An Integrated System for Multi-Rover Scientific Exploration
This paper describes an integrated system for coordinating multiple rover behavior with the overall goal of collecting planetary surface data.
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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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Satellite Servicing in Mission Design Studies at the NASA GSFC
Several NASA missions in various stages of development have undergone one-week studies in the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC) Integrated Mission Design Center (IMDC), mostly in preparation for proposals. The possible role of satellite servicing has been investigated for several of these missions, applying the lessons learned from Hubble Space Telescope (HST) servicing, taking into account the current state of the art, projecting into the future, and implementing NASA long-range plans, and is presented here. The general benefits and costs of injecting satellite servicing are detailed, including components such as mission timeline, mass, fuel, spacecraft design, risk abatement, life extension, and improved performance. The approach taken in addressing satellite servicing during IMDC studies is presented.
Leete, Stephen J.↗
DEEP Solar: Data DrivEn Modeling and Analytics for Enhanced System Layer ImPlementation
Realizing the SETO 2030 mission of reducing solar energy costs to 3-5 c/kWh will require innovative enabling research on effective, cost-efficient integration of local PV within distribution systems. However, the intermittent and variable nature of PVs compels operators to impose conservative hosting capacity constraints. Given the extremely high variability of (intermittent and unpredictable) solar energy generation, relaxing the capacity constraints (which are currently around 15%) and achieving 100% or greater integration of renewables will require a fundamental transformation of the power grid via the utilization of exponentially larger amounts of AMI enabled fine-grained data. To address the challenges in increasing the penetration of renewable energy based DERs, this project envisions an Enhanced System Layer (ESL) at the distribution network level that is reliable, cost-effective and scalable to millions of Distributed Energy Resources (DERs)/devices. This includes developing: 1) Transformative and highly scalable machine learning based predictive analytics tools that plug into distribution system planning and provide real-time situational awareness at the distribution level for short and long-term operational planning. The tools will be built using novel data-driven energy models of millions of active nodes with AMI, 2) Adaptive stochastic analysis and optimization algorithms for real-time grid operations, 3) Dynamic Scenario Analysis using parallel Cloudenabled implementations with < 1 minute computational cycle times.
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The Mars 2020 Ground Data System Architecture
The Mars 2020 Mission’s primary objective is to collect 20 geographically unique samples during its prime mission of one and a quarter Martian years, or just over 2 Earth years. Mission planners determined the project needed to develop a system that would enable the operations team to analyze engineering and science data, make science decisions, select viable rover targets at a millimeter resolution and validate an uplink bundle for a car sized rover with more complex science instruments than any previous Mars surface mission. All this had to be done within a five hour time frame. Doing this with a small team would be a challenge, but this had to be accomplished by a large team of engineers and scientists located across North America and Europe. Achieving this level of operational efficiency was unheard of in the prime mission. In addition, the mission had another set of requirements that had nothing to do with surface operations; the Mars 2020 Ground Data System (GDS) was also expected to comply with a new set of security requirements to keep up with the ever changing cybersecurity landscape. The Mars 2020 Ground Data System (GDS) is a re-architected version of the Mars Science Laboratory GDS. The primary goal was to integrate the lessons learned from previous Mars surface missions, accommodate a set of new requirements and capabilities required to ensure mission success, and comply with a new set of cybersecurity controls. The new architecture includes several unique qualities including a data lake, language-agnostic system-wide event-based operations, containerization, automated deployment, network segmentation, infrastructure-as-code, API-driven interfaces, and the first Mars surface GDS to operate primarily in the cloud. The new architecture enabled greater access to the system’s data, tighter integration with the operations team, and a higher level of traceability. The availability of the data also enabled a new set of capabilities previously not possible on surface missions. These new capabilities include an autonomous data to information, pipeline for downlink analysis, horizontal scaling of science data processing capabilities, autonomous round trip data tracking of science and engineering data, integration of flight system state into the tactical planning cycle, high fidelity targeting utilizing kinematic data, and hierarchical image and 3d meshes data representations. This paper will introduce the requirements for the Mars 2020 Mission, the heritage architecture, and the rationale for the changes to achieve the new architecture. The paper will continue to describe the fundamental changes made to the GDS architecture, how these changes enabled a more tightly integrated GDS, and the new capabilities that were enabled by the new architecture. The paper will conclude with the lessons learned from the process of rearchitecting a heritage GDS system and from the first 200 days of operations supporting over 800 users from around the world.
Lopez-Roig, Reynaldo↗
Ground Processing Affordability for Space Vehicles
Launch vehicles and most of their payloads spend the majority of their time on the ground. The cost of ground operations is very high. So, why so often is so little attention given to ground processing during development? The current global space industry and economic environment are driving more need for efficiencies to save time and money. Affordability and sustainability are more important now than ever. We can not continue to treat space vehicles as mere science projects. More RLV's (Reusable Launch Vehicles) are being developed for the gains of reusability which are not available for ELV's (Expendable Launch Vehicles). More human-rated vehicles are being developed, with the retirement of the Space Shuttles, and for a new global space race, yet these cost more than the many unmanned vehicles of today. We can learn many lessons on affordability from RLV's. DFO (Design for Operations) considers ground operations during design, development, and manufacturing-before the first flight. This is often minimized for space vehicles, but is very important. Vehicles are designed for launch and mission operations. You will not be able to do it again if it is too slow or costly to get there. Many times, technology changes faster than space products such that what is launched includes outdated features, thus reducing competitiveness. Ground operations must be considered for the full product Lifecycle, from concept to retirement. Once manufactured, launch vehicles along with their payloads and launch systems require a long path of processing before launch. Initial assembly and testing always discover problems to address. A solid integration program is essential to minimize these impacts, as was seen in the Constellation Ares I-X test rocket. For RLV's, landing/recovery and post-flight turnaround activities are performed. Multi-use vehicles require reconfiguration. MRO (Maintenance, Repair, and Overhaul) must be well-planned--- even for the unplanned problems. Defect limits and standard repairs need to be in-place as well as easily added. Many routine inspections and maintenance can be like an aircraft overhaul. Modifications and technology upgrades should be expected. Another factor affecting ground operations efficiency is trending. It is essential for RLV's, and also useful for ELV's which fly the same or similar models again. Good data analysis of technical and processing performance will determine fixes and improvements needed for safety, design, and future processing. Collecting such data on new or low-frequency vehicles is a challenge. Lessons can be learned from the Space Shuttle, or even the Concorde aircraft. For all of the above topics, efficient business systems must be established for comprehensive program management and good throughput. Drawings, specifications, and manuals for an entire launch vehicle are often in different formats from multiple vendors, plus they have proprietary constraints. Nonetheless, the integration team must ensure that all data needed is compatible and visible to each appropriate team member. Ground processing systems for scheduling, tracking, problem resolution, etc. must be well laid-out. The balance between COTS (commercial off the shelf) and custom software is difficult. Multiple customers, vendors, launch sites, and landing sites add to the complexity of efficient IT (Information Technology) tools.
Ingalls, John↗
A system safety model for developmental aircraft programs
Basic tenets of safety as applied to developmental aircraft programs are presented. The integration of safety into the project management aspects of planning, organizing, directing and controlling is illustrated by examples. The basis for project management use of safety and the relationship of these management functions to 'real-world' situations is presented. The rationale which led to the safety-related project decision and the lessons learned as they may apply to future projects are presented.
Amberboy, E. J.↗
Improved Understanding of Coupled Water and Carbon Cycle Processes through Machine Learning Approaches
Focal Area(s): This white paper addresses how explainable machine learning (ML) algorithms can improve insights gained from complex data (Focal Area 3). We will also address how ML approaches related to sensor compression and low energy AI hardware can be used for efficient data acquisition (Focal Area 1). Science Challenge: We will focus on the coupled water cycle and carbon cycle processes in terrestrial ecosystems and terrestrial-aquatic interfaces. Thus, our approaches will be centered around several data-model integration challenges indicated in EESD strategic plans for two science focus areas: Terrestrial Ecosystem Science and Hydrobiogeochemistry. We can use AI to correct systematic model errors due to biases in either data or model structure/processes. Error patterns in model predictions usually vary by region, model, season etc. But there are systematic patterns in them. e.g., soil moisture tends to be overestimated in the arid western continental United States underestimated in wetter eastern USA; some land surface models tend to underestimate moisture in wet seasons and overestimate in dry seasons. Moreover, the consequences of extreme events (e.g., drought, extreme flooding, storm surges associated with tropical storms, hurricanes) on carbon cycle processes are not well represented in ecosystem and Earth system models. Redox-sensitive processes (e.g., rapid oxidation/reduction of iron and other redox-sensitive elements in soil microsites subjected to fluctuated hydrology) in terrestrial-aquatic interfaces further challenge model predictions of hot-spots (and hot-moments) due to poor understanding of underlying mechanisms. Thus, AI/ML approaches can be used to learn patterns in the data and model errors and use them to build model equations and correct process-based model errors.
54 ENVIRONMENTAL SCIENCES↗
In-Kind Contributions: The PIP-II Project at Fermilab
The Proton Improvement Plan II (PIP-II) Project is the first U.S. accelerator project that has significant contributions from international partners. A project management framework was created to fully integrate and make consistent across all partners the design, development, and delivery of In-Kind Contributions (IKC) into PIP-II. This framework consists of planning documentation, procedures, and communication and assessment processes to control schedule, risk, quality, and technical integration over the lifetime of the project. The purpose of this paper is to present the PIP-II IKC model put in place to properly integrate the IKC deliverables into the PIP-II Linac and share experience and lessons learned from its early implementation.
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Deep Reinforcement Learning for Resilient Power and Energy Systems: Progress, Prospects, and Future Avenues
In recent years, deep reinforcement learning (DRL) has garnered substantial attention in the context of enhancing resilience in power and energy systems. Resilience, characterized by the ability to withstand, absorb, and quickly recover from natural disasters and human-induced disruptions, has become paramount in ensuring the stability and dependability of critical infrastructure. This comprehensive review delves into the latest advancements and applications of DRL in enhancing the resilience of power and energy systems, highlighting significant contributions and key insights. The exploration commences with a concise elucidation of the fundamental principles of DRL, highlighting the intricate interplay among reinforcement learning (RL), deep learning, and the emergence of DRL. Furthermore, it categorizes and describes various DRL algorithms, laying a robust foundation for comprehending the applicability of DRL. The linkage between DRL and power system resilience is forged through a systematic classification of DRL applications into five pivotal dimensions: dynamic response, recovery and restoration, energy management and control, communications and cybersecurity, and resilience planning and metrics development. This structured categorization facilitates a methodical exploration of how DRL methodologies can effectively tackle critical challenges within the domain of power and energy system resilience. The review meticulously examines the inherent challenges and limitations entailed in integrating DRL into power and energy system resilience, shedding light on practical challenges and potential pitfalls. Additionally, it offers insights into promising avenues for future research, with the aim of inspiring innovative solutions and further progress in this vital domain.
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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↗
Intelligent flight control systems
The capabilities of flight control systems can be enhanced by designing them to emulate functions of natural intelligence. Intelligent control functions fall in three categories. Declarative actions involve decision-making, providing models for system monitoring, goal planning, and system/scenario identification. Procedural actions concern skilled behavior and have parallels in guidance, navigation, and adaptation. Reflexive actions are spontaneous, inner-loop responses for control and estimation. Intelligent flight control systems learn knowledge of the aircraft and its mission and adapt to changes in the flight environment. Cognitive models form an efficient basis for integrating 'outer-loop/inner-loop' control functions and for developing robust parallel-processing algorithms.
Stengel, Robert F.↗
Designing Autonomy into Interfaces for Long-Duration Missions
As NASA develops technologies for long-duration crewed missions, we must understand how communication between ground control teams and astronauts differs from the current dynamic to adapt new concepts for long-duration mission operations. Today, ground control teams support astronauts with immediate availability to answer questions, resolve issues, and manage activities. In the near future, however, extended communication delays during long-duration missions will require astronauts to become more autonomous. As many of the responsibilities shift from the ground control teams to the astronauts on-board, the concept of operations must also change from how it functions today. With increased astronaut autonomy, software tools must be developed that enable efficient completion of mission tasks without increased mental workload. Designing software tools to facilitate crew autonomy requires development teams to know which data will enhance quick decision making while providing necessary context for situational awareness of systems being managed on-board. NASA's Autonomous Systems and Operations (ASO) team is presently developing a software interface tool, EXPRESS (EXpedite the PRocessing of Experiments for Space Station) 2.5 to enable a long-duration crew to schedule activities for and operate autonomous systems. This paper describes details of the integrated human factors approach that drove the design of the elements of the software tool, including self-managed scheduling, constraint-driven planning, autonomous system fault recovery, and recommended troubleshooting actions. Additionally, this paper will chronicle ASO modifications of the user interface after the team's first flight demonstration, how it was based on lessons learned during software development, as well as from crew feedback in order to develop the current version which will be demonstrated on ISS in 2021. In the upcoming ISS demonstration, the astronaut crew will be given scenarios for scheduling and operating autonomous system activities, including off-nominal scenarios and autonomous system recoveries. The demonstration of the EXPRESS 2.5 tool is a step towards improved levels of autonomy as our new journeys take us farther into space.
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Development testing of TSS-1 Deployer tether control system mechanisms
Successful tether deployment and retrieval, consistent with established control laws, is predicated upon statusing real time tether dynamic conditions. This paper reports on the initial phase of engineering tests performed on various components and subassemblies integral to the TSS-1 tether control system as part of the TSS Deployer. The tests were conducted as part of the tether control system development and verification plan to confirm the functionality and map the performance of the hardware in both ambient and environmental test conditions. The result of this development effort is a lessons-learned list and design upgrades to both the flight and test hardware and to the test methods and procedures.
Bentley, D. P.↗
Ground Testing of a 10 K Sorption Cryocooler Flight Experiment (BETSCE)
The Brilliant Eyes Ten-Kelvin Sorption Cryocooler Experiment (BETSCE) is a Space Shuttle side-wall-mounted flight experiment designed to demonstrate 10 K sorption cryocooler technology in a space environment. The BETSCE objectives are to: (1) provide a thorough end-to-end characterization and space performance validation of a complete, multistage, automated, closed-cycle hydride sorption cryocooler in the 10 to 30 K temperature range, (2) acquire the quantitative microgravity database required to provide confident engineering design, scaling, and optimization, (3) advance the enabling technologies and resolve integration issues, and (4) provide hardware qualification and safety verification heritage. BETSCE ground tests were the first-ever demonstration of a complete closed-cycle 10 K sorption cryocooler. Test results exceeded functional requirements. This paper summarizes functional and environmental ground test results, planned characterization tests, important development challenges that were overcome, and valuable lessons-learned.
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Z-2 Space Suit: A Case Study in Human Spaceflight Public Outreach
NASA Johnson Space Center's Z-series of planetary space suit prototypes is an iterative development platform with a Mars-forward design philosophy, targeting a Mars surface mission in the mid-2030s. The first space suit assembly, called the Z-1, was delivered in 2012. While meeting the project's stated requirements and objectives, the general public's reception primarily focused on the color scheme, which vaguely invoked similarity to a certain animated cartoon character. The public at large has and continues to be exposed to varying space suit design aesthetics from popular culture and low TRL technology maturation efforts such as mechanical counter-pressure. The lesson learned was that while the design aesthetic is not important from an engineering perspective, the perception of the public is important for NASA and human spaceflight in general. For the Z-2 space suit, an integrated public outreach strategy was employed to engage, excite and educate the public on the current technology of space suits and NASA's plans moving forward. The keystone of this strategy was a public vote on three different suit cover layer aesthetics, the winner of which would be used as inspiration in fabrication. Other components included social media, university collaboration, and select media appearances, the cumulative result of which, while intangible in its benefit, was ultimately a positive effect in terms of the image of NASA as well as the dissemination of information vital to dispelling public misconceptions.
McFarland, S. M.↗
Z-2 Space Suit: A Case Study in Human Spaceflight Public Outreach
NASA Johnson Space Center's Z-series of planetary space suit prototypes is an iterative development platform with a Mars-forward design philosophy, targeting a Mars surface mission in the mid-2030s. The first space suit assembly, called the Z-1, was delivered in 2012. While meeting the project's stated requirements and objectives, the general public's reception primarily focused on the color scheme, which vaguely invoked similarity to a certain animated cartoon character. The public at large has and continues to be exposed to varying space suit design aesthetics from popular culture and low TRL technology maturation efforts such as mechanical counterpressure. The lesson learned was that while the design aesthetic is not important from an engineering perspective, the perception of the public is important for NASA and human spaceflight in general. For the Z-2 space suit, an integrated public outreach strategy was employed to engage, excite and educate the public on the current technology of space suits and NASA's plans moving forward. The keystone of this strategy was a public vote on three different suit cover layer aesthetics, the winner of which would be used as inspiration in fabrication. Other components included social media, university collaboration, and select media appearances, the cumulative result of which, while intangible in its benefit, was ultimately a positive effect in terms of the image of NASA as well as the dissemination of information vital to dispelling public misconceptions.
McFarland, Shane M.↗
Promoting a Culture of Tailoring for Systems Engineering Policy Expectations
NASA's Marshall Space Flight Center (MSFC) has developed an integrated systems engineering approach to promote a culture of tailoring for program and project policy requirements. MSFC's culture encourages and supports tailoring, with an emphasis on risk-based decision making, for enhanced affordability and efficiency. MSFC's policy structure integrates the various Agency requirements into a single, streamlined implementation approach which serves as a "one-stop-shop" for our programs and projects to follow. The engineers gain an enhanced understanding of policy and technical expectations, as well as lesson's learned from MSFC's history of spaceflight and science missions, to enable them to make appropriate, risk-based tailoring recommendations. The tailoring approach utilizes a standard methodology to classify projects into predefined levels using selected mission and programmatic scaling factors related to risk tolerance. Policy requirements are then selectively applied and tailored, with appropriate rationale, and approved by the governing authorities, to support risk-informed decisions to achieve the desired cost and schedule efficiencies. The policy is further augmented by implementation tools and lifecycle planning aids which help promote and support the cultural shift toward more tailoring. The MSFC Customization Tool is an integrated spreadsheet that ties together everything that projects need to understand, navigate, and tailor the policy. It helps them classify their project, understand the intent of the requirements, determine their tailoring approach, and document the necessary governance approvals. It also helps them plan for and conduct technical reviews throughout the lifecycle. Policy tailoring is thus established as a normal part of project execution, with the tools provided to facilitate and enable the tailoring process. MSFC's approach to changing the culture emphasizes risk-based tailoring of policy to achieve increased flexibility, efficiency, and effectiveness in project execution, while maintaining appropriate rigor to ensure mission success.
Blankenship, Van A.↗
Securing the Clean Energy Transition with Cyber-Informed Engineering
The Bipartisan Infrastructure Law kick-started a surge in clean energy investments in the United States. Securing these systems and their supporting infrastructure from the outset is imperative, starting from the conceptual design and continuing through the entire engineering lifecycle. Cyber-Informed Engineering is an ideal approach to seamlessly integrate the necessary advanced control capabilities and adaptive protection systems, especially considering the trend towards cloud-based infrastructure and process controls. Learn more about CIE at https://www.energy.gov/ceser/articles/cyber-informed-engineering-bridge-between-cyber-and-critical-infrastructure-securing.
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