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A deep learning model for automatic analysis of cavities in irradiated materials
Transmission electron microscopy (TEM) is a commonly used technique in materials science for defect investigation. Quantitative analysis of defects is important for understanding the properties of a material, but manual analysis of TEM micrographs can be time-consuming and prone to error, especially when the defects have irregular shapes rather than spherical shapes. Many existing methods or deep learning models do not handle a wide range of sizes for the same object type within a single image. In this work, we present a framework that enables users to train an instance segmentation model called Mask R- CNN on any microstructure dataset, perform multi-detection on the same image at different scales, and obtain properties (e.g., size, area) of the objects based on the desired shape (e.g., circle, ellipse, rectangle). Additionally, we have developed a parallel detection module that uses multiple GPUs to increase the efficiency of the object detection process. We demonstrate the capabilities of our framework using a set of TEM images of cavities with different shapes, size distributions, and background contrasts. Finally, we show that the performance of our model in terms of density, size, and swelling of the cavities is comparable to the human average and that our model achieves the highest recall value compared to existing methods due to the use of image multi-rescaling.
Report of the Atmosphere to Electrons Mesoscale-to-Microscale Coupling Project (FY2019)
The overall goal of the Mesoscale-to-Microscale Coupling (MMC) project is to improve coupling between mesoscale and microscale simulations via improved guidance and new strategies for setting up simulations and the development of new tools that can be used across the community. Including the mesoscale forcing is critical to modeling the full energy transfer across scales in the atmosphere. The project-specific objectives include: Apply rigorous verification and validation (V&V) techniques to the new modeling tools that are developed as part of the project to ensure the accuracy of our codes and results and develop estimates of the relative uncertainty. Improve computational performance of the coupled MMC models through the development of methods that can be used to reduce turbulence spin-up time and hence the size of computational domains. Improve representation of the surface layer in microscale models to enhance simulations of hub-height wind speed. Develop guidance for the community describing the best ways to couple mesoscale and microscale models, including specific spatial scales at which the handoff to the microscale model should occur. Prepare documentation and a suite of software tools that can be used across the community. Transition MMC research to the offshore environment.
Modeling of Prismatic High Temperature Reactors in Pronghorn
Pronghorn is a Multiphysics Object-Oriented Simulation Environment (MOOSE) based thermal-hydraulics code developed at Idaho National Laboratory (INL) for advanced reactor analysis. It has been previously applied to model Pebble-Bed High Temperature Reactors (High Temperature Reactor (HTR)s), Liquid-Metal Cooled Reactors, and Molten Salt Reactors, among others. This work applies the coarse-mesh thermal hydraulics capabilities in Pronghorn to model Prismatic-Core HTRs. In particular, the Oregon State University (OSU)’s High Temperature Test Facility (HTTF) is modeled with Pronghorn. The HTTF is a 1:4 height scaled-down facility of General Atomics’ Modular High Temperature Gas-cooled Reactor.
Fusion Energy Research at Idaho National Laboratory: Experimentation and Simulation to Support Safety and Rapid Technology Development
Research into fusion energy is growing rapidly, responding to a call for sustainable sources of energy to replace fossil fuels and mitigate climate change. Within the United States, at least, researchers are also responding to the “Bold Decadal Vision” proposed by the White House, seeking to have a commercially relevant fusion pilot plant deployed within a decade. Before this can become a reality, many Fusion Science & Technology (FS&T) gaps remain. For over 45 years, Idaho National Laboratory has been at the forefront of addressing these FS&T gaps in the context of fusion safety and technology via the operation of world-leading experimental facilities within the Safety and Tritium Applied Research (STAR) Facility. Here, INL focuses on the tritium fuel cycle, conceptual system design studies, risk assessment, waste management, and materials safety. Modeling and Simulation (M&S) has also been a component of this portfolio of research, but, early on, focused on individual systems. Since 2019, active development and research on integrated whole device modeling tools based on the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework has been undertaken. This has culminated in a MOOSE-based version of the Tritium Migration and Analysis Program (TMAP), an INL code historically focused on tritium permeation and trapping within fusion systems. More recently, INL Laboratory Directed Research and Development funds have been used to create the Fusion ENergy Integrated multiphys-X (FENIX) code focused on scrape-off layer plasma physics and the first wall of a magnetically confined fusion device. This talk will focus on an overview of INL activities in the FS&T research area, with a particular focus on recent M&S activities and results.
3DBFSVBF (3D BatFinder Smart Video BioFilter and Multi-class BatFinder Smart Video BioFilter) [SWR-22-88]
Bats are notoriously difficult to study, therefore, identifying specific behavioral trends and the precise environmental conditions at the time of collision requires a monitoring solution that can reliably collect relevant data. To date, thermal infrared video surveillance has been extensively applied to study bats and has proven to be a powerful yet cumbersome tool. Current analytical approaches are time consuming because data processing data has not been fully automated. In the past, steps have been taken to record avian and bat activity in conjunction with complicated image processing techniques that separate species from other moving objects within the field of view (i.e. clouds and portions of the wind turbine). Once the videos are collected, the post-processing does not allow real time monitoring and identification, leading to a delay in both studying the behavior of these species and determining the effectiveness of any impact reduction strategy being studied. Moreover, object identification capability is lacking, thus limiting the usefulness of video data. To resolve these issues, we are using open source 3D computer vision and machine learning techniques allowing for automatic detection of objects in real-time with the ability to correlate these objects with environmental variables and recording the flight paths of each object. The machine learning has been trained on 3D data and allows for automated real-time data collection, identification and tracking, thereby eliminating the need for long and tedious post-analysis processing of the videos. This machine learning model is an added feature to the previous BatFinder Smart Video BioFilter and increases the accuracy of that systems classification by increasing the accuracy of identifying bats (90% accuracy) and insects (69% accuracy) to a 97% accuracy. There are two object classifier machine learning models, Binary and multi-classification. Binary object classifier labeled BatFinder_Smart_Video_BioFilter.h5 distinguishes between biological objects and non-biological objects. The main goal of this object classifier is to ignore the turbine blades while detecting biological object flying withing the rotor swept area of the turbine. Non-biological objects have a probability of 0 and biological objects have a probability of 1. Multi-classifier labeled Multiclass_BatFinder_Smart_Video_BioFilter.h5 distinguishes between bats, birds, insects and non-biological.
Attract-repel path planner system for collision avoidance
A system for determining a travel direction that avoids objects when a vehicle travels from a current location to a target location is provided. The system determines a travel direction based on an attract-repel model. The system assigns a repel value to the object locations and an attract value. A repel represents a magnitude of a directional repulsive force, and the attract value represents the magnitude of a directional repulsive force. The system calculates an attract-repel field having an attract-repel magnitude and attract-repel direction for the current location based on the repel values and their directions and the attract value and its direction. The system then determines the travel direction for a vehicle to be the direction of the attract-repel field at the current location.
Optimization and Experimental Validation of Annular Finned PCM-HX for a Domestic Hot Water Heater Application
The load profile for domestic water heating is time-dependent and can result in high energy demand during peak operating times. Shifting this peak load can have significant environmental and economic impacts. Phase change material (PCM)-based thermal energy storage (TES) is a potentially useful technology for peak load shifting in domestic hot water (DHW) applications thanks to its high latent heat and energy density. In this study, an annular finned-tube PCM-HX design concept was optimized for a load-shifting TES unit to meet the Department of Energy standard for a medium-usage DHW heater using a resistance-capacitance model (RCM) integrated with a Multi-Objective Genetic Algorithm. The optimized design comprised 70 identical annular finned-tube PCM-HX units connected in parallel and utilizing RT62HC as the PCM. A single PCM-HX unit was prototyped and tested in a vertically oriented setup with upward heat transfer fluid (HTF) flow. The hot water supply time was defined based on a cutoff temperature of 51.7°C. The as-designed mass flow rate (1.5 g/s) was tested to assess the performance of the prototyped PCM-HX unit for RCM validation. For the experimental investigation, RTD sensor bundles measured HTF temperature at the PCM-HX inlet and outlet, and a Coriolis flow meter accurately measured the HTF mass flow rate. The simulated discharging power underpredicted the experimental result by about 12%, and the simulated hot water supply time underpredicted the experimental result by approximately 13% for the as-designed mass flow rate (1.5 g/s). The average deviation of the hot water supply temperature between the experimental and RCM results during the complete PCM solidification process was 1.3 K for the as-designed mass flow rate. The overall good agreement between the experimental and RCM results provides confidence that computationally efficient models such as RCM can be utilized for design optimization of PCM-HXs.
Thermal-Mechanical Elastic-Plastic and Ductile Failure Model Calibrations for 304L Stainless Steel Alloy
Numerical simulations of metallic structures undergoing rapid loading into the plastic range require material models that accurately represent the response. In general, the material response can be seen as having four interrelated parts: the baseline response under slow loading, the effect of strain rate, the conversion of plastic work into heat and the effect of temperature. In essence, the material behaves in a thermal-mechanical manner if the loading is fast enough so when heat is generated by plastic deformation it raises the temperature and therefore influences the mechanical response. In these cases, appropriate models that can capture the aspects listed above are necessary. The material of interest here is 304L stainless steel, and the objective of this work is to calibrate thermal-mechanical models: one for the constitutive behavior and another for failure. The work was accomplished by first designing and conducting a material test program to provide data for the calibration of the models. The test program included uniaxial tension tests conducted at room temperature, 150 and 300 C and at strain rates between 10 –4 and 10 3 1/s. It also included notched tension and shear-dominated compression hat tests specifically designed to calibrate the failure model. All test specimens were extracted from a single piece of plate to maintain consistency. The constitutive model adopted was a modular $J_2$ plasticity model with isotropic hardening that included rate and temperature dependence. A criterion for failure initiation based on a critical value of equivalent plastic strain fitted the failure data appropriately and was adopted. Possible ranges of the values of the parameters of the models were determined partially on historical data from calibrations of the same alloy from other lots and are given here. The calibration of the parameters of the models were based on finite element simulations of the various material tests using relatively ne meshes and hexahedral elements. When using the model in structural finite element calculations, however, element formulations and sizes different from those in the calibration are likely to be used. A brief investigation demonstrated that the failure initiation predictions can be particularly sensitive to the element selection and provided an initial guide to compensate for the effect of element size in a specific example.
Modular HF Isolated MV String Inverters Enable a New Paradigm for Large PV Farms
The “Modular HF Isolated MV String Inverters Enable a New Paradigm for Large PV Farms” project focuses on exploring alternative power converter and system-level plant configurations to achieve the lowest cost and highest energy output for a given solar plus storage (e.g., PV + battery) plant, including the use of medium voltage (MV) collection while taking into account detailed models of all elements. To realize this objective, four approaches were utilized (i) employ a novel Medium Voltage String Inverter (MVSI) topology (soft switching solid state transformer – S4T) to convert 1000 Vdc to 4.16 kVac; (ii) plant collection using standard, low-cost overhead MV distribution network; (iii) enable energy storage integration without additional converter cost to achieve dispatchability of the PV resource; and (iv) provide advanced functionality (autonomous operation, track ISO signals for dynamic balancing and ancillary services, and PV farm operation as a virtual grid resource). Subsequently and in alignment with the previously mentioned approaches, the project was structured in five efforts (i) S4T MVSI simulation and design; (ii) system analysis and storage optimization; (iii) financial analysis; (iv) power converter prototype build and test; and (v) regulatory and commercial impact study. The outcomes provided by each effort can be summarized as follows (i) Project explored the use of MV AC distribution architecture for hybrid PV+storage utility-scale PV farms; (ii) Detailed loss and LCOE analysis for AC and DC side BESS architecture, including multiple converter topologies, as well as for proposed MVSI/MDCT systems; (iii) MVSI was built and holds promise but needs lower-cost high-voltage Si-C devices, which does not seem possible in the near term; (iv) MDCT provides a simpler modular building block – validated through HIL and farm level modeling, simulation and experimental validation; (v) 300 kVA MDCT prototype built and tested, technology is being commercialized; and (vi) Regulatory model of utility building PV plants, where PV panels are treated as DC generation (IPP), seems viable and can allow improved grid integration.
Final Project Report: Performance Models and Standards for Bifacial PV Module Technologies
This project has three main objectives: (1) to field and collect performance data from bifacial PV systems and share this information with the stakeholder community; (2) to develop and validate bifacial performance models and deployment guides that will allow users to accurately predict and assess the use of bifacial PV as compared with monofacial technologies and (3) to help develop international power rating standards for bifacial PV modules.
Generative design of de novo proteins based on secondary-structure constraints using an attention-based diffusion model
We report two generative deep-learning models that predict amino acid sequences and 3D protein structures on the basis of secondary-structure design objectives via either the overall content or the per-residue structure. Both models are robust regarding imperfect inputs and offer de novo design capacity because they can discover new protein sequences not yet discovered from natural mechanisms or systems. The residue-level secondary-structure design model generally yields higher accuracy and more diverse sequences. These findings suggest unexplored opportunities for protein designs and functional outcomes within the vast amino acid sequences beyond known proteins. Our models, based on an attention-based diffusion model and trained on a dataset extracted from experimentally known 3D protein structures, offer numerous downstream applications in the conditional generative design of various biological or engineering systems. Future work could include additional conditioning and an exploration of other functional properties of the generated proteins for various properties beyond structural objectives.
Impact of Storage Dispatch Assumptions on Resource Adequacy and Capacity Credit
Representing energy-limited resources in power system probabilistic resource adequacy assessment introduces new considerations that invalidate classical modeling assumptions. In particular, such resources have multi-period operating objectives and constraints that in real systems are addressed via a sequence of rolling intertemporal optimizations. Ideally, adequacy models would develop dispatch decisions by solving a similar sequence of problems, but this approach has historically been too computationally intensive for practical use in Monte Carlo simulations, with studies making use of simplifying approximations instead. These simplifications have the potential to distort the assessed value of energy-limited resources on the system.This work describes three classes of storage dispatch assumptions in current use and discusses their theoretical differences. It then provides an empirical analysis of their differences on test systems with different levels of storage, assessing the potential for a study's modeling assumptions to influence the perceived contribution of energy-limited resources.
Evaluation Of Glass Density to Support the Estimation of Fissile Mass Loadings in Sludge Batch 10 Glasses
Per a directive from the Department of Energy Savannah River Operations Office (DOE-SR) in 2008, the fissile mass loading concentration must remain below 897 g/m 3 in each high-level waste (HLW) glass canister produced by the Defense Waste Processing Facility (DWPF). To support Sludge Batch 5 (SB5) processing, the Savannah River National Laboratory (SRNL) developed a technical basis that facilitates the evaluation of fissile mass loading of the glass product. The calculation is based on the iron (Fe) concentration in the glass as determined by measurements from the Slurry Mix Evaporator acceptability analysis as well as the glass density. In April 2022, a subsequent DOE-SR directive increased the fissile mass loading limit to 2500 g/m 3 beginning with Sludge Batch 11. Thus, the 897 g/m 3 limit still applies to Sludge Batch 10 (SB10) processing. For SB5 through initial Sludge Batch 9 (SB9) processing prior to coupled operation with the Salt Waste Processing Facility (SWPF), SRNL provided DWPF a bounding glass density value that was based on a statistical evaluation of density measurements. To eliminate the need for experimental work, a composition-based density model for HLW glasses was developed at SRNL in 2019. The objective of this report is to present the bounding glass density determined with the composition-based density model for SB10 sludge-only (SO) and coupled processing.
Prototypical Region Proposal Networks for Few-Shot Localization and Classification
Recently proposed few-shot image classification methods have generally focused on use cases where the objects to be classified are the central subject of images. Despite success on benchmark vision datasets aligned with this use case, these methods typically fail on use cases involving densely-annotated, busy images: images common in the wild where objects of relevance are not the central subject, instead appearing potentially occluded, small, or among other incidental objects belonging to other classes of potential interest. To localize relevant objects, we employ a prototype-based few-shot segmentation model which compares the encoded features of unlabeled query images with support class centroids to produce region proposals indicating the presence and location of support set classes in a query image. These region proposals are then used as additional conditioning input to few-shot image classifiers. We develop a framework to unify the two stages (segmentation and classification) into an end-to-end classification model---PRoPnet---and empirically demonstrate that our methods improve accuracy on image datasets with natural scenes containing multiple object classes.
How Low Can You Go? Using Synthetic 3D Imagery to Drastically Reduce Real-World Training Data for Object Detection
Deep convolutional neural networks (DCNNs) currently provide state-of-the-art performance on image classification and object detection tasks, and there are many global security mission areas where such models could be extremely useful. Crucially, the success of these models is driven in large part by the widespread availability of high-quality open source data sets such as Image Net, Common Objects in Context (COCO), and KITTI, which contain millions of images with thousands of unique labels. However, global security relevant objects-of-interest can be difficult to obtain: relevant events are low frequency and high consequence; the content of relevant images is sensitive; and adversaries and proliferators seek to obscure their activities. For these cases where exemplar data is hard to come-by, even fine-tuning an existing model with available data can be effectively impossible. Recent work demonstrated that models can be trained using a combination of real-world and synthetic images generated from 3D representations; that such models can exceed the performance of models trained using real-world data alone; and that the generated images need not be perfectly realistic (Tremblay, et al., 2018). However, this approach still required hundreds to thousands of real-world images for training and fine tuning, which for sparse, global security-relevant datasets can be an unrealistic hurdle. In this research, we validate the performance and behavior of DCNN models as we drive the number of real-world images used for training object detection tasks down to a minimal set. We perform multiple experiments to identify the best approach to train DCNNs from an extremely small set of real-world images. In doing so, we: Develop state-of-the-art, parameterized 3D models based on real-world images and sample from their parameters to increase the variance in synthetic image training data; Use machine learning explainability techniques to highlight and correct through targeted training the biases that result from training using completely synthetic images; and Validate our results by comparing the performance of the models trained on synthetic data to one another, and to a control model created by fine-tuning an existing ImageNet-trained model with a limited number (hundreds) of real-world images.
Iodine Distribution During Evaporation of Hanford Waste Treatment Plant Direct Feed Low Activity Waste Effluent Management Facility Simulant
The Hanford Waste Treatment and Immobilization Plant (WTP) Low Activity Waste (LAW) vitrification facility will generate an aqueous condensate recycle stream from the off-gas system. The baseline plan for disposition of this stream during full WTP operations is to send it to the WTP Pretreatment Facility, where it will be blended with LAW, concentrated by evaporation, and recycled to the LAW vitrification facility. However, during the Direct Feed LAW (DFLAW) scenario, planned disposition of this stream involves concentrating the condensate in a new evaporator at the Effluent Management Facility (EMF) and returning it to the LAW melter. The LAW melter condensate stream will contain components, e.g. halides and sulfates, that are volatile at melter temperatures, have limited solubility in glass waste forms, and present a material corrosion concern. Further, some minor constituents in the melter condensate, such as volatile organic compounds and iodine, have been found to be at least partially volatile in the EMF. The condensate from the EMF will be sent to the Hanford Effluent Treatment Facility (ETF). The presence of some recalcitrant organics and iodine, as 129I, are expected to be problematic in the ETF. In order to better predict and prepare for the distribution of organics and 129I in the flowsheet, it is key to understand their chemistry and experimentally determine their behavior during evaporation. To do this, modeling and testing were performed to examine the speciation of iodine under relevant conditions, as well as determine partitioning of iodine and the organics in the EMF evaporator. This overall program examines the potential treatment and immobilization of the LAW melter condensate stream to enable alternative disposal. The objective of this task was to (1) perform modeling to determine the speciation and fate of iodine species, (2) prepare a simulant of the LAW Melter Off-gas Condensate expected during DFLAW operations, (3) spike in the key volatile organics and non-radioactive iodine, and (4) demonstrate evaporation in order to predict the final composition of the effluents from the EMF evaporator to aid in planning for their disposition. The results of the modeling of iodine speciation indicate that the iodine is expected to remain in the evaporator pot as iodide and possibly iodate ion if the pH is raised to 11.5-12, which is the planned condition for the EMF. Experimental testing confirmed that the iodide remained in the evaporator concentrate and was below detection limits in all condensate and knock-out pot samples, indicating it was not volatile. The organic nitrile compounds that were added to the simulant were absent from the evaporator feed solution, either lost through evaporation, or more likely, decomposition. The acetone and methylene chloride were below the target values in the feed and condensate samples, and were presumably lost to evaporation or decomposition.
Machine learning for detection of 3D features using sparse x-ray tomographic reconstruction
In many inertial confinement fusion (ICF) experiments, the neutron yield and other parameters cannot be completely accounted for with one and two dimensional models. This discrepancy suggests that there are three dimensional effects that may be significant. Sources of these effects include defects in the shells and defects in shell interfaces, the fill tube of the capsule, and the joint feature in double shell targets. Due to their ability to penetrate materials, x rays are used to capture the internal structure of objects. Methods such as computational tomography use x-ray radiographs from hundreds of projections, in order to reconstruct a three dimensional model of the object. In experimental environments, such as the National Ignition Facility and Omega-60, the availability of these views is scarce, and in many cases only consists of a single line of sight. Mathematical reconstruction of a 3D object from sparse views is an ill-posed inverse problem. These types of problems are typically solved by utilizing prior information. Neural networks have been used for the task of 3D reconstruction as they are capable of encoding and leveraging this prior information. We utilize half a dozen, different convolutional neural networks to produce different 3D representations of ICF implosions from the experimental data. Deep supervision is utilized to train a neural network to produce high-resolution reconstructions. These representations are used to track 3D features of the capsules, such as the ablator, inner shell, and the joint between shell hemispheres. Machine learning, supplemented by different priors, is a promising method for 3D reconstructions in ICF and x-ray radiography, in general.