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NASA NTRSDate not supplied

Autonomous System Operations for Lunar Safe Haven Establishment and Sustainment

To enable a sustainable, permanent human lunar presence, NASA must provide a safe haven shelter to protect astronauts and equipment from radiation, thermal extremes, and micro-meteoroids (MM). Planning and development for a robust Safe Haven includes an examination of NASA activities in site preparation, excavation, regolith transfer, surface operations, autonomous monitoring and maintenance, advanced manufacturing, and in-situ resource utilization (ISRU) for identifying the best approaches when implementing a safe haven shelter. These NASA activities were reviewed as a part of a trade study conducted at NASA Langley to assess technological needs and estimated technology readiness levels (TRL). This paper presents a thorough review of the role and level of autonomy in the establishment and sustainment operations of a Lunar Safe Haven.

Walter J Waltz↗

DOE OSTI2025

Safe Physics-Informed Machine Learning for Dynamics and Control

This tutorial paper focuses on safe physics-informed machine learning in the context of dynamics and control, providing a comprehensive overview of how to integrate physical models and safety guarantees. As machine learning techniques enhance the modeling and control of complex dynamical systems, ensuring safety and stability remains a critical challenge, especially in safety-critical applications like autonomous vehicles, robotics, medical decision-making, and energy systems. We explore various approaches for embedding and ensuring safety constraints, including structural priors, Lyapunov and Control Barrier Functions, predictive control, projections, and robust optimization techniques. Additionally, we delve into methods for uncertainty quantification and safety verification, including reachability analysis and neural network verification tools, which help validate that control policies remain within safe operating bounds even in uncertain environments. The paper includes illustrative examples demonstrating the implementation aspects of safe learning frameworks that combine the strengths of data-driven approaches with the rigor of physical principles, offering a path toward the safe control of complex dynamical systems.

Drgona, Jan↗

NASA NTRS2015

Sun Safe Mode Controller Design for LADEE

This paper presents the development of sun safe controllers which are designed to keep the spacecraft power positive and thermally balanced in the event an anomaly is detected. Employed by NASA's Lunar Atmosphere and Dust Environment Explorer (LADEE), the controllers utilize the measured sun vector and the spacecraft body rates for feedback control. To improve the accuracy of sun vector estimation, the least square minimization approach is applied to process the sensor data, which is proven to be effective and accurate. To validate the controllers, the LADEE spacecraft model engaging the sun safe mode was first simulated and then compared with the actual LADEE orbital fight data. The results demonstrated the applicability of the proposed sun safe controllers.

Sun Safe Control↗

NASA NTRS2022

Scheduling For Urban Air Mobility Using Safe Learning

This work considers the scheduling problem for Urban Air Mobility (UAM) vehicles travelling between origin-destination pairs with both hard and soft trip deadlines. Each route is described by a discrete probability distribution over trip completion times (or delay) and over interarrival times of requests (or demand) for the route along with a fixed hard or soft deadline. Soft deadlines carry a cost that is incurred when the deadline is missed. An online, safe scheduler is developed that ensures that hard deadlines are never missed and that average cost of missing soft deadlines is minimized. The system is modelled as a Markov Decision Process (MDP) and safe model based learning is used to find the probabilistic distributions over route delays and demand. Monte Carlo Tree Search (MCTS) Earliest Deadline First (EDF) is used to safely explore the learned models in an online fashion and develop a near-optimal non-preemptive scheduling policy. These results are compared with Value Iteration (VI) and MCTS (Random) scheduling solutions.

Urban Air Mobility↗

NASA NTRSDate not supplied

Scheduling for Urban Air Mobility using Safe Learning

Urban Air Mobility↗

DOE OSTI2024

Infrared-safe energy weighting does not guarantee small nonperturbative effects

Infrared and collinear (IRC) safety has long been used a proxy for robustness when developing new jet substructure observables. This guiding philosophy has been carried into the deep learning era, where IRC-safe neural networks have been used for many jet studies. For graph-based neural networks, the most straightforward way to achieve IRC safety is to weight particle inputs by their energies. However, energy-weighting by itself does not guarantee that perturbative calculations of machine-learned observables will enjoy small nonperturbative corrections. Here, in this paper, we demonstrate the sensitivity of IRC-safe networks to nonperturbative effects, by training an energy flow network (EFN) to maximize its sensitivity to hadronization. We then show how to construct Lipschitz energy flow networks (L-EFNs), which are both IRC safe and relatively insensitive to nonperturbative corrections. We demonstrate the performance of L-EFNs on generated samples of quark and gluon jets, and showcase fascinating differences between the learned latent representations of EFNs and L-EFNs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Optimal Management of Grid-Interactive Efficient Buildings via Safe Reinforcement Learning

Reinforcement learning (RL)-based methods have achieved significant success in managing grid-interactive efficient buildings (GEBs). However, RL does not carry intrinsic guarantees of constraint satisfaction, which may lead to severe safety consequences. Besides, in GEB control applications, most existing safe RL approaches rely only on the regularisation parameters in neural networks or penalty of rewards, which often encounter challenges with parameter tuning and lead to catastrophic constraint violations. To provide enforced safety guarantees in controlling GEBs, this paper designs a physics-inspired safe RL method whose decision-making is enhanced through safe interaction with the environment. Different energy resources in GEBs are optimally managed to minimize energy costs and maximize customer comfort. The proposed approach can achieve strict constraint guarantees based on prior knowledge of a set of developed hard steady-state rules. Simulations on the optimal management of GEBs, including heating, ventilation, and air conditioning (HVAC), solar photovoltaics, and energy storage systems, demonstrate the effectiveness of the proposed approach.

Huo, Xiang↗

DOE OSTI2026

Genesis Mission-Enabled Secure AI to Fortify Energy Process Safety (Genesis-SAFE)

Argonne National Laboratory is supporting the U.S. Department of Transportation’s (USDOT’s) Bureau of Transportation Statistics (BTS) with collaborative research on development and application of privacy preserving AI frameworks that leverage unmatched AI expertise and secure computing resources made available through the U.S. Genesis Mission1 . This research advances U.S. energy security goals by supporting a safe offshore energy industry with secure, domain-specific AI tools to analyze confidential industry datasets collected by BTS to rapidly improve identification of hazards, precursors, and systemic safety risks in high-risk operational environments. The staged, security-first approach begins with development and testing of Argonne’s Genesis Mission-enabled Secure AI to Fortify Energy Process Safety (Genesis-SAFE) framework within Argonne’s accredited secure computing enclave (ABLE) leveraging Argonne’s AI scientific assistant substrate (AISAC). Methods to build synthetic datasets were developed together with BTS for use in preparing synthetic datasets that can be used to validate data containment, governance, and security controls in the ABLE environment. Future research directions would focus on applying the Genesis-SAFE framework to CIPSEA-protected datasets entirely within ABLE to support confidentiality-preserving analysis of safety risks, trends, and contributing factors.

Kim, Hyekyung [Argonne National Laboratory (ANL), ↗

NASA NTRS1973

SAFE - Six axis frequency evaluation of a motion simulator.

SAFE is a digital computer program used to check the response of an aircraft motion simulator. SAFE drives each axis of the simulator near its acceleration, velocity and position limits, recording the simulator motions for comparison with nominal responses. SAFE also drives up to six axes at once to measure the frequency response and noise levels of each axis. Together these two measurements provide a sensitive check of the entire motion system, yet they can be completed in less than ten minutes.

Shirley, R. S.↗

NASA NTRS1975

Fail-safe system for activity cooled supersonic and hypersonic aircraft

A fail-safe-system concept was studied as an alternative to a redundant active cooling system for supersonic and hypersonic aircraft which use the heat sink of liquid-hydrogen fuel for cooling the aircraft structure. This concept consists of an abort maneuver by the aircraft and a passive thermal protection system (TPS) for the aircraft skin. The abort manuever provides a low-heat-load descent from normal cruise speed to a lower speed at which cooling is unnecessary, and the passive TPS allows the aircraft skin to absorb the abort heat load without exceeding critical skin temperature. On the basis of results obtained, it appears that this fail-safe-system concept warrants further consideration, inasmuch as a fail-safe system could possibly replace a redundant active cooling system with no increase in weight and would offer other potential advantages.

Jones, R. A.↗

NASA NTRS1976

Study of fail-safe abort system for an actively cooled hypersonic aircraft, volume 2

Conceptual designs of a fail-safe abort system for hydrogen fueled actively cooled high speed aircraft are examined. The fail-safe concept depends on basically three factors: (1) a reliable method of detecting a failure or malfunction in the active cooling system, (2) the optimization of abort trajectories which minimize the descent heat load to the aircraft, and (3) fail-safe thermostructural concepts to minimize both the weight and the maximum temperature the structure will reach during descent. These factors are examined and promising approaches are evaluated based on weight, reliability, ease of manufacture and cost.

Peeples, M. E.↗

NASA NTRS1976

Study of a fail-safe abort system for an actively cooled hypersonic aircraft. Volume 1: Technical summary

Pirello, C. J.↗

NASA NTRS1975

On the calculation of low-thrust fail-safe trajectories

A guidance algorithm is developed for a low-thrust spacecraft such that target intercept is possible in spite of premature thrust termination along the trajectory. Such a trajectory is called a 'fail-safe' trajectory and the spacecraft thrust is utilized to minimize the relative target-spacecraft approach speed. The fail-safe guidance algorithm is solved using the concept of a critical thrust plane and a non-critical thrust direction. Several examples of fail-safe guidance are presented for a solar-electric propulsion flyby mission to the comet Encke.

Sauer, C. G., Jr.↗

NASA NTRS1983

Safe on-orbit experiment for measurement of large structures dynamics

The Solar Array Flight Experiment (SAFE) on-orbit experiment for measurement of large structures dynamics consists of a dynamic sensing system designed to record and analyze the dynamic characteristics of the SAFE. The early availability of the SAFE and its basic large space structure characteristics make it a logical candidate for verification of the sensing system and the evaluation technique. The characteristics of the solar array which place it well within the generic class of large space structures are: (1) large size, (2) low natural frequencies, (3) mechanical complexity of its extendable/retractable mast, and (4) the inability to dynamically test in the Earth's atmosphere and in one g. The fourth characteristic is due to its size, air damping dominance of the blanket, and structural instability in one g. The specific characteristics of the solar array are shown and the applicability to large space structures is illustrated.

Schock, R. W.↗

NASA NTRS1988

A computer-based Safety Assessment for Flight Evacuation - SAFE

The Safety Assessment for Flight Evacuation (SAFE) system has been developed for the computerized evaluation of safety in civil Emergency Medical Service (EMS) operations. The speed of the microprocessor used to analyze data allows many individual factors to be considered, as well as the interactions among those factors. SAFE's data base is structured as if-then conditional statements. SAFE also allows the most important of the factors to be given greater weight in the final score. The questionnaire filled by EMS crews encompassed mission-, crew-, organization-, environment-, and aircraft-related factors; each of these was subdivided into as many as eight variables affecting the EMS-mission risk of that factor.

Shively, Robert J.↗

NASA NTRS1998

Simple Automatic File Exchange (SAFE) to Support Low-Cost Spacecraft Operation via the Internet

Various issues associated with Simple Automatic File Exchange (SAFE) are presented in viewgraph form. Specific topics include: 1) Packet telemetry, Internet IP networks and cost reduction; 2) Basic functions and technical features of SAFE; 3) Project goals, including low-cost satellite transmission to data centers to be distributed via an Internet; 4) Operations with a replicated file protocol; 5) File exchange operation; 6) Ground stations as gateways; 7) Lessons learned from demonstrations and tests with SAFE; and 8) Feedback and future initiatives.

Baker, Paul↗

NASA NTRS2002

Flywheel Rotor Safe-Life Technology

Since the 1960s, research has been conducted into the use of flywheels as energy storage systems. The-proposed applications include energy storage for hybrid and electric automobiles, attitude control and energy storage for satellites, and uninterruptible power supplies for hospitals and computer centers. For many years, however, the use of flywheels for space applications was restricted by the total weight of a system employing a metal rotor. With recent technological advances in the manufacturing of composite materials, however, lightweight composite rotors have begun to be proposed for such applications. Flywheels with composite rotors provide much higher power and energy storage capabilities than conventional chemical batteries. However, the failure of a high speed flywheel rotor could be a catastrophic event. For this reason, flywheel rotors are classified by the NASA Fracture Control Requirements Standard as fracture critical parts. Currently, there is no industry standard to certify a composite rotor for safe and reliable operation forth( required lifetime of the flywheel. Technical problems hindering the development of this standard include composite manufacturing inconsistencies, insufficient nondestructive evaluation (NDE) techniques for detecting defects and/or impact damage, lack of standard material test methods for characterizing composite rotor design allowables, and no unified proof (over-spin) test for flight rotors. As part of a flywheel rotor safe-life certification pro-ram funded b the government, a review of the state of the art in composite rotors is in progress. The goal of the review is to provide a clear picture of composite flywheel rotor technologies. The literature review has concentrated on the following topics concerning composites and composite rotors: durability (fatigue) and damage tolerance (safe-life) analysis/test methods, in-service NDE and health monitoring techniques, spin test methods/ procedures, and containment options. This report presents the papers selected for their relevance to this topic and summarizes them.

Ratner, J. K. H.↗

NASA NTRS2003

Secured Advanced Federated Environment (SAFE): A NASA Solution for Secure Cross-Organization Collaboration

This paper discusses the challenges and security issues inherent in building complex cross-organizational collaborative projects and software systems within NASA. By applying the design principles of compartmentalization, organizational hierarchy and inter-organizational federation, the Secured Advanced Federated Environment (SAFE) is laying the foundation for a collaborative virtual infrastructure for the NASA community. A key element of SAFE is the Micro Security Domain (MSD) concept, which balances the need to collaborate and the need to enforce enterprise and local security rules. With the SAFE approach, security is an integral component of enterprise software and network design, not an afterthought.

Chow, Edward↗