Knowledge-Based Autonomous Service Provisioning in Multi-Domain Elastic Optical Networks
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Engineering↗
SEARCH · Engineering Papers
Results for “knowledge engineering”
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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Rational Optimization of Microbial Processing for High Yield CO 2 -to-Isopropanol Conversion: Cooperative Research and Development Final Report, CRADA Number CRD-20-17114
This project focuses on the production of the fuel blendstock isopropanol using a CO 2 -fixing Clostridium by metabolic engineering and process optimizations. The project will initiate from a baseline isopropanol producer and pursue isopropanol production at high carbon-conversion efficiency. We will lead engineering work by in-depth pathway analyses including thermodynamics optimization, enzyme expense analysis, metabolic robustness analysis, and -omics analysis. The isopropanol production will be optimized via genome editing followed by fermentation optimizations. This project will deliver a novel microbial process that efficiently converts waste CO 2 to isopropanol at ~g/L titer level within 18-months. This project will layout a solid knowledge basis and technology platform for renewable CO 2 valorization to bio-blendstock that help achieve Co-Optima and Shell's goals.
09 BIOMASS FUELS↗
Suppressing simulation bias in multi-modal data using transfer learning
Abstract Many problems in science and engineering require making predictions based on few observations. To build a robust predictive model, these sparse data may need to be augmented with simulated data, especially when the design space is multi-dimensional. Simulations, however, often suffer from an inherent bias. Estimation of this bias may be poorly constrained not only because of data sparsity, but also because traditional predictive models fit only one type of observed outputs, such as scalars or images, instead of all available output data modalities, which might have been acquired and simulated at great cost. To break this limitation and open up the path for multi-modal calibration, we propose to combine a novel, transfer learning technique for suppressing the bias with recent developments in deep learning, which allow building predictive models with multi-modal outputs. First, we train an initial neural network model on simulated data to learn important correlations between different output modalities and between simulation inputs and outputs. Then, the model is partially retrained, or transfer learned, to fit the experiments; a method that has never been implemented in this type of architecture. Using fewer than 10 inertial confinement fusion experiments for training, transfer learning systematically improves the simulation predictions while a simple output calibration, which we design as a baseline, makes the predictions worse. We also offer extensive cross-validation with real and carefully designed synthetic data. The method described in this paper can be applied to a wide range of problems that require transferring knowledge from simulations to the domain of experiments.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Correlating Time-Resolved Pressure Measurements With Rim Sealing Effectiveness for Real-Time Turbine Health Monitoring
Purge flow is bled from the upstream compressor and supplied to the under-platform region to prevent hot main gas path ingress that damages vulnerable under-platform hardware components. A majority of turbine rim seal research has sought to identify methods of improving sealing technologies and understanding the physical mechanisms that drive ingress. While these studies directly support the design and analysis of advanced rim seal geometries and purge flow systems, the studies are limited in their applicability to real-time monitoring required for condition-based operation and maintenance. As operational hours increase for in-service engines, this lack of rim seal performance feedback results in progressive degradation of sealing effectiveness, thereby leading to reduced hardware life. To address this need for rim seal performance monitoring, the present study utilizes measurements from a one-stage turbine research facility operating with true-scale engine hardware at engine-relevant conditions. Time-resolved pressure measurements collected from the rim seal region are regressed with sealing effectiveness through the use of common machine learning techniques to provide real-time feedback of sealing effectiveness. Two modelling approaches are presented that use a single sensor to predict sealing effectiveness accurately over a range of two turbine operating conditions. Results show that an initial purely data-driven model can be further improved using domain knowledge of relevant turbine operations, which yields sealing effectiveness predictions within three percent of measured values.
Compressors↗
Automated identification of dominant physical processes
The identification of processes that locally and approximately dominate dynamical system behavior has enabled significant advances in understanding and modeling nonlinear differential dynamical systems. Conventional methods of dominant process identification involve piecemeal and ad hoc (non-rigorous, informal) scaling analyses to identify dominant balances of governing equation terms and to delineate the spatiotemporal boundaries (boundaries in space and/or time) of each dominant balance. For the first time, we present an objective global measure of the fit of dominant balances to observations, which is desirable for automation, and was previously undefined. Furthermore, we propose a formal definition of the dominant balance identification problem in the form of an optimization problem. Here, we show that the optimization can be performed by various machine learning algorithms, enabling the automatic identification of dominant balances. Our method is algorithm agnostic and it eliminates reliance upon expert knowledge to identify dominant balances which are not known beforehand.
42 ENGINEERING↗
Transfer Learning-Based Independent Component Analysis
Understanding the underlying component structure is crucial for multivariate signal analysis. Among all the techniques that try to learn the latent structure, independent component analysis (ICA) is one of the most important and popular methods, which aims to extract independent components from multivariate signals and enables further analysis. For example, in electroencephalogram (EEG) analysis, artifacts filtering and disease detection are conducted based on the independent components of the signals. One critical challenge in existing ICA approaches is that the component extraction accuracy may degrade when the available data of a unit are limited. To address this issue, this paper proposes a transfer learning-based ICA method by innovatively transferring component distribution from a source domain, so that accurate component extraction results can be achieved even when only limited data are available in the target domain. To the best of our knowledge, this is the first work that leverages transfer learning to improve ICA accuracy with limited available data. In particular, we first extract all the independent components from the source domain by maximizing the log-likelihood function with a Newton-like method on a smooth manifold. Then for the target domain, the component with the largest negentropy is extracted in each round. To effectively leverage the knowledge from the source domain and to prevent the negative transfer, we try to find a component in the source domain that matches the component we are extracting. The probability density function of the matched component will then be used to improve the component extraction accuracy if such matched component can be found; otherwise, no knowledge will be transferred. Finally, numerical simulations and a case study with electrocardiogram (ECG) data are conducted, showing the effectiveness of the proposed method in transferring knowledge and reducing negative transfer.
42 ENGINEERING↗
(Invited) Virtual Analysis of Gas-Diffusion-Electrode CO 2 Electrolyzers
The electrochemical reduction of CO 2 (CO 2 R) to value-added products is an attractive technology for tackling the rising atmospheric CO 2 levels and storing intermittent renewable energy into chemical bonds. Fundamental understanding of CO 2 R has progressed significantly in recent years and is critical in the development of CO 2 R to liquid-fuel electrolyzers, where gas-diffusion electrodes (GDEs) have been shown to be key enabling architectures. Various designs have been proposed and studied in the literature to enhance overall selectivity, rates, and maximize the conversion of CO 2 , the latter of which is only now being recognized as a critical issue. In this respect, there is a need to explore the governing phenomena inherent in these architectures to enable optimization. Mathematical modeling is ideally suited to tackle and explore these multiphysics interactions and provide virtual design analysis. In this talk, we discuss modeling methodologies and physics inherent in these devices and present our recent modeling of GDEs for CO 2 reduction. We specifically examine the impacts of multiphase flow and related phenomena on overall cell performance. We then explore the performance and limitations of various cell designs guided by simulation results and examine potential methods for improving water management and tuning catalyst selectivity including the use of different anion-exchange and bipolar membranes. Finally, we discuss the disparities in local environments between aqueous and GDE devices and propose strategies to reduce the gap in knowledge between the two systems.
42 ENGINEERING↗
Performance Validation of Pulsed Thermal Imaging System for In-Service Applications
Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of complex topology nuclear reactor parts from high-strength corrosion resistance alloys, such as stainless steel and Inconel. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which has the capability of melting metallic powder and net shaping the structures with relatively high precision. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures in high temperature nuclear reactor environment. Currently, there exist limited capabilities to evaluate actual AM structures non-destructively. Pulsed Thermography Imaging (PTI) provides a capability for non-destructive evaluation (NDE) of subsurface defects in arbitrary size structures. The PTI method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PTI method has advantages for NDE of actual AM structures because the method involves one-sided non-contact measurements and fast processing of large sample areas captured in one image. Following initial qualification of an AM component for deployment in a nuclear reactor, a PTI system can also be used for in-service nondestructive evaluation (NDE) applications. In this report, we describe recent progress in enhancing PTI capabilities in detecting microscopic defects in metallic specimens. SS316 and IN718 specimens were developed with a pattern of subsurface calibrated flat bottom hole (FBH) defects with diameters from 500µm to 200µm. FBH’s were created with EDM (electron discharge machining) drill. PTI imaging data was processed Spatial Temporal Denoised Thermal Source Separation (STDTSS) unsupervised machine learning (ML) algorithm. We show that defects as small as 200µm in SS316 and IN718 can be detected with STDTSS algorithm. To the best of our knowledge, these are the smallest detected defects which are reported in literature.
42 ENGINEERING↗
Polymer Deconstruction and Redesign Strategies for Plastics Recycling
Advancing plastics recycling requires both the selective deconstruction of existing polymers and the design of new materials that enable efficient reuse without loss of performance. This perspective highlights an integrated approach that is rooted in polymer chemistry, catalysis, and process engineering which can enable a circular plastics economy. Here, we outline recent advances in catalytic, solvolytic, and enzymatic pathways for plastic deconstruction, and examine the molecular design principles driving next-generation recyclable-by-design and bio-based polymers. Despite these advances, major knowledge gaps remain in understanding the evolution of polymer morphology and catalyst structure during deconstruction, assessing deconstruction processes with realistic polymers, and offering redesigned polymers with competitive cost and environmental advantage over conventional plastics. United States Department of Energy (U.S. DOE) national laboratories offer unique capabilities to address these challenges through in situ and operando characterization, high-throughput experimentation, environmental studies, technoeconomic and life cycle assessment, scale-up support, and collaboration networks. Advances made in understanding plastic deconstruction mechanisms and structure-property correlations of redesigned polymers inform emerging research directions including autonomous experimentation, real-time feedback-enabled process optimization, and protein engineering for enzymatic depolymerization.
36 MATERIALS SCIENCE↗
Prediction and characterization of promoters and ribosomal binding sites of Zymomonas mobilis in system biology era
Zymomonas mobilis is a model bacterial ethanologen with many systems biology studies reported. Besides lignocellulosic ethanol production, Z. mobilis has been developed as a platform for biochemical production through metabolic engineering. However, identification and rigorous understanding of the genetic origins of cellular function, especially those based in non-coding region of DNA, such as promoters and ribosomal binding sites (RBSs), are still in its infancy. This knowledge is crucial for the effective application of Z. mobilis to new industrial applications of biotechnology for fuels and chemicals production. In this study, we explored the possibility to systematically predict the strength of promoters based on systems biology datasets. The promoter strength was clustered based on the expression values of downstream genes (or proteins) from systems biology studies including microarray, RNA-Seq and proteomics. Candidate promoters with different strengths were selected for further characterization, which include 19 strong, nine medium, and ten weak ones. A dual reporter-gene system was developed which included appropriate reporter genes. These are the opmCherry reporter gene driven by the constitutive PlacUV5 promoter for calibration, and EGFP reporter gene driven by candidate promoters for quantification. This dual reporter-gene system was confirmed using the inducible promoter, Ptet, which was used to determine the strength of these predicted promoters with different strengths. In addition, the dual reporter-gene system was applied to determine four synthetic RBSs with different translation initiation rates based on the prediction from bioinformatics server RBS calculator. Our results showed that the correlations between the prediction and experimental results for the promoter and RBS strength are relatively high, with R2 values more than 0.7 and 0.9, respectively. This study not only identified and characterized 38 promoters and four RBSs with different strengths for future metabolic engineering in Z. mobilis, but also established a flow cytometry-based dual reporter-gene system to characterize genetic elements including, but not limited to the promoters and RBSs studied in this work. This study also suggested the feasibility of predicting and selecting candidate genetic elements based on omics datasets and bioinformatics tools. Moreover, the dual reporter-gene system developed in this study can be utilized to characterize other genetic elements of Z. mobilis, which can also be applied to other microorganisms.
09 BIOMASS FUELS↗
Morphogene-assisted transformation of Sorghum bicolor allows more efficient genome editing
Sorghum bicolor (L.) Moench, the fifth most important cereal worldwide, is a multi-use crop for feed, food, forage and fuel. To enhance the sorghum and other important crop plants, establishing gene function is essential for their improvement. For sorghum, identifying genes associated with its notable abiotic stress tolerances requires a detailed molecular understanding of the genes associated with those traits. The limits of this knowledge became evident from our earlier in-depth sorghum transcriptome study showing that over 40% of its transcriptome had not been annotated. Here, we describe a full spectrum of tools to engineer, edit, annotate and characterize sorghum’s genes. Efforts to develop those tools began with a morphogene-assisted transformation (MAT) method that led to accelerated transformation times, nearly half the time required with classical callus-based, non-MAT approaches. These efforts also led to expanded numbers of amenable genotypes, including several not previously transformed or historically recalcitrant. Another transformation advance, termed altruistic, involved introducing a gene of interest in a separate Agrobacterium strain from the one with morphogenes, leading to plants with the gene of interest but without morphogenes. The MAT approach was also successfully used to edit a target exemplary gene, phytoene desaturase. To identify single-copy transformed plants, we adapted a high-throughput technique and also developed a novel method to determine transgene independent integration. These efforts led to an efficient method to determine gene function, expediting research in numerous genotypes of this widely grown, multi-use crop.
54 ENVIRONMENTAL SCIENCES↗
Operational and Mission Highlights A Monthly Summary of Top Achievements January 2022
Abstract not provided.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
BESS Digital Assurance, Supply Chain Risk Management, and Emerging Regulation Session One - Abridged
The TADA BESS Supply Chain Workshops are designed to equip participants with the knowledge and tools necessary to address the evolving challenges at the intersection of battery energy storage systems (BESS), cybersecurity, and supply chain vulnerabilities. The workshops emphasize the application of Cyber-Informed Engineering (CIE) principles using INL’s procurement guide and the CIE-BAT tool. Attendees will develop risk-based security strategies and actionable compliance roadmaps tailored to their BESS projects. Additionally, the program fosters a collaborative network of practitioners and provides guidance on navigating emerging regulatory requirements, including FEOC rules under the OBBB framework, to assess and enhance organizational readiness. This is Session 1 of 3.
25 - ENERGY STORAGE↗
BESS Digital Assurance, Supply Chain Risk Management, and Emerging Regulation Session Two
The TADA BESS Supply Chain Workshops are designed to equip participants with the knowledge and tools necessary to address the evolving challenges at the intersection of battery energy storage systems (BESS), cybersecurity, and supply chain vulnerabilities. The workshops emphasize the application of Cyber-Informed Engineering (CIE) principles using INL’s procurement guide and the CIE-BAT tool. Attendees will develop risk-based security strategies and actionable compliance roadmaps tailored to their BESS projects. Additionally, the program fosters a collaborative network of practitioners and provides guidance on navigating emerging regulatory requirements, including FEOC rules under the OBBB framework, to assess and enhance organizational readiness. A comprehensive exploration of BESS cybersecurity supply chain risks, systematic vendor risk assessment through the BESS Procurement Guide, and practical application of the INL SCRM Chatbot for enhanced supply chain resilience. This is Session 2 of 3. (Full Version)
25 - ENERGY STORAGE↗
BESS Digital Assurance, Supply Chain Risk Management, and Emerging Regulation Session One
The TADA BESS Supply Chain Workshops are designed to equip participants with the knowledge and tools necessary to address the evolving challenges at the intersection of battery energy storage systems (BESS), cybersecurity, and supply chain vulnerabilities. The workshops emphasize the application of Cyber-Informed Engineering (CIE) principles using INL’s procurement guide and the CIE-BAT tool. Attendees will develop risk-based security strategies and actionable compliance roadmaps tailored to their BESS projects. Additionally, the program fosters a collaborative network of practitioners and provides guidance on navigating emerging regulatory requirements, including FEOC rules under the OBBB framework, to assess and enhance organizational readiness. This is Session 1 of 3. (Full Version)
25 - ENERGY STORAGE↗
Scaling Up: Demonstrating Risk Reduction and Cost Compression for Commercial Heat Pump Water Heaters - CRADA 625 (Abstract)
Commercial heat pump water heater (CHPWH) systems significantly decarbonize the commercial and multifamily sectors by eliminating the reliance on gas-fired water heating. CHPWH systems are also well suited to include load shift controls that enable load-up and shed commands for supporting grid reliability and time-of-use pricing structure. However, they have not had wide adoption due to factors including price, complexity, and perceived risk. Although CHPWHs have been available in the US for decades, they have not made significant market gains in part because the systems have required significant and costly engineering design expertise and proved lackluster performance. Successful widespread market adoption requires a different approach; a shift from the current custom specialized expertise project design and installation to a repeatable approach that requires little specialized knowledge or expertise and can deliver persistent performance. Using this type of holistic systems approach requires effectively integrating four CHPWH system key components: primary air-to-water heat pumps; primary thermal storage tanks, a temperature maintenance system, and a control system which has capabilities to manage the primary heat pump cycles, any back-up, supplemental, or temperature maintenance heating, alarms, and grid connectivity allowing for demand response (DR), and/or load shifting. The project team has developed and will implement a suite of tools to support faster, less expensive, and more reliable field installations of CHPWH technology and with the resulting data used to further improve the tool set. These tools include: (1) A tool for optimizing system size and costs. (2) A tool that predicts annual energy use and overall system efficiency. (3) The Advanced Water Heater Specification (AWHS 8.0) defining the components of a full CHPWH system addressing performance requirements by climate zone. (4) The Qualified Products List: (QPL) of approved products that meet the specifications requirements. (5) Training materials including online on-demand modules, instructor-led training, and virtual interactive video tours of CHPWH installations in multifamily buildings. Demonstration site identification in low-income buildings in underserved communities is currently underway. Preliminarily, the team anticipates having three demonstrations in the Pacific Northwest and three in the Northeast for a total of six sites. After the demonstration sites are finalized, and M&V instrumentation installations are complete, the team will gather performance data and confirm whether the CHPWH systems perform as predicted and use the data to improve the existing tools.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Identifying and creating pathways to improve biological lignin valorization
Biological lignin valorization to fuels and value-added chemicals enables sustainable and economic biorefineries. Even though significant progress has been made, several major challenges arose due to high recalcitrance and heterogeneity of lignin, which needs to be addressed to improve lignin processing. This work demonstrates an overview of biological lignin conversion and its regulation from a metabolic engineering and systems biology viewpoint. Biological lignin valorization includes three stages: lignin depolymerization, aromatics degradation, and target product biosynthesis. Ligninolytic microorganisms have an extensive enzymatic toolbox to break down the lignin and convert heterogeneous lignin derivatives to central intermediates, such as protocatechuate or catechol, through a peripheral pathway. These intermediates undergo ring cleavage via the β-ketoadipate pathway and are ultimately transformed into metabolites by yielding acetyl-CoA for internal product biosynthesis, such as triacylglycerols, polyhydroxyalkanoates, etc. Bioprospecting will expand the knowledge base of ligninolytic microbial communities, strains, and enzymes to facilitate the understanding of aromatics metabolism. Systems biology analyses achieve an understanding of molecular and systems-level degradation mechanisms and metabolic pathways of lignin and aromatics. By identifying these mechanisms, synthetic biology provides promising approaches to create the lignin conversion pathways and engineer ligninolytic strains suitable as potential hosts for lignin conversion. Techno-economic analysis of biological lignin upgrading to coproducts in biorefineries will guide the implementation of lignin valorization by mitigating technical risk for scale-up and improving the profitability of biorefinery. Through improving the understanding of biological lignin valorization, it should be possible to create biological lignin valorization route to effectively produce value-added products from lignin.
09 BIOMASS FUELS↗
US DOE Nuclear Criticality Safety Engineer Hands-On Subcritical and Critical Experiments Training and Education Course
Abstract not provided.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗