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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Electric power scheduling - A distributed problem-solving approach

Space Station Freedom's power system, along with the spacecraft's other subsystems, needs to carefully conserve its resources and yet strive to maximize overall Station productivity. Due to Freedom's distributed design, each subsystem must work cooperatively within the Station community. There is a need for a scheduling tool which will preserve this distributed structure, allow each subsystem the latitude to satisfy its own constraints, and preserve individual value systems while maintaining Station-wide integrity.

Mellor, Pamela A.↗
NASA NTRS2021

Maximizing Dust Devil Follow-up Observations on Mars Using Cubesats and On-board Scheduling

Several million dust devil events occur on Mars every day. These events last, on average, about 30 minutes and range in size from meters to hundreds of meters in diameter. Designing low-cost missions that will improve our knowledge of dust devil formation and evolution, and their connection to atmospheric dynamics and the dust cycle, is fundamental to informing future crewed Mars lander missions about surface conditions. In this paper we present a mission for a constellation of low orbiting Mars cubesats, each carrying imagers with agile pointing capabilities. The goal is to maximize the number of dust devil follow-up observations through real-time, on-board scheduling. We study scenarios where cubesats are equipped with a 2.5 degree boresight angle camera that accommodates five slew positions (including nadir). We assume a concept of operations where the cubesats autonomously survey the surface of Mars and can autonomously detect dust devils from their surface imagery. When a dust devil is detected, the constellation is autonomously re-tasked through an on-board distributed scheduler to capture as many follow-on images of the event as possible, so as to study its evolution. The cubesat orbits are propagated assuming two-body dynamics and the ground tracks and camera field of view are computed assuming a spherical Mars. Realistic inter-agent communication link opportunities are computed and included in our optimization, which allow for real-time event detection information to be shared within the constellation. We compare against a powerful ``omniscient'' mission which has a priori knowledge of all dust devil activity to show the gap between predicted performance and the best possible outcome. In particular, we show that the communications are especially important for acquiring follow-up observations, and that a realistic distributed scheduling mechanism is sufficient to capture nearly all dust devil observations that are possible for a given orbit configuration.

Hook, Joshua Vander↗
NASA NTRS2005

Distributed network scheduling

We investigate missions where communications resources are limited, requiring autonomous planning and execution. Unlike typical networks, spacecraft networks are also suited to automated planning and scheduling because many communications can be planned in advance.

scheduling↗
NASA NTRS1990

Distributed planning and scheduling for instrument and platform operations

Distributed planning and scheduling for instruments and platform operations in resource constrained environments are discussed. NASA has repeatedly faced the situation of multiple users having differing goals, objectives, and requirements interacting with a planning and scheduling system. Current examples include the Hubble Space Telescope and Space Station Freedom. The paper provides a brief description of the instrument and platform operations domain, discusses both traditional and distributed planning and scheduling in the context of this domain, and looks at examples of NASA environments in which planning and scheduling is or will be performed in a distributed system.

Hull, Larry G.↗
DOE OSTI2026

iDDS: intelligent distributed dispatch and scheduling for workflow orchestration

The intelligent distributed dispatch and scheduling (iDDS) service is a versatile workflow orchestration system designed for large-scale, distributed scientific computing. iDDS extends traditional workload and data management by integrating data-aware execution, conditional logic, and programmable workflows, enabling automation of complex and dynamic processing pipelines. Originally developed for the ATLAS experiment at the large hadron collider, iDDS has evolved into an experiment-agnostic platform that supports both template-driven workflows and a Function-as-a-Task model for Python-based orchestration. This paper presents the architecture and core components of iDDS, highlighting its scalability, modular message-driven design, and integration with systems such as PanDA and Rucio. We demonstrate its versatility through real-world use cases: fine-grained tape resource optimization for ATLAS, orchestration of large Directed Acyclic Graph (DAG) workflows for the Rubin Observatory, distributed hyperparameter optimization for machine learning applications, active learning for physics analyses, and AI-assisted detector design at the electron–ion collider. By unifying workload scheduling, data movement, and adaptive decision-making, iDDS reduces operational overhead and enables reproducible, high-throughput workflows across heterogeneous infrastructures. We conclude with current challenges and future directions, including interactive, cloud-native, and serverless workflow support.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1993

EOS distributed planning and scheduling prototype

Some of the more significant lessons learned during the development of the Earth Observing System (EOS) Distributed Planning and Scheduling Prototype are presented. The need for a central scheduler is not demonstrated. A mapping of scheduling and conflict-resolution responsibility across the nodes of the EOS distributed scheduling system is developed and shown to be both feasible and appropriate. Complex instrument scheduling is mostly accomplished at the ICC/IST (instrument control center/instrumental support terminal) with 'slidable' flexibility for slews and some kinds of calibrations resolved at the EOS Operations Center (EOC). All nodes have full visibility interinstrument contention for resource and environmental rights, e.g., vibration, thermal, and electromagnetic. The EOC assigns, by activity, initial action responsibility for conflict resolution to a node which is party to the conflict. Most interinstrument conflicts are resolved by the ICCs and ISTs during an intermediate scheduling phase while the EOC is negotiating a TDRS schedule with the NCC.

Hull, Larry G.↗
DOE OSTI2021

Distributed ADMM Using Private Blockchain for Power Flow Optimization in Distribution Network With Coupled and Mixed-Integer Constraints

The optimization problem for scheduling distributed energy resources (DERs) and battery energy storage systems (BESS) integrated with the power grid is important to minimize energy consumption from conventional sources in response to demand. Conventionally this optimization problem is solved in a centralized manner, limiting the size of the problem that can be solved and creating a high communication overhead because all the data is transferred to the central controller. These limitations are addressed by the proposed distributed consensus-based alternating direction method of multiplier (DC-ADMM) optimization algorithm, which decomposes the optimization problem into subproblems with private cost function and constraints. The distribution feeder is partitioned into low coupling subnetworks/regions, which solves the private subproblem locally and exchanges information with the neighboring regions to reach consensus. The relaxation strategy is employed for mixed-integer and coupled constraints introduced in the optimal power flow (OPF) problem by stationary and transportable BESS because DC-ADMM convergence is only guaranteed for strict convex problems. The information exchange and synchronization between subnetworks/regions are vital for distributed optimization. In this work, both of these aspects are addressed by the blockchain. The smart contract deployed on the blockchain network acts as a mediator for secure data exchange and synchronization in distributed computation. The blockchain-based distributed optimization problem’s effectiveness is tested for a 0.5-MW laboratory microgrid for one hour ahead and day-ahead for the IEEE 123-bus and EPRI J1 test feeders, and results are compared with a centralized solution.

25 ENERGY STORAGE↗
DOE OSTI2023

Principled Schedulability Analysis for Distributed Storage Systems Using Thread Architecture Models

In this article, we present an approach to systematically examine the schedulability of distributed storage systems, identify their scheduling problems, and enable effective scheduling in these systems. We use Thread Architecture Models (TAMs) to describe the behavior and interactions of different threads in a system, and show both how to construct TAMs for existing systems and utilize TAMs to identify critical scheduling problems. We specify three schedulability conditions that a schedulable TAM should satisfy: completeness, local enforceability, and independence; meeting these conditions enables a system to easily support different scheduling policies. We identify five common problems that prevent a system from satisfying the schedulability conditions, and show that these problems arise in existing systems such as HBase, Cassandra, MongoDB, and Riak, making it difficult or impossible to realize various scheduling disciplines. We demonstrate how to address these schedulability problems using both direct and indirect solutions, with different trade-offs. To show how to apply our approach to enable scheduling in realistic systems, we develop Tamed-HBase and Muzzled-HBase, sets of modifications to HBase that can realize the desired scheduling disciplines, including fairness and priority scheduling, even when presented with challenging workloads.

Computer Science↗
NASA NTRS1990

An innovative approach for distributed and integrated resources planning for the Space Station Freedom

This paper presents a planning approach to the Space Station Freedom program which takes into account the widely distributed nature of that program. The program management structure is organized into three major levels: a strategic level, a tactical level, and an execution level. For each level, resource availabilities are determined, the resources are distributed, schedules are built independently within the resource limits, the schedules are integrated into a single schedule, and conflicts are resolved by negotiating requirements and/or relaxing contraints. This approach distributes resources to multiple planning entities in such a way that when the multiple plans are collected, they fit together with minimal modification. The up-front distribution is planned in such a way and to a sufficient degree that a fit is virtually assured.

Hornstein, Rhoda S.↗
NASA NTRS2012

Automating Mid- and Long-Range Scheduling for NASA's Deep Space Network

NASA has recently deployed a new mid-range scheduling system for the antennas of the Deep Space Network (DSN), called Service Scheduling Software, or S(sup 3). This system is architected as a modern web application containing a central scheduling database integrated with a collaborative environment, exploiting the same technologies as social web applications but applied to a space operations context. This is highly relevant to the DSN domain since the network schedule of operations is developed in a peer-to-peer negotiation process among all users who utilize the DSN (representing 37 projects including international partners and ground-based science and calibration users). The initial implementation of S(sup 3) is complete and the system has been operational since July 2011. S(sup 3) has been used for negotiating schedules since April 2011, including the baseline schedules for three launching missions in late 2011. S(sup 3) supports a distributed scheduling model, in which changes can potentially be made by multiple users based on multiple schedule "workspaces" or versions of the schedule. This has led to several challenges in the design of the scheduling database, and of a change proposal workflow that allows users to concur with or to reject proposed schedule changes, and then counter-propose with alternative or additional suggested changes. This paper describes some key aspects of the S(sup 3) system and lessons learned from its operational deployment to date, focusing on the challenges of multi-user collaborative scheduling in a practical and mission-critical setting. We will also describe the ongoing project to extend S(sup 3) to encompass long-range planning, downtime analysis, and forecasting, as the next step in developing a single integrated DSN scheduling tool suite to cover all time ranges.

scheduling↗
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 OSTI2024

Multi-Timescale Optimal Operation Framework for Integrated Economic and Reliability Analysis of Hybrid Power Plants

This paper introduces a hierarchical modeling framework for hybrid power plants (HPP) to facilitate the operation of HPP in power systems similar to conventional generators (Congens) in the integrated multi-timescale optimal operation framework. To consider the uncertainties of HPP renewable power in the day-ahead scheduling, distributionally robust optimization (DRO) is used. To ensure that the state-of-charge (SOC) of energy storage systems in HPPs aligns closely with the planned value for long-term reliability, real-time SOC management is incorporated. In addition, an adjustable real-time control is designed for the robust delivery of HPP real-time services. Case studies performed on a revised IEEE 39-bus system demonstrate the effectiveness of the proposed framework for HPP operation. Simulation results highlight that the proposed framework not only can help operators schedule HPP similar to Congens in varying weather conditions but can also maintain the frequency reliability of the system.

frequency stability↗
NASA NTRS1995

System-Availability And Resource-Allocation Program

ACARA analyzes availability, life-cycle cost, and scheduling of resources. Uses statistical Monte Carlo method to simulate capacity states of system as well as failure and repair of components. Failures of components modeled mathematically by use of combination of exponential and Weibull probability distributions. Schedules replacement of components to optimize performance of system. Made to comply with any constraints on production of components, capacities of resupply vehicles, spares kept on site, crews, and/or equipment. Written in APL2.

Viterna, L. A.↗
NASA NTRS2010

HIFiRE Direct-Connect Rig (HDCR) Phase I Ground Test Results from the NASA Langley Arc-Heated Scramjet Test Facility

The initial phase of hydrocarbon-fueled ground tests supporting Flight 2 of the Hypersonic International Flight Research Experiment (HIFiRE) Program has been conducted in the NASA Langley Arc-Heated Scramjet Test Facility (AHSTF). The HIFiRE Program, an Air Force-lead international cooperative program includes eight different flight test experiments designed to target specific challenges of hypersonic flight. The second of the eight planned flight experiments is a hydrocarbon-fueled scramjet flight test intended to demonstrate dual-mode to scramjet-mode operation and verify the scramjet performance prediction and design tools. A performance goal is the achievement of a combusted fuel equivalence ratio greater than 0.7 while in scramjet mode. The ground test rig, designated the HIFiRE Direct Connect Rig (HDCR), is a full-scale, heat sink, direct-connect ground test article that duplicates both the flowpath lines and the instrumentation layout of the isolator and combustor portion of the flight test hardware. The primary objectives of the HDCR Phase I tests are to verify the operability of the HIFiRE isolator/combustor across the Mach 6.0-8.0 flight regime and to establish a fuel distribution schedule to ensure a successful mode transition prior to the HiFIRE payload Critical Design Review. Although the phase I test plans include testing over the Mach 6 to 8 flight simulation range, only Mach 6 testing will be reported in this paper. Experimental results presented here include flowpath surface pressure, temperature, and heat flux distributions that demonstrate the operation of the flowpath over a small range of test conditions around the nominal Mach 6 simulation, as well as a range of fuel equivalence ratios and fuel injection distributions. Both ethylene and a mixture of ethylene and methane (planned for flight) were tested. Maximum back pressure and flameholding limits, as well as a baseline fuel schedule, that covers the Mach 5.84-6.5 test space have been identified.

Hass, Neal E.↗
NASA NTRS2011

HIFiRE Direct-Connect Rig (HDCR) Phase I Scramjet Test Results from the NASA Langley Arc-Heated Scramjet Test Facility

A series of hydrocarbon-fueled direct-connect scramjet ground tests has been completed in the NASA Langley Arc-Heated Scramjet Test Facility (AHSTF) at simulated Mach 8 flight conditions. These experiments were part of an initial test phase to support Flight 2 of the Hypersonic International Flight Research Experimentation (HIFiRE) Program. In this flight experiment, a hydrocarbon-fueled scramjet is intended to demonstrate transition from dual-mode to scramjet-mode operation and verify the scramjet performance prediction and design tools A performance goal is the achievement of a combusted fuel equivalence ratio greater than 0.7 while in scramjet mode. The ground test rig, designated the HIFiRE Direct Connect Rig (HDCR), is a full-scale, heat sink test article that duplicates both the flowpath lines and a majority of the instrumentation layout of the isolator and combustor portion of the flight test hardware. The primary objectives of the HDCR Phase I tests were to verify the operability of the HIFiRE isolator/combustor across the simulated Mach 6-8 flight regime and to establish a fuel distribution schedule to ensure a successful mode transition. Both of these objectives were achieved prior to the HiFIRE Flight 2 payload Critical Design Review. Mach 8 ground test results are presented in this report, including flowpath surface pressure distributions that demonstrate the operation of the flowpath in scramjet-mode over a small range of test conditions around the nominal Mach 8 simulation, as well as over a range of fuel equivalence ratios. Flowpath analysis using ground test data is presented elsewhere; however, limited comparisons with analytical predictions suggest that both scramjet-mode operation and the combustion performance objective are achieved at Mach 8 conditions.

Cabell, Karen↗
NASA NTRS1992

Multi-agent planning and scheduling, execution monitoring and incremental rescheduling: Application to motorway traffic

This article describes a planning method applicable to agents with great perception and decision-making capabilities and the ability to communicate with other agents. Each agent has a task to fulfill allowing for the actions of other agents in its vicinity. Certain simultaneous actions may cause conflicts because they require the same resource. The agent plans each of its actions and simultaneously transmits these to its neighbors. In a similar way, it receives plans from the other agents and must take account of these plans. The planning method allows us to build a distributed scheduling system. Here, these agents are robot vehicles on a highway communicating by radio. In this environment, conflicts between agents concern the allocation of space in time and are connected with the inertia of the vehicles. Each vehicle made a temporal, spatial, and situated reasoning in order to drive without collision. The flexibility and reactivity of the method presented here allows the agent to generate its plan based on assumptions concerning the other agents and then check these assumptions progressively as plans are received from the other agents. A multi-agent execution monitoring of these plans can be done, using data generated during planning and the multi-agent decision-making algorithm described here. A selective backtrack allows us to perform incremental rescheduling.

Mourou, Pascal↗