Flow Monitoring Research Articles

Microfluidic flowmeter based on a liquid crystal-filled nested capillary

Microfluidic flowmeters are a powerful and highly accurate tool, enabling precise monitoring and measurements of flows of gases and fluids in a range of applications. Here we proposed and experimentally demonstrated a whispering gallery modes flowmeter composed of a liquid crystal-filled nested capillary. Whispering gallery modes are excited by a tapered fiber coupled perpendicularly to the nested capillary. The air flowing through the capillary cools it down, which leads to a temperature-induced change of the refractive index of the nematic liquid crystals. This change in turn leads to a spectral shift of the whispering gallery modes resonances, which can be linked to the airflow rate in the capillary. The temperature change in the liquid crystals was simulated considering the heat transfer between the liquid crystals and airflow in the capillary, which indicated that the liquid crystals temperature decreases in a nonlinear manner with the increase of the airflow rate. A flowmeter with the maximum sensitivity of 0.3423 nm·min·mL−1 in the flowrate range from 0 to 2.52 nm·min·mL−1 and a resolution of 5.72 pm was demonstrated in our experiment. The proposed sensor provides a platform for whispering gallery modes flowmeters and offers the advantages of good stability, high sensitivity, and miniature size.

Portable cerebral blood flow monitor to detect large vessel occlusion in patients with suspected stroke

BackgroundEarly detection of large vessel occlusion (LVO) facilitates triage to an appropriate stroke center to reduce treatment times and improve outcomes. Prehospital stroke scales are not sufficiently sensitive, so we...

Monitoring and Early Warning Method of Debris Flow Expansion Behavior Based on Improved Genetic Algorithm and Bayesian Network

Based on an improved genetic algorithm and debris flow disaster monitoring network, this study examines the monitoring and early warning method of debris flow expansion behavior, divides the risk of debris flow disaster, and provides a scientific basis for emergency rescue and post-disaster recovery. The function of the debris flow disaster monitoring network of the spreading behavior disaster chain is constructed. According to the causal reasoning of debris flow disaster monitoring information, the influence factors of debris flow, such as rainfall intensity and duration, are selected as the inputs of the Bayesian network, and the probability of a debris flow disaster is obtained. The probability is compared with the historical data threshold to complete the monitoring and early warning of debris flow spreading behavior. Innovatively, by introducing niche technology to improve traditional genetic algorithms by learning Bayesian networks, the optimization efficiency and convergence speed of genetic algorithms are improved, and the robustness of debris flow monitoring and warning is enhanced. The experimental results show that this method divides debris flow disasters into the following five categories based on their danger: low-risk area, medium-risk area, high-risk area, higher-risk area, and Very high-risk area. It accurately monitors the expansion of debris flows and completes early warning. The disaster management department can develop emergency rescue and post-disaster recovery strategies based on early warning results, thus providing a scientific basis for debris flow disasters. The improved genetic algorithm has a higher learning efficiency, a higher accuracy, a faster convergence speed, and higher advantages in learning time and accuracy of the Bayesian network structure.

ECT image reconstruction based on sensitive field expansion and optimization

Electrical capacitance tomography (ECT) is an efficient method for addressing the issue of two-phase flow monitoring. Most current methods result in low image reconstruction accuracy due to soft field issues. This paper propose an ECT image reconstruction method based on sensitive field expansion and optimization, which improve reconstruction efficiency and accuracy. Firstly, a sensitivity field optimization method based on flow pattern identification was proposed. The flow pattern recognition determines the flow pattern to which the input signal belongs and selects the sensitive field for the corresponding flow pattern. Secondly, a sensitive field expansion method based on feature extraction is proposed. The ECT image is modeled as a finite rate of innovation signal, sampled using an exponential reproducing kernel. Feature information is extracted from the input signal for data fusion, and the optimized sensitive field is expanded into a new sensitive field distribution matrix by zero-padding and random reorganization. Then, based on optimized and expanded sensitive fields, a sparse image reconstruction method is proposed. Image reconstruction by constructing comprehensive observation equation to obtain the original permittivity distribution vector of the ECT system using the sparsity of the sensitivity matrix and capacitance signal. Finally, the ECT hardware system and the upper computer measurement and imaging integration software are designed. The experimental results show that the method outperforms other existing algorithms in terms of imaging indexes, has better imaging results.

Wearable fiber-free optical sensor for continuous monitoring of neonatal cerebral blood flow and oxygenation.

Unstable cerebral hemodynamics places preterm infants at high risk of brain injury. We adapted an innovative, fiber-free, wearable diffuse speckle contrast flow-oximetry (DSCFO) device for continuous monitoring of both cerebral blood flow (CBF) and oxygenation in neonatal piglets and preterm infants. DSCFO uses two small laser diodes as focused-point and a tiny CMOS camera as a high-density two-dimensional detector to detect spontaneous spatial fluctuation of diffuse laser speckles for CBF measurement, and light intensity attenuations for cerebral oxygenation measurement. The DSCFO was first validated against the established diffuse correlation spectroscopy (DCS) in neonatal piglets and then utilized for continuous CBF and oxygenation monitoring in preterm infants during intermittent hypoxemia (IH) events. Significant correlations between the DSCFO and DCS measurements of CBF variations in neonatal piglets were observed. IH events induced fluctuations in CBF, cerebral oxygenation, and peripheral cardiorespiratory vitals in preterm infants. However, no consistent correlation patterns were observed among peripheral and cerebral monitoring parameters. This pilot study demonstrated the feasibility of DSCFO technology to serve as a low-cost wearable sensor for continuous monitoring of multiple cerebral hemodynamic parameters. The results suggested the importance of multi-parameter measurements for understanding deep insights of peripheral and cerebral regulations. The innovative DSCFO technology may serve as a low-cost wearable sensor for continuous bedside monitoring of multiple cerebral hemodynamic parameters in neonatal intensive care units. Concurrent DSCFO and DCS measurements of CBF variations in neonatal piglet models generated consistent results. No consistent correlation patterns were observed among peripheral and cerebral monitoring parameters in preterm neonates, suggesting the importance of multi-parameter measurements for understanding deep insights of peripheral and cerebral regulations during IH events. Integrating and correlating multiple cerebral functional parameters with clinical outcomes may identify biomarkers for prediction and management of IH associated brain injury.

Delta Blue(green)s: The Impact of Drought and Drought Management Actions on Microcystis in the Sacramento–San Joaquin Delta

Cyanobacterial phytoplankton blooms are more prevalent in the freshwater Sacramento-San Joaquin Delta (Delta) since the late 1990s, including blooms driven by overgrowths of potentially toxigenic organisms of the genus Microcystis. Data from 2014 to 2021 were used to show how flow dynamics, water temperature, and water clarity drive occurrence of Microcystis. We used a Microcystis bloom in the central Delta from 2021 as a case study for how novel monitoring tools can track blooms in real-time and be used post hoc to evaluate the effects of management actions. Microcystis was detected throughout the Delta in all but the highest-flow years, and bloom incidence and severity increased during drier years. In the South Delta, Franks Tract, lower San Joaquin River, and Old River regions, where blooms are most prevalent, higher water temperatures and clarities combined with lower exports from state and federal water projects were the best explanatory factors for the occurrence of Microcystis blooms. Nutrient concentrations were lower in summer than in winter, but only became limiting at high phytoplankton concentrations. We used satellite data and in situ continuous monitoring of flow, phytoplankton communities, and water quality to track hydro-biogeochemical conditions during the 2021 case study Microcystis bloom in the Central Delta. We did not find evidence that changes to Delta outflow regulatory standards contributed to this bloom, but changes in flow caused by a salinity barrier placed in west False River may have exacerbated the bloom. The frequency and severity of droughts are expected to increase in the future as a result of climate change, and our study demonstrates how continued monitoring of cyanotoxins, water quality, and phytoplankton communities could help improve management of cyanobacterial blooms in the Delta and other estuaries.

Traffic flow detection method based on improved SSD algorithm for intelligent transportation system.

With the development of the new generation communication system in China, the application of intelligent transportation system is more extensive, which brings higher demands for vehicle flow detection and monitoring. Traditional traffic flow detection modes often cannot meet the high statistical accuracy requirement and high-speed detection simultaneously. Therefore, an improved Inception module is integrated into the single shot multi box detector algorithm. An intelligent vehicle flow detection model is constructed based on the improved single shot multi box detector algorithm. According to the findings, the convergence speed of the improved algorithm was the fastest. When the test sample was the entire test set, the accuracy and precision values of the improved method were 93.6% and 96.0%, respectively, which were higher than all comparison target detection algorithms. The experimental results of traffic flow statistics showed that the model had the highest statistical accuracy, which converged during the training phase. During the testing phase, except for manual statistics, all methods had the lowest statistical accuracy on motorcycles. The average accuracy and precision of the designed model for various types of images were 96.9% and 96.8%, respectively. The calculation speed of this intelligent model was not significantly improved compared to the other two intelligent models, but it was significantly higher than manual monitoring methods. Two experimental data demonstrate that the intelligent vehicle flow detection model designed in this study has higher detection accuracy. The calculation speed has no significant difference compared with the traditional method, which is helpful to the traffic flow management in intelligent transportation system.

Validation of the Openwater wearable optical system: cerebral hemodynamic monitoring during a breath-hold maneuver.

Bedside cerebral blood flow (CBF) monitoring has the potential to inform and improve care for acute neurologic diseases, but technical challenges limit the use of existing techniques in clinical practice. Here, we validate the Openwater optical system, a novel wearable headset that uses laser speckle contrast to monitor microvascular hemodynamics. We monitored 25 healthy adults with the Openwater system and concurrent transcranial Doppler (TCD) while performing a breath-hold maneuver to increase CBF. Relative blood flow (rBF) was derived from changes in speckle contrast, and relative blood volume (rBV) was derived from changes in speckle average intensity. A strong correlation was observed between beat-to-beat optical rBF and TCD-measured cerebral blood flow velocity (CBFv), ; the slope of the linear fit indicates good agreement, 0.87 (95% CI: 0.83 ). Beat-to-beat rBV and CBFv were also strongly correlated, , but as expected the two variables were not proportional; changes in rBV were smaller than CBFv changes, with linear fit slope of 0.18 (95% CI: 0.17 to 0.19). Further, strong agreement was found between rBF and CBFv waveform morphology and related metrics. This first in vivo validation of the Openwater optical system highlights its potential as a cerebral hemodynamic monitor, but additional validation is needed in disease states.

Flow regime classification using various dimensionality reduction methods and AutoML

Accurate identification of flow regimes is paramount in several industries, especially in chemical and hydrocarbon sectors. This paper describes a comprehensive data-driven workflow for flow regime identification. The workflow encompasses: i) the collection of dynamic pressure signals using an experimentally verified numerical two-phase flow model for three different flow regimes: stratified, slug and annular flow, ii) feature extraction from pressure signals using Discrete Wavelet Transformation (DWT), iii) Evaluation and testing of 12 different Dimensionality Reduction (DR) techniques, iv) the application of an AutoML framework for automated Machine Learning classifier selection among K-Nearest Neighbors, Artificial Neural Networks, Support Vector Machines, Gradient Boosting, Random Forest, and Logistic Regression, with hyper-parameter tuning. Kernel Fisher Discriminant Analysis (KFDA) is the best DR technique, exhibiting superior goodness of clustering, while KNN proved to be the top classifier with an accuracy of 92.5 % and excellent repeatability. The combination of DWT, KFDA and KNN was used to produce a virtual flow regime map. The proposed workflow represents a significant step forward in automating flow regime identification and enhancing the interpretability of ML classifiers, allowing its application to opaque pipes fitted with pressure sensors for achieving flow assurance and automatic monitoring of two-phase flow in various process industries.

Advancements in Computer Vision Applications for Traffic Surveillance Systems

With the increasing need for efficient traffic surveillance systems, computer vision emerges as a promising technology to address various challenges in this domain. This paper aims to provide an overview of recent advancements in the application of computer vision in traffic surveillance. It commences by discussing fundamental concepts of computer vision and its role in traffic monitoring, followed by an exploration of various techniques and algorithms used in computer vision systems, including object detection, tracking, and classification. Furthermore, it investigates the integration of computer vision with other technologies such as machine learning and deep learning, emphasizing their impact on enhancing the accuracy and efficiency of traffic surveillance systems. The review explores specific applications of computer vision in traffic surveillance, such as traffic flow monitoring, anomaly detection, and license plate recognition. Additionally, it addresses challenges and limitations associated with the implementation of computer vision in real-world traffic scenarios. Synthesizing existing literature, case studies, and research findings, this review offers a comprehensive insight into the current state-of-the-art in the application of computer vision for traffic surveillance systems. The paper concludes with a discussion of future directions and potential research opportunities in this rapidly evolving field.

Finite difference-embedded UNet for solving transcranial ultrasound frequency-domain wavefield.

Transcranial ultrasound imaging assumes a growing significance in the detection and monitoring of intracranial lesions and cerebral blood flow. Accurate solution of partial differential equation (PDE) is one of the prerequisites for obtaining transcranial ultrasound wavefields. Grid-based numerical solvers such as finite difference (FD) and finite element methods have limitations including high computational costs and discretization errors. Purely data-driven methods have relatively high demands on training datasets. The fact that physics-informed neural network can only target the same model limits its application. In addition, compared to time-domain approaches, frequency-domain solutions offer advantages of reducing computational complexity and enabling stable and accurate inversions. Therefore, we introduce a framework called FD-embedded UNet (FEUNet) for solving frequency-domain transcranial ultrasound wavefields. The PDE error is calculated using the optimal 9-point FD operator, and it is integrated with the data-driven error to jointly guide the network iterations. We showcase the effectiveness of this approach through experiments involving idealized skull and brain models. FEUNet demonstrates versatility in handling various input scenarios and excels in enhancing prediction accuracy, especially with limited datasets and noisy information. Finally, we provide an overview of the advantages, limitations, and potential avenues for future research in this study.

An intelligent continuous bladder irrigation monitoring and control system: Development and preliminary assessment

Abstract Objectives: To design an intelligent continuous bladder irrigation monitoring and control system and preliminarily assess its sensitivity of the early warning of flushing anomalies. Methods: According to the principle of bladder irrigation, an intelligent continuous bladder irrigation monitoring and control system was designed. The Amesim fluid simulation software was used to establish a parameterized simulation model for the bladder perfusion process and simulate the dynamic changes of bladder pressure and volume under different perfusion speeds to assess the sensitivity of the early warning of flushing anomalies. Results: The intelligent system consisted of 3 parts: the monitoring part (a flushing fluid inlet flow monitoring device, a flushing fluid outlet flow monitoring device, and a flushing fluid outlet color recognition sensor), the transition part (a control motherboard), and the regulation part (an alarm device, an electric control device for flushing fluid input speed, and a squeezing device for flushing waste fluid line). The data of the parameterized simulation model for the bladder perfusion process suggested that the sensitivity of the system was acceptable. Conclusion: The intelligent bladder irrigation system can automatically detect, report, and resolve signal abnormalities, thereby achieving the closed-loop management of bladder irrigation. The intelligent system might be used in clinical settings to reduce the burdens of nurses and promote patients’ recovery.

Overview of Control and Monitoring Specification Study to Help Reach Net Zero in the UK for Hydrogen Flow Measurement Network

Currently, environmental damage appears a worldwide issue that affects everyone on the globe. With the goal of a net-zero carbon footprint by the year 2050, many nations have passed legislation, provided aid, and raised both public and private investment to encourage the use of renewable energy sources instead of traditional pathways like fossil fuels. Many other countries have made similar intentions to initiate their journey to an economy with no emissions at all including the UK. The current objective of this study is to review equipment for monitoring techniques and metering which helps to deal with the modern industry to attain net zero emission through the application of hydrogen. Ultrasonic and Coriolis flowmeters have been compared in terms of measurement techniques for hydrogen in the operative system. It has been identified that the petroleum and natural gas industrial sectors are using mass flow monitoring more frequently. Beyond absolute zero and at atmospheric pressures hydrogen becomes liquified. So, it proved essential to have a huge mass flow measuring apparatus having minimal uncertainty and proper and fixed measurement inaccuracy. Moreover, it has been evaluated that Programmable logic controllers are being used to operate the system to create a trustworthy control framework.

Noninvasive measurement of ambulatory venous pressure via column interruption duration in chronic venous disease

BackgroundColumn interruption duration (CID) is a noninvasive surrogate for venous refill time (VFT), a parameter used in ambulatory venous pressure measurement. CID is more accurate than invasive VFT measurement because it avoids errors involved with indirect access of the deep system through the dorsal foot vein. The aim of this retrospective single center study is to analyze the clinical usefulness of CID in assessment of chronic venous disease (CVD). MethodsA total of 1551 limbs (777 patients) were referred with CVD symptoms over a 5-year period (2018-2023); CID, air plethysmography, and duplex reflux data were analyzed. Of these limbs, 679 had supine venous pressure data as well. The pathology was categorized as obstruction if supine peripheral venous pressure was >11 mm Hg and as reflux if duplex reflux time in superficial or deep veins was >1 second. CID was measured via Doppler monitoring of flow in the great saphenous vein (GSV) and one of the paired posterior tibial (PT) veins near the ankle in the erect posture. The calf is emptied by rapid inflation cuff. CID is the time interval in seconds when cephalad venous flow in great saphenous vein and posterior tibial veins reappear after calf ejection. A CID <20 seconds in either vein is abnormal similar to the threshold used in VFT measurement. ResultsThirty-two percent of the limbs had obstruction, 17% had reflux, and 37% had a combination; 14% had neither. Higher clinical-etiology-anatomy-pathophysiology (CEAP) clinical classes (C4-6) were prevalent in 44% of pure reflux, significantly less (P < .0001) than in pure obstruction (73%) or obstruction plus reflux subsets (72%), partly reflecting distribution of pathology. There is a progressive increase in supine venous pressure and abnormal CID (P < .0001 and P < .0001, respectively) in successive CEAP clinical class. No such correlation between CEAP and any of the reflux severity grading methods (reflux segment score, Venous Filling Index, and Kistner axial grading) was observed. Abnormal CID (55%) was more prevalent in higher CEAP classes (>4) (P < .0001) than in lesser clinical classes (0-2) or limbs with neither obstruction nor reflux (P < .01). ConclusionsObstruction seems to be a more dominant pathology in clinical progression among CEAP clinical classes than reflux. CID is abnormal in both obstructive and refluxive pathologies and may represent a common end pathway for similar clinical manifestations (eg, ulcer). These data suggest a useful role for CID measurement in clinical assessment of limbs with CVD.

EffCause: Discover Dynamic Causal Relationships Efficiently from Time-Series

Since the proposal of Granger causality, many researchers have followed the idea and developed extensions to the original algorithm. The classic Granger causality test aims to detect the existence of the static causal relationship. Notably, a fundamental assumption underlying most previous studies is the stationarity of causality, which requires the causality between variables to keep stable. However, this study argues that it is easy to break in real-world scenarios. Fortunately, our paper presents an essential observation: if we consider a sufficiently short window when discovering the rapidly changing causalities, they will keep approximately static and thus can be detected using the static way correctly. In light of this, we develop EffCause, bringing dynamics into classic Granger causality. Specifically, to efficiently examine the causalities on different sliding window lengths, we design two optimization schemes in EffCause and demonstrate the advantage of EffCause through extensive experiments on both simulated and real-world datasets. The results validate that EffCause achieves state-of-the-art accuracy in continuous causal discovery tasks while achieving faster computation. Case studies from cloud system failure analysis and traffic flow monitoring show that EffCause effectively helps us understand real-world time-series data and solve practical problems.

Sector orifice with a correlation-based analysis

In single and multiphase flows, an orifice is a vital flow-measuring and flow-controlling device that operates on Bernoulli’s theorem on the determination of the pressure drop (∆p). Laboratory, petroleum, energy, nuclear, mining, chemical, processing, and food industries all depend on flow monitoring and flow regulations. A sector or segmental orifice meter is a much simpler device used for drainage and sediment-laden type non-homogeneous flow. Sector orifice meter analysis is the rate of flow, pressure and velocity profile concerning input parameters to find out the ∆p and coefficient of discharge (Cd) of an orifice meter. The present study varies with different sector angles (θ), Reynolds number (Re) and space ratio (s/d) of the sector orifice for the flow analysis with the use of CFD (Ansys. Inc) based study. The numerical simulation range considered for θ (30°-180°), Re (10000-100000), and s/d (0.1-0.9). To compute the flow rate, ∆p and Cd by using air as a working fluid to present a flow problem through high viscous flow. The simulation also presents the pressure and velocity profiles along the flow on the variant of Re, θ and s/d. Based on the numerical data a correlation developed which will help the measurement through sector orifice for wide range of future application. With the comparison of the existing literature, this will give a good contribution to sector orifice for accurate flow measurement for high viscous flow.

MANAJEMEN PEMBIAYAAN PENDIDIKAN UNTUK MENINGKATKAN MUTU PENDIDIKAN

This study aims to analyze education financing management based on the effectiveness and efficiency of education financing management and the use of technology in education financing management. This research uses descriptive qualitative research methods with secondary data collected through literature studies and analyzed qualitatively by reducing displays and concluding results. The results of this study indicate that effective and efficient education financing management in educational institutions is key to supporting the achievement of quality education goals. The management of education financial resources must pay attention to the essential stages, because education financing is considered an investment in human resource development. In this context, the effectiveness and efficiency of education cost management is essential to maximize educational outcomes. The current development of digital technology opens up great opportunities to optimally utilize the education financing system. With the adoption of information and communication technology, educational institutions can automate the financing process, integrate digital payment systems, and enable real-time monitoring of fund flows and financial reporting.

Sleep-induced limb vasodilation in individuals confined to bed for 24 h

The sleep/wake rhythm in limbs has been scarcely studied, especially due to the difficulty associated with continuous monitoring of arterial flow to the forearm and leg for a 24-h period. Addressing this constraint, we employed indium-gallium-in-silicone strain-gauge venous-occlusion plethysmography, an automated method facilitating the measurement of 24-h limb arterial flow in bed-confined subjects without disturbing their natural sleep. This article presents the state of the art in this field. Our examination of 60 healthy normotensive individuals revealed a distinctive sleep/wake rhythm in limb arterial flow, characterized by elevated values during sleep (32.7% in the forearm, P &lt; 0.0001; 39.1% in the leg, P &lt; 0.0001). Correspondingly, limb resistance mirrored the trend of flow (-32.7%, P &lt; 0.0001; -33.5%, P &lt; 0.0001), with these variations attributed to sleep-induced limb vasodilation. Sleep-associated vasodilation was also evident in 21 hypertensive individuals (leg resistance: -33.1%, P &lt; 0.0001) and 13 heart transplant recipients lacking vagal and sympathetic cardiac innervation (resistance: -33.6%, P &lt; 0.0001). On the contrary, among 11 subjects with an interrupted spinal cord, we observed forearm vasodilation (resistance: -36.6%, P &lt; 0.0001) but observed no leg vasodilation if the spinal lesion was under T2 (innervating the leg). Furthermore, a loss of sleep-induced vasodilation occurred in both the forearm and leg if the injury was above C7 (innervating both forearm and leg). Our conclusion posits the existence of sleep-induced limb vasodilation, a phenomenon attributed to signals traveling along the spinal cord, with the heart playing no discernible role in this rhythmic process, and arterial hypertension deemed irrelevant. Comprehensive further studies are imperative to elucidate the precise triggers of limb vasodilation during sleep.

Network topology facilitates internet traffic control in autocracies.

Recent years have seen an increase in governmental interference in digital communication. Most research on this topic has focused on the application level, studying how content is manipulated or removed on websites, blogs, or social media. However, in order for governments to obtain and maintain control of digital data flows, they need to secure access to the network infrastructure at the level of Internet service providers. In this paper, we study how the network topology of the Internet varies across different political environments, distinguishing between control at the level of individual Internet users (access) and a higher level in the hierarchy of network carriers (transit). Using a novel method to estimate the structure of the Internet from network measurements, we show that in autocratic countries, state-owned (rather than privately owned) providers have a markedly higher degree of control over transit networks. We also show that state-owned Internet providers often provide Internet access abroad, with a clear focus on other autocratic countries. Together, these results suggest that in autocracies, the network infrastructure is organized in a way that is more susceptible to the monitoring and manipulation of Internet data flows by state-owned providers both domestically and abroad.

Coordination of modular nano grid energy management using multi-agent AI architecture

Many innovative creations of businesses and academics are a result of the ever-increasing intricacy of the contemporary, renewable electricity, and fuel networks. To handle the complicated aspects of networks, the right architectural and testing techniques, methodologies, theories, and accompanying technologies are required. Intermittent sustainable energy resources like wind and solar present some difficulties in managing energy production and use. This study provides a revolutionary multi-agent AI system-based smart controlling strategy to manage the power requirements of a building with sustainable power resources. To meet the demands of the workload, the suggested approach uses a hierarchical multiple-agent network based on collaboration and communication among intelligent entities. An intelligent grid network comprising of multiple intelligent nano grids connected to a sustainable energy resource with tariff monitoring and intelligent power flow monitoring been modeled for energy-sharing and electrical reliability enhancement. The network, which features imbalanced three-phase diodes bridging rectifier output, is combined using two distinct Renewable Energy Sources (RESs). These two RESs include a photovoltaic unit for the first nano grid and a wind power generation unit for the second nano grid. This method is primarily based on auto-similar networks, which are suitable and simple to apply for multiple agent networks.