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.

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.

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.

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.

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.

Performance Prediction of Carbon Fiber Protofilament Based on SAGA-SVR

The existing optimized performance prediction of carbon fiber protofilament process model is still unable to meet the production needs. A way of performance prediction on carbon fiber protofilament was presented based on support vector regression( SVR) which was optimized by an optimization algorithm combining simulated annealing algorithm and genetic algorithm( SAGA-SVR). To verify the accuracy of the model,the carbon fiber protofilament production test data were analyzed and compared with BP neural network( BPNN). The results show that SAGA-SVR can predict the performance parameters of the carbon fiber protofilament accurately.

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.

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.

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.

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.

Molecular dynamics for electrocatalysis:Mechanism explanation and performance prediction

Designing low-cost,high-performing electrocatalysts is key to green energy development,yet relying solely on the"synthesis-characterization"catalyst screening model is time-consuming and costly.There are two main applications for Molecular dynamics(MD)simulations in electrochemical reactions:explaining mechanisms and predicting performance,which play important roles in fabricating robust electrocatalysts.MD simulations of electrocatalysis include the adsorption and desorption of reactants,intermediates,and products in this review.The structural changes in active centers under various electric field states,the effects of alkali metal cations,common anions,and pH effects in the electrolyte on the electrocatalytic process are also discussed to reveal the reaction mechanism.Then the prediction of the catalysts performance in specific reaction using MD simulations are introduced.Finally,the optimization and challenges of MD techniques are discussed.

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.

QoS-Driven Self-Healing Web Service Composition Based on Performance Prediction

Web services run in a highly dynamic environment, as a result, the QoS of which will change relatively frequently. In order to make the composite service adapt to such dynamic property of Web services, we propose a self-healing approach for web service composition. Such an approach is an integration of backing up in selection and reselecting in execution. In order to make the composite service heal itself as quickly as possible and minimize the number of reselections, a way of performance prediction is proposed in this paper. On this basis, the self-healing approach is presented including framework, the triggering algorithm of the reselection and the reliability model of the service. Experiments show that the proposed solutions have better performance in supporting the self-healing Web service composition.

Query Performance Prediction for Information Retrieval Based on Covering Topic Score

We present a statistical method called Covering Topic Score （CTS） to predict query performance for information retrieval. Estimation is based on how well the topic of a user＇s query is covered by documents retrieved from a certain retrieval system. Our approach is conceptually simple and intuitive, and can be easily extended to incorporate features beyond bag- of-words such as phrases and proximity of terms. Experiments demonstrate that CTS significantly correlates with query performance in a variety of TREC test collections, and in particular CTS gains more prediction power benefiting from features of phrases and proximity of terms. We compare CTS with previous state-of-the-art methods for query performance prediction including clarity score and robustness score. Our experimental results show that CTS consistently performs better than, or at least as well as, these other methods. In addition to its high effectiveness, CTS is also shown to have very low computational complexity, meaning that it can be practical for real applications.

LogGPO:An accurate communication model for performance prediction of MPI programs

Message passing interface （MPI） is the de facto standard in writing parallel scientific applications on distributed memory systems. Performance prediction of MPI programs on current or future parallel systems can help to find system bottleneck or optimize programs. To effectively analyze and predict performance of a large and complex MPI program, an efficient and accurate communication model is highly needed. A series of communication models have been proposed, such as the LogP model family, which assume that the sending overhead, message transmission, and receiving overhead of a communication is not overlapped and there is a maximum overlap degree between computation and communication. However, this assumption does not always hold for MPI programs because either sending or receiving overhead introduced by MPI implementations can decrease potential overlap for large messages. In this paper, we present a new communication model, named LogGPO, which captures the potential overlap between computation with communication of MPI programs. We design and implement a trace-driven simulator to verify the LogGPO model by predicting performance of point-to-point communication and two real applications CG and Sweep3D. The average prediction errors of LogGPO model are 2.4% and 2.0% for these two applications respectively, while the average prediction errors of LogGP model are 38.3% and 9.1% respectively.

Simultaneous removal of NO_(x)and chlorobenzene on V_(2)O_(5)/TiO_(2)granular catalyst:Kinetic study and performance prediction

The synergetic abatement of multi-pollutants is one of the development trends of flue gas pollution control technology,which is still in the initial stage and facing many challenges.We developed a V_(2)O_(5)/TiO_(2)granular catalyst and established the kinetic model for the simultaneous removal of NO and chlorobenzene(i.e.,an important precursor of dioxins).The granular catalyst synthesized using vanadyl acetylacetonate precursor showed good synergistic catalytic performance and stability.Although the SCR reaction of NO and the oxidation reaction of chlorobenzene mutually inhibited,the reaction order of each reaction was not considerably affected,and the pseudo-first-order reaction kinetics was still followed.The performance prediction of this work is of much value to the understanding and reasonable design of a catalytic system for multi-pollutants(i.e.,NO and dioxins)emission control.

Multi-task MIML learning for pre-course student performance prediction

In higher education,the initial studying period of each course plays a crucial role for students,and seriously influences the subsequent learning activities.However,given the large size of a course’s students at universities,it has become impossible for teachers to keep track of the performance of individual students.In this circumstance,an academic early warning system is desirable,which automatically detects students with difficulties in learning(i.e.,at-risk students)prior to a course starting.However,previous studies are not well suited to this purpose for two reasons:1)they have mainly concentrated on e-learning platforms,e.g.,massive open online courses(MOOCs),and relied on the data about students’online activities,which is hardly accessed in traditional teaching scenarios;and 2)they have only made performance prediction when a course is in progress or even close to the end.In this paper,for traditional classroom-teaching scenarios,we investigate the task of pre-course student performance prediction,which refers to detecting at-risk students for each course before its commencement.To better represent a student sample and utilize the correlations among courses,we cast the problem as a multi-instance multi-label(MIML)problem.Besides,given the problem of data scarcity,we propose a novel multi-task learning method,i.e.,MIML-Circle,to predict the performance of students from different specialties in a unified framework.Extensive experiments are conducted on five real-world datasets,and the results demonstrate the superiority of our approach over the state-of-the-art methods.

Acoustic performance prediction of Helmholtz resonator with conical neck

There is no accurate analytical approach for the acoustic performance prediction of Helmholtz resonator with conical neck,which has broad band acoustic attenuation performance in the low frequency range.To predict the acoustic performance of the resonator accurately,a general theory model based on the one-dimensional analysis approach with acoustic length corrections is developed.The segmentation method is used to calculate the acoustic parameters for sound propagation in conical tubes.And then,an approximate formula is deduced to give accurate correction lengths for conical tubes with difierent geometries.The deviations of the resonance frequency between the transmission loss results obtained by the general theory with acoustic lengths correction and the results from the finite element method and experiments are less than 2 Hz,which is much better than the results from one-dimensional approach without corrections.The results show that the method of acoustic length correction for the conical neck greatly improved the accuracy of the one-dimensional analysis approach,and it will be quick and accurate to predict the sound attenuation property of Helmholtz resonator with conical neck.