Optimal Flow Control Research Articles

Impacts of Arrhenius activation energy and oblique magnetic field on the flow of freely oscillating nanofluid over a rough surface

The significance of freely oscillating unsteady stagnation flows is attributed to their use in various kinds of engineering processes like cooling systems, heat exchangers and turbomachinery. The restrictive forces produced with the help of magnetic fields are a useful technique in providing optimum flow control in the metallurgical operations and refinement of molten metals. The present study highlights the inclined magnetic field implications on an oscillatory streaming nanofluid which impinges non-orthogonally over a non-smooth surface. The surface roughness of the wall is elaborated by the slip velocity condition. The two-phase Buongiorno nanofluid model is being considered. The energy dissipation due to the active chemical reactions occurring in the flow is adopted with Arrhenius activation energy. Suitable dimension-free variables are chosen to make the mathematical model parameter dependent. The numerical outcomes of the problem are determined through the utilization of the bvp5c solver based on the finite difference method using MATLAB R2023a. The graphical representations depict significant alteration in concentration profiles with thermal ratio and Activation energy (). The findings indicate that higher activation energy and weaker temperature conditions increased the mass transfer. In conjunction, the velocity profile is enhanced for acute magnetic field angles.

A case study of a neighborhood in Douala examining the technical feasibility of a hybrid renewable energy system connected to the grid for energy and oxygen production in the fight against respiratory diseases

The world is increasingly focusing its attention on the rapid growth in electricity consumption, a concern shared by both industrialized and emerging nations. Meeting this escalating demand has become crucial, necessitating a shift towards sustainable energy use. Renewable energy sources have emerged as a popular choice for both off-grid and grid-connected installations as a means to bridge the efficiency gap. In this context, the present paper explores the potential of supplying electricity to a neighborhood in Cameroon comprising 100 homes through the integration of solar photovoltaic cells and electrolyzers. It is worth noting that Cameroon as a whole continues to grapple with severe social, economic, and physical challenges. Some regions within the country face significant obstacles in accessing quality healthcare, particularly for conditions like respiratory illnesses. This study employs an intelligent energy management method to design an optimal power flow control system, aimed at enhancing system stability during power outages and load fluctuations by effectively utilizing available storage capacity. The analysis was conducted using the Hybrid Optimization Model for Electric Renewable (HOMER) program. Our calculations reveal that a daily output of 0.123 kg mass of oxygen is produced, with renewable factor (RF) values indicating an 85.1 % integration of renewable sources, and the crucial parameter of Loss of Power Supply Probability (LPSP) reaching 0 %. These findings demonstrate that it is feasible for 100 homes to reliably generate and connect to the grid for electricity. Moreover, this research can serve as a solid foundation for the development of hybrid renewable energy systems to address the challenge of treating respiratory disorders such as COVID-19. These results underscore the significance of this study for various stakeholders, including decision-makers, policymakers, and investors in Cameroon and beyond. The ultimate goal is to promote national growth by improving socio-medical conditions.

Dynamic mode decomposition for the tip unsteady flow analysis in a counter-rotating axial compressor

Counter-rotating axial compressor (CRAC) is a promising potential technology to improve the thrust-to-weight ratio of aero-engines, but its special aerodynamic layout usually causes more pronounced flow unsteadiness. Understanding the unsteady flow features and mechanism in the CRAC contributes to the aerodynamic optimization design and flow control strategy organization. A data-driven dynamic mode decomposition method is introduced to investigate the tip flow unsteadiness in a CRAC, and the unsteady features of the tip flow at the design point (DP) and near-stall point (NSP) conditions are revealed. The results show that the 1.0 times blade passage frequency (BPF) and its multi-order harmonic frequency are the dominant frequencies for both rotors at the DP condition. At the NSP condition, the 1.0 BPF is no longer the dominant frequency causing the tip flow unsteadiness, and the low frequency fluctuation of the tip leakage flow becomes the dominant frequency to induce the flow unsteadiness. In the front rotor R1, the unsteady dominant frequency is 1.0 BPF, whereas in the rear rotor R2, the frequency (0.801 BPF and 0.803 BPF) of the tip leakage flow is the dominant frequency. By reconstructing the flow field under the NSP condition, the spatiotemporal evolution of the tip flow during the unsteady stable manifests that the interference effect between the rotors is an important source of the tip flow unsteadiness. The increase in flow unsteadiness leads to an increase in the reconstruction error, indicating that more modes are required to obtain a more accurate reconstruction flow field.

Distributed feedback optimisation based optimal power flow control in fully inverter based islanded AC microgrids

AbstractA novel distributed feedback optimisation (FO) based control method is proposed to control grid‐forming inverters (GFMIs) in fully inverter‐based islanded AC microgrids (MGs). The proposed controller has two control layers. The upper layer uses FO to calculate the frequency and voltage setpoints of GFMIs, whereas the lower layer makes GFMIs track these setpoints. The proposed control method takes advantage of the flexibility of voltage control to regulate the system frequency, maintain both active power and reactive power sharing accuracies, keep bus voltage within allowable range and meanwhile preserves the optimality of the closed‐loop system in term of optimal power flow. The gradient descent method is used to solve the proposed FO problem based on the real‐time measurements in the MGs, which is implemented in a distributed way, and thus eliminates the need for a central controller. Case studies show the effectiveness of the proposed method.The cover image is based on the Research Article Distributed feedback optimisation based optimal power flow control in fully inverter based islanded AC microgrids by Y. Cheng et al., https://doi.org/10.1049/stg2.12132.

On Flow Control and Optimized Back-Off in Non-Saturated CSMA

Medium Access Control (MAC) main functions encompass contention for channel access, packet scheduling, error control, and data integrity. Channel contention is a collective function involving all stations in the network, while data integrity pertains to data flows of each individual station. We propose a design where contention related functions are separated from other data management functions. The hinge connecting contention and other data management functions is a flow control algorithm, aiming at guaranteeing stability of contention queues and load on the MAC channel. With reference to Carrier-Sense Multiple Access (CSMA), we define an analytical model of contention queues under non saturated traffic. An asymptotic analysis of the model for large number of stations yields a closed form of the optimal flow control rate. The insight gained from the model is used to design an adaptive flow control algorithm that guarantees throughput optimality for all values of the number of stations.

Digital Twin Design of a Turbulence Inhibitor in a Tundish Based on the Production Cluster Mining Algorithm

The lack of a direct and linear relation between inclusion removal from tundishes and the design of their turbulence inhibitors is a difficult challenge. In contrast to the traditional method of optimizing flow control devices based on the residence time distribution curve, this study used the inclusion/flow field database production clustering mining algorithm to conduct step-by-step data mining on the tundish flow field; to produce relevant facts of the flow field characteristics in the inclusion aggregation zone; and to extract the data mining results from the fact database to screen a digital twin algorithm that forecasts the inclusion aggregation area in a tundish to optimize the flow control device. The results showed that the inclusion aggregation area in the tundish impact zone is above the turbulence inhibitor and that the inclusion aggregation area outside the tundish impact zone is at the vortex center of the flow field. According to the mining results, a pseudo-code for screening the inclusion aggregation area was developed, and the turbulence inhibitor was optimized with the help of the digital convergence of the digital and physical models. Finally, in a tundish, the inclusion removal rate in molten steel was increased by 14.4%. The turbulence inhibitor designed by the digital twin method is currently being used in a Chinese steel mill.

A New Flow Control and Efficiency Enhancement Method for Horizontal Axis Wind Turbines Based on Segmented Prepositive Elliptical Wings

Flow separation occurs when wind turbines operate under large inflow conditions, which seriously affects the utilization of wind energy and reduces the output power of the blade. Therefore, a composite flow control configuration for horizontal axis wind turbines, founded on segmented prepositive elliptical wings, is proposed for efficiency enhancement. Taking a typical NREL Phase VI wind turbine as the prototype, its separation effect is evaluated by the CFD method. Then, starting from the improvement of the two-dimensional airfoil flow, the prepositive elliptic wing is designed according to the airfoil flow, and the optimal two-dimensional flow control configuration of the blade airfoil is obtained by simulation analysis. Finally, the two-dimensional configuration is extended to three-dimensional, and the aerodynamic characteristics of the blade before and after flow control are simulated and compared. The results show that, at wind speeds of 10~20 m/s, flow separation on the blade is effectively inhibited; meanwhile, the pressure difference between the pressure surface and the suction surface increases. These characteristics greatly improve the performance of wind turbine and increase its torque by more than 30%. Moreover, when the flow control effect cannot be reached, the blade torque is only reduced by approximately 2%.

Optimal battery energy storage planning and control strategy for grid modernization using improved genetic algorithm

The flexible operation of battery energy storage systems (BESS) to support electricity grid modernization requires optimal planning and an efficient control strategy. This paper proposes the optimal allocation of BESS with photovoltaic systems for microgrids to enhance grid reliability and flexibility. An adaptive BESS control strategy is developed to allow the BESS to operate in both redundant and peak shaving modes. An evolutionary programming-based genetic algorithm implemented with an optimal power flow control is proposed to determine the BESS’s optimal location, sizing, and charge/discharge operation. The objective function is to maximize the benefits of energy loss reduction and peak shaving enhancement while minimizing the installation cost. The proposed approach is conducted with MATLAB and DIgSILENT PowerFactory software, which uses an intelligent automatic data exchange process to solve the optimal solution. A practical 22 kV grid-connected microgrid of Thailand is used as a test system to demonstrate the effectiveness of the planning and control strategy for the BESS installation. The simulation test results show that the proposed optimal BESS allocation and control strategy can significantly reduce the microgrid’s energy loss and peak demand and increase energy shaving leading to enhanced grid resiliency and reliability.

Bayesian Optimization of Wall-Normal Blowing and Suction-Based Flow Control of a NACA 4412 Wing Profile

Active flow-control techniques have shown promise for achieving high levels of drag reduction. However, these techniques are often complex and involve multiple tunable parameters, making it challenging to optimize their efficiency. Here, we present a Bayesian optimization (BO) approach based on Gaussian process regression to optimize a wall-normal blowing and suction control scheme for a NACA 4412 wing profile at two angles of attack: 5 and 11 ∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\\circ$$\\end{document}, corresponding to cruise and high-lift scenarios, respectively. An automated framework is developed by linking the BO code to the CFD solver OpenFOAM. RANS simulations (validated against high-fidelity LES and experimental data) are used in order to evaluate the different flow cases. BO is shown to provide rapid convergence towards a global maximum, even when the complexity of the response function is increased by introducing a model for the cost of the flow control actuation. The importance of considering the actuation cost is highlighted: while some cases yield a net drag reduction (NDR), they may result in an overall power increase. Furthermore, optimizing for NDR or net power reduction (NPR) can lead to significantly different actuation strategies. Finally, by considering losses and efficiencies representative of real-world applications, still a significant NPR is achieved in the 11∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\\circ$$\\end{document} case, while net power reduction is only marginally positive in the 5∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\\circ$$\\end{document} case.

Collaborative passenger flow control optimization of urban rail transit network based on sliding window mechanism

In order to enhance the operational performance of urban rail transit networks under overloaded conditions, this study proposes a novel network-level collaborative passenger flow strategy based on a sliding window mechanism. The main idea is to categorize arriving passengers at stations into different groups using variable-length windows and allocate them to specific trains. With the aim of minimizing the total passenger extra waiting time under cost budget, we establish a mixed integer programming model with sliding window constraints, flow distribution constraints, and node flow constraints to build the optimal matching relation between passenger flow demands and transportation resources. Specifically, the model incorporates precise quantity constraints related to transfers, facilitating the accurate quantification of both internal and external demands of network passenger flow. Then we use logical constraint transformation and linearization techniques to enable the model to be solved directly by commercial optimization solvers. For large-scale problems, we adopt a rolling horizon approach to improve computing efficiency. To validate the effectiveness of the sliding-window based passenger control method, we conduct case studies including a small designed example and real-world research on the Beijing Subway network. The experimental results demonstrate that our proposed model assists managers in developing flexible and accurate network-level collaborative passenger flow control schemes.

Topology optimization of thermally activated building system in high-rise building

The building sector accounts for nearly 1/3 of global energy end-use and associated carbon emissions. Fulfilling buildings' thermal demand in an efficient way is critical for decarbonizing the building sector. Therefore, this paper introduces the thermally activated building system (TABS) as an energy retrofit solution. A TABS floor-by-floor return water temperature control approach is proposed to reduce buildings' energy use and carbon emissions. The approach includes the topology optimization of TABS and flow control. The bypass pipe connections of the TABS to each floor, namely, the floor-by-floor pipe network topology, have been optimized by using genetic algorithms to achieve the lowest total cost. A case study of a high-rise building located in Beijing demonstrates that the proposed approach can improve the system's energy efficiency compared to the conventional TABS without optimization. The reduction of energy cost can be up to 11.21% with a 13.53% annual emission reduction. In general, the proposed TABS floor-by-floor return water temperature control approach can significantly improve the building efficiency comprehensively, therefore can be considered as a promising energy retrofit solution for the decarbonization of high-rise buildings.

Analytical Approximation-Based Approach for Passenger Flow Control Strategy in Oversaturated Urban Rail Transit Systems

Focusing on a heavily congested urban rail corridor, this study investigates the passenger flow control strategy optimization problem from a mesoscopic perspective to reduce platform congestion and enhance service quality. Based on a quadratic functional approximation for passenger arrival rates, an analytical formula for calculating passenger waiting time is derived based on the classic deterministic queueing theory. We formulate the problem as a continuous nonlinear programming model to minimize the total passenger waiting time within transportation capacity constraints. A Lagrangian relaxation approach effectively transforms the original complex problem into an unconstrained minimization program. The analytical solution relating to optimal flow control strategy is derived by directly solving the unconstrained program. To further provide an integrated optimization framework from both the supply and demand sides, we extend the abovementioned passenger flow control optimization model into an integrated mixed-integer nonlinear programming model to jointly optimize the passenger-flow control strategy and train frequency setting. Numerical examples are presented to demonstrate the applicability and effectiveness of the proposed models. The computational results show that the produced high-quality passenger flow control strategy significantly reduces total passenger delay.

DER integrated BTB-VSI based DSTATCOM for PQ enhancement

ABSTRACT This article demonstrates the performance of back-to-back connected voltage source inverter (BTB-VSI)-based distribution static compensator (DSTATCOM). In the proposed system, a three-phase three wire electrical utility system (EUS) is integrated with distributed energy resources (DER) through BTB-VSI. It has drawn more attention due to enhanced active power filtering, simple design procedure and performs reliable operation. The objectives of the research work is to provide clean power for weak EUS with improved power quality (PQ) such as, better voltage regulation, improved power factor (P.F), reduced total harmonic distortion (THD), balanced voltage at point of common coupling (PCC) and better optimal active power flow control (OAPFC), and so on. An artificial neurocontrol technique Kernel Hebbian Least Mean Square (KHLMS) is chosen to determine the accurate reference source current for operation of voltage source inverter (VSI). The BTB-VSI performance is investigated on both shunt compensation and OAPFC. The proposed system is converted to a simulation model and hardware unit using MATLAB/Simulink software and SPARTAN−6 field programmable gate arrays (FPGA) controller. Finally, the Simulation and experimental results illustrate that the shunt compensating ability with OAPFC of BTB-VSI performs better over VSI as per IEEE Std 1159–2019 grid code.

Mixed virtual element methods for optimal control of Darcy flow

Mixed virtual element methods for optimal control of Darcy flow

An air supply system regulation method for PEMFCs based on disturbance observation and MPC control

The optimal tracking control of air pressure and air flow is an important guarantee to improve the output characteristics of fuel cells. However, under the load disturbances scenario, the optimal control effect is difficult to guarantee. In order to solve this problem, this paper proposes a new control method based on real-time disturbances observation and MPC optimal control. The decoupling of air pressure and air flow is realized by feedback linearization, and then an extended state observer is designed to achieve accurate estimation of load disturbances. Based on the principle of optimal output power of the fuel cell system, the reference trajectory of air pressure and air flow is obtained. Based on this, the optimal MPC controller is designed to achieve accurate tracking of air pressure and air flow by controlling the motor voltage of the air compressor and the opening of the back pressure valve. Under load disturbances, compared with feedback linearization control, improved tracking and robust performances of the proposed strategy can be exhibited through offline and online tests, the net power of PEMFCs is increased by 3%.

Optimal control of tidal flow

The tidal flow through a channel connecting two basins with different tidal regimes can be optimally controlled by means of a turbine fence or array to maximise the extracted mechanical power. The paper gives the optimal control strategy as a function of the blockage ratio $\sigma$ , i.e. the ratio of the turbine cross-section to the cross-section of the local passage of a turbine. The results presented are a physically consistent generalisation of the results of Garrett & Cummins (Proc. R. Soc. Lond. A, vol. 461, 2060, pp, 2563–2572), valid only for $\sigma =1$ and turbine efficiency of one, now for arbitrary blockage ratio $0 < \sigma \leqslant 1$ . Published research over the past decade on the same topic has taken the momentum equation and the turbine drag force as a starting point. The new approach presented here, in contrast, takes the energy equation as the starting point and uses the relative volume flow as the control variable. As the work shows, this new approach has three advantages. First, starting with the energy equation allows us to derive an optimal flow control problem resulting in an Euler–Lagrange equation using the physically consistent and experimentally validated actuator disk model for the free surface flow of Pelz et al. (J. Fluid Mech., vol. 889, 2020) in a direct and formal way. The optimal control problem is solved (a) numerically and (b) analytically. In the latter case, the turbine characteristics are approximated by a rational function in the relevant design and operating range. The analytical solution (b) validated against the numerical solution (a) is surprisingly concise and easy to apply in practice, as shown by use cases. Second, instead of the induction factor, we use the volume flow that is the same for all turbines in a cascade, i.e. a row of turbines in the direction of flow, which significantly reduces the complexity of the optimal control task of turbine arrays. Third, we obtain a well-founded energy estimate, whereas previous methods overestimate the energy yield due to inconsistent turbine disc models (for the consistency and valid parameter ranges of different models, also in comparison with experiments, see Pelz et al., J. Fluid Mech., vol. 889, 2020). The results can be used for the conceptual design of turbine arrays, but also for a sound physically realistic and consistent resource assessment of tidal power for a system consisting of two basins, a channel and a turbine fence with $0<\sigma \leqslant 1$ and operated in a complete tidal cycle.

Machine learning for optimal flow control in an axial compressor.

Air jets for active flow control have proved effective in postponing the onset of stall phenomenon in axial compressors. In this paper, we use a combination of machine learning and genetic algorithm to explore the optimal parameters of air jets to control rotating stall in the axial compressor CME2. Three control parameters are investigated: the absolute injection angle, the number of injector pairs and the injection velocity. Given an experimental dataset, the influence of the air jet parameters on the surge margin improvement and power balance is modeled using two shallow neural networks. Parameters of the air jets are then optimized using a genetic algorithm for three rotational velocities, i.e., [Formula: see text]. First, surge margin improvement and power balance are being maximized independently. Then, a bi-objective optimization problem is posed to explore the trade-off between the two competing objectives. Based on the Pareto front, results suggest that a globally optimal set of parameters is obtained for a velocity ratio (defined as the ratio of the injection velocity to the rotor tip speed) ranging from 1.1 to 1.6 and an injection angle attack varying from [Formula: see text]. These outcomes point out a potential generalization of the control strategy applicable to other compressors.

Experimental and industrial method of synthesis of optimal control of the temperature region of cupola melting

The object of research is the temperature regime of melting in a cupola. The synthesis of optimal control of such an object is associated with the presence of a problem consisting in the complexity of its mathematical description and the absence of procedures that allow one to obtain optimal control laws. These problems are due to the presence of links with a pure delay, non-additive random drift, and difficulties in controlling the process parameters, in particular, accurately determining the temperature profile along the horizons and the periphery of the working space of the cupola. The proposed conceptual solution for the synthesis of optimal temperature control allows the use of two levels of control: the level controller solves the problem of maintaining the constant height of the idle charge, and the problem of increasing the temperature of cast iron is solved by controlling the air supply to the tuyere box. It is shown that the problem of regulating the upper level of an idle charge can be solved by reducing the model of the regulation process to a typical form, followed by the use of the Pontryagin maximum principle. A procedure for the synthesis of optimal air flow control is proposed, which makes it possible to obtain the temperature regime control law on the basis of experimental industrial studies preceding the synthesis process. This takes into account the time delay between the impact on the object and its reaction, which makes it possible to predict the temperature value one step acharge, equal to the time interval during which the lower surface of the fuel charge reaches the upper surface of the level of the idle charge. A procedure for temperature profile control based on the use of D-optimal plans for selecting sensor installation points is proposed. Due to this, it becomes possible to determine the temperature profile of the cupola according to its horizons and the periphery of the working space of the cupola with maximum accuracy. The proposed synthesis method can be used in iron foundries equipped with cupolas, as it is a tool for studying a real production process, taking into account its specific conditions. This will allow developing or improving control systems for cupola melting, implementing different control modes: manual, automated or automatic

Adaptive control of resource flow to optimize construction work and cash flow via online deep reinforcement learning

Due to complexity and dynamics of construction work, resource, and cash flows, poor management of them usually leads to time and cost overruns, bankruptcy, even project failure. Existing approaches in construction failed to achieve optimal control of resource flow in a dynamic environment with uncertainty. Therefore, this paper proposes a model and method to adaptive control the resource flows to optimize the work and cash flows of construction projects. First, a mathematical model based on a partially observable Markov decision process is established to formulate the complex interactions of construction work, resource, and cash flows as well as uncertainty and variability of diverse influence factors. Meanwhile, to efficiently find the optimal solutions, a Deep Reinforcement Learning (DRL) based method is introduced to realize the continuous adaptive optimal control of labor and material flows, thereby optimizing the work and cash flows. To assist the training process of DRL, a simulator based on discrete event simulation is also developed to mimic the dynamic features and external environments of a project. Experiments in simulated scenarios illustrate that our method outperforms the vanilla empirical method and genetic algorithm, possesses remarkable capability in diverse projects and external environments, and a hybrid agent of DRL and empirical method leads to the best result. This paper contributes to adaptive control and optimization of coupled work, resource, and cash flows, and may serve as a step stone for adopting DRL technology in construction project management.

Field Programmable Gate Array-Based Execution on a Distributed Energy Resource Supported Electrical Distribution System for Enhanced Power Quality with Optimal Active Power Flow Control

This paper explains the experimental study of a distributed energy resource (DER) integrated three-phase, three-wire electrical distribution system (EDS) under power quality (PQ) and optimal active power flow control (OAPFC). In this research, a back-to-back connected two-level VSI (BTB-TVSI) based distribution static compensator (DSTATCOM) for unbalanced non-linear load is designed. The aim is to provide better OAPFC in EDS with shunt compensation to maintain a round-the-clock quality power supply for end users. The improved neural network-based adaptive least mean square (ALMS) control algorithms are employed for DSTATCOM. The ALMS control strategy is realized using the SPARTAN-6 field programmable gate array (FPGA) evaluation kit. This research involves the comparative study of a two-level voltage source inverter (VSI) and a BTB-TVSI in terms of their OAPFC capability and robustness in mitigating power quality (PQ) against source current disturbances, low power factor (PF), poor voltage regulation, unbalanced voltage at the point of common coupling (PCC), high switching stress, and issues in the EDS operation. The experimental results demonstrate that the BTB-TVSI using an ALMS controller is able to achieve better dynamic performance and lower total harmonic distortion (THD) under selected/permissible limits in comparison to a conventional VSI.