Results for “Automated” — Page 13

NASA NTRS2018

Human-Automation Allocations for Current Robotic Space Operations

Within the Human Research Program, one risk delineates the uncertainty surrounding crew working with automation and robotics in spaceflight. The Risk of Inadequate Design of Human and Automation/Robotic Integration (HARI) is concerned with the detrimental effects on crew performance due to ineffective user interfaces, system designs and/or functional task allocation, potentially compromising mission success and safety. Risk arises because we have limited experience with complex automation and robotics. One key gap within HARI, is the gap related to functional allocation. The gap states: We need to evaluate, develop, and validate methods and guidelines for identifying human-automation/robot task information needs, function allocation, and team composition for future long duration, long distance space missions. Allocations determine the human-system performance as it identifies the functions and performance levels required by the automation/robotic system, and in turn, what work the crew is expected to perform and the necessary human performance requirements. Allocations must take into account each of the human, automation, and robotic systems capabilities and limitations. Some functions may be intuitively assigned to the human versus the robot, but to optimize efficiency and effectiveness, purposeful role assignments will be required. The role of automation and robotics will significantly change in future exploration missions, particularly as crew becomes more autonomous from ground controllers. Thus, we must understand the suitability of existing function allocation methods within NASA as well as the existing allocations established by the few robotic systems that are operational in spaceflight. In order to evaluate future methods of robotic allocations, we must first benchmark the allocations and allocation methods that have been used. We will present 1) documentation of human-automation-robotic allocations in existing, operational spaceflight systems; and 2) To gather existing lessons learned and best practices in these role assignments, from spaceflight operational experience of crew and ground teams that may be used to guide development for future systems. NASA and other space agencies have operational spaceflight experience with two key Human-Automation-Robotic (HAR) systems: heavy lift robotic arms and planetary robotic explorers. Additionally, NASA has invested in high-fidelity rover systems that can carry crew, building beyond Apollo's lunar rover. The heavy lift robotic arms reviewed are: Space Station Remote Manipulator System (SSRMS), Japanese Remote Manipulator System (JEMRMS), and the European Robotic Arm (ERA, designed but not deployed in space). The robotic rover systems reviewed are: Mars Exploration Rovers, Mars Science Laboratory rover, and the high-fidelity K10 rovers. Much of the design and operational feedback for these systems have been communicated to flight controllers and robotic design teams. As part of the mitigating the HARI risk for future human spaceflight operations, we must document function allocations between robots and humans that have worked well in practice.

robotic allocation↗

NASA NTRSDate not supplied

Automated Software for Manned Spacecraft - Bridging the Gap from Sci Fi to Reality

With a voice command or a few taps on the console, the spacecraft pivots on a dime at high velocity and gently docks to an orbiting space platform. This is the image most people have of the complex software computations and integrated hardware performance necessary for a spacecraft to successfully perform an automated launch, rendezvous, and docking. Today’s reality is that while computer operations are advancing rapidly, science fiction over-simplifies and over-sells current capabilities. This paper discusses the integration of spacecraft computer automation into the operation of one of the United States’ new Commercial Crew vehicles - the Boeing CST-100 Starliner. Lessons learned by the Boeing Mission Operations team, a private-public partnership with NASA, from conceptual design through real-time operation of the first test flight will be discussed. Focus will center on how operations has learned to use the automated software to their advantage while also knowing how to adjust the automation in response to spacecraft or mission anomalies. One goal of advanced spacecraft automation is the ability to reduce both the crew workload and the ground control footprint while at the same time increasing spacecraft and mission flexibility. Historically, crewed spacecraft required a large number of operators on the ground to use a plethora of tools to compute nominal and contingency mission trajectories. Moving those sophisticated software tools to being onboard the vehicle can reduce the need for such complex ground support. Given that today’s spacecraft software is not yet as capable or as flexible in all circumstances as the computers depicted in movies, there is usually a trade-off between software automation cost and the flexibility of that software resulting in a trade-off between what is performed on the spacecraft and what is left to onboard crew or ground control. For missions that go beyond the Moon, software that autonomously controls nearly every aspect of a crewed mission will become a necessity given the long time delays between the spacecraft and Earth’s ground control teams. The lessons learned by Boeing and its Mission Operations team, through the design and implementation of Starliner’s hardware and software automation, will be able to inform future public and private spacecraft design. As the technologies and capabilities evolve, incorporating lessons learned in successful low Earth orbit commercial crew vehicle missions, spacecraft designs will continue to improve and be able to better enable safe execution of human missions to the Moon and beyond.

Robert C Dempsey↗

NASA NTRSDate not supplied

Automated Software for Manned Spacecraft - Bridging the Gap from Sci Fi to Reality

With a voice command or a few taps on the console, the spacecraft pivots on a dime at high velocity and gently docks to an orbiting space platform. This is the image most people have of the complex software computations and integrated hardware performance necessary for a spacecraft to successfully perform an automated launch, rendezvous, and docking. Today’s reality is that while computer operations are advancing rapidly, science fiction over-simplifies and over-sells current capabilities. This paper discusses the integration of spacecraft computer automation into the operation of one of the United States’ new Commercial Crew vehicles - the Boeing CST-100 Starliner. Lessons learned by the Boeing Mission Operations team, a private-public partnership with NASA, from conceptual design through real-time operation of the first test flight will be discussed. Focus will center on how operations has learned to use the automated software to their advantage while also knowing how to adjust the automation in response to spacecraft or mission anomalies. One goal of advanced spacecraft automation is the ability to reduce both the crew workload and the ground control footprint while at the same time increasing spacecraft and mission flexibility. Historically, crewed spacecraft required a large number of operators on the ground to use a plethora of tools to compute nominal and contingency mission trajectories. Moving those sophisticated software tools to being onboard the vehicle can reduce the need for such complex ground support. Given that today’s spacecraft software is not yet as capable or as flexible in all circumstances as the computers depicted in movies, there is usually a trade-off between software automation cost and the flexibility of that software resulting in a trade-off between what is performed on the spacecraft and what is left to onboard crew or ground control. For missions that go beyond the Moon, software that autonomously controls nearly every aspect of a crewed mission will become a necessity given the long time delays between the spacecraft and Earth’s ground control teams. The lessons learned by Boeing and its Mission Operations team, through the design and implementation of Starliner’s hardware and software automation, will be able to inform future public and private spacecraft design. As the technologies and capabilities evolve, incorporating lessons learned in successful low Earth orbit commercial crew vehicle missions, spacecraft designs will continue to improve and be able to better enable safe execution of human missions to the Moon and beyond.

Robert C. Dempsey↗

NASA NTRSDate not supplied

Automated Software for Crewed Spacecraft - Bridging the Gap from Sci Fi to Reality

With a voice command or a few taps on the console, the spacecraft pivots on a dime at high velocity and gently docks to an orbiting space platform. This is the image most people have of the complex software computations and integrated hardware performance necessary for a spacecraft to successfully perform an automated launch, rendezvous, and docking. Today’s reality is that while computer operations are advancing rapidly, science fiction over-simplifies and over-sells current capabilities. This paper discusses the integration of spacecraft computer automation into the operation of one of the United States’ new Commercial Crew vehicles - the Boeing CST-100 Starliner. Lessons learned by the Boeing Mission Operations team, a private-public partnership with NASA, from conceptual design through real-time operation of the first test flight will be discussed. Focus will center on how operations has learned to use the automated software to their advantage while also knowing how to adjust the automation in response to spacecraft or mission anomalies. One goal of advanced spacecraft automation is the ability to reduce both the crew workload and the ground control footprint while at the same time increasing spacecraft and mission flexibility. Historically, crewed spacecraft required a large number of operators on the ground to use a plethora of tools to compute nominal and contingency mission trajectories. Moving those sophisticated software tools to being onboard the vehicle can reduce the need for such complex ground support. Given that today’s spacecraft software is not yet as capable or as flexible in all circumstances as the computers depicted in movies, there is usually a trade-off between software automation cost and the flexibility of that software resulting in a trade-off between what is performed on the spacecraft and what is left to onboard crew or ground control. For missions that go beyond the Moon, software that autonomously controls nearly every aspect of a crewed mission will become a necessity given the long time delays between the spacecraft and Earth’s ground control teams. The lessons learned by Boeing and its Mission Operations team, through the design and implementation of Starliner’s hardware and software automation, will be able to inform future public and private spacecraft design. As the technologies and capabilities evolve, incorporating lessons learned in successful low Earth orbit commercial crew vehicle missions, spacecraft designs will continue to improve and be able to better enable safe execution of human missions to the Moon and beyond.

Robert C Dempsey↗

NASA NTRSDate not supplied

Initial Design Guidelines for Onboard Automation of Flight Path Management

Achieving the National Academy of Science’s vision of advanced aerial mobility will depend on significant developments in automation to achieve safe and efficient operations. Flight path management (FPM), a major category of automation functionality needed to achieve this vision, will provide dynamic management of an aircraft’s flight path, ensuring that it remains feasible to fly to mission completion, deconflicted from hazards, coordinated with other traffic, flexible to accommodate future disturbances, and optimized to meet business objectives. While efforts are underway to advance FPM technology for the Urban Air Mobility application, initial design guidelines are presented for FPM automation capabilities to achieve each of these objectives based on 15+ years of prior FPM automation research and development. Methods to efficiently account for uncertainty in the prediction of trajectories are described, as are additional considerations for prioritizing safety in the design of FPM automation capabilities and interactions between aircraft. Recommendations are supported by extensive experience gained via previous work with the FPM reference automation system, Autonomous Operations Planner, developed by NASA. By employing capable FPM automation supported by cooperative operational flight rules and information sharing, future aircraft operators will benefit from an increased ability to plan and execute safe and efficient flights and to achieve mission success in a dynamic airspace.

Flight Path Management, FPM, AOP, UAM, deconflicti↗

NASA NTRSDate not supplied

Initial Design Guidelines for Onboard Automation of Flight Path Management

Achieving the National Academy of Science’s vision of advanced aerial mobility will depend on significant developments in automation to achieve safe and efficient operations. Flight path management (FPM), a major category of automation functionality needed to achieve this vision, will provide dynamic management of an aircraft’s flight path, ensuring that it remains feasible to fly to mission completion, deconflicted from hazards, coordinated with other traffic, flexible to accommodate future disturbances, and optimized to meet business objectives. While efforts are underway to advance FPM technology for the Urban Air Mobility application, initial design guidelines are presented for FPM automation capabilities to achieve each of these objectives based on 15+ years of prior FPM automation research and development. Methods to efficiently account for uncertainty in the prediction of trajectories are described, as are additional considerations for prioritizing safety in the design of FPM automation capabilities and interactions between aircraft. Recommendations are supported by extensive experience gained via previous work with the FPM reference automation system, Autonomous Operations Planner, developed by NASA. By employing capable FPM automation supported by cooperative operational flight rules and information sharing, future aircraft operators will benefit from an increased ability to plan and execute safe and efficient flights and to achieve mission success in a dynamic airspace.

Flight Path Management↗

DOE OSTI2021

Human-automated vehicle interactions

This dissertation is proposed to answer the question: how can the interactions between human and automated vehicles be used to improve the overall performance of automated driving technology? Multiple different modules in automated vehicles such as the perception, motion plan and motion control modules can potentially be benefitted from human-automated vehicle interactions. For perception module, the self-correction of faulty sensors can be achieved using human demonstration data. For motion plan and motion control modules, the performance of the low-level motion controller can be improved with the help of human demonstration, and the behavior of the motion planner can be improved using human intervention data during automated driving. Moreover, a better model for a human driver could improve the overall efficiency and comfort of vehicles in connected mixed traffic. In this dissertation, the technical research toward these goals has been completed and has resulted in several peer-reviewed publications. Optimization methods and model predictive control are used extensively to improve energy efficiency while maintaining safe and comfort driving. An inverse model predictive control (IMPC) method has been developed and it has been proven to be effective in modeling the motion of human driven vehicles. The proposed method has demonstrated its benefits in both connected automated highway driving and the bilateral adaptation of human driver and automated driving controller in human-in-the loop simulations. The proposed future research seeks to broaden the application of IMPC by considering a more comprehensive cost function design and applying it to more complex driving situations.

Guo, Longxiang↗

DOE OSTI2021

HUMAN-AUTOMATED VEHICLE INTERACTIONS

This dissertation is proposed to answer the question: how can the interactions between human and automated vehicles be used to improve the overall performance of automated driving technology? Multiple different modules in automated vehicles such as the perception, motion plan and motion control modules can potentially be benefitted from human-automated vehicle interactions. For perception module, the self-correction of faulty sensors can be achieved using human demonstration data. For motion plan and motion control modules, the performance of the low-level motion controller can be improved with the help of human demonstration, and the behavior of the motion planner can be improved using human intervention data during automated driving. Moreover, a better model for a human driver could improve the overall efficiency and comfort of vehicles in connected mixed traffic. In this dissertation, the technical research toward these goals has been completed and has resulted in several peer-reviewed publications. Optimization methods and model predictive control are used extensively to improve energy efficiency while maintaining safe and comfort driving. An inverse model predictive control (IMPC) method has been developed and it has been proven to be effective in modeling the motion of human driven vehicles. The proposed method has demonstrated its benefits in both connected automated highway driving and the bilateral adaptation of human driver and automated driving controller in human-in-the loop simulations. The proposed future research seeks to broaden the application of IMPC by considering a more comprehensive cost function design and applying it to more complex driving situations.

Guo, Longxiang↗

DOE OSTI2013

Automated Groundwater Monitoring of Uranium at the Hanford Site, Washington - 13116

An automated groundwater monitoring system for the detection of uranyl ion in groundwater was deployed at the 300 Area Industrial Complex, Hanford Site, Washington. The research was conducted to determine if at-site, automated monitoring of contaminant movement in the subsurface is a viable alternative to the baseline manual sampling and analytical laboratory assay methods currently employed. The monitoring system used Arsenazo III, a colorimetric chelating compound, for the detection of the uranyl ion. The analytical system had a limit of quantification of approximately 10 parts per billion (ppb, μg/L). The EPA's drinking water maximum contaminant level (MCL) is 30 ppb [1]. In addition to the uranyl ion assay, the system was capable of acquiring temperature, conductivity, and river level data. The system was fully automated and could be operated remotely. The system was capable of collecting water samples from four sampling sources, quantifying the uranyl ion, and periodically performing a calibration of the analytical cell. The system communications were accomplished by way of cellular data link with the information transmitted through the internet. Four water sample sources were selected for the investigation: one location provided samples of Columbia River water, and the remaining three sources provided groundwater from aquifer sampling tubes positioned in a vertical array at the Columbia River shoreline. The typical sampling schedule was to sample the four locations twice per day with one calibration check per day. This paper outlines the instrumentation employed, the operation of the instrumentation, and analytical results for a period of time between July and August, 2012. The presentation includes the uranyl ion concentration and conductivity results from the automated sampling/analysis system, along with a comparison between the automated monitor's analytical performance and an independent laboratory analysis. Benefits of using the automated system as an alternative to traditional sample collection and analysis includes the following: - Field observations that provide more characterization information than is possible using traditional monitoring methods. - Potentially significant reductions in labor and analytical costs if traditional methods are complemented by automated systems. - The reduced cost of acquiring samples will allow for more frequent collection of samples that may be automatically introduced into real-time graphical flux programs allowing site managers to observe the changes in contaminant concentrations during remediation projects and across discrete river stage events. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2016

Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles

In 5-10 years, experts predict that new automobiles will be capable of driving themselves under limited conditions and under most conditions within 10–20 years. Automation may affect road vehicle energy consumption and greenhouse gas (GHG) emissions in a host of ways, positive and negative, by causing changes in travel demand, vehicle design, vehicle operating profiles, and choices of fuels. In this paper, we identify specific mechanisms through which automation may affect travel and energy demand and resulting GHG emissions and bring them together using a coherent energy decomposition framework. Here, we review the literature for estimates of the energy impacts of each mechanism and, where the literature is lacking, develop our own estimates using engineering and economic analysis. We consider how widely applicable each mechanism is, and quantify the potential impact of each mechanism on a common basis: the percentage change it is expected to cause in total GHG emissions from light-duty or heavy-duty vehicles in the U.S. Our primary focus is travel related energy consumption and emissions, since potential lifecycle impacts are generally smaller in magnitude. We also explore the net effects of automation on emissions through several illustrative scenarios, finding that automation might plausibly reduce road transport GHG emissions and energy use by nearly half – or nearly double them – depending on which effects come to dominate. We also find that many potential energy-reduction benefits may be realized through partial automation, while the major energy/emission downside risks appear more likely at full automation. Finally, we present some implications for policymakers and identifying priority areas for further research.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Towards a Deeper Understanding of Automation Transparency in the Operation of Nuclear Plants

Automation technologies have the potential to reduce operations and maintenance costs, ensure reliable power generation and safety, and thereby contribute to extending the lifespan of nuclear power plants. To fulfill this aspiration, automation behaviors have to be understandable and predictable to human operators and traceable for license holders and regulators in the event of automation failures. The current report investigates how automation transparency as a systems design principle can keep operators sufficiently informed about the inner workings of automation or make fail-safe automation run invisibly in the background to free up operator capacity.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

Energy-Efficient Maneuvering of Connected and Automated Vehicles (CAVs) with Situational Awareness at Intersections (Final Progress Report)

The increased development of Connected and Automated Vehicle (CAV) systems, currently used for safety and driver convenience, presents new opportunities to improve the energy efficiency of vehicles. Southwest Research Institute (SwRI) achieved a 20% energy consumption reduction in a 2017 Toyota Prius Prime plug-in hybrid by using connectivity (V2V, V2I, V2X) as part of the Next Generation Energy Technologies for Connected and Automated on-Road Vehicles (NEXTCAR) program. The energy consumption gains were achieved by a combination of vehicle dynamics and powertrain control algorithms with a focus on SAE L1 and L2 automated vehicles where a human is still responsible for safe operation. SwRI is now involved in NEXTCAR-II, focusing on energy-efficient control tech for SAE Level 4/5 automated vehicles, aiming for a 30% energy reduction compared to stock hybrids. The rise of Mobility as a Service (MaaS) is driving investments in L4 and L5 automated vehicles. A study by the University of Michigan shows these vehicles might increase energy use and emissions by 3-20%. Technology similar to NEXTCAR can enhance energy efficiency in highly automated vehicles, leveraging improved sensing and actuation capabilities. While the NEXTCAR programs targeted energy efficiency improvements for a single vehicle, this program adopts a more expansive approach. It places its focus on understanding and testing the cumulative effects within a region or corridor, aiming to assess how a subset of vehicles equipped with NEXTCAR-style technologies influence the overall energy consumption of all vehicles traveling within that area. Additionally, the program explores infrastructure-based mobility solutions to optimize efficiency, and seeks to understand and quantify public perception and likelihood of technology adoption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRSDate not supplied

Physiological and Subjective Responses of Pilots During Advanced Air Mobility Flight Testing With Automated Systems

Aviation is constantly evolving, mostly due to the integration of automated systems into the National Airspace System. This presents a host of challenges and opportunities. NASA's Advanced Air Mobility (AAM) project has taken a significant leap forward with a research flight test led by the Integration of Automated Systems (IAS) sub-project. This effort focused on assessing automated flight deck algorithms essential for supporting high-density Urban Air Mobility (UAM) operations. Carrying out a two-ship flight test in UAM Maturity Level 4 scenarios, the team evaluated state-of-the-art algorithms, including Hazard Perception and Avoidance (HPA) and flight path management (FPM) systems. This presentation focuses on results of pilots' physiological and subjective responses during the flight scenarios conducted. In collaboration with Lockheed Martin Advanced Technology Laboratories (ATL), we collected and analyzed live-flight biometric data using eye tracking, mobile brain imaging, and heart rate sensors. The data provides insights into pilots' workload and cognitive engagement while navigating automated tools, leveraging both biometric data and post-encounter subjective assessments. The research highlights the interaction between human operators and automated systems, contributing valuable lessons learned about gathering human data in live-flight environments. The knowledge acquired from this endeavor enhances our understanding of human factors in automated flight and informs future studies attempting to undertake similar feats.

air mobility↗

DOE OSTI2011

Automated Planning of Tangential Breast Intensity-Modulated Radiotherapy Using Heuristic Optimization

Purpose: To present an automated technique for two-field tangential breast intensity-modulated radiotherapy (IMRT) treatment planning. Method and Materials: A total of 158 planned patients with Stage 0, I, and II breast cancer treated using whole-breast IMRT were retrospectively replanned using automated treatment planning tools. The tools developed are integrated into the existing clinical treatment planning system (Pinnacle{sup 3}) and are designed to perform the manual volume delineation, beam placement, and IMRT treatment planning steps carried out by the treatment planning radiation therapist. The automated algorithm, using only the radio-opaque markers placed at CT simulation as inputs, optimizes the tangential beam parameters to geometrically minimize the amount of lung and heart treated while covering the whole-breast volume. The IMRT parameters are optimized according to the automatically delineated whole-breast volume. Results: The mean time to generate a complete treatment plan was 6 min, 50 s {+-} 1 min 12 s. For the automated plans, 157 of 158 plans (99%) were deemed clinically acceptable, and 138 of 158 plans (87%) were deemed clinically improved or equal to the corresponding clinical plan when reviewed in a randomized, double-blinded study by one experienced breast radiation oncologist. In addition, overall the automated plans were dosimetrically equivalent to the clinical plans when scored for target coverage and lung and heart doses. Conclusion: We have developed robust and efficient automated tools for fully inversed planned tangential breast IMRT planning that can be readily integrated into clinical practice. The tools produce clinically acceptable plans using only the common anatomic landmarks from the CT simulation process as an input. We anticipate the tools will improve patient access to high-quality IMRT treatment by simplifying the planning process and will reduce the effort and cost of incorporating more advanced planning into clinical practice.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2019

Data automation at light sources

Rapidly growing data volumes at light sources demand increasingly automated data collection, distribution, and analysis processes, in order to enable new scientific discoveries while not overwhelming finite human capabilities. We present here the case for automating and outsourcing light source science using cloud-hosted data automation and enrichment services, institutional computing resources, and high- performance computing facilities to provide cost-effective, scalable, and reliable implementations of such processes. We discuss three specific services that accomplish these goals for data distribution, automation, and transformation. In the first, Globus cloud-hosted data automation services are used to implement data capture, distribution, and analysis workflows for Advanced Photon Source and Advanced Light Source beamlines, leveraging institutional storage and computing. In the second, such services are combined with cloud-hosted data indexing and institutional storage to create a collaborative data publication, indexing, and discovery service, the Materials Data Facility (MDF), built to support a host of informatics applications in materials science. The third integrates components of the previous two projects with machine learning capabilities provided by the Data and Learning Hub for science (DLHub) to enable on-demand access to machine learning models from light source data capture and analysis workflows, and provides simplified interfaces to train new models on data from sources such as MDF on leadership scale computing resources. We draw conclusions about best practices for building next-generation data automation systems for future light sources.

Blaiszik, Ben↗

DOE OSTI2015

The development and verification of a highly accurate collision prediction model for automated noncoplanar plan delivery

Purpose: Significant dosimetric benefits had been previously demonstrated in highly noncoplanar treatment plans. In this study, the authors developed and verified an individualized collision model for the purpose of delivering highly noncoplanar radiotherapy and tested the feasibility of total delivery automation with Varian TrueBeam developer mode. Methods: A hand-held 3D scanner was used to capture the surfaces of an anthropomorphic phantom and a human subject, which were positioned with a computer-aided design model of a TrueBeam machine to create a detailed virtual geometrical collision model. The collision model included gantry, collimator, and couch motion degrees of freedom. The accuracy of the 3D scanner was validated by scanning a rigid cubical phantom with known dimensions. The collision model was then validated by generating 300 linear accelerator orientations corresponding to 300 gantry-to-couch and gantry-to-phantom distances, and comparing the corresponding distance measurements to their corresponding models. The linear accelerator orientations reflected uniformly sampled noncoplanar beam angles to the head, lung, and prostate. The distance discrepancies between measurements on the physical and virtual systems were used to estimate treatment-site-specific safety buffer distances with 0.1%, 0.01%, and 0.001% probability of collision between the gantry and couch or phantom. Plans containing 20 noncoplanar beams to the brain, lung, and prostate optimized via an in-house noncoplanar radiotherapy platform were converted into XML script for automated delivery and the entire delivery was recorded and timed to demonstrate the feasibility of automated delivery. Results: The 3D scanner measured the dimension of the 14 cm cubic phantom within 0.5 mm. The maximal absolute discrepancy between machine and model measurements for gantry-to-couch and gantry-to-phantom was 0.95 and 2.97 cm, respectively. The reduced accuracy of gantry-to-phantom measurements was attributed to phantom setup errors due to the slightly deformable and flexible phantom extremities. The estimated site-specific safety buffer distance with 0.001% probability of collision for (gantry-to-couch, gantry-to-phantom) was (1.23 cm, 3.35 cm), (1.01 cm, 3.99 cm), and (2.19 cm, 5.73 cm) for treatment to the head, lung, and prostate, respectively. Automated delivery to all three treatment sites was completed in 15 min and collision free using a digital Linac. Conclusions: An individualized collision prediction model for the purpose of noncoplanar beam delivery was developed and verified. With the model, the study has demonstrated the feasibility of predicting deliverable beams for an individual patient and then guiding fully automated noncoplanar treatment delivery. This work motivates development of clinical workflows and quality assurance procedures to allow more extensive use and automation of noncoplanar beam geometries.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Microreactor Automated Control System - Digital Twin Models and Advanced Control Systems Updates

Automation of control systems is expected to be important in the economic and safe operation of microreactors. Therefore, there is a need to develop and demonstrate automated control for microreactors, along with the development of testbeds for this purpose. This report provides updates on the status of a nonnuclear microreactor automated control system (MACS)—a real-time, hardware-in-the-loop testbed for non-nuclear testing of microreactor control system automation. A real-time hardware-in-the-loop testbed incorporates the realistic dynamics of physical systems into control system development and testing. The collaborative effort between Oak Ridge National Laboratory (ORNL) and Idaho National Laboratory (INL) resulted in the development of a prototypic microreactor plant-level digital twin that includes the reactor and a balance of plant system. Advanced control strategies were incorporated to demonstrate testing of control automation solutions. The gRPC communication protocol, which was implemented in the hardware-in-the-loop testbed by INL, was coupled to a digital twin model developed using the TRANsient Simulation Framework of Reconfigurable Models (TRANSFORM) library in Modelica. This digital twin simulation was tested with the ViBRANT hardware for realistic feedback and visual representation of control action in real time. A modular Python client structure was developed to manage functional mock-up unit-based simulation and real-time gRPC communication. Hardware-in-the-loop testing indicated that the modeled reactor—a natural-convection, molten-salt coolant loop configuration—responds well to control of drum positioning for modulation of reactor core power, as well as system-level control and downstream demand changes. Ongoing research is focused on integrating additional control algorithms that utilize data from newly included sensors within the MACS hardware testbed, as well as demonstrating and assessing the performance of the different automated control algorithms on multiple additional operational scenarios.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS1976

Pilot interaction with automated airborne decision making systems

An investigation was made of interaction between a human pilot and automated on-board decision making systems. Research was initiated on the topic of pilot problem solving in automated and semi-automated flight management systems and attempts were made to develop a model of human decision making in a multi-task situation. A study was made of allocation of responsibility between human and computer, and discussed were various pilot performance parameters with varying degrees of automation. Optimal allocation of responsibility between human and computer was considered and some theoretical results found in the literature were presented. The pilot as a problem solver was discussed. Finally the design of displays, controls, procedures, and computer aids for problem solving tasks in automated and semi-automated systems was considered.

Rouse, W. B.↗