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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 271 records · Page 15

Sub-GLE Solar Particle Events and the Implications for Lightly-Shielded Systems Flown During an Era of Low Solar Activity

Many of the large space missions must be very rigorous in their designs to reduce risk from radiation damage as much as possible. Some ways of reducing this risk have been to build in multiple redundancies, purchase/develop radiation hardened electronics parts, and plan for worst case radiation environment scenarios. These methods work well for these ambitious missions that can afford the costs associated with these meticulous efforts. However, there have been more small spacecraft and CubeSats with smaller duration missions entering the space arena, which can take some additional risks, but cannot afford to implement all of these risk-reducing methods. Therefore, one way to quantify the radiation exposure risk for these smaller spacecraft would be to investigate the radiation environment pertinent to the mission to better understand these radiation exposures, rather than always designing to the infrequent, worst-case environment. In this study, we have investigated 34 historical solar particle events (1974-2010) that occurred during a time period when the sun spot number (SSN) was less than 30. These events contain Ground Level Events (GLE), sub-GLEs, and sub-sub-GLEs(sup 1-3). GLEs are extremely energetic solar particle events (SPEs) having proton energies often extending into the several GeV range and producing secondary particles in the atmosphere, mostly neutrons, observed with ground station neutron monitors. Sub-GLE events are less energetic, extending into the several hundred MeV range, but without producing detectable levels of secondary atmospheric particles. Sub-sub GLEs are even less energetic with an observable increase in protons at energies greater than 30 MeV, but no observable proton flux above 300 MeV. The spectra for these events were fitted using a double power law fit in particle rigidity, called the Band fit method. The differential spectra were then input into the NASA Langley Research Center HZETRN 2005, which is a high-energy particle transport/dose code, to determine the dose in various thicknesses of aluminum, representing the spacecraft. This paper will detail the absorbed dose results of each of these environments, as well as analyze the data to better understand the doses over small thicknesses that are more relevant to small spacecraft and satellites, such as CubeSats. In addition, we will discuss the implications of these data and provide some recommendations that may be useful to spacecraft designers of these smaller spacecraft/CubeSat-type missions.

Atwell, William↗

Validation of Fault-Tolerant Plans for Europa Clipper

The Europa Clipper mission will explore that icy moon in a series of brief flybys through the Jovian radiation belts. A single event upset in the spacecraft flight computer during these critical scientific periods could jeopardize the success of the mission. Rather than safing and awaiting operator intervention, the Clipper mission envisions limited onboard autonomy that can restore spacecraft state sufficiently to resume the encounter observation plan as rapidly as possible. The contingency plan is contained in an Activity Restart Timeline (ART) that is transmitted in parallel with the nominal plan, which must be co-validated jointly against all spacecraft state and resource constraints amid unpredictable fault timing. A prototype validation tool was built that leverages declarative spacecraft models and automated search techniques to find such potential inconsistencies in the unified contingent mission plan. Early validation results within motivating scenarios are presented.

Ferguson, Eric↗

Integrated System for Autonomous and Adaptive Caretaking (ISAAC) Simulated Cargo Logistics Demo Video

This video shows a simulation of autonomous cargo logistics using ISAAC (Integrated System for Autonomous and Adaptive Caretaking). The video shows autonomous planning and execution of a cargo scenario in which the R2 robot moves a cargo bag from a stowed location to a transfer location in the US Lab on the International Space Station. The Astrobee robot then moves the bag from the transfer location to a new temporary stowage location in the Japanese Experiment Module (JEM).

ISAAC↗

SWS TechTalk: System Modeling in Support of IASMS Definition

Topic: System modeling in support of IASMS definition 1. What is system modeling? 2. What are the building blocks necessary for a formal model of the IASMS concept of operations, or “conops”, and “architectures”? 3. Scenario modeling to support safety demonstrator planning and technology integration The System Wide Safety Tech Talks will offer a great opportunity to keep up on each other’s work and accomplishments, as well as an opportunity to find areas of potential collaboration. The topics of these technical talks will cover any papers, presentations, special trips, or meetings that are a part of SWS.

IASMS↗

Predicting Orion Launch Abort Acoustics

Computational fluid dynamics (CFD) scale-resolving simulations were performed to help assess the Orion launch abort system’s vibro-acoustic environment in collaboration with the Orion Loads and Dynamics team at the Johnson Space Center (JSC). The primary objectives are to establish best practices and to validate CFD predictions for surface fluctuating pressure spectra with ground, wind tunnel, and flight test data. Excellent agreement is obtained between CFD predictions by the Cartesian adaptive mesh refinement (AMR) module of the Launch, Ascent, and Vehicle Aerodynamics (LAVA) solver and measurements from a ground test representative of the start of a pad abort scenario, and with measurements from a wind tunnel test case corresponding to a transonic, high total angle of attack ascent abort scenario. A simplified (static and at rest) simulation of a pad abort flight test shows good agreement with the flight data, whereas a more costly simulation where the vehicle is accelerating relative to the ground from rest to Mach 0.4 shows consistent over-prediction of the spectra. Simulation results for a supersonic ascent abort flight test are in reasonable agreement with the measurements, but show larger excursions in regions where non-linear interactions between the plume turbulence, shocks, and turbulent boundary layer are strongest – likely due to CFD modeling assumptions, the effect of which are discussed. Overall, the CFD results demonstrate that scale-resolving simulation methodologies like the one presented can help characterize full-scale launch abort vehicle near-field noise and can complement wind tunnel, ground and flight tests by providing trends and reducing uncertainty for scenarios that cannot or are not planned to be tested.

Orion↗

AI-Driven Frameworks for Characterizing Urban Energy Systems

We develop AI-driven frameworks to characterize urban energy systems with the goal of transforming planning by reducing the labor of model generation, scaling scenario exploration, and improving accuracy for localized analysis. The approach integrates top-down and bottom-up data to train different AI models that predict missing information and generate inputs and targeted scenarios for district-scale models. The result is a scalable framework that provides actionable insights for reliable and efficient planning.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

The ReSWARM microgravity flight experiments: Planning, control, and model estimation for on‐orbit close proximity operations

Abstract On‐orbit close proximity operations involve robotic spacecraft maneuvering and making decisions for a growing number of mission scenarios demanding autonomy, including on‐orbit assembly, repair, and astronaut assistance. Of these scenarios, on‐orbit assembly is an enabling technology that will allow large space structures to be built in situ, using smaller building block modules. However, like many of these scenarios, robotic on‐orbit assembly involves several technical hurdles, such as changing system models. For instance, grappled modules moved by a free‐flying “assembler” robot can cause significant changes in the combined system inertia, which have cascading impacts on motion planning and control portions of the autonomy stack. Further, on‐orbit assembly and other scenarios require collision‐avoiding motion planning, particularly when operating in a “construction site” scenario of multiple assembler robots and structures. Multiple key technologies that address these complicating factors for autonomous microgravity close proximity operations are detailed in this work, in particular: (1) application of global long‐horizon planning, accomplished using offline and online sampling‐based planner options that consider the system dynamics; (2) adaptation of the recently proposed RATTLE information‐aware planning framework for on‐orbit reconfiguration model learning; and (3) connection with robust control tools to provide low‐level control robustness using current system knowledge. These approaches were demonstrated for an autonomous on‐orbit assembly use case by the RElative Satellite sWarming and Robotic Maneuvering (ReSWARM) experiments using NASA's Astrobee robots on the International Space Station. Results of the ReSWARM experiments are provided along with significant operational and implementation detail discussing the practicalities of hardware implementation and unique aspects of working with the Astrobee free‐flyer robots in microgravity. ReSWARM provides a base set of planning and control tools for robotic close proximity operations, demonstrates them in microgravity, and outlines some of the important hardware aspects that future autonomous free‐flyers will need to consider.

Robotics↗

Using the Integrated Vehicle Health Management Research Test and Integration Plan Wiki to Identify Synergistic Test Opportunities

The National Aeronautics and Space Administration (NASA) and the aviation industry have recognized a need for developing a method to identify and combine resources to carry out research and testing more efficiently. The Integrated Vehicle Health Management (IVHM) Research Test and Integration Plan (RTIP) Wiki is a tool that is used to visualize, plan, and accomplish collaborative research and testing. Synergistic test opportunities are developed using the RTIP Wiki, and include potential common resource testing that combines assets and personnel from NASA, industry, academia, and other government agencies. A research scenario is linked to the appropriate IVHM milestones and resources detailed in the wiki, reviewed by the research team members, and integrated into a collaborative test strategy. The scenario is then implemented by creating a test plan when appropriate and the research is performed. The benefits of performing collaborative research and testing are achieving higher Technology Readiness Level (TRL) test opportunities with little or no additional cost, improved quality of research, and increased communication among researchers. In addition to a description of the method of creating these joint research scenarios, examples of the successful development and implementation of cooperative research using the IVHM RTIP Wiki are given.

Koelfgen, Syri J.↗

Planning and Optimization for Multi-robot Planetary Cave Exploration under Intermittent Connectivity Constraints

Exploring subsurface structures with autonomous robots is of growing interest in the context of planetary caves studies. Communication between robots in these environments is severely degraded which complicates coordination and information distribution. In this paper we focus on planning for mobility and communication in a cave exploration scenario where the situational awareness of a static base station is critical. We propose a notion of information-consistency where a plan itself is part of the information to be shared between robots, and propose a method for generating information-consistent plans. We discuss in detail how the resulting plan can be robustly implemented with minimal communication through local mission executives that run on individual robots. We describe preliminary results on the performance of the planning algorithm and integration of the local mission executives in a high-fidelity simulation environment.

Murray, Richard M.↗

Exploring Applications of Machine Learning for Wildfire Monitoring and Detection using Unmanned Aerial Vehicles

Wildfires are increasing in frequency and severity around the world, including the United States. The losses caused by wildfires could be mitigated if high-risk areas, hotspots, and flare-ups could be monitored continuously, such as through the use of Unmanned Aerial Vehicles (UAVs). This paper documents exploratory efforts using machine learning to determine efficient flight paths for UAVs and to detect wildfires using image classification. On path planning, three machine learning techniques—Genetic Algorithm, Simulated Annealing, and Dynamic Programming—were explored. Genetic Algorithm was found to be an effective approach for path planning for wildfire monitoring and surveillance by UAVs. For a scenario of 25 locations in a circular arrangement, the algorithm was able to return the optimal path. The accuracy and execution time was found to be sensitive to the algorithm hyperparameters selected, which was especially evident in scenarios with hundreds or thousands of locations. Simulated Annealing was also found to be an effective approach for UAV path planning, with a major benefit of avoiding getting trapped in local minima and being straightforward to implement. Like Genetic Algorithm, the performance of Simulated Annealing was also found to be sensitive to the algorithm hyperparameters selected. By comparison, Dynamic Programming guarantees optimality for any number of locations, but it was found to be less practical in terms of execution time for scenarios with more than about a couple dozen locations. On wildfire detection, image classification using deep learning with a convolutional neural network was explored. Transfer learning was found to be a useful technique to efficiently train deep learning models. Also, it was determined that GPU processing can increase training speed by an order of magnitude, which enables significantly faster development. For a validation test set of 500 images, there were only two false negatives and zero false positives. These results demonstrate that detecting wildfires in static cameras using machine learning is feasible and establish a baseline for using images captured by UAVs in flight for wildfire detection.

Wildfire management↗

Acceptance Calculation and Study of Fault Scenarios in the PIP-II Linac

The Proton Improvement Plan (PIP)-II is a proposed high intensity proton facility being developed at the Fermilab. The PIP-II is primarily based on construction of a new superconducting radio frequency (SRF) linear accelerator (linac) that would deliver an average beam current of 2mA with output energy up to 800 MeV. The acceptance of an accelerator outlines its sensitivity against deviations from its nominal design parameters. Consequently, a higher acceptance minimizes possibility of the beam loss. This paper presents longitudinal and transverse acceptance of the PIP-II SRF linac. Furthermore, paper also discusses implications of fault scenarios, such as the cavity failure, on the beam optics and acceptances.

43 PARTICLE ACCELERATORS↗

City Decision Analysis Resources and Tools

The City Decision Analysis at Any Scale Workshop was held February 17-18, 2021. This brochure was created to provide participants with a collection of tools and resources that support planning and implementation of clean energy goals for communities and businesses.

ACES↗

Prediction of pellet mass thresholds for ELM triggering in low-collisionality, ITER-like discharges

Abstract In ITER, pellets are calculated to require more than 8 times the mass than currently planned to reliably trigger edge-localized modes (ELMs). Unmitigated heat flux impulses from ELMs are intolerable in ITER at full power and current. Therefore, ITER operation relies on multiple approaches to control ELM heat fluxes. One method is pellet ELM pacing to instigate small rapid ELMs with low heat flux. Predicting the performance of pellet pacing is critical for ITER, which is expected to operate in a regime with a low-collisionality, peeling-limited pedestal. However, to trigger ELMs the local pressure increase in the expanding pellet cloud pushes the equilibrium over the ballooning stability limit. In this work, linear and nonlinear M3D-C1 simulations are used to predict pellet mass thresholds in DIII-D discharges and ITER scenarios with peeling-limited pedestals. It is found that the distance of the equilibrium’s operational point from the ballooning branch of the pedestal stability boundary strongly changes thresholds. Linear M3D-C1 simulations find a strong dependence of the pellet mass threshold on the poloidal injection location for ITER’s 15 MA, Q = 10 scenario. The required pellet mass at the planned injection locations is 8 to 17 times larger than currently considered. However, such linear simulations do not include pellet ablation physics or time evolution of density and temperature. A new scheme of 2D nonlinear simulations, coupled with linear stability analysis at various steps throughout the nonlinear time evolution, was developed to include such physics and improve on the linear results. These new nonlinear-to-linear simulations confirm previous findings. This result suggests that pellet ELM triggering in ITER could require pellets much larger than those currently planned, which makes ELM-pacing operationally challenging. On the other hand, fueling pellets injected from the high-field side will likely not unintentionally trigger ELMs in an otherwise ELM-stable plasma.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Autoplan: A self-processing network model for an extended blocks world planning environment

Self-processing network models (neural/connectionist models, marker passing/message passing networks, etc.) are currently undergoing intense investigation for a variety of information processing applications. These models are potentially very powerful in that they support a large amount of explicit parallel processing, and they cleanly integrate high level and low level information processing. However they are currently limited by a lack of understanding of how to apply them effectively in many application areas. The formulation of self-processing network methods for dynamic, reactive planning is studied. The long-term goal is to formulate robust, computationally effective information processing methods for the distributed control of semiautonomous exploration systems, e.g., the Mars Rover. The current research effort is focusing on hierarchical plan generation, execution and revision through local operations in an extended blocks world environment. This scenario involves many challenging features that would be encountered in a real planning and control environment: multiple simultaneous goals, parallel as well as sequential action execution, action sequencing determined not only by goals and their interactions but also by limited resources (e.g., three tasks, two acting agents), need to interpret unanticipated events and react appropriately through replanning, etc.

Dautrechy, C. Lynne↗

Enhancing the Survivability of Power Systems With Grid-Edge DERs Against DoS Attacks

Power system survivability, defined as the ability of a system to maintain steady-state functionality under varying operational conditions, reflects its resilience against disturbances. While existing research primarily focuses on physical-layer disturbances, the increasing prevalence of grid-edge DERs, which are primarily used for integrating renewable energy, has significantly expanded the cyber attack surface. As a result, operational disruptions caused by cyber threats are posing significant challenges to system survivability and cannot be overlooked. To fill this gap, we redefine system survivability to incorporate the cyber layer’s status and propose a Distributionally Robust Optimization (DRO) approach to enhance power system survivability against potential cyber-physical threats. In this paper, we first analyze the operational guidelines of systems with a high penetration of DERs under various cyber network conditions and redefine survivability in this context. Next, we focus on the most common cyber threat, Denial-of-Service (DoS) attacks, and develop a corresponding attack model. This model allows for the creation of a kernel-based ambiguity set that captures attack uncertainties using historical data. Finally, we transform the proposed DRO model as a tractable optimization problem, with its solution providing an optimal cyber redundancy plan to enhance system survivability in DoS attack scenarios. Simulation results on the IEEE 13-node and 123-node test feeders demonstrate the effectiveness of our proposed model in improving system survivability. This model can also be expanded to include other types of common attacks and serve as a comprehensive planning tool to improve overall cyber physical survival of the system.

cybersecurity↗

Applying a Multisector Scenario Framework to Evaluate Past and Future Public Surface Water Supply Infrastructure Strategies in Texas

Datasets supporting the index model and scenario analysis used in evaluating surface water supply strategies across different water system types in Texas. These data underpin the scenario development and application of five key indicators: Water Availability Index (WAI), Water Quality Index (WQI), Energy Requirement Index (ERI), Water Treatment Cost (WTC), and Water Infrastructure Cost (WIC). The datasets are organized by system type—stream reaches (flowlines), waterbodies, and reservoirs—and include both raw and standardized index values. The integrated datasets also provide scenario classifications (original and adjusted) based on infrastructure and planning priorities, enabling comparison across Shared Socioeconomic Pathways (SSPs). Additional strategy-level data are included to support evaluation of state-level new reservoir projects in relation to cost and availability tradeoffs. Please refer to the README file provided in Files for more details. Descriptions of the datasets are provided below. Dataset(s) Descriptions Folder: Index_model_database.zip Subfolder: Stream_reach.zip Fl_wf.csv, Fl_wq.csv, Fl_er.csv, Fl_wf_wtcUV.csv, Fl_wf_wtcnoUV.csv, Fl_allfac_wic1.csv, Fl_allfac_wic2.csvDatasets for computing WAI, WQI, ERI, WTC, and WIC for surface water systems classified as stream reaches (flowlines). Subfolder: Waterbody.zip Wb_wf.csv, Wb_wq.csv, Wb_er.csv, Wb_wf_wtcUV.csv, Wb_wf_wtcnoUV.csv, Wb_allfac_wic1.csv, Wb_allfac_wic2.csvEquivalent index model datasets for waterbodies, reflecting hydrologic and infrastructure attributes specific to impounded natural systems. Subfolder: Reservoir.zip Rs_wf.csv, Rs_wq.csv, Rs_er.csv, Rs_wf_wtcUV.csv, Rs_wf_wtcnoUV.csv, Rs_allfac_wic1.csv, Rs_allfac_wic2.csvIndex model datasets specific to regulated reservoir systems, incorporating both resource indicators and cost parameters. Folder: Integrated data.zip combined_merged_data.csv, combined_merged_data_scenario.csvDatasets integrating index model indicators (both raw and scaled) with scenario classifications, including adjustments reflecting SSP-aligned transitions and planning shifts. Folder: Additional data.zip wai_supplystrat_wic_merged.csvCurated dataset capturing proposed major reservoir-based municipal water supply strategies in Texas. Integrates site-level planning data with estimated capital infrastructure costs and water availability scores for comparative assessment.

geospatial↗

Leveraging SETS resilience capabilities for safe-to-fail infrastructure under climate change

As the rehabilitation of infrastructure is outpaced by changes in the profile, frequency, and intensity of extreme weather events, infrastructure’s service disruptions and failures become increasingly likely. Safe-to-fail approaches for infrastructure planning and design improve the capacity of cities to adapt for uncertain climate futures by identifying social, ecological, and technological systems (SETS) capabilities to prepare for potential failure scenarios. In this work, we argue for transforming infrastructure planning and design to effectively utilize safe-to-fail approaches by navigating the opportunities and trade-offs of SETS resilience capabilities. From a technological vantage point, traditional infrastructure planning approaches account for social and ecological domains as external design conditions rather than embedded system characteristics. Safe-to-fail approaches directly challenge the isolation of the technological domain by necessitating a recognition that SETS domains are interconnected and interdependent in infrastructure systems, as such risks and system capabilities for resilience must be managed cohesively.

54 ENVIRONMENTAL SCIENCES↗

Probabilistic Assessment of High-Renewables Power Systems: Current Work and Future Directions

In a future with higher penetrations of variable and energy-limited resources, the dominant sources of resource adequacy risk begin to shift from (assumed) independent thermal unit outages to spatially- and temporally-correlated weather-driven phenomena. Traditional resource adequacy assessment and planning methods can be ill-suited to such scenarios. This presentation will highlight aspects of NREL's work in adapting and developing new probabilistic methods and tools in anticipation of the needs of the power system planning exercises of tomorrow.

17 WIND ENERGY↗