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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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NASA NTRS2018

A Small Spacecraft Swarm Deployment and Stationkeeping Strategy for Sun-Earth L1 Halo Orbits

Spacecraft orbits about the Sun-Earth librarian point L1 have been of interest since the 1950s. An L1 halo orbit was first achieved with the International Sun-Earth Explorer-3 (ISEE-3) mission, and similar orbits around Sun-Earth L1 were achieved in the Solar and Heliospheric Observatory (SOHO), Advanced Composition Explorer (ACE), Genesis, and Deep Space Climate Observatory (DSCOVR) missions. With recent advancements in CubeSat technology, we envision that it will soon be feasible to deploy CubeSats at L1. As opposed to these prior missions where one large satellite orbited alone, a swarm of CubeSats at L1 would enable novel science data return, providing a topology for intersatellite measurements of heliophysics phenomena both spatially and temporally, at varying spatial scales.The purpose of this iPoster is to present a flight dynamics strategy for a swarm of numerous CubeSats orbiting Sun-Earth L1. The presented method is a coupled, two-part solution. First, we present a deployment strategy for theCubeSats that is optimized to produce prescribed, time-varying intersatellite baselines for the purposes of collectingmagnetometer data as well as radiometric measurements from cross-links. Second, we employ a loose controlstrategy that was successfully applied to SOHO and ACE for minimized stationkeeping propellant expenditure. Weemphasize that the presented solution is practical within the current state-of-the-art and heritage CubeSat technology,citing capabilities of CubeSat designs that will launch on the upcoming Exploration Mission 1 (EM-1) to lunar orbitsand beyond. Within this iPoster, we present animations of the simulated deployment strategy and resulting spacecrafttrajectories. Mission design parameters such as total v required for long-term station keeping andminimum/maximum/mean spacecraft separation distances are also presented.

Optimal control↗

NASA NTRS2019

Distributed Swarm Antenna Arrays for Deep Space Applications

It is desirable to develop a high Equivalent Isotropically Radiated Power (EIRP), autonomous, distributed, reconfigurable, on-demand Ka/X-band transmit-antenna array using small satellites, for deepspace communication (Mars and beyond). Our work shows that a distributed, free-flying swarm array composed of N CubeSats can not only be phased to provide a coherent beam in Ka/X-band, with performance (mass, power, data rate) comparable to the state-of-the-art Mars Reconnaissance Orbiter (MRO), but with N large, higher performance and data rates can be achieved, assuming a proper intra-swarm metrology system is in place.

Bevilacqua, Stefano↗

NASA NTRS2017

CARACaS multi-agent maritime autonomy for unmanned surface vehicles in the Swarm II harbor patrol demonstration

This paper describes new autonomy technology that enabled a team of unmanned surface vehicles (USVs) to execute cooperative behaviors in the USV Swarm II harbor patrol demonstration and provides a description of autonomy performance in the event. The new developments extend the NASA Jet Propulsion Laboratory’s CARACaS (Control Architecture for Robotic Agent Command and Sensing) autonomy architecture, which pro- vides foundational software infrastructure, core executive functions, and several default robotic technology mod- ules. In Swarm II, CARACaS demonstrated higher levels of autonomy and more complex cooperation than previous on-water exercises, using full-sized vehicles and real-world sensing and communication. The core au- tonomous behaviors to support the harbor patrol scenario included Patrol, Track, Inspect, and Trail, providing the capability of finding all vessels entering the patrol area, keeping track of them, inspecting them to infer intent, and trailing suspect vessels. Significantly, CARACaS assumed responsibility for not only executing tasks safely and efficiently but also recognizing what tasks needed to be accomplished, given the current state of the world. Since the heterogeneous USV teams shared world model that evolved, such as due to (dis)appearance of vessels in the area or a change in health or availability of a USV, CARACaS replanned to generate and reallocate the new task list. Thus, human intervention was never required in the loop to task USVs during mission execution, though a supervisory role was supported in the autonomy system for mission monitoring and exception handling. Finally, CARACaS also ensured the USVs avoided hazards and obeyed the applicable rules of the road, using its local motion planning modules.

Sandoval, Michael↗

NASA NTRS2020

Experimental Validation of Swarm Array Results Using Mars Cube One (Marco) A,B Downlink Signals

In this paper, we investigate the feasibility of calibrating an orbiting phased array composed of CubeSat class spacecraft together with a larger reference (or “chief”) spacecraft, comprising an overall Swarm Array. Considered as an analog to the Deep Space Network (DSN) Uplink Arraying problem [1], a spaceborne Swarm Array that can potentially deliver comparable or even greater operational performance than large monolithic spacecraft (such as increased data rates for telecommunications, or greater baselines for improved spatial resolution), thus increasing future mission capability but with significantly enhanced flexibility, evolvability and robustness.

Kahan, Daniel↗

NASA NTRSDate not supplied

Applying the Cognitive Space Gateway to Swarm Topologies

NASA’s future vision for interplanetary networking includes a lunar network, Cube Satellite (CubeSat) constellations, and deep space robotic missions, comprising what could be viewed as a network of networks. Delay-tolerant networking (DTN) architecture and protocols provide a standard network layer among these varying scenarios and mitigate many challenges of the space environment, such as long delays, unplanned service interruptions, and asymmetric links. The Cognitive Space Gateway (CSG) is a routing method in a DTN architecture that uses spiking neural networks as the learning element to optimize outing decisions in a complex environment. This work aims to further develop cognitive networking technologies in several critical areas, including DTN, the CSG algorithm, SmallSat swarm topologies, and cloud services. The CSG algorithm is tested in a realistic scenario in which the emulated network topology is based on a SmallSat swarm. The emulation environment will be built upon a commercial cloud service, such as Amazon Web Services (AWS) Elastic Compute Cloud. This work investigates the ability of such a platform to enable a flexible, lower maintenance approach to creating a multihop network outside of a physical laboratory. The cloud platform will provide a secure environment allowing for collaboration among government and academic entities.

Ricardo Lent↗

NASA NTRS2020

Experimental Validation of Swarm Array Results Using Mars Cube One (MarCO) A,B Downlink Signals

In this paper, we investigate the feasibility of calibrating an orbiting phaased array composed of CubeSat class spacecraft together with a larger reference (or “chief”) spacecraft, comprising an overall Swarm Array. Considered as an analog to the Deep Space Network (DSN) Uplink Arraying problem [1], a spaceborne Swarm Array that can potentially deliver comparable or even greater operational performance than large monolithic spacecraft (such as increased datarates for telecommunications, or greater baselines for improved spatial resolution), thus increasing future mission capability but with significantly enhanced flexibility, evolvability and robustness.

Vilnrotter, Victor↗

NASA NTRS2023

Transpolar Pc1 Wave Ducting: Swarm, DMSP, and Ground Observations

Alfvén mode Pc1 waves undergo mode conversion to the fast mode due to induced Hall current in the ionosphere. The fast mode Pc1 waves are trapped and propagate across the magnetic field through the ionospheric waveguide. This process is called Pc1 wave ducting (PWD). Ducting is expected to be in any direction, but most of the existing literature investigated only PWDs toward the equator. In this paper, we report the rare observations of PWD propagating from sub-auroral latitudes and pervading the polar cap using Swarm satellites, ground magnetometers, and Defense Meteorological Satellite Program (DMSP) satellites. We first identify the injection region of Pc1 wave where localized broadband transverse waves, isolated aurora, and energetic proton precipitations are concurrently observed. Then, we compare ducting characteristics in the ionosphere between the two hemispheres. For the three events investigated here, PWDs in the Southern Hemisphere (SH) pervaded the polar cap while Pc1 waves in the Northern Hemisphere (NH) did not. This hemispheric asymmetry is attributed to the plasma density in the SH sufficient to form the Pc1 waveguide. However, a sharp plasma density gradient on the propagation path still interrupts the ducting even in higher plasma density (> 10 5 cm -3 ) regions. The observation of two intersecting Swarm satellites indicates the PWD is not only elongated meridionally, but also can have a significant zonal extent beyond that of the injection region.

Hyangpyo Kim↗

DOE OSTI2024

Swarm path planner system for vehicles

A system for determining optimal paths without collision through a travel volume for a swarm of vehicles is disclosed. The system determines a travel path for the swarm leader vehicle using a minimal cost path derived from various measures of environmental cost for avoiding objects in traveling from leader location to target location. The system also determines, for each empty neighbor location of each follower vehicle, relational costs for follower vehicle travel relative to leader vehicle travel. The various measures of relational cost seek to maintain a prescribed positional relationship between each follower vehicle and the leader vehicle given the leader vehicle travel path. Based on various measures of environmental and relational cost, the system determines the best travel path for the each follower vehicle relative to the leader vehicle.

Paglieroni, David W.↗

DOE OSTI2025

Accelerating the Structure Exploration of Diverse Bi–Pt Nanoclusters via Physics‐Informed Machine Learning Potential and Particle Swarm Optimization

Bimetallic Bi–Pt nanoclusters exhibit diverse structural motifs, including core-shell, Janus, and mixed alloy configurations, due to the unique bonding characteristics between Bi and Pt atoms. Using density functional theory refinements from ChIMES physically machine-learned potential and CALYPSO particle swarm optimization global searches, 34 Bi20-Pt20 nanoclusters are systematically classified. The results reveal that Bi atoms predominantly occupy surface sites, driven by charge transfer effects. Cohesive energy trends alone prove insufficient for structure differentiation, necessitating a data-driven approach employing principal component analysis and K-means clustering. Furthermore, vibrational, electronic, and infrared spectral analyses provide additional insights into structure-property relationships. The findings offer an original framework for the automated classification and analysis of bimetallic nanoclusters, enhancing the understanding of their stability and functional properties.

bimetallic nanoparticles↗

DOE OSTI2024

Self‐Potential Tomography Preconditioned by Particle Swarm Optimization—Application to Monitoring Hyporheic Exchange in a Bedrock River

Abstract A self‐potential (SP) data‐inversion algorithm was developed and tested on an analytical model of electrical‐potential profile data attributed to single and multiple polarized electrical sources. The developed algorithm was then validated by an application to SP‐monitoring field data measured on the floodplain of East Fork Poplar Creek, Oak Ridge, Tennessee, to image electrical sources in areas conducive to preferential flow into the flood plain from the bedrock‐lined riverbed. The algorithm combined stochastic source‐localization by particle‐swarm‐optimization (PSO) of electrical sources characterized by simplified geometries with source tomography by regularized weighted least‐squares minimization of a quadratic objective function. Prior information was incorporated by preconditioning the tomography algorithm by PSO results. Variable percentages of random noise were added to analytical‐model data to evaluate the algorithm performance. Results indicated that true parameters of single‐source models were inverted and approximated with small residual error, whereas inversion of analytical‐model data representing multiple electrical sources accurately approximated the locations of the sources but miscalculated some parameters because of the non‐uniqueness of the inverse‐model solution. Source tomography applied to analytical model data during testing produced a spatially continuous parameter field that identified the locations of point‐scale synthetic dipole sources of electrical current flow with varying degrees of accuracy depending on the prior information incorporated into the tomography. When applied to SP‐monitoring field data, the algorithm imaged electrical sources within a known fault that intersects the bedrock riverbed and flood plain of East Fork Poplar Creek and depicted dynamic electrical conditions attributed to hyporheic exchange.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

SWARM: Reimagining scientific workflow management systems in a distributed world

Modern scientific workflows process massive amounts of data from diverse instruments and sensors, leveraging geographically distributed, heterogeneous compute and storage resources—from leadership-class systems to edge devices—connected by high-performance networks. The diversity of resources introduces challenges in harnessing their full potential, with resilience issues arising across applications, system software, networks, storage, and hardware. Today, workflow management systems (WMS) coordinate the execution of computation and data management tasks across target resources. However, WMS’s centralized nature makes them vulnerable to faults and scalability issues that may result in failures of entire computational campaigns. In conclusion, this paper introduces a novel agentic framework for workflow management, fully distributing and decentralizing the WMS functions and modeling them as swarm intelligence agents infused with advanced artificial intelligence solutions and traditional distributed computing algorithms that can make coordinated decisions in the presence of failures of the underlying cyberinfrastructure.

Swarm intelligence↗

DOE OSTI2025

Particle Swarm Optimization of Dynamic Load Model Parameters in Large Systems

This paper considers two dynamic load models that are widely used in industry to account for induction motor behavior: CMLD and CLOD. These models must be parametrized for the specific utility system in a general way so that they can be used in planning studies and provide a conservative but realistic representation of load behavior. This study considers a measurement-based approach to tuning both models. The load modeling study compares the response of the tuned models to generic candidate models using historical events. This study considers one area-based subsystem to simplify the modeling approach and reduce the number of models required for simulations. Additionally, because dynamic load models often produce similar results for different sets of parameters, a sensitivity study was conducted to assess the parameter impacts on the voltage response. The sensitivity study covers the parameters that are tuned using event measurements. The process to estimate the parameters uses the particle-swarm optimization algorithm. Overall, the performance of the tuned model more accurately captures recovery voltage, delayed recovery, and settling voltage than its predecessor models while not being overly tuned so that it remains general for peak summer conditions.

dynamic load modeling↗

DOE OSTI2026

Applying Particle Swarm Optimization and Extended Kalman Filtering to Model Kaplan Generation Dynamics for Hydropower Systems

Variable renewable generation is increasing the need for hydropower plants to provide fast and flexible grid support, which places new demands on plant-level dynamic models used for monitoring, control, and operational decision-making. This need is especially important for hydroelectric systems, where turbine and generator dynamics are strongly coupled, nonlinear, and time-varying, making accurate real-time representation difficult. To address this problem, this paper develops a digital twin (DT) framework for a synchronous generator–Kaplan turbine system using an explicit separation of slow turbine dynamics and fast generator dynamics. The turbine subsystem is represented by a six-coefficient model, whose parameters are identified offline using particle swarm optimization, while the generator subsystem is updated online through an extended Kalman filter for real-time state and parameter estimation. These models are integrated within a closed-loop simulation that includes a proportional–integral–derivative–double-derivative governor and excitation system, allowing the DT to track plant behavior under realistic operating conditions. Unlike prior studies that treat turbine and generator modeling separately or rely mainly on simulated inputs, the proposed framework is validated using real operational data from a hydropower plant. Results show that the DT reproduces terminal voltage, active power, and reactive power with a normalized root mean square error of approximately 5%. This hybrid offline–online formulation constitutes the main contribution of the work, providing an adaptive and practically deployable DT for hydropower systems with direct relevance to control improvement, performance monitoring, and grid-support applications under high renewable penetration.

13 HYDRO ENERGY↗

NASA NTRS2001

Adaptive routing in wireless communication networks using swarm intelligence

In this paper we focus on the network routing problem, and survey swarm intelligent approaches for its efficient solution, after a brief overview of power-aware routing schemes, which are important in the network examples outlined above.

swarm routing networks↗

NASA NTRS2016

Extracting Ocean-Generated Tidal Magnetic Signals from Swarm Data Through Satellite Gradiometry

Ocean-generated magnetic field models of the Principal Lunar, M2, and the Larger Lunar elliptic, N2, semidiurnal tidal constituents were estimated through a "Comprehensive Inversion" of the first 20.5 months of magnetic measurements from European Space Agency's (ESA) Swarm satellite constellation mission. While the constellation provides important north-south along-track gradiometry information, it is the unique low-spacecraft pair that allows for east-west cross-track gradiometry. This latter type is crucial in delivering an M2 estimate of similar quality with that derived from over 10 years of CHAMP satellite data but over a shorter interval, at higher altitude, and during more magnetically disturbed conditions. Recovered N2 contains nonoceanic signal but is highly correlated with theoretical models in regions of maximum oceanic amplitude. Thus, satellite magnetic gradiometry may eventually enable the monitoring of ocean electrodynamic properties at temporal resolutions of 1 to 2 years, which may have important implications for the inference of ocean temperature and salinity.

Magnetic↗

NASA NTRS2019

Modular Active Self-Assembling Space Telescope Swarms

The Phase I study included a detailed exploration of a specific mission concept based on this architecture - a 31-meter diameter version of the LUVOIR (Large Ultraviolet Optical Infrared Surveyor) mission concept with the same wavelength coverage and wave-front quality requirements. The study focused on establishing the feasibility of the proposed concept with respect to what were identified as the most challenging aspects of the mission: the ability to find orbital solutions using solar sail propulsion for both the initial transfers and subsequent rendezvous trajectories; and the ability to generate the required final primary mirror surface out of uniformly polished individual module mirrors using deformation via mechanical degrees of freedom. As a result of the Phase I work it was shown that both orbital and optical solutions are feasible, but will require technology advancement in multiple key areas. In particular, improvements to solar sail areal densities and loading factors will allow us to more easily find fully general solutions for the transfer trajectories, while lower mass, ultra-precise cryogenic linear actuators will likely be needed to form the final mirror shape. However, it was found that current state of the art in solar sails was sufficient for rendezvous trajectories once on-orbit, that the requisite wave-front quality could be achieved with actuation requirements not more stringent than those being carried by LUVOIR, and that there exists a well-defined design space in which the module spacecraft designs can be optimized. The technology development required to progress this work overlaps significantly with numerous NASA technology roadmaps, as well as the Exoplanet Exploration Program's technologies gap list, meaning that continued development of this concept would directly benefit a variety of NASA programs, as well as enabling the completely unique capabilities provided by a 31-meter optical space telescope...Our architecture requires only a single module type allowing us to directly leverage the cost and efficiency benefits of true mass production, in direct contrast to previously explored self-assembling swarm concepts, which typically include multiple module types.

Astronomy↗

NASA NTRSDate not supplied

Active Swarm Resiliency in the HelioSwarm Mission

Designed to observe plasma turbulence dynamics in solar wind over a distributed volume of space, the HelioSwarm mission comprises a primary chief spacecraft and eight smaller deputy satellites in uniquely assigned “loops” of periodic relative motion in a P/2 lunar resonant orbit. If one or more deputies fail, this multi-satellite architecture facilitates resiliency for science goals through repositioning of satellites to contingency loops. This strategy of Active Swarm Resiliency mitigates risk by modeling quantitative results ahead of time for mission operators to make informed decisions. Responsive actions meet minimum science objectives based on past and predicted system performance, an approach with applications to future missions with similar architecture and requirements.

Fault Management↗

NASA NTRSDate not supplied

Active Swarm Resiliency in the HelioSwarm Mission

Fault Management↗