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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

SU-F-T-139: Meeting the Challenges of Quality Control in the TOPAS Monte Carlo Simulation Toolkit for Proton Therapy

Purpose: Monte Carlo particle transport simulation (MC) codes have become important tools in proton therapy and biology, both for research and practice. TOPAS is an MC toolkit serving users worldwide (213 licensed users at 95 institutions in 21 countries). It provides unprecedented ease in 4D placement of geometry components, beam sources and scoring through its user-friendly and reproducible parameter file interface. Quality control (QC) of stochastic simulation software is inherently difficult, and the versatility of TOPAS introduces additional challenges. But QC is vital as the TOPAS development team implements new features, addresses user feedback and reacts to upgrades of underlying software (i.e. Geant4). Methods: Whenever code is committed to our repository, over 50 separate module tests are automatically triggered via a continuous integration service. They check that the various module options execute successfully and that their results are statistically consistent with previous reference values. Prior to each software release, longer end-to-end tests automatically validate TOPAS against experimental data and a TOPAS benchmark. These include simulating multiple properties of spread-out Bragg peaks, validating nuclear models, and searching for differences in patient simulations. Results: Continuous integration has proven effective in catching regressions at the time they are introduced, particularly when implementing new features that involve refactoring code (e.g. multithreading and ntuple output). Code coverage statistics highlight untested portions of code and guide development of new tests. The various end-to-end tests demonstrate that TOPAS accurately describes the physics of proton therapy within clinical tolerances. Conclusion: The TOPAS QC strategy of frequent short tests and pre-release long tests has led to a more reliable tool. However, the versatility of TOPAS makes it difficult to predict how users might combine different modules, and so QC ultimately remains a partnership between the developer and the user. This work was funded by National Cancer Institute grant R01 CA 140735.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Economic Sizing of Batteries for the Smart Home: Preprint

This paper presents a methodology to economically size a home battery based on parametric analysis using home energy management system (HEMS) software to optimally dispatch the battery along with controllable loads under several use cases. We account for connected equipment, controls, renewable resources, and other factors such as building characteristics and utility tariffs. The paper defines an analytical pathway for such a sizing tool, develops initial sizing guidance, and clarifies technical and market opportunities for home batteries in the context of existing and emerging equipment and control technologies. A parametric analysis that included 132 scenarios has been performed based on different combinations of pertinent parameters. Results indicate that four variables dominate the decision-making process: application scenarios (new construction or retrofit), utility tariffs, existence of HEMS, and the anticipated payback time. Life-cycle cost analysis indicated that in the absence of utility incentives, batteries plus HEMS have a payback time of longer than 10 years for new construction under a time-of-use rate structure and feed-in tariff; larger batteries have a longer payback time but may provide more benefits to utilities on reducing power backfeed under certain circumstances.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Implementation of data acquisition interface using on-board field-programmable gate array (FPGA) universal serial bus (USB) link

Typically a system consists of hardware as the controller and software which is installed in the personal computer (PC). In the effective nuclear detection, the hardware involves the detection setup and the electronics used, with the software consisting of analysis tools and graphical display on PC. A data acquisition interface is necessary to enable the communication between the controller hardware and PC. Nowadays, Universal Serial Bus (USB) has become a standard connection method for computer peripherals and has replaced many varieties of serial and parallel ports. However the implementation of USB is complex. This paper describes the implementation of data acquisition interface between a field-programmable gate array (FPGA) board and a PC by exploiting the USB link of the FPGA board. The USB link is based on an FTDI chip which allows direct access of input and output to the Joint Test Action Group (JTAG) signals from a USB host and a complex programmable logic device (CPLD) with a 24 MHz clock input to the USB link. The implementation and results of using the USB link of FPGA board as the data interfacing are discussed.

42 ENGINEERING↗
DOE OSTI2016

High density plasmas and new diagnostics: An overview (invited)

One of the limiting factors for the full understanding of Electron Cyclotron Resonance Ion Sources (ECRISs) fundamental mechanisms consists of few types of diagnostic tools so far available for such compact machines. Microwave-to-plasma coupling optimisation, new methods of density overboost provided by plasma wave generation, and magnetostatic field tailoring for generating a proper electron energy distribution function, suitable for optimal ion beams formation, require diagnostic tools spanning across the entire electromagnetic spectrum from microwave interferometry to X-ray spectroscopy; these methods are going to be implemented including high resolution and spatially resolved X-ray spectroscopy made by quasi-optical methods (pin-hole cameras). The ion confinement optimisation also requires a complete control of cold electrons displacement, which can be performed by optical emission spectroscopy. Several diagnostic tools have been recently developed at INFN-LNS, including “volume-integrated” X-ray spectroscopy in low energy domain (2-30 keV, by using silicon drift detectors) or high energy regime (>30 keV, by using high purity germanium detectors). For the direct detection of the spatially resolved spectral distribution of X-rays produced by the electronic motion, a “pin-hole camera” has been developed also taking profit from previous experiences in the ECRIS field. The paper will give an overview of INFN-LNS strategy in terms of new microwave-to-plasma coupling schemes and advanced diagnostics supporting the design of new ion sources and for optimizing the performances of the existing ones, with the goal of a microwave-absorption oriented design of future machines.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Wind Turbine Controller to Mitigate Structural Loads on a Floating Wind Turbine Platform

This paper summarizes the control design work that was performed to optimize the controller of a wind turbine on the WindFloat structure. The WindFloat is a semisubmersible floating platform designed to be a support structure for a multimegawatt power-generating wind turbine. A controller developed for a bottom-fixed wind turbine configuration was modified for use when the turbine is mounted on the WindFloat platform. This results in an efficient platform heel resonance mitigation scheme. In addition, several control modules, designed with a coupled linear model, were added to the fixed-bottom baseline controller. The approach was tested in a fully coupled nonlinear aero-hydro-elastic simulation tool in which wind and wave disturbances were modeled. This testing yielded significant improvements in platform global performance and tower-base bending loading.

17 WIND ENERGY↗
DOE OSTI2017

CMM Data Analysis Tool

Due to the increase in the use of Coordinate Measuring Machines (CMMs) to measure fine details and complex geometries in manufacturing, many programs have been made to compile and analyze the data. These programs typically require extensive setup to determine the expected results in order to not only track the pass/fail of a dimension, but also to use statistical process control (SPC). These extra steps and setup times have been addressed through the CMM Data Analysis Tool, which only requires the output of the CMM to provide both pass/fail analysis on all parts run to the same inspection program as well as provide graphs which help visualize where the part measures within the allowed tolerances. This provides feedback not only to the customer for approval of a part during development, but also to machining process engineers to identify when any dimension is drifting towards an out of tolerance condition during production. This program can handle hundreds of parts with complex dimensions and will provide an analysis within minutes.

Source record↗
DOE OSTI2023

Status Report on Regulatory Criteria Applicable to the Use of Artificial Intelligence (AI) and Machine Learning (ML)

Although the interest in the use of artificial intelligence (AI) and machine learning (ML) in nuclear energy is increasing rapidly, at present their implementation is limited. This rapid increase in interest is not surprising considering that implementing AI and ML technology would allow for continuous monitoring, facilitate the implementation of predictive maintenance with optimized staffing plans, enable automation and autonomy opportunities that could drastically reduce fixed operation and maintenance costs, and provide training for operations and maintenance. Other industries are using AI for construction, and in the nuclear arena AI could provide great benefit in decommissioning activities. The ability of AI and ML to operate in real time vastly increases their potential impact. Before AI can be used in design, operations, or as a regulatory tool, the specifics on the regulations applicable to the use of AI for nuclear power applications need to be established. The difficulty is that the specific use cases will dictate the applicability of regulations. For example, even within the application domain associated with operations, the regulations might vary if the AI is used to create a virtual reference for plant operations or is used for training, optimization of maintenance intervals, prioritization of maintenance activities, etc. Different still is if the AI is to be used for design or setting technical specifications, which will introduce additional requirements. US Nuclear Regulatory Commission (NRC) licensing reviews are based on an applicant’s design meeting its performance assessment based on (1) safety goals and objectives, (2) deterministic and/or probabilistic analysis of accident scenarios, and (3) quantitative assessment of design alternatives against the safety goals and objectives using accepted engineering tools, methodologies, and performance criteria. The current regulatory framework does not explicitly address AI or autonomous control. However, as implementing AI technology will require the use of a digital platform, it must meet the requirements of an instrumentation and control (I&C) system. The regulatory requirements for AI, which will be incorporated into the I&C system, will be very dependent on how it is used (i.e., its functionality, safety classification, etc.). The licensing process is primarily risk-based with the identification of components and systems as nonsafety, important to safety, or safety related. A risk-informed approach allows further gradation of components and systems based on risk metrics such as core damage frequency or large early release fractions. Thus, the use cases and the risk categorization of impacted systems and components will determine the regulatory requirements. Regardless of how AI is used it presents new opportunities for risk-informing operating, maintenance, and regulatory decisions. Trustworthiness, transparency, and the ability to validate and verify the results will be paramount in showing that the systems and plant still meet their performance requirements. This report describes the results of research to identify regulatory implications of AI technologies and their uses. Specifically, this report reviews current regulatory guidance relevant to the application of AI for design (including design changes or new designs including advanced reactors), construction, operations, training, maintenance, research, testing, and as a regulatory tool. AI can be automated at different levels from purely informative purposes to autonomous controls. The focus of this review included determination of constraints on the application of AI technology, identification of any regulatory gaps or uncertainties, and clarification of anticipated technical basis information likely to be important for regulatory acceptance of these technologies. Currently, any use of AI at nuclear power plants is focused on nonsafety-related applications. The NRC and other regulatory bodies are evaluating providing guidance to address gaps rather than create new regulations to address the use of AI and ML. This approach seems to be the best to encourage AI development without adding regulatory uncertainty.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Uncontrolled Learning: Codesign of Neuromorphic Hardware Topology for Neuromorphic Algorithms

Neuromorphic computing has the potential to revolutionize future technologies and our understanding of intelligence, yet it remains challenging to realize in practice. The learning-from-mistakes algorithm, inspired by the brain's simple learning rules of inhibition and pruning, is one of the few brain-like training methods. This algorithm is implemented in neuromorphic memristive hardware through a codesign process that evaluates essential hardware trade-offs. While the algorithm effectively trains small networks as binary classifiers and perceptrons, performance declines significantly with increasing network size unless the hardware is tailored to the algorithm. This work investigates the trade-offs between depth, controllability, and capacity—the number of learnable patterns—in neuromorphic hardware. This highlights the importance of topology and governing equations, providing theoretical tools to evaluate a device's computational capacity based on its measurements and circuit structure. The findings show that breaking neural network symmetry enhances both controllability and capacity. Additionally, by pruning the circuit, neuromorphic algorithms in all-memristive circuits can utilize stochastic resources to create local contrasts in network weights. Through combined experimental and simulation efforts, the parameters are identified that enable networks to exhibit emergent intelligence from simple rules, advancing the potential of neuromorphic computing.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Flexible Integration of Diverse HVAC Technologies in EnergyPlus via Python-Enabled Workflows

Analysis of advanced controls and novel system types is often not directly feasible in building energy simulation tools. Various techniques extend building energy simulation tool capabilities to allow the use of user-defined scripts and programs, but these approaches have limitations. The EnergyPlus Python plugin offers users new flexibility to use EnergyPlus to call an external Python module at specific points in the simulation, as well as to use Python to call EnergyPlus functionality through an application programming interface (API). This paper presents four case studies leveraging the EnergyPlus Python plugin to facilitate analysis of advanced controls and system types. The use of the Python plugin offers greater modularity and flexibility relative to previous approaches, is less error prone, and is simpler for users to adopt. The Python plugin allows EnergyPlus to be used in a more flexible manner and to accommodate the expanding realm of energy modeling applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Revealing functional insights into ER proteostasis through proteomics and interactomics

The endoplasmic reticulum (ER), responsible for processing approximately one-third of the human proteome including most secreted and membrane proteins, plays a pivotal role in protein homeostasis (proteostasis). Dysregulation of ER proteostasis has been implicated in a number of disease states. As such, continued efforts are directed at elucidating mechanisms of ER protein quality control which are mediated by transient and dynamic protein-protein interactions with molecular chaperones, co-chaperones, protein folding and trafficking factors that take place in and around the ER. Technological advances in mass spectrometry have played a pivotal role in characterizing and understanding these protein-protein interactions that dictate protein quality control mechanisms. Here, we highlight the recent progress from mass spectrometry-based investigation of ER protein quality control in revealing the topological arrangement of the proteostasis network, stress response mechanisms that adjust the ER proteostasis capacity, and disease specific changes in proteostasis network engagement. We close by providing a brief outlook on underexplored areas of ER proteostasis where mass spectrometry is a tool uniquely primed to further expand our understanding of the regulation and coordination of protein quality control processes in diverse diseases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Poster — Thur Eve — 28: Enabling trajectory-based radiotherapy on a TrueBeam accelerator with the Eclipse treatment planning system

The TrueBeam linear accelerator platform has a developer's mode which permits the user dynamic control over many of the machine's mechanical and radiation systems. Using this research tool, synchronous couch and gantry motion can be programmed to simulate isocentric treatment with a shortened SAD, with benefits such as smaller projected MLC leaf widths and an increased dose rate. In this work, water tank measurements were used to commission a virtual linear accelerator with an 85 cm SAD in Eclipse, from which several arc-based radiotherapy treatments were generated, including an inverse optimized VMAT delivery. For each plan, the pertinent treatment delivery information was extracted from control points specified in the Eclipse-exported DICOM files using the pydicom package in Python, allowing construction of an XML control file. The dimensions of the jaws and MLC positions, defined for an 85 cm SAD in Eclipse, were scaled for delivery on a conventional SAD linear accelerator, and translational couch motion was added as a function of gantry angle to simulate delivery at 85 cm SAD. Ionization chamber and Gafchromic film measurements were used to compare the radiation delivery to dose calculations in Eclipse. With the exception of the VMAT delivery, ionization chamber measurements agreed within 3.3% of the Eclipse calculations. For the VMAT delivery, the ionization chamber was located in an inhomogeneous region, but gamma evaluation of the Gafchromic film plane resulted in a 94.5% passing rate using criteria of 3 mm/3%. The results indicate that Eclipse calculation infrastructure can be used.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2024

CG-Kit: Code Generation Toolkit for performant and maintainable variants of source code applied to Flash-X hydrodynamics simulations

CG-Kit is a new Code Generation tool-Kit that we have developed as a part of the solution for portability and maintainability for multiphysics computing applications. The development of CG-Kit is rooted in the urgent need created by the shifting landscape of high-performance computing platforms and the algorithmic complexities of a particular large-scale multiphysics application: Flash-X. To efficiently use computing resources on a heterogeneous node, an application must have a map of computation to resources and a mechanism to move the data and computation to the resources according to the map. Most existing performance portability solutions are focussed on abstracting the expression of computations so that a unified source code can be specialized to run on different resources. However, such an approach is insufficient for a code like Flash-X, which has a multitude of code components that can be assembled in various permutations and combinations to form different instances of applications. Similar challenges apply to any code that has composability, where a single specified way of apportioning work among devices may not be optimal. Additionally, use cases arise where the optimal control flow of computation may differ for different devices while the underlying numerics remain identical. This combination leads to unique challenges including handling an existing large code base in Fortran and/or C/C++, subdivision of code into a great variety of units supporting a wide range of physics and numerical methods, different parallelization techniques for distributed and shared memory systems and accelerator devices, and heterogeneity of computing platforms requiring coexisting variants of parallel algorithms. All of these challenges demand that scientific software developers apply existing knowledge about domain applications, algorithms, and computing platforms to determine custom abstractions and granularity for code generation. There is a critical lack of tools to tackle those problems. CG-Kit is designed to fill this gap by providing a user with the ability to express their desired control flow and computation-to-resource map in the form a pseudocode-like recipe. It consists of standalone tools that can be combined into highly specific and, we argue, highly effective portability and maintainability toolchains. Here we present the design of our new tools: parametrized source trees, control flow graphs, and recipes. The tools are implemented in Python. They are agnostic to the programming language of the source code targeted for code generation. In conclusion, we demonstrate the capabilities of the toolkit with two examples, first, multithreaded variants of the basic AXPY operation, and second, variants of parallel algorithms within a hydrodynamics solver, called Spark, from Flash-X that operates on block-structured adaptive meshes.

Algorithmic portability↗
DOE OSTI2015

Illustration of VERA Capabilities to Model a Typical iPWR SMR

Small modular reactors (SMRs) are being considered by the commercial nuclear power industry as an option for more distributed generation and for replacement of older fossil fuel generating facilities. SMRs are more compact than operating pressurized water reactors (PWRs), producing about 200 MWe as compared to 1000 MWe or higher for their full-sized cousins, and are offered as “expandable” units; that is, their modular design allows the utility to add more units progressively at the same site. In Summer 2014, CASL reported on a progression of demonstrations illustrating VERA’s ability to simulate a typical SMR of the iPWR type, while developing a simple core cycle design for cycle economic studies. The work included development of fuel assembly lattices, 3D single assembly and full core (quarter symmetry) simulations of the proposed core cycle design using the CASL Virtual Environment for Reactor Applications (VERA). The report concluded that it would be difficult for the SMR to operate for the target 1400 EFPD cycle length and that the fuel cost would be on the order of 50% higher as compared with a PWR. Unfortunately, the Reference 4 core cycle design did not include control component management. The iPWR is intended to be operated using the control rods for reactivity control, as opposed to using soluble boron (like most currently operating PWRs), and the introduction of the control rods is expected to affect the length of the cycle. Therefore, this work continues the previous CASL demonstration of VERA capability to simulate a typical iPWR core, including the development of a control component management strategy using the Reference 4 core design and utilizing the soon-to-be released VERA3.3 tools. Note that this study is not intended as a validation study; rather it is merely an illustration of VERA’s capabilities to simulate a typical iPWR. Simulations included two variations on control rod banks – a two-bank scheme and a ten-bank scheme. Both schemes were modelled using the VERA Common Input, and more than six iterations on each control rod scheme were simulated to arrive at a combination of rod banks and insertion depths that provided a core reactivity near the target k eff of 1.000 ± 0.001 (more than 50 cases run) over two calendar months. In particular, VERA was used to determine the hot excess reactivity for the core design with all rods out (ARO), and then iterative simulations with the rod banks inserted to determine the critical control rod bank positions were completed. VERA options to simplify the process were exercised as possible, particularly restart functionality, coupled thermal-hydraulics using the internal T-H solver, xenon, and depletion capability. The fuel shuffling functionality was not exercised, since the iPWR SMR features a once-through cycle. The VERA method of characteristics (MOC) neutronics subcomponent was used to predict power and reactivity with control rod maneuvering, both with and without thermal-hydraulic feedback. The problem was scaled from 35 CPU cores to 3815, effectively demonstrating the solution time with respect to computing capacity. Computer clusters at Oak Ridge National Laboratory, Idaho National Laboratory and Tennessee Valley Authority were used. These studies provide an additional illustration of the application of VERA to a typical iPWR SMR. Reference 4 previously demonstrated that the codes could handle the differences in geometry for the iPWR, and this study expanded the illustration to include control rod management. Further, the work illustrates the need for in-depth simulations to support effective control rod maneuvering for an iPWR and highlights the need for control rod reactivity search capability within VERA. Additionally, the development of a control rod bank reactivity search capability will be extremely useful for not only this application, but also for BWRs as CASL develops BWR capability in Phase 2.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Leveraging Real-World Driving Data for Design and Impact Evaluation of Energy Efficient Control Strategies

Modeling and simulation are crucial in the development of advanced energy efficient control strategies. Utilizing real-world driving data as the underlying basis for control design and simulation lends veracity to projected real-world energy savings. Standardized drive cycles are limited in their utility for evaluating advanced driving strategies that utilize connectivity and on-vehicle sensing, primarily because they are typically intended for evaluating emissions and fuel economy under controlled conditions. Real-world driving data, because of its scale, is a useful representation of various road types, driving styles, and driving environments. The scale of real-world data also presents challenges in effectively using it in simulations. A fast and efficient simulation methodology is necessary to handle the large number of simulations performed for design analysis and impact evaluation of control strategies. In this study, two methods are presented of leveraging real-world data in both design optimization of energy efficient control strategies and in evaluating the real-world impact of those control strategies upon large-scale deployment. Through these methodologies, strategies with highest impact on energy savings were selected to be implemented as control algorithms. The developed algorithms were incorporated into a vehicle dynamics and powertrain control architecture implemented on a Cadillac CT6 demonstration vehicle. The control algorithms were then exercised on real-world driving scenarios to determine their impact on collective energy savings. The methodology utilizes the large-scale driving data sets maintained by the National Renewable Energy Laboratory to extract real-world driving scenarios and efficient simulation software tools. The insights obtained through this research help in guiding technology selection for energy efficient driving controls.

ARPA-E↗
DOE OSTI2023

Advanced wind turbine control development using field test analysis for generator overspeed mitigation

Abstract Turbulent and gusty wind conditions can cause generator overspeed peaks to exceed a threshold that then lead to wind turbine shutdowns, which then decrease the energy production of the wind turbines. We derive so‐called “gust measures” that predict when generator overspeed peaks may occur. These gust measures are then used to develop advanced controllers to mitigate generator overspeed peaks so that wind turbines can operate more robustly in difficult wind conditions without exceeding generator overspeed thresholds that would lead to turbine shutdown events. The advanced controllers are demonstrated in nonlinear aeroelastic simulations using the open‐source wind turbine simulation tool OpenFAST. To increase the realism of the simulations, they are run using field‐replicated wind conditions and a wind turbine model based on data from an experimental field campaign on a downscaled demonstrator of a novel extreme‐scale, two‐bladed, downwind rotor design.

17 WIND ENERGY↗
DOE OSTI2019

Surface and Interface Control in Nanoparticle Catalysis

The surface and interfaces of heterogeneous catalysts are essential to their performance as they are often considered to be active sites for catalytic reactions. With the development of nanoscience, the ability to tune surface and interface of nanostructures has provided a versatile tool for the development and optimization of a heterogeneous catalyst. In this Review, we present the surface and interface control of nanoparticle catalysts in the context of oxygen reduction reaction (ORR), electrochemical CO 2 reduction reaction (CO 2 RR), and tandem catalysis in three sections. In the first section, we start with the activity of ORR on the nanoscale surface and then focus on the approaches to optimize the performance of Pt-based catalyst including using alloying, core-shell structure, and high surface area open structures. In the section of CO 2 RR, where the surface composition of the catalysts plays a dominant role, we cover its reaction fundamentals and the performance of different nanosized metal catalysts. Finally, for tandem catalysis, where adjacent catalytic interfaces in a single nanostructure catalyze sequential reactions, we describe its concept and principle, catalyst synthesis methodology, and application in different reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

SU-E-T-100: Designing a QA Tool for Enhance Dynamic Wedges Based On Dynalog Files

Purpose: A robust quality assurance (QA) program for computer controlled enhanced dynamic wedge (EDW) has been designed and tested. Calculations to perform such QA test is based upon the EDW dynamic log files generated during dose delivery. Methods: Varian record and verify system generates dynamic log (dynalog) files during dynamic dose delivery. The system generated dynalog files contain information such as date and time of treatment, energy, monitor units, wedge orientation, and type of treatment. It also contains the expected calculated segmented treatment tables (STT) and the actual delivered STT for the treatment delivery as a verification record. These files can be used to assess the integrity and precision of the treatment plan delivery. The plans were delivered with a 6 MV beam from a Varian linear accelerator. For available EDW angles (10°, 15°, 20°, 25°, 30°, 45°, and 60°) Varian STT values were used to manually calculate monitor units for each segment. It can also be used to calculate the EDW factors. Independent verification of fractional MUs per segment was performed against those generated from dynalog files. The EDW factors used to calculate MUs in TPS were dosimetrically verified in solid water phantom with semiflex chamber on central axis. Results: EDW factors were generated from the STT provided by Varian and verified against practical measurements. The measurements were in agreement of the order of 1 % to the calculated EDW data. Variation between the MUs per segment obtained from dynalog files and those manually calculated was found to be less than 2%. Conclusion: An efficient and easy tool to perform routine QA procedure of EDW is suggested. The method can be easily implemented in any institution without a need for expensive QA equipment. An error of the order of ≥2% can be easily detected.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Physics-informed machine-learning model of temperature evolution under solid phase processes

We model temperature dynamics during Shear Assisted Proccess Extrusion (ShAPE), a solid phase process that plasticizes feedstock with a rotating tool and subsequently extrudes it into a consolidated tube, rod, or wire. Control of temperature is critical during ShAPE processing to avoid liquefaction, ensure smooth extrusion, and develop desired material properties in the extruded products. Accurate modeling of the complicated thermo-mechanical feedbacks between process inputs, material temperature, and heat generation presents a significant barrier to predictive modeling and process design. In particular, connecting micro-structural scale mechanisms of heat generation to macro-scale predictions of temperature can become computationally intractable. In this work we use a neural network (NN) model of heat generation to bridge this gap, by combining it with a simplified model of the temperature dynamics due to conduction and convection to capture the macro scale evolution of temperature. We inform the construction of the NN heat generation model using crystal plasticity simulations at the micro-structural scale to model the effects of process inputs on generation of heat. We achieved close fits of the temperature dynamics model to a diverse experimental data-set. Further, the relationships learned by the NN model between process inputs and heat generation showed qualitative agreement with those predicted by crystal plasticity simulations.

36 MATERIALS SCIENCE↗