Optimal Speed Research Articles

Elaboration of an Economic and Efficient Pulp Conditioning Method for a Low-Quality Coarse Coal Slime

ABSTRACT The high energy consumption of high-intensity conditioning (HIC) limits the popularization of this technology although it has been validated as an effective way to improve flotation. In this study, an economic and efficient pulp conditioning method for a low-quality coarse coal slime was innovatively proposed by comparing four different conditioning-flotation processes. It is found that the energy consumption per unit combustible recovery of conditioning using emulsified diesel was reduced by 8.3% compared with that of the conventional conditioning. This is attributed to the energy reduction of the optimum conditioning speed decreased from 2100 rpm to 1300 rpm, and a negligible energy consumed in the preparation of emulsified diesel. Results showed that when using an emulsified diesel, flotation performance was further improved and less affected by conditioning speed but more by conditioning time. Moreover, flotation is no longer affected by the degree of surface cleaning. It was indicated that the dispersion of collector is crucial for HIC. This study provides a technical reference for the development of economic and efficient conditioning equipment.

Optimizing Dynamic Measurement Accuracy for Machine Tools and Industrial Robots with Unscented Kalman Filter and Particle Swarm Optimization Methods

Abstract The telescoping ballbar is widely utilized for diagnosing accuracy and identifying faults in machine tools and industrial robots. Currently, there are no established standards for determining the optimal feeding speed for ballbar tests. This lack of clear guidelines results in time inefficiency in measurements and inconsistencies in dynamic measurements, which complicates the comparison of ballbar test results under various conditions or across different machine platforms. To mitigate dynamic variations in ballbar results, an updated ballbar data processing method that integrates the unscented Kalman filter (UKF) and particle swarm optimization (PSO) was developed and validated using real ballbar data measured at multiple feeding speeds and simulated data with varying vibration magnitudes generated through the Renishaw ballbar simulator. Experimental results revealed that the dynamic components extracted from the ballbar results were observed to increase in correlation with the vibration measured at different feeding speeds and from the simulations. Moreover, the variations in the results measured at different feeding speeds after PSO-UKF processing were significantly reduced. The findings confirm the effectiveness of the proposed method in minimizing the dynamics of the ballbar results. Ultimately, this approach enhances the efficiency and accuracy of ballbar testing and offers a general method for improved diagnostics.

Critical parameters to standardize the size and concentration determination of nanomaterials by nanoparticle tracking analysis

The size and concentration are critical for the diagnostic and therapeutic applications of nanomaterials but the accurate measurement remains challenging. Nanoparticle tracking analysis (NTA) is widely used for size and concentration determination. However, highly repeatable standard operating procedures (SOPs) are absent. We adopted the “search-evaluate-test” strategy to standardize the measurement by searching the critical parameters. The particles per frame are linearly proportional to the sample concentration and the measured results are more accurate and repeatable when the concentration is 108-109 particles/ml. The optimal detection threshold is around 5. The optimal camera level is such that it allows clear observation of particles without diffractive rings and overexposure. The optimal speed is ≤ 50 in AU and ∼ 10 μl/min in flow rate. We then evaluated the protocol using polydisperse polystyrene particles and we found that NTA could discriminate particles in bimodal mixtures with high size resolution but the performance on multimodal mixtures is not as good as that of resistive pulse sensing (RPS). We further analyzed the polystyrene particles, SiO2 particles, and biological samples by NTA following the SOPs. The size and concentration measured by NTA differentially varies to those determined by RPS and transmission electron microscopy.

Lightweight concrete with low-carbon artificial aggregates recycled from biomass ash and slurry waste

The development of artificial aggregates (AAs) significantly reduces carbon emissions of concrete production since the aggregates account for about 70% volume of all concrete ingredients. Therefore, a type of low-carbon AAs was prepared with full solid wastes of biomass ash (BA) and slurry waste (SW) through a pelletizing technique and carbonation curing methods. This study focused on evaluating the feasibility of AAs produced by the synergistic effect of BA and SW; identifying and optimizing the pelletization parameters through the response surface methodology (RSM) with experimental tests; analyzing the effects of blending amounts of BA on the properties of AAs; discussing the improvement of carbonation curing methods for AAs production; assessing the properties of the prepared lightweight concrete (LWC) with AAs. The results indicate the addition of 20wt% BA lightened the bulk density of AAs by about 13% but weakened strength by nearly half due to its porous characteristics. The appropriate incorporation of 5wt% BA could improve the individual pellet and cylinder compressive strength by about 2% and 16%, respectively. AAs cured in pressurized carbonation of 3 d and wet carbonation of 10min demonstrated similar enhancement effects. Pressurized and wet carbonation curing showed respective superiorities in terms of improving effect and curing efficiency. The effects of inclination angle and moisture content on the average particle size are more significant than those of rotation speed, while rotational speed has a dominant effect on the bulk density. RSM simulations identified the optimal inclination angle, rotation speed, and moisture content as 45°, 38.5r/min, and 37.5%, respectively. The LC40 high-strength LWC can be prepared by using full substitution pre-wetting AAs.

Dynamic compensation of the threading speed drop in rolling processes

This paper presents an approach for the dynamic speed drop compensation during threading in rolling processes. The feedforward control design exploits the differential flatness of the mechanical model and accelerates both the rolls and the drive train in a manner such that the acceleration torque is equal to the rolling torque during threading, while simultaneously maintaining the roll at the desired target speed. Ideally, this prevents the speed drop and enhances the quality and stability of the rolling process. The flatness-based feedforward trajectories are optimized in an online fashion to determine the optimal initial roll speed and duration of the acceleration process. An extensive experimental validation on a hot strip finishing mill shows superior performance in terms of various key performance indicators in comparison with a standard overspeed approach.

Estimation of Surge and Swab Pressures While Tripping for Minimizing Excessive Drilling Problems

Surge and swab pressures result from pipe movement upward/downward during drill string reciprocation and tripping. surge and swab create costly drilling concerns such well kick, formation breakage, and lost circulation. This study aims to find the optimum pipe tripping speed to avoid surge and swab issues, particularly in lost circulation zones. Also, to determine how drilling factors and well bore geometry affect surge and swab pressures. Mud rheology, mud properties, hole diameters, drill pipe diameter, and bottom hole assembly (BHA) dimensions were also analyzed. Dynamic surge and swab model was employed to simulate two case studies in Rumaila Iraqi oilfield. The findings indicated that the tripping speed is the most effective variable on surge and swab pressures. Annular clearance and BHA design have also a major effect on the surge and swab pressures. For the first case study, it is found that whenever the mud weight is far from the pore pressure and fracture pressure gradients, the 5" drill pipe can be tripped in/out of lost circulation formation (Shuaibba formation) at optimum tripping speed of (80 ft/min) without surge/ swab related problems. The reason is that the generated surge pressures (6110-6300 psi) are below the fracture pressure (7100-7400 psi). The results also showed that tripping 5" drill pipe within a maximum running speed of 80 ft/min in (8.5") open hole section through Tanuma formation till Rumaila formation causes a surge pressures of (4650-5050 psi) which is still below the fracture pressures of formations (4750-5660 psi). the results of the second case study (S-shaped well) revealed that the optimum tripping speed of the drill string in the (12.25") open hole through Dammam and Hartha formations is (90 ft/min). it is also concluded that pulling out of hole (POOH) process must be carried out more carefully than the running process since the mud density is near the pore pressure gradient. The results of this study can be further used to improve the drilling efficiency in Rumaila oil field with decreasing non-productive time (NPT) by employing the appropriate tripping speed and optimal drilling operations.

A Study on E-Commerce Warehousing Dynamics Navigating Order Fulfilment and Returns

The e-commerce over the past decade has experienced remarkable growth, reshaping shopping habits and challenging traditional supply chain methods. This surge in online shopping has compelled e-commerce firms to enhance their warehousing and order fulfilment strategies to meet rising consumer expectations for swift deliveries and hassle-free returns. Effective management of order fulfilment and reverse logistics has become essential for competitiveness in this industry. This study investigates e-commerce warehousing dynamics, emphasizing the optimization of order speed, delivery times, operational costs, and return management. Through industry reports, academic literature, and case studies, we identify best practices in five key areas: warehouse design and automation, demand forecasting, inventory control, order picking and packing processes, transportation strategies, last-mile delivery models, and reverse logistics for returns. Our research underscores the significance of highly automated warehouses equipped with advanced technologies such as robotics, automated storage and retrieval systems (ASRS), automated guided vehicles (AGVs), and intelligent software for material flow management. These innovations empower e-commerce firms to boost throughput, enhance operational flexibility, and adapt to changing demands effectively.

Effect of Laser Scan Speed on Defects and Texture Development of Pure Chromium Metal Fabricated via Powder Bed Fusion-Laser Beam

Chromium (Cr) metal has garnered significant attention in alloy systems owing to its exceptional properties, such as a high melting point, low density, and superior oxidation and corrosion resistance. However, its processing capabilities are hindered by its high ductile–brittle transition temperature (DBTT). Recently, powder bed fusion-laser beam for metals (PBF-LB/M) has emerged as a promising technique, offering the fabrication of net shapes and precise control over crystallographic texture. Nevertheless, research investigating the mechanism underlying crystallographic texture development in pure Cr via PBF-LB/M still needs to be conducted. This study explored the impact of scan speed on relative density and crystallographic texture. At the optimal scan speed, an increase in grain size attributed to epitaxial growth was observed, resulting in the formation of a <100> cubic texture. Consequently, a reduction in high-angle grain boundaries (HAGB) was achieved, suppressing defects such as cracks and enhancing relative density up to 98.1%. Furthermore, with increasing densification, Vickers hardness also exhibited a corresponding increase. These findings underscore the efficacy of PBF-LB/M for processing metals with high DBTT properties.

Managing port disruption through sailing speed optimization for sustainable maritime transportation

Ports, as nodes in maritime transportation, frequently face disruptions leading to congestion, adversely affecting the efficiency and sustainability of the global supply chain. This study considers the speeding-up strategy to alleviate port congestion. We model the transportation network as a closed Jackson network and adopt a tailored mean-value analysis algorithm to solve the model. To deliver the increased sailing speed, we further put forward a bi-section search algorithm. Our numerical analysis results demonstrate the feasibility of increasing speed to alleviate port congestion. However, when port congestion exceeds a certain threshold, the system faces collapse, rendering the increase in speed ineffective. Additionally, we recommend shipping companies consider using clean energy when employing the speeding-up strategy to mitigate congestion, thus safeguarding the environment. Our study combines theoretical methodologies and analytical models, providing insights regarding speeding up the vessels. The findings of this study offer guidelines for real-world sustainable maritime practice.

Optimization of mixing speed and time to disperse the composite conductive agent composed of carbon black and graphene in lithium-ion battery slurry

This paper proposed an optimal approach to disperse the composite conductive agent which is composed of carbon black (CB) and graphene (Gr) within lithium-ion battery (LIB) slurry with different mixing speeds and mixing times. The internal structures of LIB slurry are characterized by Electrochemical Impedance Spectroscopy, Scanning Electron Microscopy, and Raman experiment. Initially, a composite conductive solution is prepared by mixing the composite conductive agent with NMP solvent under the conditions of five different mixing speeds n1 (n1 = 1000, 1100, 1200, 1300, 1400 rpm) in the case of mixing time t1 = 10 min. Subsequently, LIB slurry is prepared by blending the composite conductive solution, LiCoO2 and PVDF-NMP solution under the conditions of five different mixing speeds n2 (n2 = 1000 ± 280, 1100 ± 280, 1200 ± 280, 1300 ± 280, 1400 ± 280 rpm) in the case of mixing time t2 = 6 min. By analyzing the internal structure of different LIB slurries, it shows that in the case of n1 = n2 = 1200 rpm, a conductive network structure is well formed within LIB slurry. Additionally, in order to determine the optimal time to prepare the composite conductive solution for LIB slurry, nine different t1 (t1 = 0, 10, 20, 30, 40, 50, 60, 70, 80 min) are selected. By analyzing the internal structure of different LIB slurries, a well-formed conductive network structure and a uniformly distributed composite conductive agent are deduced in LIB slurry when t1 = 50 min. Therefore, it can be concluded that the composite conductive agent composed of CB and Gr is able to be uniformly dispersed in LIB slurry by establishing a well-formed conductive network structure under the optimal mixing speed n1 = n2 = 1200 rpm and the optimal mixing time t1 = 50 min, t2 = 6 min. This kind of the internal structure has the potential to be used to further analyze the dispersion characterizations of LIB slurry under different composite conductive agent and CB/Gr ratios with the aim of improving the final performance of LIB.

Optimization of wear parameters for ECAP-processed ZK30 alloy using response surface and machine learning approaches: a comparative study

The present research applies different statistical analysis and machine learning (ML) approaches to predict and optimize the processing parameters on the wear behavior of ZK30 alloy processed through equal channel angular pressing (ECAP) technique. Firstly, The ECAPed ZK30 billets have been examined at as-annealed (AA), 1-pass, and 4-passes of route Bc (4Bc). Then, the wear output responses in terms of volume loss (VL) and coefficient of friction (COF) have been experimentally investigated by varying load pressure (P) and speed (V) using design of experiments (DOE). In the second step, statistical analysis of variance (ANOVA), 3D response surface plots, and ML have been employed to predict the output responses. Subsequently, genetic algorithm (GA), hybrid DOE–GA, and multi-objective genetic algorithm techniques have been used to optimize the input variables. The experimental results of ECAP process reveal a significant reduction in the average grain size by 92.7% as it processed through 4Bc compared to AA counterpart. Furthermore, 4Bc exhibited a significant improvement in the VL by 99.8% compared to AA counterpart. Both regression and ML prediction models establish a significant correlation between the projected and the actual data, indicating that the experimental and predicted values agreed exceptionally well. The minimal VL at different ECAP passes was obtained at the highest condition of the wear test. Also, the minimal COF for all ECAP passes was obtained at maximum wear load. However, the optimal speed in the wear process decreased with the number of billets passes for minimum COF. The validation of predicted ML models and VL regression under different wear conditions have an accuracy range of 70–99.7%, respectively.

Variable Neighborhood Search for Multi-Port Berth Allocation with Vessel Speed Optimization

This paper delves into the multi-port berth allocation problem (MBAP), enriching the traditional berth allocation problem (BAP) with vessel speed optimization (VSO). In the MBAP, it is assumed that there is cooperation between the port and the shipping companies, and the operation of the vessels and the ports is planned to maximize the overall benefits. Exploring this potential collaboration between ports and shipping entities has the potential to mitigate, or even resolve, the challenges plaguing maritime transportation, e.g., port congestion and suboptimal vessel schedules, to ultimately enhance the efficiency of maritime trade. In this paper, a new mixed-integer linear programming (MILP) model for the MBAP is formulated, which attempts to minimize the total cost incurred during operations, with various constraints such as vessel sailing, the vessel space–time relationship in ports, and the planning period. Meanwhile, an innovative variable neighborhood search (VNS) algorithm is presented, in which the initial solution generation method and neighborhood structures are proposed according to the MBAP characteristics. Furthermore, two sets of MBAP instances are generated to test the proposed MILP and VNS, of which the first set is based on real-world port data and the second on existing studies. The numerical experiments verify that the VNS can efficiently and reliably solve instances of all scales, with each neighborhood structure contributing uniquely to the iterative process. In addition, by analyzing the impact of varying oil prices on the MBAP, the study offers valuable management insights. Finally, a case study based on real data from a port group in the Yangtze River Basin is presented to further demonstrate the necessity of considering vessel service time window and planning period in the MBAP as well as the important role of the VSO in scheduling.

Numerical investigation and optimization of the melting performance of latent heat thermal energy storage unit strengthened by graded metal foam and mechanical rotation

In this paper, a combined passive graded metal foam and active mechanical rotation strategy is proposed to simultaneously solve the problem of slow melting rate and non-uniform phase change problem of the latent heat thermal energy storage (LHTES) technology. The enthalpy-porosity method and fixed grid structure are employed to numerically investigate the effects of porosity range, the number of graded layers, and the rotational angular velocity. Moreover, the response surface method (RSM) is further selected to optimize the structural parameters and obtain the function of melting performance and various factors. The result shows that there is an optimal porosity range and the total melting time decreases first and then increases with the increase of porosity range. When the porosity range is 12 % and the graded layer is three, it can shorten the melting time by 35.54 % and increase the thermal energy storage rate (TESR) by 51.57 %. In addition, the melting performance is gradually improved as the number of graded layers increases. The strengthening effect of graded metal foam with just four layers is close to that of the maximum layers considered in this paper. The trend of the influence of rotation speed on the melting performance of the LHTES unit is consistent with the porosity range, indicating the existence of the optimal rotational speed. The RSM optimization structure, with a 14.969 % porosity range, 5-layer graded number, and 2.267 rpm rotational speed, shortens the melting time by 42.42 % and increases the TESR by 62.75 % compared with the stationary case with uniform metal foam. This work verifies the effectiveness of active rotation coupling with passive graded porous structures, which could provide a new strengthening approach to enhance LHTES.

Vision-Based Reinforcement Learning Approach to Optimize Bucket Elevator Process for Solid Waste Utilization

An energy-intensive industry such as cement manufacturing requires a constant supply of high amounts of traditional fossil fuels, such as coal or gas, for the calcination process. A way to overcome this fuel need is the usage of solid waste or Alternative Fuel Resources (AFRs), such as wood or paper. An advantage of using such waste is that their combustion byproduct, “ash”, can be used as a raw material alternative in the cement manufacturing process. However, for structural reasons, only bucket elevator technology is feasible to convey the fuel vertically for feeding the calciner in most cement plants. During the fuel feeding process, the inhomogeneous characteristics of AFRs lead to discharge parabolas of these materials varying over the infeed sample. Hence, a need arises to observe these trajectories and estimate a method for their optimal discharge. Thus, the purpose of this study is to develop an intelligent high-performance bucket elevator system. As such, a vision-based reinforcement learning algorithm is proposed in this study to monitor and control the speed of the elevator depending on the material properties observed at the inlet. These inlet materials properties include the type of material used in the simulation, and the particle size distribution within the infeed sample. A relationship is established between the inlet material properties and the speed of the bucket elevator. The best possible scenario is then deduced using a reward function. Here, the reward function is formulated via the deep learning image segmentation algorithm, a novel approach. After observing the test simulation conducted with a random-parameters setup, it was noted that the optimum speed for a given infeed sample was predicted correctly. As such, it can be concluded that the goal of developing an intelligent bucket elevator system was achieved.

Bi-objective ship speed optimization based on machine learning method and discrete optimization idea

With the introduction of increasingly stringent carbon dioxide (CO2) emission regulations in the shipping industry, shipping companies face significant pressure to reduce emissions while ensuring profitable ship operations. A practical bi-objective speed optimization (BSO) algorithm for container ships is developed based on the variable weight idea, the discrete optimization idea, and the ideal point method to meet the urgent needs of shipping companies to improve operating profits and reduce emissions. By constructing two BSO models of ship main engine fuel consumption-ship sailing time and ship operating costs (SOC)-ship CO2 emissions (SCE), the effectiveness of the BSO algorithm is confirmed through extensive optimization experiments. The results show that compared to the historical sailing results, the BSO reduces the SOC by 4.43 % without causing a significant increase in the SCE, and reduces the SCE by 0.25 %. Moreover, the results demonstrate the effectiveness and feasibility of the BSO in reducing the cost of ship operations while reducing the impact on the environment. The results provide valuable references for shipping companies in the trade-off analysis between economic and environmental benefits and optimization decision-making.

Energy-saving operation in urban rail transit: A deep reinforcement learning approach with speed optimization

The energy consumption of urban rail transit plays a significant role in the operating costs of trains. It is particularly crucial to decrease the energy consumption of the traction power supply in subway systems, as it accounts for approximately half of the total energy consumption of the subway operating organization. To overcome the limitations of traditional real-time speed profile generation methods and the limited exploration capabilities of popular reinforcement learning algorithms in the speed domain, this paper presents the Energy-Saving Maximum Entropy Deep Reinforcement Learning (ES-MEDRL) algorithm. The ES-MEDRL algorithm incorporates Lagrange multipliers and maximum policy entropy as penalties to formulate a novel objective function. This function aims to intensify exploration in the speed domain, minimize train traction energy consumption, and ensure a balance between ride comfort, punctuality, and safety within the subway system. This leads to the optimization of speed profile strategies. To further reduce energy consumption, this paper proposes a secondary optimization strategy for the energy-saving speed profile. This approach involves trading acceptable travel time for improved energy efficiency. To validate the performance of the proposed model and algorithm, numerical experiments are conducted using the Yizhuang Line of the Beijing Metro. The findings demonstrate a minimum 20 % increase in energy efficiency with the ES-MEDRL algorithm compared to manual driving. This algorithm can guide energy-efficient train operations at the planning level.

Identifying Patterns of Primary Care In-Person and Telemedicine Use in the Veterans Health Administration: A Latent Class Analysis.

The Veterans Health Administration increased synchronous telemedicine (video and telephone visits) in primary care in response to the COVID-19 pandemic. Our objective was to determine veteran use patterns of in-person and telemedicine primary care when all modalities were available. A retrospective cohort analysis. We performed a latent class analysis of primary care visits over a 1-year period to identify veteran subgroup (i.e., class) membership based on amount of primary care use and modality used. Then, we used multinomial logistic regression with a categorical outcome to identify patient characteristics associated with class identification. A random national sample consistingof 564,580 primary care empaneled veterans in June 2021. Latent class membership. We identified three latent classes: those with few primary care visits that were predominantly telephone-based (45%), intermediate number of visits of all modalities (50%), and many visits of all modalities (5%). In an adjusted model, characteristics associated with the "few" visits class, compared to the intermediate class, were older age, male sex, White race, furtherdriving distance to primary care, higher Gagne, optimal internet speed, and unmarried status (OR 1.002, 1.52, 1.13, 1.004, 1.04, 1.05, 1.06, respectively; p < .05). Characteristics associated with membership in the "many" visits class, compared to the intermediate class, were Hispanic race, higher JEN Frailty Index and Gagne (OR 1.12, 1.11, 1.02, respectively; p < .05), and higher comorbidity by Care Assessment Need score quartile (Q2 1.73, Q3 2.80, Q4 4.12; p < 0.05). Veterans accessing primary care in-person or via telemedicine do so primarily in three ways: (1) few visits, predominantly telephone; (2) intermediate visits, all modalities, (3) many visits, all modalities. We found no groups of veterans receiving a majority of primary care through video.

Optimizing friction stir processing parameters for aluminium alloy 2024 reinforced with SiC particles: A taguchi approach of investigation

The present investigation focuses on the microstructural behavior and mechanical properties of Aluminium alloy 2024 reinforced with SiC nanoparticles by applying the Friction Stir Processing (FSP) technique. Taguchi L9 orthogonal array was used to find the optimal process parameters. The experiment used the expected best process parameters, which confirmed the predicted highest value for the mechanical characteristic. Traverse speed, axial load, and rotating speed are the main factors affecting aluminum metal matrix composite. Hence, this experiment examined the effects of five different tool spinning speeds, specifically 800, 900, 1000, 1100, and 1200 rpm. Weld samples are fabricated wherein the stir zone contains SiC nanoparticles. The results of the study indicate that the optimal welding rotational speed of 1000 rpm has a significant impact on the microstructure, wear rate, and microhardness. Specifically, it was observed that the stirred zone exhibited enhanced wear resistance compared to other zones. The study's findings revealed that after implementing friction stir processing (FSP), a well-defined shear zone was observed on the advancing side of the stir zone. Additionally, it is found that the uniform dispersion and strong bonding of SiC particles with an aluminium matrix further enhance wear resistance.

Advancing forensic-based investigation incorporating slime mould search for gene selection of high-dimensional genetic data

Modern medicine has produced large genetic datasets of high dimensions through advanced gene sequencing technology, and processing these data is of great significance for clinical decision-making. Gene selection (GS) is an important data preprocessing technique that aims to select a subset of feature information to improve performance and reduce data dimensionality. This study proposes an improved wrapper GS method based on forensic-based investigation (FBI). The method introduces the search mechanism of the slime mould algorithm in the FBI to improve the original FBI; the newly proposed algorithm is named SMA_FBI; then GS is performed by converting the continuous optimizer to a binary version of the optimizer through a transfer function. In order to verify the superiority of SMA_FBI, experiments are first executed on the 30-function test set of CEC2017 and compared with 10 original algorithms and 10 state-of-the-art algorithms. The experimental results show that SMA_FBI is better than other algorithms in terms of finding the optimal solution, convergence speed, and robustness. In addition, BSMA_FBI (binary version of SMA_FBI) is compared with 8 binary algorithms on 18 high-dimensional genetic data from the UCI repository. The results indicate that BSMA_FBI is able to obtain high classification accuracy with fewer features selected in GS applications. Therefore, SMA_FBI is considered an optimization tool with great potential for dealing with global optimization problems, and its binary version, BSMA_FBI, can be used for GS tasks.

Integrated design optimization method for pavement structure and materials based on further development of finite element and particle swarm optimization algorithm

The maintenance design for existing asphalt pavements, characterized by their typical structure and material composition, falls short of meeting the diverse requirements of pavements. There is an urgent need for a more rational design approach that can achieve accurate pavement design while also reducing construction costs. This paper focuses on selected conventional road surface structures and conducts an analysis of how pavement structure and materials impact fatigue life and construction expenses. It employs Python to perform three-dimensional finite element road modeling, analysis, and data extraction at specific locations, using normalized fatigue equation to calculate pavement fatigue life. The optimization variables selected are the modulus E and thickness h, with pavement maintenance cost serving as the optimization target function. The particle swarm optimization algorithm is employed for optimizing these variables. A comparative analysis is carried out for five improved optimization algorithms, optimizing combinations of inertia weight, learning factors, particle speed, number of particles, and iteration times. The results reveal that the enhanced optimization algorithms significantly improve iteration trends, featuring velocity limitations and natural selection mechanisms. Between 0 and 20 iterations, all algorithms search for the optimal solution over a wider interval with greater fluctuations. Between 20 and 30 iterations, each algorithm gradually converges towards its solution, stabilizing in the later stages. Based on optimization speed and actual engineering cost benchmarks, the NDPSO algorithm is selected, resulting in a 52.8% reduction in actual engineering costs. Adjustments to the particle count and iteration number of the NDPSO algorithm are made based on computational cost and optimization accuracy, thus choosing 20 particles and 50 iterations as the recommended parameter combination. By combining automated finite element modeling, analysis, and particle swarm optimization, this paper offers a novel and effective automated design reference for extending the lifespan of asphalt pavement structures.