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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 415 records · Page 23

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NASA NTRS2005

Mixed-Initiative Activity Planning for Mars Rovers

One of the ground tools used to operate the Mars Exploration Rovers is a mixed-initiative planning system called MAPGEN. The role of the system is to assist operators building daily plans for each of the rovers, maximizing science return, while maintaining rover safety and abiding by science and engineering constraints. In this paper, we describe the MAPGEN system, focusing on the mixed-initiative planning aspect. We note important challenges, both in terms of human interaction and in terms of automated reasoning requirements. We then describe the approaches taken in MAPGEN, focusing on the novel methods developed by our team.

Bresina, John↗

NASA NTRSDate not supplied

Automated Reconfigurable Mission Adaptive Digital Assembly Systems (ARMADAS): Robotically Assembled Sustainable Lunar Infrastructure

Introduction: The Automated Reconfigurable Mission Adaptive Digital Assembly Systems (ARMADAS) project at NASA Ames Research Center is developing autonomous infrastructure, instrumentation, and spacecraft assembly and manufacturing capabilities for next generation exploration and science missions, with a goal to change the cost scaling of these missions relative to mission size and duration. Using a building-block approach with a 'kit of parts' composed of ultra-light, high-performance mechanical metamaterials, simplified robots leverage the period environment to achieve high levels of autonomy and reliability for in-space and surface assembly of large-scale apertures, solar-arrays, towers, habitats, and other infrastructure. Robots and structure break down into a compact form factor for launch. By leveraging economies of scale and achieving high-packing ratios, ARMADAS technology can revolutionize possible space missions by breaking the tyranny of the launch shroud, decreasing development times, decreasing mission costs for transformative science capability, and providing a scalable and versatile space infrastructure strategy. Capabilities: To date, the ARMADAS project has demonstrated autonomous assembly of large numbers of structural modules into a meters-scale structure in an earth gravity environment. The mechanical performance is on par with conventional space structures, at a fraction of the cost. By using highly repeatable manufacturing processes (injection molding carbon fiber reinforced space-rated polymers), the structures are both cost effective and highly precise. The ARAMADAS system carefully designs parts for high-precision assembly–the simple and cost-effective robots build structures much larger and more precise than themselves. While maintaining structural efficiency, the ARMADAS system encompasses many functional module types, including for solar power, comm/power routing, etc., that will enable entire infrastructure systems to be engineered and autonomously assembled with modular parts. With well-defined interfaces, custom instrumentation modules can easily be added to fit many mission objectives. Lunar Infrastructure Vision: At the LSIC spring meeting, ARMADAS will present a vision for a general-purpose lunar construction kit capable of meeting a wide variety of lunar surface infrastructure needs (Figure 1). Investing in an ecosystem of reconfigurable, discretely repairable parts and robots enables systems than can expand their capability, reconfigure to meet emergency or unforeseen needs, self-repair and reduce spare-part needs. With a small set of parts (module types), a wide variety of infrastructure needs can be met. Footing modules will allow infrastructure construction at locations with no surface preparation. Footer, primary structure, and rail modules can create reconfigurable rail systems. These rail systems can be used to reduce cost-of-transport between frequently accessed sites, provide dust mitigation, and convey power and communications. High-performance structure modules combined with power-routing and solar modules can create tall towers for power and communication. Concepts for habitats and garages encompass a structure to support regolith cover for a pre-integrated module to an entirely ARMADAS system-based structure. This system can be leveraged by many in-situ resource utilization (ISRU) technologies to simplify processes and augment capabilities.

C. E. Gregg↗

NASA NTRSDate not supplied

Autonomous Lunar Infrastructure Outfitting

The Automated Reconfigurable Mission Adaptive Digital Assembly Systems (ARMADAS) project at NASA Ames Research Center is developing autonomous infrastructure, instrumentation, and spacecraft assembly and manufacturing capabilities for next generation exploration and science missions, with a goal to change the cost scaling of these missions relative to mission size and duration. Using a building-block approach with a 'kit of parts' composed of ultra-light, high-performance mechanical metamaterials, simplified robots leverage the period environment to achieve high levels of autonomy and reliability for in-space and surface assembly of large-scale apertures, solar-arrays, towers, habitats, and other infrastructure. Robots and structure break down into a compact form factor for launch. By leveraging economies of scale and achieving high-packing ratios, ARMADAS technology can revolutionize space missions by breaking the tyranny of the launch shroud, decreasing development times, decreasing mission costs for transformative science capability, and providing a scalable and versatile space infrastructure strategy. An ecosystem of reconfigurable infrastructure modules can be reused, repaired, expanded reconfigured to meet emergency or unforeseen needs, and reduce spare parts.

Christine Gregg↗

NASA NTRS2024

Integration of Automated Systems Test Campaign NC-IAS

NASA’s 2022 strategic plan included the goal to catalyze economic growth and drive innovation in the aviation industry, and to address challenges in air transportation and airspace management within the National Airspace System (NAS). The Aeronautics Research Mission Directorate (ARMD) specifically was tasked with leading aviation innovation to enable safe and sustainable air transportation through revolutionary vehicle advances and efficient flight operations. As a part of meeting these objectives and advancing air mobility concepts, NASA initiated the National Campaign (NC) in the Advanced Air Mobility (AAM) Project within the Airspace Operations and Safety Program (AOSP). The NC was designed to support operational demonstrations with industry as well as the research and development needed to support NASA-led research flight demonstrations. Within NC, the Integration of Automated Systems (IAS), an NC activity, tested and evaluated flight deck automation and airspace operations management functions needed to enable Urban Air Mobility (UAM) operations. This was accomplished through a partnership with Sikorsky Aircraft (specifically Sikorsky Innovations), a Lockheed Martin company, and DARPA (Defense Advanced Research Projects Agency), by leveraging two automation-enabled helicopters equipped with unique capabilities that enabled NASA to develop and test two-ship conflict encounters to demonstrate flight path management and hazard avoidance technologies. The enabler in this testing was NASA-developed “Middleware” (MW) software (also known as Expandable Variable Autonomy Architecture, or EVAA), which among other things allowed multiple algorithms to be incorporated into one software build that was hosted on the dissimilar-type Sikorsky helicopters. The IAS test campaign period of performance was from March 2022 through October 2023 and was structured as a phased, or spiral, approach that ultimately led to the first-ever demonstration of two-ship UAM/AAM operations designed to safely choreograph specific conflict encounters and mission scenarios to test the research algorithms for strategic and tactical aircraft deconfliction. Lessons learned are included in the body of the report. Data collected will be used to inform FAA and industry standards groups on the increasingly automated systems needed for future AAM operations. The test encounters developed for these flight tests were proven to be highly predictable, repeatable, and safely exercised flight path planning and Detect and Avoid (DAA) algorithms. These same test encounters should be leveraged by future flight test campaigns to verify that operational safety is not compromised as the AAM architecture matures. Next steps include repeating similar encounters using unmanned aircraft carrying DAA sensors in the National Airspace.

AAM↗

NASA NTRS1979

Aerospace power systems - A building surge

Dramatic changes in both aircraft and space power systems are being projected. The primary driver is mission cost, although more stringent performance requirements are also being imposed. The order-of-magnitude reductions anticipated in space power, both in cost and specific mass, are partially related to the advent of the Shuttle and the continuing evolution of the STS over the next decade. Automation, microcircuitry, batteries, and structural materials are common aircraft and space technology needs.

Barthelemy, R. R.↗

DOE OSTI2020

Leveraging Flexible Smart Manufacturing to Accelerate Industrial Supply Chain Recovery

Any major crisis such as the latest coronavirus disease pandemic will have a monumental effect on the worldwide economy and on the international supply chain. Much of the global supply chain relies on China, Germany, and the United States for manufacturing and distribution; this fragile system is subject to failure. Climactic changes in demand and rapid shortages in necessities are the results of the pandemic’s disruption. The recovery forecast for the ongoing pandemic is uncertain and, therefore, any recovery effort will need to be adaptable to build the supply chain resiliency. A wide adoption of flexible smart automated technologies in the manufacturing sector are helping to build robust supply chains and assisting in recovery. Here, a brief discussion of these smart manufacturing technologies is presented, along with a list of potential supply chain issues and corresponding solutions. Such smart manufacturing technologies possess the potential to empower and revolutionize the traditional manufacturing environment by enhancing its resiliency and flexibility. These smart technologies will play a crucial role in accelerating the worldwide supply chain recovery in and after a pandemic.

99 GENERAL AND MISCELLANEOUS↗

NASA NTRS1993

Parallel runway requirement analysis study. Volume 1: The analysis

The correlation of increased flight delays with the level of aviation activity is well recognized. A main contributor to these flight delays has been the capacity of airports. Though new airport and runway construction would significantly increase airport capacity, few programs of this type are currently underway, let alone planned, because of the high cost associated with such endeavors. Therefore, it is necessary to achieve the most efficient and cost effective use of existing fixed airport resources through better planning and control of traffic flows. In fact, during the past few years the FAA has initiated such an airport capacity program designed to provide additional capacity at existing airports. Some of the improvements that that program has generated thus far have been based on new Air Traffic Control procedures, terminal automation, additional Instrument Landing Systems, improved controller display aids, and improved utilization of multiple runways/Instrument Meteorological Conditions (IMC) approach procedures. A useful element to understanding potential operational capacity enhancements at high demand airports has been the development and use of an analysis tool called The PLAND_BLUNDER (PLB) Simulation Model. The objective for building this simulation was to develop a parametric model that could be used for analysis in determining the minimum safety level of parallel runway operations for various parameters representing the airplane, navigation, surveillance, and ATC system performance. This simulation is useful as: a quick and economical evaluation of existing environments that are experiencing IMC delays, an efficient way to study and validate proposed procedure modifications, an aid in evaluating requirements for new airports or new runways in old airports, a simple, parametric investigation of a wide range of issues and approaches, an ability to tradeoff air and ground technology and procedures contributions, and a way of considering probable blunder mechanisms and range of blunder scenarios. This study describes the steps of building the simulation and considers the input parameters, assumptions and limitations, and available outputs. Validation results and sensitivity analysis are addressed as well as outlining some IMC and Visual Meteorological Conditions (VMC) approaches to parallel runways. Also, present and future applicable technologies (e.g., Digital Autoland Systems, Traffic Collision and Avoidance System II, Enhanced Situational Awareness System, Global Positioning Systems for Landing, etc.) are assessed and recommendations made.

Ebrahimi, Yaghoob S.↗

NASA NTRS2011

Mission Operations Planning and Scheduling System (MOPSS)

MOPSS is a generic framework that can be configured on the fly to support a wide range of planning and scheduling applications. It is currently used to support seven missions at Goddard Space Flight Center (GSFC) in roles that include science planning, mission planning, and real-time control. Prior to MOPSS, each spacecraft project built its own planning and scheduling capability to plan satellite activities and communications and to create the commands to be uplinked to the spacecraft. This approach required creating a data repository for storing planning and scheduling information, building user interfaces to display data, generating needed scheduling algorithms, and implementing customized external interfaces. Complex scheduling problems that involved reacting to multiple variable situations were analyzed manually. Operators then used the results to add commands to the schedule. Each architecture was unique to specific satellite requirements. MOPSS is an expert system that automates mission operations and frees the flight operations team to concentrate on critical activities. It is easily reconfigured by the flight operations team as the mission evolves. The heart of the system is a custom object-oriented data layer mapped onto an Oracle relational database. The combination of these two technologies allows a user or system engineer to capture any type of scheduling or planning data in the system's generic data storage via a GUI.

Wood, Terri↗

DOE OSTI2025

BOPTest As a Platform for Building Controls and Grid-Interactive Buildings Workforce Training

Building automation and controls are becoming increasingly complex with the emergence of Grid Integrated Efficient Buildings (GEBs) as well as new highly efficient sequences of operation and data-driven control schemes. However, there remains a significant gap in hands-on training opportunities for building operators and technicians to gain practical experience with advanced control systems in a low-risk environment. This paper presents BOPTEST (Building Optimization Performance Test) as a suitable platform for workforce training in building controls and GEB technologies. BOPTEST provides a suite of standardized building simulation test cases with a REST API, real-time control interfaces through BACnet, semantic models connecting users to building data, and built-in calculation of control metrics and performance indicators. The platform enables trainees to interact with virtual buildings using industry-standard protocols while learning how to implement and innovate control strategies. The training platform is designed to offer a structured and interactive learning experience for building engineers, helping them effectively develop, learn, and retain skills in fault identification, troubleshooting, and correction. The workflow is divided into three main phases: 1) Setup, 2) Exercise, and 3) Review, each comprising specific activities performed by either the instructor or the student. Initial pilot training sessions have yielded positive feedback from instructors and participants and demonstrates that BOPTEST effectively fills an industry need for a low-risk training resource via simulation of real building control systems, allowing trainees to gain practical experience before working in the field. The platform's ability to provide immediate performance feedback while maintaining familiar industry interfaces makes it particularly suitable for workforce development programs. This work provides a replicable model for leveraging building simulation in control education and training.

Paul, Lazlo↗

DOE OSTI2025

BOPTEST as a Platform for Building Controls and Grid-Interactive Buildings Workforce Training

Paul, Lazlo↗

NASA NTRS1984

LANDSAT-4 MSS Geometric Correction: Methods and Results

An automated image registration system such as that developed for LANDSAT-4 can produce all of the information needed to verify and calibrate the software and to evaluate system performance. The on-line MSS archive generation process which upgrades systematic correction data to geodetic correction data is described as well as the control point library build subsystem which generates control point chips and support data for on-line upgrade of correction data. The system performance was evaluated for both temporal and geodetic registration. For temporal registration, 90% errors were computed to be .36 IFOV (instantaneous field of view) = 82.7 meters) cross track, and .29 IFOV along track. Also, for actual production runs monitored, the 90% errors were .29 IFOV cross track and .25 IFOV along track. The system specification is .3 IFOV, 90% of the time, both cross and along track. For geodetic registration performance, the model bias was measured by designating control points in the geodetically corrected imagery.

Brooks, J.↗

DOE OSTI2026

AI-Based Analytics and Energy Modeling Framework for Characterizing Urban Energy Systems

Developing location-specific district energy models is essential for understanding energy patterns and supporting efficient management and planning decisions. However, accurately characterizing these models remains challenging due to gaps in building characteristics and labor-intensive traditional modeling workflows. To address these challenges, we develop an AI-based framework that integrates top-down and bottom-up building energy data to automate urban energy model characterization. The framework trains multimodal deep learning models using heterogeneous ResStockTM datasets to infer missing building characteristics from varying levels of known information and generate simulation-ready inputs for district-scale energy modeling. It also employs a conditioning-based injection approach to generate ”what-if” scenarios, enabling users to explore retrofit, efficiency, and technology-upgrade pathways. Integrated within URBANoptTM, a bottom-up district energy modeling platform for simulating co-located buildings, the framework infers detailed building-level inputs required for bottom-up simulations. Both localized and generalized AI models are developed to learn relationships across categorical, numerical, and time-series data, enabling reconstruction of missing attributes and generation of targeted upgrade scenarios. We demonstrate this methodology on a residential neighborhood in Baltimore, MD, assessing internal consistency against ResStock reference data and URBANopt simulation, and comparing selected attributes against real-world building characteristics. Results show strong overall predictive accuracy in data completion and scenario generation, with localized and generalized models offering complementary trade-offs between precision and scalability. Overall, our automated framework streamlines energy modeling and provides a reliable framework for urban building energy characterization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Enabling Efficient Surveillance, Control, and Automation of Geothermal Operations with Advanced Predictive Analytics

Automation and control of geothermal energy production and operations require reliable and efficient predictive tools. While physics-based simulation offers a comprehensive tool for predicting energy production performance in geothermal systems, predicting the behavior of geothermal reservoirs involves complex multi-physics processes with coupling effects, highly uncertain input parameters and subsurface descriptions. Moreover, building, running, and integrating simulation models into standard model calibration and optimization workflows entail significant technical and computational efforts. An emerging alternative to physics-based simulation is data-driven predictive analytics models that have gained popularity in energy industry. In this report, we develop novel predictive models for integration into real-time fault diagnosis and model predictive control algorithms to improve the efficiency of energy production operations in geothermal reservoirs. The report includes two major research Thrust Areas, that is, the surface power plant and the subsurface reservoir.

15 GEOTHERMAL ENERGY↗

NASA NTRS1992

Automated Characterization Of Vibrations Of A Structure

Automated method of characterizing dynamical properties of large flexible structure yields estimates of modal parameters used by robust control system to stabilize structure and minimize undesired motions. Based on extraction of desired modal and control-design data from responses of structure to known vibrational excitations. Applicable to terrestrial structures where vibrations are important - aircraft, buildings, bridges, cranes, and drill strings.

Bayard, David S.↗

NASA NTRS1993

A chemical sensor and biosensor based totally automated water quality monitor for extended space flight: Step 1

The result of a literature search to consider what technologies should be represented in a totally automated water quality monitor for extended space flight is presented. It is the result of the first summer in a three year JOVE project. The next step will be to build a test platform at the Authors' school, St. John Fisher College. This will involve undergraduates in NASA related research. The test flow injection analysis system will be used to test the detection limit of sensors and the performance of sensors in groups. Sensor companies and research groups will be encouraged to produce sensors which are not currently available and are needed for this project.

Smith, Robert S.↗

NASA NTRS1994

Automating the training development process for mission flight operations

Traditional methods of developing training do not effectively support the changing needs of operational users in a multimission environment. The Automated Training Development System (ATDS) provides advantages over conventional methods in quality, quantity, turnaround, database maintenance, and focus on individualized instruction. The Operations System Training Group at the JPL performed a six-month study to assess the potential of ATDS to automate curriculum development and to generate and maintain course materials. To begin the study, the group acquired readily available hardware and participated in a two-week training session to introduce the process. ATDS is a building activity that combines training's traditional information-gathering with a hierarchical method for interleaving the elements. The program can be described fairly simply. A comprehensive list of candidate tasks determines the content of the database; from that database, selected critical tasks dictate which competencies of skill and knowledge to include in course material for the target audience. The training developer adds pertinent planning information about each task to the database, then ATDS generates a tailored set of instructional material, based on the specific set of selection criteria. Course material consistently leads students to a prescribed level of competency.

Scott, Carol J.↗

NASA NTRS2001

Adjustable Autonomy Testbed

The Adjustable Autonomy Testbed (AAT) is a simulation-based testbed located in the Intelligent Systems Laboratory in the Automation, Robotics and Simulation Division at NASA Johnson Space Center. The purpose of the testbed is to support evaluation and validation of prototypes of adjustable autonomous agent software for control and fault management for complex systems. The AA T project has developed prototype adjustable autonomous agent software and human interfaces for cooperative fault management. This software builds on current autonomous agent technology by altering the architecture, components and interfaces for effective teamwork between autonomous systems and human experts. Autonomous agents include a planner, flexible executive, low level control and deductive model-based fault isolation. Adjustable autonomy is intended to increase the flexibility and effectiveness of fault management with an autonomous system. The test domain for this work is control of advanced life support systems for habitats for planetary exploration. The CONFIG hybrid discrete event simulation environment provides flexible and dynamically reconfigurable models of the behavior of components and fluids in the life support systems. Both discrete event and continuous (discrete time) simulation are supported, and flows and pressures are computed globally. This provides fast dynamic simulations of interacting hardware systems in closed loops that can be reconfigured during operations scenarios, producing complex cascading effects of operations and failures. Current object-oriented model libraries support modeling of fluid systems, and models have been developed of physico-chemical and biological subsystems for processing advanced life support gases. In FY01, water recovery system models will be developed.

Malin, Jane T.↗

DOE OSTI2022

A High-Throughput Computing Infrastructure to Generate Custom, Open Community Geothermal Datasets

The most significant challenge facing geothermal research, development, and deployment is a lack of comprehensive datasets describing the geological and economical properties of North America. Automated knowledge base construction, the process of designing algorithms to analyze text and images to programmatically build new datasets, is one possible solution to this problem. The xDD library of full-text scientific articles (https://xdd.wisc.edu) is one of the largest collections of open and controlled-access scientific documents available for knowledge base construction in the world, but it has been underutilized by experts in geothermal research. The xDD development team attributed the lack of engagement by software developers and geothermal researchers to two perceived shortcomings of the system. First, the workflow for obtaining data from xDD for local development and testing of data mining applications was unnecessarily abstruse and required significant manual intervention by xDD systems administrators. Second, although xDD already held articles from a broad cross-section of scientific literature with an emphasis on the geosciences, it did not have an explicit set of geothermal research documents that could serve as the nucleus of a geothermal data mining application. To address these issues, the Automated Data Extraction PlaTform (ADEPT) was proposed to extend the data distribution capabilities of the xDD system. The ADEPT extension added the following four key features to xDD: 1) integration of National Geothermal Data System (NGDS) documents into the xDD library to provide an explicitly geothermally-themed collection; 2) improved RESTful (i.e., https-protocol driven) web services for external partners to access xDD data for machine learning application development; 3) a web platform for end-users and xDD administrators to coordinate the development of data mining applications from the initial step of browsing available documents to the final stage of deploying a production-quality machine learning application on high-throughput computing infrastructure; and 4) the development of demonstration data mining applications to illustrate the new workflow to potential collaborators. A total of 21,674 geothermal documents from NGDS were fully ingested into the xDD library and the associated metadata is publicly available through the xDD web services; furthermore, the ADEPT web platform is now publicly accessible and fully live at https://xdd.wisc.edu/adept/.

15 GEOTHERMAL ENERGY↗