Optimum Design Research Articles

Performance prediction and optimization of hydrogenation feed pump based on particle swarm optimization – least squares support vector regression surrogate model

Due to high power consumption and low energy efficiency of the hydrogenation feed multistage pump, conducting structural optimization design and reducing energy losses for this pump is necessary. In this study, an optimization method based on the particle swarm optimization (PSO) algorithm and least squares support vector regression (LSSVR) surrogate model is proposed. The head and efficiency are taken as the optimization objectives of the multistage pump, and the optimization design of the two impellers is carried out. Through the optimization of the surrogate model, the head and efficiency of the multistage pump are increased by 3.99% and 2.91%, respectively. It is found that the optimized impeller has more stable flow characteristics than original impeller. Further, the entropy production method is introduced to analyze the energy loss of pump. The result shows that the pump optimized by PSO-LSSVR surrogate model can reduce 9% energy consumption under nominal flow rate. It is attributed to the inhibitory effect of the optimization scheme on the horseshoe vortex at leading edge and the wake vortex at trailing edge of impeller. This study provides a new idea for the optimization design of high efficiency and low energy consumption of multistage centrifugal pumps.

Optimization Design and Experimentation of a Soil Covering Device for a Tree Planting Machine

In an effort to improve the soil covering effect of tree planting machines and reduce the lodging rate of seedlings, this paper analyzes the soil covering process and working principle of the soil covering device of tree planting machines and designs a soil covering device with adjustable soil covering width. With the help of the discrete element simulation software EDEM, the orthogonal test was designed with the width of the covering soil and the inclination angle as the test factors and the soil backfill rate as the response index. The regression analysis of the test results was carried out by Design-expert, and the optimal parameter combination of the covering soil device was determined as follows: the width of the covering soil: 600 mm, the forward angle: 45°, and the depth of the soil: 20 mm. The prototype verification test was carried out under the optimal parameters, and the soil backfill rate was 88.4%, which was basically consistent with the optimized parameters. The soil covering device with adjustable width designed in this study is of great significance to improve the soil covering rate of tree planting machines and reduce the lodging rate of seedlings.

Hybrid modeling and optimization of fiber laser hole cutting of austenitic stainless-steel sheets using response surface

In this study, an efficient approach was proposed to systematically model and optimize the laser small hole cutting process parameters using a hybrid approach for the design of experiment and multi-objective genetic algorithm optimization. The central composite design and response surface methodology were used to effectively model the impact of four main factors: cutting speed, laser power, gas pressure and focal distance on the responses. The responses considered were hole diameter circularity tolerance, spattering and cut kerf width, which were used to evaluate the quality of the laser hole cutting. The regression equations were used to model the effect of process parameters and their interactions on the responses. These regression models were then used as objective functions for optimization. The results show that the focal distance and laser power have had a significant influence on the hole diameter circularity tolerance and the variation in size of the cut kerf. In particular, the melted material spattering rate increased threefold when the focal distance increased from 0.4 to 0.8 mm. The optimization results highlighted that the best outcomes in terms of minimum deviation, spatter, and the cut-kerf width were achieved at low power (between 605 and 685 W) and low speeds (in the range of 11.1–12.7 m min−1). The optimal focal distance for all solutions was found to be 0 mm for the gas pressure (between 6.5 and 8 bars) to minimize the objective functions.

Design of water nozzle shape and structure optimization for eccentric injection mandrel in water injection wells

AbstractInjection mandrel is one of the key components to control the water injection rate of each layer in the layered injection process in the oilfield. The imbalance between the layers can be changed by an optimized injection mandrel to control the appropriate injection rate. This study introduces an approach to optimize water injection in oilfields, focusing on a new eccentric injection mandrel with adjustable flat nozzles. Firstly, computational fluid dynamics (CFD) simulations were carried out using FLUENT software to model and analyze the flow characteristics of the injection mandrel. The simulation results were validated through experimental comparisons, demonstrating excellent consistency. Secondly, the nozzle shapes were identified and optimized. The semicircle nozzle demonstrating superior regulation performance due to its smallest differential pressure and a robust linear relationship between flow rate and opening degree. Finally, orthogonal tests are designed for three influencing factors of the eccentric injection mandrel: number of injection holes, injection hole diameter, and nozzle shape. The simulation results show that the number of injection holes is 36, the diameter of injection holes is 3.2 mm, and the eccentric injection mandrel has relatively better performance. After optimization, the differential pressure between the inlet and outlet is reduced by 57.14%, and the average shear stress on the fluid is reduced by 3.88%. This innovative methodology, employing CFD simulations and orthogonal tests, offers precise water injection rate control, reducing water wastage, and optimizing energy consumption in water injection systems.

A Generative Design Approach to Improving the Environmental Performance of Educational Buildings in Hot Arid Climates. (Assiut National University as a Case Study)

The architectural design process is complex, involving diverse objectives that may be contradictory, and on which orientation exerts significant influence. The artificial intelligence application, Generative Design facilitates solving multi-objective design dilemmas through the creation and evaluation of numerous design alternatives. However, its exploration in educational buildings in hot arid climates remains limited. Given the impact of spaces’ function distribution, this study aims to optimize it in the typical plans of educational buildings. Employing a multi-objective design approach to enhance environmental performance. The study is conducted and evaluated in national universities in Egypt as a case study, specifically in Assiut City. The results revealed that the optimum design for a certain objective has not equated to optimal performance for other goals, highlighting an inherent contradiction between them. Among 26,334 possible alternatives for spaces’ function distribution, the difference between the optimal scenario and the least favourable one is significant for the parameters related to study spaces: natural daylighting, and visual comfort, ranging from 10% to 24%, besides around 1% difference for parameters related to the whole building, including energy consumption, thermal comfort, and carbon emission. This research offers a framework applicable to various building types. Additionally, it encourages decision-makers to adopt a no-cost sustainable design approach.

A computational tool for the design of rigid origami structures based on a lumped damage model

As it is well known, origami structures have broad application prospects in aerospace, medical, architectural, robotics, material science, and other engineering fields. However, the comprehension and modeling of the collapse process of this kind of structure are still in the process of development. This paper investigates one specific failure mode: ultra-low cycle fatigue under large displacements. Origami structures are represented as sets of elastic panels with small strains connected by inelastic (plastic-damaged) hinge lines with large rotations. A novel experimental study of the behavior of the Waterbomb origami structure subjected to cyclic loadings with constant amplitudes is first presented. Then, a new model of damage evolution for origami structures is proposed. Finally, parametric studies are described on the influence of several design parameters (initial folding height, number of sides of regular polygons, and length ratio) on structural collapse. The computational tools proposed in this paper can then be used for the optimum design of origami structures and the structural assessment during the two phases of loading: first, the deployment stage, and then service loads on the unfolded solid.

Design and evaluation of semi self-compacting cold mix polyurethane mixture for steel bridge deck pavement

To enhance the environmental sustainability and pavement performance of steel bridge deck paving, a novel semi self-compacting cold mix polyurethane mixture (CMPU-10) for steel bridge deck paving was specifically designed and evaluated. Herein, this study presents the first comprehensive documentation of the research process involved in the mix design of CMPU-10. Initially, based on the theory of maximum density curve, a gradation with a Talbot index (n) of 0.40 was identified as the preliminary gradation for designing CMPU-10 by void ratio, compressive strength and slump tests. Subsequently, the aggregate grade-by-grade filling test revealed the critical influence of sieve sizes (0.075 mm, 0.6 mm, and 2.36 mm) on the mixture dense degree. From these findings, an in-depth analysis of the effects of aggregate passage under the key sieve on the degree of mixture density, compressive strengths, and flow properties led to the determination of the gradation range of CMPU-10. The optimum binder-aggregate ratio for the median gradation was determined to be 9.2%. Finally, wheel tracking, semi-circular bending (SCB) and freeze-thaw splitting tests showed that the designed CMPU-10 exhibits superior high-temperature stability performance, low-temperature cracking resistance and moisture susceptibility compared to the TAF epoxy asphalt mixture (EA-10). This paper not only provides a new potential low-carbon material for steel bridge deck paving projects, but also establishes a theoretical foundation for the optimization design and promotion polyurethane mixture.

Optimization Design of Double Wishbone Front Suspension Parameters for Large Mining Dump Truck and Analysis of Ride Comfort

With the advancement of technology, mining trucks are gradually becoming larger, imposing higher performance requirements on the front suspension. There is a need to transform the original integral non-independent front axle of mining dump trucks with a payload exceeding 300 tons into an independent front suspension with a double-wishbone suspension. The changing of the front suspension is bound to have an impact on the overall vehicle’s handling stability and ride comfort. Therefore, the following research is conducted to investigate and analyze these effects. Firstly, the paper proposes a method for optimizing the parameters of the double-wishbone front suspension. The double-wishbone front suspension is modeled, and a comparison with a kinematic model is conducted to validate the accuracy of the model. Secondly, unreasonable hardpoint parameters are optimized. Thirdly, a dynamic model of the entire vehicle is established based on the optimized parameters, and an analysis of handling stability and ride comfort for the entire vehicle is performed. Finally, simulation results are compared and analyzed against experimental data. The results indicate that the optimized positioning parameters not only effectively enhance the suspension performance of the mining dump truck but also meet the requirements for handling stability and smoothness. The overall smoothness of the vehicle is significantly improved after the modification. This study not only holds significant engineering value in reducing vibrations in dump trucks and enhancing driver comfort, but also provides theoretical support for subsequent research and development in the industry.

Multi-objective optimization of composite sandwich structures using Artificial Neural Networks and Genetic Algorithm

Sandwich structures offer significant opportunities to improve the performance of many industrial applications such as aerospace, automotive, and marine. The design of a composite sandwich structure is often challenging because it is driven by the balance between the weight and cost of these structures. In this paper, a multi-objective optimization model using a Genetic Algorithm (GA) and an Artificial Neural Network (ANN) is presented to evaluate a new optimization approach in terms of weight and cost minimization for the composite sandwich structures. Classical lamination and beam bending theories were used, along with Monte Carlo simulation, to generate the design data for the proposed composite sandwich structure, which is applied as a floor panel for a high-speed train. Multilayer feedforward neural networks were used for predicting safety factors, cost, and weight of the designed structure based on the following inputs: core density, core thickness, face sheet materials’ combinations, and applied load. The trained Neural Network model was able to predict the considered results with a good performance metric, namely the coefficient of determination (R2 = 0.99) and the Mean Square Error (MSE = 1.3·10−5).Multi-objective optimization for cost and weight minimization was performed with a Genetic Algorithm using the derived ANN model. The obtained Pareto front provided several non-dominated optimal points leading to insights on the optimization process. The Finite Element Method (FEM) was used to model the key points of the optimal designs (i.e. the design with the lowest cost, weight, and Pareto optimal points). The FEM and optimization results had a maximum deviation of about 8.9%, indicating a good agreement between the two techniques.The newly elaborated methodology demonstrates a new approach for obtaining the optimum design of the investigated composite sandwich structure constructed from honeycomb core and laminated face sheets in terms the cost and weight. The study concluded that the use of Carbon Fiber-Reinforced Plastic (CFRP) or Fiber-Metal Laminate (FML) face sheets results in significant weight savings of about 59.5% and 48.6%, respectively, compared to an all-aluminum sandwich structure.

Performance Analysis and Optimization Design of a Dual-Mode Reconfigurable Ankle Joint Parallel Rehabilitation Mechanism

In this study, a dual-mode reconfigurable parallel ankle joint rehabilitation mechanism is proposed to meet the needs of patients in different ankle rehabilitation stages. This mechanism can switch between the 1T2R (where R represents rotation and T represents translation) and 2T1R motion modes. The screw theory and the modified G-K formula were used to analyze and verify the degree of freedom of the mechanism. The non-parasitic motion characteristics were analyzed by examining the topological structure of the mechanism. An inverse kinematics model was established using the closed-loop vector method, and the mechanism’s singularity was analyzed based on the Jacobian matrix. The Jacobian matrix and the numerical method were used to compare and analyze the workspace index, the rotational dexterity index and the load capacity performance index before and after the introduction of branched chains with actuation redundancy. A particle swarm optimization algorithm was used to optimize the geometric dimensional parameters of the mechanism. The results show that the mechanism exhibits the characteristics of a parallel mechanism without parasitic motion in the two motion modes. Using branched chains with actuation redundancy can significantly improve the rotational dexterity and load capacity performance index, without affecting the workspace index. Compared to the original mechanism, the kinematic performance of the optimized mechanism is significantly improved. It is concluded that the proposed mechanism can meet the needs of ankle joint activity training in the 1T2R motion mode and the needs of ankle joint proprioception training in the 2T1R motion mode, which can better meet the needs of patients in different rehabilitation stages.

Simulation Analysis and Optimization Design of Paddy Field Mud Spreader Blades for Uniform Dispersion

To improve the distribution of mud particles collected in the tray during the operation of paddy field mud spreader blades, the optimal combination of parameters for the blades that results in the best uniformity of mud dispersion needs to be identified. In this study, a thorough force analysis was conducted on the spreading process, and computational equations were formulated to describe the motion of mud particles. By utilizing the discrete element simulation technique, a simulation model was developed to accurately represent the intricate interaction between the blades and mud particles. Through the single-factor simulation experiments, the ranges of key parameters such as the rotation radius, bending angle, sub-blade tilt angle, forward velocity, and rotational speed of the blade were determined. A secondary orthogonal rotational combination design was employed to establish a regression prediction model between the non-uniformity of mud dispersion and the key blade parameters. Subsequently, a multivariate single-objective optimization method was used to develop an optimization model for the non-uniformity of mud dispersion. The results indicate that the hierarchical order of factors influencing the non-uniformity of mud dispersion is as follows: rotation radius > rotation speed > bending angle > forward velocity > sub-blade tilt angle. To achieve a minimum spreading non-uniformity of 29.63%, a specific configuration is required, which includes a blade rotation radius of 188 mm, a bending angle of 121°, a sub-blade tilt angle of 30°, a forward velocity of 400 mm/s, and a rotation speed of 191 r/min. Finally, the accuracy of the optimization results was verified by means of bench tests. The research results provide a crucial reference for enhancing the uniformity of mud dispersion in paddy field mud spreader blades.

Optimize and analyze a large-scale grid-tied solar PV-powered SWRO system for sustainable water-energy nexus

The lack of clean water and access to energy are two major obstacles to global sustainable systems. The development of solar photovoltaic (PV) technology gained the attention of water desalination projects due to its cost decline, carbon-free emission, and the on-site energy production. However, studies on in-depth analysis of the performance of these systems remain rare. This study performs a comprehensive a design optimization and seven-factor analysis (energy, exergy, economic, environmental, energoeconomic, exergoeconomic, enviroeconomic - 7E) of a grid-tied PV system powering a large-scale seawater reverse osmosis (SWRO) plant. First, an optimal design model is developed based on real hourly measured data for the SWRO load demand and renewable resources, while considering net energy metering mechanism. The objective function is delineated to diminish the energy cost of the system, concomitant with the imposition of operational constraints. Second, an analytical model is established to perform exergetic calculations for the optimal design. Third, a sensitivity analysis is conducted to show how the model parameters affect the system design and costs. The results show that the optimal system relies on a 4.25 MW solar PV array and a 3.125 MW grid-tied inverter. This configuration reduces the levelized cost of energy by 48.3 % and carbon emissions by 50 % compared to a grid-only system. The implementation of net-metering has also maximized the designed system's revenues to 263,834 $/year. Further, the optimally designed system achieves a lower levelized cost of water (0.2217 $/m3), minimal capacity shortage, and negligible unmet load ratios (0.048 %) despite grid outages. From the exergetic assessment, the proposed system has a high exergy efficiency (9.3 %) which can be achieved despite the high value of the exergy loss. Moreover, the energoeconomic and exergoeconomic indicators have recorded tiny values (3.35E-5 kWh/$ and 0.0311 kWh/$, respectively), which are considered a desired outcome. Notably, a profit of 4285.4$ is achieved through the sale of carbon emissions reduction. The sensitivity analysis revealed an increase in the system's total cost of 4.91 % and 9.36 % and the system's energy cost of 4.91 % and 8.7 % if the energy purchase price raised by 10 % and 20 %, respectively. Also, it indicates that a fall in the electricity sellback rate of 10 % would remarkably raise the system's energy cost by 47.3 %. Finally, reducing the PV-Inverter costs by 15 % would drop the system's net present and energy costs by 51.4 % and 60 %, respectively. Overall, the insights from this study have the potential to expedite the realization of sustainable development goals 6, 7, and 13, particularly in developing nations abundant in solar energy resources.

Multi-Objective Optimization Design of an Origami-Inspired Combined Cushion Airbag

To improve the cushioning performance of soft-landing systems, a novel origami-inspired combined cushion airbag is proposed. The geometry size, initial pressure, and exhaust vent area of the cushion airbags are designed preliminarily using a theoretical model. The finite element models, including the returnable spacecraft and cushion airbags, are established via the control volume method (CVM) to analyze the impact dynamic behavior and cushioning performance during the landing attenuation process. The cushioning performance of the cushion airbags in complex landing environments are studied to investigate the influence of horizontal velocity, lateral velocity and nonhorizontal landing surfaces. Four design parameters of the cushion airbags, including the initial pressure, venting threshold pressure, exhaust vent area and polygon edge number, are employed to study their influence on the cushioning performance. A multi-objective optimization model of the cushion airbags based on the neural network and multi-objective water cycle algorithm is established to realize the rapid optimization design. The Pareto front of the maximum overload and specific energy absorption is obtained. The analysis results show that the maximum overload of the proposed combined cushion airbags is 7.30 g. The system with the anti-rollover design can avoid rollover and achieve outstanding cushioning performance in complex landing environments. The maximum overload of the returning spacecraft is decreased by 16.4% from 7.30 g to 6.10 g after multi-objective optimizations. This study could provide the technical support for the soft-landing system design of returnable spacecrafts.

Design and development of prime herder optimization based BiLSTM congestion predictor model in live video streaming

High-quality content for the user in video streaming services depends critically on the ability to predict the continuous user’s quality of experience (QoE). However, continuous QoE prediction has proven challenging due to the complexity imposed by the temporal dependencies in QoE data and the non-linear correlations among QoE impact elements. In this research congestion prediction model is developed using the prime herder optimization-based BiLSTM (PHO-based BiLSTM). The input database is first gathered from the NIMS and darpa99 week 1 database and, the data collection is analyzed and the packet information is extracted after that the extracted features are then fed into the optimized BiLSTM classifier to train the classifier. The classifier’s hyperparameters are successfully tuned by the recommended prime herder optimization, which is made by fusing the herding characteristics of a prime sheepdog and herder optimization. Based on the traffic congestion prediction achievements, at training percentage (TP) 90, the accuracy is 94.81%, specificity is 94.90%, and mean square error (MSE) is 4.91 respectively for D1, similarly based on D2 the accuracy is 95.62%, specificity is 95.96%, and MSE is 0.38 respectively.

Intelligent multi-objective optimization of 3D printing low-carbon concrete for multi-scenario requirements

This study presents, for the first time, a universal theoretical framework based on machine learning (ML) algorithms for the multi-objective optimization (MOO) design of 3D printing low-carbon concrete to meet the sustainability requirements of the construction industry. The study focuses on the simultaneous reduction of carbon emissions and costs during the design process. Specifically, the applicable scenarios are primarily classified into two categories: meet aesthetics requirements (printing layer height error-HE and printing layer width error-WE approaching 0) and strengths requirements (compressive strength-CS approaching 60 MPa and interlayer bonding strength-IBS as high as possible). Through a comparison with the traditional design method, 3D printing concrete based on the intelligent design method achieves a 44% reduction in costs and a 19% reduction in carbon emissions while meeting aesthetics requirements. Under strengths requirements, there is a 57% reduction in costs and a 22% reduction in carbon emissions. This demonstrates the indispensable and crucial role of the MOO method employed in this study in achieving the lowest costs and carbon emissions. Additionally, the results of experimental verification demonstrate that the models developed in this study have successfully achieved a close alignment between design values and experimental values within the allowable error range (The errors are 3.46%–9.83%). This not only holds the potential to drive the widespread application of 3D printing technology in the construction industry but also presents new possibilities for cleaner production, contributing to the achievement of sustainable development goals.

Performance analysis and multi-objective optimization of a novel CCHP system integrated energy storage in large seagoing vessel

To advance sustainable development and emission reduction efforts within the maritime industry, a novel combined cooling, heating, power, and storage (CCHPS) system is proposed. This system comprises a cogeneration system (ORC + ARC) and a thermal energy storage (TES) system. Initially, optimal working fluids are selected for the ORC and TES discharge systems. Subsequently, an analysis is conducted regarding the correlation between sensitivity parameters and system performance. Then, the cogeneration system's optimum design parameters are determined utilizing the multi-objective grey wolf optimization (MOGWO) algorithm. Finally, the TES system is analyzed and matched with the diesel generators to meet the ship's electricity demand. The CCHPS system achieves optimal thermal efficiency, payback period, annual CO2 emission reduction, and annual fuel savings of 30.07%, 6.22 years, 8910.49 tons, and 2791.74 kL, respectively. In contrast to the TES-absent system, the CCHPS system exhibits a 12.88% increase in thermal efficiency, along with a 25.48% improvement in annual fuel savings and annual CO2 emission reduction.

Shear bearing capacity prediction of STRC shear walls using data‐augmented fusion model

AbstractMachine learning (ML) accurately predicts the shear bearing capacity of steel tube‐reinforced concrete (STRC) shear walls, aiding optimization design. However, creating a database for STRC shear walls experimentally is time‐consuming and costly. The aim of this study is to propose a method for high‐accuracy prediction of STRC shear wall shear bearing capacity using a small‐sample dataset. This study employs generative adversarial network (GAN) data augmentation techniques to address the issues of insufficient ML model training and low prediction accuracy in small‐sample databases. Based on the stacking framework, a fusion model (Stacking‐XRL) combining extreme gradient boosting (XGBoost), random forest (RF), and least absolute shrinkage and selection operator (LASSO) is established to predict the shear bearing capacity of STRC shear walls. Results show that after augmenting the training set with GAN, the prediction performance of K‐nearest neighbors (KNN), backpropagation neural network (BPNN), RF, light gradient boosting machine (LightGBM), XGBoost, and stacking‐XRL models significantly improve, with average increases of 10% in R2 and average decreases of 30% and 25% in MAE and RMSE, respectively. The proposed stacking‐XRL fusion model outperforms tested models, existing formulas, and Abaqus numerical simulations for the shear bearing capacity of STRC shear walls. Model interpretation reveals that the shear span ratios as the most important factors in predicting shear bearing capacity, followed by axial force ratio and whole section configuration steel tubular index.

Optimization design of railroad freight station computer inquiry system based on robot intelligent configuration system

In recent years, China’s railroads have been rapidly developed, but to improve the quality of service to customers and the rapid passage of passengers is a major problem facing the railroad operation. It is the era of information development at present, China’s railroad operation has also been unprecedented rapidly developed and has significant achievements. In order to meet the growing demand and improve the level of information technology, the information system platform for railroad freight transportation has been created, because each system is not connected with each other and is independent of each other, thus creating an upgrade of the system. To solve the above-mentioned problems, it is necessary first of all to establish an intelligent freight station inquiry system, which is one of the ways for the customers to experience the convenience directly. In this paper, by understanding and analyzing the inquiry system of freight stations, we address the shortcomings and deficiencies of the system now. Using the development concept of information technology and the general development goals of Chinese railroad passenger and freight stations, the development has been carried out through the two levels of the total structure and performance of the system. Current successful modifications and enhancements of the Chinese train transportation system show promising experimental results. It has been confirmed by these tests that the system demonstrates a high level of stability and dependability while skillfully meeting the intelligent inquiry needs of travelers at railroad freight stations.

Global vibration control of offshore wind turbines with a flexible monopile foundation using a pendulum-tuned mass damper: Risk mitigation and performance incrementation

This study investigates an optimized design approach for a passive-adaptive pendulum-tuned mass damper (PTMD). The PTMD is used to mitigate structural vibrations in offshore wind turbines (OWTs) with flexible monopile foundations considering Pile-Soil Interaction (PSI). The model OWT is based on the 5-MW design proposed by the National Renewable Energy Laboratory (NREL). The PSI incorporates a pile that is modeled as beam–column elements supported by nonlinear springs and accounts for lateral loads (p-y curves) and axial loads (t-z and Q-z curves) at nodal points. Wind and wave spectra estimation, as well as hydrodynamic and aerodynamic load analysis, are performed using a bespoke MATLAB® program operated in conjunction with an ANSYS® 3-D finite element global model. The resultant peak response at the OWT hub was evaluated through power spectral density (PSD) analysis. Optimal design choices for PTMD parameters are obtained via parametric analysis, which assesses the dynamic response in the along and across directions at the hub and stress at the tower base. A systematic representation considers the uncertainties stemming from the geometric and mechanical properties of the tower, environmental loads, and rotating blade dynamics. To address these uncertainties, a Monte Carlo simulation is employed to assess the structural risk via the Performance-Based Wind Engineering (PBWE) framework. Consequently, the probabilistic evaluation of structural response, foundation stresses, and power production assists in the optimal design process of PTMDs that mitigate the global structural vibrations of OWTs.

Implementation of JPEG XS entropy encoding and decoding on FPGA

AbstractJPEG XS is the latest international standard for shallow compression fields launched by the International Organization for Standardization (ISO). The coding standard was officially released in 2019. The JPEG XS standard can be encoded and decoded on different devices, but there is no research on the implementation of JPEG XS entropy codec on FPGAs. This paper briefly introduces JPEG XS encoding, proposes a modular design scheme of encoder and decoder on FPGA for the entropy encoding and decoding part, and parallelizes the algorithm in JPEG XS coding standard according to the characteristics of FPGA parallelization processing, mainly including low-latency optimization design, storage space optimization design. The optimized scheme in this paper scheme enables encoding speeds of up to 4 coefficients/clock and decoding speeds of up to 2 coefficients/clock, with a 75% reduction in encoding and decoding time. The maximum clock frequency of the entropy encoder is about 222.6 MHz, and the maximum clock frequency of the entropy decoder is about 127 MHz. The design and implementation of the FPGA-based JPEG XS entropy encoding and decoding algorithm is of great significance and provides ideas for the subsequent implementation and optimization of the entire JPEG XS standard on FPGAs. This work is the first in the world to propose the design and implementation of an algorithm that can implement the JPEG XS entropy encoding and decoding process on FPGA. It creates the possibility for the effective application of JPEG XS standard in more media.