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NASA NTRSDate not supplied

Intracranial Effects of Intermittent Lower Body Negative Pressure with Head Down Tilt Bed Rest: Comparison to Upright Posture

INTRODUCTION Spaceflight-associated neuro-ocular syndrome (SANS) affects a majority of astronauts during long-duration spaceflight. SANS is hypothesized to result from an unrelenting headward fluid shift that occurs in the microgravity environment. Altered intracranial structure and physiology documented in spaceflight and head-down tilt (HDT) experiments(1, 2) are also theorized to be related to chronic headward fluid shift and thereby can provide an independent quantitative assessment related to this mechanism. As a potential countermeasure, lower body negative pressure (LBNP) applied in the supine posture has shown efficacy in reducing headward fluid shift(3). However, it remains unknown if intermittent LBNP simulating daily upright posture fluid redistribution can mitigate SANS and intracranial changes associated with long-duration spaceflight. The goal of this study was to quantify the effects of daily application of LBNP or daily exposure to the upright posture on intracranial structure and physiology during long-term HDT in order to evaluate the potential efficacy of LBNP as a countermeasure to SANS. METHODS 22 healthy volunteers (11 men; 11 women; mean age = 35 years (SD=9.2)(age range = 24 to 52 years); mean BMI = 24.0 kg/m2 (SD=2.8)) completed an MRI study performed at the German Aerospace Facility in Cologne, Germany. Strict six-degree head-down tilt (HDT) bedrest was used as a spaceflight analog to induce a continuous headward fluid shift for 30 days. The subjects were divided equally into two groups of interventions: 1) LBNP for 3-hour sessions twice daily and 2) seated position for 3-hour sessions twice daily. Interventions were divided into morning and afternoon sessions for both groups. The LBNP intervention was maintained at 25 mmHg  2 mmHg. Pulse-gated MRI phase-contrast flow imaging was used to quantify cerebral artery stroke volume (CASV) and peak-to-peak cerebral spinal fluid (CSF) velocity (CSFVp-p) within the cerebral aqueduct. A 3D T1-MPRAGE sequence was used to quantify volumetric changes of the brain and intracranial CSF spaces using MRI Cloud software. MRI acquisitions were obtained at baseline (BDC, supine posture), 15 days into HDT (HDT15), 29 days into HDT (HDT29), and 12 days after recovery (R12, supine posture). The data were analyzed by a mixed model, which included intervention, time (four nominal levels BDC, HDT15, HDT29, R12), and intervention-time interaction as the fixed effects and included subject as a random effect. RESULTS Compared to BDC there was no statistically significant difference in CASV and CSFVp-p during HDT except for CASV at HDT29 (seated group) where there was a 1.7 mL (12%) decrease (P<0.01). Compared to BDC there was a statistically significant increase in intracranial volume (ICV; ICV = white matter + gray matter + CSF) for both interventions at HDT15 (Δ13 mL, 0.9 % (LBNP group); Δ15 mL, 1.0%, (seated group)) and HDT29 (Δ19 mL, 1.2%, (LBNP group); Δ23 mL, 1.5%) (seated group)) (All Ps<.001). Compared to baseline, lateral ventricular volume increased at HDT15 (Δ0.9 mL, 5.5%) and HDT29 (Δ1.8 mL, 10%)) for LBNP only (P=.001 for each). During HDT, white matter volume remained stable compared to baseline for both interventions. There were no significant intervention effects in the overall response to HDT (All Ps >.2). CONCLUSION LBNP had a similar response to the seated posture during 29 days of HDT. Although there was an increase in ICV and lateral ventricular volume with HDT there was no change in intracranial physiological parameters with LBNP suggesting an overall diminished response to the long-term effects of HDT. The lack of any significant increase in white matter volume during HDT with LBNP suggests maintenance of cerebral interstitial fluid transport. Final conclusions are pending the data collection for the control group (no intervention) which will be completed in 2023.

Larry A Kramer↗

DOE OSTI2024

Globally Gridded Groundwater Extraction Volumes and Costs under Six Depletion and Ponded Depth Targets

This repository contains simulated outputs from superwell – a hydro-economic tool for long-term assessment of groundwater cost and supply – providing globally gridded groundwater extractable volumes and associated unit costs ($/km³) for accessible groundwater production, based on a variety of user-defined depletion and ponded depth scenarios. Key model documentation: Niazi, H., Ferencz, S. B., Graham, N. T., Yoon, J., Wild, T. B., Hejazi, M., Watson, D. J., & Vernon, C. R. (2025). Long-term hydro-economic analysis tool for evaluating global groundwater cost and supply: Superwell v1.1. Geoscientific Model Development, 18(5), 1737-1767. https://doi.org/10.5194/gmd-18-1737-2025 Find the source code of the superwell model on GitHub: https://github.com/JGCRI/superwell Repository Overview Main output: superwell_outputs.7z contains 6 files (4.5 GB) named as superwell_py_deep_all_0.*PD_0.*DL.csv. These files present superwell outputs of global groundwater extraction volumes and cost estimates on a 0.5° scale for six scenarios with different Ponded Depth (PD; 0.3 and 0.6 m) and Depletion Limit (DL; 5%, 25%, and 40% of available volume) targets over the entire pumping lifetime of a grid cell superwell_py_deep_all_0.3PD_0.25DL_sample_100.csv contains superwell outputs for 100 data points sampled to match the global inputs' distribution superwell_py_deep_all_0.3PD_0.25DL_Grid_72548.csv contains superwell output for a single grid cell concept_v5.png provides an overview of the superwell workflow Outputs Description year_number: year of pumping depletion_limit: set depletion limit (DL) as a volume fraction of total available groundwater Mappings: continent, country, gcam_basin_id, Basin_long_name, grid_id: geographic identifiers and basin information Inputs: grid_area (km²): area of the grid cell whyclass: hydrogeological classification of the aquifer permeability (m/day), porosity (%), total_thickness (m), depth_to_water (m): aquifer properties. The geo-processed input data has been published separately: https://doi.org/10.57931/2307831 Model outputs: orig_aqfr_sat_thickness (m), aqfr_sat_thickness (m): original and remaining/instantaneous saturated thickness of the aquifer hydraulic_conductivity (m/day), transmissivity (m²/day): hydraulic properties of the aquifer radius_of_influence (m), areal_extent (km²): well radius and area of influence from the center of the well number_of_wells (-): number of wells in a grid cell determined by a ratio of well area and grid area max_drawdown (m), drawdown (m), drawdown_interference (m): well and aquifer drawdown during extraction total_head (m): total lift for the groundwater (depth to water plus drawdown) total_well_length (m): total depth of wells drilled well_yield (m³/day): pumping rate or well yield power (kW), energy (kWh): power and energy required for pumping groundwater Volume Outputs: volume_produced_perwell (m³), cumulative_vol_produced_perwell (m³): production volume metrics per well volume_produced_allwells (m³), cumulative_vol_produced_allwells (m³): aggregate extraction volumes for all wells in a grid cell available_volume (m³): available groundwater in storage for the grid cell as determined by aquifer properties depleted_vol_fraction: fraction of total volume pumped over available volumes in a grid cell (same as depletion limit) Cost Outputs: well_installation_cost ($): well installation cost based on the hydrogeological complexity of the aquifer annual_capital_cost, maintenance_cost, nonenergy_cost ($): nonenergy costs energy_cost_rate ($/kWh): electricity rate energy_cost ($): energy cost of pumping groundwater total_cost_perwell ($), total_cost_allwells ($): total annual energy and non-energy cost for each and all wells in a grid cell a unit_cost ($/m³), unit_cost_per_km3 ($/km³), unit_cost_per_acreft ($/acre-ft): total cost of pumping a unit of groundwater, indicated for different spatial units Key Resources Model documentation: Niazi, H., Ferencz, S., Graham, N., Yoon, J., Wild, T., Hejazi, M., Watson, D., & Vernon, C. (2024; In-prep). Long-term Hydro-economic Assessment Tool for Evaluating Global Groundwater Cost and Supply: Superwell v1. Geoscientific Model Development. Input data: Niazi, H., Watson, D., Hejazi, M., Yonkofski, C., Ferencz, S., Vernon, C., Graham, N., Wild, T., & Yoon, J. (2024). Global Geo-processed Data of Aquifer Properties by 0.5° Grid, Country and Water Basins. MSD-LIVE Data repository. https://doi.org/10.57931/2307831 superwell source code: https://github.com/JGCRI/superwell Cite as Niazi, H., Ferencz, S., Yoon, J., Graham, N., Wild, T., Hejazi, M., Watson, D., & Vernon, C. (2024). Globally Gridded Groundwater Extraction Volumes and Costs under Six Depletion and Ponded Depth Targets. MSD-LIVE Data repository. https://doi.org/10.57931/2307832 Contact Reach out to Hassan Niazi or Stephen Ferencz or open an issue in the superwell repository for questions or suggestions.

Earth Systems↗

DOE OSTI2014

Quantify Plasma Response to Non-Axisymmetric (3D) Magnetic Fields in Tokamaks, Final Report for FES (Fusion Energy Sciences) FY2014 Joint Research Target

The goal of the 2014 Joint Research Target (JRT) has been to conduct experiments and analysis to investigate and quantify the response of tokamak plasmas to non-axisymmetric (3D) magnetic fields. Although tokamaks are conceptually axisymmetric devices, small asymmetries often result from inaccuracies in the manufacture and assembly of the magnet coils, or from nearby magnetized objects. In addition, non-axisymmetric fields may be deliberately applied for various purposes. Even at small amplitudes of order 10 -4 of the main axisymmetric field, such “3D” fields can have profound impacts on the plasma performance. The effects are often detrimental (reduction of stabilizing plasma rotation, degradation of energy confinement, localized heat flux to the divertor, or excitation of instabilities) but may in some case be beneficial (maintenance of rotation, or suppression of instabilities). In general, the magnetic response of the plasma alters the 3D field, so that the magnetic field configuration within the plasma is not simply the sum of the external 3D field and the original axisymmetric field. Typically the plasma response consists of a mixture of local screening of the external field by currents induced at resonant surfaces in the plasma, and amplification of the external field by stable kink modes. Thus, validated magnetohydrodynamic (MHD) models of the plasma response to 3D fields are crucial to the interpretation of existing experiments and the prediction of plasma performance in future devices. The non-axisymmetric coil sets available at each facility allow well-controlled studies of the response to external 3D fields. The work performed in support of the 2014 Joint Research Target has included joint modeling and analysis of existing experimental data, and collaboration on new experiments designed to address the goals of the JRT. A major focus of the work was validation of numerical models through quantitative comparison to experimental data, in order to gain confidence in their ability to predict the performance of ITER and other burning plasmas. An important element of this effort was also to verify experimentally the limits of validity of the models. The rest of this overview will summarize the key results of the work.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS2023

Risk of Impaired Performance Due to Reduced Muscle Mass, Strength &, Endurance (Short Title: Muscle) and Risk of Reduced Physical Performance Capabilities Due to Reduced Aerobic Capacity (Short Title: Aerobic)

This report reviews the scientific literature regarding the human system risks to the microgravity environment of space flight in relation to human performance. The primary human performance-related risks involve deconditioning of the cardiovascular and skeletal muscles systems due to prolonged exposure to the reduced gravitational input. The chronological history of U.S. space flight is reviewed as a starting point to inform and understand the gaps in the knowledge to these risks. Maintenance of physical performance capabilities involves understanding the health of many organ systems (peripheral [vascular, heart, blood volume, skeletal muscle] and central [brain]) that ultimately contribute to the submaximal and maximal capacity of the aerobic (VO2peak), skeletal muscle (strength and endurance) systems. Maintaining astronaut VO2peak, muscle mass, strength, and endurance before, during, and after space flight is a significant priority to NASA for the current International Space Station (ISS) era, as well as for future exploration missions. A growing research database from both space flight and ground-based analog studies finds that the cardiorespiratory system is compromised and skeletal muscles (predominantly postural muscles of the lower limbs) undergo atrophy. These structural and metabolic responses to living in microgravity conditions contribute to physiological deconditioning during space flight that potentially increase the risks to astronauts returning to surface operations (i.e., Moon, Mars, or Earth). The time course changes from short to long-duration space flight and the relationships between in-flight performance deconditioning levels are not well characterized. Moreover, there are large interindividual variabilities that may be dependent on genetics, age, sex, preflight fitness levels, and individual exercise prescriptions that need further careful evaluations. Efforts should be made to understand the current status of preflight, in-flight, and postflight exercise performance capability and to define the operational goals and target areas for protection with the in-flight exercise program. There is a bi-directional relationship between exercise prescription and hardware countermeasures that need further understanding in-flight. For example, hardware with limited capabilities/modalities may be counterbalanced by changes in exercise prescription (i.e., frequency, time, intensity, volume) for providing effective responses to maintain fitness. Importantly, the minimal requirements for exercise prescription on ISS hardware may not translate to lower capability hardware on exploration missions. Due to limited volume on exploration vehicles, future Artemis missions to the Lunar surface will not have similar exercise hardware capabilities as ISS. This may alter the effectiveness of hardware to provide adequate physiological stress on bodily systems allowing for adaptations to maintain aerobic capacity, strength, and bone density. Thus, it will be important to understand the exercise responses of current ISS countermeasures to develop individualized exercise prescriptions that minimize aerobic and muscular risks, accounting for the large variability of responses among crewmembers. Newer exploration exercise hardware is currently being evaluated that is more compact (i.e., E4D and Orion Flywheel) and will require careful evaluation of the hardware on the stressor (i.e., metabolic rate, oxygen uptake, and heart rate work relationships, and force plate load profiles) needed the human body to protect and maintain crew health and performance. Moreover, exercise responses on the hardware need careful evaluation on the chronic adaptations. Lastly, in-flight evaluation of hardware exercise response may differ in 0-g or partial-g compared to 1-g. Therefore, it cannot be assumed that the stress on the body will be the same in each environment. Understanding this has a direct impact on exercise prescriptions. This document provides an overview of key scientific investigations that have been conducted before, during, and after human space flight missions, as well as from human ground-based analog studies that contribute to the evidence base on changes in aerobic capacity and muscle mass, strength, and endurance. Additional data from rodent and nonhuman primate experiments of skeletal muscle unloading completed during space flight or ground-based flight-simulations provide supportive information about this risk topic. Most importantly, a recent, large dataset from long-duration ISS crew has been added to give improved insight into the variability of exercise response of crew, demonstrating that a large portion of the crew population return to Earth with greater than 10-20% loss of aerobic capacity and muscle strength and endurance. Data from human space flight and ground-based studies are narrowing in on the required exercise paradigms but thus far still provide an incomplete answer to an effective approach for maintaining skeletal muscle function and aerobic fitness of all human space travelers. Finally, the relationship of this risk topic to various space flight operational scenarios is examined and discussed.

Eric Rivas↗

DOE OSTI2019

Big Data Framework with Machine Learning for D and D Applications - 19108

The nuclear industry is experiencing a steady increase in maintenance costs even though plants are maintained under high levels of safety, capability and reliability. Nuclear power plants are expected to run every unit at maximum capacity at all times, efficiently utilizing assets with minimal downtime. Surveillance and maintenance of nuclear decommissioning infrastructure provides lot challenges with respect to maintenance or decommissioning of the buildings. There is a need for a framework to analyze the huge amount of data generated by the sensors on the nuclear reactor components as well as structures, to monitor the conditions of these building over a period of time. Emerging technologies such as big data analytics have become a requirement in the nuclear industry to improve structural health monitoring and diagnostics. FIU will make use of existing mature technologies in the areas of imaging, robotics, big data, and machine learning/deep learning to assess the structural integrity of aging facilities at DOE sites. As these facilities await decommissioning, there is a need to understand the structural health of these structures. Many of these facilities were built over 50 years ago and in some cases these facilities have gone beyond the operational life expectancy. In other cases, the facilities have been placed in a state of 'cold and dark' and they are sitting unused, awaiting decommissioning. Finally, some older facilities are one-of-a-kind operational/production facilities supporting critical DOE missions and cannot be shut down. In any of these scenarios, the structural integrity of these facilities may be compromised, so it is imperative that adequate inspections and data collection/analysis be performed on a continuous and ongoing basis. The primary goals of the research include collecting various formats of data such as structured, unstructured and streaming data from the various sensors deployed in buildings, collect videos/pictures from various imaging sources, ingest them using a Hadoop distributed file system and process the data using Spark to perform batch processing and real time analytics. Research and development on various machine learning/deep learning algorithms will also be performed to analyze the heterogeneous data collected from nuclear decommission infrastructure. FIU will design the big data framework to ingest, store and process huge amounts of heterogeneous data collected from many sources and optimize the algorithms to provide insights into the data and predict anomalies observed when compared against baseline conditions. Various modules of this framework will include heterogeneous data sources, message broker, Hadoop distributed file system, Spark for stream and batch processing, machine learning/ deep learning, Cassandra - persistent data store and visualization. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2016

One System Overview - 16579

The U.S. Department of Energy (DOE), Office of River Protection (ORP) is responsible for managing and completing the River Protection Project mission, which is to 'safeguard the nuclear waste stored in Hanford's 177 underground tanks, and to manage the waste safely and responsibly until it can be treated in the Waste Treatment and Immobilization Plant for final disposition'. The Office of River Protection's work scope is currently performed through two primary contracts: - The Tank Operations Contract, held by Washington River Protection Solutions LLC, (WRPS) includes the construction, operation, and maintenance activities necessary to safely store, retrieve, and transfer tank wastes; provide supplemental tank waste treatment; provide storage and/or disposal of primary and secondary waste streams; design, construction and commissioning of the Low-Activity Waste Pretreatment System; and support ORP to integrate Hanford Tank Waste Treatment and Immobilization Plant (WTP) completion and startup with Tank Farms operations. - The WTP Contract, held by Bechtel National, Inc. (BNI), includes the design, construction, and commissioning of a Pretreatment Facility, two vitrification facilities (one for high-level waste and one for low-activity waste), a dedicated analytical laboratory, and supporting facilities to treat radioactive tank wastes by immobilizing them into a stable glass waste form. One System was established as a means to improve the integration between the tank farms and the WTP project to support its safe and successful startup. One System's scope and purpose focus on mission integration consistent with DOE's plans to treat tank waste in phases, starting with direct feed of the low-activity waste stream to the WTP's Low-Activity Waste (LAW) Vitrification Facility (referred to as DFLAW). The One System organization now has four major focus areas: - Flowsheet Integration? Integrated River Protection Project (RPP) flowsheet development and management; developing a gaps, risks, and opportunities management plan and technical roadmap; and waste feed qualification. - Mission Analysis and Planning? Strategic planning, system planning, and modeling integration. - WTP Startup, Commissioning, and Operations Integration? Interface Control Document (ICD) management, coordinating facilities startup-to-commissioning-to-operations, contract directives and standards identification, and DFLAW program integration. - Project Integration and Controls? Integrated schedule, metrics/dashboards monitoring, and One System risk register. ORP established the DFLAW Program Office in February 2015 as one of its key performance goals. ORP staff, along with contractor managers, have identified roles and responsibilities for the each of the four major focus areas. One System has established an Executive Council and Governance Board to drive program progress and make decisions on DFLAW. This approach allows appropriate government and contractor senior management involvement in the DFLAW Program. Monthly critical path analysis on the integrated DFLAW schedule is being performed. Interface control documents specific to DFLAW have been developed, and system planning and modeling tools are being used to help define the DFLAW flowsheet. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRS2018

Development of an Inflatable Airlock for a Deep Space Gateway

Inflatable structures technology utilizes high-strength fabric materials and internal pressure to create a stiffened pressure vessel that can replace traditional metallic primary structure in a habitable spacecraft. The flexibility of fabric structures allows them to be compactly stowed for launch and expanded in space, providing significant launch volume savings. The unique construction and design flexibility of these structures can be customized for a variety of uses in space including landing bags, decelerators, long duration in-space and planetary surface habitats, and even airlocks. An airlock is often a required component of a crewed spacecraft to allow for maintenance and human exploration outside of the vehicle. Airlock designs in use today rely on complex hatches and seals connected by metallic walls. Recent developments towards the design of an inflatable airlock structure show feasibility and a significant launch volume savings over a traditional metallic design. This paper will provide a high-level summary of these projects and the current state-of-the-art in inflatable airlock development with additional references and detail about previous and on-going research, providing guidance for the design of a softgoods airlock system. The use of inflatables in space has been in development since the 1960’s for both habitats and airlocks. The first ever EVA was conducted by the USSR in 1965 using an inflatable airlock known as the Volga. This airlock was attached to the Voskhod 2 spacecraft and turned the vehicle into a dual chamber airlock. The airlock was successfully deployed, used and jettisoned after Alexey Leonov’s historic spacewalk. Additional work on human-rated inflatable structures was not continued until the late 1990’s when NASA-JSC led an effort to demonstrate these structures as feasible long-term pressurized elements with the TransHab project. The technology developed and pioneered during this project led to multiple patents and proven feasibility that inflatables could be used for large habitable structures. Following TransHab, Bigelow Aerospace continued the development of inflatable structures with technical support from NASA. This partnership eventually led to the successful flight certification, launch, attachment and deployment of the Bigelow Expandable Activities Module (BEAM) on the ISS in 2016. Inflatable and expandable airlock structures have undergone various detailed feasibility studies and testing for over 15 years, most notably with the Advanced Inflatable Airlock (AIA), Dual-Chamber Hybrid Inflatable Suitlock (DCIS), Minimalistic Advanced Soft Hatch (MASH), and Lightweight External Inflatable Airlock (LEIA). During this time, full-scale articles have been built and pressure-tested, and mock-ups and demonstrators have been constructed and evaluated. During the 2001-2003 timeframe, the AIA concept was matured through requirements development, conceptual design, subscale and full-scale engineering breadboards subjecting various test articles to deployment and pressure testing up to four times operating pressure. These tests proved the feasibility of successful deployment and structural integrity of an inflatable crewlock. Additional testing was performed in the ensuing years, as funding permitted, to further refine additional structural and deployment concepts and to understand the EVA crewmember interfaces, hatches and EVA support equipment interfaces that would be required for a fully functioning airlock. This work resulted in a refinement of the structural requirements and an accounting of the systems needed in an inflatable airlock. In 2014, the MASH project developed an ultra-lightweight airlock concept with a fabric hatch that utilized a unique pressure vessel shape to minimize structural loads around a linear seal. The concept uses an automated zipper-like seal that allows for crew egress/ingress. Most of the development work on the project thus far has focused on the design, analysis and testing of the primary structure and the zipper-like seal system is in the preliminary stages of development with a successful proof-of-concept test. As part of the 2017 LEIA effort, studies were conducted on EVA crewmember interfaces on the inside of an inflatable airlock. These efforts included the design of an internal secondary structure and placement of handholds and foot restraints to enable hatch opening, closing and translation through the airlock. Structural design, analysis and testing was completed on several secondary structure candidates. Crew interface testing was also completed using an inflatable crewlock mockup and the JSC Active Response Gravity Offload System (ARGOS) to simulate the movement of an EVA crewmember through an inflatable crewlock in microgravity. The results of these tests helped demonstrate the feasibility of utilizing an inflatable structure as an airlock and informed the required volume, hatch size, and configuration and location of translation aids for crewmembers in a microgravity crewlock. The ISS Quest airlock uses a dual-chamber design with isolated compartments known as the equipment-lock and the crewlock. The equipment-lock houses the Servicing, Performance and Checkout Equipment (SPCE) items (suit batteries, consumables, etc.) while the crewlock has limited internal hardware and is the nominally depressurized compartment during US EVAs. While inflatable dual chamber airlocks have been studied, the current state of the art emphasizes an inflatable crewlock-type structure attached to a rigid equipment-lock type or habitat structure. Since a large portion of the hardware in the equipment-lock are rigid components and connectors that are installed on the ground - and an inflatable structure does not achieve full structural capabilities until pressurized in space - a depressurized fabric structure cannot provide the capabilities of a full equipment-lock. The use of an inflatable as a crewlock, however, provides all the required capabilities for EVA operations in a small launch package that offers significant volume savings over a metallic crewlock. The functions of a traditional equipment lock, including the SPCE, could be provided by a spacecraft’s habitat module or node and not necessarily in a separate equipment lock. An inflatable crewlock would be attached to the vehicle and launched in a packed and compressed state, saving volume under the launch shroud and mass for the overall airlock element compared to a rigid crewlock. Work is currently underway to continue development of an inflatable airlock with a variety of focus areas including the consideration of crew-induced loads and interfaces, the design and development of an internal sub-structure to provide translation aids and restraints, the thermal considerations of a fabric shell depressurized during an EVA, the micrometeorite environment in deep space, and the packaging and deployment of an inflatable airlock.

Deep Space Exploration↗

NASA NTRSDate not supplied

Analysis of Launch Vehicle Liftoff Debris: Historical Perspective from Space Shuttle and Application to Artemis I

Human exploration-class launch vehicles are inherently prone to debris due to the extreme environments generated during pre-launch operations, liftoff, and flight. The use of cryogenic propellants often requires thermal protection system (TPS) coatings, typically foam, to maintain the propellant conditions in the tank and prevent an accumulation ice on the external surface of the vehicle. Some ice growth is to be expected at umbilical interfaces, vents, flanges, or brackets where it is difficult to apply TPS. This ice may come loose at any time due to wind on the launch pad, structural vibration and acoustics after rocket ignition, or aerodynamic forces during flight. This phenomena is especially apparent on vehicles with no TPS, such as the Saturn V rockets used in the Apollo Program, see Figure 1. During propellant tanking, the thermal contraction of the underlying substrate may generate cracks in the TPS (Figure 1). Chunks of TPS can release due to the expansion of ingested gas from cryopumping or from aerodynamic forces if the crack creates an offset surface. Most foams will also have a certain amount of “popcorning” where small pieces of foam will pop off during flight because of the differential between the static surface pressure and the pressure of the gas trapped in the foam cell structure. There are a number of other coating or closeout materials that may be shed from the vehicle and become debris. During pre-launch operations and liftoff, the vehicle may also be exposed to debris originating from the launch pad or ground support equipment. This debris is separate from foreign object debris, or FOD, which is not intended to be present and is strictly controlled through operations and maintenance procedures. In this case, debris is generated from hardware and materials that are necessary for launch and are subject to the intense vibration, acoustics, and direct plume impingement of the launch environment. Examples include ice from umbilicals, tape and tie wraps that protect cables, and rust or corrosion from the launch platform. While NASA has historically been aware of debris as a potential issue that could cause a failure resulting in loss of mission, loss of vehicle, or loss of crew, the likelihood and severity of that risk was not always well understood or given sufficient weight in program and flight decisions. After the Space Shuttle Columbia accident (STS-107), the investigation found that foam TPS debris shed from the external tank was the proximate cause of the damage to the orbiter wing. Six previous observations of debris released from the foam ramp that covered the bipod connecting the forward end of the orbiter to the external tank resulted in minor changes or were determined to be accepted flight risks. Two occurrences of bipod ramp foam loss were not identified until the STS-107 investigation. Despite the damage inflicted by these debris strikes, the Shuttle Program Requirements Control Board deemed the vehicle safe to fly. During the Return to Flight effort following the Columbia disaster, NASA Engineering developed a process for the assessment of debris transport, impact, and damage tolerance to support independent assessments of risk by NASA Safety and Mission Assurance (S&MA). Under this system, each element (vehicle or ground system) defines a catalog of all expected debris based on launch history, component testing, or analysis. Debris transport analysis (DTA) is conducted using the debris catalog characteristics and potential flow transport mechanisms (e.g., vehicle aerodynamics, gravity, wind, plume-driven). The predicted debris impact locations and velocities are provided to the hardware owners, who use available test data and analysis to determine whether each component can withstand the impacts. In cases where the element hardware may be severely damaged or fail, the options are to mitigate the debris source through some change in design or operation, or to work with S&MA to try to characterize the probability of the impact and damage for program risk acceptance. Because of the differences in debris characteristics and transport, the DTA has been divided between the Liftoff and Ascent regimes. The development and application of Liftoff DTA methodology from the Shuttle Program to the current Artemis Program is the subject of this paper. Liftoff DTA covers the time from the start of pre-launch operations at the launch pad, up until the vehicle clears the launch tower and there is no longer any interaction with ground systems. Debris transport during this period is broadly classified as either gravity, wind, and plume-entrained (GWPE) or plume driven (PD). GWPE debris is generally lower speed, travelling in a forward-to-aft direction. PD transport includes flow features from the rocket ignition transient, as well as plume impingement and recirculation that occur as the vehicle lifts off the launch platform. In these cases, the debris typically moves in an aft-to-forward direction at higher speeds. The applicable transport mechanisms must be considered for each piece of debris depending on the material, and release location and time. For example, rust or metallic debris from the tower could fall (GWPE) and impact the vehicle before landing on the launch platform deck where it could be also be transported by plume impingement (PD). However, falling ice (GWPE) from an umbilical is unlikely to survive impact with the vehicle or launch platform and be available for PD transport. Modeling of debris transport is accomplished using a set of DTA tools which simulate debris trajectories subject to a reference frame acceleration (i.e., gravity) and aerodynamic drag. Where the trajectory encounters a solid surface, the debris is allowed to rebound with a specified coefficient of restitution. The drag is calculated by interpolating the fluid state at each point in the debris trajectory from high-fidelity computational fluid dynamics (CFD) simulations of the launch vehicle and pad. The CFD data may either be static (steady state or time averaged), typically for GWPE transport, or dynamic (time-accurate) for PD flow features like the ignition transient. Examples of the CFD flow field solutions for the Space Launch System (SLS) rocket and launch pad are shown in Figure 2. Typical SLS debris trajectory predictions from DTA are illustrated in Figure 3. The final version of this paper will include a more detailed examination of the Liftoff DTA process developed during the Shuttle Program, and how it has been augmented and applied to the SLS rocket under the Artemis Program. Comparisons with debris observations from the Artemis I launch will demonstrate validation of the tools and methodology.

Debris↗

DOE OSTI2015

Final Report for Project: Impacts of stratification and non-equilibrium winds and waves on hub-height winds

This project used a combination of turbulence-resolving large-eddy simulations, single-column modeling (where turbulence is parameterized), and currently available observations to improve, assess, and develop a parameterization of the impact of non-equilibrium wave states and stratification on the buoy-observed winds to establish reliable wind data at the turbine hub-height level. Analysis of turbulence-resolving simulations and observations illuminates the non-linear coupling between the atmosphere and the undulating sea surface. This analysis guides modification of existing boundary layer parameterizations to include wave influences for upward extrapolation of surface-based observations through the turbine layer. Our surface roughness modifications account for the interaction between stratification and the effects of swell’s amplitude and wavelength as well as swell’s relative motion with respect to the mean wind direction. The single-column version of the open source Weather and Research Forecasting (WRF) model (Skamarock et al., 2008) serves as our platform to test our proposed planetary boundary layer parameterization modifications that account for wave effects on marine atmospheric boundary layer flows. WRF has been widely adopted for wind resource analysis and forecasting. The single column version is particularly suitable to development, analysis, and testing of new boundary layer parameterizations. We utilize WRF’s single-column version to verify and validate our proposed modifications to the Mellor-Yamada-Nakanishi-Niino (MYNN) boundary layer parameterization (Nakanishi and Niino, 2004). We explore the implications of our modifications for two-way coupling between WRF and wave models (e.g.,Wavewatch III). The newly implemented parameterization accounting for marine atmospheric boundary layer-wave coupling is then tested in three-dimensional WRF simulations at grid sizes near 1 km. These simulations identify the behavior of simulated winds at the wind plant scale. Overall project conclusions include; In the presence of fast-moving swell (significant wave height Hs = 6.4 m, and phase speed cp = 18 ms -1 ), the atmospheric boundary layer grows more rapidly when waves propagate opposite to the winds compared to when winds and waves are aligned. Pressure drag increases by nearly a factor of 2 relative to the turbulent stress for the extreme case where waves propagate at 180° compared to the pressure gradient forcing. Net wind speed reduces by nearly 15% at hub-height for the 180°-case compared to the 0°-case, and turbulence intensities increase by nearly a factor of 2. These impacts diminish with decreasing wave age; Stratification increases hub height wind speeds and increases the vertical shear of the mean wind across the rotor plane. Fortuitously, this stability-induced enhanced shear does not influence turbulence intensity at hub height, but does increase (decrease) turbulence intensity below (above) hub height. Increased stability also increases the wave-induced pressure stress by ~ 10%; Off the East Coast of the United States during Coupled Boundary Layers Air-Sea Transfer - Low Wind (CBLAST-Low), cases with short fetch include thin stable boundary layers with depths of only a few tens of meters. In the coastal zone, the relationship between the mean wind and the surface fiction velocity (u*(V )) is significantly related to wind direction for weak winds but is not systematically related to the air sea difference of virtual potential temperature, δθv; since waves generally propagate from the south at the Air-Sea Interaction Tower (ASIT) tower, these results suggest that under weak wind conditions waves likely influence surface stress more than stratification does; and Winds and waves are frequently misaligned in the coastal zone. Stability conditions persist for long duration. Over a four year period, the Forschungsplattformen in Nord- und Ostsee Nr. 1 (FINO1) tower (a site with long fetch) primarily experienced weakly-unstable conditions, while stability at the ASIT tower (with a larger influence of offshore winds) experiences a mix of both unstable and stable conditions, where the summer months are predominantly stable. Wind-wave misalignment likely explains the large scatter in observed non-dimensional surface roughness under swell-dominated conditions. Andreas et al.’s (2012) relationship between u* and the 10-m wind speed under predicts the increased u* produced by wave-induced pressure drag produced by misaligned winds and waves. Incorporating wave-state (speed and direction) influences in parameterizations improves predictive skill. In a broad sense, these results suggest that one needs information on winds, temperature, and wave state to upscale buoy measurements to hub-height and across the rotor plane. Our parameterization of wave-state influences on surface drag has been submitted for inclusion in the next publicly available release. In combination, our project elucidates the impacts of two important physical processes (non-equilibrium wind/waves and stratification) on the atmosphere within which offshore turbines operate. This knowledge should help guide and inform manufacturers making critical decisions surrounding design criteria of future turbines to be deployed in the coastal zone. Reductions in annually averaged hub height wind speed error using our new wave-state-aware surface layer parameterization are relatively modest. However since wind turbine power production depends on the wind speed cubed, the error in estimated power production is close to 5%; which is significant and can substantially impact wind resource assessment and decision making with regards to the viability of particular location for a wind plant location. For a single 30-hour forecast, significant reductions in wind speed prediction errors can yield substantially improved wind power forecast skill, thereby mitigating costs and/or increasing revenue through improved; forecasting for maintenance operations and planning; day-ahead forecasting for power trading and resource allocation; and short-term forecasting for dispatch and grid balancing.

17 WIND ENERGY↗

DOE OSTI2023

Digitalization Guiding Principles and Method for Nuclear Industry Work Processes

The commercial U.S. light-water reactor fleet has been operating at historical efficiency, reliability, and safety over the last decade. Nuclear power has the highest capacity factor of any other power generation technology while also serving as the largest baseload source for carbon-free energy. Despite this remarkable achievement, continued operations for many plants are threatened due to fierce electricity market competition and rising operations and maintenance costs of which continued maintenance of obsolete analog equipment is a contributor. The digital age and associated technologies are where the future lies in process control, and nuclear has yet to take full advantage of the capabilities offered therein. The Light Water Reactor Sustainability Program (LWRS) at Idaho National Laboratory (INL), sponsored by the Department of Energy, has a mission to help the light-water reactor fleet manage its foundational capabilities to continue providing safe and reliable carbon-free power. LWRS helps support that mission by providing scientific, technology-based solutions for advanced concepts of operations with a more viable business model that will allow the fleet to continue to operate at peak levels through extended plant operation. The LWRS Digitalization Project at INL seeks to leverage digital technologies to synthesize and transform work processes. We provide a state-of-the-art analysis of digitalized work processes in nuclear power and investigate ways in which researchers at INL and the nuclear industry can work together to identify what data to access, how to access it, what to do with the data, and most importantly, how to use the insights for decision-making across all levels within the business. Borne from these considerations, we present four guiding principles for digitalization: develop a coherent digitalization plan, apply human factors engineering, establish data governance, and anticipate unintended consequences. Together, these principles form a method that plants can use to effectively to digitalize nuclear industry work processes. Our guiding principles are informed by multiple knowledge sources. First, we document activities from the Work Digitalization Initiative, which was conceived as a means for nuclear organizations to help define and standardize the industry’s approach to digitalizing work. Second, we detail primary research conducted with industry professionals regarding drivers and barriers to digitalization adoption. We present survey results that demonstrate what the industry hopes to get out of digitalization and the ways that INL can continue to support the industry’s digital transformation. Third, we present a digitalization use case with industry partners NextAxiom Technology and Xcel Energy. The project objective was to transform the current condition report work process from paper to digital, incorporating digitalized principles. We report the development of the application and lessons learned. The accomplishments achieved by this research and development serve to identify critical needs for plant guidance in support of digitalization implementation and contribute to the knowledge and strategies available for utilities considering or undertaking digitalization.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2022

Intelligent Process Visualization through Nuclear Operation Process Modeling, Reasoning, and Object Detection from Field Videos (Final Report)

This report is a deliverable for the “Final Report” task of DOE NEET Project 19-16790, "Context-Aware Safety Information Display for Nuclear Field Workers." This project's overall goal is to test the hypothesis that integrating computer vision and process reasoning methods will enable proactive visualization of the safe operation and maintenance processes of Nuclear Power Plants (NPP) for field workers. Augmented Reality (AR) glasses adopting such proactive safety information visualization techniques can significantly increase personnel safety and reduce the NPP’s operating costs. The current practice of monitoring NPPs requires workers to switch between digital models, data, and physical workspaces in identifying relevant but potentially occluded objects and in assessing the risks of operation and maintenance processes. On the other hand, frequently changed field conditions require field workers to report to supervisors for real-time guidance. Such guidance is essential to ensure that changing conditions will not invalidate or endanger the work order and other ongoing processes that may jeopardize NPP operations. Additionally, incorrect recognition of equipment objects can result in communication errors and safety problems. AR techniques can assist engineers in viewing the physical workspaces with objects labeled with detailed operation procedures and safety reminders during field operations. The project team developed an “Intelligent Context-Aware Safety Information Display” (ICAD) for supporting Nuclear Power Plant (NPP) field workers in achieving safe and efficient execution of a series of operational tasks in uncertain and changing workspaces of an NPP. Before designing the ICAD-AR prototype, the project team synthesized NPP operational knowledge models through literature review studies, surveys, interviews with domain experts, and knowledge modeling. The project team conducted an extensive study of the operational procedures of various NPPs, and digital technologies that can support the safe and efficient execution of those procedures in different NPP operational contexts. This literature review helped the project team conduct surveys and interviews with nuclear engineers and field workers to identify three categories of information. The NPP knowledge modeling efforts reveal that the three categories of information identified have different levels of importance in a typical procedure of carrying out a series of tasks to achieve a specific NPP operation goal (e.g., shutdown, mode changes). These three categories of information include 1) Workspace dynamics – the changing spatial arrangements of workspaces, tools, protection equipment, and supporting materials, 2) Workflow prognostics – the dynamic dependencies between different parts of an NPP that functionally support and influence each other in terms of safety and efficiency, and 3) Hazards – objects and spaces that contain hazardous materials or physical conditions that can pose risks to workers or mechanical systems. The project team has profiled the importance levels of these categories of information into a knowledge model. This knowledge model specifies what types of information are more critical for a given task in a given workspace so that computers can automatically identify critical objects and sensors in a scene for delivering context-ware safety information to field workers through AR devices. Significant research development of this project results in technical research outcomes and a prototyping system that illustrates the technical feasibility of establishing an ICAD-AR system supporting the proactive safety information display for nuclear field workers. This final report summarizes the project team’s technological achievements in the past three years. Overall, the project team completed the development and integration of five techniques into a prototype ICAD Augmented Reality (ICAD-AR) system and demonstrated the developed system’s real-time execution in a mechanical room. The project team completed the analysis of using this prototype in other types of workspaces based on 3D image data and digital design models collected from two additional workspaces (a water treatment plant and a flow loop training facility). The integrated techniques include 1) Natural Language Processing (NLP) algorithms supporting the generation and updates of nuclear fieldwork process models based on text analysis of work packages and operation manuals; 2) sensor log analysis for predicting control actions in given sensor reading contexts; 3) computer vision algorithms for automatic localization and navigation of workers; 4) object detection algorithms for identifying task-related objects and correlated sensors for safety checking; 5) AR technique as a platform for supporting the integration. The testing results of these five techniques have shown that 1) the sensor log analysis model can predict the next control action with an accuracy of 0.883; 2) the trained natural language processing model can extract more than 80% of the critical information from paper-based procedures (PBPs); 3) the navigation algorithm with the integration of Visual Inertial Odometry (VIO) and Non-Recursive Bayesian Filter methods make operator’s trajectory estimation resilient to drift error; 4) the computer vision algorithm can detect task-specific and safety-critical objects with an average accuracy of 95.3%. The project team used work procedures collected from a flow loop training facility and two datasets collected from two mechanical rooms simulating the workspaces of NPPs to demonstrate the technical capabilities of the developed ICAD-AR prototype. The demonstration validated the technical feasibility of establishing the ICAD-AR system for nuclear field workers and identified the challenges in 1) automatic text analysis of work packages; 2) use of limited samples of sensor logs for predicting the proper timings of control actions; 3) reliably tracking workers and their task progress in mechanical rooms with many similar objects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS2021

Validation Assessment for the Soil Moisture Active Passive (SMAP) Level 4 Carbon (L4_C) Data Product Version 5

The post-launch Cal/Val phase of the SMAP mission is guided by two primary objectives for each science product team: 1) to calibrate, verify, and improve the performance of the science algorithms, and 2) validate accuracies of the science data products as specified in the SMAP Level-1 mission science requirements. Algorithm science and product maintenance activities during the SMAP extended mission phase have also involved periodic algorithm calibration and product refinements to maintain or enhance product consistency and performance as well as science utility. This report provides an assessment of the latest (Version 5) SMAP Level 4 Carbon (L4_C) product. The L4_C Version 5 (v5) global record now spans more than six years (March 2015 – present) of SMAP operations and has benefited from five major reprocessing updates to the operational product. These reprocessing events and L4_C product release updates have incorporated various algorithm refinements and calibration adjustments to account for similar refinements to the upstream GEOS land model assimilation system, SMAP brightness temperatures, and MODIS vegetation inputs used for L4_C processing. The SMAP L4_C algorithms utilize a terrestrial carbon flux model informed by daily surface and root zone soil moisture information contributed from the SMAP Level 4 Soil Moisture (L4_SM) product along with optical remote sensing-based (e.g. MODIS-based) land cover and canopy fractional photosynthetic active radiation (fPAR), and other ancillary biophysical data. The carbon flux model estimates global daily net ecosystem CO2 exchange (NEE) and the component carbon fluxes, namely, vegetation gross primary production (GPP) and soil heterotrophic respiration (Rh). Other L4_C product elements include surface (~0-5 cm depth) soil organic carbon (SOC) stocks and associated environmental constraints to these processes, including soil moisture-related controls on GPP and ecosystem respiration (Kimball et al. 2014, Jones et al. 2017). The L4_C product addresses SMAP carbon cycle science objectives by: 1) providing a direct link between terrestrial carbon fluxes and underlying freeze/thaw and soil moisture-related constraints to these processes, 2) documenting primary connections between terrestrial water, energy and carbon cycles, and 3) improving understanding of terrestrial carbon sink activity. The SMAP L4_C algorithms and operational product are mature and at a CEOS Validation Stage 4 level (Jackson et al. 2012) based on extensive validation of the multi-year record against a diverse array of independent benchmarks, well characterized global performance, and systematic refinements gained from five major reprocessing events. There are no Level-1 mission science requirements for the L4_C product; however, self-imposed requirements have been established focusing on NEE as the primary product field for validation, and on demonstrating L4_C accuracy and success in meeting product science requirements (Jackson et al. 2012). The other L4_C product fields also have strong utility for carbon science applications (e.g., Liu et al. 2019, Endsley et al. 2020); however, analysis of these other fields is considered secondary relative to primary validation activities focusing on NEE. The L4_C targeted accuracy requirements are to meet or exceed a mean unbiased root-mean-square error (ubRMSE, or standard deviation of the error) for NEE of 1.6 g C m-2 d-1 and 30 g C m-2 yr-1, emphasizing northern (≥45°N) boreal and arctic ecosystems; this accuracy is similar to that of tower eddy covariance measurement-based observations (Baldocchi 2008). Methods used for the latest v5 L4_C product performance and validation assessment have been established from the SMAP Cal/Val plan and previous studies (Jackson et al. 2012, Jones et al. 2017) and include: 1) consistency evaluations of the product fields against earlier product releases (version 4 or earlier); 2) comparisons of daily carbon flux estimates with independent tower eddy 4covariance measurement-based daily carbon (CO2) flux observations from core tower validation sites (CVS); and 3) consistency checks against other global carbon products, including soil carbon inventory records, global GPP records derived from tower observation upscaling methods, and satellite-based observations of canopy solar induced chlorophyll fluorescence (SIF) as a surrogate for GPP. Metrics used to evaluate relative agreement between L4_C product fields and observational benchmarks include correlation (r-value), RMSE differences, bias and model sensitivity diagnostics. Following these validation criteria, the present report provides a validation assessment of the latest L4_C product release (v5). Detailed descriptions of the L4_C algorithm and additional global product accuracy and performance results are given elsewhere (Jones et al. 2017, Endsley et al. 2020). The v5 L4_C product replaces earlier product versions and continues to show: (i) accuracy and performance levels meeting or exceeding SMAP L4_C science requirements; (ii) improvement over the previous product version (version 4); and (iii) suitability for a diversity of science applications. Example L4_C applications from the recent literature include clarifying environmental trends and controls on the northern terrestrial carbon sink (Liu et al. 2019), diagnosing drought-related impacts on ecosystem productivity (Li et al. 2020), and regional monitoring of cropland conditions for projecting annual yields (Wurster et al. 2020). 2EXPECTED L4_C ALGORITHM AND PRODUCTPERFORMANCE The L4_C algorithm performance, including variance and uncertainty estimates of model outputs, was determined during the mission pre-launch phase through spatially explicit model sensitivity studies using available model inputs similar to those currently being used for operational production and evaluating the resulting model simulations over the observed range of northern (≥45 °N) and global conditions (Kimball et al. 2012, Entekhabi et al. 2014). The L4_C algorithm options were also evaluated during the mission prelaunch phase, including deriving canopy fPAR from lower order NDVI (Normalized Difference Vegetation Index) inputs in lieu of using MODIS (MOD15) fPAR; and including an explicit model representation of boreal fire disturbance recovery impacts. These results indicated that the L4_C accuracy requirements (i.e., NEE ubRMSE ≤ 30 g C m-2 yr-1or ≤ 1.6 g C m-2 d-1) could be met from the baseline algorithms over more than 82% and 89% of global and northern vegetated land areas, respectively (Yi et al. 2013, Kimball et al. 2014). The global L4_C algorithm error budget for NEE derived during the mission prelaunch phase indicated that the estimated NEE ubRMSE uncertainty is proportional to GPP and is therefore larger in higher biomass productivity areas, including forests and croplands (Kimball et al. 2014). Likewise, NEE ubRMSE uncertainty is expected to be lower in less-productive areas, including grasslands and shrublands. Expected model NEE ubRMSE levels were also generally within targeted accuracy levels for characteristically less-productive boreal and Arctic biomes, even though relative model error as a proportion of total productivity (NEE RMSE / GPP) may be large in these areas. The estimated NEE uncertainty was lower than expected in some warmer tropical high biomass productivity areas (e.g. Amazon rainforest) because of reduced low temperature and moisture constraints to the L4_C respiration calculations so that the bulk of model uncertainty is contributed by GPP in these areas. Model NEE uncertainty in the African Congo was estimated to

SMAP↗

DOE OSTI2017

Radiological Shielding Design for the Neutron Backscattering Spectrometer EMU

In this paper we present modelling and design of compact radiation shielding required for operation of the EMU instrument, located at Australia's OPAL nuclear reactor. The acceptance criteria for the shielding design were (i) safety - dose rate ≤ 3 μSv/h at external surfaces of the instrument's experimental area, (ii) floor load limit < 20 tons/m{sup 2}, (iii) low cost and (iv) for maintenance purposes 'easy access' to components of the instruments. The EMU instrument is a cold neutron backscattering spectrometer designed to measure inelastic neutron scattering. Its distinctive feature is that it has a tight elastic energy transfer resolution, of about 1 μeV. The instrument provides unique opportunities to study diffusion in complex materials, e.g. water molecules in confinement, polymers or biological molecules, particularly in the Asia-Oceania region. EMU is the first instrument located on the CG3 neutron beam line of the OPAL Neutron Guide Hall (NGH). A pre-existing evacuated neutron guide of dimensions 200 mm (height) x 50 mm (width) feeds into the shared EMU/KOOKABURRA pre-monochromator bunker shielding assembly, and its bottom cross section will be intercepted ∼200 mm further in air by the highly orientated pyrolytic graphite (HOPG) EMU pre-monochromator (take-off angles from 125.4 deg. to 148 deg.). The bunker shielding assembly for EMU also contains a beryllium filter and Focusing Guide Section D (FGD, 170 mm (height) x 50 mm (width)). To accommodate the range of take-off angles Pb cylindrical shielding blocks of radius 140 mm and 500 mm height have been designed. These blocks will be located at the section of the bunker where the neutron beam will exit towards the experimental area and they will reside on top of a hollow steel base (sides 10 mm thick), on which they move by sliding and are integrated with the tertiary shutter. The experimental area is enclosed by a concrete wall of 2.5 m height and 300 mm thickness, shared with the KOOKABURRA instrument, Gyprock Plasterboard panels with viewing windows, a wall from the Out-of-Pile Bunker and the CG3 shielding. The neutron beam enters the experimental area via the tertiary shutter, into the Focusing Guide Section C (FGC), which has an entrance guide cross-section of 158 mm (height) x 79 mm (width) and exit guide cross-section of 149 mm (height) x 85 mm (width) and is of length 816 mm. The vacuum chamber of FGC will be connected to the vacuum chamber of the Background Chopper (BC, disc of outer radius 350 mm). Focusing Guide Section B (FGB) is aligned with FGC and its vacuum shared with the BC vacuum. The FGB has an exit guide cross section of 50 mm (height) x 49 mm (width) and is of length 4046 mm, which will focus the neutron beam vertically and horizontally towards the Al window entrance (0.82 mm thickness, labelled N16) of the Scattering Tank assembly (constructed from 20 mm - 40 mm thickness stainless steel and will be under vacuum < 1 x 10{sup -3} mbar). Inside the Scattering Tank there is another section of Focusing Guide (FGA), with an exit guide cross-section of 30 mm (height) 30 mm (width) and of length 417 mm. The neutron beam exiting the FGA will be reflected by the HOPG crystal pieces of the Graphite Chopper (GC) towards the Backscattering Monochromator (BM). The reflected neutron beam will first pass through the other Al window (0.82 mm thickness, labelled N17) of the Scattering Tank and then enter into a flight tube (steel of 10 mm thickness). The BM is formed in an 1800 mm spherical radius array of Si crystal elements (0.87 mm thickness) and is oscillated by the linear motor Doppler Drive. The neutron beam will be reflected back at 180 deg. from, and focussed by the BM to reenter the Scattering Tank through the N17 Al window. It will then pass through the GC (HOPG crystals are rotated out of the way) and will reach the scattering sample of interest to the instrument end user. Also, inside the Scattering Tank there will be {sup 3}He linear position-sensitive detectors, and Analyser Arrays formed in an 1800 mm radius spherical arrays of Si crystal elements (0.87 mm thickness) glued on aluminium support frames (10 mm thickness) and span ∼160 deg. arc.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗