Automated Threat Information Generation
Manual malware analysis is time consuming and is not scalable. This poster describes tools that INL has developed that automate and codify threat information.
99 GENERAL AND MISCELLANEOUS↗
SEARCH · Engineering Papers
Results for “AUTOMATION”
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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Exploring Microsimulation Process for Energy Impact Evaluation of Connected and Automated Vehicles
In this paper, the authors present a microsimulation-based methodological approach for evaluating the energy impact of connected and automated vehicles (CAVs). They use an open-source micro-simulator, SUMO, and provide a way to set up a simulation environment that emulates real-world traffic dynamics. They also employ the Intelligent Driver Model to represent human drivers and calibrate its driving behavior using real-world traffic data and driving statistics. The authors conduct extensive simulation studies considering different penetration rates of CAVs, different car-following models, and varying car-following model parameters. Using the state-of-the-art Future Automotive System Technology Simulator (FASTSim), they estimate the fuel economy of each vehicle and analyze the energy impact of the given CAV implementation. Finally, the authors analyze the possible factors affecting the simulation results, and also discuss limitations and future work.
ADVANCED PROPULSION SYSTEMS↗
Automated Post-Mold Operations for Wind Blade Manufacturing
Three post-mold operations performed in wind blade manufacturing include: trimming, to remove excess flashing; grinding, to shape the leading edge; and sanding, to prepare the are for bonding of over-lamination or paint. This work focuses on automating these three operations. Each operation scans the blade to build a point cloud, plans a tool path for the operation, and executes the toolpath using an industrial robot arm. The results are analyzed to determine the operational speed and the finish quality.
advanced manufacturing↗
RISMC Industry Application no. 1 (ECCS/LOCA) - Core Characterization Automation: Lattice Codes Interface for PHISICS/RELAP5-3D
The U.S. NRC is currently proposing rulemaking '10 CFR 50.46c' to revise the loss-of-coolant-accident (LOCA) and emergency core cooling system (ECCS) acceptance criteria to include the effects of higher burn-up on cladding performance. The key implications of this proposition are that the cladding performance cannot be evaluated in isolation anymore. Both cladding and ECCS performance need to be considered in a coupled manner. This also implies that models for cladding performance as well as LOCA methodologies will need to be updated for regulatory purposes. Furthermore, efforts need to be made to find the limiting case within the regulatory input space. The Risk-Informed Safety Margin Characterization (RISMC) Pathway, as part of the DOE Light Water Reactor Sustainability (LWRS) research and development program initialed several programs at the Idaho National Laboratory (INL) to support the industry in the transition to the new rule and to offer potential solutions to this LOCA/ECCS analysis. One of these programs is the Industry Application no. 1 (IA1). Its purpose is to provide to the plant owner/operator a vehicle to inform decisions to manage margins related to compliance with the proposed 10 CFR 50.46c rule. IA1's goal is to develop an Integrated Evaluation Model (IEM) to revisit how uncertainties are propagated across the physical disciplines and data involved, as well as well as how risks are evaluated in a LOCA under 10 CFR 50.46c. This IEM is called LOCA Tool-kit for the US (LOTUS) and it connects five major disciplines involved in LOCA analysis. The focus of LOTUS is to establish the automation interfaces among the five disciplines. LOTUS will utilize in a first step existing computer codes. The risk-informed margins management approach for IA1 will provide a means of quantifying the impact on the key LOCA analysis figures-of-merit like peak cladding temperature (PCT), equivalent cladding reacted (ECR), etc. of a change in LOCA analysis inputs. The information that the risk analysis and associated tools provide can then be used for decision-making and margin management. (authors)
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Automated Mechanical Testing System Update
Update on Automated Mechanical Testing system Project
36 MATERIALS SCIENCE↗
Mapping the mechanical properties of rocks using automated microindentation tests
A microindentation scanner is constructed that measures the spatial fluctuation in the elastic properties of natural rocks. This novel instrument performs automated indentation tests on the surface of a rock slab and outputs 2-D maps of the indentation modulus at submillimeter resolution. Maps obtained for clean, well-consolidated, sandstone are presented and demonstrate the capabilities of the instrument. We postulate that the elastic structure of sandstones correlates well with their visual appearance. Moreover, we observe that the probability distribution of the indentation modulus fluctuations across the slab surfaces can be modeled using a lognormal probability density function. To illustrate possible use of the data obtained with the microindentation scanner, we use roughly 10 cm × 10 cm scans with millimeter resolution over four sandstone planar slabs to numerically compute the overall drained elastic moduli for each sandstone sample. We demonstrate that such numerically computed moduli are well modeled using the multicomponent form of the Hashin-Shtrikman lower bound that employs the observed lognormal probability distribution for the mesoscopic-scale moduli (the geometric mean works almost the same). We also compute the seismic attenuation versus frequency associated with wave-induced fluid flow between the heterogeneities in the scanned sandstones and report relatively small values for the inverse quality factor (Q -1 <10 -2 ) in the seismic frequency band 10 2 Hz 4 Hz. The numerically computed frequency dependence in the attenuation varies from one type of sandstone to another, and we determine vital anisotropy in the attenuation associated with waves propagating in different directions.
58 GEOSCIENCES↗
Hypothesis Learning in Automated Experiment: Application to Combinatorial Materials Libraries
Machine learning is rapidly becoming an integral part of experimental physical discovery via automated and high-throughput synthesis, and active experiments in scattering and electron/probe microscopy. This, in turn, necessitates the development of active learning methods capable of exploring relevant parameter spaces with the smallest number of steps. In this work, an active learning approach based on conavigation of the hypothesis and experimental spaces is introduced. This is realized by combining the structured Gaussian processes containing probabilistic models of the possible system's behaviors (hypotheses) with reinforcement learning policy refinement (discovery). This approach closely resembles classical human-driven physical discovery, when several alternative hypotheses realized via models with adjustable parameters are tested during an experiment. This approach is demonstrated for exploring concentration-induced phase transitions in combinatorial libraries of Sm-doped BiFeO 3 using piezoresponse force microscopy, but it is straightforward to extend it to higher-dimensional parameter spaces and more complex physical problems once the experimental workflow and hypothesis generation are available.
36 MATERIALS SCIENCE↗
Performance Prediction of High‐Entropy Perovskites La 0.8 Sr 0.2 Mn x Co y Fe z O 3 with Automated High‐Throughput Characterization of Combinatorial Libraries and Machine Learning
Perovskite oxides form a large family of materials with applications across various fields, owing to their structural and chemical flexibility. Efficient exploration of this extensive compositional space is now achievable through automated high-throughput experimentation combined with machine learning. In this study, we investigate the composition–structure–performance relationships of high-entropy La 0.8 Sr 0.2 Mn x Co y Fe z O 3±𝞭 perovskite oxides (0 < x, y, z <1; x+y+z≈1) for application as oxygen electrodes in Solid Oxide Cells. Following the deposition of a continuous compositional map using thin-film combinatorial pulsed laser deposition, compositional, structural, and performance properties are characterized using six different techniques with mapping capabilities. Random forests effectively model electrochemical performance, consistently identifying Fe-rich oxides as optimal compounds with the lowest area-specific resistance values for oxygen electrodes at 700 °C. Additionally, the models identify a statistical correlation between oxygen sublattice distortion—derived from spectral analysis of Raman-active modes—and enhanced performance.
high entropy oxides↗
Process synthesis using block superstructure with automated flowsheet generation and optimization
An alternative method for chemical process synthesis using a block–based superstructure representation is proposed. The block–based superstructure is a collection of blocks arranged in a two–dimensional grid. The assignment of different equipment on blocks and the determination of their connectivity are performed using a mixed–integer nonlinear formulation for automated flowsheet generation and optimization–based process synthesis. Based on the special structure of the block representation, an efficient strategy is proposed to generate and successively refine feasible and optimized process flowsheets. Our approach is demonstrated using two process synthesis case studies adapted from the literature and one new process synthesis problem for methanol production from biogas.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Utilization of Data Augmentation Techniques in Automated Inspection Systems for Defect Detection in Metals With Limited Data
Accurate identification of defects on metal surfaces is of great interest to many industry sectors, such as the automotive and aerospace industries. In contrast to conventional manual inspection techniques, recent automated inspection systems employ deep learning models trained to detect defects rapidly and precisely. The development of these models often requires a substantial image dataset to acquire adequate knowledge of defect features and enhance their predictive accuracy. When data is limited, augmentation techniques are often used to improve the precision and accuracy of defect detection systems. This study examined the prediction performance of two object detection models, namely Faster Region‐based Convolutional Neural Network (Faster R‐CNN) and You Only Look Once version 8 (YOLOv8), to identify dent defects in limited images of cast iron cylinder head surfaces. The original image set contains 46 images with 563 dents. To overcome limited data availability, common image augmentation techniques along with a copy‐paste method were applied. Results show that standard augmentation improved YOLOv8 accuracy by 8.00% and average precision (AP) by 3.00%. On the other hand, the copy‐paste technique achieved a 20.00% increase in accuracy and a 1% increase in AP with just 200 synthetic dents. Furthermore, these results provide support for using the copy‐paste augmentation strategy to enhance defect detection performance, with a limited dataset, contributing to more accurate defect identification in remanufacturing processes.
36 MATERIALS SCIENCE↗
Automation for Electric Vehicle Battery Pack Disassembly
Battery‐electric vehicles (EVs) are growing exponentially. The demand for these batteries is expected to increase sevenfold by 2035. The EV batteries reach their end of life when the capacity fades to 70%–80% of new, with some being removed from the primary applications with even lower levels of degradation. These batteries can be used in less demanding applications. The disassembly process is currently manual, slow, unsafe, and expensive. Automation is needed to increase the throughput. EV battery packs feature various continually changing designs and form factors, which limit the usefulness of deterministically programmed robotic solutions. The conceptual robotic disassembly of EV batteries has attracted the attention of researchers. However, while many approaches have been proposed, practical implementations are lacking. Here, we review proposed concepts for EV battery disassembly and describe the selected approach, with elements of partial solutions validated in a laboratory setting, including the selection of commercial solutions, the development of custom end effectors, and methodologies for detection, localization, and classification of fasteners. The computer vision tasks employed an overhead 2D camera to detect the type of battery pack and approximate localization of fasteners, and a 3D camera mounted on the robotic arm for precise localization (position and tilt) and classification.
Islam, Abu [Rochester Inst. of Technology, Rochest↗
A Semi‐Automated, High‐Throughput Approach for the Synthesis and Identification of Highly Photo‐Cytotoxic Iridium Complexes
Abstract The discovery of new compounds with pharmacological properties is usually a lengthy, laborious and expensive process. Thus, there is increasing interest in developing workflows that allow for the rapid synthesis and evaluation of libraries of compounds with the aim of identifying leads for further drug development. Herein, we apply combinatorial synthesis to build a library of 90 iridium(III) complexes (81 of which are new) over two synthesise‐and‐test cycles, with the aim of identifying potential agents for photodynamic therapy. We demonstrate the power of this approach by identifying highly active complexes that are well‐tolerated in the dark but display very low nM phototoxicity against cancer cells. To build a detailed structure–activity relationship for this class of compounds we have used density functional theory (DFT) calculations to determine some key electronic parameters and study correlations with the experimental data. Finally, we present an optimised semi‐automated synthesise‐and‐test protocol to obtain multiplex data within 72 hours.
Kench, Timothy↗
Automated Scoring of Morphological Changes in Images of Pentaerythritol Tetranitrate
Recent advances in characterization techniques that generate large datasets of material microstructure images require robust, automated image-processing. We applied an unsupervised anomaly detection method called feature anomaly detection system (FADS) to automatically detect and quantify microstructure changes in images of the explosive pentaerythritol tetranitrate (PETN) aged at various temperatures. We demonstrated the FADS approach on two-dimensional images extracted from computed tomography scans, but the same technique can be readily applied to other imaging modalities. FADS calculates anomaly scores on the basis of differences in filter activations of nominal and test data in pretrained convolutional neural networks. The FADS scores successfully differentiated between pristine PETN and PETN aged at a temperature where material coarsening occurred. Morphological metric analysis of segmented images verified observed trends in FADS scores as a function of aging temperature and aging time, specifically by calculating volume fractions, specific boundary lengths, two-point correlation functions, and local thicknesses. Here, the FADS technique has two important advantages compared to traditional morphological analysis: First, it uses grayscale images as input, rather than images that are segmented to separate the appropriate phases; and second, FADS scores capture any type of changes among image sets, rather than requiring prior knowledge or selection of a relevant set of metrics.
Accelerated aging↗
LipidMiner: A Software for Automated Identification and Quantification of Lipids from Multiple Liquid Chromatography-Mass Spectrometry Data Files
We have developed a tool for automated, high-throughput analysis of LC-MS/MS data files, which greatly simplifies LC-MS based lipidomics analysis. Our results showed that LipidMiner is accurate and comprehensive in identification and quantification of lipid molecular species. In addition, the workflow implemented in LipidMiner is not limited to identification and quantification of lipids. If a suitable metabolite library is implemented in the library matching module, LipidMiner could be reconfigured as a tool for general metabolomics data analysis. It is of note that LipidMiner currently is limited to singly charged ions, although it is adequate for the purpose of lipidomics since lipids are rarely multiply charged,[14] even for the polyphosphoinositides. LipidMiner also only processes file formats generated from mass spectrometers from Thermo, i.e. the .RAW format. In the future, we are planning to accommodate file formats generated by mass spectrometers from other predominant instrument vendors to make this tool more universal.
Environmental Molecular Sciences Laboratory↗
Fully Automated Laser Ablation Liquid Capture Sample Analysis using NanoElectrospray Ionization Mass Spectrometry
RATIONALE: Laser ablation provides for the possibility of sampling a large variety of surfaces with high spatial resolution. This type of sampling when employed in conjunction with liquid capture followed by nanoelectrospray ionization provides the opportunity for sensitive and prolonged interrogation of samples by mass spectrometry as well as the ability to analyze surfaces not amenable to direct liquid extraction. METHODS: A fully automated, reflection geometry, laser ablation liquid capture spot sampling system was achieved by incorporating appropriate laser fiber optics and a focusing lens into a commercially available, liquid extraction surface analysis (LESA ) ready Advion TriVersa NanoMate system. RESULTS: Under optimized conditions about 10% of laser ablated material could be captured in a droplet positioned vertically over the ablation region using the NanoMate robot controlled pipette. The sampling spot size area with this laser ablation liquid capture surface analysis (LA/LCSA) mode of operation (typically about 120 m x 160 m) was approximately 50 times smaller than that achievable by direct liquid extraction using LESA (ca. 1 mm diameter liquid extraction spot). The set-up was successfully applied for the analysis of ink on glass and paper as well as the endogenous components in Alstroemeria Yellow King flower petals. In a second mode of operation with a comparable sampling spot size, termed laser ablation/LESA , the laser system was used to drill through, penetrate, or otherwise expose material beneath a solvent resistant surface. Once drilled, LESA was effective in sampling soluble material exposed at that location on the surface. CONCLUSIONS: Incorporating the capability for different laser ablation liquid capture spot sampling modes of operation into a LESA ready Advion TriVersa NanoMate enhanced the spot sampling spatial resolution of this device and broadened the surface types amenable to analysis to include absorbent and solvent resistant materials.
alstroemeria↗
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↗
In situ Synchrotron X‐ray Metrology Boosted by Automated Data Analysis for Real‐time Monitoring of Cathode Calcination
Abstract Synchrotron X‐ray‐based in situ metrology is advantageous for monitoring the synthesis of battery materials, offering high throughput, high spatial and temporal resolution, and chemical sensitivity. However, the rapid generation of massive data poses a challenge to on‐site, on‐the‐fly analysis needed for real‐time process monitoring. Here, a weighted lagged cross‐correlation (WLCC) similarity approach is presented for automated data analysis, which merges with in situ synchrotron X‐ray diffraction metrology to monitor the calcination process of the archetypal nickel‐based cathode, LiNiO 2 . The WLCC approach, incorporating variables that account for peak shifts and width changes associated with structural transformations, enables rapid extraction of phase progression within 10 seconds from tens of diffraction patterns. Details are captured, from initial precursors to intermediates and the final layered LiNiO 2 , providing information for agile on‐site adjustments during experiments and complementing post hoc diffraction analysis by offering insights into early‐stage phase nucleation and growth. Expanding this data‐powered platform paves the way for real time calcination process monitoring and control, which is pivotal to quality control in battery cathode manufacturing.
36 MATERIALS SCIENCE↗
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↗