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Prototype Design, Analysis, and Results for a Liquid Radiological Waste Control Room

Human factors researchers at Idaho National Laboratory are collaborating with a utility partner to address human factors in the modernization of their radiological waste control room. The collaborating utility, Palo Verde, plans to remove all control boards associated with the liquid radiological waste system, which include controls, indicators, and alarm systems, and replace them with modernized digital instrumentation and controls and displays. To be sure the new system either supports current operational performance or enhances it, researchers have carried out three planning and analysis activities: expert operator review, function allocation analysis, and human factor principles and design applications. Researchers utilized access to expert operators to gain information about the system. Operators were asked to detail a procedure exemplifying typical interactions with the liquid radiological waste system and provide it to Idaho National Laboratory with a list of functionalities to incorporate (Section 4.1). Later, operators were asked to provide additional feedback to inform a prototype with adequate realism to support sound analysis of the system design (Section 4.2). Other operators not associated with the utility partner were also involved in experiment design and prototype evaluation (Section 4.2). Due to the extent of the upgrade, care was given to measure the change in the operator’s role (Section 7). As part of the modernization, some plant systems are expected to be equipped with automation or sensors that send information directly to the control where previously operators were required to enter controlled areas to find the information. Such updates made slight changes to operator role requirements, but the impact was negligible. The way the system is operated will remain largely the same, but operator convenience and safety is expected to increase. Additionally, to map operator tasks during the selected procedure, an Operational Sequence Diagram was created (Section 7.1). The diagram helped to account for every interaction and piece of equipment needed to create the prototype design. Alternatively, the diagram can be created following the final design to identify any function reallocations due to redesign. Human factors principles were also applied to the design. An ergonomic assessment was performed on the workspace resulting in various recommendations for placing and designing the new workstation (Section 6). A 3 D mockup of the control room was created to depict the options available with in-depth descriptions of each. Furthermore the prototype design incorporates well known human factors principles. However, to be sure the principles are sound in the control room context, the researches have designed an experiment to measure the impact design decision has on operator performance. The experiment is described in detail in Section 5. Lastly, a tool emerged as this effort began called the NUREG 0711 Crosswalk (Section 8). The tool has multiple uses, but it was primarily intended to communicate how the researcher’s action directly mapped to specific results suggested in the Nuclear Regulatory Commission review guideline. The tool also maps how current results impact future decisions in the modernization process.

47 OTHER INSTRUMENTATION↗

Electric Field Tuning Molecular Packing and Electrical Properties of Solution-Shearing Coated Organic Semiconducting Thin Films

Recent improvements in solution-coated organic semiconductors (OSCs) evidence their high potential for cost-efficient organic electronics and sensors. Molecular packing structure determines the charge transport property of molecular solids. However, it remains challenging to control the molecular packing structure for a given OSC. Here, the application of alternating electric fields is reported to fine-tune the crystal packing of OSC solution-shearing coated at ambient conditions. First, a theoretical model based on dielectrophoresis is developed to guide the selection of the optimal conditions (frequency and amplitude) of the electric field applied through the solution-shearing blade during coating of OSC thin films. Next, electric field-induced polymorphism is demonstrated for OSCs with both herringbone and 2D brick-wall packing motifs in 2,7-dioctyl[1]benzothieno[3,2-b][1]benzothiophene and 6,13-bis(triisopropylsilylethynyl) pentacene, respectively. Favorable molecular packing can be accessible in some cases, resulting in higher charge carrier mobilities. In conclusion, this work provides a new approach to tune the properties of solution-coated OSCs in functional devices for high-performance printed electronics.

36 MATERIALS SCIENCE↗

Automatic Traffic Queue-End Identification using Location-Based Waze User Reports

Traffic queues, especially queues caused by non-recurrent events such as incidents, are unexpected to high-speed drivers approaching the end of queue (EOQ) and become safety concerns. Though the topic has been extensively studied, the identification of EOQ has been limited by the spatial-temporal resolution of traditional data sources. This study explores the potential of location-based crowdsourced data, specifically Waze user reports. It presents a dynamic clustering algorithm that can group the location-based reports in real time and identify the spatial-temporal extent of congestion as well as the EOQ. The algorithm is a spatial-temporal extension of the density-based spatial clustering of applications with noise (DBSCAN) algorithm for real-time streaming data with an adaptive threshold selection procedure. Here, the proposed method was tested with 34 traffic congestion cases in the Knoxville, Tennessee area of the United States. It is demonstrated that the algorithm can effectively detect spatial-temporal extent of congestion based on Waze report clusters and identify EOQ in real-time. The Waze report-based detection are compared to the detection based on roadside sensor data. The results are promising: The EOQ identification time of Waze is similar to the EOQ detection time of traffic sensor data, with only 1.1 min difference on average. In addition, Waze generates 1.9 EOQ detection points every mile, compared to 1.8 detection points generated by traffic sensor data, suggesting the two data sources are comparable in respect of reporting frequency. The results indicate that Waze is a valuable complementary source for EOQ detection where no traffic sensors are installed.

99 GENERAL AND MISCELLANEOUS↗

Conquering Data Chaos: Research Data Management with Kubernetes

Managing massive volumes of data and effectively making it accessible to researchers poses significant challenges and is a barrier to scientific discovery. In many cases, critical data is locked up in unwieldy file formats or one-off databases and is too large to effectively process on a single machine. This talk explores the role of Kubernetes, an open-source container orchestration platform, in addressing research data management challenges. I will discuss how we are using a set of publicly available open-source and home-grown tools in the National Renewable Energy Lab (NREL) Data, Analysis, and Visualization (DAV) group to help researchers overcome data-related bottlenecks. The talk will begin by providing an overview of the data challenges faced in research data management, including data storage, processing, and analysis. I will highlight Kubernetes' ability to handle large-scale data by leveraging containerization and distributed computing, including distributed storage. Kubernetes allows researchers to encapsulate data processing infrastructure and workflows into portable containers, enabling reproducibility and ease of deployment. Kubernetes can then schedule and manage the resource allocation of these containers to enable efficient utilization of limited computing resources, leading to more efficient data processing and analysis. I will discuss some limitations of traditional, siloed approaches to dealing with data and emphasize the need for solutions which foster collaboration. I will highlight how we are using Kubernetes at NREL to facilitate data sharing and cooperation among research teams. Kubernetes' flexible architecture enables the deployment of shared computing environments, such as Apache Superset, where researchers can seamlessly access and analyze shared datasets. Providing the ability to have one research team easily consume data generated by another, utilizing Kubernetes' as a central data platform, is one of the major wins we've encountered by adopting the platform. Finally, I will showcase real-world use cases from NREL where we have used Kubernetes to solve some persistent data challenges involving large volumes of sensor and monitoring data. I will discuss the challenges we encountered when creating our cluster and making it available as a production-ready resource. I will also discuss the specific suite of tools, including Postgres and Apache Druid for columnar and timeseries data, and Redpanda Kafka for streaming data we have deployed in our infrastructure, and the process that went into the selection of these tools.

collaborative environment↗

Smart Pixels: In-pixel AI for on-sensor data filtering

We present a smart pixel prototype readout integrated circuit (ROIC) designed in CMOS 28 nm bulk process, with in-pixel implementation of an artificial intelligence (AI) / machine learning (ML) based data filtering algorithm designed as proof-of-principle for a Phase III upgrade at the Large Hadron Collider (LHC) pixel detector. The first version of the ROIC consists of two matrices of 256 smart pixels, each 25$\times$25 $\mu$m$^2$ in size. Each pixel consists of a charge-sensitive preamplifier with leakage current compensation and three auto-zero comparators for a 2-bit flash-type ADC. The frontend is capable of synchronously digitizing the sensor charge within 25 ns. Measurement results show an equivalent noise charge (ENC) of $\sim$30e$^-$ and a total dispersion of $\sim$100e$^-$ The second version of the ROIC uses a fully connected two-layer neural network (NN) to process information from a cluster of 256 pixels to determine if the pattern corresponds to highly desirable high-momentum particle tracks for selection and readout. The digital NN is embedded in-between analog signal processing regions of the 256 pixels without increasing the pixel size and is implemented as fully combinatorial digital logic to minimize power consumption and eliminate clock distribution, and is active only in the presence of an input signal. The total power consumption of the neural network is $\sim$ 300 $\mu$W. The NN performs momentum classification based on the generated cluster patterns and even with a modest momentum threshold, it is capable of 54.4% - 75.4% total data rejection, opening the possibility of using the pixel information at 40MHz for the trigger. The total power consumption of analog and digital functions per pixel is $\sim$ 6 $\mu$W per pixel, which corresponds to $\sim$ 1 W/cm$^2$ staying within the experimental constraints.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Bayesian inference for high confidence signal validation and sensor calibration assessment - 328

Online Monitoring (OLM) systems has been investigated for applications in the nuclear industry for the past two decades. These systems offer economic and technological advantages over the currently implemented time based testing methods for sensors and associated instrumentations. The advantages offered not only augment economic viability of current nuclear reactors but will be an inevitable change called for to suit the technologies of the advanced reactors (Gen III+). Irrespective, the acceptance of OLM into the regulatory structure in the U.S. is still hindered by technical and structural clarity on uncertainty quantification used by these systems. A Bayesian inference technique is implemented in the form of a residual model to quantify uncertainty in OLM predictions. The developed system used Gaussian processes to represent the OLM predictions and model inadequacy, the stationary representation of model based uncertainty. In addition, model selection techniques are used in conjunction with numerical methods such as Genetic Algorithm (GA) and Gibb's sampler to add robustness to the estimation model inadequacy, particularly in the presence of drifts and faulted conditions. The prediction model developed herein find applicability in fault detection, obtaining real values of processes after the onset of faults etc. Further development looks into the extension of current method for a multiple output scale implementation. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

A multifunctional chemical sensor based on a three-dimensional lanthanide metal-organic framework

A 3D lanthanide MOF with formula [Sm{sub 2}(abtc){sub 1.5}(H{sub 2}O){sub 3}(DMA)]·H{sub 2}O·DMA (1) has been successfully synthesized via solvothermal method. Luminescence studies reveal that 1 exhibits dual functional detection benzyl alcohol and benzaldehyde among different aromatic molecules. In addition, 1 displays a turn-on luminescence sensing with respect to ethanol among different alcohol molecules, which suggests that 1 is also a promising luminescent probe for high selective sensing of ethanol. - Highlights: • A three-dimensional lanthanide metal-organic framework has been synthesized. • Complex 1 exhibits dual functional detection benzyl alcohol and benzaldehyde among different aromatic molecules. • Complex 1 displays a turn-on luminescence sensing with respect to ethanol among different alcohol molecules.

36 MATERIALS SCIENCE↗

Surrogate Model of Flexible Research Platform EnergyPlus Models to Enable Sensitivity Analysis

This letter report describes the surrogate models developed from the EnergyPlus model of Oak Ridge National Laboratory’s Flexible Research Platform. Two data-driven black-box models were developed, and the outputs of the surrogate models were compared with the EnergyPlus model. The two models developed are a multilayer perceptron deep learning model, and a long short-term memory (LSTM) neural network model. The three factors for selecting the black-box models are scalability, computation time, and accuracy. A total of 107 input variables were the dominant variables in determining the outputs of building energy consumptions and thermal comfort. A total of 54 output variables were identified as the prediction targets, including the system- and zone-level outputs. The large set of the simulation cases were generated by integrating sensor errors into an emulator based on EnergyPlus and Python EMS, which includes advanced control sequences from ASHRAE Guideline 36-2018: High-Performance Sequences of Operation. The surrogate models were developed based on a set of large-scale simulation runs (i.e., 4,000 runs) on a cloud platform. The comparison analysis shows that the two black-box models had good accuracy for predicting new outputs for sensitivity analysis using the root mean square error metric. As a next step, the developed surrogate models will be used to perform sensitivity analysis for different sensor impacts (e.g., sensor types, sensor locations).

42 ENGINEERING↗

Improved Biomass Cooking Stoves and Improved Stove Emission Equipment

In developing countries, there is an urgent need for access to safe, efficient, and more affordable cooking technologies. Nearly 2.5 billion people currently use an open fire or traditional cookstove to prepare their meals, and recent models predict that use of biomass for cooking will continue to be the dominant energy use in rural, resource-poor households through 2030. For these families, cooking poses serious risks to health, safety, and income. An alarming 4 million people, primarily women and children, die prematurely each year from indoor and outdoor exposure to the harmful emissions released by solid fuel combustion. Use of traditional stoves can also have a significant impact on deforestation and climate change. This dire situation creates a critical need for cookstoves that significantly and verifiably reduce fuel use and emissions in order to reach protective levels for human health and the environment. Additionally, advances in the scientific equipment needed to measure and monitor stove fuel use and emissions have not kept pace with the significant need within the industry. While several testing centers in the developed world may have hundred thousand-dollar emissions testing systems, organizations in the field have had little more than a thermometer, a scale, and subjective observations to quantify the performance of stove designs. There is an urgent need for easy-to-use, inexpensive, accurate, and robust stove testing equipment for use by laboratory and field researchers around the world. ASAT and their research partner, Aprovecho Research Center (ARC), have over thirty years of experience addressing these two needs, improved cookstoves and emissions monitoring equipment, with expertise spanning the full spectrum of development from conceptual design to product manufacturing and dissemination. This includes: 1) research, design, and verification of clean biomass cookstove technology and emissions monitoring equipment; 2) mass production of quality-controlled stove and emissions equipment at levels scalable to meet global demand; and 3) global distribution through a variety of channels and partners. ARC has been instrumental in designing and improving more than 100 stove designs over the past thirty years. In the last four years, ASAT and ARC have played a key role in the production and sales of over 200,000 improved stoves in the developed and developing world. The ARC-designed emissions equipment is currently used by researchers in laboratories and field studies on five continents. During Phase I of the DOE STTR grant, ASAT and ARC worked together to apply their wealth of product development experience towards creating the next generation of improved cookstoves and emissions monitoring equipment. Highlights of Phase I for the biomass cookstove project include 1) the development of several new stove technologies that reached the DOE 50/90 benchmark; 2) fabrication of new stove prototypes by ASAT’s manufacturing partner, Shengzhou Stove Manufacturing (SSM); 3) field testing of prototype stoves with consumers in Puerto Rico and the US; and 4) the selection of three stove prototypes for further development and commercialization during Phase II. Highlights of Phase I for the emissions monitoring equipment project include: 1) creation of a new emissions monitoring equipment product, the Laboratory Emissions Monitoring System (LEMS 2) the addition of gravimetric PM measurements to the stove testing systems to meet International Standards Organization criteria; 3) the addition of a CO{sub 2} sensor and wireless 3G capability to the IAP Meter; and 4) and the improvement of sensors and signal quality on all systems. Twelve Regional Testing and Knowledge Centers purchased this equipment during the Phase I project period.

09 BIOMASS FUELS↗

Smart Pixels: In-pixel AI for on-sensor data filtering

We present a smart pixel prototype readout integrated circuit (ROIC) designed in CMOS 28 nm bulk process, with in-pixel implementation of an artificial intelligence (AI) / machine learning (ML) based data filtering algorithm designed as proof-of-principle for a Phase III upgrade at the Large Hadron Collider (LHC) pixel detector. The first version of the ROIC consists of two matrices of 256 smart pixels, each 25$\times$25 µm\textsuperscript{2} in size. Each pixel consists of a charge-sensitive preamplifier with leakage current compensation and three auto-zero comparators for a 2-bit flash-type ADC. The frontend is capable of synchronously digitizing the sensor charge within 25 ns. Measurement results show an equivalent noise charge (ENC) of $\sim$30e\textsuperscript{-} and a total dispersion of $\sim$100e\textsuperscript{-} The second version of the ROIC uses a fully connected two-layer neural network (NN) to process information from a cluster of 256 pixels to determine if the pattern corresponds to highly desirable high-momentum particle tracks for selection and readout. The digital NN is embedded in-between analog signal processing regions of the 256 pixels without increasing the pixel size and is implemented as fully combinatorial digital logic to minimize power consumption and eliminate clock distribution, and is active only in the presence of an input signal. The total power consumption of the neural network is $\sim$ 300 $\mu$W. The NN performs momentum classification based on the generated cluster patterns and even with a modest momentum threshold, it is capable of 54.4\% – 75.4\% total data rejection, opening the possibility of using the pixel information at 40MHz for the trigger. The total power consumption of analog and digital functions per pixel is $\sim$ 6 $\mu$W per pixel, which corresponds to $\sim$ 1 W/cm\textsuperscript{2} staying within the experimental constraints.

Parpillon, Benjamin↗

SU-F-T-174: Patient-Specific Point Dose Measurement Using Fiber Optic Radiation Sensor Using Cerenkov Radiation for Proton Therapeutic Beam

Purpose: A fiber-optic radiation sensor using Cerenkov radiation (FOCR) has been widely studied for use as a dosimeter for proton therapeutic beam. We developed the FOCR, and it applied to patient-specific point dose measurement in order to evaluate the effectiveness of the FOCR system for proton therapy QA. Methods: Calibration of FOCR was performed with an ionization chamber whose absolute doses were determined according to the IAEA TRS-398 protocol. To determine the calibration curve, the FOCR was irradiated perpendicularly to the proton beam at the 13 dose levels steps. We selected five actual patient treatment plans performed at proton therapy center and compared the resulting FOCR measurements with the ionization chamber measurements. Results: The Cerenkov light yield of the FOCR increases linearly with as the dose measured using the ionization chamber increases from 0 cGy to 500 cGy. The results indicate that the fitting curve is linear, suggesting that dose measurement based on the light yield of the FOCR is possible. The results of proton radiation dose QA performed using the FOCR for 10 proton fields and five patients are good agreement with an ionization chamber. Conclusion: We carried out the patient QA using the FOCR for proton therapeutic beam and evaluated the effectiveness of the FOCR as a proton therapy QA tool. Our results indicate that the FOCR is suitable for use in patient QA of clinical proton beams.

60 APPLIED LIFE SCIENCES↗

Sensitive Fluorescent Determination of Cobalt by Microwave Assisted Synthesized TPTZ Functionalized Carbon Dots

Novel fluorescent carbon dots (CDs) for cobalt ions sensing were synthesized from 2,4,6-tris(2′-pyridyl)-s-triazine (TPTZ) and citric acid by microwave-assisted method in one pot. This sensor was water soluble, simple, sensitive and cheap. The size of the CDs was determined from transmission electron microscope image and was in the range of 10 nm. Under optimized experimental conditions, this luminescent system had stable response for Co (II) over a concentration range from 0.4 to 50 µM with a detection limit as low as 230 nM. The proposed method showed good sensitivity and selectivity with respect to interference ions. Finally, this system was used for Co (II) determination in tap water, river water and mineral water and B{sub 12} ampoule samples.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Metabolites of Tobacco- and E-Cigarette-Related Nitrosamines Can Drive Cu 2+ -Mediated DNA Oxidation

Nitrosamine metabolites resulting from cigarette smoking and E-cigarette (E-cig) vaping cause DNA damage that can lead to genotoxicity. While DNA adducts of metabolites of nitrosamines 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) and N-nitrosonornicotine (NNN) are well-known tobacco-related cancer biomarkers, only a few studies implicate NNN and NNK in DNA oxidation in humans. NNK and NNN were found in the urine of E-cigarette users who never smoked cigarettes. This paper proposes the first chemical pathways of DNA oxidation driven by NNK and NNN metabolites in redox reactions with Cu 2+ and NADPH leading to reactive oxygen species (ROS). A microfluidic array with thin films of DNA and metabolic enzymes that make metabolites of NNN and NNK in the presence of Cu 2+ and NADPH was used to estimate relative rates of DNA oxidation. Detection by electrochemiluminescence (ECL) employed a new ECL dye [Os(tpy-benz-COOH) 2 ] 2+ that is selective for and sensitive to the primary DNA oxidation product 8-oxo-7,8-dihydro-2-deoxyguanosine (8-oxodG) in DNA. Enzyme–DNA films on magnetic beads were used to produce nitrosamine metabolites that enter ROS-forming redox cycles with Cu 2+ and NADPH, and liquid chromatography–mass spectrometry (LC–MS) was used to quantify 8-oxodG and identify metabolites. ROS were detected by optical sensors. Metabolites of NNK and NNN + Cu 2+ + NADPH generated relatively high rates of DNA oxidation. Lung is the exposure route in smoking and vaping, human lung tissue contains Cu 2+ and NADPH, and lung microsomal enzymes gave the highest rates of DNA oxidation in this study. Furthermore, E-cigarette vapor contains 6-fold more copper than that in cigarette smoke, which could exacerbate DNA oxidation.

59 BASIC BIOLOGICAL SCIENCES↗

Nanophotonic Filters and Integrated Networks in Flexible 2D Polymer Photonic Crystals

Polymers have appealing optical, biochemical, and mechanical qualities, including broadband transparency, ease of functionalization, and biocompatibility. However, their low refractive indices have precluded wavelength-scale optical confinement and nanophotonic applications in polymers. Here, we introduce a suspended polymer photonic crystal (SPPC) architecture that enables the implementation of nanophotonic structures typically limited to high-index materials. Using the SPPC platform, we demonstrate nanophotonic band-edge filters, waveguides, and nanocavities featuring quality (Q) factors exceeding 2, 300 and mode volumes (V-mode) below 1.7(l/n) 3 . The unprecedentedly high Q/V-mode ratio results in a spectrally selective enhancement of radiative transitions of embedded emitters via the cavity Purcell effect with an enhancement factor exceeding 100. Moreover, the SPPC architecture allows straightforward integration of nanophotonic networks, shown here by a waveguide-coupled cavity drop filter with sub-nanometer spectral resolution. The nanoscale optical confinement in polymer promises new applications ranging from optical communications to organic opto-electronics, and nanophotonic polymer sensors.

PHOTONIC CRYSTALS↗

Tautomerism induces bending and twisting of biogenic crystals

Understanding and exploiting material flexibility through phenomena such as the bending and twisting of molecular crystals has been a subject of increased interest owing to the number of applications that benefit from these properties, such as optoelectronics, mechanophotonics, soft robotics, and smart sensors. Here, we report the growth of spontaneously bent and twisted ammonium urate crystals induced by the keto–enol tautomerism of the urate molecule. The major tautomer is native to biogenic crystals, whereas the minor tautomer functions as an effective crystal growth modifier to induce naturally bent and twisted ammonium urate crystals. We show that the degree of curvature can be tailored based on the judicious selection of growth conditions. A combination of state-of-the-art microscopy and spectroscopy techniques are used to characterize the origin of bending. Spatially resolved nano-electron diffraction and high-resolution electron microscopy of naturally bent crystals show nearly single crystallinity with local lattice deformations generated by a combination of screw and edge dislocations. These observations are consistent with photoinduced force microscopy and contact resonance atomic force microscopy, which confirmed spatially resolved changes in the intermolecular interactions and the mechanical properties throughout the cross-sectional and axial regions of bent crystals. A mechanism of bending involving the generation of regionally specific dislocations is proposed as an alternative to more commonly reported models. These findings highlight a unique characteristic of tautomeric crystals that may have broader implications for other biogenic materials.

Science & Technology - Other Topics↗

The Dark Energy Spectrographic Instrument (DESI) Guide-Focus Assembly

The specific research of the DESI project is to study in detail the expansion history of the Universe over the past 10 billion years. In order to do this, the project designed, fabricated, tested and commissioned the DESI instrument which has been deployed at the Mayall Telescope at the Kitt Peak National Observatory near Tucson, Arizona. The DESI project was conducted by approximately 30 US and foreign national laboratories. Our CRADA with the The Institut de Física d’Altes Energies (IFAE) in Barcelona (Spain) involved both research planning for eventual observing on the DESI telescope; and in providing hardware jointly designed by LBNL and IFAE; ten guide and focus sensor systems (GFAs) that either provide feedback to the Mayall telescope tracking system or provide focus position information to the DESI corrector hexapod. The key major goals of this CRADA were achieved.For survey planning, Drs. Fernandez, Fosalba, Garcia-Bellido, and Gaztañaga were all important contributors to the working groups to which they were assigned. Their application of redshift techniques, development of simulations to support target selection, exploration of the use of alternative observables to extract cosmological information like AP and void statistics, and the use of PAU data to assist in the optimization of targeting selections have been vital in the early planning for targets during commissioning. The major role in the project involved Drs. Castander, Miquel, Padilla, and Sànchez. They were all directly involved with the development of the Guide-Focus Assembly (GFA) hardware, software and filter validation. In order to accomplish the goals of the DESI Project, the Mayall telescope must track DESI targeted galaxies for up to 15 minutes and the DESI corrector must be repositioned on its hexapod system for different telescope orientations. The guide and focus assemblies (GFA) provide in this information. There are ten GFA modules on the focal plane, six providing tracking information and four providing focus information. A common GFA design supports both functions by a simple switching of its operating mode. IFAE designed, fabricated, tested and delivered twelve GFAs. Once received at LBNL, the GFAs were integrated with the focal plane. This was an iterative process as the GFA’s were delivered as they were made. Our working relationship with the IFAE scientists and engineers was remarkable given the distance and time zone issues. The GFAs have performed extremely well during the commissioning phase of the instrument.

79 ASTRONOMY AND ASTROPHYSICS↗

DECOVALEX-2023: Task D Final Report

Task D of DECOVALEX-2023 is focused on the simulation of the coupled thermal hydraulic-mechanical (THM) behaviour in the full-scale engineered barrier system (EBS). The Horonobe EBS experiment is the demonstration of the full-scale EBS in the underground research laboratory (URL) (performed by JAEA in the Horonobe URL in Japan). Task D consisted of the three steps, a preliminary step (Step 0), simulation of the laboratory tests (Step 1) and simulation of the in-situ full-scale EBS experiment (Step 2). Since the Horonobe EBS experiment demonstrates the vertical emplacement option of the EBS, the experiment gallery is also backfilled with the backfill material. Therefore, interaction between the EBS and the backfill material can also be demonstrated, such as deformation (change of density) of the buffer material. The underground water in the Horonobe URL is saline. This fact adds chemical processes to THM behaviour. For example, mechanical properties (such as swelling pressure of the buffer material and backfill material) and hydraulic properties (such as permeability of the buffer material and backfill material) change depending on the water chemistry. Task D was therefore a challenging Task focused on not only the relatively simple THM behaviour but also complex THM behaviour including chemical processes. Six research teams (BGR, CAS, JAEA, KAERI, SNL and Taipower) participated the Task D. BGR, CAS, JAEA, KAERI and Taipower research teams selected a THM approach, while the SNL research team selected a TH approach. Step 1 involved the simulation of laboratory test results and was important to check the numerical codes developed by the research teams. Step 1 was divided into four sub steps. The simulation results through the Step 1 identified the parameters for simulation of the Step 2. Basic parameters of the materials (buffer material, backfill material, rock mass, concrete, sand) were provided by JAEA. Special parameters which research team needed were identified by back analysis of Step 1. Most notably the mechanical behaviour of swelling and displacement depended on the applied model (elastic model or elastoplastic model). Parameters such as Young’s modulus were found to need smaller values than characterised in the fundamental laboratory test results (Step 1-1, 1-2) for the elastic model. Although laboratory experiments are usually simple, test results contained some error. For example, if the saturation level is 100 % or higher, it should be considered an error. This situation was presented in the Step 1-3. A possible reason is that the buffer material is a mixture of bentonite and silica sand. When a specimen is cut to measure volume or weight, sand grains will affect the measurement data. In Step 2, boundary conditions such as temperature on the surface of the simulated overpack, heater power of the electrical heaters installed in the simulated overpack, injection pressure and inflow rate of the test water, were applied. The outer boundary conditions can be selected using measured data (injection pressure and inflow rate of the test water that is controlled by the injection systems installed in the sand layer around the buffer material and in the boundary between backfill material and concrete support). Since such measured data has some noise, research teams developed their own simplified boundary conditions. Inner boundary conditions can be selected using measured data as heater power and temperature on the surface of the simulated overpack. These data also contain some noise, so research teams developed their own simplified developed boundary conditions. Task D validated various approaches thorough the simulation of the in-situ full scale EBS system including backfill of the gallery: variations in the coupling processes (THM or THC), analysis codes, and boundary conditions. Temperature distribution in the buffer material was simulated well by all research teams. This means thermal behaviour is not sensitive to the simulation approaches. Although the water content distribution on the outside of the buffer material was well simulated by all research teams, the simulation results differ from the measured values inside the buffer material (at the centre and inside, near the simulated overpack). The buffer material is made from tap water, but in the in-situ experiment, saline groundwater infiltrates the buffer material. Therefore, the selection of the hydraulic parameters of the buffer material greatly affects the simulation results of the re saturation behaviour of the buffer material. In the Horonobe EBS experiment, measured values suitable for validating the simulation results were not obtained near the simulated overpack. When simulating the pressure and deformation of the buffer material, the measurement data is easily affected by the installation conditions of the measurement sensors, so verifying the measurement data itself remains an issue. Mechanical simulation results differ depending on whether they are considered as elastic or elastoplastic phenomena. The accuracy of measured in-situ data can be assessed by detailed analysis comparing sampling specimen analysis and measured data. The Horonobe EBS experiment is scheduled to be dismantled in the future (FY2026 and 2027). This detailed dismantling investigation will finally confirm the measured data.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Continued Evaluation of the Pulse-Echo Ultrasonic Instrument for Critical Velocity Determination during Hanford Tank Waste Transfer Operations

Laboratory (PNNL) conducted an extensive evaluation of the ability of three ultrasonic instruments to detect critical velocity for a broad range of simulated Hanford nuclear waste streams containing particles with mean particle sizes of >50 microns. Evaluations were perform using the pipe loop at the Process Development Laboratory – East (PDL-E) at PNNL that was designed and built to evaluate the pipeline plugging issue during slurry transfer operations at the Hanford Waste Treatment Plant. In 2011 the ability of the ultrasonic PulseEcho system to detect critical velocity continued to be evaluated using the PDL-E flow loop and new simulants containing high-density particles with a mean particle size of < 15 microns. The PDL-E flow loop was modified for the 2011 testing to include a new test section that contained 5-MHz and 10-MHz ultrasonic transducers non-invasively mounted to schedule 40 pipe. The test section also contained reference instrumentation to facilitate direct comparison of the real-time PulseEcho transducer responses with experimentally observed critical velocities. This paper presents the results from the 2011 PulseEcho evaluation using a variety of simulated Hanford nuclear waste streams that were selected to encompass the expected high-level waste feed properties.

Critical Velocity↗