DAMAGE IDENTIFICATION OF BEAM STRUCTURE BASED ON HYBRID NEURAL GENETIC ALGORITHM

It has established that a crack has an important effect on the dynamic behavior of a structure.To identify the location and depth of a crack on a structure,a method is presented which uses hybrid neuro-genetic technique.This method combines genetic algorithm（GA） with neural network,and uses genetic algorithm to optimize the BP network.Though the finite element analysis,acquire the first three natural frequencies of the beam,which used as inputs into neural network models for damage assessment.Genetic algorithm（GA） optimizes the weights and thresholds of the network,and using the optimization results as the initial value of the three layer BP neural network.Though the training of the sample data,then establish the suitable network.Finally,using the theoretical natural frequencies as the test values,obtain the predicted results of crack parameters.Comparing the theoretical value with the predicted value,the results show that this algorithm can identify the damage structure accurately

STRUCTURAL DAMAGE IDENTIFICATION BASED ON LMD SAMPLE ENTROPY AND RBF NETWORK

Adaptive time frequency analysis based on local mean decomposition and nonlinear quantization ability of sample entropy,combined with radial basis function（ RBF） neural network. A method of structural damage identification based on local mean decomposition（ LMD） sample entropy and radial basis function neural network is proposed. Firstly,the original signal is decomposed into a number of product function components（ PF component） by LMD to the original signal of structure vibration.Then extract the sample entropy of the first 3 PF components to realize the feature quantization of the PF component. Finally,the sample entropy of the component is used as the damage characteristic vector. The radial basis function neural network is used to identify the bottom plate of scaled carbody for high-speed train. The experimental results show that while this method is used to identify structural damage,the damage identification errors of location and degree are 96. 97% and 96. 25% respectively. The validity and accuracy of this method in structural damage diagnosis are proved

Weld failure analysis of 2205 duplex stainless steel nozzle

Failure analyses of weld joint between the nozzle and the head of the reactor made of 2205 duplex stainless steel was performed by optical microscopy (OM) and scanning electron microscopy (SEM). Cracks were found in HAZ of the weld. The depth of the cracks is equal to the thickness of the inner weld. Localized uneven distribution of ferrite/austenite with 80–90% ferrite in weld is found. Results show that the cracks occurred along columnar granular with cleavage fracture. Poor weld process probably results in these cracks

Permeability Characterization and Its Correlation with Pore Microstructure of Stress-Sensitive Tight Sandstone: Take Chang 6 in Ordos Basin for Example

Tight reservoirs are sensitive to stress changes during fracturing and oil and gas production. Facing different production modes, the variation characteristics of rock permeability and pore structure need to be further clarified. In this study, using a self-built high temperature and pressure physical simulation device and NMR equipment, the influence of the stress loading method, cyclic loading, and loading rate on rock permeability and pore characteristics were analyzed, and the relationship between them was clarified. The permeability sensitivity under variable confining pressure (63.3%) was greater than that of variable flow pressure (46.4%). The damage rate decreased with repeated loading (63.3%-35.8%) and increased loading rate (53.1%-42.3%). As for the pore features, when the net stress increased, the volume variation range of micropores was greater than that of mesopores. The damage rate of permeability (63.3%) was obviously larger than that of pore volume (10.4%). The slope of the fitted curve of permeability and pore volume decreased evidently with loading times. The structure deformation of rock skeleton and the migration of cement had a great influence on permeability in the first loading. Later, it was mainly the bulk deformation of rock particles, the particles’ contact surface increasing and the seepage space shrinking slowly. Eventually, the permeability remained stable due to the limited pore compression. This study can provide a reference for designing reasonable production parameters and reducing formation damage

Research on Prediction of Movable Fluid Percentage in Unconventional Reservoir Based on Deep Learning

In order to improve the measurement speed and prediction accuracy of unconventional reservoir parameters, the deep neural network (DNN) is used to predict movable fluid percentage of unconventional reservoirs. The Adam optimizer is used in the DNN model to ensure the stability and accuracy of the model in the gradient descent process, and the prediction effect is compared with the back propagation neural network (BPNN), K-nearest neighbor (KNN), and support vector regression model (SVR). During network training, L2 regularization is used to avoid over-fitting and improve the generalization ability of the model. Taking nuclear magnetic resonance (NMR) T2 spectrum data of laboratory unconventional core as input features, the influence of model hyperparameters on the prediction accuracy of reservoir movable fluids is also experimentally analyzed. Experimental results show that, compared with BPNN, KNN, and SVR, the deep neural network model has a better prediction effect on movable fluid percentage of unconventional reservoirs; when the model depth is five layers, the prediction accuracy of movable fluid percentage reaches the highest value, the predicted value of the DNN model is in high agreement with the laboratory measured value. Therefore, the movable fluid percentage prediction model of unconventional oil reservoirs based on the deep neural network model can provide certain guidance for the intelligent development of the laboratory’s reservoir parameter measurement

Time-Variant Reliability Optimization for Stress Balance in Press-Pack Insulated Gate Bipolar Transistors

Stress imbalance significantly affects the performance of a press-pack insulated gate bipolar transistor (IGBT). Time-variant loads and conditions lead to the stress fluctuations, exacerbating the impacts. The conventional reliability optimization faces efficiency barriers due to the nested time-variant reliability analysis and design optimization. In this paper, a time-variant reliability optimization approach for press-pack IGBTs is proposed to address the efficiency issue of the IGBT reliability optimization. The performance functions of the maximum and typical stresses are formulated as the optimization objective and constraint. A time-variant reliability optimization model is formulated considering the stress balance reliability degradation within the service cycle. A decoupling algorithm is proposed to transform the nested optimization into a sequential iteration of static reliability optimization and time-variant reliability analysis. The reliability analysis utilizes the performance function continuity in the time domain to reduce the evaluations for the most likelihood points, thereby enhancing efficiency. Numerical and experimental results on an actual IGBT demonstrate the accuracy of the stress balance performance analysis. The time-variant reliability optimization based on the performance functions improves the stress balance performance by 16.3% and meets the reliability requirements within the service cycle. Compared with the conventional double-loop approach, the difference between the solution of the proposed approach with the reference solution is 0.4%, and the efficiency is 334 times that of the double-loop approach. The performance advantages in accuracy and efficiency exhibit the application potential of this approach

Overexpression CPT1A reduces lipid accumulation via PPARα/CD36 axis to suppress the cell proliferation in ccRCC

Clear cell renal carcinoma (ccRCC) is histologically defined by its cytoplasmic lipid deposits. Lipid metabolism disorder largely increases the risk of ccRCC. In this study, we aimed to investigate the biological functions and molecular mechanisms of carnitine palmitoyl transferase 1A (CPT1A) in ccRCC. Our results showed that CPT1A is decreased in ccRCC clinical samples and cell lines compared with that in normal samples. Lentivirus overexpressing CPT1A was used to investigate the neoplastic phenotypes of ccRCC, and the results showed that lipid accumulation and tumor growth are attenuated both in vitro and in vivo. In addition, CPT1A prevents cholesterol uptake and lipid accumulation by increasing the peroxisome proliferator-activated receptor α (PPARα) level through regulation of Class B scavenger receptor type 1 (SRB1) and cluster of differentiation 36 (CD36). Furthermore, PI3K/Akt signaling pathway promotes tumor cell proliferation in ccRCC, which is related to the enhanced expression of CD36. Functionally, weakened CPT1A expression is critical for lipid accumulation to promote ccRCC development. Collectively, our research unveiled a novel function of CPT1A in lipid metabolism via PPARα/CD36 axis, which provides a new theoretical explanation for the pathogenesis of ccRCC. Targeting CPT1A may be a potential therapeutic strategy to treat ccRCC

An Experimental Study on Stress Sensitivity of Tight Sandstones with Different Microfractures

Aiming at the stress sensitivity problem of tight reservoirs with different microfractures, the cores of H oilfield and J oilfield with different microfractures were obtained through the fractures experiment, so as to study the change of gas permeability in tight sandstone core plug during the change of confining pressure. Besides, we use the nuclear magnetic resonance (NMR) spectra of the core before and after saturation to verify whether the core has been successfully fractured. Based on Terzaghi’s effective stress principle, the permeability damage rate (D) and the stress sensitivity coefficient (Ss) are used to evaluate the stress sensitivity of the core, which show consistency in evaluating the stress sensitivity. At the same time, we have studied the petrological characteristics of tight sandstone in detail using thin section (TS) and scanning electron microscope (SEM). The results show that the existence of microfractures is the main factor for the high stress sensitivity of tight sandstone. In addition, because of the small throat of the tight reservoir core, the throat closes when the overlying stress increases. As a result, the tight sandstone pore size is greatly reduced and the permeability is gradually reduced. Therefore, in the development of tight reservoirs, we should not only consider the complex fracture network produced by fracturing, but also pay attention to the permanent damage of reservoirs caused by stress sensitivity