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.

Prediction and critical transition mechanism for granite fracture:Insights from critical slowing down theory

Rock fracture warning is one of the significant challenges in rock mechanics.Many true triaxial and synchronous acoustic emission(AE)tests were conducted on granite samples.The investigation focused on the characteristics of AE signals preceding granite fracture,based on the critical slowing down(CSD)theory.The granite undergoes a transition from the stable phase to the fracture phase and exhibits a clear CSD phenomenon,characterized by a pronounced increase in variance and autocorrelation coefficient.The variance mutation points were found to be more identifiable and suitable as the primary criterion for predicting precursor information related to granite fracture,compared to the autocorrelation coefficient.It is noteworthy to emphasize that the CSD factor holds greater potential in elucidating the underlying mechanisms responsible for the critical transition of granite fracture,in comparison to the AE timing parameters.Furthermore,a novel multi-parameter collaborative prediction method for rock fracture was developed by comprehensively analyzing predictive information,including abnormal variation modes and the CSD factor of AE characteristic parameters.This method enhances the understanding and prediction of rock fracture-related geohazards.

Prediction of the viscosity of natural gas at high temperature and high pressure using free-volume theory and entropy scaling

Eighteen models based on two equations of state(EoS),three viscosity models,and four mixing rules were constructed to predict the viscosities of natural gases at high temperature and high pressure(HTHP)conditions.For pure substances,the parameters of free volume(FV)and entropy scaling(ES)models were found to scale with molecular weight,which indicates that the ordered behavior of parameters of Peng-Robinson(PR)and Perturbed-Chain Statistical Associating Fluid Theory(PC-SAFT)propagates to the behavior of parameters of viscosity model.Predicting the viscosities of natural gases showed that the FV and ES models respectively combined with MIX4 and MIX2 mixing rules produced the best accuracy.Moreover,the FV models were more accurate for predicting the viscosities of natural gases than ES models at HTHP conditions,while the ES models were superior to PRFT models.The average absolute relative deviations of the best accurate three models,i.e.,PC-SAFT-FV-MIX4,tPR-FVMIX4,and PC-SAFT-ES-MIX2,were 5.66%,6.27%,and 6.50%,respectively,which was available for industrial production.Compared with the existing industrial models(corresponding states theory and LBC),the proposed three models were more accurate for modeling the viscosity of natural gas,including gas condensate.

Prediction method of highway pavement rutting based on the grey theory

In order to make a scientific pavement maintenance decision, a grey-theory-based prediction methodological framework is proposed to predict pavement performance. Based on the field pavement rutting data,analysis of variance （ANOVA）was first used to study the influence of different factors on pavement rutting. Cluster analysis was then employed to investigate the rutting development trend.Based on the clustering results,the grey theory was applied to build pavement rutting models for each cluster, which can effectively reduce the complexity of the predictive model.The results show that axial load and asphalt binder type play important roles in rutting development.The prediction model is capable of capturing the uncertainty in the pavement performance prediction process and can meet the requirements of highway pavement maintenance,and,therefore,has a wide application prospects.

Contact Angle Prediction Model for Underwater Oleophobic Surfaces Based on Multifractal Theory

Traditional microstructure scale parameters have difficulty describing the structure and distribution of a roughmaterial’s surface morphology comprehensively and quantitatively. This study constructs hydrophilic and underwateroleophobic surfaces based on polyvinylidene fluoride (PVDF) using a chemical modification method, and the fractaldimension and multifractal spectrum are used to quantitatively characterize the microscopic morphology. A new contactangle prediction model for underwater oleophobic surfaces is established. The results show that the fractal dimension ofthe PVDF surface first increases and then decreases with the reaction time. The uniformity characterized by the multifractalspectrum was generally consistent with scanning electron microscope observations. The contact angle of water droplets onthe PVDF surface is negatively correlated with the fractal dimension, and oil droplets in water are positively correlated.When the fractal dimension is 2.0975, the new contact angle prediction model has higher prediction accuracy. Themaximum and minimum relative deviations of the contact angle between the theoretical and measured data are 18.20%and 0.72%, respectively. For water ring transportation, the larger the fractal dimension and spectral width of the materialsurface, the smaller the absolute value of the spectral difference, the stronger the hydrophilic and oleophobic properties, andthe better the water ring transportation stability.

Research and Prediction on the Properties of Concrete at Cryogenic Temperature Based on Gray Theory

To solve the cryogenic temperature problems faced by all-concrete liquefied natural gas(ACLNG)storage tanks during servicing,a low temperature resistant and high strength concrete(LHC)was designed from the perspectives of reducing water-binder ratio,removing coarse aggregates,optimizing composite mineral admixture and utilizing steel fibers.The variation laws of compressive and tensile strength,elastic modulus and Poisson’s ratio for C60 concrete and LHC were compared and analyzed under the temperatures from 10 to-165℃through uniaxial compression and tensile tests.The rapid freezing method was adopted to analyze the evolution process of mass and relative dynamic elastic modulus loss rates for C60 and LHC in 0-300 freeze-thaw cycles.The gas permeability test was carried out,and the laws of gas permeability coefficient varied with temperature and cryogenic freeze-thaw cycles were obtained.Then,the grey dynamic model GM(1,1)was used to predict the variation laws of physical and mechanical parameters on the basis of the test data.The test results demonstrate that the compressive strength,elastic modulus and Poisson’s ratio for both C60 and LHC increase significantly from 10 to-165℃,but the specific variation laws are difierent,and there is a phenomenon that some parameters decrease after reaching a critical temperature range for C60.The uniaxial tensile strength increases first and then decreases as temperature decreases,and finally increases slightly at-165℃for both C60 and LHC.The mass and relative dynamic elastic modulus loss rates of LHC are much lower than that of C60 under different freeze-thaw cycles.The gas permeability coefficient of C60 declines gradually with the drop of temperature,and increases gradually with the number of freeze-thaw cycles while the gas permeability coefficient of LHC basically remains stable and is much lower than that of C60.Therefore,such a conclusion can be drawn that LHC has better properties at cryogenic temperature.On the premise of providing consistent functional mode,GM(1,1)can predict the test data with high accuracy,which well reflects the variation laws of relevant parameters.

Study on the Yield Prediction Model of Processing Tomato Based on the Grey System Theory

[Objective] The research aimed to study the yield prediction model of processing tomato based on the grey system theory.[Method] The variation trend of processing tomato yield was studied by using the grey system theory,and GM（1,1）grey model of processing tomato yield prediction was established.The processing tomato yield in Xinjiang during 2001-2009 was as the example to carry out the instance analysis.[Result] The model had the high forecast accuracy and strong generalization ability,and was reliable for the prediction of recent processing tomato yield.[Conclusion] The research provided the reference for the macro-control of tomato industry,the processing and storage of tomato in Xinjiang.

Comparison of Linear Level I Green-Naghdi Theory with Linear Wave Theory for Prediction of Hydroelastic Responses of VLFS

Very Large Floating Structures (VLFS) have drawn considerable attention recently due to their potential significance in the exploitation of ocean resources and in the utilization of ocean space. Efficient and accurate estimation of their hydroelastic responses to waves is very important for the design. Recently, an efficient numerical algorithm was developed by Ertekin and Kim (1999). However, in their analysis, the linear Level I Green-Naghdi (GN) theory is employed to describe fluid dynamics instead of the conventional linear wave (LW) theory of finite water depth. They claimed that this linear level I GN theory provided better predictions of the hydroelastic responses of VLFS than the linear wave theory. In this paper, a detailed derivation is given in the conventional linear wave theory framework with the same quantity as used in the linear level I GN theory framework. This allows a critical comparison between the linear wave theory and the linear level I GN theory. It is found that the linear level I GN theory can be regarded as an approximation to the linear wave theory of finite water depth. The consequences of the differences between these two theories in the predicted hydroelastic responses are studied quantitatively. And it is found that the linear level I GN theory is not superior to the linear wave theory. Finally, various factors affecting the hydroelastic response of VLFS are studied with the implemented algorithm.

A CONFUSION IN THE APPLICATION OF DISTORTION ENERGY THEORY TO MULTIAXIAL FATIGUE FAILURE PREDICTION

In the application of distortion energy theory to multiaxial fatigue prediction problems,it isquite frequent to meet with a confusion in the evaluation of the extremum values of equivalentstresses.In this paper,a description about this error is presented and discussed thereafter.Neces-sary correction has been made towards this problem.

Understanding the dynamical mechanism of year-to-year incremental prediction by nonlinear time series prediction theory

Previous studies have shown that year-to-year incremental prediction （YIP） can obtain considerable skill in seasonal forecasts. This study analyzes the mathematical deRnition of YiP and derives its formula in the nonlinear time series prediction （NP） method, it is shown that the two methods are equivalent when the prediction time series is embedded in one-dimensional phase space. Compared to previous NP models, the new one introduces multiple external forcings in the form of year-to-year increments. The year-to-year increments have physical meaning, which is better than the NP model with empirically chosen parameters. The summer rainfall over the middle to lower reaches of the Yangtze River is analyzed to examine the prediction skill of the NP models. Results show that the NP model with year-to-year increments can reach a similar skill as the YiP model. When the embedded number of dimensions is increased to two, more accurate prediction can be obtained. Besides similar results, the NP method has more dynamical meaning, as it is based on the classical reconstruction theory. Moreover, by choosing different embedded dimensions, the NP model can reconstruct the dynamical curve into phase space with more than one dimension, which is an advantage of the NP model. The present study suggests that YIP has a robust dynamical foundation, besides its physical mechanism, and the modified NP model has the potential to increase the operationaJ skill in short- term climate prediction.

Subsidence prediction method based on equivalent mining height theory for solid backfilling mining

Based on the characteristics of strata movement of solid backfilling mining technology, the surface subsidence prediction method based on the equivalent mining height theory was proposed, and the parameters selection guideline of this method was also described. While comparing the parameters of caving mining with equivalent height, the subsidence efficient can be calculated according to the mining height and bulk factor of sagging zone and fracture zone, the tangent of main influence angle of solid backfilling mining is reduced by 0.2-0.5（while it cannot be less than 1.0）. For sake of safety, offset of the inflection point is set to zero, and other parameters, such as horizontal movement coefficient and main propagation angle are equal to the corresponding parameters of caving mining with equivalent height. In the last part, a case study of solid backfilling mining subsidence prediction was described. The results show the applicability of this method and the difference of the maximum subsidence point between the prediction and the observation is less than 5%.

Factors influencing pore-pressure prediction in complex carbonates based on effective medium theory

A calculation model based on effective medium theory has been developed for predicting elastic properties of dry carbonates with complex pore structures by integrating the Kuster-Toksǒz model with a differential method.All types of pores are simultaneously introduced to the composite during the differential iteration process according to the ratio of their volume fractions.Based on this model,the effects of pore structures on predicted pore-pressure in carbonates were analyzed.Calculation results indicate that cracks with low pore aspect ratios lead to pore-pressure overestimation which results in lost circulation and reservoir damage.However,moldic pores and vugs with high pore aspect ratios lead to pore-pressure underestimation which results in well kick and even blowout.The pore-pressure deviation due to cracks and moldic pores increases with an increase in porosity.For carbonates with complex pore structures,adopting conventional pore-pressure prediction methods and casing program designs will expose the well drilling engineering to high uncertainties.Velocity prediction models considering the influence of pore structure need to be built to improve the reliability and accuracy of pore-pressure prediction in carbonates.

Theory and Method of Mineral Resource Prediction Based on Synthetic Information

Metallogenic prognosis of synthetic information uses the geological body and the mineral resource body as a statistical unit to interpret synthetically the information of geology, geophysics, geochemistry and remote sensing from the evolution of geology and puts all the information into one entire system by drawing up digitalized interpretation maps of the synthetic information. On such basis, different grades and types of mineral resource prospecting models and predictive models of synthetic information can be established. Hence, a new integrated prediction system will be formed of metallogenic prognosis (qualitative prediction), mineral resources statistic prediction (determining targets) and mineral resources prediction (determining resources amount).

Online prediction for contamination of chlortetracycline fermentation based on Dezert–Smarandache theory

Fermentative production of chlortetracycline is a complex fed-batch bioprocess. It generally takes over 90 h for cultivation and is often contaminated by undesired microorganisms. Once the fermentation system is contaminated to certain extent, the product quality and yield will be seriously affected, leading to a substantial economic loss. Using information fusion based on the Dezer–Smarandache theory, self-recursive wavelet neural network and unscented kalman filter, a novel method for online prediction of contamination is developed. All state variables of culture process involving easy-to-measure and difficult-to-measure variables commonly obtained with soft-sensors present their contamination symptoms. By extracting and fusing latent information from the changing trend of each variable, integral and accurate prediction results for contamination can be achieved. This makes preventive and corrective measures be taken promptly. The field experimental results show that the method can be used to detect the contamination in time, reducing production loss and enhancing economic efficiency.

Prediction method for risks of coal and gas outbursts based on spatial chaos theory using gas desorption index of drill cuttings

Based on the evolution of geological dynamics and spatial chaos theory, we proposed the advanced prediction an advanced prediction method of a gas desorption index of drill cuttings to predict coal and gas outbursts. We investigated and verified the prediction method by a spatial series data of a gas desorption index of drill cuttings obtained from the 113112 coal roadway at the Shitai Mine. Our experimental results show that the spatial distribution of the gas desorption index of drill cuttings has some chaotic charac- teristics, which implies that the risk of coal and gas outbursts can be predicted by spatial chaos theory. We also found that a proper amount of sample data needs to be chosen in order to ensure the accuracy and practical maneuverability of prediction. The relative prediction error is small when the prediction pace is chosen carefully. In our experiments, it turned out that the optimum number of sample points is 80 and the optimum prediction pace 30. The corresponding advanced prediction pace basically meets the requirements of engineering applications.

Application of seismic multi-attribute fusion method based on D-S evidence theory in prediction of CBM-enriched area

D-S evidence theory provides a good approach to fuse uncertain inlbrmation. In this article, we introduce seismic multi-attribute fusion based on D-S evidence theory to predict the coalbed methane （CBM） concentrated areas. First, we choose seismic attributes that are most sensitive to CBM content changes with the guidance of CBM content measured at well sites. Then the selected seismic attributes are fused using D-S evidence theory and the fusion results are used to predict CBM-enriched area. The application shows that the predicted CBM content and the measured values are basically consistent. The results indicate that using D-S evidence theory in seismic multi-attribute fusion to predict CBM-enriched areas is feasible.

THE COMBINATION PREDICTION OF TRANSMEMBRANE REGIONS BASED ON DEMPSTER-SHAFER THEORY OF EVIDENCE

Transmembrane proteins are some special and important proteins in cells. Because of their importance and specificity, the prediction of the transmembrane regions has very important theoretical and practical significance. At present, the prediction methods are mainly based on the physicochemical property and statistic analysis of amino acids. However, these methods are suitable for some environments but inapplicable for other environments. In this paper, the multi-sources information fusion theory has been introduced to predict the transmembrane regions. The proposed method is test on a data set of transmembrane proteins. The results show that the proposed method has the ability of predicting the transmembrane regions as a good performance and powerful tool.

Prediction of Transverse Permeability for Unidirectional Fiber Tows Based on the Homogenization Theory

The transverse permeability of unidirectional fiber tows is calculated using homogenization method.Each fiber tow consisting of 21 filaments is arranged in uniform square packing.Stokes governing equation is analogized with Lame equation used in the linear elasticity problem and is solved by the finite element code ANSYS.The prediction for transverse permeability of unidirectional fiber obtained by the homogenization approach is compared with other analytical methods.The result shows a good agreement with Kozeny-Carman equation and Gebart square packing model.A model for nonuniform fiber distribution and measurement technology are proposed.It can be found that the experimental result is in excellent agreement with predicted permeability in the nonuniform distribution model.

Urban Growth Prediction Modelling Using Fractals and Theory of Chaos

Urban growth prediction has acquired an important consideration in urban sustainability. An effective approach of urban prediction can be a valuable tool in urban decision making and planning. A large urban development has been occurred during last decade in the touristic village of Pogonia Etoloakarnanias, Greece, where an urban growth of 57.5% has been recorded from 2003 to 2011. The prediction of new urban settlements was achieved using fractals and theory of chaos. More specifically, it was found that the urban growth is taken place within a Sierpinski carpet. Several shapes of Sierpinski carpets were tested in order to find the most appropriate, which produced an accuracy percentage of 70.6% for training set and 81.8% for validation set. This prediction method can be effectively applied in urban growth modelling, once cities are fractals and urban complexity can be successfully described through a Sierpinski tessellation.

Prediction of the maximum water inflow in Pingdingshan No.8 mine based on grey system theory

In order to prevent and control the water inflow of mines, this paper built a new initial GM（1, 1） model to torecast the maximum water inflow according to the principle of new information. The effect of the new initial GM（1, 1） model is not ideal by the concrete example. Then according to the principle of making the sum of the squares of the difference between the calculated sequences and the original sequences, an optimized GM（1, I） model was established. The result shows that this method is a new prediction method which can predict the maximum water inflow accurately. It not only conforms to the guide- line of prevention primarily, but also provides reference standards to managers on making prevention measures.