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At least 433 records · Page 24

Fault Management Techniques in Human Spaceflight Operations

This paper discusses human spaceflight fault management operations. Fault detection and response capabilities available in current US human spaceflight programs Space Shuttle and International Space Station are described while emphasizing system design impacts on operational techniques and constraints. Preflight and inflight processes along with products used to anticipate, mitigate and respond to failures are introduced. Examples of operational products used to support failure responses are presented. Possible improvements in the state of the art, as well as prioritization and success criteria for their implementation are proposed. This paper describes how the architecture of a command and control system impacts operations in areas such as the required fault response times, automated vs. manual fault responses, use of workarounds, etc. The architecture includes the use of redundancy at the system and software function level, software capabilities, use of intelligent or autonomous systems, number and severity of software defects, etc. This in turn drives which Caution and Warning (C&W) events should be annunciated, C&W event classification, operator display designs, crew training, flight control team training, and procedure development. Other factors impacting operations are the complexity of a system, skills needed to understand and operate a system, and the use of commonality vs. optimized solutions for software and responses. Fault detection, annunciation, safing responses, and recovery capabilities are explored using real examples to uncover underlying philosophies and constraints. These factors directly impact operations in that the crew and flight control team need to understand what happened, why it happened, what the system is doing, and what, if any, corrective actions they need to perform. If a fault results in multiple C&W events, or if several faults occur simultaneously, the root cause(s) of the fault(s), as well as their vehicle-wide impacts, must be determined in order to maintain situational awareness. This allows both automated and manual recovery operations to focus on the real cause of the fault(s). An appropriate balance must be struck between correcting the root cause failure and addressing the impacts of that fault on other vehicle components. Lastly, this paper presents a strategy for using lessons learned to improve the software, displays, and procedures in addition to determining what is a candidate for automation. Enabling technologies and techniques are identified to promote system evolution from one that requires manual fault responses to one that uses automation and autonomy where they are most effective. These considerations include the value in correcting software defects in a timely manner, automation of repetitive tasks, making time critical responses autonomous, etc. The paper recommends the appropriate use of intelligent systems to determine the root causes of faults and correctly identify separate unrelated faults.

O'Hagan, Brian↗

Establishing and Monitoring an Aseptic Workspace

When are aseptic operations necessary? In order to meet certain bioburden requirements, some components must undergo dry heat microbial reduction (DHMR) or other sterilizing procedures. If sensitive surfaces must be re-exposed after DHMR, this could compromise the bioburden levels. Recontaminating sterilized surfaces could be costly both in time by requiring repeated DHMR and risk to the hardware, which may not be compatible with repeated high temperature bakes. In order to prevent recontamination of the sensitive surfaces, an aseptic environment and sterile technique must be employed. Aseptic environments mean working in a space with almost no detectable bioburden in the air or on surfaces. Ideally, DHMR happens as late as possible to avoid requiring aseptic operations, as it can be considered a high-risk operations. Preparing the cleanroom for aseptic operations Establishing an ISO (International Organization for Standardization) class 5 space to minimize airborne particles. Maintain low bioburden in the cleanroom by using biocidal cleaners. Using multiple biocidal techniques decreases the likelihood of selecting for resistant microorganisms. 70% Isopropyl Alcohol (IPA) denatures the proteins in a microorganism (note: 70% IPA is better at killing microorganisms than 100% IPA) 7% hydrogen peroxide: damages DNA and proteins through oxygen radical damage. Ultraviolet-C (UV-C) lamps: causes crosslinking in DNA which prevents replication. Monitor cleanroom regularly for bioburden trending: Standard bioassay: Swab or wipe samples of cleanroom surfaces processed for colony forming unity (viable or spore selected); Rapid bioassay: Adenosine triphosphate (ATP) or Limulus amebocyte lysate (LAL) for a bioburden snapshot. High levels can signal an immediate re-cleaning before standard bioassay samples are taken. Airborne monitoring: Active (pulling air through a filter) or passive (particle fallout) for bioburden. Verify bioburden levels just before aseptic operation. Test hardware and cleanroom surfaces and air 3 days before the planned aseptic operation. Rapid bioburden just before aseptic operation to ensure room was not re-contaminated. Preparing personnel and tools: Personnel training. Everyone in the cleanroom: Standard cleanroom certification Everyone on the team: 1 day Planetary Protection overview. Aseptic operators only: Half-day aseptic operations training. Covers sterile garmenting/gloves, Sterile handling with a focus on contact transfer risk, tool/GSE preparation, and two-operator system for opening sterilized tools/components. Tool sterilization: All tools to be used during an aseptic operation need to be identified. Compatible tools are sterilized by DHMR or Autoclave. Double wrapped so that the exterior bag can be handled by a non-sterile operator, and the sterile. Tools that are not compatible with high heat do not come in contact with sensitive surfaces: either substitutes are found, or tools are isolated by wrapping in sterile foil. During an aseptic operation. Pre-task to make sure everyone understands the operations, who is handling what, and when the most critical surfaces will be exposed. Monitoring during the operation. Bioburden: active and passive airborne bioburden sampling, glove-tip dabs onto a plate after completion of operation (3 days for results). Particles: real time particle counter constantly running, with alarm for exceeding ISO 5 conditions.

aseptic processing↗

Safety in flight operations.

Manned space flight safety, discussing system design, operations planning, staffing, training and simulation

OPERATIONS RESEARCH↗

Utilizing Physics-Informed Synthetic Data to Train a Digital Twin for Predicting Reactor Operations

Understanding techniques to strengthen the nuclear nonproliferation regime is crucial in reducing the creation of nuclear weaponry on the basis of advancements in the nuclear energy industry. Prior to construction of a nuclear power plant, it is necessary to understand the proliferation potential of the plant’s reactor. Digital twins serve as a unique solution to recognizing reactor behavior indicative of nuclear proliferation. The following research conducted serves as a validation to training a digital twin on synthetic data fabricated via means of reactor physics simulations based on parameters of Idaho State University’s AGN-201 reactor. The synthetic data is utilized to train a long short-term memory (LSTM) recurrent neural network model. The accuracy of the predicted data is measured against real operational data to verify the reliability of the synthetic data creation methods and if these methods should be used in the future to inform inspectors of a reactor’s proliferation capabilities.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

ROMNet: Neural-Network-Based Surrogates for Reduced-Order Model Dynamics

SAND2023-05829O ROMNet is a toolbox built on the Tensorflow library for training reduced-order, neural operator surrogate models. The software is used for training reduced order neural operator surrogate models for dynamical systems applications. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗

Need for research and training reactors for advanced reactor designs

Full text of publication follows. Research and training reactors have served a valuable role in helping train workforce for currently operating fleet of light water reactors. These research and training reactors have been used in reactor laboratory classes to familiarize the students with such vital concepts as approach to criticality, reactor period, neutron moderation, reactivity, flux distribution and leakage, etc. As the industry moves toward advanced non-light-water reactor designs, it is critical that research and training reactors be developed and deployed at university campuses to help train the new generation of nuclear and non-nuclear engineers who are likely to design, build, and operate these advanced reactors. Among the designs currently being pursued for nuclear power generation include molten salt, sodium cooled, and gas cooled designs, with options for various fuel forms. Thus, industry and DOE in collaboration with academic institutions should devise plans on how to familiarize the next generation of nuclear workforce with hands-on experience necessary for such designs. These research and training reactors will play a vital role in familiarizing the future workforce with hands-on experience with concepts associated with fast spectrum reactors, gas cooled reactors, and other features not associated with light water reactors. In addition to classical nuclear engineering concepts, these advanced research and training reactors can also be used for hands-on training as well as for research on features being considered in the design of GEN-IV reactors: cyber security for digital control room operations, hybrid energy system, hydrogen generation, district heating, autonomous control... (author)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Real-Time Evolution and Deployment of Neuromorphic Computing at The Edge

Extremely low power neuromorphic systems are well-suited for deployment to the edge for many applications. In many use cases of neuromorphic computing for control, a spiking neural network is trained off-line using a simulation and then deployed to a neuromorphic system at the edge, where it will operate without ongoing training or learning. However, it may be desirable to continue training or learning at the edge to refine or adapt to the real-world system. In this work, we propose an approach for performing real-time evolutionary optimization for spiking neural networks for neuromorphic deployment at the edge. In particular, we propose a combination of simulation and real-world evaluations, along with feedback from the real-world environment, to train spiking neural networks for continuous deployment to the edge. We show that the real-time evolution at the edge approach achieves comparable performance to an evolution approach that requires constant evaluation in the realworld environment.

Schuman, Catherine↗

Interaction between workload and training - Converging evidence from psychophysiology and performance measurement

Traditionally, the domains of mental workload and training have been studied in isolation even though they share many common characteristics. The present paper integrates findings from psychophysiological and performance-based studies which address both of these issues. By augmenting traditional indices of skill acquisition such as RMS error scores, reaction times, and accuracy measures with psychophysiological measures such as the event-related brain potential, it is shown that workload effects can be assessed throughout the training process. More specifically, it is argued that the development of skill and the effects of workload on the human operator can be modeled within the framework of resource theories of attentional allocation. Also described is how converging evidence from psychophysiological and behavioral studies can be used to examine subtle changes in operator strategies during training.

Kramer, Arthur F.↗

Predicting failure parameters of semiconductor devices subjected to stress conditions

A method for predicting failure parameters of semiconductor devices can include receiving a set of data that includes (i) characteristics of a sample semiconductor device, and (ii) parameters characterizing a stress condition. The method further includes extracting a plurality of feature values from the set of data and inputting the plurality of feature values into a trained model executing on the one or more processors, wherein the trained model is configured according to an artificial intelligence (AI) algorithm based on a previous plurality of feature values, and wherein the trained model is operable to output a failure prediction based on the plurality of feature values. Further, the method includes generating, via the trained model, a predicted failure parameter of the sample semiconductor device due to the stress condition.

Ahmed, Moinuddin↗

Fact Sheet - Long-length Scintillating Fibers for Radiation Detection

As the world looks to underground geological repositories for storing nuclear waste, technologies to safeguard the material are needed. An example of a project that developed a new technology is Tripwire. Tripwire proposes a multi-sensor system approach for geological spent nuclear fuel repositories that relies of radiation, vibration, and electromagnetic detection. Tripwire was developed by the Applied Radiation Measurements and Systems (ARMS) group at INL and sponsored by the National Nuclear Security Administration (NNSA). The ARMS group performs research and development, testing and evaluation, operational support, and training focused on applied ionizing radiation detection and measurement. Application areas include nuclear engineering for advanced reactors and fuel cycle operations, nuclear nonproliferation, nuclear counterproliferation, nuclear forensics, and arms control and disarmament.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗

Artemis Geology Training Introduction to A3GT

Artemis Internal Science Training Team partners closely with the xEVA Trainers and Crew Training Office in FOD; together, all parties acknowledge the importance of integrated science and operations skill-building within training for surface operations.

Geology↗

Using Degradation Modeling to Identify Fragile Operational Conditions in Human- and Component-driven Resilience Assessment

Studying failure events shows that many high-impact events result from the complex interactions between precipitating failure events and degraded operational conditions. Often, when a system is put in operations, unforeseen practical realities (e.g., maintenance and/or workforce availability) lead the system to be operated in configurations outside its envisioned nominal range. However, design-time failure models often assume that the failure events are initiated in an idealized, nominal state of system operation, resulting in an incomplete assessment of future risk. To solve this, this paper develops a framework to consider degraded operational performance in scenario-based resilience models which uses a corresponding model of performance degradation to determine the values of deteriorated model parameters in the resilience model. This framework is demonstrated on a remotely-piloted rover to determine the (individual and combined) effect of drive-train wear and operator fatigue on the resilience of the rover to drive-train faults. This demonstration showed the substantial impact that degradation has on resilience, highlighting the need to account for degradation in resilience models–specifically, unconsidered degradation can lead to overestimates of resilience (and thus underestimates of safety margin) and because resilience can degrade prior to visible unreliability, which can lead to an operational environment with a high propensity for high-impact unforeseen failure events.

resilience↗

Using Degradation Modeling to Identify Fragile Operational Conditions in Human- and Component-driven Resilience Assessment

Studying failure events shows that many high-impact events result from the complex interactions between precipitating failure events and degraded operational conditions. Often, when a system is put in operations, unforeseen practical realities (e.g., maintenance and/or workforce availability) lead the system to be operated in configurations outside its envisioned nominal range. However, design-time failure models often assume that the failure events are initiated in an idealized, nominal state of system operation, resulting in an incomplete assessment of future risk. To solve this, this paper develops a framework to consider degraded operational performance in scenario-based resilience models which uses a corresponding model of performance degradation to determine the values of deteriorated model parameters in the resilience model. This framework is demonstrated on a remotely-piloted rover to determine the (individual and combined) effect of drive-train wear and operator fatigue on the resilience of the rover to drive-train faults. This demonstration showed the substantial impact that degradation has on resilience, highlighting the need to account for degradation in resilience models--specifically, unconsidered degradation can lead to overestimates of resilience (and thus underestimates of safety margin) and because resilience can degrade prior to visible unreliability, which can lead to an operational environment with a high propensity for high-impact unforeseen failure events.

Daniel Hulse↗

Creation Synthetic Data to Train a Digital Twin to Predict Reactor Operations

Understanding techniques to strengthen the nuclear safeguards regime is crucial in preventing nuclear proliferation due to recent advancements in the nuclear energy industry such as Generation IV reactors and microreactors. Prior to the construction of a nuclear power plant, it is necessary to understand the proliferation potential of the plant's reactor. Digital twins serve as a unique solution to recognizing reactor behavior indicative of nuclear proliferation. A digital twin is defined as a virtual model that works in unison to represent a physical asset, with a transference of data between the virtual and physical assets [1]. This work serves as validation for training a digital twin on synthetic data fabricated via means of Serpent reactor physics and point kinetics equations simulations. In this case this work is based on parameters of Idaho State University's AGN-201 reactor. The synthetic data can then be utilized to train machine learning models in the future to further investigate the utility of these methods. The accuracy of the predicted data is measured against real operational data to verify the reliability of the synthetic data creation methods and decide whether these methods should be used in the future to inform inspectors of a reactor's proliferation potential.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

Neural-network-directed alignment of optical systems using the laser-beam spatial filter as an example

This report describes an effort at NASA Lewis Research Center to use artificial neural networks to automate the alignment and control of optical measurement systems. Specifically, it addresses the use of commercially available neural network software and hardware to direct alignments of the common laser-beam-smoothing spatial filter. The report presents a general approach for designing alignment records and combining these into training sets to teach optical alignment functions to neural networks and discusses the use of these training sets to train several types of neural networks. Neural network configurations used include the adaptive resonance network, the back-propagation-trained network, and the counter-propagation network. This work shows that neural networks can be used to produce robust sequencers. These sequencers can learn by example to execute the step-by-step procedures of optical alignment and also can learn adaptively to correct for environmentally induced misalignment. The long-range objective is to use neural networks to automate the alignment and operation of optical measurement systems in remote, harsh, or dangerous aerospace environments. This work also shows that when neural networks are trained by a human operator, training sets should be recorded, training should be executed, and testing should be done in a manner that does not depend on intellectual judgments of the human operator.

Decker, Arthur J.↗

[X-33 Launch and Landing Facilities]

Sverdrup is responsible for the design, construction and activation of the X-33 Flight Operations Center at Edwards Air Force Base and for providing assistance in activating the X-33 Landing Sites. The past year has seen the completion of the construction of the X-33 Flight Operations Center. Construction was completed in December of 1998, with systems checkout and testing continuing into early 1999. Integration of the site with LMCMS and other partner-supplied systems began in December and will continue through rollout of the X-33 vehicle. The construction of the X-33 Launch Complex has been performed within the Edwards AFB and Air Force Research Laboratory (AFRL) systems with no substantial interference to either parties. A high level of cooperation exists between Sverdrup, Edwards AFB, and the Air Force Research Laboratory in the areas of access, training, security, and operations. There have been no conflicts between programs that have not been accommodated. Development of the landing sites is progressing with many of the modifications necessary underway. GSE commitments are in place. The personnel training program developed by Sverdrup for persons entering the launch site construction areas, was modified by Lockheed for use in training and access control to the Center during flight operations to maximize safety and minimize intrusion upon the environment. Close cooperation between Sverdrup, the construction workers, and the environmental biologist permitted construction to proceed in a timely fashion without harm to the wildlife, in particular, the Desert Tortoise. Although the entire X-33 site encompasses approximately 50 acres including a new access road, only the areas directly impacted by the construction were cleared to minimize the impact on the environment. A total of about 30 acres was actually disturbed.

Source record↗

Kivalina Biomass Reactor

This report summarizes work performed under DOE Award DE-EE00010149 to support the reliable operation of a community-scale biochar reactor system in Kivalina, Alaska. The project focused on improving sanitation and waste management in a remote community by assessing the installed system, identifying spare parts, defining key performance indicators (KPIs), preparing operator and maintenance manuals, and developing mobile reporting tools for operational data and KPI tracking. The team also produced training materials and recorded videos to support operator onboarding and continuity. The project demonstrated progress in system readiness, documentation, and digital reporting, while also identifying challenges common to remote deployments, including travel constraints, upstream system failures, and local resource limitations. This work provides a practical framework for improving the operation, monitoring, and future replication of biomass reactor systems in remote communities.

09 BIOMASS FUELS↗

Modular space station phase B extension integrated ground operations

Requirements for development test, manufacturing, facilities, GSE, training, logistics support, and launch operations are described. The prime integrating requirement is the early establishment of a common data base and its use throughout the design, development, and operational life of the station. The common data base is defined, and the concept of its use is presented. Development requirements for the station modules and subsystems are outlined along with a master development phasing chart.

Selegue, D. F.↗