Automated Mechanical Testing System Update
Update on Automated Mechanical Testing system Project
36 MATERIALS SCIENCE↗
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AMTS update for STTR
Advanced nuclear reactor systems currently in development include designs that utilize higher energy neutron spectrums, higher temperatures, and corrosive environments that are quite different from the current fleet of nuclear power plants in use today. As a result, new structural materials must be developed and qualified for use to support these advanced systems. For example, research is currently being conducted on a new class of materials known as multi-principle-element-alloys. These alloys show some promise to be able to meet the structural needs of these challenging reactor systems. However, due to the vast available compositional space, and the limited amount of research conducted thus far, Idaho National Laboratory (INL)sought a way to advanced mechanical testing capability for highly irradiated structural materials to increase both throughput and higher fidelity data analysis through incorporation of digital image correlation. The Automated Mechanical Testing System accomplishes these tasks by automating the testing and incorporating digital image correlation to provide higher fidelity data. INL has now received this system and is currently performing functional testing and training. INL is also performing some modifications to the system to allow for installation into a remote shielded enclosure where testing of irradiated structural materials can be performed. After testing, training, and modifications of the system are complete, INL will install the system into a shielded enclosure and begin using the system to test irradiated structural materials.
36 MATERIALS SCIENCE↗
Enhancing Post-Irradiation Examination (PIE) Efficiency with High-Throughput Experiments
Nuclear materials testing is essential for understanding changes in reactor environments, but traditional methods are costly and time-consuming. High-Throughput Experimentation (HTE) enables rapid screening and optimization of material properties by significantly increasing the number of specimens in the experiment. In this work, HTE methods were employed to irradiate novel materials at the Advanced Test Reactor (ATR) at Idaho National Laboratory. A total of 367 sub-sized tensile specimens were irradiated using the standard capsule design. To facilitate HTE, automated systems such as the Automated Mechanical Testing System were implemented for mechanical testing within hot cell environments, thereby reducing the need for manual interaction with hot cell manipulators and significantly lowering both the cost and duration of experiments. Digital image correlation (DIC) was used to measure displacement during mechanical testing, providing precise, non-contact measurement capabilities. The methodologies developed in this research will support the advancement of HTE techniques for future nuclear materials testing experiments at ATR. This approach allows for more efficient and cost-effective evaluation of new materials, ultimately leading to improved reactor performance, enhanced safety, and extended longevity within the nuclear industry.
22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
Mechanical and Environmental Testing [Slides]
Summary of recent developments for mechanical testing irradiated materials at INL.
11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Development and experimental study of an automated laser-foil-printing additive manufacturing system
Purpose This paper aims to present the development and experimental study of a fully automated system using a novel laser additive manufacturing technology called laser foil printing (LFP), to fabricate metal parts layer by layer. The mechanical properties of parts fabricated with this novel system are compared with those of comparable methodologies to emphasize the suitability of this process. Design/methodology/approach Test specimens and parts with different geometries were fabricated from 304L stainless steel foil using an automated LFP system. The dimensions of the fabricated parts were measured, and the mechanical properties of the test specimens were characterized in terms of mechanical strength and elongation. Findings The properties of parts fabricated with the automated LFP system were compared with those of parts fabricated with the powder bed fusion additive manufacturing methods. The mechanical strength is higher than those of parts fabricated by the laser powder bed fusion and directed energy deposition technologies. Originality/value To the best knowledge of authors, this is the first time a fully automated LFP system has been developed and the properties of its fabricated parts were compared with other additive manufacturing methods for evaluation.
Engineering↗
Advanced Monitoring and Control in the ANL METL Facility Using an Engineering Digital Twin
The potential benefits of using an engineering digital twin to achieve greater autonomy for monitoring and control functions in advanced reactors was investigated for the Mechanisms Engineering Test Loop (METL) liquid sodium facility at Argonne National Laboratory. The METL sodium purification system served as a representative system as it requires significant human in-the-loop interaction to accomplish its design function. The objective was to demonstrate how real-time operation could be automated while preserving oversight of the operator for ensuring that the system design functions are met. A digital twin model of the purification system was developed for both the cold trap purification loop and plugging meter diagnostic loop using information from the METL piping and instrumentation diagram (P&ID). Automated monitoring and diagnosis of component degradation in the METL facility was demonstrated in tests using the PRO-AID health monitoring software with the digital twin model incorporated in the library of components. Component failures were introduced and were successfully diagnosed in real time. These tests serve to demonstrate an advanced monitoring capability able to differentiate sensor degradation from component degradation, to generate a rank ordering of probabilities of different failure mechanisms that serves to circumvent the false alarm problem with current anomaly detection methods, and how facility monitoring can be transformed from anomaly detection to identification of a specific fault. Automated control of the purification system was demonstrated through simulations that exercised a model predictive controller designed using the digital twin model. Results of these simulations compared favorably with experimental data showing very good reference tracking response with negligible overshoot. In conclusion, these pilot tests and simulations successfully demonstrated the use of a digital twin for improved automation of monitoring and control. It was shown how the digital twin enables switching between control modes from cold trap operation where impurities are removed to plugging meter operation where impurity concentrations are measured.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Invite the Chaos Monkey: Test Effect Payloads for Tuning ICS Incident Response
In 2010, when Netflix moved their operational systems to the cloud, they developed a pseudo-random mechanism to cause deliberate outages and failures of key system elements to ensure that automated systems for detecting and remediating outages were mature and fully functional. This Chaos Monkey spawned an entire Simian Army designed to ensure system resilience and enable failure response before issues became outages. Through the DARPA RADICS program, Provatech, INL, and other researchers have developed a mechanism for creating controllable failures via Test Effect Payloads (TEPs) for Industrial Control Systems in order to enable an asset owner to tune network and process sensor technology, increase the effectiveness of incident response and remediation, and highlight areas where resilient technology could bring benefit to Operational Technology. This talk will explore what a TEP is, how to create a TEP, and suggest what a systematic system resilience plan using TEP’s might look like. We will also discuss how regulated entities might employ TEP’s in a compliant resilience strategy.
42 ENGINEERING↗
Development of an Unmanned Mobile Current Turbine Platform: Preprint
The design and development of a prototype unmanned mobile floating platform, equipped with a custom low-flow marine current turbine for autonomously seeking and harnessing tidal/coastal currents is described. The platform is an unmanned surface vehicle in the form of a catamaran with two electric outboard motors and capabilities for autonomous navigation. An undershot water wheel, aided by a custom flow concentrator, has been selected as the basic design for the marine current turbine, which is mounted on the stern of the unmanned surface vehicle platform. The concept of operation is that the platform would navigate to a designated marine current resource, autonomously anchor at the location, align itself in the current and deploy the current turbine using a custom cable-lift deployment mechanism. As the turbine harnesses the local current, an onboard power-take-off device converts the harnessed mechanical energy to electricity which is stored in onboard batteries. Considerations of deployment in tidal and coastal waters required obtaining the necessary environmental permits for conducting in-water testing; developing required mitigation measures in protecting local wildlife and their habitats; and identifying potential in-water test sites and surveying them for their suitability in terms of the current resource, bottom type, water depth, and local boat traffic. The design and development of the turbine and the results of initial in-water testing are discussed. The potential for scaling up the system for extended capacity is presented.
ENGINEERING,TIDAL AND WAVE POWER↗
Evaluation of automated stability testing in machining through closed-loop control and Bayesian machine learning
Here, this paper describes a system for automated identification of the optimal stable cutting parameters in milling through Bayesian machine learning and closed-loop control. The closed-loop control system consists of a process monitoring architecture, an analysis framework, and a feedback mechanism. The analysis framework consists of a Bayesian machine learning algorithm that learns a stability map given test results. The learned stability map is used to select parameters for stability testing using an expected improvement in the material removal rate criterion. The test parameters are communicated to the machine controller to complete the test cut through a feedback mechanism. The test cuts were monitored using an audio signal; the stability of the test cut was determined by analyzing the frequency content of the audio signal. The test result was fed back to the Bayesian learning algorithm to complete the loop. Experimental results demonstrate that the system can identify the optimal stable parameters without information about the cutting force model or the structural dynamics. The system provides a low-cost method for optimal stable parameter identification in an industrial environment.
Chatter↗
Summer 2024 INL Intern Poster Session Submission - Brian Schumitz
This LRS submission is my poster for the INL Intern Poster Session, Summer 2024. Abstract: The Software Engineering and Cybersecurity Lab (SECL) at Montana State University has developed PIQUE, a system for evaluating software quality. PIQUE's adaptability allows for language-specific static-analysis operations, including a model for assessing cloud microservice ecosystems. These ecosystems often rely on Docker for efficient deployment and management of containerized services. Our research focuses on evaluating the network quality within these microservice ecosystems. To automate this process, we're utilizing Snort, an open-source intrusion detection system renowned for its ability to detect and log network traffic. By leveraging Snort's customizable rules, we aim to construct comprehensive testing methods for measuring and quantifying the network quality based on traffic between Docker containers. This research aims to enhance the overall security and reliability of cloud microservice ecosystems by providing automated and robust quality evaluation mechanisms, ultimately contributing to the advancement of software engineering practices in these environments
97 MATHEMATICS AND COMPUTING↗
Automated High‐Throughput Fatigue Testing of Freestanding Thin Films
Abstract Mechanical testing at small length scales has traditionally been resource‐intensive due to difficulties with meticulous sample preparation, exacting load alignments, and precision measurements. Microscale fatigue testing can be particularly challenging due to the time‐intensive, tedious repetition of single fatigue experiments. To mitigate these challenges, this work presents a new methodology for the high‐throughput fatigue testing of thin films at the microscale. This methodology features a microelectromechanical systems‐based Si carrier that can support the simultaneous and independent fatigue testing of an array of samples. To demonstrate this new technique, the microscale fatigue behavior of nanocrystalline Al is efficiently characterized via this Si carrier and automated fatigue testing with in situ scanning electron microscopy. This methodology reduces the total testing time by an order of magnitude, and the high‐throughput fatigue results highlight the stochastic nature of the microscale fatigue response. This manuscript also discusses how this initial capability can be adapted to accommodate more samples, different materials, new geometries, and other loading modes.
Barrios, Alejandro↗
Super Conducting REBCO Cable Wrapper
Future upgrades to CERN particle accelerator magnets require flat, high-temperature superconducting cables, created by wrapping brittle REBCO tape around a Rutherford copper core. Because current manual wrapping processes are time-consuming and prone to damaging the delicate ceramic tape, our team was tasked with automating the fabrication. We developed a modular, wrapping machine featuring adjustment for repeatable 10°to 45° wrap angles and a precise magnetic tensioning mechanism. While full-scale testing is pending final machined parts, our 3D-printed prototype successfully validates the system’s motion and sets the stage for future testing.
Gillen, Kameron [Northern Illinois U.]↗
PVDeg: Enhancing Usability and AI-Driven Multi-Mechanism Degradation Modeling
PVDeg version 0.7.0, released in December 2025, introduced major enhancements to improve usability and performance. This update reorganized tutorials and tool notebooks to create a more intuitive experience, enabling users to easily follow and adapt workflows for their specific analyses. In addition to structural improvements, both the notebooks and core logic underwent significant optimization for efficiency, robustness, and style. These refinements were supported by new testing frameworks built on nbval and pytest, adherence to PEP8 standards, and extensive code refactoring, which collectively simplify onboarding for new developers. Looking ahead, version 0.8.0 will deliver advanced AI-driven capabilities. The primary focus is to further develop and automate the degradation workflow, designed to analyze PV module degradation across diverse locations and system configurations. By integrating large language models (LLMs) to scan literature and compile a comprehensive database of materials and degradation rates, this feature will enable modeling of multiple materials and mechanisms within a single, streamlined workflow. Users will be able to evaluate degradation impacts on different system architectures under varying environmental conditions, facilitating informed decisions on bill-of-materials optimization for specific deployment scenarios. These advancements position PVDeg as a powerful, user-friendly tool for accelerating PV reliability research and system design.
14 SOLAR ENERGY↗
Simple, Secure, Internet Delivery of MOOSE-based Applications
Application packaging and distribution are the final steps for delivering software to end-users; both are frequently neglected when creating scientific software. Commercial businesses rely on electronic distribution systems that have rendered disk drives obsolete. Still, national laboratories continue to rely heavily on removable media to distribute and limit access to controlled applications. With increasing concerns of unauthorized copying of sensitive applications, a modern distribution system that utilizes cryptographically secure communication and authentication protocols has been developed. This new distribution system will secure the chain of custody for nuclear software while simultaneously simplifying access to these tools. This report summarizes four primary advancements made toward the secure distribution of Nuclear Energy Advanced Modeling and Simulation (NEAMS)-developed, Multiphysics Object Oriented Simulation Environment (MOOSE)-based applications: application installation, package distribution, automated package building, and distribution of documentation. NEAMS is currently developing more than ten separate applications based on the open-source MOOSE Framework. Distribution of these applications has primarily been accomplished by distributing source code, with end-users compiling the applications themselves. This work created a mechanism where MOOSE applications can be installed in a similar way to any other software. This allows both administrators and end-users simplified access to runnable executables. With this new installation capability, it was then possible to rethink distribution. A new, secure capability for delivering MOOSE-based applications over the internet has been created. This system requires unique cryptographic tokens for authentication, greatly securing the custody chain for software. Once granted access, installation of any NEAMS code can be accomplished with these terminal commands: "conda install ncrc" "ncrc install ncrc-bison." After these two commands (and authenticating) the BISON application will be securely down- loaded from Idaho National Laboratory (INL)’s servers, installed, and ready to use. To enable this new distribution capability to be successful, the open-source Continuous Integration, Verification, Enhancement, and Testing (CIVET) Continuous Integration (CI) capability was augmented to add Continuous Delivery (CD). CD enables the automated building and packaging of MOOSE-based applications as they are modified by development teams, ensuring that our customers can obtain up-to-date versions of the software at any time. The need for instruction on how to use these applications was addressed through modifications to the MOOSE documentation system. The MooseDocs capability, which enables robust documentation of MOOSE-based applications, has been extended to allow both for the installation of documentation and the packaging of documentation with installed applications. Together, these enhancements form the core of a new, secure distribution mechanism for nuclear simulation tools. In concert with the Nuclear Computational Resource Center (NCRC), NEAMS- developed applications will now be straightforward to obtain securely.
97 MATHEMATICS AND COMPUTING↗
Investigation of Nanoparticle Degradation in Hydrogen Fuel Cell Systems through Automated Electron Microscopy
Proton exchange membrane fuel cells (PEMFC) are promising devices for the deployment of hydrogen-powered heavy-duty vehicles, providing a higher efficiency for similar driving range and fueling time than the existing ones. However, PEMFCs still encounter durability challenges mainly due to catalyst degradation in the cathode. Mitigating these performance losses requires a better understanding of the degradation mechanisms under heavy-duty accelerated stress tests (ASTs) [1]. Scanning transmission electron microscopy (STEM) combined with energy dispersive X-ray spectroscopy (EDS) are key tools for the analysis of Pt and PtCo nanoparticle size, spatial distribution and composition [2]. Electron tomography is also used to determine the rate and type of degradation of catalyst nanoparticles as a function of their position on the carbon support. In this work, automated data acquisition software, paired with a custom Python code, have been used to study the effect of different accelerated stress tests (ASTs) on nanoparticle coarsening [2]. Figure 1 shows high-angle annular dark-field (HAADF)-STEM images and EDS maps comparing the cathodes of membrane electrode assemblies (MEAs) following an electrocatalyst AST performed under H2/N2 with that of the heavy-duty AST performed under H2/air. We will discuss how AST conditions affect considerably the spatial distribution of the nanoparticles across the electrode between the membrane and microporous layer. Although the median particle size increased more in the MEA aged under the heavy-duty AST, as determined using a high-throughput image analysis, the quantitative EDS measurements demonstrate that the electrocatalyst AST resulted in more Pt and Co dissolution from the cathode, which is another important indicator of electrocatalyst degradation. We will further present the impact of the relative humidity (% RH) on the degradation mechanisms demonstrated using the same approach. Electron tomography has been used to distinguish the Pt nanoparticles residing on the carbon support surface (exterior) from those within the pore structure (interior) in order to determine the relative stability of interior and exterior nanoparticles. As shown in Figure 2, we will compare the Pt catalyst particle size at the beginning of test (BOT) and end of test (EOT), and discuss the importance of automating the electron tomography workflow, i.e. acquisition, reconstruction, and visualization, to increase sampling and determine the standard deviation of these measurement. The outlook for utilizing low-dose cryo-tomography for limiting damage to the catalyst, support, and especially proton-conducting ionomer will also be discussed [3].
Amichi, Lynda↗
Bench-Scale Development of a Novel Direct Air Capture Technology Using High-Capacity Structured Sorbents
The work performed under this project has resulted into development of a DAC technology utilizing a structured sorbent to capture CO 2 from ambient air with a key innovation of direct Joule heating of the sorbent for CO 2 desorption. A working SMA, fully integrated with an electrically resistive heating layer, high surface area support, and high CO 2 capacity sorbent coated onto a commercial ceramic monolithic substrate, was successfully developed and demonstrated over >200 adsorption-desorption cycles in a high-fidelity bench test unit directly using ambient air. A cordierite-based monolith was selected as a substrate owing to its high surface area, low bulk density, low heat capacity, and commercial availability. Reaction kinetics study conducted during this project led to development of a promoter for the base Na 2 CO 3 sorbent that could be incorporated into the sorbent to enhance to achieve higher CO 2 adsorption/desorption rates, greater working capacity, and reduced regeneration temperature. An accelerated aging study was conducted in a TGA to determine sorbent stability and no degradation in the sorbent performance was observed even after 250 adsorption-desorption cycles. An electrically resistive heating layer was developed with tunable electrical properties. The heating layer was coated onto the selected cordierite substrate. Aging studies performed showed the electrical properties and heating performance was stable after 500 heating and cooling cycles. The collective findings on the selected cordierite substrate, robust heating layer, promoter and sorbent selection were used to synthesize a full, 6”x6” SMA for bench-scale testing. The bench-scale DAC system was constructed to test full size SMAs using real ambient air for adsorption and joule heating for regeneration. After completing shakedown and commissioning of the 1 kg/day of CO 2 capacity DAC bench unit, an extended operation was performed to complete over 230 cycles with the full size SMA. This testing showed no observable degradation in sorbent performance. A detailed process model, TEA and LCA were developed for a conceptual 100,000 TPY CO 2 removal DAC facility. The LCA results show the net CO 2 e emissions from the DAC system are highly dependent on the electricity source. All other factors, including SMA manufacturing, materials for facility enclosure, etc., are minor cost contributors compared to the energy consumption required for CO 2 removal. With the successful development and validation of the SMA for the sustained performance for CO 2 removal from ambient air with joule heated regeneration in this project, a fully integrated 1 TPY bench-scale DAC system is currently in development with the support of DOE/FECM (DE-FE0032243). The project objective is to demonstrate the engineering design of the DAC system to produce a continuous, high purity CO 2 stream from ambient air. This project will address and validate key engineering features of the DAC system including the gas sealing mechanism and panels, enclosure and air contactor design, and automation sequence to achieve continuous CO 2 production.
42 ENGINEERING↗
Model predictive control for integrated control of air-conditioning and mechanical ventilation, lighting and shading systems
Modern buildings are increasingly automated and often equipped with multiple building services (e.g., air-conditioning and mechanical ventilation (ACMV), dynamic shading, dimmable lighting). These systems are conventionally controlled individually without considering their interactions, affecting the building’s overall energy inefficiency and occupant comfort. A model predictive control (MPC) system that features a multi-objective MPC scheme to enable coordinated control of multiple building services for overall optimized energy efficiency, indoor thermal and visual comfort, as well as a hybrid model for predicting indoor visual comfort and lighting power is proposed. The MPC system was implemented in a test facility having two identical, side-by-side experimental cells to facilitate comparison with a building management system (BMS) employing conventional reactive feedback control. The MPC system coordinated the control of the ACMV, dynamic façade and automated dimmable lighting systems in one cell while the BMS controlled the building services in the other cell in a conventional manner. The MPC side achieved 15.1–20.7% electricity consumption reduction, as compared to the BMS side. Simultaneously, the MPC system improved indoor thermal comfort by maintaining the room within the comfortable range (-0.5 < predicted mean vote < 0.5) for 98.3% of the time, up from 91.8% of the time on the BMS side. Visual comfort, measured by indoor daylight glare probability (DGP) and horizontal illuminance level at work plane height, was maintained for the entire test period on the MPC side, improving from having visual comfort for 94.5% and 85.7% of the time, respectively, on the BMS side.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Power generation forecasting for solar plants based on Dynamic Bayesian networks by fusing multi-source information
A Dynamic Bayesian network (DBN) model for solar power generation forecasting in solar plants is proposed in this paper. The key idea is to fuse sensor data, operational indicators, meteorological data, lagged output power information, and model errors for more accurate short-term (e.g., hours) and mid-term (e.g., days to weeks) power generation forecasting. The proposed DBN augments automated data-driven structure learning with expert knowledge encoding using continuous and categorical data given constraints to represent causal relationships within a solar inverter system. Additionally, an error compensation mechanism is proposed to capture temporal fluctuation. The effectiveness of the DBN on solar power generation forecasting was evaluated by rolling window analysis with one-year testing data collected from a local solar plant. The proposed DBN is compared with four state-of-art methods including support-vector regression (SVR), k-nearest neighbors (kNN), artificial neural network (ANN), and long short-term memory (LSTM) models. The result show that the proposed DBN achieves better accuracy in general, and it is not as data-hungry as some neural network-based models. The proposed DBN is also shown to have robust and consistent forecasting power with different forecasting horizons. The accuracy is 92% - 95% from one hour to one week ahead forecasting.
14 SOLAR ENERGY↗