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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Engineering Computational Practices (Rev. 1)

This manual will attempt to motivate the use of an automated build system for the purposes of computational science and engineering. As part of this motivation, the surrounding computational practices of version control, documen tation, compute environment management, and regression testing will also be addressed as applied to the practice of computational engineering. Specifically, this manual intends to motivate the adoption of these traditional software engineering practices for use in research and production engineering simulation projects. This manual is not the first such effort in the greater scientific computing community. In fact, the authors relied heavily on the lesson plans of the Software Carpentry, established to teach computing skills to researchers in 1998. As the intention for this manual is to lay out fundamental practices of engineering computing, it will not attempt to fully teach the underlying concepts and will instead reference the well designed lesson plans of the Software Carpentry. Where possible, this manual will explain to general computing practices and concepts and limit discussion of specific software implementations to examples or vehicles for practice in concrete application. The specific software taught by the Software Carpentry curriculum is an excellent starting point to learn the core concepts of computational engi neering. However, the authors have found that applications to engineering simulation and analysis require translation of these software development concepts into the language and workflows of computational engineers. Adopting these computational tools may require engineers to re-imagine their workflows in some combination of traditional engineer ing and software concepts. It has also been necessary to extend existing software build systems for engineering practices beyond the simple wrap ping of engineering software execution. Where necessary, examples of specific software and their method of extension to engineering simulations will be given, with reference to the User Manual for recommended practical use. Where this manual relies on specific implementation examples, it should be understood that the practicing engineer may find that different software is more amenable to their specific work. It is always the overall collection of computational practices is more important than any specific software implementation. The ability to recognize which concepts are implemented by a software package will make a practicing engineer agile to changing project needs, computing resources, numeric solvers, programming languages, and even available funding.

42 ENGINEERING↗
DOE OSTI2024

Elucidating Abnormal Grain Growth in Thermomagnetic Processed Materials with Transfer Learning and Reinforcement Learning

The goal of this research program is to establish the mechanism governing local grain boundary motion, which is needed to design and process desirable microstructures for better performance, by identifying the relative contributions of grain boundary (GB) energy and mobility to grain growth. Classical models for grain growth assume that the primary mechanism for reducing the total interfacial energy is area reduction and that GB restructuring is not significant. This assumption implies that grain growth is locally driven by curvature. However, recent experimental observations using new non-destructive 3D x-ray diffraction microscopy techniques (3D-XRM) reveal that classic descriptors (i.e., curvature, number of neighbors, grain size) do not predict real grain growth. Instead, local GB motion appears to be governed by its energy relative to its neighbors such that low-energy boundaries replace those of higher energy. However, simulations that incorporate GB energy anisotropy still fail to reproduce these observations. These discrepancies suggest that the common assumption for grain growth theory must be re-examined to predict and, thus, control microstructure evolution in real polycrystals. A significant challenge to testing this assumption is due to anisotropic GB mobility. Mobility may cause abnormal grain growth or affect the final grain shapes or growth rate but its true contributions are unknown because it is difficult to measure. For example, observations in Fe have found that grains associated with high energy and high mobility boundaries tend to experience abnormal grain growth, whereas abnormal grain growth is associated with low energy and high mobility boundaries in alumina. As mobility and energy both control GB motion, it is challenging to isolate the local driving forces necessary to test the common assumption that the primary mechanism is area reduction. The novelty of this work is the use of machine learning tools to capture GB mobility and energy from 3D-XRM measurements in polycrystals to test the common assumption used in grain growth models. Machine learning can capture high-order correlations in dynamic systems like those found in the evolving GB topology. The PIs have developed a physics-regularized interpretable machine learning microstructure evolution (PRIMME) model that accurately replicates the grain growth behavior of its trained data set.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Natural and Hydraulic Fracture Density Prediction and Identification of Controllers

Discrete Fracture Networks (DFN’s) incorporated into hydraulic fracture modeling and reservoir simulation are typically constructed and calibrated to all available high-quality natural fracture data from image logs and core, which generally results in an extremely limited calibration data set. To extrapolate these data over large areas, more broadly sampled data sets, such as discontinuity-related 3-D seismic attributes are often used. Broad spatial trending methodologies can potentially misrepresent natural fracture systems through over-reliance on seismic attributes that are commonly influenced by noise. The Hydraulic Fracture Test Site (HFTS) provides a rare insight of the subsurface natural fracture network and controlling factors on fracture distribution from a mechanical and lithological standpoint. The physical occurrence of hydraulic fractures and their interaction and relationship to preexisting natural fractures can be predicted using analytical models. Such model outputs can be applied to provide higher confidence when developing DFN’s.

Campbell, Whitney↗
DOE OSTI2022

Development of Large Scale Extrusion Deposition for Structural Applications

Large Scale Extrusion Deposition (LSED) is an evolving additive manufacturing (AM) technology that research, such as that taking place at Oak Ridge National Laboratory (ORNL) and companies like Local Motors, are continuing to utilize and develop new applications. A major LSED application of interest has been molds and tooling as it allows for much shorter production time and lower cost. However, interest has been expanding into using LSED for more structural applications due to more frequent use of high performing polymer composites as feedstock. The use of LSED for structural applications is of particular interest to Local Motors as it is currently being used to create commercially viable, energy efficient electric vehicles. LSED offers a unique opportunity when compared to traditional manufacturing methods as it can significantly reduce the number of components necessary, while decreasing embodied energy and carbon emissions. Even with these benefits however, it is important that LSED is properly understood from a structural aspect as this field has not been as heavily researched as tooling. To ensure a high level of safety and repeatability is an essential responsibility of a manufacturer whose products are structural in nature. As such it is important to understand the materials and LSED process to make structural objects that the manufacturer can be confident in. The main goals of this project were to: develop and investigate materials that are of interest for structural LSED applications, further develop and understand the current machines used in LSED and develop simulations tools of LSED and the mechanical properties of the created structure. Material development focused on composite materials that have high mechanical properties and are stable in a variety of environments. The materials were tested to determine their as printed mechanical and thermal properties as these are necessary for simulations. Once the materials were investigated thoroughly, it allowed for simulations to be performed to compare to experimental data with simulations. Materials were also vetted to determine candidates for multi-material printing. The machine development focused around the areas of process monitoring, non-destructive evaluation, and quality control. Finally, the goal of the simulations was to develop a realistic model of printed structures, including in-process simulation and dynamic simulation. The routes to get to some of these goals and the depth in which they were investigated changed throughout the project due to personnel changes and the COVID-19 pandemic. This project resulted in many valuable results such as the development of an nondestructive evaluation (NDE) technique for interlayer defects, proof of simulation for warpage in simple parts, the development and utilization of a profilometer to monitor a print for defects or inconsistencies, thorough investigation of a material used commercially for structural LSED applications and valuable experimental data on the applicability and advantages of multi-material crush structures versus their neat counterparts by creation and testing of samples.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Natural Gas Drilling near the Underground Nuclear Test Site of Rulison, Colorado - 19571

In 1969, the Project Rulison test was conducted in western Colorado using a nuclear device to stimulate natural gas production by fracturing low-permeability, gas-bearing rock. The presence of radionuclides (specifically tritium) in the produced gas persisted above acceptable levels, and the test was abandoned. Advances in hydrofracturing technology over the past 10-15 years have revolutionized the extraction of hydrocarbons from low-permeability formations, leading to a significant increase of drilling in the Rulison area. Drilling activity nearing the Rulison site has raised concerns that remnant radioactivity might be encountered. The site, now under the care of the US Department of Energy (DOE), is protected by a 16-hectare (40 acre) institutional control boundary (Lot 11) that prohibits drilling or extraction of materials below 1830 meters (6000 feet) within the lot boundary. Drilling within 0.8 kilometer (0.5 mile) of the site requires a hearing before the Colorado Oil and Gas Conservation Commission. DOE recommends that gas developers adopt a conservative, staged drilling approach, allowing gas reserves near the Rulison site to be recovered in a manner that minimizes the likelihood of encountering contamination. The staged approach calls for collecting data from wells away from the site before drilling closer. For instance, the wells 1.2 kilometer (0.75 mile) west of the site did not encounter any unexpected geologic conditions and now have 8 years of production without encountering any detonation-related radionuclides. The next set of wells would be 0.8 kilometer (0.5 mile) west of the site. This sequential method drills first at locations with a low probability of encountering contamination and then moves closer. This process protects public safety while allowing the collection of additional data to confirm that contamination has not migrated beyond the 16-hectare (40-acre) institutional control boundary. The staged drilling approach was simulated with a modeling effort that estimates the current extent of contamination and the potential for production-induced contaminant migration to nearby wells. The model incorporates geologic data from nearby wells and was calibrated to production data from both the reentry well production testing (1970-1971) and the wells 1.2 kilometer (0.75 mile) from the site that began producing in 2010. The geologic data include both sand/shale distribution statistics as well as the tendency of hydrofractures to preferentially propagate east and west due to the regional stress field. The model results indicate that it is unlikely that wells near the site would ever encounter site-related radionuclides. (authors)

03 NATURAL GAS↗
DOE OSTI2019

Tank Test of an Active Power Rectification in Wave-to-Wire Energy Conversion

Maximum power control algorithms in wave energy converter (WEC) are mainly based on the fluid-structure dynamics with impedance matching from power take-off (PTO) damping or model predictive control through the buoy dynamics. However, the conventional buoy characteristics study cannot satisfy estimation of maximum output power in a practical wave-to-wire (W2W) system due to significant losses on mechanical and electrical components from PTO to power converter in comparison with the losses on the buoy. To analysis the non-ideal effects from mechanical transmission and energy conversion characteristics of WEC with a power converter output, a W2W simulation and tank testing is performed. The 1:20 scale WEC system with 1.17m buoy diameter includes a single body heaving buoy, mechanical-motion-rectifier based power-take-off, and power converter that store its output energy to a battery load. From the tank testing results, a circuit model is built and is used to estimate the system performance. The non-ideal effects such as viscous damping between buoy and power take-off, gearbox loss, generator loss, and power converter loss in the system are included in the circuit model with efficiencies from 60 - 80%. The maximum extracted power results under various wave conditions are dominated by these non-ideal effect.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Long-Term field testing of the accuracy and HVAC energy savings potential of occupancy presence sensors in A Single-Family home

The energy-saving potential of occupancy-centric smart thermostats has been extensively explored in simulations but lacked field testing for energy savings quantification and sensor performance assessment in real buildings. This paper presents a long-term field study conducted in a single-family home in Texas, U.S. to evaluate the performance of occupancy-centric controls (OCC) of HVAC (heating, ventilation, and air-conditioning) system in terms of energy savings, sensor accuracy, and impact on electric peak demand. The test site was equipped with a commercial off-the-shelf (COTS) smart thermostat and multiple occupancy presence sensors for OCC implementation. Additionally, a sub-metering system was installed to monitor electricity consumption of various end-use equipment, including the HVAC system. A supplementary device was installed to track the ground-truth occupancy for the accuracy evaluation of the occupancy presence sensor. Scenarios of baseline and OCC controls were alternated weekly over the 20-month testing period. The results indicated an effective OCC execution, as evidenced by indoor temperature profiles. During the 2023 cooling season, OCC achieved total energy savings of 1,958 kWh, corresponding to a 17.6% energy savings ratio. Under certain conditions, daily HVAC energy savings reached as high as 17 kWh, with a savings ratio of 35%. Sensor performance showed an overall accuracy of 83.8%, a False Positive Rate (FPR) of 12.8%, and a False Negative Rate (FNR) of 47.4%. A key limitation was the sensor’s inability to detect stationary occupants during sleep, leading to a midnight FNR of nearly 100% and significantly compromising thermal comfort. Additionally, the implementation of OCC resulted in extended periods of high electricity demand on summer afternoons, affecting occupant’s thermal comfort and posing potential challenges to community-level grid operations if OCC were widely adopted. Furthermore, this study addresses a critical research gap by empirically investigating energy-saving potential and occupancy sensor performance in residential buildings. Through a comprehensive field-testing study, the research examines the interrelationship between sensor accuracy, energy savings, and thermal comfort, an area that has received limited attention in the current literature.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Nutrient Dynamics in a Coupled Terrestrial Biosphere and Land Model (ELM-FATES-CNP)

We present a representation of nitrogen and phosphorus cycling in the Functionally Assembled Terrestrial Ecosystem Simulator, a demographic vegetation model within the Energy Exascale Earth System land model. This representation is modular, and designed to allow testing of multiple hypothetical approaches for carbon-nutrient coupling in plants. Novel model hypotheses introduced in this work include, (a) the controls on plant acquisition of aqueous mineralized nutrients in the soil and (b) fairly straight forward methods of allocating nutrients to specific plant organs and their losses through live plant turnover as well as litter fluxes generated through plant mortality. This combines the new with pre-existing hypotheses (such as nitrogen fixation and soil decomposition) into a system that can accommodate plant-soil dynamics for a large number of size- and functional-type-resolved plant cohorts within a time-since-disturbance-resolved ecosystem. Root uptake of nutrients is governed by fine root biomass, and plants vary in their fine root biomass allocation in order to balance carbon and nutrient limitations to growth. We test the sensitivity of the model to a wide range of parameter variations and structural representations, and in the context of observations at Barro Colorado Island, Panama. A key model prediction is that plants in the high-light-availability canopy positions allocate more carbon to fine roots than plants in low-light understory environments, given the widely different carbon versus nutrient constraints of these two niches within a given ecosystem. This model provides a basis for exploring carbon-nutrient coupling with vegetation demography within Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Using high-fidelity computational fluid dynamics to help design a wind turbine wake measurement experiment

Here, we describe the process of using large-eddy simulations of wind turbine wake flow to help design a wake measurement campaign. The main goal of the experiment is to measure wakes and wake deflection that result from intentional yaw misalignment under a variety of atmospheric conditions at the Scaled Wind Farm Technology facility operated by Sandia National Laboratories in Lubbock, Texas. Prior simulation studies have shown that wake deflection may be used for wind-plant control that maximizes plant power output. In this study, simulations are performed to characterize wake deflection and general behavior before the experiment is performed to ensure better upfront planning. Beyond characterizing the expected wake behavior, we also use the large-eddy simulation to test a virtual version of the lidar we plan to use to measure the wake and better understand our lidar scan strategy options. This work is an excellent example of a 'simulation-in-the-loop' measurement campaign.

17 WIND ENERGY↗
DOE OSTI2018

National Renewable Energy Laboratory (NREL) Topic 2 Final Report: End-to-End Communication and Control System to Support Clean Energy Technologies

This document is the final report of a two-year development, test, and demonstration project, 'Cohesive Application of Standards- Based Connected Devices to Enable Clean Energy Technologies.' The project was part of the National Renewable Energy Laboratory's (NREL's) Integrated Network Testbed for Energy Grid Research and Technology (INTEGRATE) initiative hosted at Energy Systems Integration Facility (ESIF). This project demonstrated techniques to control distribution grid events using the coordination of traditional distribution grid devices and high-penetration renewable resources and demand response. Using standard communication protocols and semantic standards, the project examined the use cases of high/low distribution voltage, requests for volt-ampere-reactive (VAR) power support, and transactive energy strategies using Volttron. Open source software, written by EPRI to control distributed energy resources (DER) and demand response (DR), was used by an advanced distribution management system (ADMS) to abstract the resources reporting to a collection of capabilities rather than needing to know specific resource types. This architecture allows for scaling both horizontally and vertically. Several new technologies were developed and tested. Messages from the ADMS based on the common information model (CIM) were developed to control the DER and DR management systems. The OpenADR standard was used to help manage grid events by turning loads off and on. Volttron technology was used to simulate a homeowner choosing the price at which to enter the demand response market. Finally, the ADMS used newly developed algorithms to coordinate these resources with a capacitor bank and voltage regulator to respond to grid events.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

MOOSE IPL Extensions (Documentation)

In FY-2015, The development of MOOSE was driven by the needs of the NEAMS MOOSE-based applications, BISON, MARMOT, and RELAP-7. An emphasis was placed on the continued upkeep and improvement MOOSE in support of the product line integration goals. New unified documentation tools have been developed, several improvements to regression testing have been enforced and overall better software quality practices have been implemented. In addition the Multiapps and Transfers systems have seen significant refactoring and robustness improvements, as has the “Restart and Recover” system in support of Multiapp simulations. Finally, a completely new “Control Logic” system has been engineered to replace the prototype system currently in use in the RELAP-7 code. The development of this system continues and is expected to handle existing needs as well as support future enhancements.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2018

Evaluation of Spent Fuel Disposition in Crystalline Rocks: FY18 Progress Report

The U.S. Department of Energy Office of Spent Fuel Waste Disposition (SFWD) established in fiscal year 2010 (FY10) the Spent Fuel Waste Science & Technology (SFWST) Program (formerly the Used Fuel Disposition Campaign - UFDC) program to conduct the research and development (R&D) activities related to storage, transportation and disposal of used nuclear fuel and high level nuclear waste. The Mission of the SFWST is: To identify alternatives and conduct scientific research and technology development to enable storage, transportation and disposal of used nuclear fuel and wastes generated by existing and future nuclear fuel cycles. The work package of Crystalline Disposal R&D directly supports the following SFWST objectives: Develop a fundamental understanding of disposal system performance in a range of environments for potential wastes that could arise from future nuclear fuel cycle alternatives through theory, simulation, testing, and experimentation. ; Develop a computational modeling capability for the performance of storage and disposal options for a range of fuel cycle alternatives, evolving from generic models to more robust models of performance assessment. The objective of the Crystalline Disposal R&D control account is to advance our understanding of long-term disposal of used fuel in crystalline rocks and to develop necessary experimental and computational capabilities to evaluate various disposal concepts in such media. Significant progress has been made in FY18 in both experimental and modeling arenas in evaluation of used fuel disposal in crystalline rocks, especially in model demonstration using field data. The work covers a wide range of research topics identified in the R&D plan. The major accomplishments are summarized.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Pressure Gain, Stability, and Operability of Methane/Syngas Based RDEs Under Steady and Transient Conditions (Final Project Report)

The scope of this work addresses key issues associated with losses associated with the detonation wave and other processes internal to the RDE operation, as well as it develops modeling tools for the evaluation of these losses and exhaust emissions in RDEs. The main challenge in studying RDEs is that RDE performance is highly reliant on the specifics of the design so much so that simple/canonical systems alone cannot provide useful engineering information, but practical RDE designs are sufficiently complex and involve extreme operational environments that detailed access either experimentally (laser diagnostics, for instance) or computationally (direct numerical simulations) are as yet to become practical. To overcome this challenge, we have conducted a combined experimental/simulation/analytical study investigating key phenomena that control the characteristics of operation of RDEs. As a result, the study has developed tools and methods that can be used to evaluate performance and design approaches using reduced-physics models, with the assumptions validated using detailed simulations, and the model prediction tested using experimental observations. The specific objectives of the research were: (1) Develop and demonstrate a low-loss fully axial injection concept, taking advantage of stratification effects to alter the detonation structure and position the wave favorably within the combustor; (2) Obtain stability and operability characteristics of an RDE across operating conditions to aid in the development of operability and performance rules for the operations of other systems; and (3) Develop quantitative metrics for performance gain as well as quantitative description of the loss mechanisms through a combination of diagnostics development, reduced-order modeling, and detailed simulations. The work conducted here has made contribution on design of low-loss inlets that has broad application within the power generation industry for use with pressure gain combustion. The operability and stability of different designs, while focusing on axial air inlet designs, has been analyzed. The effect of nozzle and injection conditions was studied. Models and simulations of exhaust emissions, focusing on NOx emission has been developed and used to investigate how operation of the RDE affect NOx production using Lagrangian analysis of RDE simulations. This work has built on previous programs, with the goal of further understanding operation of RDEs and elevate the readiness of design consideration. In addition, a suite of diagnostic and modeling tools have been developed to obtain quantitative metrics on performance based on measurements, which can be readily transferred to other experimental configurations.

08 HYDROGEN↗
DOE OSTI2020

Dynamic Restoration Strategy for Distribution System Resilience Enhancement

In electric power distribution systems, distributed energy resources (DERs) can act as controllable power sources and support utility operators to minimize power outages after extreme weather events (e.g., hurricane, earthquake, wildfire) and thus help enhance the grid's resilience. Meanwhile, the influences of extreme events and the capabilities of DERs are dynamic and difficult to predict. Hence, the desired distribution system restoration strategy should be able to evolve according to real-time fault/disturbance information and the availability of DERs. In this paper, we propose a new dynamic restoration strategy for distribution systems to enhance system resilience against potential hazards. An efficient reconfiguration algorithm is developed to eliminate the use of integer variables to relieve the computational burden. Model predictive control is implemented to adjust the system topology and DER operation set points based on the updated fault information and DER forecasts. The effectiveness of the proposed restoration model in enhancing distribution system resilience is validated through an IEEE 123-bus test system. Simulation results also validate that the proposed restoration model can mitigate the occurrence of unexpected events and the fluctuations of DERs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Decentralized Microgrid Protection Through Relative Fault Direction Classification: Preprint

Protection in inverter-based resources (IBRs) dominated microgrids generally face significant challenges due to the low fault current and inconsistent fault behaviors from IBRs. Recently, machine learning-based approaches have attracted considerable attention to address these challenges. This paper introduces a novel decentralized protection strategy for microgrids. The proposed method decomposes the protection challenge into several distributed learning tasks, enabling individual relays to autonomously determine the direction of faults using a binary classification framework based on support vector machine (SVM) algorithms. Following the distributed fault direction estimation, classifier outcomes are shared among neighboring relays, facilitating a local decision-making process to ascertain the presence of faults within the neighborhood. Finally, a tripping signal is generated based on the classifier results of each relay to operate the circuit breaker. To test and validate this approach, a 100% renewable microgrid model is simulated in MATLAB/Simulink. In the numerical analysis, the application of SVM classifiers in our approach yields impressive results: an average relay classification accuracy of 98%, and a 96% accuracy in circuit breaker control. These findings highlight the potential of machine-learning-based approaches in enhancing the efficiency and reliability of microgrid protection systems.

decentralized algorithm↗
DOE OSTI2025

2025 Intern Poster

The Hot Fuel Examination Facility (HFEF) at the Materials and Fuels Complex (MFC) houses the largest U.S. inert atmosphere hot cell for nuclear material research. Key features include the precision gamma scanning (PGS), Fuel Accident Condition Simulator (FACS), Neutron Radiography Reactor (NRAD), and the focus of this project, the Metallograph Loading Cell (MET Cell). The MET Cell performs tests on spent nuclear fuel, such as microhardness testing, microscopy, and neutron radiography. However, the MET Cell’s existing pressure and lighting control systems are outdated and inefficient, with inadequate documentation for system changes over time. This project aims to design a new automated control system for the MET Cell, ensuring longevity (minimum ten years), ease of troubleshooting/repair, and integration into the building monitoring system. The design process addressed challenges such as space restrictions, varied voltages within enclosures, sourcing new components, and security limitations. Compliance with NFPA 70, UL508A, MFC Physical Security, and INL Engineering standards was essential. The project involves repurposing an existing PLC to manage lighting and pressure control using digital and analog signals, simplifying wiring, and ensuring thorough documentation for future reference.

42 - ENGINEERING↗
DOE OSTI2021

Process Anomaly Detection for Sparsely Labeled Events in Nuclear Power Plants

An essential aspect of online monitoring, subtle anomaly detection increases the detection lead time for equipment failure and enables a nuclear power plant (NPP) to mitigate unexpected partial or full outages, resulting in significant cost saving to the plant. Once an anomaly is detected by plant staff, its cause and severity are investigated. Because the vast majority of anomalies require some level of investigation, including some that require time-consuming examination, before they are passed over to the engineering organization for further analysis, plants are often equipped with tools to assist the staff in performing anomaly detection. Those tools operate as a black box and are often based on statistical methods that establish sensor correlations using preconfigured mathematical models and flag correlation deviations as anomalies. Due to the number of anomalies detected at a given NPP on a daily basis, a significant number of flagged anomalies usually await examination for days or weeks. A primary cause of this backlog is that the methods used by the tools generate many false positives. Though this is usually attributed to oversensitive model settings due to very narrow normal operation bands, it can also be associated with the model development being inadequate for the process being monitored, or with missing model inputs that could have explained misclassified positives. The performance of anomaly detection tools impacts their plant acceptance and utilization, especially when the effort to address false positives generated by the tool depletes the value or cost saved by using that tool. Thus, means to advance anomaly detection performance have been investigated by the Department of Energy’s Light Water Reactor Sustainability program. Previous and ongoing efforts have targeted unsupervised machine-learning (ML) methods, which do not require the labeling of any data fed into the ML model. By contrast, in supervised anomaly detection methods, every data point is labeled as either a normal or abnormal process condition, and the model is trained to replicate the classification process. Supervised methods usually outperform unsupervised methods, due to the added value in differentiating normal from anomalous states of the monitored process. An NPP’s corrective action program requires it to track and document, via a dedicated report, the resolution of any issues that occur within the plant. Once created, each report is reviewed by a plant screening committee, and several classifications and decisions are made. Recently, a collaborating NPP developed an artificial intelligence and ML-based classifier to categorize a condition report (CR) into classes that can serve to label the data as normal or anomalous. Applying CRs as labels represents a semi-supervised use case. Semi-supervised ML assumes that labels exist for some data points (i.e., labeled anomalies, in this case) but not for the rest. In this effort, semi-supervised ML methods were used to fuse data from CRs with anomaly detection methods in order to test the hypothesis that partially labeled anomalies would improve the accuracy of the anomaly detection methods. Specifically, two methods were used. The first is the deep Semi-supervised Anomaly Detection (deep SAD) method, which can handle labels ranging from fully unsupervised to fully supervised cases. The second is a newly designed ML method developed specifically for this effort and referred to as the high-order feature (HOF)-based method. To evaluate these two methods in controlled environments, synthetic data generators were developed and used. The first datasets used a spring-mass-damper (SMD) system simulator commonly found in mechanical engineering references. This was used to create two use cases: a one- and a three-mass system. Anomalies were introduced by changing the spring and damper coefficients while the system was actuated by random forces. The second datasets used the commercial Dymola-Modelica software to build a simplified nuclear reactor model. Anomalies were added in the form of corrupted sensor readings and/or control commands. The deep SAD method was tested using the SMD system, while the HOF method was tested using both datasets. Application of the deep SAD semi-supervised ML method demonstrated that labels can generate increased confidence in detecting true anomalies. This helped increase the number of true positives and decrease the number of false negatives—something that would aid in addressing the backlog of possible anomalies. Application of the HOF method demonstrated that labels can aid in down selecting from a candidate set of features to a more optimal subset in order to better differentiate between normal and anomalous conditions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

When and how to treat an IMRT patient on a second accelerator without replanning?

When a linear accelerator is unavailable for treatment, a clinical decision is imminent regarding whether a patient should be treated on a linear accelerator other than the machine the patient was scheduled on, or whether treatment should be postponed until the original Linac becomes available. This work investigates the feasibility of switching patients to different accelerators for intensity-modulated radiation therapy (IMRT). We have performed Monte Carlo simulations of photon beams from different Linac models and vendors. Prostate and head and neck (H&N) treatment plans for Siemens Primus, Primart, and Varian 21EX accelerators are studied in this work. Dose distributions for given plans are recalculated using different beam data with the same nominal energy from different Linacs. We have compared dose-volume histograms (DVHs) and the maximum, the minimum, and the mean doses to the target and critical structures because of switching accelerators. In the process of switching a treatment plan to a different accelerator, issues exist, including optimum penumbra compensation, dose distribution at the boundary of target and critical structures, and multileaf collimator (MLC) leaf-width effects, which need to be considered and verified with measurements. Our Monte Carlo simulation results confirm that, for the cases we tested, the dose received by 95% of the planning target volume differs by 0.2% to 1.5% between Siemens Primus and Varian 21EX Linacs. The discrepancy is within our clinical acceptance criteria of 3% for IMRT treatments. In making the final decision on whether to switch machines or not, the tumor control probabilities (TCPs) based on a linear-quadratic model are compared. Based on the analyses performed in this work, it is therapeutically more beneficial to switch a patient to a different machine than to postpone a treatment until the original machine is available, especially for fast-growing tumors such as H&N cancers.

62 RADIOLOGY AND NUCLEAR MEDICINE↗