of distributed BESSs, which overcomes the curse of dimensionality caused by the charge and discharge decisions of multiple BESSs. Numerical simulations validate the effectiveness of the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Scalable Unit Commitment with Security Constrained AC Power Flow via ADMM and Hybrid Modeling Strategies

This research introduces a more efficient way to optimize power grid operations, breaking the problem into manageable steps and using advanced mathematical techniques to speed up calculations. By incorporating smart heuristics, improved preprocessing, and contingency analysis, the approach allows operators to make better decisions faster. These innovations enhance our understanding of how to optimize energy generation, making it possible to anticipate failures before they happen, reduce system costs, and improve overall grid performance. Ultimately, this research helps bridge the gap between theoretical models and real-world applications, paving the way for a smarter, more resilient power grid. This research directly benefits the public by making electricity more affordable, reliable, and sustainable. By improving how power grids schedule and distribute electricity, the project helps energy providers reduce operational costs, which can lead to lower electricity prices for consumers. Additionally, the ability to predict and prevent power system failures enhances grid reliability, reducing the likelihood of blackouts that can disrupt homes, businesses, and critical infrastructure such as hospitals. From an environmental perspective, optimizing power generation reduces energy waste and lowers carbon emissions, contributing to cleaner air and a more sustainable energy system. Furthermore, with extreme weather events becoming more frequent, these advancements make the power grid more resilient, ensuring communities are better prepared for emergencies and natural disasters. By strengthening the nation's energy infrastructure, this research plays a crucial role in improving economic stability, public safety, and environmental sustainability.

24 POWER TRANSMISSION AND DISTRIBUTION↗