Simplex Optimization Algorithm Research Articles

Estimation of constitutive parameters for shape memory polymer thermo-mechanical cycle by an integrated simulation and inverse optimization method

In this work, a method for inverse optimization of shape memory polymer material parameters is proposed to determine the intrinsic model parameters more quickly and accurately for simulated experimental test analysis. First, the basic mechanical properties of the material are characterized experimentally and the parameters of the generalized Maxwell model based on SMP material are fitted. Then, a joint simulation inversion optimization method is built in the ISIGHT-MATLAB-ABAQUS integrated environment to optimize the time domain Prony series parameters using downhill simplex optimization algorithm. The results show that the average errors of the stress curves before and after the optimization of the cooling solid phase and the experimental results are 0.31 MPA and 0.23 MPA, respectively, and the average errors of the strain curves in the warming recovery phase are 1.70% and 0.40%, respectively. After the inversion optimization, the average errors of the stress curves are optimized by 25.8% and the average errors of the strain curves are optimized by 76.5%. This method makes the simulation results more stable with the experimental results, which can effectively reduce the experimental cost and promote the shape memory property of SMP to be widely used in engineering.

Enhancing understanding of moisture diffusion in wood: numerical approach and diffusion parameter optimization

Wood, an environmentally friendly construction material, is characterized by a critical attribute that significantly influences its mechanical properties and durability: its moisture absorption behavior. This study expands upon an existing one-dimensional model to develop an innovative two-dimensional numerical framework for simulating moisture propagation within wood. Utilizing the finite difference method, this approach offers a more detailed analysis of moisture behavior in wood structures. The research adopts an inverse modeling technique, integrating a simplex optimization algorithm programmed in VBA software. This algorithm is employed to deduce diffusion parameters from the evolution of moisture content over time. Focusing on the analysis of moisture diffusion parameters, the study examines both longitudinal and transverse directions in two temperate species (Beech and Fir) and two tropical species (Moabi and Ozigo). The findings provide insightful data on the hygroscopic behavior of these woods, revealing significant distinctions between temperate and tropical species. This research offers valuable information for the application of these wood species in construction and other fields, enhancing the understanding of their moisture-related properties.

Optimal CT windowing on low-monoenergetic images using a simplex algorithm-based approach for abdominal inflammatory processes

Background:ObjectivesTo determine optimal window settings for conspicuity of abdominal inflammatory processes on 50 keV low-monoenergetic images derived from dual-energy spectral CT (DECT). MethodsA retrospective study of 30 patients with clinically proven pancreatitis (15/30) or pyelonephritis (15/30) with inflammatory lesions visible on DECT scans were selected to serve as reference populations. 50 keV low-monoenergetic images in the portal venous phase were iteratively evaluated by 6 abdominal radiologists in twenty-one different windows (7-350HU center; 120-580HU width), selected using a simplex optimization algorithm. Each reader graded the conspicuity of the parenchymal hypodense lesions and image background quality. Three-dimensional contour maps expressing the relationship between overall reader grade and window center and width were constructed and used to find the ideal window for inflammatory pancreatic and renal processes and the image background quality. Finally, 15 appendicitis cases were reviewed on optimal pancreas and kidney windows and the manufacturer recommended conventional abdominal window settings for conventional imaging. ResultsConvergence to optimal windowing was achieved based upon a total of 3,780 reads (21 window settings × 6 readers × 15 cases for pancreas and kidney). Highest conspicuity grade (>4.5 ± 0.0) for pancreas inflammatory lesions was seen at 116HU/430HU, whereas hypodense pyelonephritis had highest conspicuity at 290HU/570HU. This rendered an ideal “compromise” window (>4 ± 0.2) of 150HU/450HU which differed substantially from conventional manufacturer recommended settings of 50HU/380HU (2.1 ± 1.0, p = 0.00001). Appendix mucosal enhancement was best visualized at manufacturer settings. ConclusionsOptimal visualization of inflammatory processes in abdominal organs on 50 keV low-monoenergetic images may require tailored refinement of window settings.

A Simplex Method-Based Bacterial Colony Optimization Algorithm for Data Clustering Analysis

Data clustering is the task of separating data samples into a set of clusters. [Formula: see text]-means is a popular partitional clustering algorithm. However, it has a lot of weaknesses, including sensitivity to initialization and the ability to become stuck in local optima. Hence, nature-inspired optimization algorithms were applied to the clustering problem to overcome the limitations of the [Formula: see text]-means algorithm. However, due to the high-dimensionality of a search space, the nature-inspired optimization algorithm suffers from local optima and poor convergence rates. To address the mentioned issues, this paper presents a simplex method-based bacterial colony optimization (SMBCO) algorithm. The simplex method is a stochastic variant approach that improves population diversity while increasing the algorithm’s local searching ability. The potential and effectiveness of the proposed SMBCO clustering algorithm are assessed using a variety of benchmark machine learning datasets and the generated groups were evaluated using different performance measures. When compared to several well-known nature-inspired algorithms, the experimental results reveal that the SMBCO model produces superior clustering efficiency and a faster convergence rate.

Analysis of the Response of Urban Water Consumption to Climatic Variables: Case Study of Khorramabad City in Iran

Iran is located in a dry climate belt. Such conditions have made the supply of urban water resources one of the most fundamental management challenges. The amount of water consumed in a city is affected by the weather conditions greatly such that as the weather changes, the amount of water consumed changes as well. In this study, several models including zero-order Pearson’s correlation coefficient, first-order Pearson’s correlation, generalized additive model (GAM), generalized linear model (GLM), support vector machine (SVM-Nu), and simplex optimization algorithm were used in order to identify linear/nonlinear reactions of monthly water consumption to the individual and combined associations of meteorological variables (temperature, air pressure, and relative humidity) in Khorramabad city. Zero-order and first-order correlations showed that, by controlling the air temperature, the effect of pressure and relative humidity on changes in water consumption increase. On the other hand, both individual and combined GAM models showed the same result in the nonlinear response of water consumption to the changes in relative humidity and air pressure. The spline method also revealed that, by eliminating the effect of air temperature, the nonlinear reaction of water consumption to changes in pressure and relative humidity was increasing, and by eliminating the effects of the relative humidity and air pressure, the nonlinear reaction of water consumption to the air temperature was intensified. In general, by decreasing the air pressure and temperature, the amount of urban household water consumption decreases drastically. These conditions are generally provided by entering low-pressure systems.

Random Walk와 Simplex 방법을 이용한 V-shape 매입형 영구자석 동기전동기의 코깅토크 저감 최적 설계

This paper presents random walk and simplex optimization algorithm on an optimal design of V-shape interior permanent synchronous motor (V-shape IPMSM). The IPMSM can easily generates a cogging torque because of change in magnetic circuit by interaction between permanent magnets and stator slots. Therefore minimization of the cogging torque, which generates vibration and noise, is an important factor in design of IPMSM. This paper proposes the optimal shape design of V-shape IPMSM using random walk and simplex method for minimizing cogging torque.

Effect of renewable energy sources integration on the optimal coordination of directional over-current relays in distribution system

<div data-canvas-width="397.21009877040933">The demand for power electricity is growing fast and one of the main tasks for power engineers is to generate electricity from renewable energy sources (RES).</div><div data-canvas-width="277.2073926627698">Nevertheless, problems arise when the new generation is integrated in the power distribution network. Under this condition, the main purpose in this paper, an investigation of the effect of RES integration on the optimal coordination of directional over-current relays in distribution system, a dual simplex optimization algorithm has been presented to determine the coordination of directional over-current relays (DOCRs). The system used to check the efficiency of the optimization algorithm which is IEEE 33-bus models. The analysis is performed in MATLAB software environment.</div>

Quantifying the Uncertainty of Identified Parameters of Prestressed Concrete Poles Using the Experimental Measurements and Different Optimization Methods

Prestressed concrete poles nowadays are widely used in supporting the catenary cables of train systems. Compared to their importance to the functionality of the train system, this type of structures have not yet received adequate attention from researchers. We have started tracing the changes in the dynamic behavior of these poles caused by the train passing and the degradation of the materials over a long-time period. In this aim, we installed a structural monitoring system on three of them along one of the high-speed train tracks in Germany. The efficient analysis of the recorded measurements by this system requires a well-known data covering the real material properties of the given structures considering uncertainties of the different parameters. In this paper, we inversely identify the material properties of the poles using deterministic and probabilistic approaches based on the experimental measurements of a full-scale structure and Finite Elements Models. In the deterministic approach, the parameters are identified using the simplex optimization algorithm. Uncertainty of the identified parameters is quantified using a Markov Estimator. In the probabilistic approach, Bayesian inference is utilized for better estimation of the probability distribution of the parameters. Both approaches are suitable for the estimation of mean values of the parameters. The Bayesian method, even though computationally more demanding, is additionally suitable for determining the probability distributions and quantifying the uncertainties of the identified parameters and the correlations between each pair of them. The results show the efficiency of each approach to identify the parameters of the poles. For a rough estimation of the mean values, we recommend the deterministic approach as a simple tool. Conversely, the Bayesian approach is recommended for more detailed and accurate estimation.

Back-Analysis for the Elasto-Viscoplastic Parameters of Landslides Based on the Observed Displacements: A Case Study of the Wujiang Landslide, China

The Wujiang landslide is located on the right bank of Centianhe reservoir near the entrance of the Wujiang gorge, approximately 300 m upstream of the original dam. As a typical ancient landslide, this landslide’s total volume is approximately $$1.327\times 10^{7}\hbox { m}^{3}$$ . The stability of the landslide affects the operation and safety of the dam and flood-release structures after the reservoir of the Expansion Project impounding. First, the observed internal landslide displacement is analyzed and shows that the Wujiang landslide is a long-term creep slope along the control shear plane, and the shear strength of the slip band has either approached or reached its long-term strength. Then, a typical cross section parallel to the sliding direction is selected for the calculation, and the strength parameters are back-analyzed based on the limit analysis method. Next, a finite element model of a typical cross section for the landslide is constructed, and the viscoplastic strain rate is calculated based on Mohr–Coulomb yield criterion and Perzyna’s assumption; therefore, the annual displacement creep rate calculated by the elasto-viscoplastic finite element program is obtained. Finally, the annual creep rate of the observed displacement and that of the calculated displacement are combined, and the elastic modulus and viscosity coefficient of the landslide are back-analyzed based on the orthogonal design method and simplex optimization algorithm. These parameters provide a basis for the analysis and control of the landslide and serve as a reference for similar projects.

A Novel Method for Mechanical Constitutive Parameters Estimation of Human Left Ventricle

In this paper, a new framework for estimation of myocardium constitutive parameters is established. A more realistic, cardiac magnetic resonance image based realistic human left ventricular finite element analysis model is introduced for analyzing the deformation of left ventricle during diastole firstly, the material behavior is described by the anisotropic nonlinear Holzapfel-Ogden constitutive model; then a novel hybrid simplex and quantum behavioral particle swarm optimization algorithm which is proposed to estimate the constitutive model parameters of myocardium as the inverse problem of left ventricle deformation. Numerical results show that finite element analysis results and the estimated parameters are in good agreement with the experimental data reported in literature, which demonstrate that parameter estimation framework is valid. Comparing with current optimization algorithm, the presented hybrid optimal algorithm can estimate the mechanical parameters more efficiently.

A New Framework for Myocardium Constitutive Parameters Estimation

In this study, a new inverse analysis framework for estimation of myocardium constitutive parameters is established. In this framework, by using cardiac magnetic resonance image of realistic human left ventricular, a more realistic, finite element analysis model for analyzing the deformation of left ventricle during diastole is introduced. The anisotropic nonlinear Holzapfel-Ogden constitutive model is used to describe the material behavior of myocardium. Estimating the parameters as for the inverse problem of left ventricle deformation, a novel hybrid simplex and particle swarm optimization algorithm is proposed to estimate the parameters of myocardium’s constitutive model. Numerical examples presents that finite element analysis results and the estimated parameters are in good agreement with the experimental data reported in literature, comparing with current optimization algorithm, the presented hybrid optimal algorithm can estimate the constitutive parameters more efficient. The efficiency and validity of the proposed parameter estimation framework is demonstrated.

Multidisciplinary design of a guided flying vehicle using simplex nondominated sorting genetic algorithm II

This paper presents design of a typical Guided Flying Vehicle (GFV) using the multidisciplinary design optimization (MDO). The main objectives of this multi-disciplinary design are maximizing the payload’s weight as well as minimizing the miss distance. The main disciplines considered for this design include aerodynamics, dynamic, guidance, control, structure, weight and balance. This design of GFV is applied to three and six Degree of Freedom (DOF) to show comparison of simulation results. The hybrid scheme of optimization algorithm is based on Nelder-Mead Simplex optimization algorithm and Nondominated Sorting Genetic Algorithm II (NSGA II), called Simplex-NSGA II. This scheme is implemented for finding an optimal solution through the MDO. The Simplex-NSGA II method is a heuristic optimization algorithm that applies to multi-objective functions and the results are then compared with the most famous algorithms, like Nondominated Sorting Genetic Algorithm II (NSGA II) and Multi-Objective Particle Swarm Optimization (MOPSO). Simulation results demonstrate the superior performance of the Simplex-NSGA II over NSGA II and MOPSO. Also, it is used in this study in order to achieve an optimal solution using MDO in both 3DOF and 6DOF simulations of GFV to reach desirable performance index.

Multi-objective optimization research on the start condition for a parallel hybrid electric vehicle

One of the major issues for Parallel Hybrid Electric Vehicle (Parallel HEV) powertrain is the torsional vibration in the process of start condition, which is unavoidable. This article targets at reducing the damage caused by the torsional vibration with the method of Multi-Objective Optimization (MOO). The dynamic model of the parallel HEV powertrain is established by lumped mass method. Five design variables are selected from 19 parameters by the process of Design of Experiment (DOE), and are optimized by multi-objective downhill simplex optimization algorithm. Pareto Frontier is used to describe the relationship between the two objective functions, and one of the optimization data serves as the basics data for the powertrain modification. Finally, the results of optimization before and after optimization are compared by the test bench. Experimental results under the start condition show that the maximum torque of the optimized powertrain is decreased within the safe range, and the problem of shaft breaking on the originally powertrain is solved.

A simplex method-based social spider optimization algorithm for clustering analysis

Clustering is a popular data-analysis and data-mining technique that has been addressed in many contexts and by researchers in many disciplines. The K-means algorithm is one of the most popular clustering algorithms because of its simplicity and easiness in application. However, its performance depends strongly on the initial cluster centers used and can converge to local minima. To overcome these problems, many scholars have attempted to solve the clustering problem using meta-heuristic algorithms. However, as the dimensionality of a search space and the data contained within it increase, the problem of local optima entrapment and poor convergence rates persist; even the efficiency and effectiveness of these algorithms are often unacceptable. This study presents a simplex method-based social spider optimization (SMSSO) algorithm to overcome the drawbacks mentioned above. The simplex method is a stochastic variant strategy that increases the diversity of a population while enhancing the local search ability of the algorithm. The application of the proposed algorithm on a data-clustering problem using eleven benchmark datasets confirms the potential and effectiveness of the proposed algorithm. The experimental results compared to the K-means technique and other state-of-the-art algorithms show that the SMSSO algorithm outperforms the other algorithms in terms of accuracy, robustness, and convergence speed.

Implementation and calibration of meso-scale modeling technique for simulation of tensile behavior of fabric materials

Abstract Present paper investigates the simulation of quasi-static response of fabric materials subjected to uniaxial tensile loading. The purpose of this research is to simulate the fabrics mechanical behavior accurately determining the quasi-static elastic and failure properties of fabric yarn and to rank, from the analysis point of view, the parameters which influence the fabric behavior. In particular, a meso-scale approach was employed to model the behavior of different para-aramid fabrics using LS-DYNA software whereas the Nelder & Mead Simplex optimization algorithm was implemented for the determination of material model's parameters by the fitting of numerical and experimental tensile loading curve.

Identification of the dynamic parameters of active magnetic bearings based on the transfer matrix model updating method

The stiffness and damping coefficients of Active magnetic bearings (AMBs) have a great impact on the dynamics of a high-speed rotor AMB system, from its bending critical speed to the modes of its vibration and stability. To accurately obtain the stiffness and damping coefficients of AMBs, this study proposes a new identification approach based on the transfer matrix model updating method. By minimizing the error between the unbalance response calculated through the transfer matrix approach and the experimental measurements, the stiffness and damping coefficients are obtained using the simplex optimization algorithm based on the updating method of the model. According to the experimental data, we identify the parameters from 20 Hz to 260 Hz (1200 rpm to 15600 rpm). To verify the identified results, a finite element rotor AMBs model is created, and the theoretical unbalance response is predicted using the identified parameters. The theoretical unbalance responses closely coincide with the experimental measurements, indicating the effectiveness of the proposed method.

Quantifying uncertainty in crop model predictions due to the uncertainty in the observations used for calibration

Despite modellers are paying increasing attention to analyse and manage the different sources of uncertainty affecting model predictions, the impact of the uncertainty in the observations used for calibration has been ignored. This study proposes a methodology for its quantification and provides an illustrative case study with data collected in two field experiments where rice was grown under flooded conditions in northern Italy in 2002 and 2004. Latin hypercube sampling was used to generate virtual series of observations from the mean and standard deviation of aboveground biomass values collected during the season in the two experiments. Each of the generated series was then used to calibrate the parameters maximum radiation use efficiency and optimum temperature for growth of the WARM model by means of the simplex optimization algorithm. The analysis of the distribution of key outputs (aboveground and panicle biomass at harvest) and of agreement metrics revealed that the impact of uncertainty in the observations used for calibration (explored here running calibration experiments for each of the generated series) can be large. The difference between maximum and minimum aboveground biomass at maturity was 2.79tha−1 and 3.78tha−1 for the datasets collected in 2004 and 2002, respectively. Corresponding values for panicle biomass were 0.97tha−1 and 2.36tha−1. In all cases, model outputs were normally distributed. Large differences were achieved also in the values of the agreement metrics, with RRMSE ranging from 13.64% to 36.22% and from 8.04% to 29.97% for the 2004 and 2002 datasets. The methodology proposed – although applicable to a variety of models and domains – deals only with the uncertainty due to random errors, which could derive, e.g. from non-representative sampling or from the repeatability of the method used to determine the variable of interest. Other sources of uncertainty, like those involved with systematic errors, need to be addressed in further studies. This study highlighted the need for conceptual and mathematical frameworks where the different sources of uncertainty affecting model predictions can be analysed in an integrated way.

Encapsulation of roasted coffee oil in biocompatible nanoparticles

Nanoencapsulation is a promising approach to protect the volatile compounds in natural lipid mixtures like roasted coffee oil. In this work, Nanocapsules were obtained by the miniemulsification-solvent evaporation technique using poly(l-lactic acid) (PLLA) and poly(hydroxybutyrate-co-hydroxyvalerate) (PHBV) as encapsulant polymers. The aromatic profile of both in natura and encapsulated oil was determined by Gas Head-Space Solid Phase Membrane Extraction/Chromatography coupled to Mass Spectroscopy. The total amount of effectively encapsulated oil was evaluated using a combination of full factorial experimental design and the simplex optimization algorithm and the following independent factors were evaluated: encapsulant polymer, dispersion mechanism, polymer:oil mass ratio and surfactant. The total oil content (oil recovery) was significantly influenced (p < 0.05) by two-way and three-way interactions confirming that a complex dependence between the factors took place. If PLLA was the encapsulant polymer, then sonication yielded the highest oil recovery. For PHBV as encapsulant, high shear homogenization (Ultraturrax) led to the highest oil recovery. In both cases, polymer:oil ratio and surfactant must be adjusted accordingly. FTIR spectra and HS-SPME/GCMS suggested that the major aroma constituents of coffee oil were successfully encapsulated demonstrating that miniemulsification-solvent evaporation was appropriate to encapsulate roasted coffee oil.

Optimization and robustness analysis of hybridoma cell fed-batch cultures using the overflow metabolism model

The maximization of biomass productivity in fed-batch cultures of hybridoma cells is analyzed based on the overflow metabolism model. Due to overflow metabolism, often attributed to limited oxygen capacity, lactate and ammonia are formed when the substrate concentrations (glucose and glutamine) are above a critical value, which results in a decrease in biomass productivity. Optimal feeding rate, on the one hand, for a single feed stream containing both glucose and glutamine and, on the other hand, for two separate feed streams of glucose and glutamine are determined using a Nelder-Mead simplex optimization algorithm. The optimal multi exponential feed rate trajectory improves the biomass productivity by 10 % as compared to the optimal single exponential feed rate. Moreover, this result is validated by the one obtained with the analytical approach in which glucose and glutamine are fed to the culture so as to control the hybridoma cells at the critical metabolic state, which allows maximizing the biomass productivity. The robustness analysis of optimal feeding profiles obtained with different optimization strategies is considered, first, with respect to parameter uncertainties and, finally, to model structure errors.

Cardiac T 1 Imaging

T(1) mapping of the heart has evolved into a valuable tool to evaluate myocardial tissue properties, with or without contrast injection, including assessment of myocardial edema and free water content, extracellular volume (expansion), and most recently cardiomyocyte hypertrophy. The magnetic resonance imaging pulse sequence techniques developed for these applications have had to address at least 2 important considerations for cardiac applications: measure magnetization-inversion recoveries during cardiac motion with sufficient temporal resolution for the shortest expected T(1) values, and, second, obtain these measurements within a time during which a patient can comfortably suspend breathing. So-called Look-Locker techniques, and variants thereof, which all sample multiple points of a magnetization recovery after each magnetization preparation, have therefore become a mainstay in this field. The rapid pace of advances and new findings based on cardiac T(1) mapping for assessment of diffuse fibrosis or myocardial edema show that these techniques enrich the capabilities of magnetic resonance imaging for myocardial tissue profiling, which is arguably unmatched by other cardiac imaging modalities.