Composition optimization and performance prediction for ultra-stable water-based aerosol based on thermodynamic entropy theory

Water-based aerosol is widely used as an effective strategy in electro-optical countermeasure on the battlefield used to the preponderance of high efficiency,low cost and eco-friendly.Unfortunately,the stability of the water-based aerosol is always unsatisfactory due to the rapid evaporation and sedimentation of the aerosol droplets.Great efforts have been devoted to improve the stability of water-based aerosol by using additives with different composition and proportion.However,the lack of the criterion and principle for screening the effective additives results in excessive experimental time consumption and cost.And the stabilization time of the aerosol is still only 30 min,which could not meet the requirements of the perdurable interference.Herein,to improve the stability of water-based aerosol and optimize the complex formulation efficiently,a theoretical calculation method based on thermodynamic entropy theory is proposed.All the factors that influence the shielding effect,including polyol,stabilizer,propellant,water and cosolvent,are considered within calculation.An ultra-stable water-based aerosol with long duration over 120 min is obtained with the optimal fogging agent composition,providing enough time for fighting the electro-optic weapon.Theoretical design guideline for choosing the additives with high phase transition temperature and low phase transition enthalpy is also proposed,which greatly improves the total entropy change and reduce the absolute entropy change of the aerosol cooling process,and gives rise to an enhanced stability of the water-based aerosol.The theoretical calculation methodology contributes to an abstemious time and space for sieving the water-based aerosol with desirable performance and stability,and provides the powerful guarantee to the homeland security.

Two-Way Neural Network Performance PredictionModel Based onKnowledge Evolution and Individual Similarity

Predicting students’academic achievements is an essential issue in education,which can benefit many stakeholders,for instance,students,teachers,managers,etc.Compared with online courses such asMOOCs,students’academicrelateddata in the face-to-face physical teaching environment is usually sparsity,and the sample size is relativelysmall.It makes building models to predict students’performance accurately in such an environment even morechallenging.This paper proposes a Two-WayNeuralNetwork(TWNN)model based on the bidirectional recurrentneural network and graph neural network to predict students’next semester’s course performance using only theirprevious course achievements.Extensive experiments on a real dataset show that our model performs better thanthe baselines in many indicators.

A Stacking Machine Learning Model for Student Performance Prediction Based on Class Activities in E-Learning

After the spread of COVID-19,e-learning systems have become crucial tools in educational systems worldwide,spanning all levels of education.This widespread use of e-learning platforms has resulted in the accumulation of vast amounts of valuable data,making it an attractive resource for predicting student performance.In this study,we aimed to predict student performance based on the analysis of data collected from the OULAD and Deeds datasets.The stacking method was employed for modeling in this research.The proposed model utilized weak learners,including nearest neighbor,decision tree,random forest,enhanced gradient,simple Bayes,and logistic regression algorithms.After a trial-and-error process,the logistic regression algorithm was selected as the final learner for the proposed model.The results of experiments with the above algorithms are reported separately for the pass and fail classes.The findings indicate that the accuracy of the proposed model on the OULAD dataset reached 98%.Overall,the proposed method improved accuracy by 4%on the OULAD dataset.

Numerical Research on Performance Prediction for Centrifugal Pumps

Performance prediction for centrifugal pumps is now mainly based on numerical calculation and most of the studies merely focus on one model. Therefore, the research results are not representative. To make an improvement of numerical calculation method and performance prediction for centrifugal pumps, performance of six centrifugal pump models at design flow rate and off design flow rates, whose specific speed are different, were simulated by using commercial code FLUENT. The standard k-t turbulence model and SIMPLEC algorithm were chosen in FLUENT. The simulation was steady and moving reference frame was used to consider the impeller-volute interaction. Also, how to dispose the gap between impeller and volute was presented and the effect of grid number was considered. The characteristic prediction model for centrifugal pumps is established according to the simulation results. The head and efficiency of the six models at different flow rates are predicted and the prediction results are compared with the experiment results in detail. The comparison indicates that the precision of head and efficiency prediction are all less than 5%. The flow analysis indicates that flow change has an important effect on the location and area of low pressure region behind the blade inlet and the direction of velocity at impeller inlet. The study shows that using FLUENT simulation results to predict performance of centrifugal pumps is feasible and accurate. The method can be applied in engineering practice.

Prediction of roadheaders' performance using artificial neural network approaches (MLP and KOSFM)

A pplication o f m echanical excavators is one o f th e m o st com m only used excavation m eth o d s because itcan bring th e p ro ject m ore productivity, accuracy and safety. A m ong th e m echanical excavators, roadhead ers are m echanical m iners w h ich have b een extensively u se d in tu n n elin g , m ining an d civil indu stries. Perform ance pred ictio n is an im p o rta n t issue for successful ro a d h e a d e r application andgenerally deals w ith m achine selection, p ro d u ctio n rate an d b it consu m p tio n . The m ain aim o f thisresearch is to investigate th e c u ttin g p erfo rm an ce （in stan tan eo u s c u ttin g rates （ICRs）） o f m ed iu m -d u tyro ad h ead ers by using artificial neural n etw o rk （ANN） approach. T here are d ifferent categories forANNs, b u t based o n train in g alg o rith m th e re are tw o m ain k in d s： supervised and u n su p erv ised . Them u lti-lay er p ercep tro n （MLP） an d K ohonen self-organizing feature m ap （KSOFM） are th e m o st w idelyused neu ral netw o rk s for supervised an d u n su p erv ised ones, respectively. For gaining this goal, ad atab ase w as prim arily provided from ro ad h e a d e rs＇ p erfo rm an ce an d geom echanical characteristics o frock form ations in tu n n els and d rift galleries in Tabas coal m ine, th e larg est an d th e only fullymech an ized coal m ine in Iran. T hen th e datab ase w as analyzed in o rd e r to yield th e m ost im p o rtan tfactor for ICR by using relatively im p o rta n t factor in w hich G arson eq u atio n w as utilized. The MLPn etw o rk w as train ed by 3 in p u t p ara m e te rs including rock m ass pro p erties, rock quality d esignation（RQD）, in tact rock p ro p erties such as uniaxial com pressive stre n g th （UCS） an d Brazilian ten sile stren g th（BTS）, and o n e o u tp u t p a ra m e te r （ICR）. In o rd e r to have m ore v alidation o n MLP o u tp u ts, KSOFM visualizationw as applied. The m ean square e rro r （MSE） an d regression coefficient （R ） o f MLP w e re found tobe 5.49 an d 0.97, respectively. M oreover, KSOFM n etw o rk has a m ap size o f 8 x 5 and final qu an tizatio nan d topographic erro rs w e re 0.383 an d 0.032, respectively. The results show th a t MLP neural n etw orkshave a strong capability to p red ict an d ev alu ate th e perfo rm an ce o f m ed iu m -d u ty ro ad h ead ers in coalm easu re rocks. Furtherm ore, it is concluded th a t KSOFM neural n etw o rk is an efficient w ay for u n d e rstand in g system beh av io r an d know ledge extraction. Finally, it is indicated th a t UCS has m ore influenceo n ICR b y applying th e b e st train ed MLP n etw o rk w eig h ts in G arson eq u atio n w h ich is also confirm ed byKSOFM.

THREE-DIMENSIONAL COUPLED IMPELLER-VOLUTE SIMULATION OF FLOW IN CENTRIFUGAL PUMP AND PERFORMANCE PREDICTION

A three-dimensional turbulent flow through an entire centrifugal pump is simulated using k-ε turbulence model modified by rotation and curvature, SIMPLEC method and body-fitted coordinate. The velocity and pressure fields are obtained for the pump under various working conditions, which is used to predict the head and hydraulic efficiency of the pump, and the results correspond well with the measured values. The calculation results indicate that the pressure is higher on the pressure side than that on the suction side of the blade; The relative velocity on the suction side gradually decreases from the impeller inlet to the outlet, while increases on the pressure side, it finally results in the lower relative velocity on the suction side and the higher one on the pressure side at the impeller outlet; The impeller flow field is asymmetric, i.e. the velocity and pressure fields arc totally different among all channels in the impeller; In the volute, the static pressure gradually increases with the flow route, and a large pressure gratitude occurs in the tongue; Secondary flow exists in the rear part of the spiral.

Tunnelling performance prediction of cantilever boring machine in sedimentary hard-rock tunnel using deep belief network

Evaluating the adaptability of cantilever boring machine(CBM) through in-depth excavation and analysis of tunnel excavation data and rock mass parameters is the premise of mechanical design and efficient excavation in the field of underground space engineering.This paper presented a case study of tunnelling performance prediction method of CBM in sedimentary hard-rock tunnel of Karst landform type by using tunneling data and surrounding rock parameters.The uniaxial compressive strength(UCS),rock integrity factor(Kv),basic quality index([BQ]),rock quality index RQD,brazilian tensile strength(BTS) and brittleness index(BI) were introduced to construct a performance prediction database based on the hard-rock tunnel of Guiyang Metro Line 1 and Line 3,and then established the performance prediction model of cantilever boring machine.Then the deep belief network(DBN) was introduced into the performance prediction model,and the reliability of performance prediction model was verified by combining with engineering data.The study showed that the influence degree of surrounding rock parameters on the tunneling performance of the cantilever boring machine is UCS > [BQ] > BTS >RQD > Kv > BI.The performance prediction model shows that the instantaneous cutting rate(ICR) has a good correlation with the surrounding rock parameters,and the predicting model accuracy is related to the reliability of construction data.The prediction of limestone and dolomite sections of Line 3 based on the DBN performance prediction model shows that the measured ICR and predicted ICR is consistent and the built performance prediction model is reliable.The research results have theoretical reference significance for the applicability analysis and mechanical selection of cantilever boring machine for hard rock tunnel.

Intelligent prediction on performance of high-temperature heat pump systems using different refrigerants

Two new binary near-azeotropic mixtures named M1 and M2 were developed as the refrigerants of the high-temperature heat pump(HTHP).The experimental research was used to analyze and compare the performance of M1 and M2-based in the HTHP in different running conditions.The results demonstrated the feasibility and reliability of M1 and M2 as new high-temperature refrigerants.Additionally,the exploration and analyses of the support vector machine(SVM)and back propagation(BP)neural network models were made to find a practical way to predict the performance of HTHP system.The results showed that SVM-Linear,SVM-RBF and BP models shared the similar ability to predict the heat capacity and power input with high accuracy.SVM-RBF demonstrated better stability for coefficient of performance prediction.Finally,the proposed SVM model was used to assess the potential of the M1 and M2.The results indicated that the HTHP system using M1 could produce heat at the temperature of 130°C with good performance.

Performance prediction of gravity concentrator by using artificial neural network-a case study

In conventional chromite beneficiation plant, huge quantity of chromite is used to loss in the form of tailing. For recovery these valuable mineral, a gravity concentrator viz. wet shaking table was used.Optimisation along with performance prediction of the unit operation is necessary for efficient recovery.So, in this present study, an artificial neural network(ANN) modeling approach was attempted for predicting the performance of wet shaking table in terms of grade(%) and recovery(%). A three layer feed forward neural network(3:3–11–2:2) was developed by varying the major operating parameters such as wash water flow rate(L/min), deck tilt angle(degree) and slurry feed rate(L/h). The predicted value obtained by the neural network model shows excellent agreement with the experimental values.

PREDICTION OF THE AERODYNAMIC PERFORMANCE OF HORIZONTAL AXIS WIND TURBINES IN CONDITION OF UNIFORM WIND

The classical momentum-blade element theory is improved by using the empirical formula while part of rotor blades enters into the turbulent wake state, and the performance of a horizontal-axis wind turbine (HAWT) at all speed ratios can be predicted. By using an improved version of the so-called secant method, the convergent solutions of the system of two-dimensional equations concerning the induced velocity factors a and a' are guaranteed. Besides, a solving method of multiple solutions for a and a' is proposed and discussed. The method provided in this paper can be used for computing the aerodynamic performance of HAWTs both ofrlow solidity and of high solidity. The calculated results coincide well with the experimental data.

Performance Prediction of Packet Mobile Communication through Wireless Multipath Fading Channel Modeling

This paper presents a software simulator applicable to multipath fading channels in urban environments of mobile communication networks. The simulator is constructed by a two-state Markov model and several statistical models for simulating the characterizations of different environments. A core idea of the simulator is to construct a Rice distribution-based multipath fading module produced by a modified Gans Doppler power spectrum, and in combination with a Markov model to predict the time-dependent characteristics of packet in different radio circumstances. It can simply predict the packet performance of the future channel and evaluate the relations between the radio channel and the modulation schemes, error control protocols and channel coding. Simulation results demonstrate that it is a reliable and efficient method.

Prediction and Optimization Performance Models for Poor Information Sample Prediction Problems

The prediction process often runs with small samples and under-sufficient information.To target this problem,we propose a performance comparison study that combines prediction and optimization algorithms based on experimental data analysis.Through a large number of prediction and optimization experiments,the accuracy and stability of the prediction method and the correction ability of the optimization method are studied.First,five traditional single-item prediction methods are used to process small samples with under-sufficient information,and the standard deviation method is used to assign weights on the five methods for combined forecasting.The accuracy of the prediction results is ranked.The mean and variance of the rankings reflect the accuracy and stability of the prediction method.Second,the error elimination prediction optimization method is proposed.To make,the prediction results are corrected by error elimination optimization method(EEOM),Markov optimization and two-layer optimization separately to obtain more accurate prediction results.The degree improvement and decline are used to reflect the correction ability of the optimization method.The results show that the accuracy and stability of combined prediction are the best in the prediction methods,and the correction ability of error elimination optimization is the best in the optimization methods.The combination of the two methods can well solve the problem of prediction with small samples and under-sufficient information.Finally,the accuracy of the combination of the combined prediction and the error elimination optimization is verified by predicting the number of unsafe events in civil aviation in a certain year.

Performance Prediction Model for Parallel Computing on Network of Workstation

In order to effectively program Parallel Computing on NOW (Network of workstation),users must be able to evaluate how well the system performs for a given application.In this paper,we present an framework that can be used to evaluate tree structured computing on NOW.Based on this framework,we derive a model for the famous parallel programming paradigm divide and conquer.We discuss how this model can be used to evaluate performance and how it can be used to restructure the application to improve performance.

STUDY ON TURBOMACHINERY PERFORMANCE PREDICTION WITH NEURAL NETWORKS

Traditional methods for performance prediction of a turbomachinery are usually based on certain computations from a set of data obtained in limited experiment measurements of the machine, or the machinemodels. Since the computational (mathematical) models used in such performance prediction are often crude, most of the predicted results are only correct in very small ranges around the known data points. Beyond the limited ranges, the accuracy of the resultant predictions decrease abruptly. Therefore, an alternative approach, neural network technique, is studied for performance prediction of turbomachinery. The new approach has been applied to two typical performance prediction cases to verify its feasibility and reliability.

Aeroengine Performance Parameter Prediction Based on Improved Regularization Extreme Learning Machine

Performance parameter prediction technology is the core research content of aeroengine health management,and more and more machine learning algorithms have been applied in the field.Regularized extreme learning machine(RELM)is one of them.However,the regularization parameter determination of RELM consumes computational resources,which makes it unsuitable in the field of aeroengine performance parameter prediction with a large amount of data.This paper uses the forward and backward segmentation(FBS)algorithms to improve the RELM performance,and introduces an adaptive step size determination method and an improved solution mechanism to obtain a new machine learning algorithm.While maintaining good generalization,the new algorithm is not sensitive to regularization parameters,which greatly saves computing resources.The experimental results on the public data sets prove the above conclusions.Finally,the new algorithm is applied to the prediction of aero-engine performance parameters,and the excellent prediction performance is achieved.

Performance prediction for Grid workflow activities based on features-ranked RBF network

Accurate performance prediction of Grid workflow activities can help Grid schedulers map activitiesto appropriate Grid sites.This paper describes an approach based on features-ranked RBF neural networkto predict the performance of Grid workflow activities.Experimental results for two kinds of real worldGrid workflow activities are presented to show effectiveness of our approach.

Data-Driven Probabilistic S for Batsman Performance Prediction in a Cricket Match

Batsmen are the backbone of any cricket team and their selection is very critical to the team’s success.A good batsman not only scores run but also provides stability to the team’s innings.The most important factor in selecting a batsman is their ability to score runs.It is a generally accepted notion that the future performance of a batsman can be predicted by observing and analyzing their past record.This hypothesis is based on the fact that a player’s batting aver-age is generally considered to be a good indicator of their future performance.We proposed a data-driven probabilistic system for batsman performance prediction in the game of cricket.It captures the dependencies between the runs scored by a batsman in consecutive balls.The system is evaluated using a dataset extracted from the Cricinfo website.The system is based on a Hidden Markov model(HMM).HMM is used to generate the prediction model to foresee players’upcoming performances.The first-order Markov chain assumes that the probabil-ity of a batsman scoring runs in the next ball is only dependent on how many runs he scored in the current ball.We use a data-driven approach to learn the para-meters of the HMM from data.A probabilistic matrix is made that predicts what scores the batter can do on the upcoming balls.The results show that the system can accurately predict the runs scored by a batsman in a ball.

Prediction of Lubricant Physicochemical Properties Based on Gaussian Copula Data Expansion

The composition of base oils affects the performance of lubricants made from them.This paper proposes a hybrid model based on gradient-boosted decision tree(GBDT)to analyze the effect of different ratios of KN4010,PAO40,and PriEco3000 component in a composite base oil system on the performance of lubricants.The study was conducted under small laboratory sample conditions,and a data expansion method using the Gaussian Copula function was proposed to improve the prediction ability of the hybrid model.The study also compared four optimization algorithms,sticky mushroom algorithm(SMA),genetic algorithm(GA),whale optimization algorithm(WOA),and seagull optimization algorithm(SOA),to predict the kinematic viscosity at 40℃,kinematic viscosity at 100℃,viscosity index,and oxidation induction time performance of the lubricant.The results showed that the Gaussian Copula function data expansion method improved the prediction ability of the hybrid model in the case of small samples.The SOA-GBDT hybrid model had the fastest convergence speed for the samples and the best prediction effect,with determination coefficients(R^(2))for the four indicators of lubricants reaching 0.98,0.99,0.96 and 0.96,respectively.Thus,this model can significantly reduce the model’s prediction error and has good prediction ability.

Performance prediction of expressway pavement in high maintenance level areas based on cosine deterioration equation: A case study of Zhejiang Province in China

Accurate prediction of performance decay law is an important basis for long-term planning of maintenance strategy.The statistical regression prediction model is the most widely employed method to calculate pavement performance due to its advantages such as the small amount of calculation and good accuracy,but the traditional prediction model seems not applicable to the high maintenance level areas with excellent pavement conditions.In this paper,the service life and the cumulative number of the axle load were determined as the independent variables of prediction models of pavement performance.The pavement condition index(PCI)and rutting depth index(RDI)were selected as maintenance decision control indexes to establish the unified prediction model of PCI and RDI respectively by applying the cosine deterioration equation.Results reveal that the deterioration law of PCI presents an anti-S type or concave type and the deterioration law of RDI shows an obvious concave type.The prediction model proposed in this study added the pavement maintenance standard factor d,which brings the model parameterα(reflecting the road life)and the deterioration equations are more applicable than the traditional standard equations.It is found that the fitting effects of PCI and RDI prediction models with different traffic grades are relatively similar to the actual service state of the pavements.

A machine learning approach for accelerated design of magnesium alloys.Part B: Regression and property prediction

Machine learning(ML) models provide great opportunities to accelerate novel material development, offering a virtual alternative to laborious and resource-intensive empirical methods. In this work, the second of a two-part study, an ML approach is presented that offers accelerated digital design of Mg alloys. A systematic evaluation of four ML regression algorithms was explored to rationalise the complex relationships in Mg-alloy data and to capture the composition-processing-property patterns. Cross-validation and hold-out set validation techniques were utilised for unbiased estimation of model performance. Using atomic and thermodynamic properties of the alloys, feature augmentation was examined to define the most descriptive representation spaces for the alloy data. Additionally, a graphical user interface(GUI) webtool was developed to facilitate the use of the proposed models in predicting the mechanical properties of new Mg alloys. The results demonstrate that random forest regression model and neural network are robust models for predicting the ultimate tensile strength and ductility of Mg alloys, with accuracies of ~80% and 70% respectively. The developed models in this work are a step towards high-throughput screening of novel candidates for target mechanical properties and provide ML-guided alloy design.