Flow Analysis Techniques Research Articles

Power flow solution using a novel generalized linear Hopfield network based on Moore–Penrose pseudoinverse

This paper proposes a novel generalized linear Hopfield neural network-based power flow analysis technique using Moore–Penrose Inverse (MPI) to solve the nonlinear power flow equations (PFEs). The Hopfield neural network (HNN) with linear activation function augmented by a feed forward layer is used to compute the MPI. In this work, the inverse of Jacobian matrix in solving the PFEs is determined by including feed forward network along with feedback network. The developed power flow technique is coded in MATLAB, and its effectiveness is tested on well-conditioned IEEE bus systems (9-bus, 14-bus, 30-bus, and 118-bus), naturally ill-conditioned systems (11-bus and 13-bus), and real-time Malaysian 87-bus system. The results of voltage magnitude and phase angle obtained are compared with standard Newton–Raphson method in case of well-conditioned system. Further, the sensitivity analysis of this approach is carried out against change in initial conditions, line outage, and increase in power generation to validate its robustness. The computational cost of convergence time is compared with well-known power flow techniques of Modified HNN, fourth-order Runge–Kutta (RK4), Iwamoto, and Euler method. The convergence of solution obtained from proposed technique is ensured by Lyapunov notion of stability.

Coupling Bayesian-Monte Carlo simulations with substance flow analysis for efficient pollutant management: A case study of phosphorus flows in China

Phosphorus is an essential nutrient for plants and also a natural component of healthy rivers. Yet, an overload of phosphorus could lead to the degradation of water quality in rivers and streams, thereby causing eutrophication of surface waters. Substance flow analysis (SFA) as a well-established analytical method has been widely used by scholars and scientists to quantify and manage the major nutrient flows entering water bodies over different spatial scales, e.g., the watershed or regional or national scale. However, despite its utility, this method has a notable limitation in that it cannot quantify the data uncertainty efficiently. This may significantly limit its application and lead to less reliable results. In this study, we propose a conceptual framework which enables integration of Bayesian networks, Monte Carlo simulations and substance flow analysis. The Bayesian-Monte Carlo simulation-based substance flow analysis (BMC-SFA) technique is able to account for random uncertainty, and thus can help to deliver an improved understanding of nutrient flows in surface waters. We use the BMC-SFA technique to carry out a systematic analysis of phosphorus flows in China. The results show that the livestock droppings on farmlands account for the most considerable uncertainty of phosphorus flows in China. Suggestions for uncertainty reduction and risk management are given to enable efficient phosphorus flow management in the future. Overall, the BMC-SFA technique can be a useful tool for guiding pollution control and water quality improvement efforts in surface waters.

Upgradation in efficiency of centrifugal pump

The main objective of this work is to understand role of the computational fluid dynamics (CFD) technique in analyzing and predicting the performance of centrifugal pump. Computational Fluid Dynamics (CFD) is the present-day state-of-art technique for fluid flow analysis. The critical review of CFD analysis of CFD analysis of centrifugal pump along with future scope for further improvement is presented in this paper. Different solver like ANSYS-CFX, FLUENT etc. can be used for simulations. Shear stress transport model has been found appropriate as turbulence model. Study of pressure contours, velocity contours, flow streamlines etc. can be studied by CFD techniques. Unsteady Reynolds Averaged Navier Stokes (URANS) equations are solved by solver to get flow simulation results inside centrifugal pump. CFD results has to be validated with testing results or with performance characteristics curves. Performance prediction at design and off-design conditions, parametric study, cavitation analysis, diffuser pump analysis, performance of pump running in turbine mode etc. are possible with CFD simulation techniques.

Permissive runtime information flow control in the presence of exceptions

Information flow control (IFC) has been extensively studied as an approach to mitigate information leaks in applications. A vast majority of existing work in this area is based on static analysis. However, some applications, especially on the Web, are developed using dynamic languages like JavaScript where static analyses for IFC do not scale well. As a result, there has been a growing interest in recent years to develop dynamic or runtime information flow analysis techniques. In spite of the advances in the field, runtime information flow analysis has not been at the helm of information flow security, one of the reasons being that the analysis techniques and the security property related to them (non-interference) over-approximate information flows (particularly implicit flows), generating many false positives. In this paper, we present a sound and precise approach for handling implicit leaks at runtime. In particular, we present an improvement and enhancement of the so-called permissive-upgrade strategy, which is widely used to tackle implicit leaks in dynamic information flow control. We improve the strategy’s permissiveness and generalize it. Building on top of it, we present an approach to handle implicit leaks when dealing with complex features like unstructured control flow and exceptions in higher-order languages. We explain how we address the challenge of handling unstructured control flow using immediate post-dominator analysis. We prove that our approach is sound and precise.

Renewable Energy Supported Bi-directional Electric-Vehicle Charging Station Allocation in Distribution Network using INBPSO Technique

With the sustained growth in the electric-vehicle (EV) industry, the installation of a large number of electric-vehicle charging stations (EVCSs) is the utmost important in recent energy policies of all countries. On the other side, due to the augmented awareness on renewable energy especially the photovoltaic (PV) and wind energies, the installation of renewable energy based EVCSs and their integration into the radial distribution networks have become a promising choice with the feasible bi-directional power flow facility catered by the EVCSs. Bi-directional EVCSs can enable power flow from renewable source/EV to grid and grid to EV to balance power demand for EV as well as distribution network. The optimum allotment of renewable energy powered EVCSs in existing power distribution system is a matter of great significance to maintain the power balance as well as the voltage profile and minimize the total active power loss of the network. This paper presents a strategy for optimum allocation of renewable energy powered EVCSs in a large distribution network with the focus on power balance, voltage profile improvement and active power loss minimization. An improved new binary particle swarm optimization (INBPSO) technique is applied in collaboration with a fast and efficient power flow analysis technique for performing dynamic power flow computation. Validation is conducted on IEEE 15-bus system considering different cases of power flow directions and power ratings. Additionally, voltage as well as loss sensitivity analysis are performed to ascertain the acceptability of the optimum locations obtained by the computation approach. https://dorl.net/dor/20.1001.1.13090127.2021.11.2.24.3

Waste treatment and soil cultivation in a zero emission integrated system for catfish farming in Mekong delta, Vietnam

Abstract The purpose of this study is to investigate the process of waste treatment (wastewater and solid waste) within an intensive catfish farming agro-based zero emission integrated system (AZEIS) in which the fish farming, livestock and cultivation are combined in the same area, as well as the role of this process for soil cultivation of the garden in the same integrated system. The material flow analysis (MFA) technique was used throughout the study for calculating efficiency of waste treatment processes and also for conversion/accumulation of materials with purpose to improve the soil quality for cultivation purpose in the garden. The experimental results showed that the garden in the system plays an important role in receiving, conversion, uptake and accumulation of the nutrients from waste flows, and by that way, in effective removal the pollutants from waste flows as well. In this system, the wastewater from catfish ponds was treated by using water morning glory, a local aquatic plant, before to be reused for watering the cultivation plants in the garden, and the removal percentage of Total Organic Carbon, Nitrogen, Phosphorus and Kallium in wastewater were 33.3, 21.7, 59,5 and 37,5%, respectively. All the solid wastes (sludge from catfish ponds, cow manure from livestock, and absorbed biochar material) were reused as organic fertilizer (compost), and served as sources of additional nutrients for cultivation of Pennisennon purpurecums-schumach grass and maize plants in the garden. Biochar material, which was produced from dried leaves in the garden, was used for treatment of biogas effluent before using later for watering the garden. Total wastewater after being treated and reused for maize plants was 9575 m3/ha, in which absorbed water by maize plant was 48.02 m3/ha, and 9527 m3/ha was evaporated and accumulated in soil. The Nitrogen absorbed by maize plant was 4.12 g/m2, and the soil organic carbon stock in the AZEIS system was 0.19 mg/ha/crop.

Deep feature learning of in-cylinder flow fields to analyze cycle-to-cycle variations in an SI engine

Machine learning (ML) models based on a large data set of in-cylinder flow fields of an IC engine obtained by high-speed particle image velocimetry allow the identification of relevant flow structures underlying cycle-to-cycle variations of engine performance. To this end, deep feature learning is employed to train ML models that predict cycles of high and low in-cylinder maximum pressure. Deep convolutional autoencoders are self-supervised-trained to encode flow field features in low dimensional latent space. Without the limitations ascribable to manual feature engineering, ML models based on these learned features are able to classify high energy cycles already from the flow field during late intake and the compression stroke as early as 290 crank angle degrees before top dead center ([Formula: see text]) with a mean accuracy above chance level. The prediction accuracy from [Formula: see text] to [Formula: see text] is comparable to baseline ML approaches utilizing an extensive set of engineered features. Relevant flow structures in the compression stroke are revealed by feature analysis of ML models and are interpreted using conditional averaged flow quantities. This analysis unveils the importance of the horizontal velocity component of in-cylinder flows in predicting engine performance. Combining deep learning and conventional flow analysis techniques promises to be a powerful tool for ultimately revealing high-level flow features relevant to the prediction of cycle-to-cycle variations and further engine optimization.

Aerodynamic performance improvement using a micro-cylinder as a passive flow control around the S809 airfoil

Aerodynamic performance improvement of airfoilsis the first step towards enhancement ofthe wind turbine performance in electricity generation and energy conversion inrenewable energy applications. The flow behavioraround wind turbine blades profilecan be improved by introducing active and/or passive flow controls. This work numerically describes the impact of adding micro-cylinder, as a passive flow control around S809 airfoil, on aerodynamic performance under various operating conditions. A suitable combination of flow analysis and optimization technique has been used in the current work.The numerical simulation has been performed using ANSYSFluent 18.2 software. The airfoil was numerically analysed in flow atReynolds number of 106; aerodynamic coefficients (lift and drag coefficients) at different angle of attacks were validated with the experimental data reported by Somers in NREL. The Response Surface Method (RSM) is applied to obtain the optimum position of micro-cylinder to achieve maximum lift to drag ratio. It has been found that the total aerodynamic forces are sensitive to the location of the micro-cylinder. A significant enhancement of lift to drag ratio can be achieved by adding micro-cylinder in front of S809 airfoil especially at high Reynolds number.

Computer Vision-based Early Fire Detection Using Enhanced Chromatic Segmentation and Optical Flow Analysis Technique

Recent advances in video processing technologies have led to a wave of research on computer vision-based fire detection systems. This paper presents a multi-level framework for fire detection that analyses patterns in chromatic information, shape transmutation, and optical flow estimation of fire. First, the decision function of fire pixels based on chromatic information uses majority voting among state-of-the-art fire color detection rules to extract the regions of interest. The extracted pixels are then verified for authenticity by examining the dynamics of shape. Finally, a measure of turbulence is assessed by an enhanced optical flow analysis algorithm to confirm the presence of fire. To evaluate the performance of the proposed model, we utilize videos from the Mivia and Zenodo datasets, which have a diverse set of scenarios including indoor, outdoor, and forest fires, along with videos containing no fire. The proposed model exhibits an average accuracy of 97.2% for our tested dataset. In addition, the experimental results demonstrate that the proposed model significantly reduces the rate of false alarms compared to the other existing models

The nine year basic education policy and secondary school internal efficiency

This study examined the effect of implementing Nine Year Basic Education (9YBE) policy on secondary school internal efficiency. The study followed descriptive design. Data on students’ enrolments and repetitions for consecutive school years, cohort 2014/15 and 2017/18, were collected from all five lower secondary schools in Ngoma and Tumba Sectors using survey questionnaire. Besides, focus group discussions (FGDs) were used to collect views of head-teachers on the study variables. The reconstructed cohort flow analysis technique was used to determine indicators of internal efficiency. Summary statistics are presented in tables. Qualitative data from FGDs were analysed through thematic content analysis with consideration of similarities and differences. The findings revealed (i) a positive change in students' promotion and repetition rates, (ii) students’ survival rate to the last grade increased to 69.39% for the cohort 2017/18 from 50.72% for the cohort 2014/15 and (iii) wastage ratio declined from 1.62 for cohort 2014/15 to 1.33 for cohort 2017/18. Thus, school efficiency rose to 75.19% in 2017/18. Head-teachers attributed the positive change in internal efficiency indicators to the combination of 9YBE interventions including removal of school fees, school feeding, and flexibility in student progression and increasing day secondary school in proximity of home. However, persistent high dropout rates indicated that the implementation of 9YBE policy had not alleviated all disruptive forces against students’ participation rates. It was therefore suggested to relook at policy interventions to address individual students’ challenges at school and household levels and the management of older children in the school system.

A General Review of the Current Development of Mechanically Agitated Vessels

The mixing process in a mechanically agitated vessel is a widespread phenomenon which plays an important role among industrial processes. In that process, one of the crucial parameters, the mixing efficiency, depends on a large number of geometrical factors, as well as process parameters and complex interactions between the phases which are still not well understood. In the last decade, large progress has been made in optimisation, construction and numerical and experimental analysis of mechanically agitated vessels. In this review, the current state in this field has been presented. It shows that advanced computational fluid dynamic techniques for multiphase flow analysis with reactions and modern experimental techniques can be used with success to analyse in detail mixing features in liquid-liquid, gas-liquid, solid-liquid and in more than two-phase flows. The objective is to show the most important research recently carried out.

Three-Phase Distribution System Load Flow Analysis Using Sequence Components

Electric power distribution system are usually unbalance. Therefore, a power flow method that can handle the three-phase configuration of the power system is needed so that the system planning and operation can properly be carried out. In the case of three-phase distribution system power flow analysis, for each system bus (except for substation bus), the voltage magnitude and angle of the three phases must be calculated. These calculations are carried out under certain loading conditions. After these voltages have been calculated, the electric power flows and losses in the distribution lines, and the substation power can also be determined. This paper proposes a new technique for three-phase distribution system power flow analysis using sequence components. The new formulation for the power flow problem in terms of sequence components is also proposed and developed in this paper. The application of sequence components has the advantage that the size of the problem can effectively be reduced, and solution to the power flow problem will be easier to find. Case study using a representative distribution test system confirms the validity of the proposed method where comparative studies between the proposed (i.e. sequence components based) method and the phase components based method are carried out.

The Definite Referring Expressions of the Prophet Muhammad's Implicated Speech and its Educational Value

The aims of this study is to analyze the definite referring expressions of the Prophet Muhammad's Implicated Speech and its Educational Value. This is qualitative study with the data in the form of dialogical utterances speech involving two parties, namely the Prophet Muhammad and his best friend. While the source of this research data is a collection of valid hadith narrated by Bukhari and Muslim. In this study, the data collected was analyzed with an explanatory comparative technique with Miles and Huberman's flow analysis techniques, namely data reduction, data presentation, and inference / verification. The results of this study indicate that the expression of certain reference in the implied implications of the Prophet Muhammad can be identified based on certain sciences, such as mathematics and linguistics. The use of this knowledge base is adjusted to the number and type of certain reference expressions used. By using a certain knowledge base, all definite reference expressions in the explicator can be identified so that the meaning is clear. Clarity of meaning that is associated with the context of communication or shared knowledge in this case is an important property of understanding implicates. For further explained, the expression of reference must be in the process of refinement, the process of refinement is in the explanatory, the exploitation is in the implied speech, the implied speech is in the pragmatic or speaking practice. This shows that the expression of reference must be at the point in speaking activities. The use of definite reference expressions, thus implies high-level speaking skills. Higher-level speaking skills are even better when identifying certain expressions of reference in the speech used requires mastering knowledge in mathematics, linguistics, and so on. In other words, the better the quality of a speech if the speaker is conditioned to operate the multidisciplinary knowledge he has in identifying the expression of certain reference because it has educational value.

A Survey on the Methods and Results of Data-Driven Koopman Analysis in the Visualization of Dynamical Systems

Koopman mode decomposition is a flow analysis technique developed by Igor Mezic in 2004, based upon the Koopman operator first proposed by Bernard Koopman in 1931. Via Koopman decomposition any non-chaotic well-sampled dynamic system - linear, non-linear, laminar or turbulent - is broken down into single-frequency repetitive components (modes). This paper presents a survey consolidating published information regarding data-driven Koopman analysis techniques. It is intended to aid researchers exploring the suitability of data-driven Koopman analysis in anticipation of developing their own modeling. A basic mathematical explanation of Koopman analysis is given with emphasis toward the data-driven Dynamic Mode Decomposition (DMD) solution, which converges to the Koopman operator given a highly-sampled dataset. The four primary uses of Koopman analysis: flow analysis, power grid analysis, building thermal analysis, and biomedical analysis are discussed, along with other publications. Finally, weaknesses and problems inherent within Koopman analysis/DMD will be enumerated, alongside potential solutions. Koopman analysis is a computationally complex, yet often suitable method for determining periodic motion in any highly-sampled dataset. When compared to a similar analysis method, Proper Orthogonal Decomposition, Koopman analysis often provides additional detail regarding the structure of less significant modes present, albeit at the cost of increased computational complexity.

ANALISIS KONFLIK BATIN TOKOH UTAMA DAN NILAI PENDIDIKAN KARAKTER NOVELET KETIKA MAS GAGAH PERGI KARYA HELVY TIANA ROSA SERTA RELEVANSINYA SEBAGAI BAHAN AJAR SASTRA DI SMA

<em>This form of research is descriptive qualitative research. The data sources of this research are novel and informants. Technique of taking research subject is purposive sampling. Data collection techniques used is technique read dan write dan interviews. Test the validity of data using triangulation of data sources and theory. Data analysis technique used is flow analysis technique. The result show that: (1) the theme raised in the novelet is the deity, the protagonist, the plot is a forward flow, the story set in Jakarta in 1994, the point of view of the first person the main character, his message is to be a good agent of Goodness; (2) Gagah and Yudi characters have elements id, ego, and superego, Gita character has high id and ego and experiencing the most powerful inner conflicts; (3) the value of character education contained in the novelet includes religious, honest, tolerant, disciplined, creative, independent, democratic, curiosity, friendly/communicative, peace loving, reading, caring, caring, responsible; and (4) the novelet Ketika Mas Gagah Pergi is relevant to be used as a teaching material when viewed from its relation with instructional objectives to be achieved, also from the aspect of content, presentation, language, and graph.</em>

The impact of collection portfolio expansion on key performance indicators of the Dutch recycling system for Post-Consumer Plastic Packaging Waste, a comparison between 2014 and 2017

The recycling network of post-consumer plastic packaging waste (PCPPW) was studied for the Netherlands in 2017 with material flow analysis (MFA) and data reconciliation techniques. In comparison to the previous MFA of the PCPPW recycling network in 2014, the predominant change is the expansion of the collection portfolio from only plastic packages to plastic packages, beverage cartons and metal objects. The analysis shows that the amounts of recycled plastics products (as main washed milled goods) increased from 75 to 103 Gg net and the average polymeric purity of the recycled products remained nearly constant. Furthermore, the rise in the amounts of recycled products was accompanied with a rise in the total amount of rejected materials at cross docking facilities and sorting residues at the sorting facilities. This total amount grew from 19 Gg in 2014 to 70 Gg gross in 2017 and is over-proportional to the rise in recycled products. Hence, there is a clear trade-off between the growth in recycled plastics produced and the growth in rejects and residues. Additionally, since the polymeric purity of the recycled plastics did not significantly improve during the last years, most of the recycled plastics from PCPPW are still only suited for open-loop recycling. Although this recycling system for PCPPW is relatively advanced in Europe, it cannot be considered circular, since the net recycling yield is only 26 ± 2% and the average polymeric purity of the recycled plastics is 90 ± 7%.

A Multi-criteria Approach for Distribution Network Expansion Through Pooled MCDEA and Shannon Entropy

Abstract Power distribution network expansion planning (DNEP), based on innovative load flow analysis and optimization techniques, has drawn great attention of researchers around the world to cater for ever increasing demand of electrical power. In the present work, a new approach based on Multi-criteria Data Envelopment Analysis and Shannon Entropy analysis (MCDEA-SEA) is presented that strengthens the solution hunt process of DNEP problems. In the first stage of proposed methodology, various probable configurations are determined through load flow analysis considering different objective functions and constraints. In the next stage, large amount of complex data sets generated for various configurations from power flow perusal, then analyzed using pooled MCDEA-SEA. This multi-stage approach expedites search for best and impeccable solution, which leaves no space for sub-optimality. The efficacy of the proposed method has been verified by applying it on IEEE 33-bus test distribution system, considering impending load scenarios and the results show that the proposed methodology can effectively solve DNEP problem and provide optimal solution for DNEP problems, consuming lesser computation time compared to conventional approaches.

Computational Techniques for three-phase Load Flow Analysis

This paper aims to compute a three-phase load flow for balanced load and unbalanced loads conditions. Unbalanced loading can occur if the power demand of each phase is not equal, this happens because the load usage of each phase varies. Three-phase load flow is a study in the electric power system that will provide information on voltage, phase angle, current, power and power losses of each bus on phases a, b and c. Three phase power flow is carried out for operation planning and control of three-phase electric power systems. The imbalance in the electric power system will affect the use of electrical energy, that is, there will be a decrease in voltage received by consumers. Three-phase load flow computing will be done using the Etap version 12.0 software which will be tested on the IEEE 6 bus system. The computational results show that the unbalanced load flow converges on the 2nd iteration of the bus. The IEEE 6 bus system has the largest voltage drop on bus 3 by 3%, 2.9% and 2.2% for each phase a, b and c. The biggest power losses of the IEEE 6 bus system occur in the connecting line between bus 1 with bus 2 which is 32.5 KW, 28.1 KW and 37.1 KW, with total losses of 254.1 KW and 778.5 KVAR.

Predicting process performance: A white‐box approach based on process models

AbstractPredictive business process monitoring methods exploit historical process execution logs to provide predictions about running instances of a process. These predictions enable process workers and managers to preempt performance issues or compliance violations. A number of approaches have been proposed to predict quantitative process performance indicators for running instances of a process, including remaining cycle time, cost, or probability of deadline violation. However, these approaches adopt a black‐box approach, insofar as they predict a single scalar value without decomposing this prediction into more elementary components. In this paper, we propose a white‐box approach to predict performance indicators of running process instances. The key idea is to first predict the performance indicator at the level of activities and then to aggregate these predictions at the level of a process instance by means of flow analysis techniques. The paper develops this idea in the context of predicting the remaining cycle time of ongoing process instances. The proposed approach has been evaluated on real‐life event logs and compared against several baselines.

3D CFD wind flow analysis technique applied to a parabolic solar tracker for two extreme weather conditions with experimental results and a controller proposition

This work presents a Computational Fluid Dynamics (CFD) wind flow analysis technique applied to a Parabolic Solar Tracker (PST) using a three-dimensional Reynolds-Averaged Navier-Stokes Method. The modeling technique considered two extreme weather conditions named: (i) North Wind (NW) and (ii) Severe Convective Storms (SCSs), both of which can severely affect the integrity and the position control of a PST under operational conditions. The NW case simulated was validated using experimental data without bottom effects in order to verify the degree of approximation between the numerical results obtained in each approach and measurement data. It was observed that the simulated data were satisfactory in most cases, with predictions on the order of 77% compared to the measured data. The numerical CFD results at different heights for both cases, NW and SCS, were also verified. Finally, the CFD wind flow analysis technique results were used to: (i) establish both analytical and numerical models for dynamic and steady state analysis, control design, and simulation purposes and (ii) consequently design a closed loop controller which can minimize the wind effects.