Optimum Design Research Articles

Multi-objective aerodynamic optimization of expansion–deflection nozzle based on B-spline curves

An expansion–deflection nozzle (EDN) is an altitude-compensated nozzle that can accommodate a wide range of nozzle pressure ratio (NPR) variations. An EDN design with good thrust performance under both high- and low-NPR conditions can effectively improve the launch capability of the vehicle. In this study, a multi-objective aerodynamic optimization design is developed considering the performance of the EDN in both open and closed wake modes. The design method of the outer wall and pintle of the EDN is developed using B-spline curves. The design variables are the coordinates of the control points of the curves, and the objective functions are the thrust efficiencies of the EDN at NPRs of 30 and 400. The nozzle thrust is obtained using a Reynolds Averaged Navier–Stokes (RANS) equation solver and validated by experimental results. A radial basis function neural network and a non-dominated sorting genetic algorithm II (NSGA-II) are used to construct the surrogate model and search for the Pareto front, respectively. The relationship between the nozzle contour geometry and the nozzle thrust characteristics of the four optimised individuals on the Pareto front is discussed and analysed. The results demonstrate the effectiveness of the multi-objective optimization design for an EDN. One of the optimized designs improved the thrust efficiency at NPR = 30 by 3.5 % while improving the thrust efficiency at NPR = 400 by 0.6 %. Moreover, to ensure superior performance of the EDN in both operating modes, the nozzle length and pintle ending angle must be designed as a compromise, whereas a larger nozzle exit angle is favoured for both conditions. This study demonstrates the potential of the B-spline method for nozzle optimization design.

Advance optimum design of multi-layer compound cylinders

The purpose of this work is to find the optimum design for two-layer compound cylinders of the same material with an open end condition by using the elimination method. The optimization method depends on reducing the number of design variables with a simultaneous yield hypothesis for all layers of the compound cylinder. A combination of von Mises and Tresca criteria is used as a yield criterion, and the superposition principle is used to evaluate the equivalent stresses for each cylinder. By considering the working pressure and the internal diameter of the inner cylinder as the design parameters (constants during optimization) and the total thickness of the compound cylinder as the objective function, the optimization algorithm has been programmed in C# with a user-friendly graphical interface. The optimization results (outer diameter, interference diameter, and shrinkage pressure) are obtained for different working pressures and compared with the optimum design, which is based only on the Tresca criterion. The total mass of the compound cylinder can be reduced by up to 50% by using the von Mises–Tresca combination criterion. The optimized results are validated numerically by using finite element analysis in the ANSYS Workbench. The theoretical result and the FEA result agree with each other with errors of about 2%. The behavior of the optimized parameters for different working pressures is also observed and presented.

Investigation on the Compressive Characteristics and Optimization of Design Parameters of a Novel Functionally Graded Cell Structure

Novel structural conceptualizations frequently incorporate inventive ideas, materials, or construction techniques. This study presents a unique design inspired by the traditional practice of sikku rangoli, a cultural tradition prevalent in the southern region of India, particularly in Tamil Nadu. Because it was novel, it was necessary to optimize the fundamental design for maximal outputs. In contrast to honeycomb structures, intercellular interactions are believed to contribute to the overall strengthening of the structure. By eliminating sharp corners from the structure, stress accumulation is prevented, resulting in improved stress distribution. Therefore, the design aspects that were deemed significant were taken into consideration and through the implementation of experimental design, an optimum design was determined. Utilizing the optimal base design as a foundation, the structure underwent several printing processes using diverse materials and incorporated multiple fillers. Furthermore, the structure was subjected to modifications employing the functional grading design concept. The study employed the functional grading design concept to examine the variations in load bearing capability, load distribution, and failure mode. The findings indicate that the compression strength of the composite structure was mostly influenced by the wall thickness. The combination of a carbon fiber reinforced base material with silicone rubber as filler, together with a functional graded cell structure featuring top and bottom densification, exhibited the highest compression strength compared to all other combinations. In order to investigate the accurate impact of the FG structures, every cell design was printed using PLA-CF, subjected to testing devoid of any additives, and the output parameters were computed. The results indicated that the center densified cell design exhibited significant values for specific energy absorption, relative density, and compressive strength (52.63 MPa, 0.652, and 2.95 kJ kg−1, respectively). The design of the base cell exhibited the greatest crushing force efficacy of 0.982.

Neutronic Analysis for The Radial Direction Heterogeneous Core Configuration of GFR with Thorium Fuel

A neutronic analysis has been carried out for the radial direction heterogeneous core configuration in GFR with thorium nitride fuel to obtain the most optimal heterogeneous design has keff > 1 and excess reactivity below one percent. Neutronic analysis was performed using openMC version 0.13.2 based on open-source Monte Carlo simulation with ENDF/B-VII.1 data library. The first calculation is benchmarking openMC and SRAC where the largest error is 2.49% is caused by different data library. SRAC data library is JENDL 4.0. The second calculation is a homogeneous core configuration with 5% to 15% uranium-U233 fuel variation, the stability lies at 8% for the reference of heterogeneous core configuration. The heterogeneous core configuration was performed with three types of fuel percentage and five different geometry variations. Optimum design for geometry F1:F2:F3 = 1 ring:4 rings:1 ring using fuel type F1:F2:F3 = 6.5%:8%:9.5%. Optimum design for geometry F1:F2:F3 = 2 rings:2 rings:2 rings using fuel type F1:F2:F3 = 7.5%:8%:8.5%. Optimum design for geometry F1:F2:F3= 1 ring:2 rings:3 rings using fuel type F1:F2:F3=7%:8%:9%. Optimum design for geometry F1:F2:F3= 1 ring:3 rings:2 rings using fuel type F1:F2:F3 = 7.5%:8%:8.5%. Optimum design for geometry F1:F2:F3 = 1 ring:1 ring: 4 rings using fuel type F1:F2:F3 = 7.5%:8%:8.5%. The most optimum design among the other five designs is the F1:F2:F3 = 2 rings:2 rings:2 rings geometry design because it has a maximum keff of 1.00999 and excess reactivity of 0.99%. This design is carried out for an extended burn-up 15 years and still stable with the characteristics of the neutron flux and fission rate in this design decrease with burn-up time. Fission product in this design also decreased according to the purpose of the generation IV reactor is the prevention of nuclear weapons.

Efficient and exquisite concurrent optimization of hierarchical structures with non-uniform eccentric body centered cubic lattice

To obtain lightweight structures with desirable mechanical performances and to enlarge the design space, this work presents an innovative concurrent optimization methodology for the precise inverse design of micro lattices and the modeling of hierarchical structures. The inverse design of micro lattices with continuously changing anisotropic properties is achieved by offsetting the center nodes of eccentric body centered cubic lattices (EBCC) to designated locations. Simultaneously, the macro material distribution is adjusted utilizing 3D density-based topology optimization method. To enhance the computation efficiency and significantly amplify the versatility in optimization design, a Parameterized Interpolation for Anisotropic Lattice material (PIAL) is established based on elastic stiffness matrices of micro lattices with 21 independent components. Specifically, a cubic multinomial function is employed to construct a direct correlation between micro topology configurations and equivalent elasticities of the EBCC lattices. Several numerical examples of compliance optimization are carried out to confirm the validity of the proposed approach. Concurrently, Additive Manufacturing (AM) using AlSi10Mg is adopted to manufacture the obtained hierarchical structures, which exhibit well-improved mechanical performance, as confirmed by experimental testing, when compared to the classical density-graded structures.

Optical lens optimization design and tolerance analysis for a depth camera

This paper describes a depth camera lens design composed of eight glass lenses, where the field of view is 100°, the focal length is 1.88 mm, and the F/# is 2. The lens design requires the lens to be fixed and the object distance to be fixed at −745.964 mm. The depth of focus ranges from −0.0047 mm to 0.0096 mm and can be obtained by selecting a spatial frequency of 60 lp/mm for the Through-focus MTF optimization design. The object distance ranges from negative infinity (-∞) to −250 mm (for the depth of field), the MTF is greater than 0.73 at 60 lp/mm, the lateral color difference is less than 1.58 μm, the optical distortion is less than 1%, and the relative illumination is greater than 64%. When conducting tolerance analysis, we select a spatial frequency requirement of tolerance MTF>0.3. At an object distance of −745.964 mm, the lens resolution can reach 80 lp/mm. The object distance is -∞, and the lens resolution is above 59 lp/mm. The object distance is −250 mm, and the lens resolution can reach over 43 lp/mm. In this work, the depth camera lens is designed to have an object distance shooting range of −6 m to −0.25 m. When the object distance is −6 m, the object depth resolution is 61 mm; while at the object distance of −0.25 m, the object depth resolution is 0.1045 mm.

Optimal design of composite grid/skin structures based on deep learning and Double-Double layup strategy

This study introduces a novel two-stage optimization framework combining deep learning and genetic algorithms for the design of composite grid/skin structures. Firstly, an image dataset of 131,022 potential design configurations based on the Double-Double (DD) layup strategy is created. Subsequently, a deep-learning based Auto-encoder model is employed to extract the structural genes from this dataset. These structure genes are then used to establish a surrogate model using transfer learning, which yields accurate predictions of the buckling loads for various grid/skin structures. Finally, the Genetic Algorithm (GA) is employed to explore the optimal design configuration based on the surrogate model, with the objective of maximizing the buckling load while minimizing the weight. Results show that the optimized structure surpasses the traditional Ortho-grid and Iso-grid designs in terms of buckling load by 145 % and 72 %, respectively. Additionally, it improves the structure efficiency by 31 % and 56 %, respectively. These findings emphasize the effectiveness of the two-stage optimization method in addressing complex, multi-objective challenges, offering valuable insights and promising prospects for the optimization design of composite grid/skin structures.

Beamforming Design for the Performance Optimization of Intelligent Reflecting Surface Assisted Multicast MIMO Networks

Beamforming Design for the Performance Optimization of Intelligent Reflecting Surface Assisted Multicast MIMO Networks

Consensus Clustering With Co-Association Matrix Optimization.

Consensus clustering can derive a more promising and robust clustering result by integrating multiple partitions strategically. However, there are several limitations in the existing approaches: 1) most of the methods compute the ensemble-information matrix heuristically and lack of sufficient optimization; 2) the information from the original dataset is rarely considered; and 3) the noise in both label space and feature space is ignored. To address these issues, we proposed a novel consensus clustering method with co-association matrix optimization (CC-CMO), which aims at improving the co-association matrix by taking abundant information from both label space and feature space into consideration. In label space, CC-CMO derives a weighted partition matrix capturing the intercluster correlation and further designs a least squares regression (LSR) model to explore the global structure of data. In feature space, CC-CMO minimizes the reconstruction error with doubly stochastic normalization in the projective subspace to eliminate noise features as well as learn the local affinity of data. To improve the co-association matrix by jointly considering the subspace representation, global structure, and local affinity of data, we explicitly propose a unified optimization framework and design an alternating optimization algorithm for the optimal co-association matrix. Extensive experiments on a variety of real-world datasets demonstrate the superior performance of CC-CMO to the state-of-the-art consensus clustering approaches.

Analysis and optimization of the automated TIG welding process parameters on SS304 incorporating Taguchi optimization technique

AbstractThe present research work focused on optimizing the input parameters of the automated TIG welding process of SS304 metal. Since SS304 possesses high inter‐granular toughness and corrosion resistance with increased lifespan in the pressure vessel and automobile sector, SS304 is mainly referred for gas arc welding compared with SS 202. The SS304 workpieces of 60 mm × 40 mm × 4 mm with dimension are utilized in the experiment and the same metal has been used as the filler material. The TIG welding experimental lab‐scale setup utilizes a 2‐axis servo workbench programmed with PLC to perform an automatic trajectory path using Taguchi design of optimization to obtain the optimal welding parameters for the SS304 welding process. To analyze the influence of welding current, welding speed, gas flow rate, and welding arc length on the tensile strength and hardness based on predicted R‐squared, p‐value and co‐efficient of the sum of squares from are verified. From the regression analysis, the predicted model R‐squared value holds 95.78% and 94.83% for the hardness and tensile strength respectively associating with the actual coefficient confirming the model which has maximum precision. Further, it is inferred that on increasing welding current and welding speed, the hardness of the welded joints seems to increase whereas when the minimum gas flow rate is maintained, the tensile strength of the SS304 decreases drastically. Overall, among the four input factors, the welding current is a major influencing parameter on the SS304 which is directly proportional to tensile strength and hardness.

Optimum Design of Mixture Proportion and Microscopic Analysis of Cement-Stabilized Macadam by Rubber Powder and Fiber

Abstract Based on the orthogonal test method, the mixture proportion design of cement-stabilized macadam (CSM) modified with rubber powder and fiber is studied. With strength and dry-shrinkage as key indices, the influence of length of polyvinyl alcohol (PVA) fiber, fiber content, particle size of rubber powder, and rubber powder content on the performance of CSM is analyzed, and the optimum mixing proportion is determined. Based on this, the modification mechanism of CSM by rubber powder and fiber is further studied from a micro perspective. The results show that the 7-day unconfined compressive strength of CSM gradually increases with the increase of fiber length. With the increase of fiber content, the compressive strength shows a trend of first increasing and then decreasing. The 7-day unconfined compressive strength of CSM gradually increases with the increase of rubber powder mesh size and linearly decreases with the increase of rubber powder content. The 28-day average dry-shrinkage coefficient of CSM shows a trend of first decreasing and then increasing with the increase of fiber length and fiber content, and gradually increasing with the increase of rubber powder mesh number and rubber powder content. Considering the strength and shrinkage characteristics, the optimum mixing parameters are 40 mesh rubber powder, 0.5 % rubber powder, 18-mm fiber length, and 1.2-kg/m3 fiber content. Scanning electron microscopy results show that there is a gap between the rubber powder and the cement matrix, and the interface between the two is discontinuous, with weak bonding, resulting in a decrease in the compressive strength of CSM. PVA fiber can improve the toughness of CSM. It has good adhesion with cement matrix. It forms a three-dimensional network support structure in the CSM mixture and can restrain water loss in the cement matrix, thus greatly improving the cracking resistance of CSM.

Optimum design of pile cap considering minimization of environmental impacts

Optimum design of pile cap considering minimization of environmental impacts

Theoretical exploration of photoemission characteristics of GaAs nanowire array cathode based on photon-enhanced thermionic emission

The theoretical photoemission models of single GaAs nanowire and nanowire array cathode based on photon-enhanced thermionic emission (PETE) mechanism are respectively developed by utilizing two-dimensional continuity equations. Based on the built models in this work, the effect of several key factors on the quantum efficiency are also discussed. Results show that GaAs nanowire cathode exhibits the superiority of photoemission performance compared with GaAs film cathode at the spectral range of 300∼600 nm. Besides, the optimal wire diameter of GaAs nanowire cathode is in the range of 340∼360 nm. In addition, the nanowire cathode with low electron affinity of 0.3 eV achieves PETE effect under low temperature of 400 K, which is conducive to improve the stability of cathode. Moreover, increasing the incident light angle or decreasing the array spacing provides an effective method of obtaining high quantum efficiency of nanowire array cathode. Furthermore, the numerical difference between quantum efficiency and effective quantum efficiency implies that the low collection efficiency of emitted electrons is the main issue that hinders the application of PETE cathodes of GaAs nanowire array. The proposed GaAs nanowire array model would provide theoretical guidance for optimum design of PETE cathodes with nanoarray structure.

Optimization design of configurable standard-type coils based on singular value decomposition for transcranial magnetic stimulation

Objective: This study aims to introduce a configurable standard-type (CST) coil design methodology that can accommodate different stimulation needs through the overlapping of standard coil components, while ensuring the performance of CST coil approaches the existing optimal physical limit as closely as possible. Approach: The study utilizes planar, spherical, and hemispherical stream function to represent the spatial geometry of the coils. Singular value decomposition is employed to design the standard coil components while minimizing the number of coil components. Initially, this study delineates the relationship between singular vectors and coil geometry, defining a multi-objective optimization problem for the research. By contrasting the decay characteristics of singular values in the inductance and electric field matrices of the stream functions, the singular vectors of the electric field matrix are utilized as the foundation for CST coil optimization. Subsequently, the relationships between the number of singular vectors and the coil’s Pareto fronts are determined through the optimization algorithms, and the geometric patterns of the singular vectors, i.e., the standard coil components, are analyzed. Main results: By constructing the CST coil components using seven singular vectors and comparing with the Pareto front of the performance of over 50 existing coil types, the stimulation depth range of the CST coil based on the planar stream function decreased by only 7.7%; the focality performance of the CST coil based on the spherical stream function improved by 22.4%; the focality performance of the CST coil based on the hemispherical stream function decreased by only 1.6%. Significance: The construction of CST coils with a minimal number of components is explored to approximate the performance at any discrete point on the physical limit curve. This approach overcomes the limitation of one coil associating with one performance point in traditional designs, presenting a novel perspective on coil design.

Finite element analysis and optimization design of large vibrating screen based on equivalent static load method

Vibrating screens have wide applications in various industries, ranging from agriculture to coal mining. In recent years, improvement in production have put forward higher requirements on the efficiency of vibrating screens. However, these large screens are prone to fatigue damage, particularly crack formation, over time. The emergence of substantial dynamic inertial forces can be attributed to the considerable body mass of a large vibrating screen. This study analyzed the structural strength of the HZXZ200x300 large vibrating screen, employing finite element simulation to identify the maximum equivalent stress and primary stress distribution. The main frame model of the vibrating screen box was constructed by integrating the equivalent static load and submodule methods. The model transitioned from dynamic response to static optimization under equivalent static and dynamic loads, significantly reducing calculation scale and enhancing optimization efficiency. Equivalent static sub-models were employed for topology optimization, determining the optimal structure for material performance distribution. This process yielded an optimal conceptual model for reconstructing the actual model. The structural strength was further improved by comparing three-dimensional and dynamic local stress relationships post-topological optimization, followed by reinforcing the vibrating screen structure and introducing reinforced bars for increased stability. The study showed that lightweight topology optimization significantly decreased the stress levels and improved the fatigue durability of beams. Local strengthening, accomplished via topology optimization, effectively reduced the maximum equivalent stress to 76.487 MPa, a decrease of 46.8%. The vibrating screen mass also decreased by 225 kg, a reduction of 14.9%. In summary, this study employed structural analysis, topology optimization, and local reinforcement to mitigate stress levels, enhance the fatigue life of a vibrating screen, and reduce its weight. This study offers an effective solution to the dynamic optimization challenges of complex structures.

Optimization design of surface optical characteristics of space solar cells based on transfer matrix method

The antireflection coating (ARC) can improve the photoelectric conversion efficiency of photovoltaic (PV) cells. In this paper, the influence of film thickness and refractive index of single-layer and double-layer ARC on solar light absorption under different spectral conditions is simulated by the transfer matrix method. The optimum values of ARC film thickness and refractive index are obtained. To optimize it at AM 0 (air mass 0) solar irradiance, a 66 nm thick SiN x ARC with a refractive index of 2.0 was used. The PV cell’s maximum power density is 89.87. The maximum power density of the PV cell with double-layer SiN x as ARC is 90.94. This work provides a theoretical basis for the application of ARC in ground PV power generation systems and space solar power systems.

Hierarchical Velocity Optimization for Connected Automated Vehicles With Cellular Vehicle-to-Everything Communication at Continuous Signalized Intersections

The rapid development of intelligent connected technologies and cellular vehicle-to-everything communication (C-V2X) provide new opportunities to solve the connected automated vehicle (CAV) traffic problem for eco-driving at continuous signalized intersections. With C-V2X, a hierarchical velocity optimization design based on hybrid model predictive control technique (HVO-HMPC) is presented to reduce the fuel consumption and pollution emission. First, a distance–domain velocity optimization problem, with distance as the independent variable, was constructed. Second, a hybrid MPC scheme was developed by combining the multiple shooting method and MPC technique to calculate the optimal velocity profile of a high-level controller, which acts as the reference velocity in a low-level controller. Then, a car-following model was built, the low-level controller tracked the reference velocity with the predictive control as the backbone, and the optimal velocity was calculated while ensuring that the safety velocity constraint is satisfied. Next, the proposed HVO-HMPC was tested in Prescan, and the effect comparisons with different control methods in terms of fuel consumption, pollution emission, braking time, and number of braking applications were studied under different driving scenarios. Results show that once the maximal speed is limited to 40 km/h under short-period signals and 20 km/h under long-period signals, the HVO-HMPC effectively reduces fuel consumption by 27.21%, 25.89%, and pollution emissions by 25.3%, 25.97%, respectively, while achieving best performance. Finally, an experimental prototype is built to confirm the validity of the HVO-HMPC.

Quality by Design Enabled Formulation Optimization of Rabeprazole Sodium Mucoadhesive Microcapsules for the Treatment of Gastroesophageal Reflux Disease

Aims: The present research describes the implementation of quality by-design principles for developing the mucoadhesive microcapsules of rabeprazole sodium for treating gastroesophageal reflux disease conditions. Background: In addition, a holistic QbD-based product development strategy was implemented, where the target product profile was defined based on desired product quality of mucoadhesive microcapsules. Based on TPP, the critical quality attributes were identified. The identification of CMAs was carried out with the help of risk assessment and factor screening exercises, which indicated drugpolymer ratio (X1), temperature (X2), and stirring speed (X3) as the influential factors. Methods: The mucoadhesive microcapsules of rabeprazole sodium were prepared by a solvent evaporation method, and 33 Box-Behnken optimization design was used for the optimization of the selected factors, and mucoadhesive microcapsules formulations were evaluated for particle size (μm), drug entrapment efficiency (%), mucoadhesion (%), and in vitro drug release (Q18h) in percentage characteristics. Mathematical data analysis was performed to fit the two-factor interaction model, and optimized mucoadhesive microcapsules formulation was selected. Results: The optimized mucoadhesive microcapsules indicated desired formulation characteristics with smaller particle size, good entrapment efficiency, better mucoadhesion, and sustained drug release characteristics. Conclusion: In a nutshell, the studies vouch for the successful development of mucoadhesive microcapsules for oral delivery of rabeprazole sodium which could be used to manage gastroesophageal reflux disease condition.

Optimization Design and Thermodynamic Analysis of Thermal Management System for New Energy Vehicle Power Batteries

Optimization Design and Thermodynamic Analysis of Thermal Management System for New Energy Vehicle Power Batteries

Drug Discovery and the Role of Artificial Intelligence in Human Healthcare

Abstract: The integration of artificial intelligence (AI) into healthcare and drug development has catalyzed an era of change in medical research and patient care. This research paper examines the evolving landscape of drug development and highlights the central role of artificial intelligence in advancing human healthcare. By combining data-driven approaches with advanced machine learning algorithms, AI can transform target identification, molecular screening, lead optimization and clinical trial design. In healthcare, AI applications include medical image analysis, disease diagnosis, personalized medicine, electronic health information management and more, promising better patient care, cost reduction and advances in medical research. However, these innovations come with challenges such as data protection, bias, regulatory barriers and ethical issues. The article highlights the potential of AI in drug development and healthcare and discusses similar AI-based solutions and methods. It also addresses issues related to data protection, interoperability, data quality, regulatory approval, ethical issues and resource constraints. Responsible development and implementation are critical to the safety and effectiveness of healthcare AI applications. Future work includes improving explainability, promoting knowledge sharing and collaboration, creating ethical frameworks, adapting regulatory processes, integrating AI into clinical practice, and addressing global health disparities. Through collaboration and continued research, AI holds the promise of revolutionizing healthcare, leading to better patient outcomes and a brighter future for medicine.