Optimizing Point-in-Space Continuous Monitoring System Sensor Placement on Oil and Gas Sites
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1. Introduction. Wear and erosion damage of materials from solid particle and surface interactions is a major issue in various industries. Although more common in mining and oil and gas production, erosion is becoming a critical issue in renewable technologies as well such as particle based concentrated solar thermal power (CSP) systems. In particle based CSP systems, solid particles are used to absorb solar energy and as thermal storage. However, these particles may cause significant amount of wear to system components while through the system at high temperatures. This damage can be costly, and therefore, requires a greater understanding of solid particle erosion in CSP systems. Throughout the years, models and tools have been developed to predict and control erosion in industries such as oil and gas production. However, these erosion models and erosion prediction tools have been mainly developed based on erosion data for much higher velocities and lower temperatures, i.e. the operating conditions for which the existing models have been built are not pertinent to those expected in CSP system. It is known that erosion depends on many factors such as material properties, erodent particle properties, and particle impact speed and angle. However, the effect of temperature on erosion is not vastly investigated. In this work, a temperature-based correlation is introduced that will be used to modify the existing erosion models to predict erosion rates at velocities and temperatures relevant to Gen3 CSP systems. The existing models are first validated against erosion experiments run at low temperatures and low velocities. Subsequently, a temperature correction term is developed that can extend the existing models to high temperatures, based on the available experimental data showing the effect of temperature on impact erosion. 2. Erosion Models. Continuing sequence of impacts from solid particles on surfaces would result in loss of material due to mechanical interaction between solid surface and particles. Erosion equations have been developed to predict erosion under different conditions and erosion mechanism, including cutting and deformation erosion. These models are mainly either mechanistic, empirical, and semi-mechanistic models, when the latter combine the theory of the erosion mechanism and particle motion with the available experimental data. One of the first empirical correlation was introduced by American Petroleum Institute (API) Recommended Practice (RP) 14E [1]. This correlation was very conservative, and several improved empirical and semi-mechanistic equations were developed in the years following. More recently, the models introduced by Erosion/Corrosion Research Center (E/CRC) at the University of Tulsa and Arabnejad. et al. [2] are frequently used in the literature and industry, as they account for many parameters affecting erosion including particle impact speed and angle, material density and hardness, and particle size and shape. However, both these models were developed based on data at relatively high velocities and also do not take into account any mechanical changes in material associated with thermal cycling at high temperatures. 3. Erosion Prediction at High Temperatures. In this work, the accuracy of Arabnejad et al. model and E/CRC model is investigated to predict impact erosion at conditions relevant to Gen3 CSP systems. Two significant deviations expected in Gen3 systems compared to operating conditions used to build these models are particle impact velocities and system temperature. As a first step, the performance of these models was validated at low velocities and temperatures. Impact erosion experiments were conducted on SS316 coupons using HSP 40/70 ceramic particles. Assuming particle rate of 1 kg/s/m, 0.0254 m of particle curtain thickness, and particle velocity of 1.5 m/s in the system, an overall erosion of 0.59 mm/year was calculated. The experimental results were subsequently compared to those from computational simulations and erosion of 0.47 mm/year was obtained using the Arabnejad et al. model. The results indicated that the models work well at low velocities and low temperature conditions. To account for temperature effect, a mathematical correlation was developed using data published by DUCOM [3] for Inconel 600 eroded by alumina particles at high velocities. The correlation was applied to both Arabnejad et. al model and the E/CRC model. The prediction results from these modified models were within 20% of this experimental data. Predictions of of erosion by the modified E/CRC model at three different temperatures are made. Similar to the calculation at low temperature, a typical CSP system with 1 kg/s/m of particle flow rate per unit length of the particle curtain and a curtain thickness of 0.0254 m is assumed. The annual thickness loss calculations were run for different particle impact velocities, assuming a uniform particle impact area equal to the cross-sectional area of the curtain (particle-particle interactions and dispersion of particles are not considered in obtaining the results). It is observed that erosion increases exponentially as temperature increases. Furthermore, it is also noted that, erosion changes non-linearly with impact velocity. Currently, experiments are also being conducted to measure erosion of SS316 at 800 ºC with HSP 40/70 particles at low impact velocities. We expect to use the results from high temperature testing to further improve the temperature correlation function. Similar models are also being developed for abrasion erosion resulting from particle sliding along the surfaces as well as attrition from particle to particle and particle to surface interactions. References. [1] Institute, A. P. (1991). API Recommended Practice for Design and Installation of Offshore Production Platform Piping System, API RP 14E. [2] Arabnejad, H., Mansouri, A., Shirazi, S. A., and McLaury, B. S. (2015a). Development of mechanistic erosion equation for solid particles. Wear, 332–333, 1044–1050. http://doi.org/10.1016/j.wear.2015.01.031. [3] https://ducom.com/high-temperature-erosion-evaluating-sample-wear/
Lipids have numerous roles critical to living systems. Many of these vital functions are tightly linked or regulated by proteins. Even though the function of many lipid-related enzymes is known, direct links between most individual lipid species and the enzymes that metabolize them are severely lacking. This missing connection impedes biological progress. A resource that allows users to quickly identify protein-lipid relationships will advance our understanding of lipid homeostasis and metabolic perturbed systems. Databases are readily available for mapping small molecule metabolites (i.e., polar metabolites) and specific oxidized fatty acid lipids (e.g., arachidonic acid derivatives) to proteins/enzymes known to be directly involved in metabolism of these molecules (e.g. SMPDB, Reactome, HMDB, BioCyc). This enables the integration of metabolite and protein (as well as transcript) data when performing functional enrichment and metabolic modeling allowing researchers to rapidly examine metabolic pathways from multiple omics sources. Currently, linking lipidomics data with proteomics data is conducted using a time-intensive, user defined, manual process. Here we propose creating a tool that will link lipidomics and proteomics data and also conduct functional enrichment analysis and visualization allowing for full statistical integration. Using text-mining approaches, we will extract lipid references from uniprot and create a database linking lipid species to enzymes directly related to their metabolism. We will also extract directional annotation, which will allow even greater specificity when linking data types and will help eliminate false positives from functional associations.
This report provides details of reservoir characterization and techno-economic analysis for enhanced geothermal systems and closed-loop geothermal systems in the Denver-Julesburg Basin Wattenberg area. The analysis contributes to the Geothermal Limitless Approach to Drilling Efficiencies (GLADE) project, a multi-institutional research initiative focused on advancing geothermal energy development by reducing drilling costs and improving the rate of penetration. The GLADE project brings together national laboratories, academic institutions, and industry partners, including the U.S. Department of Energy's Geothermal Technologies Office, Occidental Petroleum, the National Laboratory of the Rockies (NLR), Los Alamos National Laboratory, Colorado School of Mines, Louisiana State University, Texas A&M University, and several drilling technology companies. As part of this effort, NLR developed and applied slender-body theory modeling tools and the GEOPHIRES simulator to support techno-economic analysis. The slender-body theory wellbore simulator evaluated thermal performance in U-loop, Eavor-type multilateral, and enhanced geothermal system well configurations. Results indicated that an optimized single U-loop design delivers an outlet temperature of 168.8 degrees Celsius,stabilizing at a final outlet temperature of 135.3 degrees Celsius. Heat production also improved significantly, with an average thermal output of 16.9 MWth and a final thermal output of 11.0 MWth. These results provided critical baseline data for selecting optimized systems for techno-economic analysis. Building on these outputs, NLR applied the GEOPHIRES techno-economic simulator to estimate the levelized cost of electricity for both the Eavor multilateral and enhanced geothermal systems. The sensitivity analysis showed that drilling costs exert a particularly strong impact on the levelized cost of electricity of multilateral systems. Therefore, advances in drilling technology and improved drilling rates could yield significant gains in the economic performance of these systems. In this context, the GLADE project directly supports the U.S. Department of Energy's mission to reduce the cost of geothermal energy and accelerate the deployment of next-generation geothermal technologies.
A structured approach for recording data quality and contextual information about how and why a signal exists – i.e. metadata – is central to interpret and use sensor data correctly. This is becoming increasingly important with the global trend with data-driven applications such as digital twins and AI-models. But a structured metadata collection and organization of sensor data is not routine in most plants, which can result in lost information and missed opportunities to make use of the investments made in the data collection. Therefore, the IWA task group on Metadata Collection and Organization in wastewater resource recovery systems (MetaCO) was initiated in 2020 and recently delivered the IWA scientific and technical report number 31. The report gives and in-depth description about metadata in water resources recovery facilities (WRRFs) and is available as open access at IWA publishing. The report is the outcome of the collaboration between more than 80 water professionals with the intention to serve WRRF data users with a guide on how to structure and make use of metadata throughout the data pipeline in order to maximize the value of sensor data.
Malware is one of the most persistent and costly cyber threats endangering reputation, confidentiality, integrity, and availability for organizations and national security. Consequently, many of the incident detection and prevention systems, and incident responders have begun to utilize machine learning as a helper in the fight against malware and other cyber threats. However, cyber defenders rely on interpretability and generalizability, yet the popular machine learning methods are black-box and often use traditional supervised solutions that do not generalize to novel malware. Therefore, there is a need to improve the existing solutions. At the same time, the majority of the prior research ignored essential evaluation criteria when reporting the results of their methods, which disables the safe reproducibility of the methods in a production environment. Tensor decomposition, on the other hand, enables interpretable unsupervised analysis of the large-scale data for the discovery of hidden patterns. Our findings, performed on real-world and large-scale experiments, show that tensor factorization-based methods yield performance results that surpasses or competes with existing supervised solutions with the added benefit of interpretability and generalizability. With the ability to analyze complex and large-scale data using tensors, we report results that reflect real-world production environments. We propose to develop new game- changing tools for malware identification and characterization that can trace malware evolution, rank the infected or malicious hosts, and streamline the work of incident response teams, malware analysts, and incident detection and prevention systems.
The United States Department of Energy, Geothermal Technologies Office (GTO) is funding a collaborative investigation of enhanced geothermal systems (EGS) processes at the meso-scale. This study, referred to as the EGS Collab project, is a unique opportunity for scientists and engineers to investigate the creation of fracture networks and circulation of fluids across those networks under in-situ stress conditions. The EGS Collab project is envisioned to comprise three experiments and the site for the first experiment is on the 4850 Level in phyllite of the Precambrian Poorman formation, at the Sanford Underground Research Facility, located at the former Homestake Gold Mine, in Lead, South Dakota. Principal objectives of the project are to develop a number of intermediate-scale field sites and to conduct well-controlled in situ experiments focused on rock fracture behavior and permeability enhancement. Data generated during these experiments will be compared against predictions of a suite of computer codes specifically designed to solve problems involving coupled thermal, hydrological, geomechanical, and geochemical processes. Comparisons between experimental and numerical simulation results will provide code developers with direction for improvements and verification of process models, build confidence in the suite of available numerical tools, and ultimately identify critical future development needs for the geothermal modeling community. Moreover, conducting thorough comparisons of models, modelling approaches, measurement approaches and measured data, via the EGS Collab project, will serve to identify techniques that are most likely to succeed at the Frontier Observatory for Research in Geothermal Energy (FORGE), the GTO’s flagship EGS research effort. As noted, outcomes from the EGS Collab project experiments will serve as benchmarks for computer code verification, but numerical simulation additionally plays an essential role in designing these meso-scale experiments. This paper describes specific numerical simulations supporting the design of Experiment 1, a field test involving hydraulic stimulation of two fractures from notched sections of the injection borehole and fluid circulation between sub-horizontal injection and production boreholes in each fracture individually and collectively, including the circulation of chilled water. Whereas the mine drift allows for accurate and close placement of monitoring instrumentation to the developed fractures, active ventilation in the drift cooled the rock mass within the experimental volume. Numerical simulations were executed to predict seismic events and magnitudes during stimulation, initial fracture orientations for smooth horizontal wellbores, pressure requirements for fracture initiation from notched wellbores, fracture propagation during stimulation between the injection and production boreholes, tracer travel times between the injection and production boreholes, and produced fluid temperatures with chilled water injections, and pressure limits on fluid circulation to avoid fracture growth.
The United States Department of Energy, Geothermal Technologies Office (GTO) is funding a collaborative investigation of enhanced geothermal systems (EGS) processes at the meso-scale. This study, referred to as the EGS Collab project, is a unique opportunity for scientists and engineers to investigate the creation of fracture networks and circulation of fluids across those networks under in-situ stress conditions. The EGS Collab project is envisioned to comprise three experiments and the site for the first experiment is on the 4850 Level in phyllite of the Precambrian Poorman formation, at the Sanford Underground Research Facility, located at the former Homestake Gold Mine, in Lead, South Dakota. Principal objectives of the project are to develop a number of intermediate-scale field sites and to conduct well-controlled in situ experiments focused on rock fracture behavior and permeability enhancement. Data generated during these experiments will be compared against predictions of a suite of computer codes specifically designed to solve problems involving coupled thermal, hydrological, geomechanical, and geochemical processes. Comparisons between experimental and numerical simulation results will provide code developers with direction for improvements and verification of process models, build confidence in the suite of available numerical tools, and ultimately identify critical future development needs for the geothermal modeling community. Moreover, conducting thorough comparisons of models, modelling approaches, measurement approaches and measured data, via the EGS Collab project, will serve to identify techniques that are most likely to succeed at the Frontier Observatory for Research in Geothermal Energy (FORGE), the GTO’s flagship EGS research effort. As noted, outcomes from the EGS Collab project experiments will serve as benchmarks for computer code verification, but numerical simulation additional plays an essential role in designing these meso-scale experiments. This paper describes specific calculations and numerical simulation approaches conducted in support of the designs for the stimulation and circulation experiments, and their monitoring, including those for predicting seismic energies during stimulation, stimulation pressure requirements, impacts of borehole orientation on fracture development and trajectories, influence of notch shapes on hydraulic fracture initiation, and pressure limits on fluid circulation to avoid fracture growth.
Heating, ventilation, and air conditioning (HVAC) is a prime source of energy consumption in the US. In 2006, approximately 35% of energy in the US was used for HVAC [1]. Usually building operators use a static schedule for controlling HVAC systems without having a deeper understanding of how many people use the building at different times of the day. In addition, HVAC systems operate by assuming maximum occupancy in each room, which leads to a significant energy waste, e.g., an HVAC system providing ventilation for 30 people when there are only 10 people in a room. Considering how many people use different rooms at different times of the day is very crucial for achieving building energy efficiency and providing occupant comfort. There have been several attempts to collect long term occupancy patterns from office buildings, e.g., Mitsubishi’s Electronic Research Lab (MERL) dataset [14] and Colorado School of Mines (CSMBB) dataset. However, PIR motion detectors are used in these projects for sensing occupancy and hence these datasets only reflect whether a room is occupied or not without revealing the actual person count. Similar datasets are also collected from households. Only a few datasets from commercial spaces capture actual person count, and even in these cases, the data collection period was very limited, e.g., in [3] only 5 days of occupancy data is collected as ground truth. We are the first to collect long term (9 months) occupancy data (people count) from an 11,000 square foot commercial office (second floor of Bosch Research and Technology Center Pittsburgh). In this paper, we outline some statistical results from our empirical study and explore their implications for saving energy.
The development of deep repository concepts in the USA is evaluating generic options for disposal of heat-generating nuclear spent nuclear fuel (SNF) waste for a suite of host rock media (clay rock, granite, salt). Large waste canister designs (e.g., dual purpose canisters or DPC’s) are currently being considered to accommodate many SNF assemblies. Large amounts of SNF in the waste packages will produce high thermal loads generating temperatures in excess of 200 °C for long periods of time. For a bentonite backfilled repository concept, prolonged exposure to high temperatures will induce chemical reactions in the engineered barrier system (EBS), particularly at barrier interfaces. Our focus is on experimental investigations and the application of thermodynamic modeling to evaluate clay-zeolite phase equilibria as a function of temperature and fluid chemistry. Experimental work on barrier material interactions under hydrothermal conditions (150 – 300 °C, 15-16 MPa) has elucidated mineral phase changes in Wyoming (Colony mine) bentonite in the presence of steel phases. Glassy material in bentonite is replaced by analcime-wairakite phases, and through apparent clinoptilolite recrystallization. The initial increase in dissolved silica leads to authigenic quartz formation. Such mineral assemblage suggests an initially silica-rich environment (analcime, clinoptilolite) moving towards Si-depleted conditions. Analcime-wairakite compositions suggest a well-defined solid-solution between these Na and Ca end-members. Smectite clay in these experiments is stable with Fe-saponite and chlorite growth co-existing with binary/ternary sulfides at steel interfaces. Little or no illite was observed in the reaction products which could be tied to silica oversaturation and low K in the system. The thermodynamic analysis is used to evaluate thermodynamic data and develop phase diagrams to describe stability field relations of secondary mineral phase occurrences. This analysis allows for delineation of potential reaction pathways in bentonite clay degradation and interactions with metallic phases. One example of this is the important role of dissolved silica plus other phase components to the formation of alteration mineralogy observed in these interactions. All these investigations are key to the assessment of thermally-induced degradation zones in the EBS during the thermal period and their effect on barrier performance in the safety assessment.
Cell-free systems offer many advantages over traditional biological conversion by eliminating biological growth constraints. It also offers easy manipulation and finetuning of the reaction conditions for each individual enzyme. The conversion of cellulosic glucose to Limonene, a terpene, is a promising pathway for producing fuels and chemicals. Recent advances in developing cell-free systems focuses on bench scale optimization of terpene yield and to demonstrate its feasibility towards commercialization [1,2]. There is significant knowledge gap regarding reaction kinetics of these cell-free systems to further study how it will perform at larger scale. We present here, our studies on reaction kinetics and reactor design implications of cell-free glucose to Limonene conversion to facilitate the further development and commercialization of this process. We developed a novel kinetic model based on the metabolic-network structure of the cell-free system with multi-substrate reversible Michaelis-Menten rate law. To estimate kinetic parameters for this system of rate equations, we employed Bayesian optimization to perform global search with the assistance of gaussian processes to balance exploration and exploitation. The model parameters estimated showed good results compared with experimental data. The estimated parameters were used to perform sensitivity analysis. We found that Hexokinase is one of the most critical enzymes that affect the conversion of the glucose. We also observed that abundance of co-factors is also critical to the conversion of glucose to limonene. We investigated packed bed reactors with enzymes immobilized on the surface of particles to convert glucose stream into Limonene for larger scale production. The reactor design such as particle size, enzyme loading, and flow rate are found to be critical for improving yields. [1] Dudley, Q.M., Nash, C.J. and Jewett, M.C., 2019. Synthetic Biology, 4(1), p.ysz003. [2] Korman, T.P., Opgenorth, P.H. and Bowie, J.U., 2017. Nature communications, 8(1), p.15526.
LLNL completed the FY20 workplan entailing application of the Dynamic Correlation Processor (DCP) to International Monitoring System (IMS) primary seismic data in 2019 and 2020. The effective analysis period was curtailed to January 1 through November 12, 2019 due to a data compression change that rendered later data unusable. Fixing the data compression error is a priority for our data source, and we plan to extend the period of data analysis when the fix is implemented. We found that the percentage overlap between detections generated by the International Data Centre (IDC) DFX software and detections generated by DCP varied widely by station. Overlap is 30-40% at some stations, suggesting that a large percentage of DFX detections could be screened (removed) from the detection-event association algorithm. However, detection overlap was much lower, a few percent, at many stations and the percentage overlap was low at stations known to have many mines and other local sources of seismicity. Unexpectedly low detection overlap at many stations prompted us to examine results for the ARCES station in detail. There is considerable mine activity near ARCES, but our initial FY20 analysis found only ~2% DCP/DFX detection overlap. Differences in the pre-processing (e.g. beam recipes) used by DCP and DFX appear to be the cause of low detection overlap at ARCES. DCP uses wideband filters to improve detector robustness and the IDC uses narrow-band pre-filtering to improve sensitivity. After reprocessing ARCES using the IDC beam recipe, DCP/DFX overlap increased to ~74%. This result shows that we must reprocess the IMS network using the IDC station-specific beam recipes if we are to effectively screen DFX detections and improve automatic event building performance at the IDC.
The United States Department of Energy, Geothermal Technologies Office (GTO) is funding a collaborative investigation of enhanced geothermal systems (EGS) processes at the meso-scale. This study, referred to as the EGS Collab project, is a unique opportunity for scientists and engineers to investigate the creation of fracture networks and circulation of fluids across those networks under in-situ stress conditions. The EGS Collab project is envisioned to comprise three experiments and the site for the first experiment is on the 4850 Level (4,850 feet below ground surface) in phyllite of the Precambrian Poorman formation, at the Sanford Underground Research Facility, located at the former Homestake Gold Mine, in Lead, South Dakota. Principal objectives of the project are to develop a number of intermediate-scale field sites and to conduct well-controlled in situ experiments focused on rock fracture behavior and permeability enhancement. Data generated during these experiments will be compared against predictions of a suite of computer codes specifically designed to solve problems involving coupled thermal, hydrological, geomechanical, and geochemical processes. Comparisons between experimental and numerical simulation results will provide code developers with direction for improvements and verification of process models, build confidence in the suite of available numerical tools, and ultimately identify critical future development needs for the geothermal modeling community. Moreover, conducting thorough comparisons of models, modelling approaches, measurement approaches and measured data, via the EGS Collab project, will serve to identify techniques that are most likely to succeed at the Frontier Observatory for Research in Geothermal Energy (FORGE), the GTO's flagship EGS research effort. As noted, outcomes from the EGS Collab project experiments will serve as benchmarks for computer code verification, but numerical simulation additionally plays an essential role in designing these meso-scale experiments. This paper describes specific numerical simulations supporting the design of Experiment 1, a field test involving hydraulic stimulation of two fractures from notched sections of the injection borehole and fluid circulation between sub-horizontal injection and production boreholes in each fracture individually and collectively, including the circulation of chilled water. Whereas the mine drift allows for accurate and close placement of monitoring instrumentation to the developed fractures, active ventilation in the drift cooled the rock mass within the experimental volume. Numerical simulations were executed to predict seismic events and magnitudes during stimulation, initial fracture orientations for smooth horizontal wellbores, pressure requirements for fracture initiation from notched wellbores, fracture propagation during stimulation between the injection and production boreholes, tracer travel times between the injection and production boreholes, produced fluid temperatures with chilled water injections, pressure limits on fluid circulation to avoid fracture growth, temperature environment surrounding the 4850 Level drift, and fracture propagation within a stress field altered by drift excavation, ventilation cooling, and dewatering.
While understanding individual stakeholders' perspectives on the adoption and conversion to a biofuel-based landscape has been a subject of many previous studies on biofuels, there has been relatively little attention given to understanding how the interaction between multiple stakeholders involved in biofuel development could influence the widespread adoption of biofuel production. Here, this paper analyzes the key stakeholder interactions utilizing various data sources including survey results, social media posts, and empirical and theoretical analyses. An intensive review is conducted for a number of surveys and research papers on different aspects of biofuel development such as land use choices, biorefinery and transportation, infrastructure development, consumer priorities, environmental impacts, etc. Following that, a stakeholder synergy approach is applied to synthesizing typical responses of stakeholders, such as producers, consumers, biorefineries, rural communities, and the government, and discussing how their responses influence each other's decisions and the overall system performance. Based on the findings of inadequate stakeholder synergy, it is recommended that new surveys and further research should be conducted to understand why synergy between stakeholders in biofuel development is absent. Additionally, this paper provides research perspectives, including (1) applying cutting-edge text-mining techniques to conduct sentiment analysis, and research and public attention analysis; (2) using an agent-based model to simulate stakeholder interactions and understand the factors that influence stakeholder synergy and the emergence of a bioeconomy.
Microreactors are one promising advanced-reactor concept being pursued by the nuclear industry. They are distinguished by a relatively low power output of 20 MWth or less. These microreactors are intended for deployment in applications where conventional small-capacity power solutions, such as diesel generators, are either economically unfeasible or logistically challenging. Such applications include providing electric power and/or heat for remote communities, mining sites, defense installations, and humanitarian and disaster-relief missions. An important feature for the successful deployment of microreactors is their capability to be operated remotely. This capability can significantly reduce staffing costs by eliminating the need for licensed operators to be physically present at each reactor site. Instead, operators can be centralized in a single remote operations center placed in an economically advantageous location, thereby optimizing resources by consolidating expertise and enhancing operational efficiency. However, the implementation of a remote operation system for nuclear reactors raises new concerns regarding the security, reliability, and resilience of such a system. One way in which remote operations can be supported in a manner that maintains system security, reliability, and resilience is through the use of digital twins in a novel framework designed to verify and validate sensor data and commands communicated between the remote operations center and reactor. This framework, known as the Digital Twin Certification System (DTCS), has previously been proposed as an operations architecture that can bring security and resiliency levels of remote nuclear-reactor operations to a level acceptable for commercial deployment. This paper moves the proposed DTCS architecture from concept to reality by presenting the implementation and testing of the system. The rationale and implementation of the DTCS using tools such as DeepLynx and Apache Airflow, is covered in-depth. This is followed by a demonstration of the DTCS by applying the implemented system architecture to the Single Primary Heat Extraction and Removal Emulator, a small-scale non-nuclear test bed that emulates thermal behavior of a microreactor. The demonstration includes both normal and abnormal operating scenarios to highlight how the DTCS can increase the security, reliability, and resilience of a remote operations system.
Microreactors are one promising advanced-reactor concept being pursued by the nuclear industry. They are distinguished by a relatively low power output of 20 MWth or less. These microreactors are intended for deployment in applications where conventional small-capacity power solutions, such as diesel generators, are either economically unfeasible or logistically challenging. Such applications include providing electric power and/or heat for remote communities, mining sites, defense installations, and humanitarian and disaster-relief missions. An important feature for the successful deployment of microreactors is their capability to be operated remotely. This capability can significantly reduce staffing costs by eliminating the need for licensed operators to be physically present at each reactor site. Instead, operators can be centralized in a single remote operations center placed in an economically advantageous location, thereby optimizing resources by consolidating expertise and enhancing operational efficiency. However, the implementation of a remote operation system for nuclear reactors raises new concerns regarding the security, reliability, and resilience of such a system. One way in which remote operations can be supported in a manner that maintains system security, reliability, and resilience is through the use of digital twins in a novel framework designed to verify and validate sensor data and commands communicated between the remote operations center and reactor. This framework, known as the Digital Twin Certification System (DTCS), has previously been proposed as an operations architecture that can bring security and resiliency levels of remote nuclear-reactor operations to a level acceptable for commercial deployment. This paper moves the proposed DTCS architecture from concept to reality by presenting the implementation and testing of the system. The rationale and implementation of the DTCS using tools such as DeepLynx and Apache Airflow, is covered in-depth. This is followed by a demonstration of the DTCS by applying the implemented system architecture to the Single Primary Heat Extraction and Removal Emulator, a small-scale non-nuclear test bed that emulates thermal behavior of a microreactor. The demonstration includes both normal and abnormal operating scenarios to highlight how the DTCS can increase the security, reliability, and resilience of a remote operations system.
Lignocellulosic biomass is a feedstock for fuels and chemicals that does not compete with food resources and has less contaminants than refuse-derived biomass feedstocks. To convert lignocellulosics to biofuels or value-added products, multiple processing steps are typically necessary. One method of producing biofuels from lignocellulosic biomass utilizes a deacetylation and mechanical refining pretreatment and an enzymatic hydrolysis reaction to produce fermentable sugars from cellulose and hemicellulose. The rheological properties of biomass, such as yield stress and plastic viscosity, change during enzymatic hydrolysis and alter the energy requirements for pumping and mixing, an important consideration for the design of processing equipment. The dynamic changes in rheological properties that occur in a corn stover feedstock undergoing enzymatic hydrolysis are characterized in this work, and the influence on pressure losses in piping systems is estimated. Two rheometer geometries were fabricated with stereolithography 3D printing to reduce wall slip and sample ejection. The slurries have complex rheological behaviors that include shear-thinning behavior. Shear stress ramps were performed on samples at 20 and 50 degrees C using the custom geometries, and the Herschel-Bulkley model was fit to the data. The dynamic nature of the rheological properties is correlated with changes in the average fiber length at various extents of reaction, and the influence of solids concentration on the observed rheology and piping pressure losses is discussed.
This is the research performance final report for the project entitled: Agent-Based, Bottom-Up Medium- and Heavy-duty Electric Vehicle Economics, Operation, Charging and Adoption This project was able to achieve the DOE’s goals of developing new modeling tools to understand MDHD vehicle operation and adoption. The first modeling tool is a fleet-level techno-economic analysis model capable of estimating energy use and associated environmental and cost impacts for electrified and conventional vehicles of any MDHD vocation, using real-world cost and operations data, including approaches to optimizing schedules for charging and/or vehicle dispatch. The second modeling tool is a system-level, bottom-up, agent-based adoption model capable of generating geographically-resolved estimates of market projections for MDHD vehicles and charging infrastructure. These tools will be developed and published to serve dual purposes as analysis tools for researchers, and decision-support tools for decision makers within the MDHD system.