Optimization Procedure Research Articles

D3S: Decision support system for sectorization

Sectorization problems refer to dividing a large set, area or network into smaller parts concerning one or more objectives. A decision support system (DSS) is a relevant tool for solving these problems, improving optimisation procedures, and finding feasible solutions more efficiently. This paper presents a new web-based Decision Support System for Sectorization (D3S). D3S is designed to solve sectorization problems in various areas, such as school and health districting,planning sales territories and maintenance operations zones, or political districting. Due to its generic design, D3S bridges the gap between sectorization problems and a state-of-the-art decision support tool. The paper aims to present the generic and technical attributes of D3S by providing detailed information regarding the problem-solution approach (based on Evolutionary Algorithms), objectives (most common in sectorization), constraints, structure and performance.

Development of an automated and cost-effective apparatus for sol-gel solution deposition using spray coating technique and its application for TiO[formula omitted]-based photocatalytic films

This study presents the development of a novel apparatus for automated sol–gel solution deposition through a cost-effective spray coating technique. The focus was on producing TiO2 films for photocatalytic applications, addressing environmental pollution and energy scarcity issues. The apparatus, constructed using economical materials, facilitated the production of TiO2 films with an anatase-like crystalline structure and uniform surface morphology. The methodology included detailed experimental procedures for solution preparation, substrate cleansing, and deposition process optimization. Characterization techniques such as scanning electron microscope (SEM), energy-dispersive X-ray spectroscopy (EDS), thermogravimetric analysis (TGA), X-ray diffraction analysis (XRD), Fourier-transform infrared spectroscopy (FTIR), and UV–Vis spectroscopy were employed to assess the structural, morphological, and optical properties of the films. The results indicated the successful synthesis of anatase-phase TiO2 thin films with desirable optical properties and efficient photocatalytic activity, as demonstrated through the degradation of methylene blue and methyl orange. The study highlights the apparatus’s efficiency, versatility, and potential for industrial-scale production, thus offering promising prospects for practical applications and commercialization in the field of sustainable environmental solutions.

Practical Design Guidelines for Topology Optimization of Flexible Mechanisms: A Comparison between Weakly Coupled Methods

Industrial robots are complex systems, as they require the integration of several sub-assemblies to perform accurate operations. Moreover, they may experience remarkable dynamic actions due to high kinematic requirements, which are necessary to obtain reduced cycle times. The dynamic design of industrial robots can therefore be demanding, since the single structural component can induce an impact both in the design phase (development strategy and computational time) and at the machine level (global stiffness and natural frequencies). To this end, the present paper proposes first a topology optimization procedure based on the Equivalent Static Loads (ESL) method that integrates flexible multibody simulation outputs. The same procedure also foresees an intermediate static reduction to reduce and to precisely define the application points of the ESL. Secondly, an optimization procedure based on the Quasi-Static Loads (QSL) method integrating flexible multibody simulation outputs is proposed as well. The objective is to carry out a comparison between the two methods and consequently evaluate the benefits and drawbacks of the two. In the end, practical guidelines regarding the selection and application of the two methods are also provided to the reader.

Proposing a procedure for multi-objective optimization of cascade thermoelectric coolers to achieve maximum cooling capacity and COP

Abstract A novel approach is proposed for optimal designing two-stage thermoelectric coolers using the biogeography-based optimization algorithm. For the comprehensiveness of the research, both types of two-stage coolers, including electrically serial and separated stages, have been investigated. Using the proposed procedure, first by selecting the most important performance parameters of the cooler, i.e. COP and the cooling capacity as objective functions, the efficiency and the validity of the approach are proved by comparing the results with results of the previous researchers. In all the examined cases, the proposed approach has led to the enhancing of the cooler performance. The results of single-objective optimization showed that using the proposed method, the cooling capacity and COP can be improved up to 17 % and 8.5 % compared to the previous designs, respectively. In the following, multi-objective optimization of thermoelectric coolers is conducted, taking into account both the objectives of the COP and the cooling capacity. Finally, Pareto optimal solutions and the exergetic efficiency of the optimized cooler were calculated for different values of the weight factor of the multi-objective function. The results of multi-objective optimization showed that by increasing the COP weight factor in the objective function, the exergetic efficiency of the refrigeration system enhances.

Evaluation of the impact of breast-conserving surgery on cancer outcomes of multiple (multifocal or multicentric) ipsilateral breast cancer

Background Breast conservative therapy (BCT) includes conservative breast surgery (CBS) with adjuvant radiotherapy and is now considered a standard treatment for early-stage breast cancer to achieve survival with acceptable aesthetic outcomes. Management of multiple ipsilateral breast cancer (MIBC) is a challenge, since the choice of optimal surgical procedures is controversial. Recently, many breast surgeons have advocated CBS is technically feasible to safely excise MIBC in selected cases. Aim This study aimed to evaluate the outcome of MIBC patients who received CBS with special attention on local control and recurrence. Patient and method This retrospective study was carried out on 90 patients at Al Azhar University Hospital and Ain Shams University Hospitals between January 2021 and January 2023. Results The majority of patients 85 (94.4%) had two foci of disease while five (5.5%) patients had three foci. The distance between the lesions ranges between 0.5 and 6.5 cm. The most common type of CBS was wide local excision en bloc resection of all lesions with normal tissue in between was done in 85 (94.4%) patients and five (5.5%) patients had two separate incisions leaving normal tissue in between. Clear margins were reported in 79 (87.7%) patients. 15 (5.5%) patients had different pathological lesion types (heterogeneous). One patient had a recurrence and another had distant metastases. Conclusion With good patient selection, there is no difference between CBS and mastectomy in the management of MIBC. CBS is oncological safe provided that an adequate excision with clear margins followed by whole-breast radiation therapy and adjuvant systemic therapy.

Abstract 947: A nanodroplet cell processing platform for identifying synergistic drug combinations for neuroblastoma treatment in TH-MYCN transgenic mice

Abstract Neuroblastoma is the most common pediatric extracranial solid tumor and contributes to more than 15% of child mortality. It is characterized by extreme heterogeneity and high-risk tumors often resulting in poor prognosis. Currently, chemotherapy is a prevailing standard treatment for neuroblastoma. However, it is plagued by multiple challenges such as drug resistance and debilitating side effects. Synergistic drug combination is proposed as an innovative approach to potentially improve clinical outcomes of single-agent chemotherapy. We propose a novel approach to screen for effective chemo-synergistic drug combinations using Bioinspired nanodroplet processing (BioNDP) screening platform. This platform overcomes the limitations of conventional drug screening approaches, including the requirement for large cell numbers and the low translation rate from bench to bedside. In this study, we utilize TH-MYCN transgenic mice, a well-known genetically engineered mouse model of neuroblastoma that recapitulates critical genetic and clinical aspects of high-risk MYCN-amplified neuroblastoma. With its genetically homogeneous background and spontaneous tumor development, TH-MYCN mice offer a valuable comparison to traditional xenograft mouse models. We have also developed an optimal procedure of primary murine tumor isolation using both negative and positive selection approaches. The purity of the isolated cells was verified with GD2 expression, one of well-known marker of neuroblastoma. In addition, we have successfully identified an effective drug combination of cyclophosphamide, doxorubicin, and vincristine, using primary neuroblastoma tumor samples derived from TH-MYCN transgenic mice as a target. The in vitro synergistic effect of primary mouse tumor cell cytotoxicity was confirmed by using luminescent cell viability assay. Further in vivo experiments have demonstrated the therapeutic efficacy of this selected drug combination in suppressing tumor growth and improving survival rate in all TH-MYCN transgenic mice treated. To sum up, we aim to establish a future patient-centric paradigm by which minimal primary cell samples from a personalized biopsy of patients may undergo drug screening, leading to the identification of a tailored drug combination. The BioNDP platform we developed may become a pivotal resource for personalized therapy to ensure a faster and more efficient treatment strategy for patients. Citation Format: Yen-Tzu Liao, Zhi-Kai Yu, Ching-Te Kuo, Wen-Ming Hsu, Yi-Xun Huang, Hsinyu Lee. A nanodroplet cell processing platform for identifying synergistic drug combinations for neuroblastoma treatment in TH-MYCN transgenic mice [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 947.

An anisotropic topology optimization procedure for continuous fiber reinforced polymer structures with biaxial fiber layout for improved intersection point design

Fiber placement technologies allow for manufacturing of complex-shaped composite parts with load-adapted fiber orientation. For design of corresponding structures, topology optimization is well-established. However, anisotropic topology optimization approaches are often limited to unidirectional fiber orientations. In truss-like topology optimized structures, the intersections between the individual trusses are subject to multiaxial stress states. For those, a unidirectional fiber orientation is not appropriate and may not be suitable for manufacturing.In this work we propose a topology optimization procedure that allows for unidirectional but also biaxial fiber orientations. The choice between unidirectional and biaxial fiber layout is based on the stress state within the finite elements. For the biaxial fiber layout, the first fiber angle is aligned with the maximum absolute principal stress direction, while the second fiber orientation is calculated using composite net theory.Results show that considering biaxial fiber orientations leads to fiber orientations that can be better manufactured by fiber placement technologies and are considered suitable for automatic derivation of manufacturing-tolerant placement paths. Furthermore, the stress state at intersections is improved reducing the probability of inter fiber failure. In addition, consideration of biaxial fiber orientations could increase the stiffness at constant weight compared to purely unidirectional topology optimization.

Laboratory Investigation and Empirical Modelling of Polymer Solution Viscosity

Polymer flooding, an improved oil recovery technique, is a well-established method for improving oil production. Although numerous polymers have been suggested in the literature, identifying suitable ones for applications in high-temperature and high-salinity carbonate reservoirs remains a challenging task. The viscosity of polymer solutions depends on various parameters, including polymer concentration, temperature, and shear rate. Therefore, screening polymers for these demanding conditions necessitates meticulous experimental work. In this experimental study, polymer solutions' physical property—viscosity—was measured as a function of polymer concentration, temperature, and shear rate. Subsequently, the generated rheological data were used to develop empirical models capable of predicting the viscosity of polymer solutions under various conditions. An acceptable match was achieved between the experimental and model-predicted data. Furthermore, a validation was conducted for the empirical models, yielding a root mean square error of 1.75. These models will significantly reduce the number of experiments required for the screening process and prove valuable for optimization procedures.

Artificial intelligence-based surrogate model for computation of the electric field of high voltage transmission line ceramic insulator with corona ring

Purpose The ionization of the air surrounding the phase conductor in high-voltage transmission lines results in a phenomenon known as the Corona effect. To avoid this, Corona rings are used to dampen the electric field imposed on the insulator. The purpose of this study is to present a fast and intelligent surrogate model for determination of the electric field imposed on the surface of a 120 kV composite insulator, in presence of the Corona ring. Design/methodology/approach Usually, the structural design parameters of the Corona ring are selected through an optimization procedure combined with some numerical simulations such as finite element method (FEM). These methods are slow and computationally expensive and thus, extremely reducing the speed of optimization problems. In this paper, a novel surrogate model was proposed that could calculate the maximum electric field imposed on a ceramic insulator in a 120 kV line. The surrogate model was created based on the different scenarios of height, radius and inner radius of the Corona ring, as the inputs of the model, while the maximum electric field on the body of the insulator was considered as the output. Findings The proposed model was based on artificial intelligence techniques that have high accuracy and low computational time. Three methods were used here to develop the AI-based surrogate model, namely, Cascade forward neural network (CFNN), support vector regression and K-nearest neighbors regression. The results indicated that the CFNN has the highest accuracy among these methods with 99.81% R-squared and only 0.045468 root mean squared error while the testing time is less than 10 ms. Originality/value To the best of the authors’ knowledge, for the first time, a surrogate method is proposed for the prediction of the maximum electric field imposed on the high voltage insulators in the presence Corona ring which is faster than any conventional finite element method.

Samarium(III) Removal by Weak Acid Exchanger Amberlite IRC-50 in (H+) and (Na+) Forms

Adsorption of samarium(III) on a weakly macroporous cation exchanger Amberlite IRC-50 (H+) and (Na+) forms is studied as a function of the initial pH of the aqueous solution, time and temperature, initial samarium(III) ion concentration, and the amount of resin at a fixed temperature (20 ± 1 °C). The concentration range was between 0.1–5 mmol/L, the pH range was between 1.8 and 10.5; the stirring time was between 2 and 60 min; and the amount of resin was between 0.025 and 0.15 g. Both the film and particle diffusion equations are applied to explain the kinetic data. The rate constant values for samarium(III) adsorption were calculated for both film and particle diffusion processes. It is observed to follow the order (Na+) > (H+). Temperature is found to have an insignificant effect on both diffusional processes. Various thermodynamic parameters (ΔH°, ΔS°, and ΔG°) from samarium(III) exchange on the resin were calculated. The optimum conditions were found to be a concentration of 1 mmol/L, pH of 9.3, stirring time of 20 and 5 min for Amberlite IRC-50 (H+) and (Na+) forms, respectively, and 0.15 g of resin. The equilibrium extraction of samarium was 22.2 mg/g for Amberlite IRC-50 (H+) and 21.9 mg/g for Amberlite IRC-50 (Na+) at an initial concentration of 1 mmol/L. The results obtained show that the Amberlite IRC-50 weak cationexchange resin performed well for the removal and recovery of samarium(III). The optimization procedure provides access to industrial-scale Sm(III) removal processing.

Innovative multiobjective optimization of a curved‐blade vertical axis wind turbine modeled by modified double multiple streamtube method

AbstractThe attractive specifications of vertical axis wind turbine (VAWT) are its operating with low noise and wind in various directions. To achieve a higher performance of these turbines, modeling of a curved‐blade VAWT by the modified double multiple streamtube (DMST) method and optimizing this wind turbine are performed. The power coefficient and the total normal force acting on blades were selected as two objective functions. Minimizing the normal force acting on turbine blades as the second objective function was not observed in the open literature. The amount of normal force is crucial in the structural design of VAWT because it generates both the bending moment on blades and forces on supporting arms of VAWT. Another innovation of this study is to consider the shape coefficient parameter that determines the shape of the rotor geometry as a design variable in optimization procedure. A 3 m height curved‐blade VAWT type was optimized by this multi‐objective optimization technique. The optimum values of the design variables obtained by the Genetic Algorithm were selecting an elliptical rotor geometry, pitch angle −1.4°, diameter/height 1.3, blade aspect ratio 19.8, tip speed ratio 4.3 and selecting three blades which could provide a power coefficient of 0.49 and a normal force of 158.7 N.

Entanglement catalysis for quantum states and noisy channels

Many applications of the emerging quantum technologies, such as quantum teleportation and quantum key distribution, require singlets, maximally entangled states of two quantum bits. It is thus of utmost importance to develop optimal procedures for establishing singlets between remote parties. As has been shown very recently, singlets can be obtained from other quantum states by using a quantum catalyst, an entangled quantum system which is not changed in the procedure. In this work we take this idea further, investigating properties of entanglement catalysis and its role for quantum communication. For transformations between bipartite pure states, we prove the existence of a universal catalyst, which can enable all possible transformations in this setup. We demonstrate the advantage of catalysis in asymptotic settings, going beyond the typical assumption of independent and identically distributed systems. We further develop methods to estimate the number of singlets which can be established via a noisy quantum channel when assisted by entangled catalysts. For various types of quantum channels our results lead to optimal protocols, allowing to establish the maximal number of singlets with a single use of the channel.

Rapid prototyping of a modular optical flow cell for image-based droplet size measurements in emulsification processes

AbstractEmulsification processes are often found in the process industry and their evaluation is crucial for product quality and safety. Numerous methods exist to analyze critical quality attributes (CQA) such as the droplet sizes and droplet size distribution (DSD) of an emulsification process. During the emulsification process, the optical process accessibility may be limited due to high disperse phase content of liquid-liquid systems. To overcome this challenge, a modular, optical measurement flow cell is presented to widen the application window of optical methods in emulsification processes. In this contribution, the channel geometry is subject of optimization to modify the flow characteristics and produce high optical quality. In terms of rapid prototyping, an iterative optimization procedure via SLA-3D printing was used to increase operability. The results demonstrated that the flow cell resulting from the optimization procedure provides a broad observation window for droplet detection. Graphical abstract

The hybrid algorithms in constrained optimization of the permanent magnet motors

AbstractHere, different hybrid optimization algorithms have been applied to the constrained optimization of the line‐start permanent magnet synchronous motor. The optimal results for the hybrid Cuckoo Search and hybrid Grey Wolf Optimization have been compared. In the primary objective function, the parameters describing motor functional parameters in the steady state have been included. Moreover, the non‐linear constraint function has been taken into account. The optimization procedures have been elaborated in the Delphi and Matlab environments. The mathematical model of the line‐start permanent magnet motor has been developed in the Maxwell environment. Selected optimization results are presented and discussed.

Implementation of optimal HF welding procedure of steel pipes for high-quality and energy-efficient welds

Electric currents of medium and high frequencies are used for inductive heating and welding of steel products. Furthermore, magnetic concentrators are used for the optimisation of transfer so that they direct the currents at a specific area of the heat-treated steel product. After years of study and detailed experimental research, we have found out that the material behaviour during inductive heat treatment also depends on the type of concentrator applied. The character of the electric current passing through the steel products changes through the application of different concentrators which directly alter the structure and quality of the final product. Ferritic and magneto-dielectric concentrators from different producers have been used in the research with HF inductive welding of steel pipes.

Adaptive Turbulence Model for Leading Edge Vortex Flows Preconditioned by a Hybrid Neural Network

Eddy-viscosity-based turbulence models provide the most commonly used modeling approach for computational fluid dynamics simulations in the aerospace industry. These models are very accurate at a relatively low cost for many cases but lack accuracy in the case of highly rotational leading edge vortex flows for mid to low aspect-ratio wings. An enhanced adaptive turbulence model based on the one-equation Spalart–Allmaras turbulence model is fundamental to this work. This model employs several additional coefficients and source terms, specifically targeting vortex-dominated flow regions, where these coefficients can be calibrated by an optimization procedure based on experimental or high-fidelity numerical data. To extend the usability of the model from single or cluster-wise calibrated cases, this work presents a preconditioning approach of the turbulence model via a neural network. The neural network provides a case-unspecific calibration approach, enabling the use of the model for many known or unknown cases. This extension enables aircraft design teams to perform low-cost Reynolds-averaged Navier–Stokes simulations with increased accuracy instead of complex and costly high-fidelity simulations.

Optimization of plasmonic nanofluid filters for spectral splitting photovoltaic/thermal systems

There remains a scarcity of studies on the utilization and optimization of core–shell plasmonic nanofluid filter although it shows potential in solar energy exploitation. To fill this research gap, this paper introduces an optimization methodology founded on merit function (MF) to optimize the Au@SiO2-based nanofluid filters for enhancing the photovoltaic/photothermal (PV/T) system performance. Initially, the MF model of the nanofluid filtered-PV/T system was constructed based on a reliable optical model and validated by referenced experimental results. Using these models, the effect of nanofluid parameters on the system performance and the mechanism of optimization procedure were investigated. Results indicated that modifying SiO2 shell thickness is a cost-effective means of tailoring the transmittance of the nanofluids compared to adjusting the Au core diameter. The optimization mechanism can be summarized as enhancing electrical efficiency or improving thermal efficiency to offset electricity losses. Furthermore, the MF of the Au@SiO2/EG nanofluid increased from 1.3737 to 1.3844 with optical thickness decreasing from 44 mm to 40 mm by mixing with Ag@SiO2 nanoparticles, indicating the cost-effectivity of blended nanofluid. The MF of the optimized blended nanofluids are 0.007–0.084 higher than those of the referenced nanofluids. This work offers a feasible pathway toward achieving high-performance nanofluid optical filters.

Reliability-based optimization of supported pendulum TMDs’ nonlinear track shape using Padé approximants

Supported Pendulum Tuned Mass Dampers (PTMDs) are vibration absorbers whose mass is constrained to oscillate along a curved path, enabling to use gravity as a restoring force. Through a proper track shape selection, they can adapt to the so-called Nonlinear Energy Sinks (NES) concept, consolidated for spring–mass–damper TMDs. Accordingly, NESs are recognized for their ability to resonate in a wide range of frequencies due to their nonlinear nature, becoming more effective when variations on the host structure’s natural frequencies occur. However, regardless of the recent advances in the last few years, gaps remain to be explored in the track shape optimization of supported PTMDs. For instance, a comprehensive literature review shows that Reliability-Based Design Optimization (RBDO) studies to determine the track shape of supported PTMDs cannot be found to the best of the author’s knowledge. Within this context, this paper proposes an original RBDO approach to define the track shape of supported PTMDs in buildings subjected to seismic excitation. For this purpose, a generic rational function with an arbitrary curvature, determined from the Padé approximation of the circle equation, is constructed to allow the PTMD’s path shape search. This procedure allows optimizing fewer parameters than required in Taylor series expansions while providing a wider range of alternatives for numerical optimization. Finally, an active-learning Kriging-based Efficient Global Optimization (EGO) procedure is employed to find the optimum solutions with few objective function evaluations. The application case consists of a typical soft story building subjected to seismic loadings. The optimal track differs from the circle equation, with a higher slope for moderate displacements.

Many-Stage Optimal Stabilized Runge–Kutta Methods for Hyperbolic Partial Differential Equations

A novel optimization procedure for the generation of stability polynomials of stabilized explicit Runge–Kutta methods is devised. Intended for semidiscretizations of hyperbolic partial differential equations, the herein developed approach allows the optimization of stability polynomials with more than hundred stages. A potential application of these high degree stability polynomials are problems with locally varying characteristic speeds as found for non-uniformly refined meshes and spatially varying wave speeds. To demonstrate the applicability of the stability polynomials we construct 2N-storage many-stage Runge–Kutta methods that match their designed second order of accuracy when applied to a range of linear and nonlinear hyperbolic PDEs with smooth solutions. These methods are constructed to reduce the amplification of round off errors which becomes a significant concern for these many-stage methods.

Two-stage Taguchi-based optimization on dynamically-vented blade flaps to enhance the rotor performance of a Savonius turbine

Among the vertical-axis turbine groups for small-scale wind and hydrokinetic applications, the Savonius type is considered practical due to its simple construction, easy maintenance, and good self-starting characteristics. Unfortunately, it has a poor efficiency due to the exertion of negative torques during its returning sweeps. Recently, the technique of controlled dynamic venting has been shown to reduce this negative torque on a two-bladed Savonius rotor while preserving its omnidirectional capability. That investigation was extended in this numerical study by refining the controllable flaps of the same rotor to improve its performance using a two-stage optimization procedure. In the first stage, the design parameters of the controllable flaps were optimized using the Taguchi and Analysis of Variance (ANOVA) methods. In this analysis, the flap length was found to be the most significant parameter affecting the rotor performance with a contribution ratio of 79.8%, in contrast to a ratio of only 6.7% by the least effective parameter: the flap opening angle. The first optimization stage produced an improvement of 16.7% on the average power coefficient (CP) of the vented rotor, compared to the unvented one, at the optimal tip-speed ratio (TSR) of 1.0. In the second stage, the flap length was refined further to a shorter flap of 22.5% of the blade length, resulting in an improvement of 21% on the same metric at the same TSR. Crucially, these controllable flaps only used an energy cost of 6.1% of the total energy produced by the rotor, a significant improvement from the 12.2% energy cost obtained in the previous study.