Results for “automated reasoning” — Page 17

DOE OSTI2026

Balance of Plant Modeling and Real-Time Hardware-in-the-Loop Integration with the Microreactor Automated Control System

The advent of novel microreactor technology has driven a focused effort to explore safety and efficiency improvements that can be achieved through the use of automated system control. Development of control strategies, especially for initial demonstration, requires an adequate surrogate environment to safely research failure modes and control integration with realistic hardware delay. However, efficiency gains from control strategies are improved when the scope of controller action is expanded to include system-level dynamics such as downstream heat extraction and mass flow. For this reason, a balance-of-plant (BOP) model of a representative microreactor system has been developed using the TRANsient Simulation Framework of Reconfigurable Models library in Modelica. This model captures a reactor and primary NaK coolant loop that represent corresponding system components of the Microreactor Applications Research Validation and EvaLuation (MARVEL) design as well as a secondary coolant loop and heat extraction representative of the Microreactor Agile Non-Nuclear Experimental Test Bed (MAGNET). This model configuration allows for hardware-in-the-loop (HIL) integration with microreactor automated control system (MACS) hardware in real time through a Python-based gRPC client. Real-time simulation of model performance with emulated hardware and communication delay suggests that under independent proportional-integral-derivative control of BOP model drum dynamics and downstream heat extraction, stable power load following is achievable. A slight delay in load following, filtering of high-frequency dynamics, and localized temperature fluctation suggest room for improvement through the development of higher-level control strategies. The simulated coupling of the MAGNET facility lays the groundwork for future digital twin analysis with a coupled MACS-MAGNET HIL demonstration.

McConnell, Jono [ORNL] (ORCID:0000000238984741)↗

NASA NTRS2021

Defining and Reasoning about Model-based Safety Analysis: A Review

Model-based safety analysis (MBSA) has been around for over two decades. The benefits of MBSA have been well-documented in the literature, such as tackling complexity, introducing Formal Methods to eliminate the ambiguity in the traditional safety analysis, using automation to replace the error-prone manual safety modeling process, and ensuring consistency between the design model and the safety model. However, there is still a lack of consensus on what MBSA even is. This paper provides an approach towards developing such a consensus

model-based↗

DOE OSTI2021

Large-scale dissolution of sintered Mo disks

Non-uranium production of 99 Mo via neutron capture or by photonuclear reaction using an electron accelerator is receiving considerable attention. For commercial production of 99 Mo, post-irradiation processes must incorporate a robust design for easy scale-up. Once developed, large-scale chemical processes must be optimized for hot-cell operations and potential automation. In this work, we present experimental results on the large-scale dissolution of sintered Mo disks using three 50% hydrogen peroxides from different suppliers. Dissolution rates obtained for 600-g Mo batches and potential reasons for different dissolution behaviors is discussed.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

NASA NTRS1989

Cognitive performance deficits in a simulated climb of Mount Everest - Operation Everest II

Cognitive function at simulated altitude was investigated in a repeated-measures within-subject study of performance by seven volunteers in a hypobaric chamber, in which atmospheric pressure was systematically lowered over a period of 40 d to finally reach a pressure equivalent to 8845 m, the approximate height of Mount Everest. The automated performance test system employed compact computer design; automated test administrations, data storage, and retrieval; psychometric properties of stability and reliability; and factorial richness. Significant impairments of cognitive function were seen for three of the five tests in the battery; on two tests, grammatical reasoning and pattern comparison, every subject showed a substantial decrement.

Kennedy, R. S.↗

NASA NTRS1994

The Stanford how things work project

We provide an overview of the Stanford How Things Work (HTW) project, an ongoing integrated collection of research activities in the Knowledge Systems Laboratory at Stanford University. The project is developing technology for representing knowledge about engineered devices in a form that enables the knowledge to be used in multiple systems for multiple reasoning tasks and reasoning methods that enable the represented knowledge to be effectively applied to the performance of the core engineering task of simulating and analyzing device behavior. The central new capabilities currently being developed in the project are automated assistance with model formulation and with verification that a design for an electro-mechanical device satisfies its functional specification.

Fikes, Richard↗

NASA NTRS1986

Optimum full-scale subsonic wind tunnel

The needs and reasons for performing full-scale subsonic research have been studied. Full-scale wind-tunnel requirements are described; recommended size, airspeed, acoustic capabilities, and flow quality are developed; and data-acquisition systems, productivity, automation, and some cost elements are discussed. It is proposed that the optimum full-scale, subsonic wind tunnel be large enough to accommodate aerodynamic and acoustic investigations on most advanced aircraft, which exhibit complex engine/lifting-surface flow-field interactions, including advanced rotorcraft.

Mort, K. W.↗

NASA NTRS2017

Assessment of Air Traffic Controller Acceptability of Aircrew Route Change Requests

NASA developed the traffic aware strategic aircrew requests concept for a cockpit automation that identifies route improvements and advises the aircrew to request the change from the air traffic controller. In order to increase the chance of air traffic control approval, the automation ensures that the route is clear of known traffic, weather, and airspace restrictions. Hence the technology is anticipated to provide benefits in areas such as flight efficiency, flight schedule compliance, passenger comfort, and pilot and controller workload. In support of a field trial of a prototype of the technology, observations were conducted at the Atlanta and Jacksonville air traffic control centers to identify the main factors that affect the acceptability of aircrew requests by air traffic controllers. Observers shadowed air traffic controllers as the test flight pilot made pre-scripted requests to invoke acceptability issues and then they interviewed voluntarily fifty controllers with experience ranging from one to thirty-five years. The most common reason for rejecting requests is conflicting with traffic followed by violating air traffic procedures, increasing sector workload, and conflicting with major arrival and departure flows and flow restrictions. Quantitative parameters such as the distance that a route should maintain from sector boundaries and special use airspace were identified and recommended for inclusion in the automation.

Flight Optimization; Field Trial↗

NASA NTRS2017

Assessment of Air Traffic Controller Acceptability of Aircrew Route Change Requests

NASA developed the traffic aware strategic aircrew requests concept for a cockpit automation that identifies route improvements and advises the aircrew to request the change from the air traffic controller. In order to increase the chance of air traffic control approval, the automation ensures that the route is clear of known traffic, weather, and airspace restrictions. Hence the technology is anticipated to provide benefits in areas such as flight efficiency, flight schedule compliance, passenger comfort, and pilot and controller workload. In support of a field trial of a prototype of the technology, observations were conducted at the Atlanta and Jacksonville air traffic control centers to identify the main factors that affect the acceptability of aircrew requests by air traffic controllers. Observers shadowed air traffic controllers as the test flight pilot made pre-scripted requests to invoke acceptability issues and then they interviewed voluntarily fifty controllers with experience ranging from one to thirty-five years. The most common reason for rejecting requests is conflicting with traffic followed by violating air traffic procedures, increasing sector workload, and conflicting with major arrival and departure flows and flow restrictions. Quantitative parameters such as the distance that a route should maintain from sector boundaries and special use airspace were identified and recommended for inclusion in the automation.

Flight Optimization; Field Trial↗

NASA NTRS2001

Information Handling is the Problem

This slide presentation reviews the concerns surrounding the automation of information handling. There are two types of decision support software that supports most Space Station Flight Controllers. one is very simple, and the other is very complex. A middle ground is sought. This is the reason for the Human Centered Autonomous and Assistant Systems Testbed (HCAAST) Project. The aim is to study flight controllers at work, and in the bigger picture, with particular attention to how they handle information and how coordination of multiple teams is performed. The focus of the project is on intelligent assistants to assist in handling information for the flight controllers.

Malin, Jane T.↗

NASA NTRS1989

Parallel plan execution with self-processing networks

A critical issue for space operations is how to develop and apply advanced automation techniques to reduce the cost and complexity of working in space. In this context, it is important to examine how recent advances in self-processing networks can be applied for planning and scheduling tasks. For this reason, the feasibility of applying self-processing network models to a variety of planning and control problems relevant to spacecraft activities is being explored. Goals are to demonstrate that self-processing methods are applicable to these problems, and that MIRRORS/II, a general purpose software environment for implementing self-processing models, is sufficiently robust to support development of a wide range of application prototypes. Using MIRRORS/II and marker passing modelling techniques, a model of the execution of a Spaceworld plan was implemented. This is a simplified model of the Voyager spacecraft which photographed Jupiter, Saturn, and their satellites. It is shown that plan execution, a task usually solved using traditional artificial intelligence (AI) techniques, can be accomplished using a self-processing network. The fact that self-processing networks were applied to other space-related tasks, in addition to the one discussed here, demonstrates the general applicability of this approach to planning and control problems relevant to spacecraft activities. It is also demonstrated that MIRRORS/II is a powerful environment for the development and evaluation of self-processing systems.

Dautrechy, C. Lynne↗

NASA NTRS1989

Parallel plan execution with self-processing networks

A critical issue for space operations is how to develop and apply advanced automation techniques to reduce the cost and complexity of working in space. In this context, it is important to examine how recent advances in self-processing networks can be applied for planning and scheduling tasks. For this reason, the feasibility of applying self-processing network models to a variety of planning and control problems relevant to spacecraft activities is being explored. Goals are to demonstrate that self-processing methods are applicable to these problems, and that MIRRORS/II, a general purpose software environment for implementing self-processing models, is sufficiently robust to support development of a wide range of application prototypes. Using MIRRORS/II and marker passing modelling techniques, a model of the execution of a Spaceworld plan was implemented. This is a simplified model of the Voyager spacecraft which photographed Jupiter, Saturn, and their satellites. It is shown that plan execution, a task usually solved using traditional artificial intelligence (AI) techniques, can be accomplished using a self-processing network. The fact that self-processing networks were applied to other space-related tasks, in addition to the one discussed here, demonstrates the general applicability of this approach to planning and control problems relevant to spacecraft activities. It is also demonstrated that MIRRORS/II is a powerful environment for the development and evaluation of self-processing systems.

D'Autrechy, C. Lynne↗

DOE OSTI2020

Artificial Intelligence and Computer Vision for Cost-Effective Benthic Habitat Characterizations

Machine learning applied to computer vision and pattern recognition is a type of artificial intelligence that has advanced rapidly in the last 10 to 15 years, spurred forward by breakthroughs in deep convolutional neural networks. These state-of-the-art methods are poised to become widely used in environmental monitoring applications as a result of the increasing abundance of data available from different imaging platforms (e.g., fixed-point cameras, drone surveys, high-resolution satellite data) that can be analyzed to observe, model, and understand environmental conditions. Computer vision and pattern recognition (CVPR) tools advance our ability to use imagery and camera-based tools in cost-effective ways for environmental monitoring. Although these techniques offer great potential, some challenges remain, such as the need for large sets of labeled images for model training and validation and optimized hardware and software to ensure that the models can be trained effectively and in a reasonable amount of time. Here, we have overcome many of these rate-limiting challenges by using a diverse image library built across multiple projects coupled with staff expertise and onsite computing resources. We are working toward a fully automated SPI processing system and also are moving to develop CVPR analytical tools for other imaging platforms and data sets.

02 PETROLEUM↗

NASA NTRS1993

Expert diagnostics system as a part of analysis software for power mission operations

The operation of interplanetary spacecraft at JPL has become an increasingly complex activity. This complexity is due to advanced spacecraft designs and ambitious mission objectives which lead to operations requirements that are more demanding than those of any previous mission. For this reason, several productivity enhancement measures are underway at JPL within mission operations, particularly in the spacecraft analysis area. These measures aimed at spacecraft analysis include: the development of a multi-mission, multi-subsystem operations environment; the introduction of automated tools into this environment; and the development of an expert diagnostics system. This paper discusses an effort to integrate the above mentioned productivity enhancement measures. A prototype was developed that integrates an expert diagnostics system into a multi-mission, multi-subsystem operations environment using the Galileo Power / Pyro Subsystem as a testbed. This prototype will be discussed in addition to background information associated with it.

Harris, Jennifer A.↗

DOE OSTI2021

Artificial Intelligence for Autonomous Molecular Design: A Perspective

Domain-aware artificial intelligence has been increasingly adopted in recent years to expedite molecular design in various applications, including drug design and discovery. Recent advances in areas such as physics-informed machine learning and reasoning, software engineering, high-end hardware development, and computing infrastructures are providing opportunities to build scalable and explainable AI molecular discovery systems. This could improve a design hypothesis through feedback analysis, data integration that can provide a basis for the introduction of end-to-end automation for compound discovery and optimization, and enable more intelligent searches of chemical space. Several state-of-the-art ML architectures are predominantly and independently used for predicting the properties of small molecules, their high throughput synthesis, and screening, iteratively identifying and optimizing lead therapeutic candidates. However, such deep learning and ML approaches also raise considerable conceptual, technical, scalability, and end-to-end error quantification challenges, as well as skepticism about the current AI hype to build automated tools. To this end, synergistically and intelligently using these individual components along with robust quantum physics-based molecular representation and data generation tools in a closed-loop holds enormous promise for accelerated therapeutic design to critically analyze the opportunities and challenges for their more widespread application. This article aims to identify the most recent technology and breakthrough achieved by each of the components and discusses how such autonomous AI and ML workflows can be integrated to radically accelerate the protein target or disease model-based probe design that can be iteratively validated experimentally. Taken together, this could significantly reduce the timeline for end-to-end therapeutic discovery and optimization upon the arrival of any novel zoonotic transmission event. Our article serves as a guide for medicinal, computational chemistry and biology, analytical chemistry, and the ML community to practice autonomous molecular design in precision medicine and drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1990

Automated simulation as part of a design workstation

A development project for a design workstation for advanced life-support systems (called the DAWN Project, for Design Assistant Workstation), incorporating qualitative simulation, required the implementation of a useful qualitative simulation capability and the integration of qualitative and quantitative simulation such that simulation capabilities are maximized without duplication. The reason is that to produce design solutions to a system goal, the behavior of the system in both a steady and perturbed state must be represented. The Qualitative Simulation Tool (QST), on an expert-system-like model building and simulation interface toll called ScratchPad (SP), and on the integration of QST and SP with more conventional, commercially available simulation packages now being applied in the evaluation of life-support system processes and components are discussed.

Cantwell, Elizabeth↗

NASA NTRS1991

Thermal Control System Automation Project (TCSAP)

Information is given in viewgraph form on the Space Station Freedom (SSF) Thermal Control System Automation Project (TCSAP). Topics covered include the assembly of the External Thermal Control System (ETCS); the ETCS functional schematic; the baseline Fault Detection, Isolation, and Recovery (FDIR), including the development of a knowledge based system (KBS) for application of rule based reasoning to the SSF ETCS; TCSAP software architecture; the High Fidelity Simulator architecture; the TCSAP Runtime Object Database (RODB) data flow; KBS functional architecture and logic flow; TCSAP growth and evolution; and TCSAP relationships.

Boyer, Roger L.↗

NASA NTRS1998

Automated Rendezvous and Capture in Space: A Technology Assessment

This paper presents the results of a study to assess the technology of automated rendezvous and capture (AR&C) in space. The outline of the paper is as follows: First, the history of manual and automated rendezvous and capture and rendezvous and dock is presented. Next, the need for AR&C in space is reviewed. In light of these, AR&C systems are proposed that meet NASA's future needs, but can be developed in a reasonable amount of time with a reasonable amount of money. Technology plans for developing these systems are presented; cost and schedule are included.

Polites, Michael E.↗

NASA NTRS1991

Decision blocks: A tool for automating decision making in CLIPS

The human capability of making complex decision is one of the most fascinating facets of human intelligence, especially if vague, judgemental, default or uncertain knowledge is involved. Unfortunately, most existing rule based forward chaining languages are not very suitable to simulate this aspect of human intelligence, because of their lack of support for approximate reasoning techniques needed for this task, and due to the lack of specific constructs to facilitate the coding of frequently reoccurring decision block to provide better support for the design and implementation of rule based decision support systems. A language called BIRBAL, which is defined on the top of CLIPS, for the specification of decision blocks, is introduced. Empirical experiments involving the comparison of the length of CLIPS program with the corresponding BIRBAL program for three different applications are surveyed. The results of these experiments suggest that for decision making intensive applications, a CLIPS program tends to be about three times longer than the corresponding BIRBAL program.

Eick, Christoph F.↗