Effect Of Coriolis And Centrifugal Forces On Turbulence And Transport At High Rotation And Buoyancy Numbers

This study attempts to understand one of the most fundamental and challenging problems in fluid flow and heat transfer for rotating machines. The study focuses on gas turbines and electric generators for high temperature and high energy density applications, respectively, both which employ rotating cooling channels so that materials do not fail under high temperature and high stress environment. Prediction of fluid flow and heat transfer inside internal cooling channels that rotate at high rotation number and high density ratio similar to those that are existing in turbine blades and generator rotors is the main focus of this study. Both smooth-wall and rib-roughened channels are considered here. Rotation, buoyancy, bends, ribs and boundary conditions affect the flow inside theses channels. Ribs are introduced inside internal cooling channel in order to enhance the heat transfer rate. The use of ribs causes rapid increase in the supply pressure, which is already limited in a turbine or a generator and requires high cost for manufacturing. Hence careful optimization is needed to justify the use of ribs. Increasing rotation number (Ro) is another approach to increase heat transfer rate to values that are comparable to those achieved by introduction of ribs. One objective of this research is to study and compare theses two approaches in order to decide the optimum range of application and a possible replacement of the high-cost and complex ribs by increasing Ro. A fully computational approach is employed in this study. On the basis of comparison between two-equation (k-[epsilon] and k-[omega]) and RSM turbulence models, against limited available experimental data, it is concluded that the two-equation turbulence models cannot predict the anisotropic turbulent flow field and heat transfer correctly, while RSM showed improved prediction. For the near wall region, two approaches with standard wall functions and enhanced near wall treatment were investigated. The enhanced near wall approach showed superior results to the standard wall functions approach. Thus RSM with enhanced near wall treatment is validated against available experimental data (which are primarily at low rotation and buoyancy numbers). The model was then used for cases with high Ro (as much as 1.29) and high-density ratios (DR) (up to 0.4). Particular attention is given to how turbulence intensity, Reynolds stresses and transport are affected by Coriolis and buoyancy/centrifugal forces caused by high levels of Ro and DR. Variations of flow total pressure along the rotating channel are also predicted. The results obtained are explained in view of physical interpretation of Coriolis and centrifugal forces. Investigation of channels with smooth and with rib-roughened walls that are rotating about an orthogonal axis showed that increasing Ro always enhances turbulence and the heat transfer rate, while at high Ro, increasing DR although causes higher turbulence activity but does not necessarily increase Nu and in some locations even decreases Nu. The increasing thermal boundary layer thickness near walls is the possible reason for this behavior of Nu. The heat transfer enhancement for smooth-wall cases correlates linearly with Ro (with other parameters are kept constant) and hence it is possible to derive linear correlation for the increase in Nu as a function of Ro. Investigation of channels with rib-roughened walls that rotate about orthogonal axis showed that 4-side-average Nur correlates with Ro linearly, where a linear correlation for Nur/Nus as a function of Ro is derived. It is also observed that the heat transfer rate on smooth-wall channel can be enhanced rapidly by increasing Ro to values that are comparable to the enhancement due to the introduction of ribs inside internal cooling channels. This observation suggests that ribs may be unnecessary in high-speed machines, and has tremendous implications for possible cost savings in these machines. In square channels that rotate about parallel axis, the heat transfer rate enhances with Ro on three surfaces of the square channel and decreases on the inner surface (that is the one closest to the axis of rotation). However, the four-sides average Nu increases with Ro. Increasing wall heat flux at high Ro does not necessarily increase Nu on walls although higher turbulence activity is observed. This study examines the rich interplay of physics under the simultaneous actions of Coriolis and centrifugal/buoyancy forces in one of the most challenging internal flow configurations. Several important conclusions are reached from this computational study that may have far-reaching implications on how turbine blades and generator rotors are currently designed. Since the computation study in not validated for high Ro cases, these important results call for a experimental investigation

Experimental determination and computational fluid dynamics predictions of pressure loss in close-coupled elbows (RP-1682)

An experimental program was implemented to study pressure losses in HVAC duct systems associated with 305 mm (12 in.) diameter close-coupled round five-gore elbows. The goal of this program was to experimentally verify a computational fluid dynamics model to accurately predict pressure losses in order to design duct systems more effectively. The results of this study showed that the loss coefficient increased as a function of separation distance between the elbows in a Z-configuration and decreased in a U-configuration. For both 305 mm (12 in.) and 203 mm (8 in.) diameter elbows, power law expressions correlating the combination loss coefficient data as a function of intermediate length for close-coupled elbows arranged in a Z-configuration or a U-configuration were presented. Computational fluid dynamics modeling with enhanced wall treatment using the k-ϵ method was generally able to correctly predict elbow loss coefficients with an error of less than 15%.Scopu

Novel dual-mixed refrigerant precooling process for high capacity hydrogen liquefaction plants with superior performance

Liquid hydrogen is a superior alternative for the current energy storage methods and energy carriers as it has higher energy density and cleanliness. However, hydrogen liquefaction is an energy-intensive process. In particular, the precooling process of hydrogen consumes a tremendous portion of about 30 % of the total compression power of the plant. Several previous studies introduced various pure-refrigerant and single mixed refrigerant (SMR) precooling processes, however, their specific energy consumption (SEC) still very high especially at large-scale capacities. Therefore, this study presents a novel, efficient, and large-scale dual-mixed refrigerant (DMR) process to precool the hydrogen from 25 °C to -192 °C at a pressure of 21 bar. New heavyweight-based mixed refrigerant MR1 and lightweight-based mixed refrigerant MR2 are developed for the DMR process using a new-proposed systematic approach. The proposed DMR process is capable of handling a wide range of hydrogen flow from 100 TPD to 1000 TPD with SEC of 0.862 kWh/kgH2Feed, which is 20.33 % lower than the most competitive SMR process available in the literature. Based on the sensitivity analysis, further optimization of the DMR operating parameters reduced the SEC to 0.833 kWh/kgH2Feed at an optimal capacity of 500 TPD. Furthermore, the COP of the new process is improved by 14.47 % and the total annualized cost is reduced by 12.24 %. Compared to five other technologies that use the pure-refrigerant and other SMR precooling processes, the DMR reduces the SEC by 39.0 % to 63.0 %. The novel precooling process presented herein has the potential to drive the development of large-scale hydrogen liquefaction processes.The work presented in this publication was made possible by NPRP-S grant # [ 11S-1231-170155 ] from the Qatar National Research Fund (a member of Qatar Foundation). The findings herein reflect the work, and are solely the responsibility, of the authors.Scopu

Performance analysis of various machine learning algorithms for CO2 leak prediction and characterization in geo-sequestration injection wells

The effective detection and prevention of CO2 leakage in active injection wells are paramount for safe carbon capture and storage (CCS) initiatives. This study assesses five fundamental machine learning algorithms, namely, Support Vector Regression (SVR), K-Nearest Neighbor Regression (KNNR), Decision Tree Regression (DTR), Random Forest Regression (RFR), and Artificial Neural Network (ANN), for use in developing a robust data-driven model to predict potential CO2 leakage incidents in injection wells. Leveraging wellhead and bottom-hole pressure and temperature data, the models aim to simultaneously predict the location and size of leaks. A representative dataset simulating various leak scenarios in a saline aquifer reservoir was utilized. The findings reveal crucial insights into the relationships between the variables considered and leakage characteristics. With its positive linear correlation with depth of leak, wellhead pressure could be a pivotal indicator of leak location, while the negative linear relationship with well bottom-hole pressure demonstrated the strongest association with leak size. Among the predictive models examined, the highest prediction accuracy was achieved by the KNNR model for both leak localization and sizing. This model displayed exceptional sensitivity to leak size, and was able to identify leak magnitudes representing as little as 0.0158% of the total main flow with relatively high levels of accuracy. Nonetheless, the study underscored that accurate leak sizing posed a greater challenge for the models compared to leak localization. Overall, the findings obtained can provide valuable insights into the development of efficient data-driven well-bore leak detection systems.<br/

Experimental Investigation of Innovative Thermal Mechanical Refrigeration System

The current electrical refrigeration and air condition systems are considered as one of the major sources for ozone depletion and global warming problems. Furthermore, they consume a large percentage of the worldwide gross production of electricity (around 17%). Therefore, developing new refrigeration systems that might be able to work using renewable sources (solar, geothermal, etc.) and waste heat sources is necessary to address these problems. In this paper, the experimental investigation of an innovative thermal-mechanical refrigeration (TMR) system is presented. The TMR system replaces the electric compressor of the conventional refrigeration systems with an innovative expander-compressor unit (two connected double-acting cylinders). The proposed ECU can be driven by ultra-low heat temperature sources, has simple configuration, and high flexibility for the operating conditions. A hybrid electric-compressor and ECU refrigeration setup was developed to investigate the performance of the ECU and compare it to that of an electric compressor. The experiment was conducted using R134a as a working fluid at different masses. The results show that a maximum COP of 0.57 is obtained at a refrigerant mass of 30g (in electric mode) and a maximum COP of 0.41 is obtained at a refrigerant mass of 60g (in ECU mode)

Digital Twin for Power Plants, Energy Savings and other Complex Engineering Systems

Digital Twin (DT) is a digital representation of a machine, service, or production system that consists of models, information, and data used to characterize properties, conditions, and behavior of the system. Renewable energy integration will make future power plants more complex with addition of varieties of Power-to-X technologies, Electrolysis to green hydrogen, onsite storage and transport of hydrogen, and use of pure or blended hydrogen, etc. These future power plants need robust DT architecture to achieve high Reliability, Availability and Maintainability at lower cost. In this research work, a comprehensive and robust DT architecture for power plants is proposed that also can be implemented in other similar complex capital-intensive large engineering systems. The novelty and advantages of the proposed DT is asserted by reviewing the state-of-the-art of DT in energy industries and its potential to transform these industries. Then the proposed DT architecture and its five components are explained and discussed. More specifically, the main contributions of the present work include: 1. Overview of DT key research and development for energy savings applications to consider important findings, research gaps and the needed future development for the proposed DT for power plants. 2. Overview of DT key research for power plants including applications, frameworks and architectures to consider important findings and to confirm the novelty and robustness of the proposed DT. 3. Proposing and demonstrating new robust DT architecture for power plants and other similar complex capital-intensive large engineering systems

NET-ZERO ENERGY BUILDING OPERATOR TRAINING PROGRAM (NZEBOT)

The primary objective of the Net-Zero Energy Building Operator Training Program (NZEBOT) was to develop certificate level training programs for commercial building owners, managers and operators, principally in the areas of energy / sustainability management. The expected outcome of the project was a multi-faceted mechanism for developing the skill-based competency of building operators, owners, architects/engineers, construction professionals, tenants, brokers and other interested groups in energy efficient building technologies and best practices. The training program draws heavily on DOE supported and developed materials available in the existing literature, as well as existing, modified, and newly developed curricula from the Department of Engineering Technology & Construction Management (ETCM) at the University of North Carolina at Charlotte (UNC-Charlotte). The project goal is to develop a certificate level training curriculum for commercial energy and sustainability managers and building operators that: 1) Increases the skill-based competency of building professionals in energy efficient building technologies and best practices, and 2) Increases the workforce pool of expertise in energy management and conservation techniques. The curriculum developed in this project can subsequently be used to establish a sustainable energy training program that can contribute to the creation of new “green” job opportunities in North Carolina and throughout the Southeast region, and workforce training that leads to overall reductions in commercial building energy consumption. Three energy training / education programs were developed to achieve the stated goal, namely: 1. Building Energy/Sustainability Management (BESM) Certificate Program for Building Managers and Operators (40 hours); 2. Energy Efficient Building Technologies (EEBT) Certificate Program (16 hours); and 3. Energy Efficent Buildings (EEB) Seminar (4 hours). Training Program 1 incorporates the following topics in the primary five-day Building Energy/Sustainability Management Certificate program in five training modules, namely: 1) Strategic Planning, 2) Sustainability Audits, 3) Information Analysis, 4) Energy Efficiency, and 5) Communication. Training Program 2 addresses the following technical topics in the two-day Building Technologies workshop: 1) Energy Efficient Building Materials, 2) Green Roofing Systems, 3) Energy Efficient Lighting Systems, 4) Alternative Power Systems for Buildings, 5) Innovative Building Systems, and 6) Application of Building Performance Simulation Software. Program 3 is a seminar which provides an overview of elements of programs 1 and 2 in a seminar style presentation designed for the general public to raise overall public awareness of energy and sustainability topics

Experimental Investigations of Gas Kick for Single and Two-Phase Gas-liquid Flow in near Horizontal Wells

Multiphase flow in pipelines is of great importance and broadly used in several industries for various applications. A multiphase flow is a complex physical phenomenon where more than one phase occurs. In oil and gas exploration process, more attention has been given to the well drilling operation to fulfill the extreme high demand of natural gas. Well drilling operation and technology has transformed to ultra-high pressure and high temperature reservoirs. This transformation has negatively impacted the drilling conditions and the safety of the drilling rig, as a gas kick would become more likely to occur at these extreme conditions. The resulting uncontrolled gas kicks may ignite and explode causing dramatic blowouts associated with very serious consequences, including financial losses, damaging the environment, and loss of personnel's lives. The early detection of a gas kick is therefore essentially needed for timely response with appropriately well control measures

Visual Twin for Pipeline Leak Detection

We describe a visual digital twin system to allow for both operation and training of a data-driven pipeline leak detection system. We show system design in terms of its data inputs and the software system which incorporates this data in real time. This system allows visualization of pipeline data and machine learning-driven leak detection in a pipeline sitting in a subsea context. The intended purpose of the system is to both train operators of the leak detection system in its use and also provide high situational awareness to those tasked with monitoring pipeline deployments. The visual digital twin system uses gaming engine technology to achieve high visual quality. We also construct a novel software system enhancement to incorporate live data streams into the gaming engine environment. This allows real-time driving of gaming engine visualization elements with which we may augment the gaming engine environment. In terms of visualization, we focus on addressing problems of large ranges of multiple scales and providing high situational awareness which minimize operator fatigue and cognitive load. We show how multiple camera views in combination with a convenient user interface can help to address these issues. We demonstrate a digital twin system for leak detection. We show its realtime operation in a gaming engine environment with the ability to instantaneously incorporate outside data sources into the visualizations. We demonstrate using simulated pipeline flow data from sensors such as pressure, temperature, etc. This is visualized in the context of a subsea pipeline on a sea floor. Given the large range of scales, we demonstrate how we can view both the full kilometer scale pipeline and smaller subsections in the context of specific sensor data streams. The overall system demonstrates a novel combination of advanced software systems which incorporates real-time data stream with visualization using a high-fidelity gaming engine. The data used represents a leak detection scenario where both operator training and situational awareness are key desired outcomes

Visual Twin for Pipeline Leak Detection

We describe a visual digital twin system to allow for both operation and training of a data-driven pipeline leak detection system. We show system design in terms of its data inputs and the software system which incorporates this data in real time. This system allows visualization of pipeline data and machine learning-driven leak detection in a pipeline sitting in a subsea context. The intended purpose of the system is to both train operators of the leak detection system in its use and also provide high situational awareness to those tasked with monitoring pipeline deployments. The visual digital twin system uses gaming engine technology to achieve high visual quality. We also construct a novel software system enhancement to incorporate live data streams into the gaming engine environment. This allows real-time driving of gaming engine visualization elements with which we may augment the gaming engine environment. In terms of visualization, we focus on addressing problems of large ranges of multiple scales and providing high situational awareness which minimize operator fatigue and cognitive load. We show how multiple camera views in combination with a convenient user interface can help to address these issues. We demonstrate a digital twin system for leak detection. We show its realtime operation in a gaming engine environment with the ability to instantaneously incorporate outside data sources into the visualizations. We demonstrate using simulated pipeline flow data from sensors such as pressure, temperature, etc. This is visualized in the context of a subsea pipeline on a sea floor. Given the large range of scales, we demonstrate how we can view both the full kilometer scale pipeline and smaller subsections in the context of specific sensor data streams. The overall system demonstrates a novel combination of advanced software systems which incorporates real-time data stream with visualization using a high-fidelity gaming engine. The data used represents a leak detection scenario where both operator training and situational awareness are key desired outcomes