Combination Of Design Parameters Research Articles

Optimized Machine Learning Model for Predicting Compressive Strength of Alkali-Activated Concrete Through Multi-Faceted Comparative Analysis

Alkali-activated concrete (AAC), produced from industrial by-products like fly ash and slag, offers a promising alternative to traditional Portland cement concrete by significantly reducing carbon emissions. Yet, the inherent variability in AAC formulations presents a challenge for accurately predicting its compressive strength using conventional approaches. To address this, we leverage machine learning (ML) techniques, which enable more precise strength predictions based on a combination of material properties and cement mix design parameters. In this study, we curated an extensive dataset comprising 1756 unique AAC mixtures to support robust ML-based modeling. Four distinct input variable schemes were devised to identify the optimal predictor set, and a comparative analysis was performed to evaluate their effectiveness. After this, we investigated the performance of several popular ML algorithms, including random forest (RF), adaptive boosting (AdaBoost), gradient boosting regression trees (GBRTs), and extreme gradient boosting (XGBoost). Among these, the XGBoost model consistently outperformed its counterparts. To further enhance the predictive accuracy of the XGBoost model, we applied four state-of-the-art optimization techniques: the Gray Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), beetle antennae search (BAS), and Bayesian optimization (BO). The optimized XGBoost model delivered superior performance, achieving a remarkable coefficient of determination (R2) of 0.99 on the training set and 0.94 across the entire dataset. Finally, we employed SHapely Additive exPlanations (SHAP) to imbue the optimized model with interpretability, enabling deeper insights into the complex relationships governing AAC formulations. Through the lens of ML, we highlight the benefits of the multi-faceted synergistic approach for AAC strength prediction, which combines careful input parameter selection, optimal hyperparameter tuning, and enhanced model interpretability. This integrated strategy improves both the robustness and scalability of the model, offering a clear and reliable prediction of AAC performance.

Experimentation, Taguchi and machine learning-based optimisation of parameters concerning heat losses from the trapezoidal box-type solar cooker

ABSTRACT The optimal design parameter combinations for a trapezoidal solar cooker that yield the lowest heat loss coefficient have been determined using a thorough Taguchi and Machine learning-based optimization approach that has been established for this study. Data for the previously described optimization have been generated through the use of an experimental approach in a laboratory environment. Numerous influencing factors are taken into account, including the temperature of the bottom plate, the depth of the cavity, the emissivity of the bottom cover, and the wind-induced heat transfer coefficient on the top glass cover. For the current investigation, the ideal factor combination is found to be a parametric combination plate temperature of 350K with 0.4 plate emissivity, 15W/m2 convective heat transfer coefficient, and 0.25 m height. To determine the percentage contribution of each control element to the heat loss coefficient, analysis of variance (ANOVA) has been used for the collected data and signal-to-noise ratios. Plate temperature at 74.72% and emissivity at 23.84% are the primary contributing variables found. Likewise, the least contributing factors like height (0.57%) and convective heat transfer coefficient (0.32%) are discovered. A regression equation is used to represent the connection between the response and the control factor. This formula is suggested as a forecast formula for calculating the heat loss coefficient.

Bio-mechanical analysis of porous Ti-6Al-4V scaffold: a comprehensive review on unit cell structures in orthopaedic application.

A scaffold is a three-dimensional porous structure that is used as a template to provide structural support for cell adhesion and the formation of new cells. Metallic cellular scaffolds are a good choice as a replacement for human bones in orthopaedic implants, which enhances the quality and longevity of human life. In contrast to conventional methods that produce irregular pore distributions, 3D printing, or additive manufacturing, is characterized by high precision and controlled manufacturing processes. AM processes can precisely control the scaffold's porosity, which makes it possible to produce patient specific implants and achieve regular pore distribution. This review paper explores the potential of Ti-6Al-4V scaffolds produced via the SLM method as a bone substitute. A state-of-the-art review on the effect of design parameters, material, and surface modification on biological and mechanical properties is presented. The desired features of the human tibia and femur bones are compared to bulk and porous Ti6Al4V scaffold. Furthermore, the properties of various porous scaffolds with varying unit cell structures and design parameters are compared to find out the designs that can mimic human bone properties. Porosity up to 65% and pore size of 600 μm was found to give optimum trade-off between mechanical and biological properties. Current manufacturing constraints, biocompatibility of Ti-6Al-4V material, influence of various factors on bio-mechanical properties, and complex interrelation between design parameters are discussed herein. Finally, the most appropriate combination of design parameters that offers a good trade-off between mechanical strength and cell ingrowth are summarized.

Thermal performance optimization for a tapered heat sink of bus bar conductor using definitive screening design

This study examines and optimizes four design parameters of a bus duct conductor's heat sink: fin pitch, fin height, fin thickness, and the number of fin valleys. Average surface temperature and Nusselt number are chosen as the thermal performance criterion of the heat sink. A Definitive Screening Design is employed as a statistical method to reduce the number of optimization runs required while minimizing the aliasing. The regression analysis, analysis of variance, main effect analysis and optimization are conducted to optimize the heat sink design parameter and its thermal performance. The current results provide an ideal heat sink design for the casing of bus duct conductors. A fin pitch of 4 mm, fin height of 6.5 mm, fin thickness of 1 mm, and six fin valleys are determined to be the most optimal combination of design parameters. The optimized responses' average surface temperature and Nusselt numbers are 72.05 °C and 21.59, respectively, with 2.97 % and 6.25 % deviation from the predicted values of the empirical equation. The experimental results are benchmarked against the IEC 60439-1 and IEC 60439-2 standards. The current analysis is expected to provide more insight into the impact of design factors on the thermal performance of a bus duct conductor.

A Computational Framework to Optimize the Mechanical Behavior of Synthetic Vascular Grafts.

The failure of synthetic small-diameter vascular grafts has been attributed to a mismatch in the compliance between the graft and native artery, driving mechanisms that promote thrombosis and neointimal hyperplasia. Additionally, the buckling of grafts results in large deformations that can lead to device failure. Although design features can be added to lessen the buckling potential, the addition is detrimental to decreasing compliance (e.g., reinforcing coil). Herein, we developed a novel finite element framework to inform vascular graft design by evaluating compliance and resistance to buckling. A batch-processing scheme iterated across the multi-dimensional design parameter space, which included three parameters: coil thickness, modulus, and spacing. Three types of finite element models were created in FEBio for each unique coil-reinforced graft parameter combination to simulate pressurization, axial buckling, and bent buckling, and results were analyzed to quantify compliance, buckling load, and kink radius, respectively, from each model. Importantly, model validation demonstrated that model predictions agree qualitatively and quantitatively with experimental observations. Subsequently, data for each design parameter combination were integrated into an optimization function for which a minimum value was sought. The optimization values identified various candidate graft designs with promising mechanical properties. Our investigation successfully demonstrated the model-directed framework identified vascular graft designs with optimal mechanical properties, which can potentially improve clinical outcomes by addressing device failure. In addition, the presented computational framework promotes model-directed device design for a broad range of biomaterial and regenerative medicine strategies.

Influence and optimization of dual-orifice jet nozzles on oil capture performance of under-race lubrication with a radial oil scoop for high-speed bearings in aero-engine

The objective of this research is to determine the influence of dual-orifice jet nozzles on the oil–air flow dynamics and oil capture performance in the under-race lubrication for high-speed bearings and to identify the optimal design parameter combinations for optimizing the oil capture performance. An experimental setup for under-race lubrication is specifically designed, complemented by a multiphase computational fluid dynamics model to elucidate the oil–air flow behavior. The response surface methodology is combined with a multi-objective particle swarm optimization algorithm to perform multi-objective optimization of the oil capture performance. The findings indicate that oil capture efficiency initially rises to a peak and subsequently declines with increasing orifice spacing, identifying an optimal orifice spacing that maximizes efficiency. While the oil flow rate from dual-orifice jet nozzles is lower than twice that of single-orifice jet nozzles, appropriate parameter matching allowed for an increase in both captured lubricant oil and efficiency. Optimized configurations achieved substantial enhancements in oil capture efficiency, with the greatest improvement exceeding 16%, all while maintaining precise oil supply to the high-speed bearings.

A case study of multi-objective design optimization of a healthy building in Shanghai, China

Energy efficient healthy buildings design is important in achieving carbon neutrality and occupants’ health, yet not sufficiently explored. This paper aims to optimize a healthy building in Shanghai, China, based on energy consumption, indoor air quality and visual comfort. A four-step optimization method was proposed. Firstly, Latin Hypercube-Soubert sampling method was used to generate 375 design samples and performance data through computer simulation and calculation. Secondly, five different machine learning approaches were applied for prediction model development. Thirdly, the best prediction models were coupled with eleven optimization algorithms to find Pareto solutions. Finally, optimization on different combinations of design parameters were conducted. It was found that the back propagation neural network and Pareto Envelope-based Selection Algorithm II were the best prediction models and optimization algorithm. The average reduction of building energy consumption, visual discomfort, and improper indoor air quality hours were found to be 25.73 %, 46.24 %, and 38.34 %, respectively. The recommended windows to wall ratios of the east, south, west and north walls, absorptance of solar radiation and filter types are in the range of 20%–35 %, 10%–45 %, 10%–30 %, 10%–40 %, 0.7–0.9, and S7-S9, respectively. This study contributes to enrich the case study of healthy buildings, provide a quick design optimization approach and analysis on the importance of each design parameter. Its originality lies in the optimization methodology, comparative analysis of prediction models, optimization algorithms, and combination effects of design parameters. The outcomes can guide the building designers in performing energy efficient design while maximizing indoor air quality and visual comfort.

Balcony design recommendations to enhance daylight, thermal and energy performance of mixed-mode office buildings

The use of balconies in hot climates holds the potential to block direct solar radiation thus reducing energy consumption for cooling and enhancing visual comfort. However, balconies may also diminish daylight availability within the room, thereby affecting occupants’ satisfaction and wellbeing. This study aimed to provide balcony design recommendations through a parametric analysis of various combinations of balcony design parameters (balcony depth, width, location on the façade, and parapet type), along with key building design parameters (façade orientation, floor level, and glazed door width), considering a multi-domain objectives assessment. The results demonstrate that balcony design can effectively enhance visual comfort, thermal performance, and energy efficiency while ensuring optimal daylight availability in office rooms located in subtropical climates. Recommendations for balcony design were tailored to each façade orientation and thoughtfully combined with the glazed door width. One optimal combination, featuring a 3-meter-wide glazed door and a 2-meter-deep balcony, proved beneficial for all façade orientations across all floor levels. However, the choice of parapet type should align with the balcony’s location, which is directly correlated with the room’s depth. The insights gained from this study offer valuable information for building designers, highlighting that balconies should not be designed solely as decorative façade elements or spaces for building services. Furthermore, the cross-analysis method developed in this study can be applied by researchers and designers to their own case studies.

Prediction of Optimal Design Parameters for Reinforced Soil Embankments with Wrapped Faces Using a GA–BP Neural Network

Under the same geological conditions, the thickness and length of the reinforced strip, the slope ratio of the reinforced embankment, the modulus of elasticity of the fill and the reinforced strip, and the friction angle at the interface between the reinforcement and the soil, are the main design parameters that have an important influence on the stress, deformation, and stability of the encompassing reinforced soil embankment. To quickly and accurately determine the optimal design parameters for reinforced soil embankments with wrapped faces, ensuring minimal cost, while maintaining structural safety, we propose a design parameter prediction model based on a GA–BP neural network. This model evaluates parameters within their specified ranges, using maximum lateral displacement, maximum vertical displacement, maximum stress in the XZ direction, the maximum shear strain increment, and the safety factor, as assessment criteria. The primary objective is to minimize the overall cost of the embankment. A comparison with five machine learning algorithms shows that the model has high prediction accuracy, and the optimal design parameter combinations obtained from the optimization search can significantly reduce the cost of the embankment, while controlling the displacement and stability of the embankment. Therefore, the GA–BP network is suitable for predicting the optimal design parameters of reinforced soil embankments with wrapped faces.

Performance prediction and optimization of lateral exhaust hood based on back propagation neural network and genetic algorithm

The lateral exhaust hood (LEH) is commonly used to capture contaminated airflow in industrial plants. Its performance is influenced by numerous factors and exhibits a strong nonlinear relationship, optimizing this performance demands significant computational time and cost. This study proposes an LEH optimization design method that combines a backpropagation neural network (BPNN) with a genetic algorithm (GA), verified through experiments and numerical simulations. A BPNN prediction model is established based on 520 CFD simulation results. This study discusses seven factors influencing LEH performance, including the buoyant jet's velocity and temperature, the LEH's geometry and position, and the exhaust velocity. The results indicate that within the parameter range of the prediction model, there is a critical aspect ratio value that significantly affects LEH capture efficiency. The horizontal distance between the LEH and the pollution source significantly affects capture efficiency, especially when the LEH's installation height is low (H/D < 0.5). In addition, when combined with GA for fast searching, the expected combination of LEH design parameters can be obtained. This study aims to enhance the design efficiency of industrial exhaust hoods and improve worker health protection.

The synergistic effect of multiple design factors on building energy consumption of office blocks: A case study of Wuhan, China

Design factors such as block morphology, building form and building envelope performance are key parameters affecting building energy consumption of urban blocks. However, the synergistic effect of multi-design factors on building energy consumption has not been studied in detail. This paper aims to investigate the synergistic effect of multi-design factors on building energy consumption of office blocks in Wuhan, China. This study employs field survey, satellite map and open street map to extract the typical building forms and construct parametric models of office blocks. Then, 81 representative scenarios considering the synergy of multiple design factors in office blocks are built. Then, based on the Rhino & Grasshopper parametric platform, a building energy assessment tool that considers the synergistic effect of multiple design factors is developed to simulate and record the building energy consumption of each scenarios. Finally, the synergistic effect of multi-design parameters on building energy consumption of office blocks is quantified using correlation and multiple linear regression methods. The findings reveal that the synergistic effect of multi-design factors on building energy consumption is 18.53 %. Building energy consumption for high-rise pavilion typology is saved by 9.64 % than multi-storey courtyard. The key design parameter combinations affecting building energy consumption in office block are building shape factor, solar heat gain coefficient, wall K-value, building width and window-wall ratio of west and east. This study contributes to a deeper understanding of the synergistic effects of multiple design factors on building energy consumption in office blocks and presents a scalable model that can be applied globally to promote sustainable urban development.

Optimal design of an inclined barrier for performance improvement of an oscillating water column device with an artificial neural network model

An oscillating water column (OWC) device with a surface-piercing inclined barrier has been studied in irregular waves. This study is to determine the optimal shape of an inclined barrier utilizing an Artificial Neural Network (ANN) model for the performance enhancement of the conventional OWC system. We employed the dual boundary element method (DBEM) as our numerical approach, which resolves two separate boundary integral equations (singular and hyper-singular) formulated at the coincident source points situated on either side of the degenerate barrier boundary. The design parameters affecting the performance of the OWC system have been optimized to maximize the power extraction using the trained ANN model. The training dataset was prepared using the DBEM, which is then used to train and validate the ANN model. Subsequently, the trained model was employed to make predictions using a broader dataset to identify the optimal combination of design parameters that maximize the conversion efficiency. The findings reveal that the well-trained ANN model can learn complex nonlinear relationships, make good predictions with high accuracy using a larger dataset of input conditions, and offer the optimal values of an inclined barrier appropriate for the wave conditions at the sea installation site.

A global sensitivity analysis method based on IBWO-SVR-SS

PurposeAiming at the inefficiency of solving the Sobol index using the traditional mathematical analytical method, a Sobol global sensitivity analysis method is proposed.Design/methodology/approachIn this paper, a support vector regression (SVR) surrogate model is constructed to solve the Sobol index. The optimal combination of SVR hyperparameters is obtained by using the improved beluga whale optimization (IBWO). Meanwhile, in order to solve the problem that Sobol sequences will form correlation regions in high-dimensional space leading to the uneven distribution of sampling points, a scrambled strategy is introduced in the Sobol sensitivity analysis using IBWO-SVR. Thus, the IBWO-SVR-SS sensitivity analysis model is established.FindingsThe results of two test functions show that the method further improves the accuracy of the sensitivity analysis. Finally, the first-order Sobol index and second-order Sobol index are solved by the IBWO-SVR-SS method using the metro bogie frame as an engineering example. Through the analysis results, the key design parameters of the frame and the design parameter combinations with more obvious coupling relationships are identified, providing a strong reference for the subsequent analysis and structural optimization.Originality/valueSobol sensitivity analysis using the surrogate model method can effectively improve the efficiency of the solution. In addition, IBWO is used for the optimization of the SVR hyperparameters to improve the accuracy and efficiency of the optimization, and finally, the correction of the Sobol sequence through the introduction of the disruption strategy also further improves the accuracy of the sensitivity analysis of Sobol.

Optimized Design of Carbon Nanotube Field-Effect Transistor Using Taguchi Method for Enhanced Current Ratio Performance

Abstract Presently, the integrated circuit (IC) industry grapples with obstacles in downsizing MOSFET technology further, hindered by its inherent physical constraints. Therefore, the substitution of silicon with carbon nanotubes (CNTs) holds promise for paving a novel path in semiconductor industries, driven by their diminutive dimensions and superior electrical properties. Hence, this project employed SILVACO ATLAS software in conjunction with the Taguchi method to refine a CNTFET design for optimal performance. In this work, response variables consists of on-current (Ion), current ratio (Ion/Ioff) and threshold voltage (Vth) are extracted. In this particular design, the Taguchi method was employed to ascertain the most effective combination of design parameters and materials to achieve optimal CNTFET performance, as assessed by the three key response variables. The design parameter and material that had been chosen were the diameter of carbon nanotube (Dcnt), dielectric material (K) and oxide thickness (tox). Each of the design parameters and material had three different values. For K, the values are 3.9 (SiO2), 25 (ZrO2) and 80 (TiO2). While for Dcnt and tox, the values are 4.0 nm, 6.0 nm, 8.0 nm and 2.0 nm, 4.0 nm, 6.0 nm respectively. According to the Taguchi optimization findings, the ideal combination of parameters comprises a CNT diameter of 4.0 nm, an oxide thickness of 2.0 nm, and the use of TiO2 (80) as the dielectric material. The ANOVA analysis underscores the significance of prioritizing optimization efforts towards the CNT diameter parameter. This is attributed to its substantial contribution, accounting for 93.55% of the variation in the Ion/Ioff value, surpassing the influence of dielectric materials and oxide thickness.

Comparative Assessment of ESI‐MS Softness for Inorganic Complexes: How Soft is Your ESI‐MS?

Electrospray ionization mass spectrometry (ESI‐MS) is a powerful tool for identifying and characterizing organometallic and coordination compounds. However, detection of fragile structures bound by weaker intermolecular forces can be significantly limited in ESI‐MS owing to the use of relatively harsh instrument conditions and configurations. In this study, a set of tests was developed to assess the softness of ESI‐MS systems. Two variants are presented: positive ion mode, utilizing a mixture of sodium ions and triphenylphosphine oxide producing [Na(OPPh3)n]+ ions (n = 1–4), and negative ion mode, utilizing Pd(PPh3)4 and sulfonated triphenylphosphine producing [Pd(L)(PPh3)n]– ions (n = 0–2), where softer instrument conditions preserve a higher proportion of the high‐coordinate ions and harsher conditions will result in increased detection of products of ion fragmentation. The results revealed notable variations in instrument softness, which were influenced by a combination of instrument design and experimental parameters. Meticulously optimizing experimental conditions and ESI‐MS parameters is essential to achieving the softest ionization possible, ensuring reliable analysis where applicable. This study offers valuable insight through straightforward tests that can be employed to assess the suitability of an instrument for specific research needs.

Optimising Building Energy and Comfort Predictions with Intelligent Computational Model

Building performance prediction is a significant area of research, due to its potential to enhance the efficiency of building energy management systems. Its importance is particularly evident when such predictions are validated against field data. This paper presents an intelligent computational model combining Monte Carlo analysis, Energy Plus, and an artificial neural network (ANN) to refine energy consumption and thermal comfort predictions. This model addresses various combinations of architectural building design parameters and their distributions, effectively managing the complex non-linear relationships between the response variables and predictors. The model’s strength is demonstrated through its alignment with R2 values exceeding 0.97 for both thermal discomfort hours and energy consumption during the training and testing phases. Validation with field investigation data further confirms its accuracy, demonstrating average relative errors below 2.0% for total energy consumption and below 1.0% for average thermal discomfort hours. In particular, an average underestimation of −12.5% in performance discrepancies is observed when comparing the building energy simulation model with field data, while the intelligent computational model presented a smaller overestimation error (of +8.65%) when validated against the field data. This discrepancy highlights the model’s potential and reliability for the simulation of real-world building performance metrics, marking it as a valuable tool for practitioners and researchers in the field of building sustainability.

Neutronic Analysis of the Conceptual Molten Uranium Breeder Reactor Using MCNP and SCALE Tools

The Molten Uranium Breeder Reactor (MUBR) is a radically new reactor concept with a mixed-energy spectrum. MUBR is fueled with molten uranium metal in large-diameter fuel tubes and is cooled by circulating molten uranium fuel through a heat exchanger. The reactor has heavy water as moderator, and the reactivity of the reactor is primarily controlled by the voiding effect of the moderator through an innovative control cavity structure design. Because the MUBR design is vastly different from most existing fission reactors, neutronics analysis must be performed for many different combinations of design parameters to identify viable and optimum design configurations. To facilitate the neutronics analysis, a proprietary program called MUBR6gen is being developed to provide a pipeline tool to expedite the process. MUBR6gen employs two well-established neutronics codes, i.e., MCNP and SCALE, to perform standard neutronics calculations for MUBR by automating input preparation and output processing. In addition, MUBR6gen ensures consistency of the MCNP and SCALE inputs and compares the outputs of the two codes to warrant the simulation results. Augmented with MUBR6gen, standard neutronics analysis was carried out on a small-scale MUBR design, which serves as a model problem in the paper. The neutronics performance characteristics of the model reactor were obtained and discussed in a code-to-code pattern. An overall very good agreement between the results of the two neutronics codes was established. Based on the success of the model problem analysis, further neutronics analysis using MUBR6gen was extended for a set of MUBR variant designs. Meaningful and promising fuel cycle analysis results for the 10 different designs were achieved and discussed. These results are used to identify the best MUBR candidates in terms of fuel lifetime and utilization efficiency for future applications.

Examination of convective heat transfer and entropy generation performance in twisted elliptical tubes using response surface method

In this paper, the convective heat transfer (CHT) and flow properties of twisted elliptical tubes (TETs) are numerically simulated within the range of Reynolds numbers (Re) 710 to 4790. The effects of design parameters such as twist ratio (P/D), aspect ratio (A/B), and Re on the CHT and flow resistance properties of TETs are highlighted. The response surface method (RSM) is used to research the effect of interaction between design parameters on the performance of CHT in TETs and establish the relationship between the objective function, i.e., Nusselt number (Nu), friction factor (f), and design parameters. The objective function fitted by RSM are optimized by the non-dominated sequential genetic algorithm (NSGA-II) to obtain the Pareto optimal solution. Besides, the entropy generation minimization principle explores entropy generation characteristics to reveal the mechanism of CHT enhancement in TETs. The results show that the CHT performance, flow resistance, and total entropy generation in TET increase, decrease, and increase with the growth of Re, respectively. They all increase with the reduction of P/D or the growth of A/B. The maximum values of Nu and pressure drop (ΔP) occur when A/B = 2.16 and P/D = 11.90; the minimum value of total entropy generation is reached when A/B = 1.34 and P/D = 26.33; and the Pareto optimal solution corresponds to the best combination of design parameters of Re = 3980, P/D = 13.32, and A/B = 1.82.

Dynamics optimization of small branch pipes in nuclear power plants based on machine learning algorithms

Vibration fatigue failure of small branch pipes poses a great threat to the safe operation of nuclear power plants. However, the transient and wide-band vibration problems are not adequately considered in ASME and RCC-M code, resulting in repeated fatigue failures. In addition, the current research mainly focuses on vibration test methods and fatigue analysis methods, neglecting the study of pipeline vibration characteristics. Therefore, innovative approaches were essential for effectively managing complex dynamic loads. In this study, an innovative approach combining backpropagation artificial neural networks (BP-ANN) and non-dominated sorting Genetic Algorithm II (NSGA-II) was proposed to optimize the vibration of these pipes. The goal was mitigating vibration-induced failures by enhancing operational stability. The methodology progressed through several key stages. Firstly, BP-ANN was utilized for regression analysis, correlating pipe characteristics to vibration effects. Through regression analysis, the complex interrelationships governing the pipes' dynamic behavior was revealed. Subsequently, based on the regression model, NSGA-II was used to derive an optimal combination of design parameters to minimize the vibration response. The proposed technique was validated on an L-shaped cantilevered pipe via finite element simulations and physical experiments. The analysis case shows that the BP-ANN model demonstrated excellent accuracy in predicting vibration responses. Meanwhile, NSGA-II successfully revealed the trade-offs between conflicting objectives, generating a Pareto-optimal set balancing stability under different excitation directions. This study highlights the potential of machine learning methods for dynamic optimization of small branch pipes in nuclear power plants, and verifies the accuracy and effectiveness of the method through experiments. The research results provide a new idea for small branch pipe design and vibration control of nuclear power plant, which contributes to enhancing the safety and reliability of small branch pipes.

Preparation and performance analysis of polyamide-based rapid repair material for asphalt pavement shallow groove

In order to effectively remedy shallow grooves on asphalt pavements, this study presents an innovative solution, termed Polyamide-based Asphalt Pavement Shallow Groove Rapid Repair Material (PA-SGRRM). This material employs polyamide 6 (PA6) as the matrix resin, in conjunction with polypropylene (PP), ethylene-vinyl acetate copolymer (EVA), and maleic anhydride-grafted polyethylene-octene copolymer (POE-g-MAH). It is manufactured through an extrusion granulation process. At the outset, a molecular dynamics approach was employed to assess the compatibility of the primary constituents and the adhesion between the matrix resin and asphalt, confirming the rationale behind the material selection. Subsequently, the orthogonal experimental design method was utilized to investigate the influence of design parameters (POE-g-MAH content, PP content, and EVA content) on the flexural strength, tensile strength, low-temperature toughness, and melt flow rate of PA-SGRRM. Following this, we prepared samples with the optimal proportions of EVA and POE-g-MAH and conducted a separate analysis to evaluate the impact of PP on the saturated moisture content of PA-SGRRM. Finally, we employed Scanning electron microscopy (SEM) to observe the microstructures of the low-temperature fracture surface and ambient temperature tensile fracture surface of the PA-SGRRM and employed Fourier transform infrared spectroscopy (FTIR) to deduce the primary chemical reactions occurring within the PA-SGRRM. The results demonstrated that POE-g-MAH played a crucial role in enhancing the compatibility of the simulated hybrid system consisting of PP and PA6. Additionally, it was observed that adhesion between PA6 and asphalt existed, with a gradual decrease as temperature rose. The content of POE-g-MAH and PP were the two primary parameters influencing the performance of PA-SGRRM. POE-g-MAH exerted a significant adverse impact on the flexural strength, tensile strength, and melt flow rate of PA-SGRRM, while substantially improving its low-temperature toughness. The PP positively reduced the water absorption characteristics of PA-SGRRM. Based on the experimental results, the optimal combination of design parameters for PA-SGRRM is recommended.