Tensile Properties and Prediction Model of Recombinant Bamboo at Different Temperatures

The destruction of recombinant bamboo depends on many factors,and the complex ambient temperature is an important factor affecting its basic mechanical properties.To investigate the failure mechanism and stress–strain relationship of recombinant bamboo at different temperatures,eighteen tensile specimens of recombinant bamboo were tested.The results showed that with increasing ambient temperature,the typical failure modes of recombinant bamboo were flush fracture,toothed failure,and serrated failure.The ultimate tensile strength,ultimate strain and elastic modulus of recombinant bamboo decreased with increasing temperature,and the ultimate tensile stress decreased from 154.07 to 96.55 MPa,a decrease of 37.33%,and the ultimate strain decreased from 0.011 to 0.008,a decrease of 26.57%.Based on the Ramberg-Osgood model and the pseudo‒elastic design method,a predictive model was established for the tensile stress–strain relationship of recombinant bamboo considering the temperature level.The model can accurately evaluate the tensile stress–strain relationship of recombinant bamboo under different temperature conditions.

Monthly Mean Temperature Prediction Based on a Multi－level Mapping Model of Neural Network BP Type

In terms of 34-year monthly mean temperature series in 1946-1979,the multi-level maPPing model of neural netWork BP type was applied to calculate the system's fractual dimension Do=2'8,leading tO a three-level model of this type with ixj=3x2,k=l,and the 1980 monthly mean temperture predichon on a long-t6rm basis were prepared by steadily modifying the weighting coefficient,making for the correlation coefficient of 97% with the measurements.Furthermore,the weighhng parameter was modified for each month of 1980 by means of observations,therefore constrcuhng monthly mean temperature forecasts from January to December of the year,reaching the correlation of 99.9% with the measurements.Likewise,the resulting 1981 monthly predictions on a long-range basis with 1946-1980 corresponding records yielded the correlahon of 98% and the month-tO month forecasts of 99.4%.

Fuzzy Modeling of Prediction M_s Temperature for Martensitic Stainless Steel

A method of fuzzy modeling based on fuzzy clustering and Kalman filtering was proposed for predicting M s temperature from chemical composition for martensitic stainless steel. The membership degree of each sample was calculated by the fuzzy clustering algorithm. Kalman filtering was used to identify the consequent parameters. Only Grade 95 steel are available for training and validation, and the fuzzy model is valid for the following element concentration ranges (wt%): 0.01<C<0.7; 0<Si<1.0; 0.10<Mn<1.25; 11.5<Cr< 17.5; 0<Ni<2.5; 0<Mo<1.0. Compared with that of several empirical models reported, the accuracy of the fuzzy model was almost 5 times higher than that of the best empirical model. Furthermore, the compositional dependences of Ms were successfully determined and compared with those of the empirical formulae. It was found that the specific element dependences were a function of the overall composition, something could not easily be found using conventional statistics.

High-Temperature Overpressure Basin Reservoir and Pressure Prediction Model

Yinggehai Basin locates in the northern South China Sea. Since the Cainozoic Era, crust has several strong tension: the basin subsides quickly, the deposition is thick, and the crust is thin. In the central basin, formation pressure coefficient is up to 2.1;Yinggehai Basin is a fomous high-temperature overpressure basin.YinggehaiBasin’s in-depth, especially high-temperature overpressure stratum has numerous large-scale exploration goals. As a result of high-temperature overpressure basin’s perplexing geological conditions and geophysical analysis technical limitations, this field of gas exploration can’t be carried out effectively, which affects the process of gas exploration seriously. A pressure prediction model of the high-temperature overpressure basin in different structural positions is summed up by pressure forecast pattern research in recent years, which can be applied to target wells pre-drilling pressure prediction and post drilling pressure analysis of Yinggehai Basin. The model has small erroneous and high rate of accuracy. The Yinggehai Basin A well drilling is successful in 2010, and gas is discovered in high-temperature overpressure stratum, which proved that reservoir can be found in high-temperature overpressure stratum. It is a great theoretical breakthrough of reservoir knowledge.

Analysis of Variation Characters and Prediction Model of Soil Temperature in Solar Greenhouse

[Objective] The aim was to study the soil temperature changes and its forecast model in greenhouse by solar heat. [Method] Annual and daily variation characters of soil temperature were analyzed in this paper by using the observation data of air temperature out of solar greenhouse and different layers soil temperature in it. The soil temperature (daily maximum, daily minimum and daily mean) forecasting models were also studied. Simulation and test were conducted to the forecast model of soil temperature in the greenhouse. [Result] The annual changes and daily changes of soil temperature of each layer in the greenhouse were in single peak curve. The lower layer temperature changes were smaller than the upper layer. The soil temperature of each layer within the greenhouse was closely related to the relevance of same type temperature outside the greenhouse of the day. Taking the average daily temperature, daily maximum temperature and daily lowest temperature of the day and the day before as forecast factors, soil temperature forecast model of different layer of same type within greenhouse was constructed. The simulation outcome of average daily temperature of each layer within the greenhouse was better than the simulation outcome of highest temperature of corresponding layer, worse than the simulation of lowest temperature of corresponding layer. The highest temperature of lower soil and daily temperature of soil were better than the upper layer. The simulated soil temperature was much more close to the observation when the observation was during 15-30 ℃. In other interval, it was lower than the observation. [Conclusion] The study offered theoretical reference for the growth environment of sunlight greenhouse plantation.

Low Temperature Performance Prediction Model of Cold-Filled SMA-13 Asphalt Mixture

Sets of cold-filled SMA-13 asphalt mixture were designed by means of orthogonal design method. The bending and low temperature creep tests of the cold-filled SMA-13 asphalt mixture were carried out. The related models of the fractal dimension and the road performance evaluation index including low temperature bending failure strain εB and bending strength RB are established by using fractal theory. The model can be used to predict the low temperature performance of cold-filled SMA-13 asphalt mixture according to the design gradation, which can reduce the test workload and improve the working efficiency, so as to provide the reference for engineering design.

IMPROVEMENT OF REGIONAL PREDICTION OF SEA FOG ON GUANGDONG COASTLAND USING THE FACTOR OF TEMPERATURE DIFFERENCE IN THE NEAR-SURFACE LAYER

The relationship between the factor of temperature difference of the near-surface layer(T_(1000 hPa)-T_(2m))and sea fog is analyzed using the NCEP reanalysis with a horizontal resolution of l°xl°(2000 to 2011) and the station observations(2010 to 2011).The element is treated as the prediction variable factor in the GRAPES model and used to improve the regional prediction of sea fog on Guangdong coastland.(1) The relationship between this factor and the occurrence of sea fog is explicit:When the sea fog happens,the value of this factor is always large in some specific periods,and the negative value of this factor decreases significantly or turns positive,suggesting the enhancement of warm and moist advection of air flow near the surface,which favors the development of sea fog.(2) The transportation of warm and moist advection over Guangdong coastland is featured by some stages and the jumping among these states.It also gets stronger over time.Meanwhile,the northward propagation of warm and moist advection is quite consistent with the northward advancing of sea fog from south to north along the coastland of China.(3) The GRAPES model can well simulate and realize the factor of near-surface temperature difference.Besides,the accuracy of regional prediction of marine fog,the relevant threat score and Heidke skill score are all improved when the factor is involved.

Seasonal prediction skills of FIO-ESM for North Pacific sea surface temperature and precipitation

The seasonal prediction of sea surface temperature(SST) and precipitation in the North Pacific based on the hindcast results of The First Institute of Oceanography Earth System Model(FIO-ESM) is assessed in this study.The Ensemble Adjusted Kalman Filter assimilation scheme is used to generate initial conditions, which are shown to be reliable by comparison with the observations. Based on this comparison, we analyze the FIO-ESM 6-month hindcast results starting from each month of 1993–2013. The model exhibits high SST prediction skills over most of the North Pacific for two seasons in advance. Furthermore, it remains skillful at long lead times for midlatitudes. The reliable prediction of SST can transfer fairly well to precipitation prediction via air-sea interactions.The average skill of the North Pacific variability(NPV) index from 1 to 6 months lead is as high as 0.72(0.55) when El Ni?o-Southern Oscillation and NPV are in phase(out of phase) at initial conditions. The prediction skill of the NPV index of FIO-ESM is improved by 11.6%(23.6%) over the Climate Forecast System, Version 2. For seasonal dependence, the skill of FIO-ESM is higher than the skill of persistence prediction in the later period of prediction.

Composition control and temperature inferential control of dividing wall column based on model predictive control and PI strategies

The dividing wall column(DWC) is considered as a major breakthrough in distillation technology and has good prospect of industrialization. Model predictive control(MPC) is an advanced control strategy that has acquired extensive applications in various industries. In this study, MPC is applied to the process for separating ethanol,n-propanol, and n-butanol ternary mixture in a fully thermally coupled DWC. Both composition control and temperature inferential control are considered. The multiobjective genetic algorithm function "gamultiobj" in Matlab is used for the weight tuning of MPC. Comparisons are made between the control performances of MPC and PI strategies. Simulation results show that although both MPC and PI schemes can stabilize the DWC in case of feed disturbances, MPC generally behaves better than the PI strategy for both composition control and temperature inferential control, resulting in a more stable and superior performance with lower values of integral of squared error(ISE).

Prediction of Al(OH)_3 fluidized roasting temperature based on wavelet neural network

The recycle fluidization roasting in alumina production was studied and a temperature forecast model was established based on wavelet neural network that had a momentum item and an adjustable learning rate. By analyzing the roasting process, coal gas flux, aluminium hydroxide feeding and oxygen content were ascertained as the main parameters for the forecast model. The order and delay time of each parameter in the model were deduced by F test method. With 400 groups of sample data (sampled with the period of 1.5 min) for its training, a wavelet neural network model was acquired that had a structure of {7 211}, i.e., seven nodes in the input layer, twenty-one nodes in the hidden layer and one node in the output layer. Testing on the prediction accuracy of the model shows that as the absolute error ±5.0 ℃ is adopted, the single-step prediction accuracy can achieve 90% and within 6 steps the multi-step forecast result of model for temperature is receivable.

Development and application of model for predicting molten metal temperature in continuous casting process

An unsteady mathematical thermal model was developed for predicting the time,molten-steel weight,induction heating power,and temperature changes of the steel from the end of ladle refining to the end of the continuous-casting process of a tundish. The calculations revealed that for a specific strip-casting process,the ladle tonnage should be controlled to about 90 t. If the ladle capacity reaches 130 t,the provision of a 1 500-kW tundish induction heating device is recommended. By comparing the measured and predicted molten-steel temperature values in the Ningbosteel-Baosteel strip casting industrialization demo project( NBS) of a tundish,it was determined that the prediction accuracy of the model could meet the forecasting accuracy requirements for the molten-steel temperature in the tundish during mass production. Simultaneously,the heat flux density on each surface of the tundish was found at about 50 min,which is entirely consistent with the values reported in the related literature,and the tundish had not reached a heat balance during the casting test period. This model can also be applied to calculate the suitable size of a tundish for a specific continuous-casting process,thereby providing a theoretical basis for the design of the continuous-casting tundish.

Microstructural characteristics and low-cycle fatigue properties of AZ91 and AZ91-Ca-Y alloys extruded at different temperatures

The commercial AZ91 alloy and nonflammable SEN9(AZ91-0.3Ca-0.2Y,wt%)alloy are extruded at 300°C and 400°C.Their microstructure,tensile and compressive properties,and low-cycle fatigue(LCF)properties are investigated,with particular focus on the influence of the extrusion temperature.In the AZ91 and SEN9 materials extruded at 300°C(300-materials),numerous fine Mg_(17)Al_(12)particles are inhomogeneously distributed owing to localized dynamic precipitation during extrusion,unlike those extruded at 400°C(400-materials).These fine particles suppress the coarsening of recrystallized grains,decreasing the average grain size of 300-materials.Although the four extruded materials have considerably different microstructures,the difference in their tensile yield strengths is insignificant because strong grain-boundary hardening and precipitation hardening effects in 300-materials are offset almost completely by a strong texture hardening effect in 400-materials.However,owing to their finer grains and weaker texture,300-materials have higher compressive yield strengths than400-materials.During the LCF tests,{10-12}twinning is activated at lower stresses in 400-materials than in 300-materials.Because the fatigue damage accumulated per cycle is smaller in 400-materials,they have longer fatigue lives than those of 300-materials.A fatigue life prediction model for the investigated materials is established on the basis of the relationship between the total strain energy density(ΔW_(t))and the number of cycles to fatigue failure(N_(f)),and it is expressed through a simple equation(ΔW_(t)=10·N_(f)-0.59).This model enables fatigue life prediction of both the investigated alloys regardless of the extrusion temperature and strain amplitude.

Three-Dimensional Pressure Modeling of South China Sea in High Temperature High Pressure Field

Yingqiong basin is a proven hydrocarbon-rich basin in South China Sea. There are a number of large exploration prospects in high temperature and over-pressured formations, especially in Yacheng Block of Qiongdongnan basin and Dongfang District of Yinggehai Basin. Owing to good exploration situation, we have already achieved proven geological reserves over 1000 × 108 m3. In recent years, a few drilled HPHT wells have confirmed that pressure predicted by conventional method was wildly inaccurate. From the view of regional stress, the accuracy of the pressure prediction will be substantially improved. Accurate pressure prediction and three-dimensional pressure modeling which are based on three-dimensional lithology modeling are the cornerstone to achieve exploration breakthrough. In this paper, the use of the triple constraint trend lithology model broke through the traditional method of seismic lithology prediction only by means of impedance threshold value. Compared with actual data and prediction, it confirms that three-dimensional pressure modeling method is reasonable and effective, and has a wide prospect of application.

ConvLSTM Based Temperature Forecast Modification Model for North China

The correction of model forecast is an important step in evaluating weather forecast results.In recent years,post-processing models based on deep learning have become prominent.In this paper,a deep learning model named EDConvLSTM based on encoder-decoder structure and ConvLSTM is developed,which appears to be able to effectively correct numerical weather forecasts.Compared with traditional post-processing methods and convolutional neural networks,ED-ConvLSTM has strong collaborative extraction ability to effectively extract the temporal and spatial features of numerical weather forecasts and fit the complex nonlinear relationship between forecast field and observation field.In this paper,the post-processing method of ED-ConvLSTM for 2 m temperature prediction is tested using The International Grand Global Ensemble dataset and ERA5-Land data from the European Centre for Medium-Range Weather Forecasts(ECMWF).Root mean square error and temperature prediction accuracy are used as evaluation indexes to compare ED-ConvLSTM with the method of model output statistics,convolutional neural network postprocessing methods,and the original prediction by the ECMWF.The results show that the correction effect of EDConvLSTM is better than that of the other two postprocessing methods in terms of the two indexes,especially in the long forecast time.

A coupled model of temperature and pressure based on hydration kinetics during well cementing in deep water

Considering the complicated interactions between temperature,pressure and hydration reaction of cement,a coupled model of temperature and pressure based on hydration kinetics during deep-water well cementing was established.The differential method was used to do the coupled numerical calculation,and the calculation results were compared with experimental and field data to verify the accuracy of the model.When the interactions between temperature,pressure and hydration reaction are considered,the calculation accuracy of the model proposed is within 5.6%,which can meet the engineering requirements.A series of numerical simulation was conducted to find out the variation pattern of temperature,pressure and hydration degree during the cement curing.The research results show that cement temperature increases dramatically as a result of the heat of cement hydration.With the development of cement gel strength,the pore pressure of cement slurry decreases gradually to even lower than the formation pressure,causing gas channeling;the transient temperature and pressure have an impact on the rate of cement hydration reaction,so cement slurry in the deeper part of wellbore has a higher rate of hydration rate as a result of the high temperature and pressure.For well cementing in deep water regions,the low temperature around seabed would slow the rate of cement hydration and thus prolong the cementing cycle.

Predicting the martensite transformation start-temperature of low alloy steel based on fuzzy identification

A method of fuzzy identification based on T-S fuzzy model was proposed for predicting temperature Ms from chemical composition, austenitizing temperature and time for low alloy steel. The degree of membership of each sample was calculated with fuzzy clustering algorithm. Kalman filtering was used to identify the consequent parameters. Compared with the results obtained by empirical models based on the same data, the results by the fuzzy method showed good precision. The accuracy of the fuzzy model is almost 6 times higher than that of the best empirical model. The influence of alloying elements, austenitizing temperature and time on Ms was analyzed quantitatively by using the fuzzy model. It is shown that there exists a nonlinear relationship between the contents of alloying elements in steels and their Ms, and the effects of austenitizing temperature and time on Ms temperature cannot be neglected.

Predictive Model for Corrosion Rate of Oil Tubes in CO_2/H_2S Coexistent Environment Part Ⅰ: Building of Model

Based on an analysis of the existing models of CO 2 corrosion in literatures and the autoclave simulative experiments, a predictive model of corrosion rate (r corr) in CO 2/H 2S corrosion for oil tubes has been established, in which r corr is expressed as a function of pH, temperature (T), pressure of CO 2 (P CO 2) and pressure of H 2S (P H 2S). The model has been verified by experimental data obtained on N80 steel. The improved features of the predictive model include the following aspects: (1) The influence of temperature on the protectiveness of corrosion film is taken into consideration for establishment of predictive model of the r corr in CO 2/H 2S corrosion. The Equations of scale temperature and scale factor are put forward, and they fit the experimental result very well. (2) The linear relationship still exists between ln r corr and ln P CO 2 in CO 2/H 2S corrosion (as same as that in CO 2 corrosion). Therefore, a correction factor as a function of P H 2S has been introduced into the predictive model in CO 2/H 2S corrosion. (3) The model is compatible with the main existing models.

Simple model for predicting hourly air temperatures inside Chinese solar greenhouses

For an efficient energy greenhouse,temperature is the most important climate parameter,which not only affects crop growth and health but also determines the management of energy consumption.So reliable monitoring of temperature is of great significance,and often hourly values are required.However,due to the low level of automation for Chinese solar greenhouse,the loss or poor quality of climate data often occurs.In order to accurately supplement the missing data,as well as for the generation of future temperature,a 24-hour indoor temperature prediction model was established.It uses a piecewise Bezier curve equation that takes the characteristic temperature as the control point which was determined by the outside weather recording.The 130 d of observed hourly temperature data were used to build and validate the model,and the results showed that the temperature model proposed was accurate and sufficient for the simulation of the trend curve of hourly temperature change inside a solar greenhouse.(EF=0.98,R2=0.89).After validation,this temperature model proposed can be useful for the quantitative analysis of crop growth and optimal management.

Hot spot temperature prediction and operating parameter estimation of racks in data center using machine learning algorithms based on simulation data

In this paper,models to predict hot spot temperature and to estimate cooling air’s working parameters of racks in data centers were established using machine learning algorithms based on simulation data.First,simulation models of typical racks were established in computational fluid dynamics(CFD).The model was validated with field test results and results in literature,error of which was less than 3%.Then,the CFD model was used to simulate thermal environments of a typical rack considering different factors,such as servers’power,which is from 3.3 kW to 20.1 kW,cooling air’s inlet velocity,which is from 1.0 m/s to 3.0 m/s,and cooling air’s inlet temperature,which is from 16℃ to 26℃ The highest temperature in the rack,also called hot spot temperature,was selected for each case.Next,a prediction model of hot spot temperature was built using machine learning algorithms,with servers’power,cooling air’s inlet velocity and cooling air’s inlet temperature as inputs,and the hot spot temperatures as outputs.Finally,based on the prediction model,an operating parameters estimation model was established to recommend cooling air’s inlet temperatures and velocities,which can not only keep the hot spot temperature at the safety value,but are also energy saving.