Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “network flow”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,711 records · Page 95
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Highly resolved Eulerian simulations of fuel spray transients in single and multi-hole injectors: Nozzle flow and near-exit dynamics

In high pressure fuel injectors, needle opening and closing transients cause complex off-design fluid dynamics behaviors that profoundly impact the spray and mixture formation processes. These dynamics are completely different from what is known to occur in steady state conditions. In this study, diesel spray transients have been investigated in single-hole and 3-hole nozzles, encompassing internal and external nozzle flow and including needle motion, performing highly resolved (2.5 μm) computational fluid dynamics (CFD) simulations. We focused on end-of-injection (EOI) and start-of-injection (SOI) processes, in order to provide insights in to the physics. The liquid fuel, vapor and gas species are modeled with a single-fluid multiphase mixture approach, with diffuse interface, and with large eddy simulations (LES) of the turbulence. Occurrence of phase change due to cavitation is accounted for, and the spray dispersion is described with a turbulent dispersion model. Detailed needle motion data and orifice internal surface are available from xray synchrotron source measurements carried out at Argonne National Laboratory, and shared through the Engine Combustion Network (ECN) community. Simulations are compared against x-ray phase contrast imaging and radiography of the internal and near-exit flow, in addition to optical microscopy data of the near-exit sprays. Simulation results are found to agree well with available experimental data, and are able to realistically capture local and global features. The simulations allow to gain insight into the physics of gas ingestion and dribbles at EOI, for different hole diameters, operating conditions and number of holes. At SOI, timing of liquid appearance out of the injector and spray tip penetration are adequately predicted, by using the EOI flow field as in-nozzle initialization, and by prescribing the measured tip needle displacement with an informed effective valve opening point inferred from the x-ray observations. Lastly, the variation of spreading angle over time is also discussed in detail for the multi-hole case, including hole-to-hole variations. Due to real geometry features and asymmetric needle motion with eccentric components, it is found that the three holes exhibit swirling flows of increasing intensity as the lift decreases, causing the near cone angle to open and spread, in a quasi-hollow cone structure. These features are not observed in axial single-hole injectors because of their relative simplicity and intrinsic symmetry.

42 ENGINEERING↗
DOE OSTI2019

A Fixed-Flexible BESS Allocation Scheme for Transmission Networks Considering Uncertainties

Battery energy storage systems (BESSs) can play a key role in mitigating the intermittency and uncertainty associated with adding large amounts of renewable energy to the bulk power system (BPS). Two BESS technologies that have gained prominence in this regard are Lithium-ion (LI) BESS and Vanadium redox flow (VRF) BESS. Here, this paper proposes a fixed-flexible BESS allocation scheme that exploits the complementary characteristics of LI and VRF BESSs to attain optimal techno-economic benefits in a wind-integrated BPS. Studies carried out on relatively large transmission networks demonstrate that benefits such as reduction in system operation cost, wind spillage, voltage fluctuations, and discounted payback period, can be realized by using the proposed scheme.

42 ENGINEERING↗
DOE OSTI2020

Distributed Detection of Malicious Attacks on Consensus Algorithms with Applications in Power Networks

Consensus-based distributed algorithms are well suited for coordination among agents in a cyber-physical system. These distributed schemes, however, suffer from their vulnerability to cyber attacks that are aimed at manipulating data and control ow. In this article, we present a novel distributed method for detecting the presence of such intrusions for a distributed multi-agent system following ratio consensus. We employ a Max-Min protocol to develop low cost, easy to implement detection strategies where each participating node detects the intrusion independently, eliminating the need for a trusted certifying agent in the network. The effectiveness of the detection method is demonstrated by numerical simulations on a 1000 node network to demonstrate the efficacy and simplicity of implementation.

27 ARPA - Advanced Research Projects Agency-Energy↗
NASA NTRS2006

Single- and Multiple-Objective Optimization with Differential Evolution and Neural Networks

Genetic and evolutionary algorithms have been applied to solve numerous problems in engineering design where they have been used primarily as optimization procedures. These methods have an advantage over conventional gradient-based search procedures became they are capable of finding global optima of multi-modal functions and searching design spaces with disjoint feasible regions. They are also robust in the presence of noisy data. Another desirable feature of these methods is that they can efficiently use distributed and parallel computing resources since multiple function evaluations (flow simulations in aerodynamics design) can be performed simultaneously and independently on ultiple processors. For these reasons genetic and evolutionary algorithms are being used more frequently in design optimization. Examples include airfoil and wing design and compressor and turbine airfoil design. They are also finding increasing use in multiple-objective and multidisciplinary optimization. This lecture will focus on an evolutionary method that is a relatively new member to the general class of evolutionary methods called differential evolution (DE). This method is easy to use and program and it requires relatively few user-specified constants. These constants are easily determined for a wide class of problems. Fine-tuning the constants will off course yield the solution to the optimization problem at hand more rapidly. DE can be efficiently implemented on parallel computers and can be used for continuous, discrete and mixed discrete/continuous optimization problems. It does not require the objective function to be continuous and is noise tolerant. DE and applications to single and multiple-objective optimization will be included in the presentation and lecture notes. A method for aerodynamic design optimization that is based on neural networks will also be included as a part of this lecture. The method offers advantages over traditional optimization methods. It is more flexible than other methods in dealing with design in the context of both steady and unsteady flows, partial and complete data sets, combined experimental and numerical data, inclusion of various constraints and rules of thumb, and other issues that characterize the aerodynamic design process. Neural networks provide a natural framework within which a succession of numerical solutions of increasing fidelity, incorporating more realistic flow physics, can be represented and utilized for optimization. Neural networks also offer an excellent framework for multiple-objective and multi-disciplinary design optimization. Simulation tools from various disciplines can be integrated within this framework and rapid trade-off studies involving one or many disciplines can be performed. The prospect of combining neural network based optimization methods and evolutionary algorithms to obtain a hybrid method with the best properties of both methods will be included in this presentation. Achieving solution diversity and accurate convergence to the exact Pareto front in multiple objective optimization usually requires a significant computational effort with evolutionary algorithms. In this lecture we will also explore the possibility of using neural networks to obtain estimates of the Pareto optimal front using non-dominated solutions generated by DE as training data. Neural network estimators have the potential advantage of reducing the number of function evaluations required to obtain solution accuracy and diversity, thus reducing cost to design.

Rai, Man Mohan↗
DOE OSTI2015

Artificial Neural Network-Based Monitoring of the Fuel Assembly Temperature Sensor and FPGA Implementation

Numerous methods have been developed around the world to model the dynamic behavior and detect a faulty operating mode of a temperature sensor. In this context, we present in this study a new method based on the dependence between the fuel assembly temperature profile on control rods positions, and the coolant flow rate in a nuclear reactor. This seems to be possible since the insertion of control rods at different axial positions and variations in flow rate of the reactor coolant results in different produced thermal power in the reactor. This is closely linked to the instant fuel rod temperature profile. In a first step, we selected parameters to be used and confirmed the adequate correlation between the chosen parameters and those to be estimated by the proposed monitoring system. In the next step, we acquired and de-noised the data of corresponding parameters, the qualified data is then used to design and train the artificial neural network. The effective data denoising was done by using the wavelet transform to remove a various kind of artifacts such as inherent noise. With the suitable choice of wavelet level and smoothing method, it was possible for us to remove all the non-required artifacts with a view to verify and analyze the considered signal. In our work, several potential mother wavelet functions (Haar, Daubechies, Bi-orthogonal, Reverse Bi-orthogonal, Discrete Meyer and Symlets) were investigated to find the most similar function with the being processed signals. To implement the proposed monitoring system for the fuel rod temperature sensor (03 wire RTD sensor), we used the Bayesian artificial neural network 'BNN' technique to model the dynamic behavior of the considered sensor, the system correlate the estimated values with the measured for the concretization of the proposed system we propose an FPGA (field programmable gate array) implementation. The monitoring system use the correlation. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

FLISR in the Presence of DERs ADMS Test Bed Use Case

Fault location, isolation, and restoration (FLISR) is one of the distribution automation applications that utilities are most interested in. It operates groups of switches on distribution feeders to improve the reliability of power delivery after localizing outages. FLISR is an essential function for enabling a self-healing grid, and it directly affects grid reliability and resilience. The presence of DERs brings both new opportunities and challenges for FLISR applications. For instance, some DERs can work as backup generation sources and help energize local networks so more switching options can be achieved with the coordination of FLISR operations; however, the existence of DERs also brings new challenges. The bidirectional power flow resulting from DERs might require dynamic changes to protective devices' fault current settings, and thus the FLISR application will need to dynamically adjust its operations in coordination with the dynamic protection settings. Also, some FLISR applications make their service restoration decisions and close tie-switches based on predefined rules; however, the presence of DERs and their impact on altering net loads on the local feeder network are not considered. For this use case, the project team is using the FLISR application of the Survalent ADMS operating on a real-time simulation of a feeder from Central Georgia EMC, a cooperative electric utility. The performance of the FLISR application is being evaluated for different locations and combinations of DERs, including grid-forming battery energy storage systems.

ADMS↗
DOE OSTI2021

Performance Testing and Assessment of Protection Scheme Using Real-Time Hardware-in-the-Loop and IEC 61850 Standard

The main challenge of the microgrid is to design a suitable protection scheme due to the complexity of the architecture of the microgrid. The importance of the proposed protection technique is threefold. First, it presents a co-simulation platform to integrate between a simulated model on power system computer aided design (PSCAD)/real time digital simulator computer aided design (RSCAD) software’s and physical devices schweitzer engineering laboratories (SEL) 421-7 relays to protect the microgrid that includes different resources connected based on inverter interface. Second, it presents a comprehensive hardware/software setup to test the protective relays in a closed loop system and shows how to configure the protective relay’s International Electrotechnical Commission 61850 communications. Third, IEEE 1588 standard is used to provide sub nanoseconds latency between the simulated model that emulated on real time digital simulator (RTDS) and the external devices. Also, the measurement signals are synchronized between RTDS and the external devices using giga-transceiver synchronization card (GTSYNC) interface card and SEL-2488 satellite-synchronized network clock. The results showed that the co-simulation infrastructure introduces a highly dependable design, analysis, and testing environment for cyber and physical data flow in the system. Besides that, the voltages at ac/dc sides and frequency at fault condition were maintained due to the energy storage device contributions at different modes of operation.

42 ENGINEERING↗
DOE OSTI2022

An Evaluation of Co-Simulation for Modeling Coupled Natural Gas and Electricity Networks

Reliance on natural gas for power generation has increased the coupling between gas and power networks. While this coupling can bring operational and economic benefits, it can also yield challenges, as the constraints in one system can impact the other. Co-simulation can capture the constraints and interactions between these systems, but so far, there has been limited comparison of co-simulation results to those of an integrated model. In this work, we develop a new co-simulation framework using the HELICS platform and the SAInt tool for modeling transient gas and AC optimal power flow. We evaluate this co-simulation framework against a fully integrated version of the SAInt power and gas simulators, thus providing a benchmarking of the co-simulation approach. We compare results across the two approaches for two test networks and a network representing the Belgian power and gas networks, testing both normal operating conditions and cases with compressor disruptions. In each of the cases tested, we find nearly identical results from the two approaches across various metrics of interest, such as nodal pressure, gas flow rates, and active power generation. This alignment suggests that co-simulation can yield comparable results to fully integrated models for modeling coupled gas and electricity networks.

03 NATURAL GAS↗
DOE OSTI2020

Refueling infrastructure planning in intercity networks considering route choice and travel time delay for mixed fleet of electric and conventional vehicles

The range anxiety has been a major factor that affects the market acceptance of electric vehicles. Even with the recent development of battery technologies, a lack of charging stations and range anxiety are still significant concerns, specifically for intercity trips. This calls for more investments in building charging stations and advancing battery technologies to increase the market share of electric vehicles and improve sustainability. This study suggests a configuration for plug-in electric vehicle charging infrastructure to support long-distance intercity trips of electric vehicles at the network level. A model is proposed to minimize the total system cost including infrastructure investment (building charging stations/spots) and travel time delays (charging time, waiting time in the queue, and detour time to access charging stations). This study fills existing gaps in the literature by capturing realistic patterns of travel demand and considering flow-dependent charging delays at charging stations. Furthermore, the proposed model, which is formulated as a mixed-integer program with nonlinear constraints, solves the optimization problem at the network level. At the network level, impacts of charging station locations on the traffic assignment problem with a mixed fleet of electric and conventional vehicles need to be considered. To this end, a traffic assignment module is integrated with a simulated annealing algorithm. The numerical experiments show a satisfactory application of the model for a full-scale case study (intercity network in Michigan). The solution quality and efficiency of the proposed solution algorithm are evaluated against those of an enumeration approach for a small case study. The results suggest that even for the current market share and charging stations’ setting, a significant investment is needed to support intercity trips without range anxiety issues and with acceptable delays. Additionally, through sensitivity analyses, the required infrastructure and battery investments to support intercity trips with acceptable delays are established for hypothetical increased market shares and battery size in the future.

42 ENGINEERING↗
DOE OSTI2025

Criticality analysis of nuclear binding energy neural networks

Machine learning methods, in particular deep learning methods such as artificial neural networks (ANNs) with many layers, have become widespread and useful tools in nuclear physics. However, these ANNs are typically treated as ‘black boxes’, with their architecture (width, depth, and weight/bias initialization) and the training algorithm and parameters chosen empirically by optimizing learning based on limited exploration. We test a non-empirical approach to understanding and optimizing nuclear physics ANNs by adapting a criticality analysis based on renormalization group flows in terms of the hyperparameters for weight/bias initialization, training rates, and the ratio of depth to width. This treatment utilizes the statistical properties of neural network initialization to find a generating functional for network outputs at any layer, allowing for a path integral formulation of the ANN outputs as a Euclidean statistical field theory. We use a prototypical example to test the applicability of this approach: a simple ANN for nuclear binding energies. We find that with training using a stochastic gradient descent optimizer, the predicted criticality behavior is realized, and optimal performance is found with critical tuning. However, the use of an adaptive learning algorithm leads to somewhat superior results without concern for tuning and thus obscures the analysis. Nevertheless, the criticality analysis offers a way to look within the black box of ANNs, which is a first step towards potential improvements in network performance beyond using adaptive optimizers.

artificial neural network↗
DOE OSTI2018

A Method to Identify Seawater Pycnocline Boundaries Based on LSTM Neural Network and Its Application in Underwater Glider Missions

Underwater glider is a kind of buoyancy driven underwater vehicle; it has the features of low operating noise, long endurance and flexible maneuverability. During the glider long-time navigation, there is a high probability to encounter the pycnocline, which has a great influence on underwater glider’s navigation as well as application. In this work, an operation flow of glider hovering movement in the pycnocline was discussed in detail in order to improve the observation performance of glider. We firstly developed an LSTM (Long Short-Term Memory) neural network to identify the pycnocline boundaries. Then the accurate buoyancy adjustment of glider hovering movement was calculated based on the obtained pycnocline information. Finally a hardware in the loop simulation was implemented to verify the feasibility of the method, and the simulation results showed a reasonable performance of glider hovering in the pycnocline.

Song, Dalei↗
DOE OSTI2020

Creation of a Mixed-Mode Fracture Network at Mesoscale Through Hydraulic Fracturing and Shear Stimulation

Enhanced Geothermal Systems could provide a substantial contribution to the global energy demand if their implementation could overcome inherent challenges. Examples are insufficient created permeability, early thermal breakthrough, and unacceptable induced seismicity. In this study we report on the seismic response of a mesoscale hydraulic fracturing experiment performed at 1.5-km depth at the Sanford Underground Research Facility. We have measured the seismic activity by utilizing a 100-kHz, continuous seismic monitoring system deployed in six 60-m length monitoring boreholes surrounding the experimental domain in 3-D. The achieved location uncertainty was on the order of 1 m and limited by the signal-to-noise ratio of detected events. These uncertainties were corroborated by detections of fracture intersections at the monitoring boreholes. Three intervals of the dedicated injection borehole were hydraulically stimulated by water injection at pressures up to 33 MPa and flow rates up to 5 L/min. We located 1,933 seismic events during several injection periods. The recorded seismicity delineates a complex fracture network comprised of multistrand hydraulic fractures and shear-reactivated, preexisting planes of weakness that grew unilaterally from the point of initiation. We find that heterogeneity of stress dictates the seismic outcome of hydraulic stimulations, even when relying on theoretically well-behaved hydraulic fractures. Once hydraulic fractures intersected boreholes, the boreholes acted as a pressure relief and fracture propagation ceased. In order to create an efficient subsurface heat exchanger, production boreholes should not be drilled before the end of hydraulic stimulations.

58 GEOSCIENCES↗
DOE OSTI2018

Solver for hardware based computing

Full-AC load flow constitutes a core computation in power system analysis. A performance gain with a hardware implementation of a sparse-linear solver using a Field Programmable Gate Array (FPGA) is achieved by use of a DC network emulation of the power system bus. Analog Behavioral Models (ABMs) are used in an efficient strategy for designing analog emulation engines for large-scale power system computation. A generator model is also developed using analog circuits for load flow emulation for power system analysis to reduce computation time. The generator model includes reconfigurable parameters using operational transconductance amplifiers (OTAs). The circuit module is used with other reconfigurable circuits, i.e., transmission lines and loads.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2020

PHALANX: Expendable Projectile Sensor Networks for Planetary Exploration

Technologies enabling long-term, wide-ranging measurement in hard-to-reach areas are a critical need for planetary science inquiry. Phenomena of interest include flows or variations in volatiles, gas composition or concentration, particulate density, or even simply temperature. Improved measurement of these processes enables understanding of exotic geologies and distributions or correlating indicators of trapped water or biological activity. However, such data is often needed in unsafe areas such as caves, lava tubes, or steep ravines not easily reached by current spacecraft and planetary robots. To address this capability gap, we have developed miniaturized, expendable sensors which can be ballistically lobbed from a robotic rover or static lander - or even dropped during a flyover. These projectiles can perform sensing during flight and after anchoring to terrain features. By augmenting exploration systems with these sensors, we can extend situational awareness, perform long-duration monitoring, and reduce utilization of primary mobility resources, all of which are crucial in surface missions. We call the integrated payload that includes a cold gas launcher, smart projectiles, planning software, network discovery, and science sensing: PHALANX. In this paper, we introduce the mission architecture for PHALANX and describe an exploration concept that pairs projectile sensors with a rover “mothership.” Science use cases explored include reconnaissance using ballistic cameras, volatiles detection, and building timelapse maps of temperature and illumination conditions. Strategies to autonomously coordinate constellations of deployed sensors to self-discover and localize with peer ranging (i.e. a “local GPS”) are summarized, thus providing communications infrastructure beyond-line-of-sight (BLOS) of the rover. Capabilities were demonstrated through both simulation and physical testing with a terrestrial prototype. The approach to developing a terrestrial prototype is discussed, including design of the launching mechanism, projectile optimization, micro-electronics fabrication, and sensor selection. Results from early testing and characterization of commercial-off-the-shelf (COTS) components are reported. Nodes were subjected to successful burn-in tests over 48 hours at full logging duty cycle. Integrated field tests were conducted in the Roverscape, a half-acre planetary analog environment at NASA Ames, where we tested up to 10 sensor nodes simultaneously coordinating with an exploration rover. Ranging accuracy has been demonstrated to be within +/-10cm over 20m using commodity radios when compared to high-resolution laser scanner ground truthing. Evolution of the design, including progressive miniaturization of the electronics and iterated modifications of the enclosure housing for streamlining and optimized radio performance are described. Finally, lessons learned to date, gaps toward eventual flight mission implementation, and continuing future development plans are discussed.

Dille, Michael↗
DOE OSTI2019

Refocusing an Employee Engagement Organization to Improve Participation, Member Benefits, and Impact - 19036

Washington River Protection Solutions, LLC (WRPS) provides its employees with programs and opportunities to develop professional skills and to network. Among these is an employee engagement organization called 'WRPSpeed'. WRPSpeed was founded in 2009 initially to support new employees, specifically early-career employees and those new to the Columbia Basin area surrounding the Hanford Site. It was, and remains today, an opt-in program, meaning that in order to join, employees must request to become a member; the only requirement to join is to be a WRPS employee. WRPSpeed has historically offered presentations on professional development topics along with various events and activities supporting professional networking, such as after-work-hours events and community sports teams. Since its creation, WRPSpeed has experienced an ebb and flow of interest, participation, and success. Beginning in early 2017, existing members of the WRPSpeed board examined the stagnant growth trend of the organization and determined change was needed in order to truly accomplish the mission and goals visualized by the board and by WRPS management. These changes culminated in the establishment of a new board position, Marketing and Branding, which allowed WRPSpeed to refocus their messaging and image expressed to the WRPS employee base at large. The primary objectives of the new Marketing and Branding board position were 1) to communicate the identity of WRPSpeed, 2) to communicate the benefits of participation to the WRPS employee base, and 3) to grow WRPSpeed's presence as an active community steward. During the previous years of operation, its brand had developed two main audience perception problems: a perception of exclusivity, which limited membership growth; and a lack of recognition and awareness. Though initially founded as a group for new and early-career employees, the board recognized the benefits of offering all employees the opportunity to participate. This allows more people to engage in the organization and experience the benefits, and also offers the membership and program a more robust network of professional and personal experiences and connections. Concurrent with the launch of Marketing and Branding, the WRPSpeed board reviewed their current functions and activities to determine opportunities for improvement and scale-up. The goal of this internal review was to ensure the benefits offered and advertised by WRPSpeed were high quality, valuable, and engaging. It was up to Marketing and Branding to develop strategies for the success of these efforts and reach all parties in the expanded membership. A marketing communications strategy was developed to establish a revitalized WRPSpeed image and clearly project that new image to a wider audience. The first step was to rewrite the mission statement for the organization, which established, in writing, its primary pillars: community stewardship, professional networking, and professional development. With a new mission statement and the WRPSpeed board in concurrence with - and coached on - the new image, the Marketing and Branding team launched its rebranding campaign. The campaign included a refreshed logo and theme, a new monthly newsletter for existing membership, increased and consistently stylized communications about events, a revitalized organization web page, and a ramp-up schedule for large-scale, multimedia communications to the entire company. From July 2017, when they began, to June 2018, the Marketing and Branding efforts have garnered a 20% increase in membership overall. Additionally, members surveyed in May 2018 showed 49% of respondents had been participating in WRPSpeed between 0 and 12 months; 13% responded '1-2 years'; and 38% responded '2+ years'. These results confirm an upward trend in membership, and attendance records show an increase in new people attending events. Also, parallel increases in number and quality of activities and increases in networking with employee groups from other local companies such as Bechtel National, Inc. and AECOM have improved member satisfaction and positive word of mouth among the employee population. As with any organization, employee engagement-and-retention groups like WRPSpeed face the challenges of staying relevant and beneficial to existing members while continuing to grow their program and impact. However, employee engagement organizations also have the unique opportunity to connect employees across the company and to provide them opportunities otherwise unavailable via other programs. Often these groups also have the flexibility to tailor the offerings, events, and activities to suit the specific needs and aspirations of the employees. With this exceptional position, it is important to continue to strive to offer high quality, significant, and useful benefits to the employees and, thus, the company. The strategies and campaigns employed to revitalize WRPSpeed have proven exceptionally effective in-house and are useful to other employee groups as templates for their own improvement and as opportunities for benchmarking. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems

Abstract Predicting complex dynamics in physical applications governed by partial differential equations in real-time is nearly impossible with traditional numerical simulations due to high computational cost. Neural operators offer a solution by approximating mappings between infinite-dimensional Banach spaces, yet their performance degrades with system size and complexity. We propose an approach for learning neural operators in latent spaces, facilitating real-time predictions for highly nonlinear and multiscale systems on high-dimensional domains. Our method utilizes the deep operator network architecture on a low-dimensional latent space to efficiently approximate underlying operators. Demonstrations on material fracture, fluid flow prediction, and climate modeling highlight superior prediction accuracy and computational efficiency compared to existing methods. Notably, our approach enables approximating large-scale atmospheric flows with millions of degrees, enhancing weather and climate forecasts. Here we show that the proposed approach enables real-time predictions that can facilitate decision-making for a wide range of applications in science and engineering.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Convolutional Neural Network–Aided Temperature Field Reconstruction: An Innovative Method for Advanced Reactor Monitoring

In this study, the capabilities of a physics-informed convolutional neural network (CNN) for reconstructing the temperature field from a limited set of measurements taken at the boundaries of internal flows are demonstrated. Such an approach enables the development of less invasive monitoring methods for real-time plant diagnostics. As a test case, a Molten Salt Fast Reactor (MSFR) design was selected. This circulating fuel reactor has received interest from both scientific and industrial communities due to its intrinsic safety and sustainability. Molten salt flows in such reactors, however, can present highly localized temperature peaks that can induce significant thermal stresses onto the vessel walls. At these local maxima, the salt temperature may exceed a thousand kelvins, which makes a direct measurement challenging or even unfeasible. The proposed CNN algorithm allows one to detect indirectly such discontinuities through an accurate, albeit indirect, temperature measurement method during reactor operation. The datasets employed to train and test the machine learning models in the present work were generated with Nek5000, a computational fluid dynamics (CFD) code developed at Argonne National Laboratory. The CNN algorithm is trained with CFD results that span a set of MSFR operational power and flow ranges. Here, to demonstrate the efficacy of the algorithm, predictions are made for test cases contained within the training range but for which the CFD data were not used when training. Results demonstrate that the proposed technique properly characterizes temperature peaks and distributions within the domain for a broad range of scenarios.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2017

Evaluating the Potential for Remote In-Process Monitoring of Tool Wear in Friction Stir Welding of Stainless Steel

A wear characterization study was performed to determine the useful lifetime of Polycrystalline Cubic Boron Nitride tooling for the friction stir welding (FSW) of stainless steel samples in support of a nuclear repair welding research and development program. In-situ and ex-situ laser profilometry were utilized as primary methods of monitoring tool geometry degradation, and volumetric defects were detected through both non-destructive and destructive techniques, as repeated welds of a standard sample configuration were produced. The spectral content of weld forces were examined to search for indications of evolving material flow conditions, caused by significant tool wear, that would result in the formation of defects, and an artificial neural network was trained and evaluated as a means of automatically detecting these conditions. Here, the resulting performance constituted a successful demonstration of in-process monitoring of tool wear and weld quality in FSW of a high melting temperature, high hardness material.

36 MATERIALS SCIENCE↗