Epistemic Uncertainty-Aware Barlow Twins Reduced Order Modeling for Nonlinear Contact Problems
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42 ENGINEERING↗
SEARCH · Engineering Papers
Results for “twins”
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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Data-driven Modeling for Grid Edge IBRs: A Digital Twin Perspective of User-Defined Models
Recent events in Odessa have brought attention to the challenges associated with the interaction between Inverter- Based Resources (IBRs) and the transmission and distribution system. The NERC event diagnosis report has highlighted sev- eral issues, emphasizing the need for continuous performance monitoring of these IBRs by system operators. Key areas of concern include the mismatch of control and protection perfor- mance of IBRs between the original equipment manufacturer (OEM)-provided models and field measurements. The inability to replicate the realistic response can result in incorrect reliability and resilience studies. In this paper, we developed an approach on how to emulate the behavior of an IBR using measurement data obtained for system operators to utilize in real-time and long- term planning. Two experiments are conducted in the phasor domain and electromagnetic transients (EMT) domain to emulate the behavior for grid forming and grid following inverters under various operating conditions and the effectiveness of the proposed model is demonstrated in terms of accuracy and ease of utilizing user-defined models (UDMs)
Mahapatra, Kaveri [BATTELLE (PACIFIC NW LAB)]↗
Towards an Islanding Detection Method Using a Digital Twin Concept.
Abstract not provided.
Saavedra-Pena, Nelson E.↗
Self-Evolving Digital Twin-Based Online Health Monitoring of Multiphase Boost Converters
Not provided.
Engineering↗
Effects of soybean 7S protein on the quality and digestibility of dry rice noodles under twin‐screw extrusion process
Summary Starch and protein are important components of food, the mixed system formed between them (complexes, gels, mixtures, etc.) can improve the physical and chemical properties of starch and protein, and its potential application in the food industry has also attracted widespread attention. In this study, indica rice flour and soybean 7S protein (β‐Conglycinin) were used as the experimental materials to explore the effect of soybean 7S protein addition on the quality and digestibility of rice noodles. Aims to develop rice noodles with high protein and low GI, so that people can take care of their health while satisfying their appetites. The results showed that with the increase of soybean 7S protein addition, the starch digestibility decreased, resulting in the estimated glycaemic index (eGI) and glycaemic load (GL) decreasing. However, the cooking loss rate and breakage rate of rice noodles gradually increased, the water absorption first increased and then decreased, and the taste of rice noodles is acceptable when the 7S protein addition amount is within 9%. The interaction between starch and soybean 7S protein was mediated by weak interactions such as hydrogen bonds and hydrophobic forces indicated by Fourier transform infrared spectroscopy (FTIR) and X‐ray diffraction (XRD).
Qiao, Fan↗
Synthetic threat injection using digital twin informed augmentation .
Abstract not provided.
Krofcheck, Daniel↗
Twin-free, directly synthesized MFI nanosheets with improved thickness uniformity and their use in membrane fabrication
Zeolite nanosheets can be used for the fabrication of low-defect-density, thin, and oriented zeolite separation membranes. However, methods for manipulating their morphology are limited, hindering progress toward improved performance. We report the direct synthesis (i.e., without using exfoliation, etching, or other top-down processing) of thin, flat MFI nanosheets and demonstrate their use as high-performance membranes for xylene isomer separations. Our MFI nanosheets were synthesized using nanosheet fragments as seeds instead of the previously used MFI nanoparticles. The obtained MFI nanosheets exhibit improved thickness uniformity and are free of rotational and MEL intergrowths as shown by transmission electron microscopy (TEM) imaging. The nanosheets can form well-packed nanosheet coatings. Upon gel-free secondary growth, the obtained zeolite MFI membranes show high separation performance for xylene isomers at elevated temperature (e.g., p-xylene flux up to 1.5 × 10 –3 mol m –2 s –1 and p-/o-xylene separation factor of ~600 at 250°C).
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Athena - Digital Twin and Infrastructure Model [SWR 20-106]
The software provides database access and analysis code for project ATHENA data. In particular, it provides (1) access to the AWS RDS database and (2) supporting code for analysis. Additionally, the software contains all the code related to modeling the Dallas-Fort Worth International Airport (DFW) curbside (CTA modeling) using SUMO simulator. It demonstrates how to install, run and derive useful data from SUMO simulations. We will describe the basic files needed to run a simulation and how those files are generated. The software also has code for generating simulations representing different traffic management policies for DFW and simulating those policies on personal computer or on NREL's Eagle supercomputer. The examples provided are a representation of the DFW airport for curbside dropoff and and pickup.
Severino, Joseph↗
Digital Twin Analytics Search
The purpose of the software is to provide initial machine learning results to an unknown data set.
Jesse, KatherineN. [Idaho National Laboratory (INL↗
1982 The Twin Cities Metropolitan Area Travel Behavior Inventory
The purpose of the 1982 Travel Behavior Inventory was to update socioeconomic and travel data gathered in a 1970 home interview survey. The area in Minnesota studied included the counties of Ramsey, Hennepin, Dakota, Anoka, Washington, Carver, and Scott. The inventory collected demographic and travel data from September 1, 1982, to February 27, 1983. The survey contains data from 2,640 participating households. From participants' travel records, a total of 21,944 trips were reported by 6,059 household members for a 24-hour period.
1Hz data↗
Mechanism of Protein Transport on the Twin Arginine Translocation Pathway
This project was awarded in September 2018 and ran for one year. The overarching goals of this multi-award project was to understand the mechanism and energetics of the Tat protein transport system responsible to the transport of proteins into the chloroplast thylakoid lumen. This pathway is responsible for the delivery of a number of proteins required for chloroplast biogenesis and homeostasis, including subunits of the photosynthetic oxygen-evolving complex and of the three large protein complexes of the photosynthetic electron transport chain. The manner through which proteins are transported on this pathway is highly unusual, and through the research supported by his set of DOE grants are leading to a mechanism of action that is previously unknown for protein trafficking. During the course of this short project we have continued to uncover evidence that the Tat pathway allows proteins to cross membranes through lipid-lined toroidal pores. The experiments undertaken during this project have been continued during the subsequent on-going DOE award period.
59 BASIC BIOLOGICAL SCIENCES↗
Developing Robust Digital Twins and Reinforcement Learning for Accelerator Control Systems at the Fermilab Booster
Explore the source record for details and available documents.
Kafkes, Diana↗
Phasor-Measurement-Unit-Based Data Analytics Using Digital Twin and PhasorAnalytics Software
A major objective of this project was to apply GE’s commercial machine learning and data analytics toolsets to large-scale, real-world, anonymized Phasor Measurement Unit (PMU) datasets in order to extract signatures, correlated and/or causal factors, and precursor patterns associated with significant power system phenomena. The project had a particular emphasis on extraction of insights relevant to asset health monitoring, real-time load modeling and cybersecurity monitoring. Additionally, the team was directed to undertake a comprehensive data quality analysis for the provided datasets and encouraged to estimate the ‘machine-learning readiness’ of the datasets by documenting any major obstacles to the application of commercial machine learning algorithms. To accomplish the aforementioned objectives, the project team’s work centered around the identification of key event signatures and application of the identified event signatures for event detection and event classification. The industry-validated, semi-supervised machine learning strategy employed for event signature identification involved several major tasks, including data-preprocessing, generation of an overabundance of features, normal data identification, normality modeling, and event signature identification through a methodical, quantitative ranking of features in order of relevance to each studied event type. Throughout the project, data quality issues and mitigation techniques were investigated. In this report, insights are provided regarding the readiness of the provided synchrophasor datasets for application of machine learning and data analytics. The methodologies employed for this technical strategy are summarized in this report. With regards to data preprocessing and feature generation, the provided Training and Test Datasets were ingested into GE’s big data environment. Subsequently, the team applied bad data cleansing and data imputation scripts, event detection scripts, and application programming interfaces (APIs) to the datasets for convenient data access. The project team completed development and validation of dozens of physics-based, statistics-based and transformation-based feature functions used for the extraction of over 60 synchrophasor features. Using a new parallel feature generation technology developed on this project, over 60 features have been rapidly generated for the full two years’ worth of Training and Test Dataset data associated with both the Eastern and Western interconnects. Even accommodating for temporal down-sampling inherent to the feature extraction procedure, this parallel feature generation activity resulted in a massive feature set with a storage requirement approximately equal to that of the raw training dataset itself. With regards to normal data identification and normality modeling, a normality model was built using the feature data extracted from the Training Dataset and iteratively refined subsequent to incremental adjustments and expansions of the Training Dataset feature data. With respect to event characterization and signature identification, an event signature identification pipeline was developed and used in conjunction with the normality model to identify over 15 event signatures for key event categories within the Training Dataset. The identified event signatures were used to characterize hundreds of key events in terms of relative severity, duration, and location of the event. An investigation was undertaken to identify correlated and causal factors involved in transformer events. A separate investigation into temporal trends in ring-down analysis results was undertaken to determine possible associations between system dynamics and various other factors such as loading, season or year. To validate the identified event signatures, additional work was undertaken to develop signature-based anomaly detection and classification tools suitable for convenient application to the synchrophasor datasets. The anomaly detection and classification tools, suitable for online application, were then applied to the entirety of the Eastern Interconnect Training and Test Datasets. Performance of the event detection and classification tools was evaluated upon receipt of the Test Dataset event logs (i.e., the labels for events contained in the Test Dataset), and promising results were obtained despite several challenges (documented herein) associated with application of supervised or semi-supervised machine learning methods to large-scale, anonymized datasets. Finally, the detection and classification tools were used to detect, classify, and characterize thousands of new events not included in the original event logs provided by the DOE within both the Training and Test Datasets.
24 POWER TRANSMISSION AND DISTRIBUTION↗
University of Minnesota, Twin Cities Final Scientific/Technical Report Small Scale Ammonia Synthesis Using Stranded Wind Energy
Ammonia absorber columns offer an alternative separation unit to replace condensation in the Haber-Bosch synthesis loop. Metal halide salts can selectively separate ammonia from the reactor outlet gas mixture and incorporate it into their crystal lattice with remarkably high thermodynamic capacity. While the salts’ working capacity can be limited and unstable when they are in their pure form, the capacity is stable and can be high when using a porous ceramic support. In this project, we found optimal conditions for sorbent fabrication, we demonstrated benchmark levels of uptake and release of ammonia, and we fabricated and demonstrated a prototype at a scale of 1 kg/day ammonia. This task allowed us to assess improvement in the techno-economic prospects for this new separation approach, making viable more distributed production of green ammonia from geographically dispersed renewable energy sources.
08 HYDROGEN↗
Increasing Z-Strength and Testing the Capabilities of Twin Screw Extruders in Large Format Polymer Additive Manufacturing
During the first phase of this project, small and large Strangpresse extruders were assessed at Oak Ridge National Laboratory’s Manufacturing Demonstration Facility (MDF). This phase resulted in Strangpresse exclusively licensing some of ORNL’s previously developed extruder technology for high-volume thermoplastic additive manufacturing (AM). Phase II of this project will focus on further development and optimization of Strangpresse extruders for increased efficiency and increased throughput. The objective of this project is to develop a new extruder with a higher output and less energy usage than current AM thermoplastic extruders and to develop and evaluate a new tamper mechanism for increasing Z-strength resulting in an advanced and optimized extruder and tamper system.
42 ENGINEERING↗
Status Report on Regulatory Criteria Applicable to the Use of Digital Twins
This report describes the research results to date to identify regulatory implications of DT technologies and their uses. Specifically, this report reviews current regulatory guidance relevant to the application of predictive maintenance DTs, artificial intelligence (AI), and automation. The focus of this review included determination of constraints on the application of DT technology, identification of any regulatory gaps or uncertainties, and clarification of anticipated technical basis information likely to be important for regulatory acceptance of these technologies. The research included review of topical reports and other submissions to the US Nuclear Regulatory Commission (NRC) on technologies relevant to using DTs for predictive maintenance, NRC safety evaluation (SE) reports, and other relevant literature to identify specific regulatory concerns.
97 MATHEMATICS AND COMPUTING↗