Proposed Optimization Method Research Articles

Radial basis function‐based Pareto optimization of an outer rotor brushless DC motor

AbstractThis paper presents the development of an optimization and modeling method for the objective functions of output power, efficiency and weight of an outer rotor permanent magnet brushless DC (BLDC) motor based on radial basis function (RBF) approximation technique. The proposed RBF‐based Pareto optimization method requires less knowledge about electric/magnetic formulas and can replace conventional optimizations based on these equations with higher accuracy. To apply the proposed optimization method, the initial design should be developed using such equations. Therefore, RBFs are used to model and predict engine behavior. To optimize the objective functions, we used a genetic algorithm optimization technique with nonlinear electric and magnetic constraints to find the Pareto front set. The design obtained by the proposed radial basis function Pareto optimization (RBFPO) method was finally verified by Ansoft Maxwell. The results of optimal design using the RBFPO method have higher output power and efficiency. Also, in addition to the advantage of a favorable accuracy, RBF‐based models are significantly faster than models available in simulation tools.

Adversarial robustness analysis of LiDAR-included models in autonomous driving

In autonomous driving systems, perception is pivotal, relying chiefly on sensors like LiDAR and cameras for environmental awareness. LiDAR, celebrated for its detailed depth perception, is being increasingly integrated into autonomous vehicles. In this article, we analyze the robustness of four LiDAR-included models against adversarial points under physical constraints. We first introduce an attack technique that, by simply adding a limited number of physically constrained adversarial points above a vehicle, can make the vehicle undetectable by the LiDAR-included models. Experiments reveal that adversarial points adversely affect the detection capabilities of both LiDAR-only and LiDAR–camera fusion models, with a tendency for more adversarial points to escalate attack success rates. Notably, voxel-based models are more susceptible to deception by these adversarial points. We also investigated the impact of the distance and angle of the added adversarial points on the attack success rate. Typically, the farther the victim object to be hidden and the closer to the front of the LiDAR, the higher the attack success rate. Additionally, we have experimentally proven that our generated adversarial points possess good cross-model adversarial transferability and validated the effectiveness of our proposed optimization method through ablation studies. Furthermore, we propose a new plug-and-play, model-agnostic defense method based on the concept of point smoothness. The ROC curve of this defense method shows an AUC value of approximately 0.909, demonstrating its effectiveness.

Structural Optimization of the Main Bearing in a Tunnel Boring Machine Considering Clearance

Abstract An optimal design method for the main bearing of a tunnel boring machine is proposed. In this method, the fatigue life is used as the objective function. Structural parameters, including clearance, are considered as design variables. First, a quasi-static model of the main bearing and a calculation model of the fatigue life are established. The correctness of the theoretical method is verified by comparing it with the calculation results of the finite element method. Next, the influence of clearance on the load-carrying performance under external loads is analyzed. There is an optimal negative clearance for the axial loaded and radial rows. With the increase in the external loads, the optimal negative clearance gradually decreases. The variation laws of the load-carrying performance for the axial loaded and supporting rows affected by axial clearance mainly depend on the bias load degree of the main bearing. Finally, based on the optimal design model of the main bearing, the optimal internal structure is obtained using the genetic algorithm. The optimized fatigue life is improved by 92.2%. The load-carrying performance of the optimal main bearing has also been significantly enhanced compared to the initial design. Therefore, the proposed optimization method provides a practical approach to the main bearing design.

Empirical study on optimization methods of building energy operation for the sustainability of buildings with integrated renewable energy

Owing to the challenges caused by climate change, the spread of green buildings is increasing. Moreover, it is becoming necessary to secure sustainability through the management of new and renewable energy operations. Accordingly, this paper describes the proposal and empirical demonstration of an energy operation optimization method for renewable energy-integrated buildings utilizing photovoltaics (PVs) and energy storage systems (ESSs). The proposed optimization method begins with the calculation of a weather index (WI) based on environmental and climatic data, with particular emphasis on its relevance to PV production. Additionally, we develop the weight-prox algorithm (W-PA) for controlling and optimizing daily building energy operations. The goal of this optimization method is to enhance the performance of three sustainability indicators of green buildings. Simulations are then conducted in a digital transformation environment using real green buildings as targets. The results demonstrate the improved performance of sustainability indicators, including renewable energy efficiency, economic resilience, and eco-friendliness. The output of this study can serve as a set of guidelines that considers the convenience of implementation for building operators. This study is also significant by raising awareness of the need for climate change response and contributing to the use of renewable energy for the sustainability of green buildings.

Tuning a Kubernetes Horizontal Pod Autoscaler for Meeting Performance and Load Demands in Cloud Deployments

In the context of scaling a business-critical medical service that involves electronic medical record storage deployed in Kubernetes clusters, this research addresses the need to optimize the configuration parameters of horizontal pod autoscalers for maintaining the required performance and system load constraints. The maximum entropy principle was used for calculating a load profile to satisfy workload constraints. By observing the fluctuations in the existing workload and applying a kernel estimator to smooth its trends, we propose a methodology for calculating the threshold parameter of a maximum number of pods managed by individual autoscalers. The results obtained indicate significant computing resource savings compared to autoscalers operating without predefined constraints. The proposed optimization method enables significant savings in computational resource utilization during peak loads in systems managed by Kubernetes. For the investigated case study, applying the calculated vector of maximum pod count parameter values for individual autoscalers resulted in about a 15% reduction in the number of instantiated nodes. The findings of this study provide valuable insights for efficiently scaling services while meeting performance demands, thus minimizing resource consumption when deploying to computing clouds. The results enhance our comprehension of resource optimization strategies within cloud-based microservice architectures, transcending the confines of specific domains or geographical locations.

Optimization Design of Oil Groove Shape on the Surface of Friction Plate in High-Speed Wet Clutch Aimed at Minimizing Drag Torque

Abstract The wet clutch, a crucial component of the vehicle transmission system, experiences high-frequency impact and friction between the friction plate and the steel plate under high-speed separation conditions. This leads to a significant increase in drag torque, which is detrimental to the transmission efficiency and reliability of the high-speed wet clutch. This article aims to reduce the high-speed drag torque in a no-load wet clutch by optimizing the oil groove shape on the surface of the friction plate. Initially, a theoretical model of high-speed drag torque for the friction plate with arbitrary oil groove shape is established. Subsequently, a low-drag oil groove shape optimization model is proposed by establishing design parameters, constraint conditions, and an objective function, combined with an experiment design method, approximation modeling, and a global search optimization method. Finally, the stiffness and damping coefficients of the clearance flow field and the drag torque with the optimized oil groove shape are calculated. The simulation results and the proposed optimization method are validated through experiments. The experiment indicates that a friction plate with the optimized oil groove shape can significantly decrease high-speed drag torque, demonstrating strong practical engineering value.

Edge smoothing optimization method in DMD digital lithography system based on dynamic blur matching pixel overlap technique.

Due to digital micromirrors device (DMD) digital lithography limited by non-integer pixel errors, the edge smoothness of the exposed image is low and the sawtooth defects are obvious. To improve the image edge smoothness, an optimized pixel overlay method was proposed, which called the DMD digital lithography based on dynamic blur effect matching pixel overlay technology. The core of this method is that motion blur effect is cleverly introduced in the process of pixel overlap to carry out the lithography optimization experiment. The simulation and experimental results showed that the sawtooth edge was reduced from 1.666 µm to 0.27 µm by adopting the 1/2 dynamic blur effect to match pixel displacement superposition, which is far less than half of the sawtooth edge before optimization. The results indicated that the proposed method can efficiently improve the edge smoothness of lithographic patterns. We believe that the proposed optimization method can provide great help for high fidelity and efficient DMD digital lithography microfabrication.

Optimized adhesive application

In the manufacturing industry, the process of applying adhesive to join two substrates can be challenging. It requires precise placement of adhesive so that it is distributed in the desired pattern, ensuring a strong bond between the substrates. Overfilling is a common practice to ensure that the bond is not underfilled after the substrates are pressed together. However, this approach leads to unnecessary adhesive waste and can result in additional cleaning work. To address these issues, an optimization algorithm has been developed that can compute an initial adhesive pattern to flow into any desired geometry. The algorithm is based on a transient squeeze flow simulation, which iteratively removes fluid material as it flows out of the target bond domain. For example, to obtain a square bond geometry after a parallel squeezing process, the adhesive needs to be initially applied in a star shape. However, the algorithm is highly adaptable and can be used for many desired bond geometries as shown in this paper. The results are independent of the mesh resolution and fluid flow law, thus eliminating the need for complex rheological fluid characterization. The algorithm requires only two input parameters, namely the target bond geometry and the degree of compression. This highly simplifies the adaption of the proposed optimization method for manufacturers, and their design requirements can be considered with ease. The results are reliable and consistent despite the simulation’s complexity. Overall, this new optimization methodology represents a significant advance in adhesive application technology, eliminating the need of overfilling and thus making the manufacturing process more efficient, sustainable and cost-effective.

Incentive-based demand response optimization method based on federated learning with a focus on user privacy protection

Considering the flexible capacity and privacy needs of numerous flexible energy users in the context of demand response (DR), this study establishes an influence model (IM) to describe the DR participation capabilities of users considering privacy budget. Using the designed Stackelberg game mechanism that can achieve optimal selection for DR responders from users with different characteristics described by IM, a federated learning (FL)-based optimization method that uses differential privacy (DP) as the data transmission mechanism for DR economic optimal dispatch is proposed. Ideas of controlling the number of participating users and financially compensating for the privacy leakage risk by the FL-based optimization method are the guarantees for users with private data to participate in DR. The performance of the proposed optimization method is also compared with that of the Moth-flame optimization algorithm in a case study, and the guiding value of the former in selecting among user groups with different characteristics is then discussed. Results show that the proposed method exhibits good economic benefits and universal applicability.

Integrated optimization method for transonic turbine based on direct free-form deformation and prescreening differential evolutionary optimizer

With the increased load of turbine stages, shock waves and secondary flow influence the performance of turbines more and more significantly. Aiming at improving the aerodynamic efficiency of transonic turbines, an integrated aerodynamic optimization platform for turbines has been established. The aerodynamic shape of the flow path and blades is simultaneously parametrized by a directly manipulated free-form deformation (DFFD) method, and the optimization is conducted via a data-driven differential evolutionary algorithm. Furthermore, a prescreening strategy is introduced to enhance the convergence speed of the optimizer. The TTM-stage is selected as the research object. The result shows that the turbine isentropic efficiency at the design point increases from 90.45 % to 91.94 % after optimization. The main reasons for aerodynamic performance enhancement are the weakening of shock waves and the reduction of separation regions. As a comparison, another two optimization cases are carried out. One optimizes the blades individually with the same methods and configurations, and the other optimizes the blades and flow path simultaneously without the prescreening strategy. The optimized isentropic efficiency in the latter two cases is 91.31 % and 90.92 %, whichis 0.63 % and 1.02 % lower than the first optimization result, respectively. This shows the effectiveness of the proposed optimization method for turbine performance improvement.

Overhead crane bridge structure design optimization based on safety assessment

In order to achieve safe and economical design results for crane bridge structures, an optimization method with safety assessment constraints is proposed. In this approach, the limit state method is used to test the structure design. Safety assessment is conducted based on the fuzzy analytic network process, allowing the safety score of designs to be quantified. Aiming to minimize the self-weight of the bridge, the dimensions of critical sections are regarded as design variables and constraints related to the process size, strength, stiffness, stability, and safety score are imposed, utilizing the artificial hummingbird algorithm for the structure optimal design. After validating through an engineering example, the results demonstrated that under the settled safety constraints, the proposed optimization method successfully reduced the structural self-weight by 19.709%, while maintaining a safety score proximate to the original design. In comparison to optimization without safety assessment constraints, this method resulted in a 10.441% increase in weight, but its safety was significantly improved by 21.740%. This validates the effectiveness and practicality of incorporating safety assessment into structure optimization, thereby ensuring a balanced trade-off between safety and material economy in the design of crane bridge structures.

Topology optimization for additive manufacturing of CFRP structures

Combining design optimization and additive manufacturing (AM) enables the full exploitation of the potential for heightening the performance of carbon fiber reinforced plastic (CFRP) structures. This study simultaneously conducts topology optimization and fiber path design by employing the radial basis function (RBF) based level set function (LSF). Fiber paths are determined instinctively for the inherent advantages of the LSF, and fiber orientations are parameterized accordingly. Manufacturing drawbacks such as gaps and overlaps can be avoided by introducing a fast-marching method. To verify the effectiveness of the optimization method, three groups of optimized and empirical designs are fabricated by the AM technique, respectively, and the experimental tests are further carried out. Finite element (FE) models are also reconstructed based on the printing schemes, and then the FE simulation is validated by the experimental tests. With the proposed optimization method, stiffnesses for all three groups of the optimal samples are significantly improved compared with the empirical counterparts. The FE modeling technique is capable of reproducing the experimental results. This study paves a new way to develop an integrated framework of optimization, additive manufacturing, experimentation, and validation to deliver high-performance CFRP structures.

Optimizing Logistics System Monitoring with FIFO Method Using Intelligent System

Efficient and well-organized logistics systems are crucial for maintaining operational smoothness and customer satisfaction. In this research, we delve into the optimization of logistics system monitoring using the First-In, First-Out (FIFO) method based on smart system technology. The FIFO method has become a widely used approach in inventory management and product distribution. However, achieving optimal FIFO implementation can be challenging due to demand fluctuations and changing priorities. In the proposed optimization method, we integrate data analysis and artificial intelligence to enhance the utilization of the FIFO method in logistics system monitoring. This method gathers real-time data on inventory levels, customer demands, and resource availability. Employing intelligent algorithms, the method analyzes data to identify demand trends, predict future inventory needs, and optimize the sequence of product deliveries using the FIFO method. The conducted tests indicate that this optimization method successfully improves delivery efficiency and reduces operational costs within the logistics system. Moreover, the method can adapt to real-time changes in demand and priorities, enhancing flexibility and responsiveness in logistics system monitoring. This research makes a significant contribution to the development of more efficient and intelligent logistics monitoring systems

Optimising hierarchical emergency logistics network under ambiguous demand and transportation cost

This article studies the response problem of an emergency logistics network with a decision hierarchy relationship under uncertainty. To account for the partial distribution information about uncertain demand and transportation costs, we construct a moment-based ambiguity set based on limited historical data, where the pivot variable method is employed to determine the confidence interval of the mean value. Based on the constructed ambiguity set, we develop a novel distributionally robust bi-level post-disaster emergency logistics location-routeing model. By exploiting the structural characteristic, chance-constrained models under box-ellipsoid and budget perturbation sets are reformulated as bi-level mixed-integer conic programming models. To accelerate the solution procedure, the bi-level models are further converted into single-level ones via Karush-Kuhn-Tucker condition, which can be directly solved to optimality using CPLEX software. Supply risk value for each supplier is obtained by applying analytic hierarchy process. We conduct extensive experiments using the Iranian flood as the case study to address the computational performance of our proposed optimisation method.

Parametric design and modeling method of carbon fiber reinforcement plastic-laminated components applicable for multi-material vehicle body development

Abstract Combined application of steel, aluminum, and carbon fiber reinforcement plastic (CFRP) is the main direction of future lightweight body development. However, the anisotropy and additional lamination design variables of CFRP parts pose significant challenges for the development of multi-material bodies. This study establishes a parametric design method for the variable-thickness lamination scheme based on non-uniform rational B-splines, it can be coupled with existing parametric design methods for structural shapes to formulate a complete parametric design and modeling of CFRP components. On this basis, a homogenized intermediate material property is derived from classic laminate theory by introducing lamination assumptions, it enables a stepwise multi-material body optimization method to solve the challenge that components’ material design variables switching between CFRP and alloy will introduce/eliminate lamination design variables iteratively, posing a great optimization convergence difficulty. The proposed parametric modeling method for CFRP components was validated by experimental tests of a fabricated roof beam, and the proposed optimization method was applied to a vehicle body, achieving 15.9%, 23.9%, 18.6%, and 12.2% increase in bending and torsional stiffness and modal frequencies; 20.2%, 9.3%, and 12.7% reduction of weight and peak acceleration in frontal and side collisions. This study enables the forward design of multi-material bodies compatible with CFRP parts.

Optimization of Food Processing Parameters Based on Parametric Models

Abstract In this paper, a food processing process optimization model is constructed based on the improved particle swarm algorithm. By adding the chaos mapping strategy to the basic particle swarm algorithm, the optimization efficiency of the particle swarm under the constraints is improved. Combining the multi-objective optimization capability of the NDWPSO algorithm, the process parameter optimization process is constructed to optimize the parameters of food processing processes. The proposed method for process parameter optimization is applied to the vacuum drying process parameter optimization experiment of Jujube to test the effectiveness of particle swarm process parameter optimization. The results show that the diversity value and the best-adapted value of the NDWPSO algorithm in the single-peak function only use a small number of iterations to drop to a level close to 0, indicating that the NDWPSO algorithm has a faster convergence speed. In the vacuum drying process of jujube, the theoretical values of the optimal process parameters for freeze-dried jujube slices are: jujube slices thickness 5mm, sublimation drying temperature -22℃, resolution drying temperature 20℃.

Reprojection Error Analysis and Algorithm Optimization of Hand-Eye Calibration for Manipulator System.

The Euclidean distance error of calibration results cannot be calculated during the hand-eye calibration process of a manipulator because the true values of the hand-eye conversion matrix cannot be obtained. In this study, a new method for error analysis and algorithm optimization is presented. An error analysis of the method is carried out using a priori knowledge that the location of the augmented reality markers is fixed during the calibration process. The coordinates of the AR marker center point are reprojected onto the pixel coordinate system and then compared with the true pixel coordinates of the AR marker center point obtained by corner detection or manual labeling to obtain the Euclidean distance between the two coordinates as the basis for the error analysis. We then fine-tune the results of the hand-eye calibration algorithm to obtain the smallest reprojection error, thereby obtaining higher-precision calibration results. The experimental results show that, compared with the Tsai-Lenz algorithm, the optimized algorithm in this study reduces the average reprojection error by 44.43% and the average visual positioning error by 50.63%. Therefore, the proposed optimization method can significantly improve the accuracy of hand-eye calibration results.

A two-phase method for compaction path planning and control of the impact roller

Compaction efficiency and quality are extraordinarily significant for branch-line airport construction engineering. However, a human-determined compaction path is generally adopted during the compaction, negatively impacting compaction efficiency and quality. A two-phase approach to the compaction path is proposed to address the problem. The first phase focuses on the establishment of a model to optimize the impact roller’s complete-coverage compaction path (CCCP) to improve compaction efficiency. In this phase, the optimization problem of the CCCP is equivalent to the traveling salesman problem (TSP). Next, the optimization of the CCCP is formulated and solved as a binary integer programming problem. Finally, a method for determining the optimal traversal compaction sequence is proposed to obtain the optimal CCCP based on the secondary development of the Gurobi optimizer. In the second phase, to guarantee the compaction quality, the path navigation modulus is designed to precisely control the compaction path. Combining the compaction path planning with the path control, an integrated system is developed to provide a better service for practical earthwork compaction engineering in airport construction. The results of the simulation experiment and field experiment indicate that the CCCP determined by the proposed optimization method in the first phase can improve the compaction efficiency compared to the human-determined compaction path. In addition, the developed system can effectively generate the CCCP. Meanwhile, the developed system can provide a visual path navigation and precise path control during the compaction to ensure the compaction quality, verifying that the proposed method can offer a novel idea for compaction path control.

Resilience-economy-environment equilibrium based configuration interaction approach towards distributed energy system in energy intensive industry parks

Because unexpected, high-impact, extreme events and disasters can seriously threaten power system safety and resilience, there has been an increased research focus on power system resilience. This study proposes a bi-level multi-objective model for industrial park distributed energy configuration optimization to deal with extreme events, which considers the interactions between the authority and the industrial park in a leader–follower decision process and seeks to trade-off between economic cost, environmental protection, and power system resilience. An interactive algorithm is designed and the ɛ-constraint method is used to convert the model into a bi-level single objective model to solve the proposed complex model. A case example from an industrial park in Wuxi, Jiangsu Province, China, is given to demonstrate the practicality and efficiency of the proposed optimization method. The joint analysis of the power supply and financial losses found that a distributed energy system with an optimal resilience configuration could guarantee 95.85% of demand for three consecutive hours after an extreme event, maintain more than 50% of demand at any time, and reduce power outage financial losses at any time to a minimum of 0.232 million CNY. Based on the analyses and discussion, this model was proven to provide a reasonable and practical strategy for a resilience-economy-environment trade-off in industrial park distributed energy systems.

Dimensional Optimization of a Modular Robot Manipulator

The mechanism parameters of the manipulator not only have a great influence on the size of the working space but also affect flexible performance distribution. Aimed at obtaining a 6 DOF modular manipulator, mechanism parameters were optimized in order to explore the effect of upper arm and forearm dimensions on the end dexterity of the manipulator. First, forward kinematic equations were derived using the DH method, and the Jacobian matrix of the manipulator was solved. Second, three indicators, including the condition number index, structural length index, and global conditioning index, were employed as optimization indicators for the mechanism parameters of the manipulator, and an orthogonal experiment was designed based on the Grey–Taguchi method and robot toolbox. Third, the grey relational analysis method was used to process the experimental results, and the grey relational grade for each group was solved. Last, the variation curve between the grey relational grade and the parameter level of each mechanism was drawn, and optimized mechanical arm mechanism parameters were derived. It was found that although the overall dimension of the manipulator was slightly decreased, as determined via comparing the original and optimized manipulator length, the performance indexes were improved. The results not only verified the correctness of the proposed optimization method but also laid a foundation for subsequent research on the dynamic performance of modular robot systems.