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DOE OSTI2017

Integrating Sudo-Kaminaga Correlation to the Safety Analysis Code PARET-ANL

The accuracy and reliability of the reactor safety analysis code are critical because the specifications of the safety systems will depend on the analytical results for initiating events that could occur to the reactor. In safety analyses for power reactors, there exist many proficient and applicable computer codes such as RELAP5, CATHARE, RETRAN, or CATHENA. For non-power research reactors, an issue arises, however, due to lacking of such specialized codes. As a result, computer codes developed for the transient analysis of power reactors need to be applied carefully after proving their applicability to a specified research reactor. The safety analysis discussed in this paper concerns the prevention of fuel damage in hypothetical accidental scenarios for the new research reactor design at National Institute of Standards and Technology (NIST). One fuel integrity criterion is analyzed by investigating the Minimum Critical Heat Flux Ratio (MCHFR) in the core during the accidental transients. Some previous studies develop a relationship that correlates the nominal value of the minimum CHFR with the probability of CHF occurring and causing fuel damage. Using a statistical approach, the nominal value of the MCHFR for the low-enriched uranium (LEU) reactor needs to remain above the recommended limiting value to better ensure the safety of the research reactor. The probability of no fuel failure increases as the MCHFR increases. More importantly, as long as the MCHFR remains above the recommended limiting value of 1.301, the reactor will have a 90% probability that CHF is not reached. The safety analysis program PARET, developed by Argonne National Laboratory (ANL), is employed to obtain the CHFR information in this study. PARET/ANL is a digital computer programming code intended primarily for the analysis of test and research reactors that use plate-type (flat) fuel elements. This program has its own set of correlations that are used to calculate the CHFR. One of them is the Mirshak correlation, which was believed to provide the best estimation of the critical heat flux under the operating conditions of the new reactor at the time. Following the critical heat flux (CHF) experiments for vertical rectangular channels in the JRR-3 (Japan Research Reactor unit 3), the CHF calculations now use the Sudo- Kaminaga correlation. This correlation is of our interest because this method has an enhanced geometric similarity and an increased range of applicability that are more representative of the actual operating conditions of our current design, and has a more mechanistic approach [9]. Since the source code of PARET/ANL is inaccessible, a MATLAB-based utility has been written to integrate the Sudo-Kaminaga correlation as a substitute for the Mirshak correlation in the code. As a result, both the Mirshak and the Sudo-Kaminaga correlations are used to analyze the standard output from the PARET program to calculate the CHFR.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS2006

Education and Training Module in Alertness Management

The education and training module (ETM) in alertness management has now been integrated as part of the training regimen of the Pilot Proficiency Awards Program ("WINGS") of the Federal Aviation Administration. Originated and now maintained current by the Fatigue Countermeasures Group at NASA Ames Research Center, the ETM in Alertness Management is designed to give pilots the benefit of the best and most recent research on the basics of sleep physiology, the causes of fatigue, and strategies for managing alertness during flight operations. The WINGS program is an incentive program that encourages pilots at all licensing levels to participate in recurrent training, upon completion of which distinctive lapel or tie pins (wings) and certificates of completion are awarded. In addition to flight training, all WINGS applicants must attend at least one FAA-sponsored safety seminar, FAA-sanctioned safety seminar, or industry recurrent training program. The Fatigue Countermeasures Group provides an FAA-approved industry recurrent training program through an on-line General Aviation (GA) WINGS ETM in alertness management to satisfy this requirement. Since 1993, the Fatigue Countermeasures Group has translated fatigue and alertness information to operational environments by conducting two-day ETM workshops oriented primarily toward air-carrier operations subject to Part 121 of the Federal Aviation Regulations pertaining to such operations. On the basis of the information presented in the two-day ETM workshops, an ETM was created for GA pilots and was transferred to a Web-based version. To comply with the requirements of the WINGS Program, the original Web-based version has been modified to include hypertext markup language (HTML) content that makes information easily accessible, in-depth testing of alertness-management knowledge, new interactive features, and increased informational resources for GA pilots. Upon successful completion of this training module, a participant receives a computer- screen display of a certificate of completion. The certificate, which includes the pilot s name and an identifying number, can be printed out and submitted, for ground training credit, with the pilot s WINGS application.

Mallis, M. M.↗

NASA NTRS2009

Human Factors in Training - Space Medicine Proficiency Training

The early Constellation space missions are expected to have medical capabilities very similar to those currently on the Space Shuttle and International Space Station (ISS). For Crew Exploration Vehicle (CEV) missions to ISS, medical equipment will be located on ISS, and carried into CEV in the event of an emergency. Flight Surgeons (FS) on the ground in Mission Control will be expected to direct the Crew Medical Officer (CMO) during medical situations. If there is a loss of signal and the crew is unable to communicate with the ground, a CMO would be expected to carry out medical procedures without the aid of a FS. In these situations, performance support tools can be used to reduce errors and time to perform emergency medical tasks. Work on medical training has been conducted in collaboration with the Medical Training Group at the Space Life Sciences Directorate and with Wyle Lab which provides medical training to crew members, Biomedical Engineers (BMEs), and to flight surgeons under the JSC Space Life Sciences Directorate s Bioastronautics contract. The space medical training work is part of the Human Factors in Training Directed Research Project (DRP) of the Space Human Factors Engineering (SHFE) Project under the Space Human Factors and Habitability (SHFH) Element of the Human Research Program (HRP). Human factors researchers at Johnson Space Center have recently investigated medical performance support tools for CMOs on-orbit, and FSs on the ground, and researchers at the Ames Research Center performed a literature review on medical errors. The work proposed for FY10 continues to build on this strong collaboration with the Space Medical Training Group and previous research. This abstract focuses on two areas of work involving Performance Support Tools for Space Medical Operations. One area of research building on activities from FY08, involved the feasibility of just-in-time (JIT) training techniques and concepts for real-time medical procedures. In Phase 1, preliminary feasibility data was gathered for two types of prototype display technologies: a hand-held PDA, and a Head Mounted Display (HMD). The PDA and HMD were compared while performing a simulated medical procedure using ISS flight-like medical equipment. Based on the outcome of Phase 1, including data on user preferences, further testing was completed using the PDA only. Phase 2 explored a wrist-mounted PDA, and compared it to a paper cue card. For each phase, time to complete procedures, errors, and user satisfaction ratings were captured.

Connell, Erin↗

NASA NTRS2015

Robonaut 2 - Building a Robot on the International Space Station

In 2010, the Robonaut Project embarked on a multi‐phase mission to perform technology demonstrations on‐board the International Space Station (ISS), showcasing state of the art robotics technologies through the use of Robonaut 2 (R2). This phased approach implements a strategy that allows for the use of ISS as a test bed during early development to both demonstrate capability and test technology while still making advancements in the earth based laboratories for future testing and operations in space. While R2 was performing experimental trials onboard the ISS during the first phase, engineers were actively designing for Phase 2, Intra‐Vehicular Activity (IVA) Mobility, that utilizes a set of zero‐g climbing legs outfitted with grippers to grasp handrails and seat tracks. In addition to affixing the new climbing legs to the existing R2 torso, it became clear that upgrades to the torso to both physically accommodate the climbing legs and to expand processing power and capabilities of the robot were required. In addition to these upgrades, a new safety architecture was also implemented in order to account for the expanded capabilities of the robot. The IVA climbing legs not only needed to attach structurally to the R2 torso on ISS, but also required power and data connections that did not exist in the upper body. The climbing legs were outfitted with a blind mate adapter and coarse alignment guides for easy installation, but the upper body required extensive rewiring to accommodate the power and data connections. This was achieved by mounting a custom adapter plate to the torso and routing the additional wiring through the waist joint to connect to the new set of processors. In addition to the power and data channels, the integrated unit also required updated electronics boards, additional sensors and updated processors to accommodate a new operating system, software platform, and custom control system. In order to perform the unprecedented task of building a robot in space, extensive practice sessions and meticulous procedures were required. Since crew training time is at a premium, the R2 team took a skills‐based training approach to ensure the astronauts were proficient with a basic skill set while refining the detailed procedures over several practice sessions and simulations. In addition to the crew activities, meticulous ground procedures were required in order to upgrade firmware on the upper body motor drivers. The new firmware for the IVA mobility unit needed to be deployed using the old software system. This also provided an opportunity to upgrade the upper body joints with new software and allowed for limited insight into the success of the updates. Complete verification that the updated firmware was successfully loaded was not confirmed until the rewiring of the upper body torso was complete.

Diftler, Myron↗

NASA NTRS2010

Multi-Stage System for Automatic Target Recognition

A multi-stage automated target recognition (ATR) system has been designed to perform computer vision tasks with adequate proficiency in mimicking human vision. The system is able to detect, identify, and track targets of interest. Potential regions of interest (ROIs) are first identified by the detection stage using an Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter combined with a wavelet transform. False positives are then eliminated by the verification stage using feature extraction methods in conjunction with neural networks. Feature extraction transforms the ROIs using filtering and binning algorithms to create feature vectors. A feedforward back-propagation neural network (NN) is then trained to classify each feature vector and to remove false positives. The system parameter optimizations process has been developed to adapt to various targets and datasets. The objective was to design an efficient computer vision system that can learn to detect multiple targets in large images with unknown backgrounds. Because the target size is small relative to the image size in this problem, there are many regions of the image that could potentially contain the target. A cursory analysis of every region can be computationally efficient, but may yield too many false positives. On the other hand, a detailed analysis of every region can yield better results, but may be computationally inefficient. The multi-stage ATR system was designed to achieve an optimal balance between accuracy and computational efficiency by incorporating both models. The detection stage first identifies potential ROIs where the target may be present by performing a fast Fourier domain OT-MACH filter-based correlation. Because threshold for this stage is chosen with the goal of detecting all true positives, a number of false positives are also detected as ROIs. The verification stage then transforms the regions of interest into feature space, and eliminates false positives using an artificial neural network classifier. The multi-stage system allows tuning the detection sensitivity and the identification specificity individually in each stage. It is easier to achieve optimized ATR operation based on its specific goal. The test results show that the system was successful in substantially reducing the false positive rate when tested on a sonar and video image datasets.

Chao, Tien-Hsin↗

NASA NTRS2017

Work Term Assignment Spring 2017

My tour in the Engineering Robotics directorate exceeded my expectations. I learned lessons about Creo, manufacturing and assembly, collaboration, and troubleshooting. During my first tour, last spring, I used Creo on a smaller project, but had limited experience with it before starting in the Dynamic Systems Test branch this spring. I gained valuable experience learning assembly design, sheet metal design and designing with intent for manufacturing and assembly. These skills came from working both on the hatch and the floor. I also learned to understand the intent of other designers on models I worked with. While redesigning the floor, I was modifying an existing part and worked to understand what the previous designer had done to make it fit with the new model. Through working with the machine shop and in the mock-up, I learned much more about manufacturing and assembly. I used a Dremel, rivet gun, belt sander, and countersink for the first time. Through taking multiple safety training for different machine shops, I learned new machine shop safety skills specific to each one. This semester also gave me new collaborative opportunities. I collaborated with engineers within my branch as well as with Human Factors and the building 10 machine shop. This experience helped me learn how to design for functionality and assembly, not only for what would be easiest in my designs. In addition to these experiences, I learned many lessons in troubleshooting. I was the first person in my office to use a Windows 10 computer. This caused unexpected issues with NASA services and programs, such as the Digital Data Management Server (DDMS). Because of this, I gained experience finding solutions to lockout and freeze issues as well as Creo specific settings. These will be useful skills to have in the future and will be implemented in future rotations. This co-op tour has motivated me more to finish my degree and pursue my academic goals. I intend to take a machining Career Gateway Elective in the Fall to improve my skills in building as well as designing with manufacturing intent. I am also inspired to take more mechatronics CGE courses before I graduate to learn more about the crossover between mechanical and electrical engineering. This semester, I worked on multiple projects and had the opportunity to learn from engineers of different disciplines. I became proficient in Creo 2.0, a program I had not used significantly before. I finished modifying the hatch for 3D print and made sizeable modifications to the nose floor support design. I also gained hands-on experience that will be useful in my engineering career in the future. I would consider all of these major achievements from this spring semester. Lastly, I learned to ask more questions and to search for the right people to find answers which I know will be a valuable skill in the future. This summer, I will be completing my last rotation in the Flight Operations Directorate at Ellington Air Field. After this last tour, I will be returning to school for the Fall, Spring and Summer. I will graduate in August of 2018. I am looking forward to learning more about the different jobs available to engineers. This division works directly with many different types of aircrafts and I am excited to learn more about this focus of engineering. The Aircraft Operations Division values team work greatly and I intend to improve my interpersonal skills by working with them this summer.

Sico, Mallory↗

NASA NTRS2014

Robonaut 2 - IVA Experiments On-Board ISS and Development Towards EVA Capability

Robonaut 2 (R2) has completed its fixed base activities on-board the ISS and is scheduled to receive its climbing legs in early 2014. In its continuing line of firsts, the R2 torso finished up its on-orbit activities on its stanchion with the manipulation of space blanket materials and performed multiple tasks under teleoperation control by IVA astronauts. The successful completion of these two IVA experiments is a key step in Robonaut's progression towards an EVA capability. Integration with the legs and climbing inside the ISS will provide another important part of the experience that R2 will need prior to performing tasks on the outside of ISS. In support of these on-orbit activities, R2 has been traversing across handrails in simulated zero-g environments and working with EVA tools and equipment on the ground to determine manipulation strategies for an EVA Robonaut. R2 made significant advances in robotic manipulation of deformable materials in space while working with its softgoods task panel. This panel features quarter turn latches that secure a space blanket to the task panel structure. The space blanket covers two cloth cubes that are attached with Velcro to the structure. R2 was able to open and close the latches, pull back the blanket, and remove the cube underneath. R2 simulated cleaning up an EVA worksite as well, by replacing the cube and reattaching the blanket. In order to interact with the softgoods panel, R2 has both autonomously and with a human in the loop identified and localized these deformable objects. Using stereo color cameras, R2 identified characteristic elements on the softgoods panel then extracted the location and orientation of the object in its field of view using stereo disparity and kinematic transforms. R2 used both vision processing and supervisory control to successfully accomplish this important task. Teleoperation is a key capability for Robonaut's effectiveness as an EVA system. To build proficiency, crewmembers have attempted increasingly difficult tasks using R2 inside the Station. After donning motion capture equipment and a virtual reality visor, Expedition 34/35 flight engineer Tom Marshburn began operations with simple hand movements. Having gained confidence, Marshburn guided R2's arms in a leader-follower exercise with crewmate Chris Cassidy. He was also able to use the hand to grab a tumbling roll of tape, a task only demonstrable in microgravity. Later efforts saw Cassidy handle softgoods through shared control with ground operators, mimicking an activity previously achieved using only autonomy. Robotic climbing through the ISS on handrails requires both precision motion and compliant grasps in order to both position grippers on handrails/seat track and prevent large internal forces. R2 climbs using actively controlled compliance and torque limiting to meet both the precision and softness requirements. During a step, the attached leg is controlled to be strong and stiff in order to maintain precision trajectory tracking. The swing leg is controlled to be stiff but weak to minimize unintentional impact forces while maintaining precision. During a simulated dual limb grasp (as shown in Figure 1), the R2 controller maintains one limb rigid and one limb soft to prevent large internal forces from building up. R2's grippers also use a form of force control to limit grip force while not fully closed on either a handrail or seat track thus limiting unintentional forces on cables/objects that may be present in R2's translational path. The on-board torso R2 safety system relies on a single end-effector velocity limit to prevent potential impact forces from exceeding Station maximum load requirements. R2's mobile configuration required modifications to the velocity limiting safety function due to its large, dynamic inertia. R2's legs maneuver the robot's mass creating configuration dependent, joint-relative inertias. A single all-encompassing velocity limit to cover worst case inertia is prohibitively low. The upgraded R2 control and safety systems solve this problem using momentum limiting, momentum control, and kinetic energy minimization. Momentum and kinetic energy take the robot mass into account relieving low velocity restrictions on low inertia end-effectors while ensuring that the overall mass of R2 is limited from hazardous velocities. The momentum of R2's five safety nodes (each of the four end-effectors and the body) is monitored and compared to a single momentum limit. If any of the five nodes exceeds the safety limit, the motor power is removed and the robot comes to a stop. Momentum control/limiting also provides a simple, reliable method to integrate hand held tools into the safety system by providing the tool mass to the control system thus automatically reducing the allowable velocity of the end-effector with the tool. Work on the ground continues to build the skill set for an EVA Robonaut. Recent experiments (Figure 2) demonstrate how a teleoperator can use R2 to manipulate a tether hook, an important safety precaution on spacewalks. Another task displayed Robonaut's ability to pull back a protective jacket over a hose and search for damage, as well as inspect a quick-disconnect fitting for debris. Demonstrations such as these are indicative of EVA work done on ISS, specifically seen during a series of spacewalks over 2012 and 2013 where astronauts searched for an ammonia leak in one of the external cooling loops. Through experiments both on ISS and on the ground, R2 is evolving and providing the information needed to plan out the upgrades that will make an EVA Robonaut an effective tool. With the addition of legs, R2 will start climbing inside the space station and supply invaluable information on how the climbing strategies and task stabilization techniques must be refined. Ground R2 systems will continue to work with additional EVA tools and equipment in preparation for onboard IVA testing and future EVA applications.

Diftler, Myron↗