Optimal Power Flow Using A Hybridization Algorithm Of Arithmetic Optimization And Aquila Optimizer

In this paper, a hybridization method based on Arithmetic optimization algorithm (AOA) and Aquila optimizer (AO) solver namely, the AO-AOA is applied to solve the Optimal Power Flow (OPF) problem to independently optimize generation fuel cost, power loss, emission, voltage deviation, and L index. The proposed AO-AOA algorithm follows two strategies to find a better optimal solution. The first strategy is to introduce an energy parameter (E) to balance the transition between the individuals\u27 procedure of exploration and exploitation in AO-AOA swarms. Next, a piecewise linear map is employed to reduce the energy parameter\u27s (E) randomness. To evaluate the performance of the proposed AO-AOA algorithm, it is tested on two well-known power systems i.e., IEEE 30-bus test network, and IEEE 118-bus test system. Moreover, to validate the effectiveness of the proposed (AO-AOA), it is compared with a famous optimization technique as a competitor i.e., Teaching-learning-based optimization (TLBO), and recently published works on solving OPF problems. Furthermore, a robustness analysis was executed to determine the reliability of the AO-AOA solver. The obtained result confirms that not only the AO-AOA is efficient in optimization with significant convergence speed, but also denotes the dominance and potential of the AO-AOA in comparison with other works

Introduction

For more than a century, concrete research has focused on improving the mechanical properties and durability of concrete, focusing on the effects of mix design parameters and exposure conditions on the responses to different mechanical, physical and chemical solicitations

Sustainable Pathways For Solar Desalination Using Nanofluids: A Critical Review

Water is a fundamental requirement for the survival of human beings. Although water is abundantly available across the globe, access to freshwater still remains a major concern. Most of the water available is saline or brackish, which is not fit for human consumption. Desalination is the optimum solution for production of potable water from saline water. A major shortcoming of conventional desalination technologies is their dependence on fossil fuel that results in environmental degradation, global warming, etc. Therefore, sustainable desalination technology has evolved as a need of hour. Among all renewable energy resources, solar energy is abundantly available and can be potentially harvested. Therefore, solar energy can be used to drive sustainable desalination technologies. A solar still converts saline water into freshwater in a single step using solar energy. But the major drawbacks of solar still are relatively lower efficiency and lower yield. Nanofluids are widely used to overcome these limitations due to their extraordinary and unique properties. This paper critically reviews the recent research performed on the application of nanofluids in solar desalination systems. Methods of nanofluid preparation, their types and properties are also discussed in detail. Application of nanofluids in solar desalination systems is discussed with special attention on performance enhancement of solar stills. Combinations of nanofluids with various other performance enhancement techniques are also considered. The effectiveness of nanofluids in solar stills is found to be dependent majorly on the nature and concentration of the nanofluid used

A Review Of Dielectric Barrier Discharge Cold Atmospheric Plasma For Surface Sterilization And Decontamination

Numerous investigations have shown that non-equilibrium discharges at atmospheric pressure, also known as cold atmospheric plasma (CAP) are efficient to remove biological contaminants from surfaces of a variety of materials. Recently, CAP has quickly advanced as a technique for microbial cleaning, wound healing, and cancer therapy due to the chemical and biologically active radicals it produces, known collectively as reactive oxygen and nitrogen species (RONS). This article reviews studies pertaining to one of the atmospheric plasma sources known as Dielectric Barrier Discharge (DBD) which has been widely used to treat materials with microbes for sterilization, disinfection, and decontamination purposes. To advance research in cold atmospheric plasma applications, this review discusses various types and configurations of barrier discharge, the role played by reactive species and other DBD-CAP agents leading to its antimicrobial efficacy, a few collection of DBD-CAP past studies specifically on surface, and emerging applications of DBD-CAP technology. Our review showed that non-thermal/equilibrium plasma generated from DBD could sterilize or disinfect surface of materials without causing any thermal damage or environmental contamination

Synergetic Effect Of Viscosity Modifying Admixtures And Polycarboxylate Ether Superplasticizer On Key Characteristics Of Thixotropic UHPC For Bonded Bridge Deck Overlay Rehabilitation

Thin bonded ultra-high-performance concrete (UHPC) overlay is an advanced technology for bridge deck rehabilitation. UHPC should be tailored to secure adequate flowability and high thixotropy to facilitate mixing and placement with a low risk of sagging of the material on sloped bridge deck surfaces. The synergetic effect between nano clay (NC) and polycarboxylate ether (PCE) superplasticizer or cellulose-based viscosity modifying admixture (VMA) and PCE on the rheological properties (yield stress, plastic viscosity, and thixotropy), cement hydration, autogenous shrinkage, compressive strength, and porosity was systematically investigated. The bond strength between conventional concrete (CC) representing existing bridge deck concrete and thixotropic UHPC and the flexural performance of UHPC-CC composite beams were determined. Test results indicate that the incorporation of 1% cellulose based VMA combined with 0.4% PCE can increase thixotropy from 12 to 60 Pa/min when the initial fluidity of the mortar phase of the UHPC was maintained at 200 mm. Such increase was limited to 19 Pa/min in mixtures prepared with 1% NC and 1.55% PCE. The thixotropic UHPC containing NC and cellulose based VMA provided tensile bond strength higher than 3 MPa. The combination of NC and PCE improved the flexural toughness and strength by up to 45% and 30%, respectively, for composite beam specimens. It also led to slightly higher compressive strength associated with greater cement hydration and decreased porosity. The combined use of cellulose based VMA and PCE led to 15% and 40% greater flexural strength and toughness, respectively, despite the 10% lower compressive strength due to a 30% increase in the volume of pores with diameters larger than 5 µm

The Hydration, Microstructure, And Mechanical Properties Of Vaterite Calcined Clay Cement (VC³)

Limestone (calcite) calcined clay cement (LC3) is a promising low-CO2 binder, but the low activity of calcite cannot compensate the reduction in clinker factor, resulting in low one-day strength and limiting its broad applications. As recent carbon capture and utilization technologies allow scalable production of vaterite, a more reactive CaCO3 polymorph, we overcome the challenge by introducing vaterite calcined clay cement (VC3), inspired by the vaterite-calcite phase change. In the present study, VC3 exhibits higher compressive strengths and faster hydration than LC3. Compared to hydrated LC3, hydrated VC3 exhibits increased amount of hemi- and mono-carboaluminate formation and decreased amount of strätlingite formation. With gypsum adjustment, the 1-day strength of VC3 is higher than that of pure cement reference. VC3, a low-CO2 binder, presents great potential as a host of the metastable CaCO3 for carbon storage and utilization and as an enabler of carbon capture at gigaton scales

Assessing The Potential Of UAV-Based Multispectral And Thermal Data To Estimate Soil Water Content Using Geophysical Methods

Knowledge of the soil water content (SWC) is important for many aspects of agriculture and must be monitored to maximize crop yield, efficiently use limited supplies of irrigation water, and ensure optimal nutrient management with minimal environmental impact. Single-location sensors are often used to monitor SWC, but a limited number of point measurements is insufficient to measure SWC across most fields since SWC is typically very heterogeneous. To overcome this difficulty, several researchers have used data acquired from unmanned aerial vehicles (UAVs) to predict the SWC by using machine learning on a limited number of point measurements acquired across a field. While useful, these methods are limited by the relatively small number of SWC measurements that can be acquired with conventional measurement techniques. This study uses UAV-based data and thousands of SWC measurements acquired using geophysical methods at two different depths and before and after precipitation to predict the SWC using the random forest method across a vineyard in the central United States. Both multispectral data (five reflectance bands and eleven vegetation indices calculated from these bands) and thermal UAV-based data were acquired, and the importance of different reflectance data and vegetation indices in the prediction of SWC was analyzed. Results showed that when both thermal and multispectral data were used to estimate SWC, the thermal data contributed the most to prediction accuracy, although multispectral data were also important. Reflectance data contributed as much or more to prediction accuracy than most vegetation indices. SWC measurements that had a larger sample size and greater penetration depth (~30 cm sampling depth) were more accurately predicted than smaller and shallower SWC estimates (~18 cm sampling depth). The timing of SWC estimation was also important; higher accuracy predictions were achieved in wetter soils than in drier soils, and a light precipitation event also improved prediction accuracy

A Fully Decoupled Numerical Method for Cahn–Hilliard–Navier–Stokes–Darcy Equations based on Auxiliary Variable Approaches

A Fully Decoupled, Linearized, and Unconditionally Stable Finite Element Method is Developed to Solve the Cahn–Hilliard–Navier–Stokes–Darcy Model in the Coupled Free Fluid Region and Porous Medium Region. by Introducing Two Auxiliary Energy Variables, We Derive the Equivalent System that is Consistent with the Original System. the Energy Dissipation Law of the Proposed Equivalent Model is Proven. to Lay a Solid Foundation, We First Present a Coupled Linearized Time-Stepping Method for the Reformulated System, and Prove its Unconditionally Energy Stability. in Order to Further Improve the Computational Efficiency, Special Treatment for the Interface Conditions and the Artificial Compression Approach Are Utilized to Decouple the Two Subdomains and the Navier–Stokes Equation. Therefore, with the Discretization Techniques of Two Existing Auxiliary Variable Approaches, a Fully Decoupled and Linearized Numerical Scheme Can Be Developed, under the Framework of a Semi-Implicit Semi-Explicit Scheme for Temporal Discretization and Galerkin Finite Element Method for Spatial Discretization. the Grad-Div Stabilization is Also Employed to Further Improve the Stability of Auxiliary Variable Algorithm. the Full Discretization Obeys the Desired Energy Dissipation Law Without Any Temporal Restriction. Moreover, the Implementation Process is Discussed, Including the Adaptive Mesh Strategy to Accurately Capture the Diffuse Interface. Ample Numerical Experiments Are Performed to Validate the Typical Features of Developed Numerical Schemes, Such as the Accuracy, Energy Stability Without Restriction for Time Step Size, and Adaptive Mesh Refinement in Space. Furthermore, We Apply the Proposed Numerical Method to Simulate the Shape Relaxation and the Buoyancy-Driven Flows, Which Demonstrate the Applicability of the Proposed Method

Role Of Greener Default Options On Consumer Preferences For Renewable Energy Procurement

As options for renewable procurement have proliferated to meet consumer demand, it is more complicated for consumers to navigate the available choices. In addition to installing distributed energy resources (e.g., solar PV), consumers can subscribe to green tariffs. Depending on the electricity supplier, the default amount of renewable content will vary (e.g., Community Choice Aggregation vs. investor-owned utilities). There are also options to purchase greener electricity, up to 100%. It is unclear how this context influences household-level decisions to install solar and vice versa. This study uses a discrete choice experiment to estimate the influence of renewable content, solar PV installation, change in electricity costs, engagement level, and procurement duration on household-level decisions. Data were collected from 600 participants randomly assigned to either a 15% or 30% renewable default option. The results suggest that (1) effort is a relatively minor factor in renewable procurement decisions even when comparing PV adoption versus green electricity, (2) relative perceptions of renewable procurement options change as the default renewable content increases, and (3) some consumers are more sensitive to the default level and will shift their behavior accordingly. This research may improve program design to encourage adoption of multiple kinds of renewable energy

Optimal Tilt-Wing EVTOL Takeoff Trajectory Prediction Using Regression Generative Adversarial Networks

Electric vertical takeoff and landing (eVTOL) aircraft have attracted tremendous attention nowadays due to their flexible maneuverability, precise control, cost efficiency, and low noise. The optimal takeoff trajectory design is a key component of cost-effective and passenger-friendly eVTOL systems. However, conventional design optimization is typically computationally prohibitive due to the adoption of high-fidelity simulation models in an iterative manner. Machine learning (ML) allows rapid decision making; however, new ML surrogate modeling architectures and strategies are still desired to address large-scale problems. Therefore, we showcase a novel regression generative adversarial network (regGAN) surrogate for fast interactive optimal takeoff trajectory predictions of eVTOL aircraft. The regGAN leverages generative adversarial network architectures for regression tasks with a combined loss function of a mean squared error (MSE) loss and an adversarial binary cross-entropy (BC) loss. Moreover, we introduce a surrogate-based inverse mapping concept into eVTOL optimal trajectory designs for the first time. In particular, an inverse-mapping surrogate takes design requirements (including design constraints and flight condition parameters) as input and directly predicts optimal trajectory designs, with no need to run design optimizations once trained. We demonstrated the regGAN on optimal takeoff trajectory designs for the Airbus (Formula presented.) Vahana. The results revealed that regGAN outperformed reference surrogate strategies, including multi-output Gaussian processes and conditional generative adversarial network surrogates, by matching simulation-based ground truth with 99.6% relative testing accuracy using 1000 training samples. A parametric study showed that a regGAN surrogate with an MSE weight of one and a BC weight of 0.01 consistently achieved over 99.5% accuracy (denoting negligible predictive errors) using 400 training samples, while other regGAN models require at least 800 samples