Flow Constraints Research Articles

Thermal features of Jeffrey hybrid nanofluid based on the upgraded version of Yamada-Ota and Xue models with convective flow constraint

Thermal features of Jeffrey hybrid nanofluid based on the upgraded version of Yamada-Ota and Xue models with convective flow constraint

Financial Crisis Arising in Small-Scale Industries After Pandemic Periods

The global COVID-19 pandemic has deeply impacted the economy, with small-scale industries (SSIs) facing severe financial crises. These industries, which are the backbone of many economies due to their contribution to employment and GDP, were disproportionately affected by the pandemic. This research article will focus on the financial challenges faced by SSIs post-pandemic, specifically addressing diminished demand, cash flow constraints, supply chain disruptions, and obstacles in securing financial assistance.This paper explores the financial challenges encountered by small-scale industries during and after the pandemic, focusing on liquidity crunches, disruptions in supply chains, reduced consumer demand, and labour shortages. Furthermore, it examines the recovery strategies employed by SSIs and suggests long-term solutions for resilience against future crises.

A numerical approach to overcome the very-low Reynolds number limitation of the artificial compressibility for incompressible flows

We propose a numerical approach to solve a long-standing challenge which is the applicability of the artificial compressibility (AC) formulation for solving the incompressible Navier—Stokes equations at very-low Reynolds numbers. A wide range of engineering applications involves very-low Reynolds number flows in Micro-ElectroMechanical Systems (MEMS) and in the fields of chemical-, agricultural- and biomedical engineering. It is known that the already existing numerical methods using the AC approach fail to provide physically correct results at very-low Reynolds numbers (Re ≤ 1). To overcome the limitation of the AC method for these engineering applications, we propose a higher-order Neumann-type pressure outflow boundary condition treatment along with their up to fourth-order numerical approximations. We found that the numerical treatment of the pressure at the outlet boundary plays the main role in overcoming the limitation of the AC method at very-low Reynolds numbers (Re << 1). Therefore, we provide numerical evidence on the accuracy of the AC method beyond its previously reported limitations, e.g., the low Reynolds number Oseen flow (Re << 1) is first presented in this work. A third-order explicit total-variation diminishing (TVD) Runge–Kutta scheme has been employed with standard finite difference spatial discretisation schemes for improving the accuracy of the numerical solution. For modelling strongly viscous flows, the Reynolds number ranges from 10−1 to 10−4. Overall, we found that the accuracy limitation of the AC method below Re < 1 can be overcome with an accurate numerical treatment of the outlet pressure boundary condition instead of using high-order schemes in the governing equations. For the investigated Reynolds number range (10−1 ≤ Re ≤ 10−4), the obtained results show that the relative errors were smaller than 1% for the numerical simulations performed on the configurations of both the two- and three-dimensional, straight microfluidic channels. The imposition of high-order derivative Neumann-type pressure outflow boundary conditions reduced the maximum relative errors of the numerical solutions from 85% and 95% to below than 1% at the outlet section of the two- and three-dimensional, straight microfluidic channel flows, respectively. Taking the advantage of the numerical approach proposed here, two- and three-dimensional benchmark problems employed in the current investigation in comparison with analytical solutions available in the literature, clearly demonstrate that the artificial compressibility can be used beyond its previously known constraints for very-low Reynolds number incompressible flows.

Lateral flow assay sensitivity and signal enhancement via laser µ-machined constrains in nitrocellulose membrane

Lateral flow assay (LFA) is a handful diagnostic technology that can identify severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and other common respiratory viruses in one strip, which can be tested at the point-of-care without the need for equipment or skilled personnel outside the laboratory. Although its simplicity and practicality make it an appealing solution, it remains a grand challenge to substantially enhance the colorimetric LFA sensitivity. In this work, we present a straightforward approach to enhance the sensitivity of LFA by imposing the flow constraints in nitrocellulose (NC) membranes via a number of vertical femtosecond laser micromachined microchannels which is important for prolonged specific binding interactions. Porous NC membrane surfaces were structured with different widths and densities µ-channels employing a second harmonic of the Yb:KGW femtosecond laser and sample XYZ translation over a microscope objective-focused laser beam. The influence of the microchannel parameters on the vertical wicking speed was evaluated from the video recordings. The obtained results indicated that µ-channel length, width, and density in NC membranes controllably increased the immunological reaction time between the analyte and the labeled antibody by 950%. Image analysis of the colorimetric indicators confirmed that the flow rate delaying strategy enhanced the signal sensitives by 40% compared with pristine NC LFA.

Collaborative operation of active distribution network considering PV-storage-charging-compensation

Abstract With the conjunction of clean energy generation and electric cars, the power distribution network is gradually shifting from a traditional one-way radiative network to an active network. Coordinated control of these controllable elements of “source-grid-load-storage” can improve the operating voltage level and economic benefits, and reduce the load peak and off-peak difference. A collaborative dispatch model for active power distribution networks including PV-storage-charging-compensation is constructed in this paper, with the goal of minimizing network losses and PV curtailment costs. The cone transformation is used to handle non-linear constraints of power flow. Compared with existing ordered charging control methods, the constructed model can maximize the control efficiency of the distribution grid, enhance the PV absorption capacity, and effectively slow down the upgrading and renovation of the distribution network.

A novel constrained skew-Gaussian filter and its application to maneuverable reentry vehicle tracking

The state of the system generally satisfies specific constraints imposed by material properties or physical laws, so the application of these constraints can improve the accuracy of state estimation. In this paper, a novel recursive filter referred as constrained high-degree cubature skew-Gaussian filter (CHCSGF) is proposed, which achieves soft-constrained state estimation by compressing the probability density of unconstrained states with constraint information. First, the probability density of the state under inequality soft constraints is modeled as a skew-Gaussian (SG) distribution, rather than truncated or single Gaussian distributions. Then, a recursive constrained SG filter is developed to handle inequality soft constraints in linear systems. Addressing nonlinear challenges, a 5th-degree spherical-radial cubature approximation method is presented to numerically calculate SG-weighted integrals for the nonlinear transformation of SG distribution. Finally, the CHCSGF algorithm is proposed using this method to tackle nonlinear filtering problems. The CHCSGF is applied to reentry trajectory tracking to improve estimation accuracy by dealing with heat flow, dynamic pressure and overload constraints during reentry flight. Simulation results demonstrate that the CHCSGF achieves higher estimation accuracy than unconstrained methods under nonlinear inequality soft constraints, and is robust to the constraints with a prior error. Compared to particle filter and moving horizon estimation, the computational complexity of CHCSGF is significantly reduced.

Improved air traffic flow prediction in terminal areas using a multimodal spatial–temporal network for weather-aware (MST-WA) model

Accurately predicting air traffic flow in terminal areas is critical for balancing demand and capacity, particularly under challenging weather conditions. However, the complex interactions between weather patterns and air traffic make reliable predictions difficult. To address this issue, we propose a novel approach called the Multimodal Spatial-Temporal network for Weather-Aware prediction (MST-WA), designed to enhance air traffic flow prediction (ATFP) in terminal areas. Our method begins by constructing a spatial–temporal graph that captures the topology of the terminal area, including airports, routes, and fixes as nodes. A weather-aware module is then introduced, leveraging a Residual Network (ResNet) and attention mechanism to model the deep spatial–temporal correlations in the Weather Avoidance Field (WAF). The proposed model architecture integrates five key branches: arrival flow, departure flow, graph network topology, weather conditions, and flow constraint control, with predictions generated via an attention-based Long Short-Term Memory (LSTM) network. Experimental results using real-world data from Guangzhou Baiyun Airport, China, show that MST-WA outperforms baseline models in ATFP. Furthermore, a case study in convective weather scenarios demonstrates the model’s adaptability and effectiveness. We believe that the proposed model can serve as a valuable tool for air traffic controllers, enhancing decision-making and improving overall air traffic management.

Optimal operation model of ecological flow of hydropower station based on genetic algorithm and neural network

Amid diverse water demands, the conflict between ecological flow adjustments and power generation flow adjustments in hydropower station water resource dispatching, coupled with an increasing diversity of constraints, complicates the issue of water resource optimization and dispatching. How to scientifically develop feedback regulation mechanisms to promote a virtuous ecological cycle, fine tune existing hydropower station scheduling plans, and achieve multi-objective optimization scheduling is the core issue in researching the comprehensive utilization of water resources. This study establishes a reservoir flow regulation model with the objective functions of maximizing power generation and achieving optimal average ecological protection. The model, constrained by water balance, reservoir capacity, unit output, and unit overflow, was optimized and solved using the genetic algorithm-backpropagation neural network (GA-BPNN) approach. This study establishes a reservoir flow regulation model with the GA-BPNN algorithm was applied in multi-objective optimization to identify an optimal solution that maximizes overall system performance while adhering to ecological flow constraints. The results indicate that the optimized plan has performed effectively in hydropower station operations. It not only satisfies ecological flow requirements, but also significantly enhances power generation efficiency. Specifically, power generation increased by 32,680 kw·h, a 23.85% growth rate. The optimized plan significantly enhances the comprehensive utilization efficiency of water resources. It offers a scientific basis for the rational planning, dispatching and utilization of water resources in hydropower stations and reservoirs.

Real-Time Power Regulation of Flexible User-Side Resources in Distribution Networks via Dual Ascent Method

Flexible user-side resources are of great potential in providing power regulation so as to effectively address the challenges of reverse power flow and overvoltage issues in distribution networks characterized by high photovoltaic (PV) penetration. However, existing distributed algorithms typically implement control signals after the convergence of the algorithms, making it difficult to track frequent and rapid fluctuations in PV power outputs in real time. Given this background, an online-distributed control algorithm for the real-time power regulation of flexible user-side resources is proposed in this paper. The objective of the established control model is to minimize network losses by dynamically adjusting active power outputs of flexible user-side resources and reactive power outputs of PV inverters while respecting branch power flow and voltage magnitude constraints. Furthermore, by deconstructing the centralized problem into a primal–dual one, a distributed control strategy based on the dual ascent method is implemented. With the proposed method, agents can achieve global optimality by exchanging limited information with their neighbors. The simulation results verify the good balance between economic efficiency and voltage control performance of the proposed method.

A Bi-Level Peak Regulation Optimization Model for Power Systems Considering Ramping Capability and Demand Response

In the context of constructing new power systems, the intermittency and volatility of high-penetration renewable generation pose new challenges to the stability and secure operation of power systems. Enhancing the ramping capability of power systems has become a crucial measure for addressing these challenges. Therefore, this paper proposes a bi-level peak regulation optimization model for power systems considering ramping capability and demand response, aiming to mitigate the challenges that the uncertainty and volatility of renewable energy generation impose on power system operations. Firstly, the upper-level model focuses on minimizing the ramping demand caused by the uncertainty, taking into account concerned constraints such as the constraint of price-guided demand response, the constraint of satisfaction with electricity usage patterns, and the constraint of cost satisfaction. By solving the upper-level model, the ramping demand of the power system can be reduced. Secondly, the lower-level model aims to minimize the overall cost of the power system, considering constraints such as power balance constraints, power flow constraints, ramping capability constraints of thermal power units, stepwise ramp rate calculation constraints, and constraints of carbon capture units. Based on the ramping demand obtained by solving the upper-level model, the outputs of the generation units are optimized to reduce operation cost of power systems. Finally, the proposed peak regulation optimization model is verified through simulation based on the IEEE 39-bus system. The results indicate that the proposed model, which incorporates ramping capability and demand response, effectively reduces the comprehensive operational cost of the power system.

Putting the HYPE in hypertension: peripheral chemoreflex constraint of skeletal muscle blood flow.

Putting the HYPE in hypertension: peripheral chemoreflex constraint of skeletal muscle blood flow.

Fuzzy credibility chance-constrained multi-objective optimization for multiple transactions of electricity–gas–carbon under uncertainty

This paper proposes a fuzzy credibility chance-constrained multi-objective optimization model to optimize market transactions in the electricity–gas–carbon sectors under uncertainty. The model aims to maximize the profit of an integrated energy service provider by incorporating a ladder-type carbon trading mechanism, which adjusts carbon prices based on emission levels, and detailed multi-energy flow constraints. To effectively manage uncertainties in electricity, gas, and carbon markets, we derive credibility distributions for uncertain variables and introduce fuzzy credibility chance constraints—tools that assess the likelihood of meeting multiple transactions under uncertainty. The proposed model hedges the risks associated with multiple uncertainties while maximizing the credibility of expected costs, effectively balancing risk and cost. Through simulation analysis on an IEEE 33-node power network and a 32-node heat network, the proposed model achieved a 9.8% reduction in total system cost and an 8.5% reduction in total carbon emissions. Additionally, the model effectively determined credibility levels under different risk preferences, demonstrating its robustness in enhancing electricity–gas–carbon trading and promoting a low-carbon economy. This research provides a novel planning method for formulating trading strategies in multi-energy markets, with significant real-world implications for energy management and environmental sustainability.

Cooperative energy interaction for neighboring multiple distribution substation areas considering demand response

With the growing integration of renewable energy into medium- and low-voltage distribution networks, the distribution substation area (DSA) has emerged, encompassing energy storage and loads. This paper introduces an energy interaction framework for multiple DSAs aimed at enhancing local renewable energy consumption. The energy interaction issue among various DSAs is modeled as a Nash bargaining problem to encourage energy exchanges. However, the variability in pricing and internal demand response may influence scheduling decisions, necessitating further investigation. To address price forecast errors, scenarios are developed using a stochastic programming approach to represent price uncertainties while adjusting the DSA’s load accordingly. Optimal power flow constraints are integrated into the model to bolster power system operation security. Additionally, the transmission capacity can impact scheduling outcomes and operational costs. The influence of transmission limitations on operational strategies is examined within the allowable capacity. To solve this issue, the bargaining model is divided into two subproblems, and an enhanced alternating direction multiplier method (ADMM) is used to maintain the privacy of DSAs. The simulation results obtained using the IEEE-33 bus system indicate that energy interaction among multiple DSAs significantly lowers operating costs and facilitates the integration of renewable energy.

Anisotropy scaling functions in heavy-ion collisions: Insights into the ultracentral flow puzzle and constraints on transport coefficients and nuclear deformation

Anisotropy scaling functions in heavy-ion collisions: Insights into the ultracentral flow puzzle and constraints on transport coefficients and nuclear deformation

Identifying the Characteristics and Implications of Post-Earthquake Debris Flow Events Based on Multi-Source Remote Sensing Images

Strong earthquakes often bring amounts of loose material, disrupting the balance of material transportation within a watershed and severely impacting the restoration of the ecological environment and human safety downstream. Therefore, it is crucial to identify the frequency and scale of these debris flow events, as well as to explore their long-term development and impact on internal and external channels. Using multi-source remote sensing images from four perspectives, hillslope, channel, accumulation fan, and their relationship with the mainstream, we reconstructed a debris flow event dataset from 2008 to 2020, explored a method for identifying these events, and analyzed their impacts on channels and accumulation fans in Mozi Gully affected by the Wenchuan earthquake. Loose matter was predominantly found in areas proximate to the channel and at lower elevations during debris flow events. Alterations in channel width, accumulation fans downstream, and their potential to obstruct rivers proved to be vital for identifying the large scale of debris flow event. Finally, we encapsulated the evolution patterns and constraints of post-earthquake debris flows. Determination in frequency and scale could offer valuable supplementary data for scenario hypothesis parameters in post-earthquake disaster engineering prevention and control.

Decoding Information Flow and Sensory Pollution: A Systematic Framework for Understanding Species Interactions.

Information transmission among species is a fundamental aspect of natural ecosystems that faces significant disruption from rapidly growing anthropogenic sensory pollution. Understanding the constraints of information flow on species' trophic interactions is often overlooked due to a limited comprehension of the mechanisms of information transmission and the absence of adequate analytical tools. To fill this gap, we developed a sensory information-constrained functional response (IFR) framework, which accounts for the information transmission between predator and prey. Through empirical evaluation, the IFR provided a biologically grounded explanation for the systematic variation of functional responses. Specifically, it posits that the variation of different functional-response shapes, associated with community stability, is attributable to limitations in sensory information transmission among species. This not only deepens our mechanistic understanding of species interactions but also elucidates how anthropogenic activities are reshaping species interactions and community dynamics by disrupting information exchange through sensory pollution.

An optimal grid scheduling method considering coupling degree of nodes and duality of reciprocal inverse mapping

The optimal scheduling method of the power grid considering the node coupling degree and the duality of mutual inverse mapping is studied to effectively establish the random dynamic scene of the power grid and improve its optimal scheduling effect. Considering the duality of the mutual inverse mapping, a random dynamic scene of the power grid is generated. The optimization model of the grid random dynamic scene partition was established considering the coupling degree of the nodes in the region. The anti-prey particle swarm optimization algorithm was used to solve the optimization model, and the scene partition results were obtained. To construct an optimization scheduling model for the regional power grid with the objective functions of minimizing energy abandonment rate and grid loss and the constraints of power flow, output, and climbing power. A predator-prey particle swarm optimization algorithm is used to solve the optimization scheduling model, and the minimum energy abandonment rate, minimum grid loss, and corresponding optimal scheduling strategies are obtained. The experimental results show that this method can effectively generate random dynamic scenes of a power grid and has a better scene partitioning effect. Under different working conditions, this method can achieve optimal dispatching of the power grid and reduce the power grid energy abandonment rate and network loss.

Estimating exploitable groundwater for agricultural use under environmental flow constraints using an integrated SWAT-MODFLOW model

Traditionally, the impact of groundwater abstraction on streamflow and aquifers has been assessed primarily in terms of water availability and depletion, often neglecting critical environmental flow (Eflow) requirements. To address this gap, our study employed an Eflow perspective to evaluate exploitable groundwater for agricultural use at a watershed scale using an integrated modeling approach. We used the SWAT-MODFLOW model, integrating the Soil and Water Assessment Tool (SWAT) and the Modular Finite-Difference Flow Model (MODFLOW), to analyze water balance segments and stream-aquifer interactions in the study region. Eflow for different management classes was determined using the flow duration curve (FDC) approach. The FDC method classifies Environmental Flow Management Classes (EMC) from “A” (natural or minor alteration) to “F” (critically modified). The relationship between the selected EMC and simulated streamflow, under the pressure of agricultural groundwater pumping, served as a constraint in evaluating exploitable groundwater alongside groundwater level fluctuations. As the traditional approach depends on average recharge for determining exploitable groundwater, this study also assessed the spatiotemporal distribution of groundwater recharge. Simulation periods were aligned with agricultural pumping schedules. Results indicated that groundwater pumping significantly impacts streamflow at the start of the irrigation period. Under moderately affected EMC and with an average groundwater level fluctuation of one meter, the exploitable groundwater during the irrigation period can reach up to 2.96 Mm3/day. With this amount of pumping, the average groundwater discharge to streams declined by around 13 %. While groundwater level fluctuations near the watershed outlet were minimal, in the upstream area, levels could decrease by as much as 2.5 m. The groundwater level fluctuation underscores the necessity of accounting for both spatial and temporal factors, particularly in highly affected regions.

The interplay of cash flow uncertainty and firm life cycle on sustainability disclosure

AbstractThis study investigates the role of cash flow uncertainty and firm life cycle on sustainability disclosures using environmental, social and governance (ESG) disclosure score data. We conduct our analysis on a sample of 190 Indian firms for a period of 12 years from 2010 to 2021. Drawing upon the real‐option theory and organisational‐inertia theory, we document a negative impact of cash flow uncertainty and firm life cycle on a firm's commitment to disclose sustainability information. We also find a significant impact of managerial risk inclination and risk tolerance at the firm level in moderating the above relationships. Our findings can help managers equip themselves to meet the challenges of sustainability commitments during cash flow constraints and firm life‐cycle transitions. Moreover, our results can also help policymakers in assessing the need to enforce sustainability disclosures depending on the hurdles confronted by a firm.

Impact of electric vehicles and wave energy systems on OPF of power networks using hybrid Osprey-PSO approach

In this article, a novel optimization algorithm named hybrid Osprey Optimization and Particle Swarm Optimization (OOPSO) is proposed to solve the Probabilistic Optimal Power Flow (POPF) problem in power systems with high penetration of renewable energy resources (RERs), such as photovoltaic, wind, and wave energy and integration of electric vehicles (EVs). The algorithm's results are compared with established optimization algorithms like Osprey Optimization Algorithm (OOA), Particle Swarm Optimization (PSO), and Heap Optimization. Applied to the 30 and 118-bus IEEE test systems, the OOPSO algorithm efficiently deals with the uncertainties produced by RERs generation. This research study includes EV charging profiles for weekdays and weekends. It considers home and work-level charging scenarios. The RERs and EVs penetration ensure system reliability while minimizing generation costs and satisfying power flow constraints. Compared to existing algorithms, the OOPSO algorithm achieves superior performance, faster convergence, and higher solution accuracy. The simulation results show the effectiveness of OOPSO. It appears to be a robust tool for POPF, integrating RERs in power systems with EVs. This work highlights the potential of OOPSO as a feasible approach for power system optimization in the context of RERs and EV integration, paving the way for further research into unconventional grid optimization techniques.