Simultaneous Optimization Method Research Articles

Robot base position and spacecraft cabin angle optimization via homogeneous stiffness domain index with nonlinear stiffness characteristics

The use of mobile robots for machining large components has received considerable research interest for the application of industrial robots in the machinery manufacturing sector. However, the low structural stiffness of industrial robots can result in poor machining quality under the action of cutting forces. Therefore, this paper proposes a simultaneous optimization method the mobile robot base position and cabin angle using homogeneous stiffness domain (HSD) index for large spacecraft cabins. First, a nonlinear joint stiffness model that considers the gravity compensator mechanism is established to describe the stiffness characteristics of heavy-duty robots more accurately. Subsequently, a HSD index is proposed to evaluate the overall stiffness values and stiffness fluctuation for all robot postures in the machining program. An optimization model is then established based on the HSD under the constraints of machining accessibility, joint angle limitation and singularity. The optimal base position and cabin angle are determined simultaneously using the sparrow search algorithm. Finally, simulation and milling experiments are used to demonstrate that the optimization method proposed in this paper can effectively improve the machining quality.

Additive manufacturing-driven simultaneous optimization of topology and print direction for thermoelastic structures considering strength failure

Abstract This paper presents a strength-based simultaneous optimization method for optimizing thermoelastic structural topology and print direction in the presence of anisotropy induced by additive manufacturing. The approach utilizes the bi-directional evolutionary structural optimization framework and defines design variables including element density and print-off angle. Firstly, an anisotropic thermoelastic constitutive model is established for finite element analysis. By introducing the Tsai–Hill failure criteria, the strength constraint to evaluate the stress level of additively manufactured anisotropic components is formulated. The P-norm aggregation function is employed to approximate the maximum strength failure coefficient. Then, the aggregated strength constraint is augmented to the optimization objective through a Lagrange multiplier. Sensitivity analysis of the new objective function with respect to the elemental design variables is performed, and an analytical approach is proposed to optimize the print-off angle. To improve the stability of the optimization procedure, a series of numerical algorithms and parameter updating strategies are developed. The effectiveness of our proposed method is demonstrated through typical numerical examples, highlighting a desirable match between the structural topology and the print direction can greatly improve the structural performance.

Visual Simultaneous Localization and Mapping Optimization Method Based on Object Detection in Dynamic Scene

SLAM (Simultaneous Localization and Mapping), as one of the basic functions of mobile robots, has become a hot topic in the field of robotics this year. The majority of SLAM systems in use today, however, disregard the impact of dynamic objects on the system by defining the external environment as static. A SLAM system suitable for dynamic scenes is proposed, aiming at the issue that dynamic objects in real scenes can affect the localization accuracy and map effect of traditional visual SLAM systems. Initially, the enhanced lightweight YOLOv5s target detection algorithm is employed to detect dynamic objects in each frame of the image. Simultaneously, an assessment is conducted on the feature points present on dynamic objects to determine their potential impact on system accuracy, subsequently guiding the decision to retain or exclude these feature points. The preserved static feature points are then utilized for pose estimation and map construction. Experiments on the publicly available TUM dataset and the KITTI dataset are conducted to compare the system in this paper with ORB-SLAM 3, DS-SLAM, and DynaSLAM, and the algorithm is verified to have better performance.

Bioethanol production in bioreactor assisted by magnetic field: Correlation between S. cerevisiae H+ effluxes and fermentative efficiency

This study simultaneously evaluated fermentation kinetics and H+ fluxes during fermentation by Saccharomyces cerevisiae in order to elucidate the biochemical mechanisms behind bioethanol production induced by ELF magnetic fields. Fermentation kinetics were monitored by glucose uptake, ethanol production, secondary products (glycerol), pH, biomass, and biochemical characterization of ATPase activity through proton efflux density measurements.A face-centered central composite design 22 was performed, considering independent variables such as recycling arrangements (bioreactor with a spiral-shaped tube, whole bioreactor, and bioreactor with a U-shaped tube) and magnetic flux density (5, 10, and 15 mT), while alcoholic fermentation efficiency and H+ efflux density of the P-ATPase were the response variables.ELF magnetic fields induced substrate consumption and bioethanol formation in all experimental configurations when compared to fermentations without the field. However, the whole bioreactor exposed to 10 mT was the most attractive condition, resulting in an average increase of approximately 40 % and 18 % in the overall volumetric productivity of glucose consumption and ethanol production, respectively, compared to the control experiment. The effect of magnetic flux density on fermentative efficiency and H+ efflux density was statistically significant (p < 0.1). A direct correlation between H+ efflux and yeast performance during fermentation under magnetic fields was found through the application of a method for simultaneous optimization of the experimental results. The data are discussed in relation to the biological mechanism of action involved and the potential applications of this technology at an industrial scale are envisioned.

A special method for simultaneous optimization of electrical grid interconnections and cable size in wind farms

&lt;span lang="EN-US"&gt;Modern wind farms are considered one of the most important sources of energy today. The biggest cost of the farm is the cost of electrical connection to the farmers (constituting the largest part of the aesthetic cost of the farm) and finding the best method for electrical connection (the optimal method) for wind turbines (WTS), as well as the optimal size and measurement of electrical cables. Here, the (harmonious) search algorithm solves the optimization problem and the method of digging the soil in order to lay the cables in it. Two cases will be discussed in order to calculate the optimal size of the cables, namely partial calculation and optimal calculation for connection schemes in the best method for wind farms (OWFS). Also, the total cost, shipping methods, methods of digging extensions into the soil and the different classifications for turbines (wind turbines) will be addressed in order to obtain the best investment for them, so that we reach the optimal delivery and the lowest possible cost in a coordinated and thoughtful manner.&lt;/span&gt;

Topology optimization and parameter optimization hybridized by mesh smoothing for IPMSM design

PurposeTopology optimization (TO) methods have shown their unique advantage in the innovative design of electric machines. However, when introducing the TO method to the rotor design of interior permanent magnet (PM) synchronous machines (IPMSMs), the layout parameters of the magnet cannot be synchronously optimized with the topology of the air barrier; the full design potential, thus, cannot be unlocked. The purpose of this paper is to develop a novel method in which the layout parameters PMs and the topology of air barriers can be optimized simultaneously for aiding the innovative design of IPMSMs.Design/methodology/approachThis paper presents a simultaneous TO and parameter optimization (PO) method that is applicable to the innovative design of IPMSMs. In this method, the mesh deformation technique is introduced to make it possible to make a connection between the TO and PO, and the multimodal optimization problem can thereby be solved more efficiently because good topological features are inherited during iterative optimization.FindingsThe numerical results of two case studies show that the proposed method can find better Pareto fronts than the traditional TO method within comparable time-consuming. As the optimal design result, novel rotor structures with better torque profiles and higher reluctance torque are respectively found.Originality/valueA method that can simultaneously optimize the topology and parameter variables for the design of IPMSMs is proposed. The numerical results show that the proposed method is useful and practical for the conceptual and innovative design of IPMSMs because it can automatically explore optimal rotor structures from the full design space without relying on the experience and knowledge of the engineer.

Simultaneous shape and topology optimization of wings

This paper presents a method for simultaneous optimization of the outer shape and internal topology of aircraft wings, with the objective of minimizing drag subject to lift and compliance constraints for multiple load cases. The physics are evaluated by the means of a source-doublet panel method for the aerodynamic response and linear elastic finite elements for the structural response, which are one-way coupled. At each design iteration, a mapping procedure is applied to map the current wing shape and corresponding pressure loads to the unfitted finite element mesh covering the design domain. Wings of small fixed-wing airplanes both with and without a stiffening strut are optimized. The resulting wings show internal topologies with struts and wall-truss combinations, depending on the design freedom of the shape optimization. The lift distributions of the optimized wings show patterns like the ones obtained when performing optimization of wing shapes with constraints on the bending moment at the root.

Physics-informed deep learning for simultaneous surrogate modeling and PDE-constrained optimization of an airfoil geometry

We use a physics-informed neural network (PINN) to simultaneously model and optimize the flow around an airfoil to maximize its lift to drag ratio. The parameters of the airfoil shape are provided as inputs to the PINN and the multidimensional search space of shape parameters is populated with collocation points to ensure that the Navier–Stokes equations are approximately satisfied throughout. We use the fact that the PINN is automatically differentiable to calculate gradients of the lift-to-drag ratio with respect to the airfoil shape parameters. This allows us to optimize with the L-BFGS gradient-based algorithm, which is more efficient than non-gradient-based algorithms, without deriving an adjoint code. We train the PINN with adaptive sampling of collocation points, such that the accuracy of the solution improves as the solution approaches the optimal point. We demonstrate this on two examples: one that optimizes a single parameter, and another that optimizes eleven parameters. The method is successful and, by comparison with conventional CFD, we find that the velocity and pressure fields have small pointwise errors and that the method converges to optimal parameters. We find that different PINNs converge to slightly different parameters, reflecting the fact that there are many closely-spaced local minima when using stochastic gradient descent. This method can be applied relatively easily to other optimization problems and avoids the difficult process of writing adjoint codes. It is, however, more computationally expensive than adjoint-based optimization. As knowledge about training PINNs improves and hardware dedicated to neural networks becomes faster, this method of simultaneous training and optimization with PINNs could become easier and faster than using adjoint codes.

Concurrent shape and build orientation optimization for FDM additive manufacturing using the principal stress lines (PSL)

Additive Manufacturing (AM) with the consisting constantly evolving technologies is a particularly popular research area. Based on the shape forming freedom, size, shape, and topology optimization techniques can be validated by AM produced parts. However, in every manufacturing process, AM also has some adverse inherent properties. One and maybe the most significant optimization problem is the mechanical anisotropy caused by the layered structure. In this paper, a simultaneous build orientation and shape optimization method is presented. Both of the approaches are intended to increase the mechanical performance of the produced parts. Shape optimization was accomplished by varying the cross-section of the beam geometries, based on the angle between a PSL section and the characteristic load direction. To test the efficiency and validate the method 2D structures (with relatively small 3rd dimension) and their tensile properties were tested. Based on the results, we can prove that the PSL method works and help to increase the mechanical performance by 19.2% with only 7.8% size increment.

A novel simultaneous grey model parameter optimization method and its application to predicting private car ownership and transportation economy

&lt;p style='text-indent:20px;'&gt;Common models used for grey system predictions include the GM (1, 1), GM (1, N), and GM (N, 1). Their whitening equations are ordinary differential equations. However, the objects and factors are generally characterized by mutual restrictions and variable interactions. Thus, the relationship cannot be adequately described using a single differential equation. Therefore, this paper proposes a novel simultaneous grey model. A parameter optimization method is developed, and the time response equation of a simultaneous grey model with 2 interactive variables is derived. The model has high simulation and prediction precision because the parameter optimization's objective function minimizes the average relative errors of the simulation and prediction. The proposed model with two mutually influencing variables is used to predict the private car ownership and the added value of the transportation industry in China. The results indicate that the simultaneous grey model has significantly higher precision than the conventional single grey model. The novel model and method improve the grey modeling system and are significant for in-depth studies, popularization, and application of grey models.&lt;/p&gt;

A study on simultaneous optimization method for production and transportation scheduling with Lagrangian decomposition and coordination method

A study on simultaneous optimization method for production and transportation scheduling with Lagrangian decomposition and coordination method

An integrated collaborative decision-making method for optimizing energy consumption of sail-assisted ships towards low-carbon shipping

Energy conservation and emission reduction have become an urgent and important issue for the shipping industry in the context of low-carbon shipping. For sail-assisted ships, the application of wind energy can reduce energy consumption and thus greenhouse gas emissions. However, there still exists a high potential for energy efficiency improvement due to the lack of an effective optimization method for maintaining the hybrid system operating at the optimal state continuously. In this paper, a novel integrated collaborative decision-making method, considering the coupling influence of the sailing path, speed, wing-sail's angle of attack, and various environmental factors, is investigated to achieve energy conservation and emission reduction for sail-assisted ships. First, an integrated collaborative optimization model of the sailing path, speed, and sail's angle of attack is established. On this basis, a PSO-based integrated collaborative decision-making method for simultaneous optimization of sailing path, speed, and sail's angle of attack under various operational conditions is proposed. The study results show that the proposed method is capable of fully utilizing wind energy, meanwhile maintaining the hybrid power system operating at the optimal state under various operational conditions, and can reduce energy consumption and CO2 emissions by approximately 8.9% for a voyage.

Simultaneous Optimization of Nuclear-Electronic Orbitals.

Accurate modeling of important nuclear quantum effects, such as nuclear delocalization, zero-point energy, and tunneling, as well as non-Born-Oppenheimer effects, requires treatment of both nuclei and electrons quantum mechanically. The nuclear-electronic orbital (NEO) method provides an elegant framework to treat specified nuclei, typically protons, on the same level as the electrons. In conventional electronic structure theory, finding a converged ground state can be a computationally demanding task; converging NEO wavefunctions, due to their coupled electronic and nuclear nature, is even more demanding. Herein, we present an efficient simultaneous optimization method that uses the direct inversion in the iterative subspace method to simultaneously converge wavefunctions for both the electrons and quantum nuclei. Benchmark studies show that the simultaneous optimization method can significantly reduce the computational cost compared to the conventional stepwise method for optimizing NEO wavefunctions for multicomponent systems.

Parametric analysis and multi-objective optimization of a new combined system of liquid carbon dioxide energy storage and liquid natural gas cold energy power generation

Liquid carbon dioxide energy storage is a potential energy-storage technology. However, it is hindered by the difficulty of condensing CO 2 using high-temperature cooling water because the critical temperature of CO 2 is close to the temperature of the cooling water. Therefore, this study proposes a new combined liquid CO 2 energy storage and two-stage condensation organic Rankine cycle system. Thereby, the condensation of CO 2 is realized, and liquid natural gas (LNG) cold energy is utilized. The impact of key variables on the performance of the system is examined by establishing a mathematical model of the system and analyzing the parameters. In addition, the systems that use pure working fluid and mixed working fluid are optimized and compared using a simultaneous multi-objective optimization method. The results indicate that the system using mixed working fluids is better than that using pure working fluids, with an η R T E of 375.49%, η E X of 81.09%, and LCOS of 0.08 USD/kW·h. The proposed system performs better than the combined liquid air energy storage and LNG cold energy system in the literature from thermodynamic and economic perspectives. • A new combined system of LCES and LNG power generation is proposed. • It improves the difficulty of CO 2 to be condensed by high-temperature cooling water. • The impact of key variables on the performance of the system is explored. • Simultaneous multi-objective optimization method is conducted. • System using pure and mixed working fluid is compared.

Optimization of Pre-Chamber Geometry and Operating Parameters in a Turbulent Jet Ignition Engine

A turbulent jet ignition engine enables operation with lean mixtures, decreasing nitrogen oxide (NOX) emissions up to 92%, while the engine efficiency can be increased compared to conventional spark-ignition engines. The geometry of the pre-chamber and engine operating parameters play the most important role in the performance of turbulent jet ignition engines and, therefore, must be optimized. The initial experimental and 3D CFD results of a single-cylinder engine fueled by gasoline were used for the calibration of a 0D/1D simulation model. The 0D/1D simulation model was upgraded to capture the effects of multiple flame propagations, and the evolution of the turbulence level was described by the new K-k-ε turbulence model, which considers the strong turbulent jets occurring in the main chamber. The optimization of the pre-chamber volume, the orifice diameter, the injected fuel mass in the pre-chamber and the spark timing was made over 9 different operating points covering the variation in engine speed and load with the objective of minimizing the fuel consumption while avoiding knock. Two optimization methods using 0D/1D simulations were presented: an individual optimization method for each operating point and a simultaneous optimization method over 9 operating points. It was found that the optimal pre-chamber volume at each operating point was around 5% of the clearance volume, while the favorable orifice diameters depended on engine load, with optimal values around 2.5 mm and 1.2 mm at stoichiometric mixtures and lean mixtures, respectively. Simultaneous optimization of the pre-chamber geometry for all considered operating points resulted in a pre-chamber volume equal to 5.14% of the clearance volume and an orifice diameter of 1.1 mm.

Optimization of Operating Parameters for Tyre Pyrolysis Oil Blended Single Cylinder Diesel Engine

Abstract: Increasing industrialization and motorization led to a significant rise in demand for petroleum products. As these are the non renewable resources, it is difficult to predict the availability of these resources in the future resulting in uncertainty in its supply and price and impacting growing economies like India, importing 80% of the total demand of the petroleum products. Many attempts have been made by different researchers to find out alternate fuels for Internal Combustion engines. Many alternate fuels like Biodiesel, LPG (Liquefied Petroleum Gas), CNG (Compressed Natural Gas) and Alcohols are being used nowadays by different vehicles. In this context pyrolysis of scrap tyres can be used effectively to produce oil, thereby solving the problem of waste tyre disposal. In the present study Experimental investigations were carried out to evaluate the performance and emission characteristics of a single cylinder diesel engine fuelled by TPO10, TPO15, and TPO20 keeping the blend quality by controlling density and viscosity of tyre pyrolysis oil within permissible limit of euro IV diesel requirement to study its replace ability. The investigation involves three parameters such as blend proportion, injection timing and injection pressure, a simultaneous optimization method called Taguchi was used in this work. This method requires fewer numbers of trials for fixing optimum levels. As per this method nine experiments were required to be conducted and the results were used for optimization. This is the primary advantage as well as disadvantage of this method. In order to eliminate this difficulty and improve the quality of the research work, twenty-seven experiments were conducted in this work and the results were used for optimization. In addition, an ANOVA was also performed for the parameters to evaluate its percentage contribution over the desired output. Using the optimum levels, a full range experiment was conducted to compare its performance and emission behaviour with standard diesel operation. Keywords: Operating parameters, Diesel Engine, Tyre Pyrolysis Oil (TPO), Performance, Emission

Exploration of Effects of Graduated Compression Stocking Structures on Performance Properties Using Principal Component Analysis: A Promising Method for Simultaneous Optimization of Properties.

This paper focuses on the comfort properties of graduated and preventive compression stockings for people who work long hours in standing postures and for athletes for proper blood circulation. The present study was conducted in order to investigate the effects of the yarn insertion density and inlaid stitches on the performance of the compression stockings. The effects of these parameters on the thermo-physiological comfort properties were tested with standard and developed methods of testing. All compression stockings were maintained with class 1 pressure as per German standards. The structural parameters of the knitted fabric structures were investigated. The stretching and recovery properties were also investigated to determine the performance properties. The theoretical pressure was predicated using the Laplace’s law by testing the stockings’ tensile properties. The compression interface pressures of all stockings were also investigated using a medical stocking tester (MST) from Salzmann AG, St. Gallen, Switzerland. Correlation between the theoretical pressures and pressures measured using the MST system were also assessed. The current research used a multi-response optimization technique, i.e., principal component analysis (PCA), to identify the best structure based on the optimalization of the above-mentioned properties. The results also revealed that samples with higher insertion density levels exhibit better comfort properties. The results showed that sample R1 was the best sample, followed by R2 and P. In addition, all developed stocking samples exhibited better comfort properties than the control sample from the market.

Simultaneous optimization of material orientation angle and shape/topology of CFRP shell structure

The objective of this study is to develop a method for simultaneous optimization of shape, topology and orientation angle to maximize the stiffness of CFRP shell structures under volume constraints. In the optimization of orientation angle, the material directions of orthotropic anisotropic material in finite elements are used as design variable. The optimal shape is determined by the H1 gradient method for vector variables, and the optimal solutions for topology optimization and orientation angle are determined by the H1 gradient method for scalar variables. The effectiveness of the proposed method is confirmed through a numerical example.

Global Simultaneous Optimization of Oil, Hysteretic and Inertial Dampers Using Real-Valued Genetic Algorithm and Local Search

A method for global simultaneous optimization of oil, hysteretic and inertial dampers is proposed for building structures using a real-valued genetic algorithm and local search. Oil dampers has the property that they can reduce both displacement and acceleration without significant change of natural frequencies and hysteretic dampers possess the characteristic that they can absorb energy efficiently and reduce displacement effectively in compensation for the increase of acceleration. On the other hand, inertial dampers can change (prolong) the natural periods with negative stiffness and reduce the effective input and the maximum acceleration in compensation for the increase of deformation. By using the proposed simultaneous optimization method, structural designers can select the best choice of these three dampers from the viewpoints of cost and performance indices (displacement, acceleration). For attaining the global optimal solution which cannot be attained by the conventional sensitivity-based approach, a method including a real-valued genetic algorithm and local search is devised. In the first stage, a real-valued genetic algorithm is used for searching an approximate global optimal solution. Then a local search procedure is activated for enhancing the optimal character of the solutions by reducing the total quantity of three types of dampers. It is demonstrated that a better design from the viewpoint of global optimality can be obtained by the proposed method and the preference of damper selection strongly depends on the design target (displacement, acceleration). Finally, a multi-objective optimization for the minimum deformation and acceleration is investigated.

Simultaneous dose and dose rate optimization (SDDRO) of the FLASH effect for pencil-beam-scanning proton therapy.

Compared to CONV-RT (with conventional dose rate), FLASH-RT (with ultra-high dose rate) can provide biological dose sparing for organs-at-risk (OARs) via the so-called FLASH effect, in addition to physical dose sparing. However, the FLASH effect only occurs, when both dose and dose rate meet certain minimum thresholds. This work will develop a simultaneous dose and dose rate optimization (SDDRO) method accounting for both FLASH dose and dose rate constraints during treatment planning for pencil-beam-scanning proton therapy. SDDRO optimizes the FLASH effect (specific to FLASH-RT) as well as the dose distribution (similar to CONV-RT). The nonlinear dose rate constraint is linearized, and the reformulated optimization problem is efficiently solved via iterative convex relaxation powered by alternating direction method of multipliers. To resolve and quantify the generic tradeoff of FLASH-RT between FLASH and dose optimization, we propose the use of FLASH effective dose based on dose modifying factor (DMF) owing to the FLASH effect. FLASH-RT via transmission beams (TB) (IMPT-TB or SDDRO) and CONV-RT via Bragg peaks (BP) (IMPT-BP) were evaluated for clinical prostate, lung, head-and-neck (HN), and brain cases. Despite the use of TB, which is generally suboptimal to BP for normal tissue sparing, FLASH-RT via SDDRO considerably reduced FLASH effective dose for high-dose OAR adjacent to the target. For example, in the lung SBRT case, the max esophageal dose constraint 27Gy was only met by SDDRO (24.8Gy), compared to IMPT-BP (35.3Gy) or IMPT-TB (36.6Gy); in the brain SRS case, the brain constraint V12Gy≤15cc was also only met by SDDRO (13.7cc), compared to IMPT-BP (43.9cc) or IMPT-TB (18.4cc). In addition, SDDRO substantially improved the FLASH coverage from IMPT-TB, e.g., an increase from 37.2% to 67.1% for lung, from 39.1% to 58.3% for prostate, from 65.4% to 82.1% for HN, from 50.8% to 73.3% for the brain. Both FLASH dose and dose rate constraints are incorporated into SDDRO for FLASH-RT that jointly optimizes the FLASH effect and physical dose distribution. FLASH effective dose via FLASH DMF is introduced to reconcile the tradeoff between physical dose sparing and FLASH sparing, and quantify the net effective gain from CONV-RT to FLASH-RT.