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Use of the Manuela 3D/Radiation Scanning Tool at Nuclear Facilities - 20437

Licensees, facility operators and decommissioning contractors have a need for comprehensive, accurate and precise radiological and topographical data to plan and execute safe and efficient Decontamination and Decommissioning (D and D) projects. Traditional approaches rely greatly upon labor and administratively intensive data collection methods that require considerable time and effort to glean the relevant data to plan and perform work in a radiological work area. Orano Decommissioning Services has developed and utilized at French nuclear facilities a lightweight portable scanning instrument called MANUELA (Mobile Apparatus for Nuclear Expertise Localization Assistance). Recently Orano conducted demonstrations at two United States nuclear facilities using MANUELA that may prove useful in areas of the fuel cycle including: - ALARA (As Low As Reasonably Achievable) planning during facility operations; - Scoping and site characterization during the D and D planning phase; - Remedial action support during active decommissioning work; - Radiation and modeling planning for plant modifications; - Support Final Status Survey and demonstration of 'end state' compliance for license termination. MANUELA is currently being evaluated for use during decommissioning of nuclear facilities and could aide in demonstrating compliance with end state criteria after decommissioning. Of particular interest are decommissioning projects planned for various sites across the United States. Most of these sites present unique challenges and opportunities to safely and efficiently decommission in a regulatory compliant manner. The two demonstration site's current conditions of relatively low radiation levels inside containment proved to be ideal platforms to deploy the MANUELA instrument for evaluation of the complex geometries typically found in a nuclear facility. The MANUELA instrument was able to precisely identify local hot spots of the reactor system's primary equipment and was able to simultaneously overlay the dose information onto the scanned 3D images. Using data like this, a licensee would be able to make informed technical and personnel decisions on methods to remediate, shield or properly dispose of the radioactive source term. MANUELA can be used throughout the decommissioning process to validate progress and continue to refine the work activities to optimize the efforts of the crew which reduces downtime waiting for survey results, whereas traditional survey methodology relies on a physical survey to be performed and the data transcribed onto survey maps that are only two dimensional. The traditional format tends to be inaccurate and difficult to interpret the precise location(s) of hot spots or generalized dose rates. The resultant scan also allows the user to know the exact extent of the survey versus transposing information onto a map with only a portion of the map area scanned. MANUELA also compiles all of the relevant survey data into a downloadable table format which includes the measurement identification number, time stamp, point space location on the XYZ plane, and the dose rate value. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

The EGS Collab Project – Stimulations at Two Depths

The EGS Collab project, supported by the US Department of Energy, is performing intensively monitored rock stimulation and flow tests at the 10-m scale in an underground research laboratory to address challenges in implementing enhanced geothermal systems (EGS). Data and observations from the field tests are compared to simulations to understand processes and build confidence in numerical modeling of the processes. We have completed Experiment 1 (of 3), which examined hydraulic fracturing in a well-characterized underground fractured phyllite test bed at a depth of approximately 1.5 km at the Sanford Underground Research Facility (SURF) in Lead, South Dakota. Testbed characterization included fracture mapping, borehole acoustic and optical televiewers, full waveform sonic, conductivity, resistivity, temperature, campaign p- and s-wave investigations and electrical resistance tomography. Borehole geophysical techniques including passive seismic, continuous active source seismic monitoring, electrical resistance tomography, fiber-based distributed strain, distributed temperature, and distributed acoustic monitoring, were used to carefully monitor stimulation events and flow tests. More than a dozen stimulations and nearly one year of flow tests were performed. Quality data and detailed observations were collected and analyzed during stimulation and water flow tests using ambient temperature and chilled water. We achieved adaptive control of the tests using real-time monitoring and rapid dissemination of data and near-real-time simulation. More detailed numerical simulation was performed to answer key experimental design questions, forecast fracture propagation trajectories and extents, and analyze and evaluate results. Data are freely available from the Geothermal Data Repository. Experiment 2 examines the potential for hydraulic shearing in amphibolite at a depth of about 1.25 km at SURF. This site has a different set of stress and fracture conditions than Experiment 1. The Experiment 2 testbed consists of nine subhorizontal boreholes configured in two fans of two boreholes which surround the testbed and contain grouted-in electrical resistance tomography, seismic sensors, active seismic sources and distributed fiber sensors. A “five-spot” set of test wells that extends from a custom mined alcove includes an injection well and four production/monitoring wells. The testbed was characterized geophysically and hydrologically, and three stimulations have been performed using the Step-Rate Injection Method for Fracture In-Situ Properties (SIMFIP) tool to measure strains, and a new strain quantifying tool (downhole robotic strain analysis tool -DORSA) was deployed in a monitoring hole during stimulation. Real-time data were broadcast during stimulations to allow real-time response to arising issues.

EGS Collab, Enhanced Geothermal Systems, EGS, fiel↗

Lightweight and Statistical Techniques for Petascale Debugging: Correctness on Petascale Systems (CoPS) Preliminry Report

Petascale platforms with O(10{sup 5}) and O(10{sup 6}) processing cores are driving advancements in a wide range of scientific disciplines. These large systems create unprecedented application development challenges. Scalable correctness tools are critical to shorten the time-to-solution on these systems. Currently, many DOE application developers use primitive manual debugging based on printf or traditional debuggers such as TotalView or DDT. This paradigm breaks down beyond a few thousand cores, yet bugs often arise above that scale. Programmers must reproduce problems in smaller runs to analyze them with traditional tools, or else perform repeated runs at scale using only primitive techniques. Even when traditional tools run at scale, the approach wastes substantial effort and computation cycles. Continued scientific progress demands new paradigms for debugging large-scale applications. The Correctness on Petascale Systems (CoPS) project is developing a revolutionary debugging scheme that will reduce the debugging problem to a scale that human developers can comprehend. The scheme can provide precise diagnoses of the root causes of failure, including suggestions of the location and the type of errors down to the level of code regions or even a single execution point. Our fundamentally new strategy combines and expands three relatively new complementary debugging approaches. The Stack Trace Analysis Tool (STAT), a 2011 R&D 100 Award Winner, identifies behavior equivalence classes in MPI jobs and highlights behavior when elements of the class demonstrate divergent behavior, often the first indicator of an error. The Cooperative Bug Isolation (CBI) project has developed statistical techniques for isolating programming errors in widely deployed code that we will adapt to large-scale parallel applications. Finally, we are developing a new approach to parallelizing expensive correctness analyses, such as analysis of memory usage in the Memgrind tool. In the first two years of the project, we have successfully extended STAT to determine the relative progress of different MPI processes. We have shown that the STAT, which is now included in the debugging tools distributed by Cray with their large-scale systems, substantially reduces the scale at which traditional debugging techniques are applied. We have extended CBI to large-scale systems and developed new compiler based analyses that reduce its instrumentation overhead. Our results demonstrate that CBI can identify the source of errors in large-scale applications. Finally, we have developed MPIecho, a new technique that will reduce the time required to perform key correctness analyses, such as the detection of writes to unallocated memory. Overall, our research results are the foundations for new debugging paradigms that will improve application scientist productivity by reducing the time to determine which package or module contains the root cause of a problem that arises at all scales of our high end systems. While we have made substantial progress in the first two years of CoPS research, significant work remains. While STAT provides scalable debugging assistance for incorrect application runs, we could apply its techniques to assertions in order to observe deviations from expected behavior. Further, we must continue to refine STAT's techniques to represent behavioral equivalence classes efficiently as we expect systems with millions of threads in the next year. We are exploring new CBI techniques that can assess the likelihood that execution deviations from past behavior are the source of erroneous execution. Finally, we must develop usable correctness analyses that apply the MPIecho parallelization strategy in order to locate coding errors. We expect to make substantial progress on these directions in the next year but anticipate that significant work will remain to provide usable, scalable debugging paradigms.

97 MATHEMATICS AND COMPUTING↗

Rain rate instrument for deployment at sea, phase 2

This report describes, in detail, the SBIR Phase 2 contracting effort provided for by NASA Contract Number NAS8-38481 in which a prototype Rain Rate Sensor was developed. FWG Model RP101A is a fully functional rain rate and droplet size analyzing instrument. The RP101A is a fully functional rain rate and droplet size analyzing instrument. The RP101A consists of a fiber optic probe containing a 32-fiber array connected to an electronic signal processor. When interfaced to an IBM compatible personal computer and configured with appropriate software, the RP101A is capable of measuring rain rates and particles ranging in size from around 300 microns up to 6 to 7 millimeters. FWG Associates, Inc. intends to develop a production model from the prototype and continue the effort under NASA's SBIR Phase 3 program.

Steele, Jimmy W.↗

Viewpoint Set on Nuclear Materials Science

Materials science has historically played critical roles in developing today’s nuclear energy systems. The current mainstream nuclear materials such as the zirconium alloys for fuels and core components and the reactor pressure vessel (RPV) steels were essential enabling materials for present day nuclear power and they undergo continued science-based fine tuning. Discovery of void swelling and other radiation effect phenomena in a fast neutron spectrum is largely responsible for the development of modern nuclear steels with enhanced radiation tolerance and, to a certain extent, the struggle of fast spectrum reactor development. Understandings the effects of neutron irradiation and the operating thermochemical environment in heat-resistant alloys, core graphite, and SiC-coated particle fuels have enabled the high temperature helium cooled reactors as a passively safe nuclear energy technology that is now anticipating mass deployment.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

PV Ramping in a Distributed Generation Environment: A Study Using Solar Measurements; Preprint

Variability in Photovoltaic (PV) generation resulting from variability in the solar radiation over the PV arrays is a topic of continuing concern for those involved with integrating renewables onto existing electrical grids. The island of Lanai, Hawaii is an extreme example of the challenges that integrators will face due to the fact that it is a small standalone grid. One way to study thisproblem is to take high-resolution solar measurements in multiple locations and model simultaneous PV production for various sizes at those locations. The National Renewable Energy Laboratory (NREL) collected high-resolution solar data at four locations on the island where proposed PV plants will be deployed in the near future. This data set provides unique insight into how the solar radiationmay vary between points that are proximal in distance, but diverse in weather, due to the formation of orographic clouds in the center of the island. Using information about each proposed PV plant size, power output was created at high resolution. The team analyzed this output to understand power production ramps at individual locations and the effects of aggregating the production from all fourlocations. Hawaii is a unique environment, with extremely variable events occurring on a daily basis. This study provided an excellent opportunity for understanding potential worst-case scenarios for PV ramping. This paper provides an introduction to the datasets that NREL collected over a year and a comprehensive analysis of PV variability in a distributed generation scenario.

14 SOLAR ENERGY↗

2015 South Carolina PV soft cost and workforce development Part I. Initial survey results

The South Carolina solar industry has surged over the past two years, largely due to the implementation of Act 236, and continues to grow at a rapid pace. At the beginning of 2014, there was little more than 3MW total spread across the state, but by the end of 2021, that state solar industry will have grown to over 300MW across all sectors. Prior to this study, there has been little publically available information on the solar industry in SC and throughout the Southeastern US. This makes SC a key case study of an emerging market, enabling the development of regional best practices in order to decrease associated costs and increase deployment.

14 SOLAR ENERGY↗

Spacecraft Data and Relay Management using Delay Tolerant Networking

NASA's demonstration of the successful transmission of relay data through the orbiting Mars Odyssey, Mars Global Surveyor, and Mars Express by the Mars Exploration Rovers has shown not only the benefit of using a relay satellite for multiple landed assets in a deep space environment but also the benefit of international standards for such architecture. As NASA begins the quest defined in the Vision for Exploration with robotic and manned missions to the Moon, continues its study of Mars, and is joined in these endeavors by countries world-wide, landed assets transmitting data through relay satellites will be crucial for completing mission objectives. However, this method of delivery of data will result in increased complexity in routing and prioritization of data transmission as the number of missions increases. Also, there is currently no standard method among organizations conducting such missions to return these data sets to Earth given a complex environment. One possibility for establishing such a standard is for mission designers to deploy protocols which fall under the umbrella of Delay Tolerant Networking (DTN). These developing standards include the Bundle Protocol (BP) which provides a standard, secure, store and forward mechanism designed for high latency and asymmetric communication links and the Licklider Transmission Protocol (LTP) which is used to provide a reliable deep space link transmission service.

network congestion control algorithm↗

Greenhouse Gas Concentration Data Recovery Algorithm for a Low Cost, Laser Heterodyne Radiometer

The goal of a coordinated effort between groups at GWU and NASA GSFC is the development of a low-cost, global, surface instrument network that continuously monitors three key carbon cycle gases in the atmospheric column: carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), as well as oxygen (O2) for atmospheric pressure profiles. The network will implement a low-cost, miniaturized, laser heterodyne radiometer (mini-LHR) that has recently been developed at NASA Goddard Space Flight Center. This mini-LHR is designed to operate in tandem with the passive aerosol sensor currently used in AERONET (a well established network of more than 450 ground aerosol monitoring instruments worldwide), and could be rapidly deployed into this established global network. Laser heterodyne radiometry is a well-established technique for detecting weak signals that was adapted from radio receiver technology. Here, a weak light signal, that has undergone absorption by atmospheric components, is mixed with light from a distributed feedback (DFB) telecommunications laser on a single-mode optical fiber. The RF component of the signal is detected on a fast photoreceiver. Scanning the laser through an absorption feature in the infrared, results in a scanned heterodyne signal io the RF. Deconvolution of this signal through the retrieval algorithm allows for the extraction of altitude contributions to the column signal. The retrieval algorithm is based on a spectral simulation program, SpecSyn, developed at GWU for high-resolution infrared spectroscopies. Variations io pressure, temperature, composition, and refractive index through the atmosphere; that are all functions of latitude, longitude, time of day, altitude, etc.; are modeled using algorithms developed in the MODTRAN program developed in part by the US Air Force Research Laboratory. In these calculations the atmosphere is modeled as a series of spherically symmetric shells with boundaries specified at defined altitudes. Temperature, pressure, and species mixing ratios are defined at these boundaries. Between the boundaries, temperature is assumed to vary linearly with altitude while pressure (and thus gas density) vary exponentially. The observed spectrum at the LHR instrument will be the integration of the contributions along this light path. For any absorption measurement the signal at a particular spectral frequency is a linear combination of spectral line contributions from several species. For each species that might absorb in a spectral region, we have pre-calculated its contribution as a function of temperature and pressure. The integrated path absorption spectrum can then by calculated using the initial sun angle (from location, date, and time) and assumptions about pressure and temperature profiles from an atmospheric model. The modeled spectrum is iterated to match the experimental observation using standard multilinear regression techniques. In addition to the layer concentrations, the numerical technique also provides uncertainty estimates for these quantities as well as dependencies on assumptions inherent in the atmospheric models.

Miller, J. Houston↗

Aerodynamic Load Analysis of a Variable Camber Continuous Trailing Edge Flap System on a Flexible Wing Aircraft

This paper presents an investigative aerodynamic analysis conducted on the novel control surface known as a Variable Camber Continuous Trailing Edge Flap (VCCTEF). The VCCTEF is modeled as a control effector on the NASA Generic Transport Model (GTM) where wing flexibility is considered. Aerodynamic modeling of the aircraft is conducted using vortex-lattice method (VLM), and an aeroelastic model of the aircraft that utilizes a coupled finite-element analysis (FEA) vortex-lattice solution is employed. VLM solutions are used to determine quasi-steady aerodynamic loading over the aeroelastic wing structures with VCCTEF. The load data is used to calculate aerodynamic sensitivities to control surface deployment and is also integrated to determine overall hinge moments. This analysis is conducted for different flight conditions, where control sensitivities compare the VCCTEF effectiveness against conventional control surfaces. Hinge moment results provide insight into aeroelastic wing loads, and worst case hinge moments for the VCCTEF can be estimated. Results show that the VCCTEF offers greater control authority than conventional ailerons at cruise, and nominal flap settings are determined for a low-speed take-off condition where the VCCTEF maintains comparable control effectiveness. Worst case hinge moment values are presented, where for a possible VCCTEF configuration, the stiff wing model demonstrates up to a 5.90 percent increase in flap hinge moment relative to a rigid model, and a reduced stiffness model demonstrates up to an 11.42 percent increase.

Aeroelasticity↗

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

J Lemery↗

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

Medical Operations↗

Used Fuel Dry Storage Systems and Components for Shutdown Plants to Expedite Defueling of Spent Fuel Pool - 18378

For plants that have announced plans to shut down, their main priority is to off-load the Used Nuclear Fuel from the spent fuel pool to dry storage at an ISFSI so that they can proceed to 'Safe store' or other storage options, and start their decommissioning process as soon as possible. One of the major expenses for a decommissioned plant is the cost of maintenance of the spent fuel pool. Therefore, the ability of the dry storage system to store short cooled fuel is an important aspect of dry storage system for shutdown plants. When a shutdown plant evaluates the dry storage options, total cost of the transferring the fuel from the pool to ISFSI site needs to be evaluated. One of the major costs to the utility for dry storage is the cost of ISFSI pad (basemat) along with the cost of dry storage system hardware. Smaller ISFSI footprint saves utility for the cost of ISFSI site excavation, cost of engineered backfill and cost of ISFSI pad. In order to minimize the cost of dry storage systems, besides the size of the ISFSI pad as described above, other important considerations is to use high capacity systems. The highest capacity systems available in the dry storage market today are with 37 PWR or 89 BWR used fuel assemblies per canister. These dry storage systems are expected to be at the plant sites for extended duration due to the unavailability of Interim Storage Facilities or a permanent disposal sites like Yucca Mountain. Maintaining the integrity of the dry storage systems canisters and storage overpack so that they can safely protect the fuel and integrity of used nuclear fuel is maintained during storage period is of utmost importance. Robust Aging Management Program is an important aspect for dry storage systems not only at the shutdown plants but also at the operating plants. This paper will provide the update of a reduced footprint version of TN Americas NUHOMS{sup R} System with MATRIX HSM and NUHOMS{sup R} EOS Canisters that addresses the need for maximizing safety, ISFSI capacity while optimizing cost. It will provide an update on the current capabilities, design features and current status of the licensing and deployment of NUHOMS{sup R} EOS System. It will provide the current status of the MATRIX HSM along with the significant enhancement included for a robust Canister Evolved Safety Program for Aging Management. It will also describe the construction sequence; loading operations, and inspections during extended storage as a part of aging management program. The compact version of TN Americas NUHOMS{sup R} System with MATRIX HSM offers a 'Universal' Storage Overpack which is designed to accept all different designs dry storage canisters available in the industry. As UNF storage continues to drives the industry towards innovations that address the need for optimized dry storage systems, compact ISFSIs and prudent aging management programs, this new evolution of the TN Americas NUHOMS{sup R} system with MATRIX HSM and EOS Canisters offers a significant leap forward in addressing all of these challenges. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

A Conceptual Framework for HPC Operational Data Analytics

This paper provides a broad framework for under- standing trends in Operational Data Analytics (ODA) for High- Performance Computing (HPC) facilities. The goal of ODA is to allow for the continuous monitoring, archiving, and analysis of near real-time performance data, providing immediately actionable information for multiple operational uses. In this work, we combine two models to provide a comprehensive HPC ODA framework: one is an evolutionary model of analytics capabilities that consists of four types, which are descriptive, diagnostic, predictive and prescriptive, while the other is a four- pillar model for energy-efficient HPC operations that covers facility, system hardware, system software, and applications. This new framework is then overlaid with a description of current development and production deployments of ODA within leading- edge HPC facilities. Finally, we perform a comprehensive survey of ODA works and classify them according to our framework, in order to demonstrate its effectiveness.

Netti, Alessio↗

Remote Monitoring of the Structural Health of Hydrokinetic Composite Turbine Blades (Final Report)

A health monitoring approach is investigated for hydrokinetic turbine blade applications. In-service monitoring is critical due to the difficult environment for blade inspection and the cost of inspection downtime. Composite blade designs have advantages that include long life in marine environments and great control over mechanical properties. Experimental strain characteristics are determined for static loads and free-vibration loads. These experiments are designed to simulate the dynamic characteristics of hydrokinetic turbine blades. Carbon/epoxy symmetric composite laminates are manufactured using an autoclave process. Four-layer composite beams, eight-layer composite beams, and two-dimensional eight-layer composite blades are instrumented for strain. Experimental results for strain measurements from electrical resistance gages are validated with theoretical characteristics obtained from in-house finite-element analysis for all sample cases. These preliminary tests on the composite samples show good correlation between experimental and finite-element strain results. A health monitoring system is proposed in which damage to a composite structure, e.g. delamination and fiber breakage, causes changes in the strain signature behavior. The system is based on embedded strain sensors and embedded motes in which strain information is demodulated for wireless transmission. In-service monitoring is critical due to the difficult environment for blade inspection and the cost of inspection downtime. Composite blade designs provide a medium for embedding sensors into the blades for in-situ health monitoring. The major challenge with in-situ health monitoring is transmission of sensor signals from the remote rotating reference frame of the blade to the system monitoring station. In the presented work, a novel system for relaying in-situ blade health measurements in hydrokinetic systems is described and demonstrated. An ultrasonic communication system is used to transmit sensor data underwater from the rotating frame of the blade to a fixed relay station. Data are then broadcast via radio waves to a remote monitoring station. Results indicate that the assembled system can transmit simulated sensor data with an accuracy of ±5% at a maximum sampling rate of 500 samples/sec. A power investigation of the transmitter within the blade shows that continuous max-sampling operation is only possible for short durations (~days), and is limited due to the capacity of the battery power source. However, intermittent sampling, with long periods between samples, allows for the system to last for very long durations (~years). Finally, because the data transmission system can operate at a high sampling rate for short durations or at a lower sampling rate/higher duty cycle for long durations, it is well-suited for short-term prototype and environmental testing, as well as long-term commercially-deployed hydrokinetic machines.

13 HYDRO ENERGY↗

Machine Learning Based Cavity Fault Classification and Prediction

We report on the development of machine learning models for classifying C100 superconducting radio frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a continuous-wave recirculating linac utilizing 418 SRF cavities to accelerate electrons up to 12 GeV through 5-passes. Of these, 96 cavities (12 cryomodules) are designed with a digital low-level RF system configured such that a cavity fault triggers waveform recordings of 17 RF signals for each of the eight cavities in the cryomodule. Subject matter experts (SME) can analyze the collected time-series data, identify which of the eight cavities faulted first, and classify the type of fault. This information is used to find trends and strategically deploy mitigations to problematic cryomodules. However, manually labeling the data is laborious and time-consuming. By leveraging machine learning, near real-time – rather than post-mortem – identification of the offending cavity and classification of the fault type has been implemented. We discuss the performance of the ML models during a recent physics run.

43 PARTICLE ACCELERATORS↗

Re-evaluating the Thermal-Spectrum Molten-Salt Breeder Reactor Fuel Cycle in a Modern Context

The current need to rapidly and substantially expand clean power production combined with limited capability for domestic uranium enrichment results in a U.S. nuclear power production planning environment similar to that of the first nuclear era. The U.S. thermal-spectrum molten-salt breeder reactor (TS-MSBR) program emerged during the first nuclear era as means to support rapid nuclear power expansion while minimizing demand on fissile resources. The primary reasons that the TS-MSBR program was discontinued in the 1970s were the comparatively large amount of required effort to mature its technology sufficiently for commercial deployment and the access to separated fissile material provided by the design of its integrated fuel cycle. The slowdown in U.S. electrical load growth after 1980 and the abundance of inexpensive fossil fuels inhibited reconsideration of TS-MSBR development for decades. The purpose of this discussion is to re-examine the value and technical challenges of TS-MSBRs with integrated fuel salt chemical processing considering the renewed need for rapid expansion of clean energy production worldwide, the current state of technology, the challenge of actinide wastes, and the continuing need to maintain a high degree of proliferation resistance and to integrate safeguards into the design.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Free-Flying Magnetometer Data System

The Free-Flying Magnetometer (FFM) is an autonomous "sensorcraft" developed at the Jet Propulsion Laboratory (JPL) for the Enstrophy sounding rocket mission. This mission was a collaborative project between the University of New Hampshire, Cornell University and JPL. The science goal of the mission was the study of current filamentation phenomena in the northern auroral region through multipoint measurements of magnetic field. The technical objective of the mission was the proof of concept of the JPL FFM design and the demonstration of an in-situ multipoint measurement technique employing many free-flying spacecraft. Four FFMs were successfully deployed from a sounding rocket launched from Poker Flats, Alaska on February 11, 1999. These hockey-puck-sized (80 mm diameter, 38 mm. height, 250 gram mass) free flyers each carry a miniature 3-axis flux-gate magnetometer that output +/- 2 V signals corresponding to a +/- 60,000 nT measurement range for each axis. The FFM uses a synchronized four-channel Sigma(Delta) Analog-to-Digital Converter (ADC) having a dynamic range of +/- 2.5V and converting at a rate of 279 samples/second/channel. Three channels are used to digitize the magnetometer signals to 17-bit (1.144 nT/bit) resolution. The fourth ADC channel is multiplexed for system monitoring of four temperature sensors and two battery voltages. The FFM also contains two sun sensors, a laser diode which emits a fan-shaped beam, a miniature S-band transmitter for direct communication to the ground station antennas, an ultra-stable Temperature Compensated Crystal Oscillator (TCXO) clock, an integrated data subsystem implemented in a Field-Programmable Gate Array (FPGA), a 4 Mbit Static Random Access Memory (SRAM) for data storage and Lithium Thionyl Chloride batteries for power. Communicating commands to the FFM prior to deployment is achieved with an infrared (IR) link. The FFM IR receiver responds to 9-bit pulse coded signals that are generated by an IR Light Emitting Diode (LED) in the payload for turning FFM power on or off and placing the FFM in a test mode or flight mode. The IR links are also used to synchronize (zero) the clocks onboard all the FFMs through a reset pulse originating from the payload GPS receiver that is issued when the FFMs are in flight mode. The FPGA based data subsystem manages continuous data collection from the four ADC channels and sun sensors, formatting and storing the data to SRAM, and controlling downlink transmission. The transmitter is powered only after a 2547 frame SRAM buffer has been filled (approx. 5 minutes of data). The data is Viterbi encoded and sent to the S-band transmitter via a First-In-First-Out (FIFO) buffer who's output is clocked at 100 bits/second. After the 26-second transmission, the transmitter is turned off to reduce noise coupling to the sensitive magnetometer. The data subsystem control consists of a master state machine that performs data flow management and is interfaced through a prioritized interrupt scheme to state machines that service the ADC, sun sensors and transmitter FIFO. Continuous data collection prevents the missing of data during transmission and provides implicit time tagging of the data acquired by the ADC because of synchronization with the TCXO clock.

Blaes, B.↗