Optimum Design Research Articles

Efficient Error-Rate Estimation for Optical Transmission Systems Using Artificial Neural Networks

ABSTRACT With the advancement in modulation formats, optical transmission systems are becoming more adaptable and dynamic. Predicting bit errors of higher-order modulation formats is a challenging problem for the optimum design of communication systems. The existing simulation methods have persisted by employing iterative procedures that frequently incur high computing expenses and time consumption. On the other hand, deep learning (DL) algorithms have demonstrated remarkable efficacy as practical computing tools, presenting a viable approach to accelerate modulation simulations. In this paper, an artificial neural network (ANN) based bit error rate (BER) estimation scheme is proposed for the popular modulation forms including 56 Gbps 16-quadrature amplitude modulation (16QAM), 100 Gbps 32QAM, and 120 Gbps 64QAM optical system. Based on constellation diagrams (CDs) acquired with different launch power, laser linewidth, transmission distance, and OSNR, amplitude histograms (AHs) are generated via a manual preprocessing method. The properly trained ANN architecture exhibits a computational speed that surpasses traditional simulation methods by a factor exceeding 347. Moreover, the design considerations including the number of layers, nodes, activation functions, learning rate, optimizers, and evolution epochs are also investigated in detail. This research paves the way for optical transmission systems to use fast DL-based optimization strategies.

Optimum design of a coaxial magnetic gear integrated with a permanent magnet synchronous generator of a wind turbine based on the pelican optimization algorithm

This paper presents an optimum design of a coaxial magnetic gear (CMG) Integrated with a Permanent Magnet Synchronous Generator (PMSG) of a wind turbine using a pelican optimization algorithm (POA) launched in 2022. In order to evaluate the proposed system design based on POA, the well-known Particle Swarm Optimization (PSO) is implemented for the same CMG under the same conditions and constraints. Also, a Taguchi-method-based sensitivity analysis for the key design variables is employed to determine their impact on the system’s performance. The optimization process for the CMG design based on POA and PSO is executed based on the 2-D Finite element analysis (FEA) using the Finite Element Method Magnetics (FEMM) program. The results obtained from the POA and PSO are compared and analyzed. The results underline that the optimum design for the CMG/PMSG integrated system based on POA achieves more torque/weight ratio and then more generated power and currents as compared to PSO. It is concluded that the proposed design based on POA gives a superior and efficient design with substantial stability and rapid convergence.

An iteration-based design algorithm for high frequency transformer in SSTs and its validation by finite element analysis

This paper proposes an iteration-based algorithm for the optimum design of a high frequency transformer for solid-state transformer (SST) applications. This algorithm minimizes the total owning cost (TOC) of a distribution type solid-state transformer. The unique features of this algorithm compared with the available algorithms in the literature are as follows: it iterates eight design variables, four constraints are defined for selecting the valid designs, and it works with different core materials and AC test voltages. The algorithm uses various user-defined data inputs to calculate loss capitalization values for TOC calculation. In every iteration, TOC is estimated, and calculated values of design constraints are compared with their threshold limits. A case study is conducted on a high-frequency transformer (HFT) incorporated in 1000-kVA, 11-kV/415-V, Dyn11 three-phase wound core SST. This is to determine the optimum design parameters. In this case study, the algorithm was iterated with 2,100,000 design data inputs, generating 258,272 designs that satisfied all design constraints. The optimum design with minimum TOC is selected from the generated 258,272 designs. The optimum design is validated using finite element analysis in ANSYS software. Comparing the results of both analyses, the deviation is less than 5%. Hence, the algorithm’s reliability is proved.

Quality by design approach to improve quality and decrease cost of in vitro transcription of mRNA using design of experiments.

In vitro transcription (IVT) reaction is an RNA polymerase-catalyzed production of messenger RNA (mRNA) from DNA template, and the unit operation with highest cost of goods in the mRNA drug substance production process. To decrease the cost of mRNA production, reagents should be optimally utilized. Due to the catalytic, multicomponent nature of the IVT reaction, optimization is a multi-factorial problem, ideally suited to design-of-experiment approach for optimization and identification of design space. We derived a data-driven model of the IVT reaction and explored factors that drive process yield (in g/L), including impact of nucleoside triphosphate (NTP) concentration and Mg:NTP ratio on reaction yield and how to optimize the main cost drivers RNA polymerase and DNA template, while minimizing dsRNA formation, a critical quality attribute in mRNA products. We report a methodological approach to derive an optimum reaction design, with which cost efficiency of the reaction was improved by 44%. We demonstrate the validity of the model on mRNA construct of different lengths. Finally, we maximized the yield of the IVT reaction to 24.9 ± 1.5 g/L in batch, thus doubling the highest ever reported IVT yield.

Optimum Model-Based Design of Diagnostics Experiments (DOE) with Hybrid Pulse Power Characterization (HPPC) for Lithium-Ion Batteries

Abstract Diagnostics of lithium-ion batteries are frequently performed in battery management systems for optimized operation of lithium-ion batteries or for second-life usage. However, attempting to extract dominant degradation information requires long rest times between diagnostic pulses, which compete with the need for efficient diagnostics. Here, we design a set of efficient optimal hybrid pulse power characterization (HPPC) diagnostics using model-based design of experiment (DOE) methods, applying knowledge of degradation effects on pulse kinetics and cell properties. We validate that these protocols are effective through minimization of uncertainty, and robust with Markov Chain Monte Carlo (MCMC) simulations. Contrary to traditional HPPC diagnostics which use fixed pulse magnitudes at uniformly distributed state of charges (SOC), we find that well-designed HPPC protocols using our framework outperform traditional protocols in terms of minimizing both parametric uncertainties and diagnostic time. Trade-offs between minimizing parametric uncertainty and total diagnostic time can be made based on different diagnostics needs.

Design of Experiments-Driven Optimization of Spray Drying for Amorphous Clotrimazole Nanosuspension.

This study employed a Quality by Design (QbD) approach to spray dry amorphousclotrimazole nanosuspension (CLT-NS) consisting of Soluplus® and microcrystallinecellulose. Using the Box-Behnken Design, a systematic evaluation was conducted toanalyze the impact of inlet temperature, % aspiration, and feed rate on the criticalquality attributes (CQAs) of the clotrimazole spray-dried nanosuspension (CLT-SDNS). In this study, regression analysis and ANOVA were employed to detect significantfactors and interactions, enabling the development of a predictive model for the spraydrying process. Following optimization, the CLT-SD-NS underwent analysis using Xraypowder diffraction (XRPD), Fourier transform infrared spectroscopy (FTIR), Dynamic Scanning Calorimetry (DSC), and in vitro dissolution studies. The resultsshowed significant variables, including inlet temperature, feed rate, and aspiration rate,affecting yield, redispersibility index (RDI), and moisture content of the final product. The models created for critical quality attributes (CQAs) showed statistical significanceat a p-value of 0.05. XRPD and DSC confirmed the amorphous state of CLT in theCLT-SD-NS, and FTIR indicated no interactions between CLT and excipients. In vitrodissolution studies showed improved dissolution rates for the CLT-SD-NS (3.12-foldincrease in DI water and 5.88-fold increase at pH 7.2 dissolution media), attributed torapidly redispersing nanosized amorphous CLT particles. The well-designed studyutilizing the Design of Experiments (DoE) methodology.

Optimum design parameters for double concave friction pendulum system considering ground motion characteristics and friction heating

Optimum design parameters for double concave friction pendulum system considering ground motion characteristics and friction heating

Physical, mechanical, and microstructural characteristics of fly ash replaced cement deep mixing columns

The novel approach of the study is implementing the installation procedure of fly ash (FA) replaced cement deep mixing (DM) columns to field cases aiming at managing FA waste and reducing cement utilization. FA replaced cement DM columns (diameter of 30 cm and length of 80 cm) were installed on clayey soils using a laboratory type DM machine. The effect of installation parameters such as the binder dosage, FA replacement ratio, superplasticizer content, water/binder ratio, and the liquidity index (LI) of the soil on column performance was investigated. The design of experiments and optimization process were conducted using the Taguchi method, S/N and ANOVA analyses, and the desirability function method. Observations have shown that the mixing time required for a homogeneously mixed column depends on the LI of the soil and the volume ratio (VR) of the slurry. A key parameter (LI∙VR) is defined to decide the minimum number of the mixing process. The blade rotation number should be minimum of 252 rev/m to obtain a homogeneous soil-slurry mixture. The highest strength of the column was obtained when LI of fresh soilcrete (LImix) is 1.25∙LI. Optimum installation parameters were determined as binder dosage of 425 kg/m3, FA replacement ratio is 40%, superplasticizer content is 3%, water/binder ratio is 0.8, and LI of the untreated soil is 1. In the optimum design, the mixing efficiency of the soil-slurry mixture increased and the best column performance was obtained. In addition, cement utilization and binder cost decrease 40% and 33%, respectively, in FA-replaced cement DM columns. SEM images prove the increase in column performance due to the cementation products (CSH and CAH gels) formed in the microstructure of the column.

Optimum Design of Granular Pile Anchor System for Ground Improvement on Expansive and Shrinking Soil

Optimum Design of Granular Pile Anchor System for Ground Improvement on Expansive and Shrinking Soil

Packed Bed Thermal Energy Storage System: Parametric Study

The use of thermal energy storage (TES) contributes to the ongoing process of integrating various types of energy resources in order to achieve cleaner, more flexible, and more sustainable energy use. Numerical modelling of hot storage packed bed storage systems has been conducted in this paper in order to investigate the optimum design of the hot storage system. In this paper, the effect of varying design parameters, including the diameter of the packed bed, the storage material, the void fraction, and the aspect ratio of the packed bed, on storage performance was investigated. COMSOL Multiphysics 5.6 software has been used to design, simulate, and validate an axisymmetric model, which was then applied to evaluate the performance of the storage system based on the total energy stored, the heat transfer efficiency, and the capacity factor. In this paper, a novel-packed bed was proposed based on the parametric analysis. This involved a 0.2 void fraction, 4 mm porous media particle diameter, and Magnesia as the optimum storage material with air as the heat transfer fluid.

Investigate the optimum design of atomizing nozzles for coal dust suppression by using multifactor level response surface methodology

Mechanized coal extraction generates substantial dust, adversely affecting the physical and mental wellbeing of underground workers and degrading the operational environment. To address the limitations of traditional spray systems, which often suffer from poor atomization effects and limited ability to withstand wind interference, the innovative vortex atomizing nozzle has been developed specifically for dust suppression. This advanced solution was rigorously tested through detailed numerical simulations and comprehensive experimental investigations. These studies focused on examining the impact of critical parameters, including the velocity at the airflow director's exit, the angle of the exit hole, and its radius, on the efficacy of dust suppression. Findings highlighted the paramount importance of the airflow director's exit velocity in enhancing dust removal, with the exit hole's radius having the least impact. Optimal conditions for dust suppression were identified as an airflow director exit velocity of 20 m/s, an angle of 60°, and a radius of 0.6 mm. Field validations under these optimum parameters demonstrated unparalleled dust control efficiency, achieving a reduction in coal dust concentration exceeding 90 %. This apparatus offers a highly effective, user-friendly solution for comprehensive coal dust management across various mine locations.

Optimum Design of Elastomer-Based Shock Isolation System for a Naval Component

Optimum Design of Elastomer-Based Shock Isolation System for a Naval Component

Optimum Design of Elastomer-Based Shock Isolation System for a Naval Component

Optimum Design of Elastomer-Based Shock Isolation System for a Naval Component

Fabrication Strategies for Bioceramic Scaffolds in Bone Tissue Engineering with Generative Design Applications

The aim of this study is to provide an overview of the current state-of-the-art in the fabrication of bioceramic scaffolds for bone tissue engineering, with an emphasis on the use of three-dimensional (3D) technologies coupled with generative design principles. The field of modern medicine has witnessed remarkable advancements and continuous innovation in recent decades, driven by a relentless desire to improve patient outcomes and quality of life. Central to this progress is the field of tissue engineering, which holds immense promise for regenerative medicine applications. Scaffolds are integral to tissue engineering and serve as 3D frameworks that support cell attachment, proliferation, and differentiation. A wide array of materials has been explored for the fabrication of scaffolds, including bioceramics (i.e., hydroxyapatite, beta-tricalcium phosphate, bioglasses) and bioceramic–polymer composites, each offering unique properties and functionalities tailored to specific applications. Several fabrication methods, such as thermal-induced phase separation, electrospinning, freeze-drying, gas foaming, particle leaching/solvent casting, fused deposition modeling, 3D printing, stereolithography and selective laser sintering, will be introduced and thoroughly analyzed and discussed from the point of view of their unique characteristics, which have proven invaluable for obtaining bioceramic scaffolds. Moreover, by highlighting the important role of generative design in scaffold optimization, this review seeks to pave the way for the development of innovative strategies and personalized solutions to address significant gaps in the current literature, mainly related to complex bone defects in bone tissue engineering.

Design of Experiments-Based Optimization of an Electrochemical Decarboxylative Alkylation Using a Spinning Cylinder Electrode Reactor

Design of Experiments-Based Optimization of an Electrochemical Decarboxylative Alkylation Using a Spinning Cylinder Electrode Reactor

Optimization of two-wheeler Bike engine among three different designs using design of experiments

This research presents the results of a fin heat transfer study of three different models of the motorcycle engine. The objective of this study is to minimize the external body temperature because the optimum design will be found when the heat transfer rate is maximized. The study obtains the procurement of a balance between the outer boundary temperature due to convective heat transfer and air-cooled fin design. Heat transfer coefficient varies according to wind velocity (Km/hr) and Nusselt Number. The analysis was performed on three different engine models, named A, B and C. The optimum design was design B through simulation which has lower temperature gain, lower deformation and lower normal stress. DOE (Design of Experiments) was performed on the optimum design of the engine among all with three parameters thickness of fin, size of fin, and shape of fin, and again analysis was performed according to DOE cases. The material used for manufacturing the models was aluminum alloy 6061 which has a thermal conductivity of 200 W/mK. The study was performed on the designed models by taking the outer boundary temperature of 750oC. The heat transfer coefficient was about 77.28 W/m2K at 40 Km/h velocity.

Ultra-high-sensitive plasmonic Sensor Based on Asymmetric Hexagonal Nano-ring Resonator for Cancer Detection

A highly sensitive sensor based on two metal-insulator-metal waveguides coupled to an asymmetric hexagonal nano-ring resonator detecting cancerous cells is proposed. This novel design is utilized to facilitate the sensing of human cells. The sensing mechanism of the presented optical structure can act as a refractive index measurement in biological, chemical, biomedical diagnosis, and bacteria detection, which leads to achieving high sensitivity in the structure. The main goal is to achieve the highest sensitivity concerning the optimum design. As a result, the sensitivity of the designed topology reaches a maximum value of about 1800 nm/RIU (nm/refractive index unit) by controlling the angle of the resonator. It is evident that the sensitivity parameter is improved, and the reason for the increase in sensitivity is due to the asymmetry of the resonator, which has an 81% increase in sensitivity compared to the symmetrical resonator, especially for blood cancer cells. The maximum quality factor obtains 131.65 with a FOM of 90.4 (RIU-1). The sensing performance of this proposed structure is numerically investigated using the finite difference time domain (FDTD) method with the perfectly matched layer (PML). Accordingly, the suggested high sensitivity sensor makes this structure a promising therapeutic candidate for sensing applications that can be used in on-chip optical devices to produce highly complex integrated circuits.

Friction and Wear Mathematical Modeling and Optimization Design of Surface Microstructure Parameters of Unfolding Wheel based on Experiment and Numerical Simulation

Objective: In order to improve the friction-increasing and wear-reducing performance of the unfolding wheel surface, the surface microstructure of the unfolding wheel used in the detection of 8 kinds of steel balls was optimized by parameter matching. Method: Firstly, based on Hertz's theory, the contact area between steel balls of different sizes and the unfolding wheel are analyzed. The wear depth model is established based on Archard adhesive wear model. Secondly, the appropriate microstructure parameters for friction and wear experiments were selected. The finite element analysis software is used to simulate the stress on the surface of the microstructure unfolding wheel and calculate the wear depth. According to the experimental results, the relationship between friction coefficient, wear depth and microstructure parameters is obtained by data fitting, and the objective function of optimization design is established. Finally, based on the genetic algorithm DNSGA-II and Python, the parameters are optimized, and the optimal solution is obtained by using the TOPSIS method. Results: The feasibility of the simulation method is verified by friction and wear experiments, and the correctness of the optimization method is verified. Some existing patents on friction and wear of microstructure surfaces are introduced, and the future development of this field is prospected Conclusion: The research shows that the optimal parameters matching of microstructure for steel ball diameters of Ф16.6688 mm~Ф22.2250 mm: the shape is rhombus; the area of a single pit is close to the contact area, which is 0.0319 mm2 ~ 0.0554 mm2; the pit depth is 145 μm~150 μm, and the surface density of microstructure is (5.4~5.6) /mm2.

Optimum design of a novel Ku-band rasorber for RADAR warfare systems using ML neural network

The design, fabrication, and testing of a conformal Frequency Selective Rasorber (FSR) operating at A-T-A mode is designed for RADAR warfare systems. In the design of a miniaturized single-layer FSR element, multiple parameters influencing the absorption and transmission characteristics need to be optimized. This simulation using conventional methods consumes more simulation time. Thus, the geometrical parameters are optimized and predicted using supervised machine learning (ML) techniques to expedite the process. The multiple output regression neural network (MORNN) is used to generate multiple input and output features from the dataset. The ML algorithm is trained using the datasets generated from the electromagnetic solver using which a scalable FSR is synthesized. The reflection coefficient, (|S11|), and transmission coefficient (|S21|) are used as input data, and the dimension of the FSR unit cell for the user input frequency requirements are derived as output data. The extracted dimensions of the FSR offered a small mean square error (MSE) of 0.02 between the desired and observed results. The designed FSR offers absorption at 11.5 GHz and 18.9 GHz while the transmission window extends from 15.02 GHz to 16.09 GHz. The neural network results are endorsed using the EM simulation tool and validated by experimental measurements.

Distributed Unbalanced Optimization Design Over Nonidentical Constraints

Distributed Unbalanced Optimization Design Over Nonidentical Constraints