Efficiency Point Research Articles

Assessment of Fiber Bragg Grating sensors for monitoring induced strains on the draft tube cone of hydraulic turbines

Abstract In hydraulic turbines, several flow instabilities can take place inside the draft tube cone during off-design and transient operating conditions such as the rotating vortex rope which can severely damage the structure if sustained in time. In the frame of the AFC4Hydro H2020 research project, an extensive measurement campaign has been carried out to monitor and predict this rotating vortex rope phenomenon in a reduced scale Kaplan turbine model at the Vattenfall Research and Development facility in Älvkarleby, Sweden. The hydraulic turbine model has been operated in propeller mode with a fixed blade angle corresponding to its best efficiency point. Several sensors have been placed along the test stand to monitor vibrations, strains and pressures. Concretely, the present paper assesses the performance of using Fiber Bragg Grating sensors to measure the strains induced on the draft tube cone walls with high-spatial resolution in three different zones of influence: the upper and lower flanges and the vertical cone wall between the runner outlet and the elbow. To do so, a total of 3 arrays embedding a total of 48 Fiber Bragg Grating sensors were glued inside three grooves previously machined on these particular areas of the draft tube cone. Analysing the frequency response of the different Fiber Bragg Grating sensors, the strain patterns induced by the rotating and plunging components of the rotating vortex rope have been precisely determined. Moreover, their impacts at the different part load conditions tested have also been quantified.

Numerical Investigation of Impeller Parameters on the Performance and Internal Flow Characteristics in Regenerative Flow Pumps

Abstract As a particular vane pump, regenerative flow pump (RFP) is not only featured by relatively high head under low flow rate, but also is characterized by compact volume and simple structure. However, the efficiency of RFP is quite low. For the purpose of improving RFP’s hydraulic performance and internal flow, this paper investigates the influence of the matching relation between impeller diameter and height on RFP’s performance. Two cases through changing impeller’s diameter and height are designed and compared. The results show that, as impeller’s diameter increases while keeping the blade’s radial length constant, RFP’s head and efficiency are improved, and the best efficiency point (BEP) shifts gradually from lower flow rate to larger flow rate. Additionally, the ratio of the flow rates at BEP is approximately equal to the ratio of the blade’s middle diameters. On the other hand, as impeller’s height increases, the BEPs among different pump models stay in same flow rate, and there is always an optimal impeller’s height for this RFP model to achieve optimal performance. Finally, the pressure distribution, velocity distribution and mass exchange of different models are analysed to reveal the differences.

Energy loss analysis of a double-suction centrifugal pump using in pump mode and turbine mode

As an economical energy recovery device, pump as turbine (PAT) is widely used in micro-hydropower stations and the chemical industry. The inlet and outlet pipelines connected to the double-suction pump are on the same horizontal line. As the pipeline layout is very convenient, in some chemical industries, the way of residual pressure energy utilization is increasing using the double-suction pump as a turbine. Based on numerical simulation, experimental verification, and entropy generation theory, the energy loss rule of each flow component in the pump mode and turbine mode under different flow rates is compared and analyzed. The results show that when the double-suction pump is used as a turbine, the flow rate at the best efficiency point (BEP) in the turbine mode shifts to a large flow rate by 30.89% and the BEP efficiency decreases by 1.30%. In the pump mode and turbine mode, the main energy loss component is the impeller, and the turbulent entropy generation power and the wall entropy generation power are the main sources of energy loss. The energy loss in the suction chamber and impeller increases sharply, and the energy loss is primarily enhanced in the blade trailing edge and the tongue near due to the unsteady flow in the turbine mode. Due to the complex structure, the spiral suction chamber is not suitable for the flow direction of the fluid flow out of the impeller, and the flow state inside the impeller is negatively affected by the suction chamber in the turbine mode. This paper provides a theoretical basis for the design and application of double-suction centrifugal PAT.

Deep learning the efficient frontier of convex vector optimization problems

In this paper, we design a neural network architecture to approximate the weakly efficient frontier of convex vector optimization problems (CVOP) satisfying Slater’s condition. The proposed machine learning methodology provides both an inner and outer approximation of the weakly efficient frontier, as well as an upper bound to the error at each approximated efficient point. In numerical case studies we demonstrate that the proposed algorithm is effectively able to approximate the true weakly efficient frontier of CVOPs. This remains true even for large problems (i.e., many objectives, variables, and constraints) and thus overcoming the curse of dimensionality.

Structural Designs Meet Optimality: Exploring Optimized LSM-tree Structures in a Colossal Configuration Space

Mainstream LSM-tree-based key-value stores face challenges in optimizing performance for point lookup, range lookup, and update operations concurrently due to their constrained configurations. They typically follow fixed patterns to specify the level capacity and the number of sorted runs per-level. This confines their designs to a restricted space, limiting opportunities for broader optimizations. To address this challenge, we consider a more flexible configuration that enables independent adjustments of the number of runs per-level, size ratio, and Bloom filter settings at each LSM-tree level. By carefully analyzing the cost of each operation based on the new design space, we unveil two critical insights for optimizing the tradeoff among the three operations. Firstly, achieving efficient point lookup requires a large last level. Secondly, there is a specific correlation between the number of runs per level and size ratio that is advantageous for overall update and range lookup performance. Based on these insights, we introduce Moose, a structure delivering an impressive overall performance for point lookup, range lookup, and update concurrently. Furthermore, we also introduce a new framework, Smoose, to navigate the design space for adapting specific workloads. We implemented Moose and Smoose on top of RocksDB and experimental results demonstrate that our proposed approach outperforms state-of-the-art LSM-tree structures across diverse workloads.

Off-Design Operation and Cavitation Detection in Centrifugal Pumps Using Vibration and Motor Stator Current Analyses.

Centrifugal pumps are essential in many industrial processes. An accurate operation diagnosis of centrifugal pumps is crucial to ensure their reliable operation and extend their useful life. In real industry applications, many centrifugal pumps lack flowmeters and accurate pressure sensors, and therefore, it is not possible to determine whether the pump is operating near its best efficiency point (BEP). This paper investigates the detection of off-design operation and cavitation for centrifugal pumps with accelerometers and current sensors. To this end, a centrifugal pump was tested under off-design conditions and various levels of cavitation. A three-axis accelerometer and three Hall-effect current sensors were used to collect vibration and stator current signals simultaneously under each state. Both kinds of signals were evaluated for their effectiveness in operation diagnosis. Signal processing methods, including wavelet threshold function, variational mode decomposition (VMD), Park vector modulus transformation, and a marginal spectrum were introduced for feature extraction. Seven families of machine learning-based classification algorithms were evaluated for their performance when used for off-design and cavitation identification. The obtained results, using both types of signals, prove the effectiveness of both approaches and the advantages of combining them in achieving the most reliable operation diagnosis results for centrifugal pumps.

Optimal efficiency point membership incorporating fuzzy logic and automatic control of various charging stages for electric vehicles

Introduction: The rapid development of electric vehicle technology in the field of renewable energy has brought significant challenges to wireless charging systems. The efficiency of these systems is crucial for improving availability and sustainability. The main focus of the research is to develop an intelligent charging strategy that utilizes fuzzy logic to optimize the efficiency of wireless charging systems for electric vehicles.Method: Introduce a model that combines fuzzy logic algorithm with automatic control system to improve the wireless charging process of electric vehicles. The model adopts dynamic tracking and adaptive control methods by analyzing the characteristics of static wireless charging systems. Utilizing primary phase shift control and secondary controllable rectifier regulation, combined with optimized fuzzy control algorithm.Result and discussion: The experimental results show that when the secondary coil is stable, the model maintains a stable duty cycle of about 75.6% and a stable current of 5A. It was observed that when the mutual inductance values were set to 10, 15, and 20 uH, the efficiency of the primary coil before applying control decreased with increasing resistance.Conclusion: The proposed system has shown great potential for application in real-world electric vehicle charging systems, demonstrating good applicability and feasibility in controlling the charging process and tracking the optimal efficiency point. The integration of fuzzy logic enhances the system’s ability to adapt to different operating conditions, which may lead to wider implementation and improved operational efficiency.

Improving fuel efficiency and durability in fuel cell vehicles through component sizing and power distribution management

The proposed study presents a novel approach to determining the optimal component sizes and power distribution for a vehicle, taking into account various power sources in order to prolong the lifespan of the fuel cell stack. Moreover, this technique aims to increase the vehicle's overall fuel economy. The available experimental data of Toyota Mirai has been used to model the vehicle in the MATLAB-SIMULINK software. The validated model is implemented to evaluate the vehicle power request in different standard driving cycles and routes to size the vehicle components. The arithmetic average of the steady power of different driving cycles and routes has been attributed to the fuel cell stack's maximum efficiency point. The battery size should support the requested dynamic power in any situation. Thus, the maximum power of the fuel cell stack, the high voltage battery size, and the electric motor maximum power are assigned as 96 kW, 3.05 kWh, and 59 kW, respectively. The results of fuel consumption of the vehicle is also improved at least by 10% up to 21% in different driving conditions. Economic analysis reveals that adopting this strategy can lead to a substantial reduction in the initial vehicle cost by $1,328. The operational cost has also decreased 691$ annually due to lower fuel consumption of the new strategy implementation. These advancements along with better fuel cell stack durability hold promise for prospective buyers, allowing for greater accessibility and financial flexibility.

Analysis Study of Available Alternatives for Mitigation of Aromatic Hydrocarbon Emissions from a Glycol Dehydration Unit

A natural gas (NG) dehydration unit based on glycol absorption is considered one of the most important gas processing units, aiming to decrease water content and consequently adjust its dew point. However, during this process, not only water is absorbed by the glycol solvent, but also some aromatic compounds, including benzene, toluene, ethylbenzene, and xylene (BTEX), in addition to volatile organic compounds (VOC), are absorbed. These compounds are released during glycol regeneration into the atmosphere, resulting in environmental pollution and consequent catastrophic mental and physical health problems. This study aims to minimize BTEX emissions while ensuring efficient dew point control. Various strategies have been adopted to control BTEX emissions, but the present work focuses on optimizing operating conditions and investigating the influence of operational variables on BTEX emissions, as well as NG water content. LINGO optimization software and HYSYS (version 11) are used to find the plant’s optimum conditions for minimizing BTEX emissions and satisfying efficient dew point control. Simulation results show that stripping gas, triethylene glycol (TEG) circulation rate, and inlet feed gas temperature significantly affect BTEX emissions. The proposed optimum operating conditions in this work resulted in a reduction in BTEX emissions by about 81% while satisfying the required NG dew point. Furthermore, two quadratic equations are developed based on regression analysis for efficient calculation of the BTEX emissions and water dew point at any operational variables.

Subdifferentials and Coderivatives of Efficient Point Multifunctions in Parametric Convex Vector Optimization

Subdifferentials and Coderivatives of Efficient Point Multifunctions in Parametric Convex Vector Optimization

Towards environment perception for walking-aid robots: an improved staircase shape feature extraction method

This paper introduces an innovative staircase shape feature extraction method for walking-aid robots to enhance environmental perception and navigation. We present a robust method for accurate feature extraction of staircases under various conditions, including restricted viewpoints and dynamic movement. Utilizing depth camera-mounted robots, we transform three-dimensional (3D) environmental point cloud into two-dimensional (2D) representations, focusing on identifying both convex and concave corners. Our approach integrates the Random Sample Consensus algorithm with K-Nearest Neighbors (KNN)-augmented Iterative Closest Point (ICP) for efficient point cloud registration. The results show an improvement in trajectory accuracy, with errors within the centimeter range. This work overcomes the limitations of previous approaches and is of great significance for improving the navigation and safety of walking assistive robots, providing new possibilities for enhancing the autonomy and mobility of individuals with physical disabilities.

Improved fiber orientation measurement in nonwovens with corner removal

The orientation of fibers or filaments in nonwovens is critical in determining their mechanical characteristics. Image processing techniques, prized for their minimal human intervention and rapid processing speed, are widely utilized in nonwoven fiber orientation measurement. However, these techniques often face substantial challenges, such as low accuracy in corner detection, errors in fiber segmentation, and inefficiencies in fiber orientation calculation. Addressing these concerns, this study introduces a novel, enhanced method accompanied by two innovative optimization algorithms to enhance accuracy. The first innovation involves the development of a newly developed fiber corner detection algorithm, dubbed the T-detector, specifically tailored for the unique characteristics of fiber images, enabling efficient corner point detection and removal. Subsequently, we introduce and employ a fiber length restriction algorithm to further segment the processed longer fibers into the remaining fiber fragments and utilize a skeleton projection algorithm to calculate the fiber orientation. These algorithms overcome the existing technology’s inherent shortcomings, thereby heightening measurement accuracy. The results illustrate an improvement in measurement precision over other orientation distribution measurement algorithms, with the fiber information retention (covering ratio) reaching an impressive 95%. Our proposed method not only calculates fiber orientation distribution in nonwovens with remarkable accuracy and efficiency, but its innovative approach also stands to provide a theoretical foundation for the design of three-dimensional filtering models with specific fiber orientation.

Numerical evaluation of low-head inverted cross-flow turbine design method: A comprehensive study

This study aimed to address the challenges of micro-hydropower generation in low-head environments to expand the proportion of hydropower in renewable energy and achieve a carbon neutrality policy. To achieve these goals, various factors affecting the micro-hydro systems, including geographical constraints, construction costs, and grid connectivity, were reviewed and assessed. The results concluded that a novel type of cross-flow turbine with inverted structures, improved the economic feasibility, productivity, and installability of micro-hydropower plants by reducing head losses in open channels or small streams. The parametric studies consisted of four steps: first, designing the initial inverted cross-flow turbine based on the traditional design rules; for the second and third steps, a parametric study for comparing the runner diameter ratio and the number of blades is conducted; and in the last step optimized turbine was designed. The best efficiency point of the optimized inverted cross-flow turbine was 72.92 % at a runner rotational speed of 170 RPM with a 0.75 runner diameter ratio and 35 blades. The optimized turbine was improved by 7.8 % in power and 7.35 % in efficiency, higher than the initial turbine. From the hill chart comparison between the initial and optimized turbine, operation range expansion was confirmed. Overall, this research demonstrates the potential of the inverted cross-flow turbine under low-head conditions.

Analytical results for uncertainty propagation through trained machine learning regression models

Machine learning (ML) models are increasingly being used in metrology applications. However, for ML models to be credible in a metrology context they should be accompanied by principled uncertainty quantification. This paper addresses the challenge of uncertainty propagation through trained/fixed ML regression models. Analytical expressions for the mean and variance of the model output are obtained/presented for certain input data distributions and for a variety of ML models. Our results cover several popular ML models including linear regression, penalised linear regression, kernel ridge regression, Gaussian Processes (GPs), support vector machines (SVMs) and relevance vector machines (RVMs). We present numerical experiments in which we validate our methods and compare them with a Monte Carlo approach from a computational efficiency point of view. We also illustrate our methods in the context of a metrology application, namely modelling the state-of-health of lithium-ion cells based upon Electrical Impedance Spectroscopy (EIS) data.

Maximizing the capacity and benefit of CO2 storage in depleted oil reservoirs

Sequestering CO2 in depleted oil reservoirs provides one of the most appealing measures to reduce greenhouse gases (GHG) concentration in the atmosphere. The remaining liquids after enhanced oil recovery (EOR) processes, including residual oil and remaining water, lead to the main challenges to this approach. How to effectively evacuate a depleted oil reservoir by recovering not only residual oil but also remaining water is a critical consideration for this type of CO2 sequestration. This paper presents conceptual investigations concerning the methods which effectively evacuate depleted oil reservoirs from both the displacement efficiency and the sweep efficiency points of view. To improve the displacement efficiency, surfactant slug and solvent slug injection was examined using a core scale numerical model. Investigations regarding improving sweep efficiency, such as horizontal well pattern infilling and foam injection, were carried out based on a typical row well pattern. Simulation results showed that surfactant slug which modified the relative permeability and capillary pressure remarkably reduced both residual oil saturation and remaining water saturation. A CO2 slug injected before surfactant slug can help improve the oil recovery. Solvent enriched CO2 slug also remarkably reduced the residual oil saturation to as low as 2%. Horizontal well pattern infilling had great advantage for thick or inclined reservoirs, and foam slug injection greatly improved CO2 storage capacity in thin reservoirs by improving the sweep efficiency. Maximum mobility reduction (MRF) is the most important parameter to maximize the storage capacity and the benefit. The variation of CO2 storage capacity along with CO2 slug size. Larger foam slug size will play a better storage performance. The conceptual simulation investigations confirmed that depleted oil reservoirs can be effectively evacuated for CO2 storage. Depleted oil reservoirs with maximum evacuation are the best candidates for CO2 sequestrations.

Autonomous ultrasound scanning robotic system based on human posture recognition and image servo control: an application for cardiac imaging.

In traditional cardiac ultrasound diagnostics, the process of planning scanning paths and adjusting the ultrasound window relies solely on the experience and intuition of the physician, a method that not only affects the efficiency and quality of cardiac imaging but also increases the workload for physicians. To overcome these challenges, this study introduces a robotic system designed for autonomous cardiac ultrasound scanning, with the goal of advancing both the degree of automation and the quality of imaging in cardiac ultrasound examinations. The system achieves autonomous functionality through two key stages: initially, in the autonomous path planning stage, it utilizes a camera posture adjustment method based on the human body's central region and its planar normal vectors to achieve automatic adjustment of the camera's positioning angle; precise segmentation of the human body point cloud is accomplished through efficient point cloud processing techniques, and precise localization of the region of interest (ROI) based on keypoints of the human body. Furthermore, by applying isometric path slicing and B-spline curve fitting techniques, it independently plans the scanning path and the initial position of the probe. Subsequently, in the autonomous scanning stage, an innovative servo control strategy based on cardiac image edge correction is introduced to optimize the quality of the cardiac ultrasound window, integrating position compensation through admittance control to enhance the stability of autonomous cardiac ultrasound imaging, thereby obtaining a detailed view of the heart's structure and function. A series of experimental validations on human and cardiac models have assessed the system's effectiveness and precision in the correction of camera pose, planning of scanning paths, and control of cardiac ultrasound imaging quality, demonstrating its significant potential for clinical ultrasound scanning applications.

A clustering-based partially stratified sampling for high-dimensional structural reliability assessment

Assessing structural reliability problem with high-dimensional random inputs is still challenging due to the “curse of dimensionality”. In this paper, this challenge is addressed by extending the Generalized Distribution Reconstruction Method via Characteristic Function Inversion (GDRM-CFI). Specifically, a clustering-based partially stratified sampling method is proposed for selecting high-dimensional points to numerically evaluate the characteristic function (CF) curve of complex high-dimensional problems. An improved number-theoretical method (i-NTM) is used to establish a uniform, efficient point set, ensuring determinism and reducing variability. Subsequently, a partial stratification approach partitions the high-dimensional space into orthogonal two-dimensional subspaces. The fundamental point set is projected into each subspace, and the k-means clustering algorithm identifies centroids within each, acting as representative points. The complete set of representative points from all subspaces formulates the high-dimensional point set. Numerical examples are investigated, which demonstrate the proposed method is effective for high-dimensional structural reliability assessment.

From Point Cloud to BIM: A New Method Based on Efficient Point Cloud Simplification by Geometric Feature Analysis and Building Parametric Objects in Rhinoceros/Grasshopper Software

The aim of the paper is to identify an efficient method for transforming the point cloud into parametric objects in the fields of architecture, engineering and construction by four main steps: 3D survey of the structure under investigation, generation of a new point cloud based on feature extraction and identification of suitable threshold values, geometry reconstruction by semi-automatic process performed in Rhinoceros/Grasshopper and BIM implementation. The developed method made it possible to quickly obtain geometries that were very realistic to the original ones as shown in the case study described in the paper. In particular, the application of ShrinkWrap algorithm on the simplify point cloud allowed us to obtain a polygonal mesh model without errors such as holes, non-manifold surfaces, compenetrating surfaces, etc.

Graphical Interface for Electrical Dimensioning of an On-Grid Photovoltaic System

Abstract Energy is an essential element in the development of mankind and is the engine that has made possible all the discoveries and innovations of recent decades. It is found behind the comfort we have in everyday life, whether at home, at the office, or when traveling: an optimal temperature in the room, light at anytime, appliances to ease us and make our lives more beautiful. An energy system based on renewable energy has the ability to provide electricity in an economical, sustainable, long-term, non-polluting way and can serve the increasing population. One such example is represented by photovoltaic (PV) panels, which produces electricity from captured solar energy and is one of the solutions considered for reducing energy. The dimensioning of a photovoltaic system, from a technical and economic efficiency point of view, involves the analysis of input data, output data and hence the characteristics of an efficient system. The analysis can be done analytically, but also with the help of dedicated software, the result being, in the end, a safe system in terms of preventive operation and maintenance. In the article was made the dimensioning of a system, analytical and automatic – using software application – to determine if the result obtained – the photovoltaic system – is the same regardless of the method used.

Assessment of viscosity effects on high-speed coolant pump performance

The high-speed coolant pump facilitates thermal regulation in electric vehicle components, including batteries and motors, by circulating an ethylene glycol solution. This commonly used circulating fluid exhibits a notable negative correlation with temperature in terms of viscosity. Numerical simulations investigate the transient dynamics of a high-speed coolant pump operating at 6000 rpm, driving coolant flow at various temperatures. A high-speed coolant pump test rig is established, and the performance is evaluated under different temperature conditions. The numerical simulations at different temperatures align well with the experimental outcomes. Decreasing temperatures, from 100 to −20 °C, lead to reduced pump head and efficiency due to increased viscosity. Specifically, at a flow rate of 30 L/min, head decreases by 40.03% and efficiency by 44.19%. With escalating viscosity, the best efficiency point shifts toward lower flow rates. Notable impacts on both disk efficiency and hydraulic efficiency are observed due to viscosity fluctuations. It exerts minimal influence on volumetric efficiency at elevated flow rates but has a substantial impact on volumetric efficiency at lower flow rates. Increased fluid viscosity causes uneven pressure distribution within the pump, altering velocity profiles within the impeller. High-viscosity fluids tend to form large-scale vortex structures around the blades, reducing the thrust exerted by the blades on the fluid. Higher viscosity results in larger vortex structures around the blades, reducing thrust and increasing fluid frictional resistance. The study findings provide valuable insights for the advancement of high-efficiency, energy-saving, high-speed coolant pumps tailored for electric vehicles.