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DOE OSTI2017

Automated work package: capabilities of the future - 275

As part of the ongoing efforts at the U.S. Department of Energy's Light Water Reactor Sustainability Program, Idaho National Laboratory (INL) is conducting several pilot projects in collaboration with the nuclear industry to improve the reliability, safety, and economics of the nuclear power industry, especially as the nuclear power plants extend their operating licenses to 80 years. One of these pilot projects is the automated work package (AWP) pilot project. An AWP is an electronic intelligent and interactive work package. It uses plant condition, resources status, and user progress to adaptively drive the work process in a manner that increases efficiency while reducing human error. To achieve this mission, the AWP acquires information from various systems of a nuclear power plant and incorporates several advanced instrumentation and control technologies along with modern human factors techniques. With the current rapid technological advancement, it is possible to envision several available or soon-to-be-available capabilities that can play a significant role in improving the work package process. In this pilot project, the researchers develop an AWP prototype of an expanding set of capabilities and evaluates them in an industrial environment. While some of the proposed capabilities are based on using technological advances in other applications, others are conceptual; thus, they require significant research and development to be applicable in an AWP. The scope of this paper is to introduce a set of envisioned capabilities, their need for the industry, and the industry difficulties they resolve. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

NASA NTRS1990

Beam rider for an Articulated Robot Manipulator (ARM) accurate positioning of long flexible manipulators

Laser beam positioning and beam rider modules were incorporated into the long hollow flexible segment of an articulated robot manipulator (ARM). Using a single laser beam, the system determined the position of the distal ARM endtip, with millimetric precision, in six degrees of freedom, at distances of up to 10 meters. Preliminary designs, using space rated technology for the critical systems, of a two segmented physical ARM, with a single and a dual degree of freedom articulation, were developed, prototyped, and tested. To control the positioning of the physical ARM, an indirect adaptive controller, which used the mismatch between the position of the laser beam under static and dynamic conditions, was devised. To predict the behavior of the system and test the concept, a computer simulation model was constructed. A hierarchical artificially intelligent real time ADA operating system program structure was created. The software was designed for implementation on a dedicated VME bus based Intel 80386 administered parallel processing multi-tasking computer system.

Malachowski, M. J.↗

NASA NTRS2000

IVHM for the 3rd Generation RLV Program: Technology Development

The objective behind the Integrated Vehicle Health Management (IVHM) project is to develop and integrate the technologies which can provide a continuous, intelligent, and adaptive health state of a vehicle and use this information to improve safety and reduce costs of operations. Technological areas discussed include: developing, validating, and transfering next generation IVHM technologies to near term industry and government reusable launch systems; focus NASA on the next generation and highly advanced sensor and software technologies; and validating IVHM systems engineering design process for future programs.

Kahle, Bill↗

DOE OSTI2017

10th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technologies, San Francisco, CA, USA, June 11–15, 2017

As part of the ongoing efforts at the U.S. Department of Energy’s Light Water Reactor Sustainability Program, Idaho National Laboratory is conducting several pilot projects in collaboration with the nuclear industry to improve the reliability, safety, and economics of the nuclear power industry, especially as the nuclear power plants extend their operating licenses to 80 years. One of these pilot projects is the automated work package (AWP) pilot project. An AWP is an electronic intelligent and interactive work package. It uses plant condition, resources status, and user progress to adaptively drive the work process in a manner that increases efficiency while reducing human error. To achieve this mission, the AWP acquires information from various systems of a nuclear power plant’s and incorporates several advanced instrumentation and control technologies along with modern human factors techniques. With the current rapid technological advancement, it is possible to envision several available or soon-to-be-available capabilities that can play a significant role in improving the work package process. As a pilot project, the AWP project develops a prototype of an expanding set of capabilities and evaluates them in an industrial environment. While some of the proposed capabilities are based on using technological advances in other applications, others are conceptual; thus, require significant research and development to be applicable in an AWP. The scope of this paper is to introduce a set of envisioned capabilities, their need for the industry, and the industry difficulties they resolve.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NASA NTRS2018

Serious Gaming for Building a Basis of Certification via Trust and Trustworthiness of Autonomous Systems

Autonomous systems governed by a variety of adaptive and nondeterministic algorithms are being planned for inclusion into safety-critical environments, such as unmanned aircraft and space systems in both civilian and military applications. However, until autonomous systems are proven and perceived to be capable and resilient in the face of unanticipated conditions, humans will be reluctant or unable to delegate authority, remaining in control aided by machine-based information and decision support. Proving capability, or trustworthiness, is a necessary component of certification. Perceived capability is a component of trust. Trustworthiness is an attribute of a cyber-physical system that requires context-driven metrics to prove and certify. Trust is an attribute of the agents participating in the system and is gained over time and multiple interactions through trustworthy behavior and transparency. Historically, artificial intelligence and machine learning systems provide answers without explanation - without a rationale or insight into the machine “thinking”. In order to function as trusted teammates, machines must be able to explain their decisions and actions. This transparency is a product of both content and communication. NASA’s Autonomy Teaming & TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) project seeks to build a basis for certification of autonomous systems via establishing metrics for trustworthiness and trust in multi-agent team interactions, using AI (Artificial Intelligence) explainability and persistent modeling and simulation, in the context of mission planning and execution, with analyzable trajectories. Inspired by Massively Multiplayer Online Role Playing Games (MMORPG) and Serious Gaming, the proposed ATTRACTOR modeling and simulation environment is similar to online gaming environments in which player (aka agent) participants interact with each other, affect their environment, and expect the simulation to persist and change regardless of any individual agent’s active participation. This persistent simulation environment will accommodate individual agents, groups of self-organizing agents, and large-scale infrastructure behavior. The effects of the emerging adaptation and coevolution can be observed and measured to building a basis of measurable trustworthiness and trust, toward certification of safety-critical autonomous systems.

Allen, B. Danette↗

DOE OSTI2025

Next-Generation Energy Technologies for Connected and Automated On-Road Vehicles (NEXTCAR) - Predictive Data-Driven Vehicle Dynamics and Powertrain Control: from ECU to the Cloud (Final Scientific/Technical Report)

This project developed and demonstrated a predictive, data-driven vehicle control system designed to improve energy efficiency and driving performance. The team created intelligent self-driving car technology that optimizes fuel and electricity use by proactively planning vehicle actions. By combining Level 4 autonomous driving capabilities with vehicle-to-everything (V2X) connectivity, the system enables vehicles to adjust speed and change lanes in response to traffic signals, surrounding vehicles, and road conditions, reducing unnecessary stops and delays. In testing, the system improved vehicle fuel economy by more than 30% and reduced travel time by approximately 10%, compared to a conventional adaptive cruise control baseline. These results demonstrate the technical effectiveness of using predictive, V2X-enabled strategies, such as traffic light timing and surrounding traffic awareness, to inform real-time vehicle powertrain control and driving behavior. Additionally, a supporting cloud platform was developed to provide dispatch and route recommendations as well as to log vehicle data, demonstrating the economic feasibility of this approach at the fleet level. By optimizing dispatching and routing operations, this technology enables electric fleet operators to use their vehicles more efficiently and reduce reliance on diesel backups, lowering both operating costs and energy consumption. Overall, this project’s technology advances the future of clean, energy-efficient transportation, enabling vehicles and fleets to reduce energy waste, cut costs, and lower emissions through intelligent automation and connectivity.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRSDate not supplied

Human Interfaces and Management of Information (HIMI) Challenges for “In-time” Aviation Safety Management Systems (IASMS)

The envisioned transformation of the National Airspace System to integrate an In-time Aviation Safety Management System(IASMS)to assure safety in Advanced Air Mobility(AAM)brings unprecedented challenges to the design of human interfaces and management of safety information. Safety in design and operational safety assurance are critical factors for how humans will interact with increasingly autonomous systems. The IASMS Concept of Operations builds from traditional commercial operator safety management and scales in complexity to AAM. The transformative changes in future aviation systems pose potential new critical safety risks with novel types of aircraft and other vehicles having different performance capabilities, flying in increasingly complex airspace, and using adaptive contingencies to manage normal and non-normal operations. These changes compel development of new and emerging capabilities that enable innovative ways for humans to interact with data and manage information. In-creasing complexity of AAM corresponds with use of predictive modeling, data analytics, machine learning, and artificial intelligence to effectively address known hazards and emergent risks. The roles of humans will dynamically evolve in increments with this technological and operational evolution. The interfaces for how humans will interact with increasingly complex and assured systems designed to operate autonomously and how information will need to be presented are important challenges to be resolved.

Lawrence J Prinzel↗

NASA NTRS1980

Physiology of man and animals in the Tenth Five-Year Plan: Proceedings of the Thirteenth Congress of the I. P. Pavlov All-Union Physiological Society

Research in the field of animal and human physiology is reviewed. The following topics on problems of physiological science and related fields of knowledge are discussed: neurophysiology and higher nervous activity, physiology of sensory systems, physiology of visceral systems, evolutionary and ecological physiology, physiological cybernetics, computer application in physiology, information support of physiological research, history and theory of development of physiology. Also discussed were: artificial intelligence, physiological problems of reflex therapy, correlation of structure and function of the brain, adaptation and activity, microcirculation, and physiological studies in nerve and mental diseases.

Lange, K. A.↗

NASA NTRS2021

Computational Inference of Vibratory System with Incomplete Modal Information Using Parallel, Interactive and Adaptive Markov Chains

Inverse analysis of vibratory system is an important subject in fault identification, model updating, and robust design and control. It is challenging subject because 1) the problem is oftentimes underdetermined while the measurements are limited and/or incomplete; 2) many combinations of parameters may yield results that are similar with respect to actual response measurements; and 3) uncertainties inevitably exist. The aim of this research is to leverage upon computational intelligence through statistical inference to facilitate an enhanced, probabilistic framework using incomplete modal response measurement. This new framework is built upon efficient inverse identification through optimization, whereas Bayesian inference is employed to account for the effect of uncertainties. To overcome the computational cost barrier, we adopt Markov chain Monte Carlo (MCMC) to characterize the target function/distribution. Instead of using single Markov chain in conventional Bayesian approach, we develop a new sampling theory with multiple parallel, interactive and adaptive Markov chains and incorporate it into Bayesian inference. This can harness the collective power of these Markov chains to realize the concurrent search of multiple local optima. The number of required Markov chains and their respective initial model parameters are automatically determined via Monte Carlo simulation-based sample pre-screening followed by K-means clustering analysis. These enhancements can effectively address the aforementioned challenges in finite element inverse analysis. The validity of this framework is systematically demonstrated through case studies.

K Zhou↗

DOE OSTI2024

Employing artificial intelligence to steer exascale workflows with colmena

Computational workflows are a common class of application on supercomputers, yet the loosely coupled and heterogeneous nature of workflows often fails to take full advantage of their capabilities. We created Colmena to leverage the massive parallelism of a supercomputer by using Artificial Intelligence (AI) to learn from and adapt a workflow as it executes. Colmena allows scientists to define how their application should respond to events (e.g., task completion) as a series of cooperative agents. In this paper, we describe the design of Colmena, the challenges we overcame while deploying applications on exascale systems, and the science workflows we have enhanced through interweaving AI. The scaling challenges we discuss include developing steering strategies that maximize node utilization, introducing data fabrics that reduce communication overhead of data-intensive tasks, and implementing workflow tasks that cache costly operations between invocations. These innovations coupled with a variety of application patterns accessible through our agent-based steering model have enabled science advances in chemistry, biophysics, and materials science using different types of AI. In conclusion, our vision is that Colmena will spur creative solutions that harness AI across many domains of scientific computing.

Workflows↗

DOE OSTI2019

Institutional Solutions to Enhance Power Sector Resilience

How will power system operators, personnel, and end-users react during a major disruption? Will the system operations adapt to changing conditions and recover quickly? A balanced approach to power sector resilience that includes both institutional and technical solutions will help answer these questions. Institutional solutions include: building the capacity of personnel, Improving access to data, Enhancing operational intelligence, engaging stakeholders, developing a culture of resilience, and implementing other relevant institutional operational processes.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS2014

Full Scale Advanced Systems Testbed (FAST): Capabilities and Recent Flight Research

At the NASA Armstrong Flight Research Center research is being conducted into flight control technologies that will enable the next generation of air and space vehicles. The Full Scale Advanced Systems Testbed (FAST) aircraft provides a laboratory for flight exploration of these technologies. In recent years novel but simple adaptive architectures for aircraft and rockets have been researched along with control technologies for improving aircraft fuel efficiency and control structural interaction. This presentation outlines the FAST capabilities and provides a snapshot of the research accomplishments to date. Flight experimentation allows a researcher to substantiate or invalidate their assumptions and intuition about a new technology or innovative approach Data early in a development cycle is invaluable for determining which technology barriers are real and which ones are imagined Data for a technology at a low TRL can be used to steer and focus the exploration and fuel rapid advances based on real world lessons learned It is important to identify technologies that are mature enough to benefit from flight research data and not be tempted to wait until we have solved all the potential issues prior to getting some data Sometimes a stagnated technology just needs a little real world data to get it going One trick to getting data for low TRL technologies is finding an environment where it is okay to take risks, where occasional failure is an expected outcome Learning how things fail is often as valuable as showing that they work FAST has been architected to facilitate this type of testing for control system technologies, specifically novel algorithms and sensors Rapid prototyping with a quick turnaround in a fly-fix-fly paradigm Sometimes it's easier and cheaper to just go fly it than to analyze the problem to death The goal is to find and test control technologies that would benefit from flight data and find solutions to the real barriers to innovation. The FAST vehicle is a flexible laboratory for nascent technologies that would benefit from early life cycle flight research data It provides a robust and safe environment where innovative techniques can be explored in a fly-fix-fly rapid prototyping paradigm IRAC Simple adaptive control technologies can provide real benefits without undo complexity Adverse pilot/adaptive system interactions can be mitigated and tools have been developed to evaluate those interactions ICP Substantial fuel savings can be achieved over a broad range of vehicles and configurations with intelligent control solutions LVAC The AAC design is robust and effective for the SLS mission, and promises to provide benefits to other platforms as well OCLA Hopefully will show that structural feedback can be seamlessly integrated with performance and stability objectives All of these control technologies have been implemented into the same baseline control law and could be combined into one control solution that answers many pressing questions for modern vehicle configurations

flight controls↗

NASA NTRS2020

Adaptive, Model-Driven Observation for Earth Science

In this article we introduce a preliminary effort to apply an adaptive and model-driven sensing framework to the study of large scale storms. Such systems pose great challenges as studying complex, fast developing Earth science phenomena such as hurricanes have significant spatial extent and complex temporal evolution making comprehensive sensing of the entire phenomena prohibitive. We use adaptive sensing to direct sensing in an autonomous and intelligent cycle. We show how online analysis would increase the knowledge of the event and decrease uncertainty in predictions.

Swope, J.↗

NASA NTRS1992

Intelligent data management for real-time spacecraft monitoring

Real-time AI systems have begun to address the challenge of restructuring problem solving to meet real-time constraints by making key trade-offs that pursue less than optimal strategies with minimal impact on system goals. Several approaches for adapting to dynamic changes in system operating conditions are known. However, simultaneously adapting system decision criteria in a principled way has been difficult. Towards this end, a general technique for dynamically making such trade-offs using a combination of decision theory and domain knowledge has been developed. Multi-attribute utility theory (MAUT), a decision theoretic approach for making one-time decisions is discussed and dynamic trade-off evaluation is described as a knowledge-based extension of MAUT that is suitable for highly dynamic real-time environments, and provides an example of dynamic trade-off evaluation applied to a specific data management trade-off in a real-world spacecraft monitoring application.

Schwuttke, Ursula M.↗

DOE OSTI2020

High-Performance Integrated Window and Façade Solutions for California

The researchers developed a new generation of high-performance façade systems and supporting design and management tools to support industry in meeting California’s greenhouse gas reduction targets, reduce energy consumption, and enable an adaptable response to minimize real-time demands on the electricity grid. The project resulted in five outcomes: (1) The research team developed an R-5, 1-inch thick, triplepane, insulating glass unit with a novel low-conductance aluminum frame. This technology can help significantly reduce residential cooling and heating loads, particularly during the evening. (2) The team developed a prototype of a windowintegrated local ventilation and energy recovery device that provides clean, dry fresh air through the façade with minimal energy requirements. (3) A daylight-redirecting louver system was prototyped to redirect sunlight 15–40 feet from the window. Simulations estimated that lighting energy use could be reduced by 35–54 percent without glare. (4) A control system incorporating physics-based equations and a mathematical solver was prototyped and field tested to demonstrate feasibility. Simulations estimated that total electricity costs could be reduced by 9-28 percent on sunny summer days through adaptive control of operable shading and daylighting components and the thermostat compared to state-of-the-art automatic façade controls in commercial building perimeter zones. (5) Supporting models and tools needed by industry for technology R&D and market transformation activities were validated. Attaining California’s clean energy goals require making a fundamental shift from today’s ad-hoc assemblages of static components to turnkey, intelligent, responsive, integrated building façade systems. These systems offered significant reductions in energy use, peak demand, and operating cost in California.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2018

An Evolutionary Computation System Design Concept for Developing Controlled Closed Ecosystems: An Intelligent Systems Approach to Foster Gravitational Ecosystem Research for Developing Sustainable Communities in Space and on Earth

An adjustably-autonomous intelligent systems approach for developing Closed Ecosystems (CESs) is presented, which includes a design concept and preliminary design details for the Controlled Closed-Ecosystem Development System (CCEDS) and the Orbiting Modular Artificial-Gravity Spacecraft (OMAGS). The paper is divided into three sections: CESs, the CCEDS Design Concept, and Orbiting Fractional-Gravity Closed Ecosystems OMAGS design concept. The first section briefly describes Closed EcoSystems (CESs), complex adaptive systems, biomes, microbial microbiomes, and their relevance for the study of astrobiology. This section also discusses initial efforts in the development of Closed Environment Life Support Systems (CELSSs) for sustainable communities in space and on Earth. This section concludes with a discussion of the bioregenerative life support system challenge of and the corresponding consequences due to the inverse relationship of the very small human biomass/non-human biomass ratio overall on the Earth with respect to the extremely large human biomass/non-human-biomass ratio found in cities and the International Space Station. The second section describes the CCEDS design concept, which consists of a population of controlled colonies of CES Modules (CESMs), each an integrated CES, continually generating data for an intelligent system that operates the CESs and their CESMs. A variety of CESM types and their use are briefly described. The CCEDS intelligent system uses an evolutionary computation algorithm described in this section to develop and optimize these CESs to increase their viability duration and the size of the animals they support with the ultimate goal to support populations of humans, both on Earth and in space. The CCEDS architecture, its five control subsystems, and its five evolutionary computation levels are also discussed. The section concludes with a discussion of several CCEDS design strategies. The third section summarizes the OMAGS design concept for a spacecraft with a payload consisting of CESs in an orbiting spacecraft centrifuge that operates for at least 5 years. The spacecraft concept is described including its 150cm-radius centrifuge with a 2 ton & 3,000 liter bioscience payload capacity for 24 CESMs. The centrifuge design has four physical levels for its CESMs, each level subject to a different fractional gravity level. This section presents the spacecraft benefits of being designed and operated such that the spacecraft and payload centrifuge wheel counter-rotate resulting in net zero angular momentum and zero gyroscopic forces. Artificial-gravity generation by centripetal acceleration is also discussed. This section concludes by showing the external specifications of the CESMs and their layout in the centrifuge, followed by discussing the multi-payload module rationale. In tandem, the CCEDS and OMAGS systems can be used to foster gravitational ecosystem research for developing sustainable communities in space and on Earth.

Dorais, Gregory A.↗

NASA NTRS2006

Lessons Learned and Flight Results from the F15 Intelligent Flight Control System Project

A viewgraph presentation on the lessons learned and flight results from the F15 Intelligent Flight Control System (IFCS) project is shown. The topics include: 1) F-15 IFCS Project Goals; 2) Motivation; 3) IFCS Approach; 4) NASA F-15 #837 Aircraft Description; 5) Flight Envelope; 6) Limited Authority System; 7) NN Floating Limiter; 8) Flight Experiment; 9) Adaptation Goals; 10) Handling Qualities Performance Metric; 11) Project Phases; 12) Indirect Adaptive Control Architecture; 13) Indirect Adaptive Experience and Lessons Learned; 14) Gen II Direct Adaptive Control Architecture; 15) Current Status; 16) Effect of Canard Multiplier; 17) Simulated Canard Failure Stab Open Loop; 18) Canard Multiplier Effect Closed Loop Freq. Resp.; 19) Simulated Canard Failure Stab Open Loop with Adaptation; 20) Canard Multiplier Effect Closed Loop with Adaptation; 21) Gen 2 NN Wts from Simulation; 22) Direct Adaptive Experience and Lessons Learned; and 23) Conclusions

Bosworth, John↗

NASA NTRS2022

Run Time Assurance for Electric Vertical Takeoff and Landing Aircraft

NASA is conducting research to demonstrate and evaluate the application of Run Time Assurance (RTA) as a means to assure safety in Electric Vertical Takeoff and Landing (eVTOL) aircraft with highly automated or autonomous flight capability supervised by a single onboard pilot. The work described in this report demonstrates an application of RTA and examines the implications for design and analysis of aircraft functions and systems; aircraft safety hazards; safety assurance; development assurance; and pilot tasks and performance. This research effort also seeks to assess the efficacy of the combined application of traditional Functional Hazard Analysis (FHA) and the more modern System Theoretic Process Analysis (STPA) techniques to perform hazard analyses on aircraft with complex automated and autonomous systems and an onboard pilot. During the research effort we developed architectural designs of two alternate eVTOL aircraft, generally following the process characterized in the SAE standards ARP4754 and ARP4761. The design has focused on the control architectures of these aircraft, which are identical except that one incorporates RTA techniques to reduce the criticality of some key software components. Artifacts of this process include a taxonomy of aircraft-level functions, aircraft-level architecture diagrams, aircraft-level functional hazard assessments (AFHA), function allocations onto aircraft systems and subsystems, functional block diagrams for a select set of control-related functions, and system-level functional hazard assessments (SFHA) for those functions. This project has highlighted the notion that DAL D is something of a sweet spot for low-confidence controllers in an RTA-based design. Among the many activities described in DO-178C, the activities related to requirement verifiability, algorithmic accuracy, and test coverage can be the most challenging for the kinds of advanced control techniques that may be desirable in novel UAM designs, such as adaptive control, machine-learning, artificial intelligence, numerical search, and Monte Carlo based algorithms. Moreover, the standard requires that development teams demonstrate that errors leading to unacceptable failure conditions have been removed from the software. The RTA architecture, which cordons off the low-confidence function, makes it much easier to show this for these kinds of algorithms. With regard to the use of STPA and FHA as complementary hazard analysis techniques, our research effort led us to the conclusion that STPA should be used to derive requirements for hardware and software systems and/or components. Also, STPA is a natural complement to other processes in ARP4754A involving design studies and iteration.

Run-time assurance↗