Results for “SAFE” — Page 4

NASA NTRS2016

Dawn Safing Approach to Ceres Re-Design

In mid-2014 the Dawn team finalized preparations for the spacecraft’s approach to Ceres. The design was the product of careful planning that began several years in advance, when the spacecraft left Vesta in September 2012. However, a few months prior to the anticipated start of approach, the spacecraft entered a safe mode. While temporary loss of thrust for ion propulsion missions is usually not mission-critical, it still presents many challenges. The safing event occurred at a time when thrusting to match Ceres’ orbital velocity was most effective, which meant the spacecraft must fly past Ceres before capturing into orbit. The new approach geometry changed the team’s strategy for navigating the spacecraft safely into orbit around Ceres with optical data. In this paper we describe the challenges of navigating the Dawn spacecraft into orbit around Ceres, including the safing event and re-design of the approach architecture.

Grebow, Daniel J.↗

NASA NTRS2016

Dawn Safing Approach to Ceres Re-Design

In mid-2014 the Dawn team finalized preparations for the spacecraft’s approach to Ceres. The design was the product of careful planning that began several years in advance, when the spacecraft left Vesta in September 2012. However, a few months prior to the anticipated start of approach, the spacecraft entered a safe mode. While temporary loss of thrust for ion propulsion missions is usually not mission-critical, it still presents many challenges. The safing event occurred at a time when thrusting to match Ceres’ orbital velocity was most effective, which meant the spacecraft must fly past Ceres before capturing into orbit. The new approach geometry changed the team’s strategy for navigating the spacecraft safely into orbit around Ceres with optical data. In this paper we describe the challenges of navigating the Dawn spacecraft into orbit around Ceres, including the safing event and re-design of the approach architecture.

Grebow, Dan↗

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↗

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 OSTI2017

Safe Packaging of Chemically Reactive Radioactive Waste - Addressing the Data Needs - 17487

Industry-standard thermal hazard screening is an effective, cost efficient approach to quickly obtain the required data typically utilized for safe scale-up of chemical processes and to accommodate changes to process recipes. Such thermal hazard screening is directly relevant to the packaging, transport, and storage of radioactive waste that is or can become chemically reactive. For such waste streams it is vital to identify safe temperature and pressure conditions and quantify adiabatic heat and gas generation rates in order to safely accommodate (or preclude) thermal instability within the waste package or storage facility. This paper illustrates widely-used thermal hazard screening bench-scale techniques that lend themselves to quickly identifying reactive hazards while providing directly scalable data for package/storage facility design. Example data are presented with discussion of how the data are analyzed for application to safe packaging and storage. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

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↗

DOE OSTI2022

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↗

DOE OSTI2020

Multi-Agent Safe Policy Learning for Power Management of Networked Microgrids

This paper presents a supervised multi-agent safe policy learning (SMAS-PL) method for optimal power management of networked microgrids (MGs) in distribution systems. While unconstrained reinforcement learning (RL) algorithms are black-box decision models that could fail to satisfy grid operational constraints, our proposed method considers AC power flow equations and other operational limits. Accordingly, the training process employs the gradient information of operational constraints to ensure that the optimal control policy functions generate safe and feasible decisions. Furthermore, we have developed a distributed consensus-based optimization approach to train the agents’ policy functions while maintaining MGs’ privacy and data ownership boundaries. After training, the learned optimal policy functions can be safely used by the MGs to dispatch their local resources, without the need to solve a complex optimization problem from scratch. Lastly, numerical experiments have been devised to verify the performance of the proposed method.

, policy gradient↗

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

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↗

DOE OSTI2022

Fast charging of Li-ion cells: Effect of separator membranes and mapping of “safe lines” to avoid Li plating

An experimental method is introduced that uses microprobe Li/Cu reference electrodes to map "safe" charging regimes to avoid Li metal plating on graphite anodes at rates to 8C. With this method, a Li-ion cell closely approaches the Li plating condition but avoids descending into this regime. This "safe" approach is used to demonstrate effects of the microporous separator on Li plating in cells consisting of a layered nickel-manganese -cobalt oxide cathode and graphite anode. We argue that most of the difference in behaviors for different separators arises from time delayed Li + ion percolation through the membrane. In conclusion, our study suggests that explicitly treating kinetics of phase transitions in the lithiated graphite anode is essential for electrochemical models predicting "safe" lines for fast charging cells.

25 ENERGY STORAGE↗

DOE OSTI2015

Bifurcation characteristics and safe basin of MSMA microgripper subjected to stochastic excitation

A kind of magnetic shape memory alloy (MSMA) microgripper is proposed in this paper, and its nonlinear dynamic characteristics are studied when the stochastic perturbation is considered. Nonlinear differential items are introduced to explain the hysteretic phenomena of MSMA, and the constructive relationships among strain, stress, and magnetic field intensity are obtained by the partial least-square regression method. The nonlinear dynamic model of a MSMA microgripper subjected to in-plane stochastic excitation is developed. The stationary probability density function of the system’s response is obtained, the transition sets of the system are determined, and the conditions of stochastic bifurcation are obtained. The homoclinic and heteroclinic orbits of the system are given, and the boundary of the system’s safe basin is obtained by stochastic Melnikov integral method. The numerical and experimental results show that the system’s motion depends on its parameters, and stochastic Hopf bifurcation appears in the variation of the parameters; the area of the safe basin decreases with the increase of the stochastic excitation, and the boundary of the safe basin becomes fractal. The results of this paper are helpful for the application of MSMA microgripper in engineering fields.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Asymptotically safe clockwork mechanism

In this paper, we demonstrate that safe quantum field theories can accommodate the clockwork mechanism upgrading it to a fundamental theory a lá Wilson. Additionally, the clockwork mechanism naturally sources Yukawa hierarchies for safe completions of the Standard Model. As proof of concept, we investigate a safe Pati-Salam clockwork structure.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

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 OSTI2021

Safe Reinforcement Learning for Emergency Load Shedding of Power Systems

The paradigm shift in the electric power grid necessitates a revisit of existing control methods to ensure the grid’s security and resilience. In particular, the increased uncertainties and rapidly changing operational conditions in power systems have revealed outstanding issues in terms of either speed, adaptiveness, or scalability of the existing control methods for power systems. On the other hand, the availability of massive real-time data can provide a clearer picture of what is happening in the grid. Recently, deep reinforcement learning (RL) has been regarded and adopted as a promising approach leveraging massive data for fast and adaptive grid control. However, like most existing machine learning (ML)- based control techniques, RL control usually cannot guarantee the safety of the power systems. In this paper, we introduce a novel method for safe RL-based load shedding of power systems that can enhance the safe voltage recovery of the electric power grid after experiencing faults. Numerical simulation on the IEEE 39-bus testcase is performed to demonstrate the effectiveness of the proposed safe RL emergency control, as well as its adaptive capability to faults not seen in the training.

reinforcement learning, emergency control, power g↗

DOE OSTI2019

Good Practice for Safe Handling of Lithium Metal Anode and High Energy Rechargeable Lithium Metal Batteries

High-energy rechargeable lithium metal batteries have been intensively revisited in recent years. Since more researchers started to use pouch cell as the platform to study the fundamentals at relevant scales, safe testing and handling of lithium metal and high-energy lithium metal batteries have become critical. Cautions and safety procedures are needed when handling cycled pouch cells with pulverized lithium particles inside. From cell design, electrode preparation, cell fabrication to testing procedure, this work aims to discuss all possible root causes that may initiate cell internal shorting and raise safety concerns. Safe transfer and disposal of cycled pouch cells are also discussed. The insights provided in this article are applicable for the research on high-energy lithium-ion batteries as well and may inspire more safety strategies to accelerate research innovation by using large-format batteries as the testing vehicle and conduct the research safely.

25 ENERGY STORAGE↗