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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 OSTI2021

A New MCNP6 Electron-Photon Transport Validation Test: The Lockwood Energy Deposition Experiment (V.1.0)

This memo announces the availability of a new validation test for quantifying the accuracy of the MCNP6 electron-photon transport algorithm for use in energy-deposition calculations. Specifically, energy-deposition results are compared with the Lockwood energy-deposition experiment. The comparison includes energy-deposition profiles in a variety of different single-element materials including beryllium, aluminum, carbon, copper, iron, molybdenum, tantalum, and uranium for pencil beam electron sources with energies including 0.05-, 0.1-, 0.3-, 0.5, and 1-MeV and angles of incidence including normal, 30°, and 60° off-normal. The purpose of this memo is to discuss the contents of the Lockwood validation directory and to outline the procedure for generating the input files, running the tests, processing the results, and comparing results to the experimental and numerical benchmark. Each step is mostly automated by a makefile that executes the necessary perl script.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

A system of shared autonomous vehicles for Chicago: Understanding the effects of geofencing the service

With autonomous vehicles (AVs) still in the testing phase, researchers and planners must resort to simulation techniques to explore possible futures regarding shared and automated mobility. An agent-based discrete-event transport simulator, POLARIS, is used in this study to simulate travel in the 20-county Chicago region with a shared AV (SAV) mobility option. Using this framework, the effect of an SAV fleet on system performance when constrained to serve within geofences is studied under four distinct scenarios: service restricted to the city, to the city plus suburban core, to the core plus exurban areas, and to the entire region — along with the choice of dynamic ridesharing (DRS) versus solo travel in an SAV. Results indicate that service areas need a balanced mix of trip generators and attractors, and an SAV fleet’s empty VMT (eVMT) can be noticeably reduced through suitable geofencing and DRS. Geofences can also help lower response times, reduce systemwide VMT across all modes, and ensure uniform access to SAVs. DRS is most useful in lowering VMT and %eVMT that arises from sprawled land development, but with insufficient demand to share rides, savings from the use of geofences is higher. Geofences targeting neighborhoods with high trip density bring about low response times and %eVMT, but fleet sizes in these regions need to be designed for uniformly low response times throughout a large region, as opposed to maximizing vehicle use in a 24-hour day.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2017

ROBUST ONLINE MONITORING FOR CALIBRATION ASSESSMENT OF TRANSMITTERS AND INSTRUMENTATION

Robust online monitoring (OLM) technologies are expected to enable the extension or elimination of periodic sensor calibration intervals in operating and new reactors. Specifically, the next generation of OLM technology is expected to include newly developed advanced algorithms that improve monitoring of sensor/system performance and enable the use of plant data to derive information that currently cannot be measured. These advances in OLM technologies will improve the safety and reliability of current and planned nuclear power systems through improved accuracy and increased reliability of sensors used to monitor key parameters. In this paper, we discuss an overview of research being performed within the Nuclear Energy Enabling Technologies (NEET)/Advanced Sensors and Instrumentation (ASI) program, for the development of OLM algorithms to use sensor outputs and, in combination with other available information, 1) determine whether one or more sensors are out of calibration or failing and 2) replace a failing sensor with reliable, accurate sensor outputs. Algorithm development is focused on the following OLM functions: • Signal validation – fault detection and selection of acceptance criteria • Virtual sensing – signal value prediction and acceptance criteria • Response-time assessment – fault detection and acceptance criteria selection A GP-based uncertainty quantification (UQ) method previously developed for UQ in OLM, was adapted for use in sensor-fault detection and virtual sensing. For signal validation, the various components to the OLM residual (which is computed using an AAKR model) were explicitly defined and modeled using a GP. Evaluation was conducted using flow loop data from multiple sources. Results using experimental data from laboratory-scale flow loops indicate that the approach, while capable of detecting sensor drift, may be incapable of discriminating between sensor drift and model inadequacy. This may be due to a simplification applied in the initial modeling, where the sensor degradation is assumed to be stationary. In the case of virtual sensors, the GP model was used in a predictive mode to estimate the correct sensor reading for sensors that may have failed. Results have indicated the viability of using this approach for virtual sensing. However, the GP model has proven to be computationally expensive, and so alternative algorithms for virtual sensing are being evaluated. Finally, automated approaches to performing noise analysis for extracting sensor response time were developed. Evaluation of this technique using laboratory-scale data indicates that it compares well with manual techniques previously used for noise analysis. Moreover, the automated and manual approaches for noise analysis also compare well with the current “gold standard”, hydraulic ramp testing, for response time monitoring. Ongoing research in this project is focused on further evaluation of the algorithms, optimization for accuracy and computational efficiency, and integration into a suite of tools for robust OLM that are applicable to monitoring sensor calibration state in nuclear power plants.

calibration interval extension↗
DOE OSTI2023

Field evaluation of semi‐automated moisture estimation from geophysics using machine learning

Geophysical methods can provide three-dimensional (3D), spatially continuous estimates of soil moisture. However, point-to-point comparisons of geophysical properties to measure soil moisture data are frequently unsatisfactory, resulting in geophysics being used for qualitative purposes only. This is because (1) geophysics requires models that relate geophysical signals to soil moisture, (2) geophysical methods have potential uncertainties resulting from smoothing and artifacts introduced from processing and inversion, and (3) results from multiple geophysical methods are not easily combined within a single soil moisture estimation framework. To investigate these potential limitations, an irrigation experiment was performed wherein soil moisture was monitored through time, and several surface geophysical datasets indirectly sensitive to soil moisture were collected before and after irrigation: ground penetrating radar, electrical resistivity tomography (ERT), and frequency domain electromagnetics (FDEM). Data were exported in both raw and processed form, and then snapped to a common 3D grid to facilitate moisture prediction by standard calibration techniques, multivariate regression, and machine learning. A combination of inverted ERT data, raw FDEM, and inverted FDEM data was most informative for predicting soil moisture using a random regression forest model (one-thousand 60/40 training/test cross-validation folds produced root mean squared errors ranging from 0.025–0.046 cm 3 /cm 3 ). This cross-validated model was further supported by a separate evaluation using a test set from a physically separate portion of the study area. Machine learning was conducive to a semi-automated model-selection process that could be used for other sites and datasets to locally improve accuracy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

The EB factory project. II. Validation with the Kepler field in preparation for K2 and TESS

Large repositories of high precision light curve data, such as the Kepler data set, provide the opportunity to identify astrophysically important eclipsing binary (EB) systems in large quantities. However, the rate of classical “by eye” human analysis restricts complete and efficient mining of EBs from these data using classical techniques. To prepare for mining EBs from the upcoming K2 mission as well as other current missions, we developed an automated end-to-end computational pipeline—the Eclipsing Binary Factory (EBF)—that automatically identifies EBs and classifies them into morphological types. The EBF has been previously tested on ground-based light curves. To assess the performance of the EBF in the context of space-based data, we apply the EBF to the full set of light curves in the Kepler “Q3” Data Release. We compare the EBs identified from this automated approach against the human generated Kepler EB Catalog of ∼2600 EBs. When we require EB classification with ⩾90% confidence, we find that the EBF correctly identifies and classifies eclipsing contact (EC), eclipsing semi-detached (ESD), and eclipsing detached (ED) systems with a false positive rate of only 4%, 4%, and 8%, while complete to 64%, 46%, and 32%, respectively. When classification confidence is relaxed, the EBF identifies and classifies ECs, ESDs, and EDs with a slightly higher false positive rate of 6%, 16%, and 8%, while much more complete to 86%, 74%, and 62%, respectively. Through our processing of the entire Kepler “Q3” data set, we also identify 68 new candidate EBs that may have been missed by the human generated Kepler EB Catalog. We discuss the EBF's potential application to light curve classification for periodic variable stars more generally for current and upcoming surveys like K2 and the Transiting Exoplanet Survey Satellite.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

A reusable neural network pipeline for unidirectional fiber segmentation

Abstract Fiber-reinforced ceramic-matrix composites are advanced, temperature resistant materials with applications in aerospace engineering. Their analysis involves the detection and separation of fibers, embedded in a fiber bed, from an imaged sample. Currently, this is mostly done using semi-supervised techniques. Here, we present an open, automated computational pipeline to detect fibers from a tomographically reconstructed X-ray volume. We apply our pipeline to a non-trivial dataset by Larson et al . To separate the fibers in these samples, we tested four different architectures of convolutional neural networks. When comparing our neural network approach to a semi-supervised one, we obtained Dice and Matthews coefficients reaching up to 98%, showing that these automated approaches can match human-supervised methods, in some cases separating fibers that human-curated algorithms could not find. The software written for this project is open source, released under a permissive license, and can be freely adapted and re-used in other domains.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

An automated approach to the alignment of compound refractive lenses

Compound refractive lenses (CRLs) are established X-ray focusing optics, and are used to focus the beam or image the sample in many beamlines at X-ray facilities. While CRLs are quite established, the stack of single lens elements affords a very small numerical aperture because of the thick lens profile, making them far more difficult to align than classical optical lenses that obey the thin-lens approximation. This means that the alignment must be very precise and is highly sensitive to changes to the incident beam, often requiring regular readjustments. Some groups circumvent the full realignment procedure by using engineering controls ( e.g. mounting optics) that sacrifice some of the beam's focusing precision, i.e. spot size, or resolution. While these choices minimize setup time, there are clear disadvantages. This work presents a new automated approach to align CRLs using a simple alignment apparatus that is easy to adapt and install at different types of X-ray experiments or facilities. This approach builds on recent CRL modeling efforts, using an approach based on the Stochastic Nelder–Mead (SNM) simplex method. This method is outlined and its efficacy is demonstrated with numerical simulation that is tested in real experiments conducted at the Advanced Photon Source to confirm its performance with a synchrotron beam. This work provides an opportunity to automate key instrumentation at X-ray facilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Intelligent Hierarchical Resilient Operation of Distribution Systems: Implementation and Validation in a Power Hardware-in-the-Loop Simulation Testbed

This paper reports on the structure of a power hardware-in-the-loop (PHIL) simulation testbed that implements, tests, and validates a novel AI-based hierarchical resilient operation model for distribution systems. The testbed implements the central and distributed controllers of the hierarchical resilient operation model and integrates a Digital Real-Time Simulator (DRTS), protective relays, a Real-Time Automation Controller (RTAC), a Software Defined Network (SDN) switch, and a battery energy storage (BES) system. The testbed provides comprehensive real-time visualization and monitoring capability as an advanced situational awareness and operator interface solution. The IEEE 33-node system is used as a test case to test and validate the operation of the model in normal operation and recovery operation after major outages in a fully automated fashion.

Ganjkhani, Mehdi↗
DOE OSTI2016

TH-AB-201-07: Filmless Treatment Localization QA for the CyberKnife System

Purpose: Accuray recommends daily evaluation of the treatment localization and delivery systems (TLS/TDS) of the CyberKnife. The vendor-provided solution is a Winston-Lutz-type test that evaluates film shadows from an orthogonal beam pair (known as AQA). Since film-based techniques are inherently inefficient and potentially inconsistent and uncertain, this study explores a method which provides a comparable test with greater efficiency, consistency, and certainty. This test uses the QAStereoChecker (QASC, Standard Imaging, Inc., Middleton, WI), a high-resolution flat-panel detector with coupled fiducial markers for automated alignment. Fiducial tracking is used to achieve high translational and rotational position accuracy. Methods: A plan is generated delivering five circular beams, with varying orientation and angular incidence. Several numeric quantities are calculated for each beam: eccentricity, centroid location, area, major-axis length, minor-axis length, and orientation angle. Baseline values were acquired and repeatability of baselines analyzed. Next, errors were induced in the path calibration of the CK, and the test repeated. A correlative study was performed between the induced errors and quantities measured using the QASC. Based on vendor recommendations, this test should be able to detect a TLS/TDS offset of 0.5mm. Results: Centroid shifts correlated well with induced plane-perpendicular offsets (p < 0.01). Induced vertical shifts correlated best with the absolute average deviation of eccentricities (p < 0.05). The values of these metrics which correlated with the threshold of 0.5mm induced deviation were used as individual pass/fail criteria. These were then used to evaluate induced offsets which shifted the CK in all axes (a clinically-realistic offset), with a total offset of 0.5mm. This test provided high and specificity and sensitivity. Conclusion: From setup to analysis, this filmless TLS/TDS test requires 4 minutes, as opposed to 15–20 minutes for film-based methods. The techniques introduced can potentially isolate errors in individual joints of the CK robot. Spectrum Medical Physics, LLC of Greenville, SC has a consulting contract with Standard Imaging of Middleton, WI.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

SU-E-J-126: An Online Replanning Method for FFF Beams Without Couch Shift

Purpose: In a situation that couch shift for patient positioning is not preferred or prohibited (e.g., MR-Linac), segment aperture morphing (SAM) can address target dislocation and deformation. For IMRT/VMAT with flattening filter free (FFF) beams, however, SAM method would lead to an adverse translational dose effect due to the beam unflattening. Here we propose a new 2-step process to address both the translational effect of FFF beams and the target deformation. Methods: The replanning method consists of an offline and an online steps. The offline step is to create a series of pre-shifted plans (PSP) obtained by a so called “warm start” optimization (starting optimization from the original plan, rather from scratch) at a series of isocenter shifts with fixed distance (e.g. 2 cm, at x,y,z = 2,0,0 ; 2,2,0 ; 0,2,0; …;− 2,0,0). The PSPs all have the same number of segments with very similar shapes, since the warm-start optimization only adjusts the MLC positions instead of regenerating them. In the online step, a new plan is obtained by linearly interpolating the MLC positions and the monitor units of the closest PSPs for the shift determined from the image of the day. This two-step process is completely automated, and instantaneously fast (no optimization or dose calculation needed). The previously-developed SAM algorithm is then applied for daily deformation. We tested the method on sample prostate and pancreas cases. Results: The two-step interpolation method can account for the adverse dose effects from FFF beams, while SAM corrects for the target deformation. The whole process takes the same time as the previously reported SAM process (5–10 min). Conclusion: The new two-step method plus SAM can address both the translation effects of FFF beams and target deformation, and can be executed in full automation requiring no additional time from the SAM process. This research was supported by Elekta inc. (Crawley, UK)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Mercury Speciation via Direct Mercury Analyzer

Mercury has no known biological role, but is present in every living thing and widespread in the environment. As mercury enters an aqueous system, it is subject to methylation or demethylation. Methylmercury selectively partitions into biota and biomagnifies through the food chain. Nearly all methylmercury exposures in the U.S. occur through eating fish and shellfish that contain higher levels of methylmercury. Methyl mercury and other organomercury compounds are more toxic than inorganic mercury. Thus, monitoring and understanding mercury speciation are key to effective environmental management. It is particularly important to be able to separate and measure bioaccumulative-toxic species of mercury such as methyl mercury. We are developing a protocol for utilizing the Direct Mercury Analyzer (DMA-80) for mercury speciation of water samples and to monitor methylmercury (meHg). Objectives: - Advance the understanding of the performance of the direct mercury analysis (DMA) under a range of DOE relevant water chemistry conditions to quantify total mercury: - Demonstrate the applicability of DMA to analysis of typical DOE high level liquid 'tank' waste samples using nonradioactive simulants; Test innovative sorbents to extend the range of the direct mercury analyzer to environmental concentration levels. - Perform scoping tests to demonstrate proof of principle for the chemical reduction amalgamation modular microcolumns for mercury speciation: Examine various microcolumn configurations and document speciation performance for typical DOE high level liquid 'tank' waste samples using nonradioactive simulants; Perform tests to examine extending the speciation paradigm to environmental concentration levels. General Method: all samples analyzed using a direct mercury analyzer (DMA 80) which automates the following steps: 1) pyrolysis to release the mercury, 2) catalysis to convert all mercury to elemental, 3) amalgamation to trap the mercury, 4) desorption to release the mercury as a pulse and 5) spectroscopy to detect the mercury.. Instrument analyzes all mercury species in solids and liquids. The DMA 'eliminates' sample preparation, reduces labor, decreases waste, and results in more practical and cost effective methods. To test the performance of the DMA for DOE relevant conditions, we analyzed samples representative of at high (mg/L) tank waste concentrations in water and in diluted tank waste simulant and analyzed samples representative of environmental (ng/L levels) in water. High concentration samples were analyzed directly in the instrument. Low concentration samples were analyzed by concentrating the mercury onto sorbents such as silica thiol or a mixture of silica thiol and copper. The concentration was performed using microcolumns and placing the columns into the DMA (see below) or by mixing the sorbent into the bulk sample and then filtering ad analyzing the filter in the DMA. Speciation Methods: The speciation tests were based on the selective reduction of inorganic mercury ions - Hg{sup (+} {sup or} {sup 2+)} - to elemental mercury - Hg(0). The elemental mercury is then trapped by amalgamation into the metal. These reactions do not impact methyl mercury allowing for separation. All reactions were performed using powdered elemental copper. To demonstrate proof-of-principle for the selective collection of inorganic mercury, samples were exposed to different amounts of powdered copper (and a control with no copper). This was done using microcolumns and a vacuum box. The columns were placed directly on the DMA for analysis. All materials and conditions were tested using solutions of both inorganic mercury and methyl mercury and all trials were in triplicate. Results and Observations: Validated DMA for high concentration Hg samples such as DOE tank waste: - Liquid inHg - samples were stable on autosampler carousel and results were accurate; - Liquid meHg - samples required a stabilizer. Si-Thiol (0.02 g) in the sample boat generated reliable meHg results. Stabilizer can be added for routine operations. Successful 'proof of principal' in extending DMA range to near environmental levels - using both microcolumns and filtration; - Filtration - Adding Si-Thiol (0.5 g) to sample extracted both inHg and meHg and preserved sample. Handling filters was labor intensive. - Microcolumns - best performance using a combination of copper and Si-Thiol - columns clogged and flowed slowly in some cases; - Practical limit may be in the range of 1 to 5 ng/L due to blanks; - Path Forward - Test additional packing materials and methods to improve flow and consistency, and approaches to minimize blanks. Successful 'proof of principal' for Hg speciation using chemical reduction and amalgamation; - Copper microcolumns (0.5 g Cu) selectively trapped inHg for high concentration mercury samples; - Copper microcolumns (0.5 g Cu selectively trapped inHg for large volume samples at environmental concentrations; - Some sorption of me Hg so that separations not quantitative; - Path Forward - test additional packing materials and alternate eluents to better trap inHg and minimize meHg retention in column.

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

UNIVERSAL AUTO-CALIBRATION FOR A RAPID BATTERY IMPEDANCE SPECTRUM MEASUREMENT DEVICE

Electrochemical impedance spectroscopy has been shown to be a valuable tool for diagnostics and prognostics of energy storage devices such as batteries and ultra-capacitors. Although measurements have been typically confined to laboratory environments, rapid impedance spectrum measurement techniques have been developed for on-line, embedded applications as well. The prototype hardware for the rapid technique has been validated using lithium-ion batteries, but issues with calibration had also been identified. A new, universal automatic calibration technique was developed to address the identified issues while also enabling a more simplified approach. A single, broad-frequency range is used to calibrate the system and then scaled to the actual range and conditions used when measuring a device under test. The range used for calibration must be broad relative to the expected measurement conditions for the scaling to be successful. Validation studies were performed by comparing the universal calibration approach with data acquired from targeted calibration ranges based on the expected range of performance for the device under test. First, a mid-level shunt range was used for calibration and used to measure devices with lower and higher impedance. Next, a high excitation current level was used for calibration, followed by measurements using lower currents. Finally, calibration was performed over a wide frequency range and used to measure test articles with a lower set of frequencies. In all cases, the universal calibration approach compared very well with results acquired following a targeted calibration. Additionally, the shunts used for the automated calibration technique were successfully characterized such that the rapid impedance measurements compare very well with laboratory-scale measurements. These data indicate that the universal approach can be successfully used for onboard rapid impedance spectra measurements for a broad set of test devices and range of measurement conditions.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Towards Unlocking Insights from Logbooks Using AI

Electronic logbooks contain valuable information about activities and events concerning their associated particle accelerator facilities. However, the highly technical nature of logbook entries can hinder their usability and automation. As natural language processing (NLP) continues advancing, it offers opportunities to address various challenges that logbooks present. This work explores jointly testing a tailored Retrieval Augmented Generation (RAG) model for enhancing the usability of particle accelerator logbooks at institutes like DESY, BESSY, Fermilab, BNL, SLAC, LBNL, and CERN. The RAG model uses a corpus built on logbook contributions and aims to unlock insights from these logbooks by leveraging retrieval over facility datasets, including discussion about potential multimodal sources. Our goals are to increase the FAIR-ness (findability, accessibility, interoperability, and reusability) of logbooks by exploiting their information content to streamline everyday use, enable macro-analysis for root cause analysis, and facilitate problem-solving automation.

43 PARTICLE ACCELERATORS↗
DOE OSTI2019

An Automated Vehicle Fuel Economy Benefits Evaluation Framework Using Real-World Travel and Traffic Data

Increasing automation is a consistent development trend in the automobile industry. However, real-world evaluation of the operational and energy consumption differences between automated vehicles and comparable manually driven vehicles has been limited. This study helps fill the information gap by comparing the operation and fuel economy of vehicles in adaptive cruise control (ACC) and non-ACC modes based on large-scale field test data collected by Volvo Car Corporation (Volvo Cars) from vehicles traveling on the designated 'Drive Me' project road network in Gothenburg, Sweden. The test vehicles' travel data are classified by driving mode (ACC vs. non-ACC) and driving conditions, which refer to traffic speed and road grade in this study. The results from the data logging fleet are used to estimate the aggregate fuel consumption differences at the Drive Me road-network level for vehicles traveling in ACC vs. non-ACC mode based on appropriately weighting the total amount of travel that took place on the network under different driving conditions. At the ACC penetration levels observed in the field test data, vehicles tended to drive more smoothly in ACC mode than in non-ACC mode. The corresponding travel-weighted fuel consumption rate for vehicles in ACC mode was about 5%-7% lower than for vehicles in non-ACC mode when traveling at similar conditions. Sensitivity analyses impart confidence in this result, and in the future, the established evaluation framework could be used to objectively quantify potential on-road fuel consumption impacts from vehicles with even higher levels of automated driving capability.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2014

Poster — Thur Eve — 40: Automated Quality Assurance for Remote-Afterloading High Dose Rate Brachytherapy

High dose rate (HDR) remote afterloading brachytherapy involves sending a small, high-activity radioactive source attached to a cable to different positions within a hollow applicator implanted in the patient. It is critical that the source position within the applicator and the dwell time of the source are accurate. Daily quality assurance (QA) tests of the positional and dwell time accuracy are essential to ensure that the accuracy of the remote afterloader is not compromised prior to patient treatment. Our centre has developed an automated, video-based QA system for HDR brachytherapy that is dramatically superior to existing diode or film QA solutions in terms of cost, objectivity, positional accuracy, with additional functionalities such as being able to determine source dwell time and transit time of the source. In our system, a video is taken of the brachytherapy source as it is sent out through a position check ruler, with the source visible through a clear window. Using a proprietary image analysis algorithm, the source position is determined with respect to time as it moves to different positions along the check ruler. The total material cost of the video-based system was under $20, consisting of a commercial webcam and adjustable stand. The accuracy of the position measurement is ±0.2 mm, and the time resolution is 30 msec. Additionally, our system is capable of robustly verifying the source transit time and velocity (a test required by the AAPM and CPQR recommendations), which is currently difficult to perform accurately.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2020

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↗
DOE OSTI2021

Serial crystallography using automated drop dispensing

Automated, pulsed liquid-phase sample delivery has the potential to greatly improve the efficiency of both sample and photon use at pulsed X-ray facilities. In this work, an automated drop on demand (DOD) system that accelerates sample exchange for serial femtosecond crystallography (SFX) is demonstrated. Four different protein crystal slurries were tested, and this technique is further improved here with an automatic sample-cycling system whose effectiveness was verified by the indexing results. Here, high-throughput SFX screening is shown to be possible at free-electron laser facilities with very low risk of cross contamination and minimal downtime. The development of this technique will significantly reduce sample consumption and enable structure determination of proteins that are difficult to crystallize in large quantities. This work also lays the foundation for automating sample delivery.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Towards Unlocking Insights from Logbooks Using AI

Electronic logbooks contain valuable information about activities and events concerning their associated particle accelerator facilities. However, the highly technical nature of logbook entries can hinder their usability and automation. As natural language processing (NLP) continues advancing, it offers opportunities to address various challenges that logbooks present. This work explores jointly testing a tailored Retrieval Augmented Generation (RAG) model for enhancing the usability of particle accelerator logbooks at institutes like DESY, BESSY, Fermilab, BNL, SLAC, LBNL and CERN. The RAG model uses a corpus built on logbook contributions and aims to unlock insights from these logbooks by leveraging retrieval over facility datasets, including discussion about potential multimodal sources. Our goals are to increase the FAIR-ness (findability, accessibility, interoperability, and reusability) of logbooks by exploiting their information content to streamline everyday use, to enable macro-analysis for root cause analysis, and to facilitate problem-solving automation.

43 PARTICLE ACCELERATORS↗