Performance Prediction Model Research Articles

Strength Prediction of Smart Cementitious Materials Using a Neural Network Optimized by Particle Swarm Algorithm

Because of the improved physical, mechanical and crack–resistant properties, smart cementitious materials have garnered significant attention in civil engineering. However, the method of predicting performance of smart cementitious materials remains a formidable task. To address this issue, this study develops a neural network optimized by particle swarm algorithm, specifically designed for predicting the strength of smart cementitious materials. Particle swarm optimization is used to determine the initial weights and biases of the neural network in this algorithm. Two types of smart cementitious materials, namely 3D printed fiber reinforced concrete and graphene nanoparticles–reinforced cementitious composites, are studied as examples. Utilizing the PSO–BPNN method and data gathered from the existing articles, the predictive models for the mechanical properties of these materials are developed. Five commonly used statistical metrics are applied to evaluate the predictive performance. The results indicate suggest the PSO–BPNN outperforms the traditional back propagation neural network. Thus, a reliable and robust performance predictive model can be built for smart cementitious materials using the proposed approach.

Performance prediction and regression analysis of scroll expander based on response surface methodology

The Organic Rankine Cycle (ORC) plays a pivotal role in the domain of renewable energy, adeptly harnessing thermal energy from sources such as solar and geothermal to generate electrical power. Central to this system is the scroll expander, whose operational efficacy directly influences the ORC's overall efficiency. This study initiates with a detailed numerical analysis of the impact of various operational parameters on both the steady and transient output performance of the scroll expander. Following this, a sophisticated performance prediction model is developed employing the response surface methodology. The model boasts high coefficients of determination, R2,values of 0.9967 and 0.9916, respectively, underscoring its superior predictive accuracy. Further, this model delves into the complex interactions among operational parameters and their consequent effects on the expander's performance. Validation of this predictive model is achieved through rigorous testing on an experimental platform designed for the ORC low-temperature waste heat power generation system. The discrepancies between predicted and actual performance measurements fall within a commendable margin of 15 %, affirming the model's robustness in forecasting scroll expander performance. The culmination of this research furnishes a novel and efficacious optimization strategy, establishing a robust theoretical foundation and offering fresh insights for the enhancement and prediction of scroll expander performance. This not only underscores the innovation of the approach but also its practical applicability in advancing the efficiency of renewable energy systems.

Study on rotary bending fatigue performance of TC4 lattice structure fabricated by selective laser melting

Prediction of the fatigue performance of lattice structures in additive manufacturing still lacks widely applicable methods. To address this issue, we investigated the effect of a BCC lattice structure combined with thin walls and ribs on the fatigue performance of TC4 alloy samples. The lattice-structured samples were fabricated by selective laser melting. In addition, we proposed a prediction model of fatigue performance that combined ABAQUS finite element analysis with FE-safe fatigue analysis. The simulation and experimental results verified the reliability of the model. Based on this method, the effects of lattice and ribbed plate structures on the bending fatigue performance were investigated. The experimental results showed that the stress concentration generated by lattice structures weakened the fatigue performance. On the contrary, ribbed plates can enhance fatigue performance, which also depends on the orientation of the rib structure. The simulation results showed that increasing the volume fraction of unit cells can mitigate the stress concentration, leading to improved fatigue performance. The lattice structure combined with the rib structure exhibited superior fatigue performance. In addition, the proposed models can also be applied to other metallic materials. The findings in this study can provide a theoretical basis for the design of lattice structures with better fatigue performance.

A Novel Analytical Model for On-Load Performance Prediction of Delta-Type IPM Motors Based on Rotor Simplification

A Novel Analytical Model for On-Load Performance Prediction of Delta-Type IPM Motors Based on Rotor Simplification

Machine learning based education data mining through student session streams

Recently, significant growth in using online-based learning stream (i.e., elearning systems) have been seen due to pandemic such as COVID-19. Forecasting student performance has become a major task as an institution is focusing on improving the quality of education and students' performance. Data mining (DM) employing machine learning (ML) techniques have been employed in the e-learning platform for analyzing student session streams and predicting academic performance with good effects. A recent, study shows ML-based methodologies exhibit when data is imbalanced. In addressing ensemble learning by combining multiple ML algorithms for choosing the best model according to data. However, the existing ensemblebased model does not incorporate feature importance into the student performance prediction model. Thus, exhibits poor performance, especially for multi-label classification. In addressing this, this paper presents an improved ensemble learning mechanism by modifying the XGBoost algorithm, namely modified XGBoost (MXGB). The MXGB incorporates an effective cross-validation scheme that learns correlation among features more efficiently. The experiment outcome shows the proposed MXGBabased student performance prediction model achieves much better prediction accuracy contrary to the state-of-art ensemble-based student performance prediction model.

Development of a Thermal Energy Storage Model for Performance Prediction of Heat Pump Systems Linked with Multiple Heat Sources

Development of a Thermal Energy Storage Model for Performance Prediction of Heat Pump Systems Linked with Multiple Heat Sources

Degradation performance rapid prediction and multi-objective operation optimization of gas turbine blades

The performance degradation of high-temperature blades will affect the safety and efficiency of operation. In response to issues such as ignoring strength characteristics, slow degradation performance prediction, insufficient applicability and accuracy of traditional proxy models, and long operation optimization cycle, a degradation performance prediction model of turbine blades based on the back propagation neural network is established in this research. Furthermore, the efficiency-stress multi-objective operation optimization method based on genetic algorithm is proposed. The results show that the R-square value of the degradation performance neural network prediction model is greater than 0.9 for predicting characteristic parameters. The prediction speed is improved by 104 orders of magnitude compared to the traditional computational fluid dynamics method. The influence mechanisms of turbine outlet flow rate, cooling gas inlet total temperature, and cooling gas flow rate on the degradation performance are revealed. Under fouling extreme conditions and corrosion/wear extreme conditions, the operating parameters of turbine blades are optimized. The maximum values of the efficiency improvement, power increase and maximum von Mises stress reduction are 0.74 %, 2.13 MW and 43 MPa, respectively. This study can provide an effective and novel method for real-time degradation performance prediction and rapid operating condition optimization of turbine blades.

System reliability study of geothermal energy walls in subway stations based on rapid thermal performance prediction model

The energy conservation potential of relative systems in subway stations can be improved via the implementation of energy walls. However, current research mainly focuses on analyzing the factors affecting the energy walls' heat transfer performance, neglecting the reliability and performance assessment for ground source heat pump systems based on energy walls. This paper focuses on a subway station in hot-summer and cold-winter climate zone. Initially, both a resistance-capacity model and a 3D finite element model of energy walls are established. The resistance-capacity model is validated to be integrated with heat pump units to propose the rapid system performance prediction. Subsequently, the system thermal performance with the input of dynamic loads and conventional operational strategies is investigated. The results reveal that due to the heating and cooling load imbalance, the system inevitably experiences thermal imbalances and enters an inefficient operational state by the 6th year. Finally, to address this issue, a hybrid heat pump system with cooling towers is proposed, which demonstrates better thermal performance when used during non-cooling seasons. Another hybrid heat pump system for simultaneous cooling and heating is also proposed. These approaches both delay the onset of the system's inefficient operational state by 1–2 years, thereby improving the thermal performance.

Intelligent learning methods for improving student performance through virtual reality technology

With the rapid development of information technology, the field of education is undergoing a profound change, in which intelligent hybrid learning and virtual reality technology are increasingly valued. This study proposes an intelligent hybrid learning method based on virtual reality for student performance improvement. This paper reveals the limitations of traditional learning methods in meeting the needs of modern education, and expounds the theoretical basis of intelligent hybrid learning and virtual reality technology. This paper collects and processes a large amount of learning data, based on which a new model of student learning performance prediction is established. The verification results of the model show that the model in this study has excellent performance in predicting students’ learning performance. This paper gives some suggestions for future educational practice and research. In general, this study provides a new learning method for the field of education and has important reference value for education reform and teaching practice.

A Study on the Prediction Method of English Performance in Universities based on the Stacking Integrated Model

Students’ performance in higher education reflects their overall quality in higher education. By predicting the performance, students with greater learning problems can be screened out early and given appropriate guidance. To predict students’ performance in English, the knowledge information of courses, examination papers, and historical examination records are used to build a feature project of students’ examinations. Meanwhile, the features strongly correlated with their performance are filtered out. Then the next step of performance prediction is carried out. The results showed that a neural network long and short-term memory performance prediction model incorporating an attention mechanism was more effective than other models in predicting English performance in higher education. Further experiments found that the model reduced the error by 1.04% on the MAE metric, 0.53% on the RMSE metric, and increased its value by 4.12% on the R2 metric. Adding the new feature dataset led to better forecasting by the Att-LSTM model in all metrics. This indicated that the enhanced dataset temporality could improve the effectiveness of the Att-LSTM model in predicting English grades in higher education. The stacked integrated prediction model, by integrating multiple strong regressors, can avoid poor prediction and excessive overall bias due to one regressor and increase the soundness and prediction precision of the mode.

High-Temperature Characteristics of Polyphosphoric Acid-Modified Asphalt and High-Temperature Performance Prediction Analysis of Its Mixtures

To promote the application of economical and sustainable polyphosphoric acid (PPA)-modified asphalt in road engineering, styrene-butadiene block copolymer (SBS), styrene-butadiene rubber (SBR), and PPA were used to prepare PPA/SBS and PPA/SBR composite-modified asphalts, which were tested and the data analyzed. Fourier transform infrared spectroscopy (FTIR) tests and thermogravimetric analysis (TG) tests were carried out to study the modification mechanisms of the composite-modified asphalts, and the high-temperature performance of the PPA-modified asphalt and asphalt mixtures was analyzed by dynamic shear rheology (DSR) tests and wheel tracking tests. A gray correlation analysis and a back-propagation (BP) neural network were utilized to construct a prediction model of the high-temperature performance of the asphalt and asphalt mixtures. The test results indicate that PPA chemically interacts with the base asphalt and physically integrates with SBS and SBR. The PPA-modified asphalt has a higher decomposition temperature than the base asphalt, indicating superior thermal stability. As the PPA dosage increases, the G*/sinδ value of the PPA-modified asphalt also increases. In particular, when 0.6% PPA is combined with 2% SBS/SBR, it surpasses the high-temperature performance achieved with 4% SBS/SBR, suggesting that PPA may be a good alternative for polymer modifiers. In addition, the creep recovery of PPA-modified asphalt is influenced by the stress level, and as the stress increases, the R-value decreases, resulting in reduced elastic deformation. Furthermore, the BP neural network model achieved a fit of 0.991 in predicting dynamic stability, with a mean percentage of relative error (MAPE) of 6.15% between measured and predicted values. This underscores the feasibility of using BP neural networks in predictive dynamic stability models.

Ballistic response mechanism and resistance-driven evaluation method of UHMWPE composite

The use of ultra-high molecular weight polyethylene (UHMWPE) composite in the design of lightweight protective equipment, has gained a lot of interest. However, there is an urgent need to understand the ballistic response mechanism and theoretical prediction model of performance. This paper explores the ballistic response mechanism of UHMWPE composite through experimental and simulation analyses. Then, a resistance-driven modeling method was proposed to establish a theoretical model for predicting the bulletproof performance. The ballistic response mechanism of UHMWPE composite encompassed three fundamental modes: local response, structural response, and coupled response. The occurrence ratio of these fundamental response modes during impact was dependent on the projectile velocity and laminate thickness. The bulletproof performance of laminate under different response modes was assessed based on the penetration depth of the projectile, the bulging height on the rear face of the laminate, the thickness of remaining sub-laminate, and residual velocity of the projectile. The absolute deviations of bulletproof performance indicator between theoretical value and experimental value were well within 11.13%, demonstrating that the established evaluation model possessed high degree of prediction accuracy.

Mathematical modeling of reverse osmosis system design and performance

ABSTRACT Reverse osmosis (RO) is one of the most effective technologies for water desalination. However, the RO system's performance is contingent on its design parameters and operating conditions. In this study, a computational model for RO desalination performance prediction was developed. The study compares the performance parameters of the RO system utilizing various types of membranes with the established model using a single pressure vessel and seven membrane elements within the pressure vessel. This study looked at two different feed water concentrations. The first is for saltwater with a concentration of 40,000 mg/L, while the second is for seawater with a concentration of 32,000 mg/L. WAVE software assessed the findings of the generated model to the recovery ratio, feed pressure, salt rejection, and permeate concentration of each membrane element inside the pressure vessel. The results of the study show good similarity between the simulation model results and the results obtained by WAVE software as they are illustrated in Figures 4–7.

Statistical modeling and multi-objective optimization of a flat tubular indirect evaporative cooler for enhanced performance

The flat tubular indirect evaporative cooler complements the conventional round tubular indirect evaporative cooler owing to its superior wetting surface area and enhanced heat transfer capacity. Given the multitude of influencing parameters, achieving a comprehensive analysis and optimization of the flat tubular indirect evaporative cooler demands a significant number of experimental tests or numerical simulations. Consequently, this study seeks to employ statistical techniques for constructing a precise and expeditious performance prediction model for the flat tubular indirect evaporative cooler. Nine pivotal parameters were selected to evaluate their impact on the cooling performance of the flat tubular indirect evaporative cooler. A numerical model was developed and verified experimentally. The response surface method was applied to establish polynomial regression equations for the cooling performance prediction of flat tubular indirect evaporative coolers. In addition, this work proposed a multi-objective optimization strategy for the proposed cooler. By innovatively using weighting factors to assign different weights to response values, multi-objective optimization with preferences was performed for the flat tubular indirect evaporative cooler with different demands to increase the applicability of the proposed cooler. The optimization results showed that when only the optimized wet-bulb efficiency was considered, the wet-bulb efficiency can reach up to 81.1%. When optimizing both the wet-bulb efficiency and the coefficient of performance simultaneously, the coefficient of performance can be up to 58.0, and the wet-bulb efficiency was 64.7%.

A Visual Survey of Tunnel Boring Machine (TBM) Performance in Tunneling Excavation: Mainstream Direction, Brief Review and Future Prospects

This study employs scientometric analysis to investigate the current trajectory of research on tunnel boring machine (TBM) performance and collaborative efforts. Utilizing software tools like Pajek 5.16 and VOSviewer 1.6.18, it scrutinizes literature from 2000 to 2021 sourced from the Web of Science (WOS). The findings illuminate TBM research as an interdisciplinary and intersectoral field attracting increasing national and institutional attention. Notable contributions from China, Iran, the United States, Turkey, and Australia underscore the global significance of TBM research. The recent upsurge in annual publications, primarily driven by Chinese research initiatives, reflects a renewed vigor in TBM exploration. Additionally, the paper presents a succinct evaluation of TBM advantages and drawbacks compared to conventional drill and blast methods, discussing key considerations in excavation methodology selection. Moreover, the study comprehensively reviews TBM performance prediction models, categorizing them into theoretical, empirical, and artificial intelligence-driven approaches. Finally, rooted in metaverse theory, the discourse delves into the immersive learning model and the architecture of a TBM metaverse. In the future, the immersive training and learning model diagram can be employed in scenarios such as employee training and the promotion of safety knowledge. Additionally, the TBM metaverse architecture can simulate, monitor, diagnose, predict, and control the organization, management, and service processes and behaviors of TBMs. This will enhance efficient collaboration across various aspects of the project production cycle. This forward-looking perspective anticipates future trends in TBM technology, emphasizing societal impact and enhancement of economic benefits.

Performance prediction model for desalination plants using modified grey wolf optimizer based artificial neural network approach

Desalination represents an effective method for alleviating water scarcity, applying algorithmic techniques to predict the performance of reverse osmosis (RO) desalination plants, Modified Grey Wolf Optimizer (MGWO) based Artificial Neural Networks (ANN) can predict the performance of membrane distillation (MD) equipment. Four experimental inputs are selected: feed salt concentration(35–140 g/h), feed flow rate(400–600 L/h), evaporator inlet temperature (60–80 ℃), and condenser inlet temperature (20–30 ℃). The permeate flux (L/h m2) is selected as the experimental output. Ten prediction models were proposed and compared with the existing models (ANN, WOA-ANN, and GWO-ANN). The results showed that the MGWO-ANN model-5 showed the best regression results: R2 = 99.3 %, mean square error (MSE)= 0.004. This model outperformed the existing ANN model (R2 =98.8 %, MSE=0.060), WOA-ANN model (R2 =99.1 %, MSE=0.005) and GWO-ANN model (R2 =98.9 %, MSE=0.007). Model-5 has a single hidden layer (H=1), 13 hidden nodes (n = 13), 10 search agents (SA=10), and 75 %− 20 %− 05 % dataset division. Its residual error is within acceptable limits (spanning −0.1 to 0.2). Optimizing the number of hidden layer nodes (n) and the number of search agents (SA) can improve the training efficiency and prediction accuracy of the model, and the MGWO-ANN model is capable of more accurately predicting the performance of desalination plants.

Improving part-load performance of combined-cycle gas turbines by optimizing variable geometry control strategy for compressor and power turbine combined adjustment

The variable geometry methods currently used in combined-cycle gas turbines are compressor variable inlet guide vanes (VIGV) or power turbine variable area nozzles (VAN). On this basis, this study presents the optimal variable geometry control strategy for compressor and power turbine combined adjustment ([Formula: see text]) using the Differential Evolutionary Algorithm with the LM2500+ gas turbine. The aim is to further improve the part-load performance of the combined-cycle gas turbine. Firstly, a part-load performance prediction model for variable geometry gas turbines is established based on the component method. Subsequently, a variable geometry gas turbine part-load performance optimization model is developed by combining the Differential Evolution Algorithm. Finally, the optimum combination of stagger angles for the compressor inlet vane and power turbine nozzle is calculated at each part-load condition. Compared to the VIGV and VAN control strategies, the [Formula: see text] control strategy proposed in this paper shows a higher stability margin and better economy. The [Formula: see text] control strategy maintains a constant exhaust temperature within a part load range from 20% to 100% with the stability margin exceeding 14%. In comparison with the VAN control strategy, the fuel flow rate decreases by 1.152% at 45% relative load power and by 3.435% at 20.0% relative load power with the [Formula: see text] control strategy.

Risk‐based student performance prediction model for engineering courses

AbstractHigh academic failure and dropout rates in engineering courses are significant worldwide concerns attributed to various factors, with academic performance being a critical variable. This article provides a methodology to estimate the performance risk of students in engineering schools. Risk analysis is a strategy to evaluate academic success, which provides a set of methods to analyze, understand, and predict student outcomes before enrolling in specific majors or challenging college courses. This article develops a methodology to estimate fragility curves for students entering an engineering course. The fragility function concept, borrowed from the earthquake engineering field, estimates the likelihood of success in a course, given relevant student metadata, such as the grade point average, thus comprehensively addressing student performance variability. A student academic success prediction model enables instructional designers to make informed decisions. For example, fragility curves can help achieve two goals: (i) assessing the population at risk for a course to take actions to improve student success rates and (ii) assessing a course's relative difficulty based on its fragility function parameters. We demonstrate this methodology through a case study comparing the relative difficulty of two engineering courses, Statics and Solid Mechanics, at a university in Colombia. Given that Statics serves as a prerequisite for Solid Mechanics, deficiencies in the former can significantly impact student performance in the latter. The case study results reveal that Solid Mechanics poses a higher risk of academic failure than Statics, underscoring the importance of a strong foundation in prerequisite courses.

Implementation of filtration models for filter performance prediction in bioprocess dead-end membrane filtration

Mechanistic modeling is frequently applied during process development and scale-up in bioprocessing. For normal flow filtration (NFF, also called dead-end filtration) processes, such as aseptic filtration, harvest clarification, and virus removal etc., the modeling based on a theoretical fouling law has long been used to assess filter performance and filtration area requirements. This case study describes how an appropriate fouling model is selected and provides guidance for using the fouling model for bioprocess application. There is general agreement that the modeling and process simulation enables accurate filter size calculations for scale-up. Their predictions can then be used to identify optimal filter formats for each unit operation. However, because feed characteristics and conditions vary between processes, fouling models need to consider several filter fouling mechanisms for different bioprocess applications and unit operations. This study reviewed several fouling models and considered various bioprocess filter fouling scenarios, as well as the accuracy of filtration area predictions from the models. It was determined that filtration runs in small scale sizing studies should be run to at least 40 % flux decay to minimize the risk of prediction error due to extrapolation.

A Survey of Flow Field and Combustion Characteristics under Subatmospheric Pressure

This paper presents a summary of and introduction to research on high-altitude and subatmospheric combustion concerning turbine and scramjet engines. The investigation includes theoretical analysis, experimental studies, and numerical simulations. The analysis encompasses the flow field structure, fuel atomization, and combustion performance. Subsequently, recent research on the combustion performance of liquid fuels, solid fuels, and gaseous fuels under high-altitude and low-pressure plateau environments is reviewed. This includes an evaluation of flame height, flame temperature, combustion rate, fire spread rate, and heat radiation flux. Additionally, combustion performance prediction models for high-altitude environments based on experimental and theoretical analysis have been introduced. Lastly, issues in subatmospheric combustion in the aerospace and plateau fire fields are presented based on the current research.