Static and dynamic optimization of a pose adjusting mechanism considering parameter changes during construction

Recent advances in non-gradient based optimization methods (e.g., Genetic Algorithms, Particle Swarm Optimization) enhanced the abilities of discrete, integer, and mixed optimization problems. However, the very nature of non-gradient based algorithms is that the number of analyses required to get to an optimal solution is several orders of magnitude higher than for traditional gradient based optimization methods or response surface optimization methods, when considering continuous problems. Instead of these approaches we propose to use a response surface approximate optimization method modified to work with discrete design variables. In this case whenever it is required to perform the actual analysis of responses for the purpose of fitting a response surface approximation, the design variables will be converted to corresponding discrete values. Two discretization techniques are proposed. We demonstrate that although lacking global search properties like Genetic Algorithms and Particle Swarm Optimization, the discrete response surface optimization provides a computationally efficient way of improving an initial design and getting into a region of an optimum using only discrete points for analysis.

<div class="htmlview paragraph">To overcome the trade-off between NOx and particulate emissions for future diesel vehicles and engines it is necessary to seek methods to lower pollutant emissions. The desired simultaneous improvement in fuel efficiency for future DI (Direct Injection) diesels is also a difficult challenge due to the combustion modifications that will be required to meet the exhaust emission mandates.</div> <div class="htmlview paragraph">This study demonstrates the emission reduction capability of split injections, EGR (Exhaust Gas Recirculation), and other parameters on a High Speed Direct Injection (HSDI) diesel engine equipped with a common rail injection system using an RSM (Response Surface Method) optimization method. The optimizations were conducted at 1757 rev/min, 45% load. Six factors were considered for the optimization, namely the EGR rate, SOI (Start of Injection), intake boost pressure, and injection pressure, the percentage of fuel in the first injection, and the dwell between injections. In addition, the effects of swirl were investigated.</div> <div class="htmlview paragraph">The benefit of the combined effects of EGR with supercharging was well realized by the RSM optimization, which resulted in simultaneous reductions in both NOx and PM emissions, while even improving BSFC (Brake Specific Fuel Consumption). Through the present optimization process, it was shown that RSM optimization is an effective and powerful tool for realizing the full advantages of the combined effects of combustion control techniques. In addition, observation of the optimization process provides a more thorough understanding of HSDI diesel combustion since the observed trends can be explained by monitoring the effects of the operating parameters.</div>

In this study, an adaptive space mapping method is introduced. A Coarse Model (CM) uses quadratic response surfaces for the objective and constraint functions. A fine simulation model is not only used for the correction of the coarse simulation model and validation of the final solution, but is also applied for construction of the Space Mapping (SM) expressions. The intelligent sampling method is implemented for the generation of initial samples and the Particle Swarm Optimisation (PSO) method is applied to optimise the response surface based on the CM in this work. The main purpose is to combine the SM algorithm and the modification of the distribution of the samples strategy. It promises that the response surface based on the CM should be updated stepwise in the right search direction. To demonstrate the application with the proposed optimisation method, it is successfully applied to the optimisation of geometric parameters of the addendum surface and Blank Holder Forces (BHFs) in a sheet-forming problem in a remarkably short computational time.

The hazard and increased risk associated with slope failures in the natural habitat are attributed to uncertainty in soil parameters. These soil parameters, that is, cohesion, internal friction angle, and shear modulus, are estimated using the back-analysis method. The method incorporates machine learning algorithms such as particle swarm optimization and support vector machine. However, to capture the uncertainties in the soil parameters, probabilistic analysis is required. This study implements support vector machine for estimating soil parameters as the function of displacement in the soil. The Bayesian method is used to account for uncertainty in soil parameters. The Noto Hanto earthquake in 2007 and the consequent slope failure at Noto Yuryo Road, Japan, is considered as the case study for the proposed model. The finite-element method is used to simulate this slope failure. The soil parameters are treated as random variables to account for uncertainties in these parameters. The predicted displacement is comparable with the measured displacement in the field, which validates the application of the proposed model. The proposed method can efficiently predict experiment soil parameters by capturing uncertainty in soil parameters through a probabilistic approach.

Effect of Variable Selection on Multidisciplinary Design Optimization: a Flight Vehicle Example

At present, response surface methodology (RSM) is the most preferred method for fermentation media optimization. However, in the last two decades, artificial intelligence algorithm has become one of the most efficient methods for empirical modelling and optimization. One of the popular developed approaches is Particle Swarm Optimization (PSO), which is used in optimizing a problem. This paper focuses on comparative studies between RSM and PSO in fermentation media optimization for the production of flexirubin production using Chryseobacterium artocarpi CECT 8497T. Two methodologies were compared for in terms of their modeling, sensitivity analysis, and optimization abilities. All experiments were performed accordingly to box-behnken design (BBD), and the generated data was analyzed using RSM and PSO. The sensitivity analysis performed by both methods has given comparative results. Based on the correlation coefficient, the model developed with PSO was found to be superior to the model developed with RSM. The result shows that PSO gives a better pigmentation yield with optimal fermentation concentration.

This study aimed to enhance the sensory and bioactive properties of pilafs prepared from three geographically indicated bulgur varieties–Siyez, Firik, and Karakilçik–through an AI-guided optimization approach combining Response Surface Method (RSM) and Particle Swarm Optimization (PSO). Different bulgur (130–150 g) and water (350–450 mL) ratios were tested to determine optimal formulations. Sensory evaluation revealed that Firik bulgur pilaf achieved the highest overall acceptability (8.49), while Karakilçik bulgur pilaf scored highest in color (7.68) and aroma (8.58), and Siyez bulgur pilaf received the highest taste score (7.50). In terms of bioactive properties, Karakilçik bulgur pilaf showed the highest antioxidant capacity (75.57% DPPH radical scavenging activity), whereas Firik bulgur pilaf had the highest total phenolic (842.39 mg GAE/kg) and flavonoid contents (6.38 mg CE/g). Color analysis indicated that Siyez bulgur pilaf had the lightest color (L = 52.18), while Firik pilaf exhibited the most intense red hue (a = 8.12) and Karakilçik pilaf the darkest appearance (L = 35.42). PSO-based validation confirmed the accuracy of RSM models by reaching global optima within 40 iterations and minimal deviation from experimental values. This is the first study to apply an integrated RSM–PSO modeling approach to traditional bulgur pilafs, enabling the prediction and optimization of their sensory and bioactive characteristics. The results provide a novel framework for enhancing the nutritional value and consumer appeal of heritage cereal-based foods and support the development of standardized, functional bulgur products for the food industry.

In this inquisition, a passive damper namely Stockbridge Damper (SBD) has been introduced to the field of vibration control of Offshore Wind Turbine (OWT) to reduce the earthquake excitations. The dynamic responses of the structure have been analyzed for three recorded earthquakes and the responses have been assessed. To find an optimum SBD, the parameters of damper have been optimized using Response Surface Methodology (RSM) based on Box-Behnken Design (BBD) and Particle Swarm Optimization (PSO). The influence of the design variables of SBD such as the diameter of messenger cable, the length of messenger cable and logarithmic decrement of the damping has been investigated through response variables such as maximum displacement, RMS displacement and frequency amplitude of structure under an artificially generated white noise. After that, the structure with optimized and non-optimized damper has been analyzed with under the same earthquakes. Moreover, the comparative results show that the structure with optimized damper is 11.78%, 18.71%, 11.6% and 7.77%, 7.01%, 10.23% more effective than the structure with non-optimized damper with respect to the displacement and frequency response under the earthquakes. The results show that the SBD can obviously affect the characteristics of the vibration of the OWT and RSM based on BBD and PSO approach can provide an optimum damper.

This paper presents design optimization technique of Axial Flux Permanent Magnet (AFPM)Brushless DC (BLDC)micromotor. The objective of the optimization process is to minimize the motor volume and improve joules efficiency with the constraints of minimum required torque and maximum back EMF using response surface modeling and Bat Algorithm (BA). Finite element computations have been used for numerical experiments on geometrical design variables in order to evaluate the coefficients of a second-order empirical model for the response surface representation. BA is used as an optimization technique to minimize volume and improve joules efficiency in separate optimization problem in terms of design variables. The optimization results were compared with other metaheuristic algorithms, including Genetic Algorithms (GA), and Particle Swarm Optimization (PSO). The bat algorithm shows a potential competitive against GA and PSO.

This computational work deals with the optimal design of the thermal ablation treatment of skin cancer, by considering uncertainties in the model parameters. The tumor and other tissues were heated by a laser. Nanoparticles were used to improve the effects of the heating procedure and to promote thermal damage localized in the region containing the tumor. Treatment protocols examined in this work involved one single heating session with different prespecified durations, where the design variables were considered as the volume fraction of nanoparticles in the epidermis and tumor, as well as the time variation of the incident laser fluence rate. The optimal design problems were solved with the Markov Chain Monte Carlo method, by applying a modified version of the Metropolis-Hastings algorithm with sampling by blocks of parameters. The two parameter blocks were given by the properties of the tissues and by the design variables. The prior for the volume fraction of nanoparticles was given by a truncated Gaussian distribution, while a noninformative Gaussian Markov random field prior was used for the time variation of the laser fluence rate. The posterior distributions of the design variables were estimated by taking into account uncertainties in the model parameters and the desired statistical distribution of the thermal damage in the region of interest. The stochastic simulations resulted in optimal thermal damages with small uncertainties, which closely followed their desired statistical distribution functions.

This paper presents a novel integer programming approach for the design of a class of impulsive-correction projectile systems with discrete, flexible-time interval and finite-energy control. In terms of its impulsive characteristics, the task is described as the formulation of minimizing the working number of impulses and minimum control error (i.e., miss distance) with integer design variables. In order to solve such integer programming problem, particle swarm optimization (PSO) mechanism is employed to find optimal setting of impulsive control. A modification of the basic PSO algorithm is developed to improve the convergence speed of this optimization through linearly decreasing the inertial weight. Meanwhile, a rounding function operation is applied to the modified PSO considering the constraints of integer design variables. Additionally, an efficient way to design the dimension of the search space is investigated to acquire both satisfactory precision and less iterative number. Finally, simulations with nonlinear dynamics are conducted to validate the PSO-based integer programming algorithm by comparing with conventional optimization methods. It is illustrated that the proposed optimal control algorithm has a good performance in obtaining the optimal control efficiently and accurately.

Lactic Acid Bacteria (LAB) strains were isolated from sourdough samples collected from five provinces of Türkiye and the Cyprus region. The gamma-aminobutyric acid (GABA) production ability of these LAB strains with (+) gad gene regions was evaluated. Response Surface Method (RSM) and Particle Swarm Optimization (PSO) techniques were used together to optimize the GABA environment of Levilactobacillus brevis SD48 strain with the highest GABA production. Optimization of fermentation conditions for GABA production of Levilactobacillus brevis SD48 strain was designed with Box-Behnken design. Optimization parameters tested were pH, temperature, time and monosodium glutamate (MSG) concentration. Fermentation time (24, 48, 72 hours), pH values (4.5, 5.75 and 7.0), temperature (30, 35 and 40 °C), MSG concentration (50, 225, 400 mM) were taken. It was seen that the best process parameters that allow maximum GABA yield should be temperature 30 °C, MSG concentration 130.71 mM, pH 4.74 and time 72 hour. According to the results, it can be thought that microbial production of GABA is both cheap and the optimized conditions we determined will play an important role in the production of GABA-rich fermented foods.

The application of coaxial magnetic gear (CMG) in wave generator instead of traditional variable speed gearbox can effectively improve torque density and reduce torque ripple. Thus, the operation principle of CMG from the perspective of magnetic field modulation is firstly analyzed in this paper. Then the air gap magnetic fields induced by high-speed and low-speed rotors are studied. And the radial height, inner diameter angle, and outer diameter angle of the modulating pieces (MPs) are optimized by using the combination of response surface method (RSM) and particle swarm optimization (PSO) algorithm to improve the torque transmission capacity of CMG. The CMG is combined with the permanent magnet synchronous machine to get the magnetic-gear permanent magnet synchronous generator (MG-PMSG). The design feasibility of the MG-PMSG is verified from four aspects: static magnetic field analysis, no-load characteristics, load characteristics and load disturbance analysis.

The three‐dimensional (3D) printed poly lactic acid (PLA) bone plates lack mechanical strength, resulting in premature failure. Coating these plates with polydopamine (PDM) forms covalent bonds with the PLA molecular structure, enhancing their mechanical properties. The mechanical strength of the coated bone plates is influenced by infill density, submersion time, shaker speed, and coating solution concentration. However, conducting experiments for each parameter value to achieve maximum biomechanical tensile strength (BTS) and biomechanical flexural strength (BFS) is time‐consuming and costly. Overall, the combination of response surface methodology (RSM) and machine learning (ML) enables determination of the best printing parameters, leading to reduced material waste, personalized bone plates tailored to individual anatomy, improved implant fit, and functionality. Moreover, this approach has the potential to reduce the need for additional surgeries and overall costs. To optimize coating parameters, this study employs RSM and ML techniques, including genetic algorithm (GA), particle swarm optimization (PSO), random search optimization (RSO), and differential evolution (DE). Experimental validation of the optimized process parameters and their corresponding fitness values is carried out using both RSM and ML approaches. The results demonstrate that GA has the closest relationship between experimental and fitness values, followed by DE, RSM, PSO, and RSO. Highlights Direct immersion coating of polydopamine on 3D printed PLA bone plates. Evaluating mechanical strength for bone plates coated at varying parameters. Statistical modeling and optimization of mechanical strength using RSM. Mechanical strength optimization and convergence properties for ML models. Experimental validation of RSM and ML‐based optimization algorithms.

ABSTRACTBackground: High-dose fluconazole is an alternative for patients with candidemia caused by Candida glabrata or other Candida species with decreased fluconazole susceptibility. However, empiric high-dose fluconazole is not currently recommended and may result in higher drug costs and toxicity.Objective: To determine the cost-effectiveness of using empiric high-dose fluconazole in intensive care unit (ICU) with suspected invasive candidiasis.Design: Decision analytic model. Target population: ICU patients with suspected invasive candidiasis. Time horizon: Lifetime. Perspective: Societal.Interventions: Low-dose fluconazole (loading dose of 800 mg followed by 400 mg daily) vs. high-dose fluconazole (loading dose of 1600 mg followed by 800 mg daily). Generic fluconazole costs were used for the analysis. Outcome measures: Incremental life expectancy and incremental cost per discounted life year (DLY) saved.Result of base-case analysis: Based on current national levels of fluconazole resistance and ability to correctly identify patients with candidemia, high-dose fluconazole was the more effective but more expensive treatment strategy. Empiric high-dose fluconazole therapy decreased the mortality rate by 0.15% compared to low-dose strategy with a cost-effectiveness rate of $55 526 per DLY saved.Results of sensitivity analysis: Empirical high-dose fluconazole was an acceptable treatment strategy (using $100 000 per DLY saved as threshold) unless the physical age of an ICU survivor was 66 years or older. Empirical high-dose fluconazole was an acceptable treatment strategy using $50 000 per DLY saved with minor changes in parameters estimates.Limitations: The estimates of our model may not be applicable to all ICU patients. Other hospitals with differences in fluconazole resistance, prevalence of invasive candidiasis, or duration of fluconazole therapy may produce different results.Conclusion: These results suggest that empiric high-dose fluconazole therapy should reduce the mortality associated with invasive candidiasis at an acceptable cost.