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In structural simulation and design, an accurate computational model directly determines the effectiveness of performance evaluation. To establish a high-fidelity dynamic model of a complex assembled structure, a Hierarchical Model Updating Strategy (HMUS) is developed for Finite Element (FE) model updating with regard to uncorrelated modes. The principle of HMUS is first elaborated by integrating hierarchical modeling concept, model updating technology with proper uncorrelated mode treatment, and parametric modeling. In the developed strategy, the correct correlated mode pairs amongst the uncorrelated modes are identified by an error minimization procedure. The proposed updating technique is validated by the dynamic FE model updating of a simple fixed–fixed beam. The proposed HMUS is then applied to the FE model updating of an aeroengine stator system (casings) to demonstrate its effectiveness. Our studies reveal that (A) parametric modeling technique is able to build an efficient equivalent model by simplifying complex structure in geometry while ensuring the consistency of mechanical characteristics; (B) the developed model updating technique efficiently processes the uncorrelated modes and precisely identifies correct Correlated Mode Pairs (CMPs) between FE model and experiment; (C) the proposed HMUS is accurate and efficient in the FE model updating of complex assembled structures such as aeroengine casings with large-scale model, complex geometry, high-nonlinearity and numerous parameters; (D) it is appropriate to update a complex structural FE model parameterized. The efforts of this study provide an efficient updating strategy for the dynamic model updating of complex assembled structures with experimental test data, which is promising to promote the precision and feasibility of simulation-based design optimization and performance evaluation of complex structures.

For accurate Finite Element (FE) modeling for the structural dynamics of aeroengine casings, Parametric Modeling-based Model Updating Strategy (PM-MUS) is proposed based on efficient FE parametric modeling and model updating techniques regarding uncorrelated/correlated mode shapes. Casings structure is parametrically modeled by simplifying initial structural FE model and equivalently simulating mechanical characteristics. Uncorrelated modes between FE model and experiment are reasonably handled by adopting an objective function to recognize correct correlated modes pairs. The parametrized FE model is updated to effectively describe structural dynamic characteristics in respect of testing data. The model updating technology is firstly validated by the detailed FE model updating of one fixed–fixed beam structure in light of correlated/uncorrelated mode shapes and measured mode data. The PM-MUS is applied to the FE parametrized model updating of an aeroengine stator system (casings) which is constructed by the proposed parametric modeling approach. As revealed in this study, (A) the updated models by the proposed updating strategy and dynamic test data is accurate, and (B) the uncorrelated modes like close modes can be effectively handled and precisely identify the FE model mode associated the corresponding experimental mode, and (C) parametric modeling can enhance the dynamic modeling updating of complex structure in the accuracy of mode matching. The efforts of this study provide an efficient dynamic model updating strategy (PM-MUS) for aeroengine casings by parametric modeling and experimental test data regarding uncorrelated modes.

A fiber-optic sensor based on a microfiber coupler (MFC) for dual parameter detection of acetaminophen and temperature is proposed. The microfiber structure is manufactured by hydrogen-oxygen flame-heating technology. The characteristics of the transmission spectrum were studied theoretically and experimentally under conditions of different acetaminophen concentrations and temperatures. Acetaminophen concentration detection sensitivities of 102.4 pm/( $\mu $ mol/mL) and −0.01 dB/( $\mu $ mol/mL) in the range of 10 - $50~\mu $ mol/mL, as well as temperature sensitivities of −0.32 nm/°C and −0.001 dB/°C, were obtained for the proposed sensor. These results indicate that the photonic sensor has potential as a tool for pharmaceutical quality control and hospital wastewater treatment.

A high-sensitivity interferometric optical microfiber sensor based on liquid cladding is proposed to detect liquid samples temperatures in special environments. The sensor is manufactured by arc discharge and flame-brushing technology and is encapsulated in a capillary tube and covered with deionized water. The sensor is more susceptible to the influence of the external environment due to the strong evanescent field on the tapered fiber surface. High-sensitivity measurement near room temperature is important, which can be used for effective diagnosis and treatment of local temperature of biological tissues in the field of biosensing. Compared with the silica material, the sensor based on liquid encapsulation has a larger thermo-optical coefficient, and the difference between the thermo-optic coefficient of water and the optical fiber is greater. The results show that with increasing ambient temperature, the wavelength of the transmission spectrum shifts towards shorter wavelengths. The device without liquid packaging has a temperature sensitivity of −57 pm/°C, and the liquid-encapsulated microfiber can reach −415 pm/°C ranging from 30-50 °C, which is 7+ times higher than the former. In addition to improving the temperature sensitivity of the sensor, the water cladding can protect the microfiber from damage. This sensor can be used as a basic sensing unit in remote and intelligent optical fiber sensor networks with great application potential in marine environment monitoring, biomedicine diagnosis and chemical reaction research.

Landing gear lower drag stay is a key component which connects fuselage and landing gear and directly effects the safety and performance of aircraft takeoff and landing. To effectively design the lower drag stay and reduce the weight of landing gear, Global/local Linked Driven Optimization Strategy (GLDOS) was developed to conduct the overall process design of lower drag stay in respect of optimization thought. The whole-process optimization involves two stages of structural conceptual design and detailed design. In the structural conceptual design, the landing gear lower drag stay was globally topologically optimized by adopting multiple starting points algorithm. In the detailed design, the local size and shape of landing gear lower drag stay were globally optimized by the gradient optimization strategy. The GLDOS method adopts different optimization strategies for different optimization stages to acquire the optimum design effect. Through the experimental validation, the weight of the optimized lower dray stay with the developed GLDOS is reduced by 16.79% while keeping enough strength and stiffness, which satisfies the requirements of engineering design under the typical loading conditions. The proposed GLDOS is validated to be accurate and efficient in optimization scheme and design cycles. The efforts of this paper provide a whole-process optimization approach regarding different optimization technologies in different design phases, which is significant in reducing structural weight and enhance design precision for complex structures in aircrafts.

For the nonlinear dynamic analyses of complex mechanical components, it is necessary to apply efficient modeling framework to reduce computational burden. The accurate surrogate model for approximating the nonlinear responses of several failures is a vital issue to provide robust and safe design conditions in complex engineering applications. In this paper, two different Modified multi-extremum Response Surface basis Models (MRSM) are proposed for dynamic nonlinear responses of failure capacities for turbine blisk responses. The proposed MRSM is established using two regression processes including regressed the input variables by linear or exponential basis functions in first calibrating phase and regressed the second-order polynomial basis function using inputs data provided by first stage in second calibrating procedure. A sensitivity analysis using MRSM is proposed to consider the variation of input variables on the nonlinear responses. In the sensitivity analysis procedure, the effects of input variables are evaluated using the calibrating results given from the first regressed process. To evaluate the performance of the proposed MRSM, three multi-extremum failure modes including radial deformation of compressor blisk, maximum strain, and stress of compressor blade and disk are considered. the prediction of MRSM of nonlinear responses for Thermal-fluid–structure system with dynamical nonlinear finite-element analyses is compared with response surface method (RSM) and artificial neural network (ANN). The predicted results of modeling approaches showed that the sensitivity analysis based on MRSM accurately provided the effective degree for input variables. The gas temperature has the highest effects on nonlinear responses of turbine blisk which is followed by angular speed and material density. The MRSM combined with basic exponential function performs better than other models, while the MRSM coupled with linear function is more accurate than ANN and RSM. The proposed MRSM models have illustrated the accurate and efficient framework for approximating dynamic structural analysis of complex components.

Aim: To uncover the molecular mechanisms of early-onset ovarian serous cystadenocarcinoma (EOOSC; patients

The probabilistic design of complex structure usually involves the features of numerous components, multiple disciplines, nonlinearity, and transients and, thus, requires lots of simulations as well. To enhance the modeling efficiency and simulation performance for the dynamic probabilistic analysis of the multicomponent structure, we propose an improved decomposed-coordinated Kriging modeling strategy (IDCKMS), by integrating decomposed-coordinated (DC) strategy, extremum response surface method (ERSM), genetic algorithm (GA), and Kriging surrogate model. The GA is used to resolve the maximum-likelihood equation and achieve the optimal values of the Kriging hyperparameter θ . The ERSM is utilized to resolve the response process of outputs in surrogate modeling by extracting the extremum values. The DC strategy is used to coordinate the output responses of analytical objectives. The probabilistic analysis of an aeroengine high-pressure turbine blisk with blade and disk is conducted to validate the effectiveness and feasibility of this developed method, by considering the fluid–thermal–structural interaction. In respect of this investigation, we see that the reliability of turbine blisk is 0.9976 as the allowable value of radial deformation is 2.319 × 10−3 m. In terms of the sensitivity analysis, the highest impact on turbine blisk radial deformation is of gas temperature, followed by angular speed, inlet velocity, material density, outlet pressure, and inlet pressure. By the comparison of methods, including the DC surrogate modeling method (DCSMM) with quadratic polynomial, the DCSMM with Kriging, and the direct simulation with finite-element model, from the model-fitting features and simulation performance perspectives, we discover that the developed IDCKMS is superior to the other three methods in the precision and efficiency of modeling and simulation. The efforts of this article provide a highly efficient and highly accurate technique for the dynamic probabilistic analysis of complex structure and enrich reliability theory.

A structure-modulated ultralong-period microfiber grating (SULPMG) was successfully designed for simultaneous measurement of microdisplacement and temperature. The SULPMG was fabricated based on the standard single-mode fiber using the arc discharge method followed by hydrogenoxygen flame drawing technology. The resonance wavelength of the two notches shifts toward a shorter range when the microdisplacement varies from 0 μm to 65 μm. The results show that the wavelength sensitivity could reach a maximum value of -288.9 pm/μm with a total depth change of 2.95 dB and a high resolution of 69 nm. The temperature sensitivity of the proposed sensor could reach approximately -26.4 pm/°C over an ambient temperature range of 22-90 °C. The proposed sensor can be expected to be applied in fields pertaining to microfabrication and many industrial applications.

The dynamic probabilistic analysis of complex engineering structures requires thousands of simulations with nonlinear characteristics and hyperparameters, indicating that unacceptable computational loads exist. In this case, the performance of model directly determines the efficiency and accuracy of complex structural dynamic probabilistic analysis. To improve the computational efficiency and accuracy of probabilistic fatigue life prediction for complex structure, we introduce a time-dependent particle swarm optimization (PSO)-based general regression neural network (GRNN) surrogate model (called as TD/PSO-GRNN) by integrating the proposed space-filling Latin hypercube sampling technique and PSO-GRNN regression function. The related theory and method of TD/PSO-GRNN model is first investigated in details. Then the probabilistic fatigue life prediction framework is illustrated in respect of the TD/PSO-GRNN surrogate model. Moreover, the probabilistic fatigue life prediction of an aircraft turbine blisk under multi-physics interaction is performed to validate the TD/PSO-GRNN model. We obtain the distributional characteristics, reliability degree and sensitivity degree of fatigue failure cycle, which are useful for the turbine blisk design. By comparing the direct simulation (FE/FV model), RSM, GRNN, PSO-GRNN and TD/PSO-GRNN, it is revealed that the TD/PSO-GRNN surrogate model is promising to perform the probabilistic fatigue life prediction of the turbine blisk with high computational efficiency and acceptable computational accuracy. The efforts of this study offer a useful insight for the probabilistic design optimization of complex structure and enrich mechanical reliability theory and methods.

An enhanced photothermal signal detection method based on graphene oxide (GO) integrated long period fiber grating (LPFG) for on-site sodium copper chlorophyllin (SCC) quantification is proposed. SCC, as a porphyrin compound, can be photonically excited to induce a stronger photothermal effect. GO offers superior molecular adsorption and thermal conductivity properties; depositing it on the LPFG surface significantly improves the sensitivity and detection efficiency of the SCC photothermal signal, when irradiated with a 405 nm laser. The experimental results showed improved performance compared with those from uncoated LPFG, with a sensitivity of 0.0587 dB (mg L-1)-1 and a limit of detection (LOD) of 0.17 mg kg-1, which is also an order of magnitude lower than that of traditional high-performance liquid chromatography. The proposed method has potential applications in the fields of real-time food safety monitoring, environmental pollutant detection, and disease diagnosis.

The performance and reliability of aircraft engine are seriously affected by multiple failures induced by multi-physical loads. Multi-failure probabilistic design is an effective measure to estimate the multi-failure response traits and quantify the multi-failure risk for the improvement of component reliability. In this paper, we propose a neural network regression-distributed collaborative strategy (NNR-DCS) based on a developed two-step error control technique, to improve the efficiency and accuracy of multi-failure probabilistic analysis. We firstly mathematically model NNR-DCS and then introduce the corresponding multi-failure probabilistic framework. With respect to various failure modes such as deformation failure, stress failure and strain failure, the multi-failure probabilistic analysis of a turbine bladed disk is conducted to evaluate the proposed method. From this simulation, we gain the probabilistic distribution features, reliability degree and sensitivity degree of each failure mode and overall failure modes on turbine bladed disk, which provides a useful reference for improving the reliability and performance of aircraft engine. The comparison of methods (Monte Carlo method, RSM, DCRSM, DCFRM, NNR and NNR-DCS) shows that the proposed NNR-DCS holds high efficiency and accuracy for multi-failure probabilistic analysis. The efforts of this study offer an effective way for multi-failure evaluation from a probabilistic perspective and shed light on the multi-objective reliability-based design optimization of complex structures besides turbine bladed disk.

Air-induced force generated in seals is one key factor on the stability of the rotor system. In this paper, a novel negative dislocated seal (NDS) was developed in respect of dislocated bearing theory, to reduce hydrodynamic pressure effect and air-induced force and improve rotor stability as well. A test rig was built to test rotordynamic characteristics and rotor stability of the NDS. The rotordynamic characteristics of seals were investigated based on the unbalanced synchronous excitation method, and seal-rotor system stability was evaluated by the identification method with an electromagnetic bearing exciter. The effects of both rotating speed and inlet/outlet pressure ratio on the rotordynamic characteristics and rotor stability of both NDS and conventional cylindrical labyrinth seal were experimentally investigated. The results show that with the increasing rotating speed, inlet/outlet pressure ratio is promising to reduce the direct stiffness coefficients of seals and the logarithmic decrement rate of seal-rotor system and enhance both cross stiffness and damping coefficient as well. Besides, the developed NDS effectively reduces cross-stiffness coefficients and increases direct damping coefficients and the logarithmic decrement rate of the seal-rotor system, relative to the conventional cylindrical seal. The proposed seal can effectively improve seal stability of turbomachinery.

Low cycle fatigue (LCF) seriously affects the performance and reliability of turbomachinery like aeroengine. Probabilistic LCF assessment can effectively quantify risk and improve components reliability. To improve the accuracy and efficiency of probabilistic LCF life assessment, a distributed collaborative (DC)-wavelet neural network regression (WNNR) (called as DC-WNNR) surrogate model is developed by proposing Bayesian regularization-Quasi Newton (BR-QN) error control technique. The mathematical model of DC-WNNR is structured and the corresponding probabilistic framework of LCF life assessment is introduced. The probabilistic LCF life assessment for turbine discs is regarded as one case to evaluate the proposed method with respect to various uncertainties such as material property, load fluctuation and model variability. The analysis results reveal that the reliability-based LCF life of a turbine disc under reliability 99.87% is 6 058 cycles; fatigue strength coefficient σf′ and strain range Δɛt play a leading role on the fatigue life failure since their effect probabilities of 45% and 36%, respectively. The comparison of methods (Monte Carlo method, WNNR, DC response surface method and DC-WNNR) shows that the DC-WNNR holds high efficiency and accuracy for the probabilistic LCF assessment. The present efforts offer an effective way for predicting and evaluating structural LCF failure from a probabilistic perspective.

To accurately describe the dynamic features of inter-shaft bearings with localized defect under operation, the dynamic model of inter-shaft bearing with localized defects was established with respect to time-varying displacement excitation. Based on fault simulations on a birotor experimental rig, the developed dynamic model of inter-shaft bearing is validated to have high accuracy (over 99%) when localized defects happen on inner and outer race with co- and counter-rotation, which indicates that the model can be adopted to simulate the faults of inter-shaft bearing instead of experiment. Through investigation of the square-root (SR) amplitudes of bearing vibration with different defect sizes, radial loads, and rotational directions, we find that the SR amplitudes of bearing vibration increase with increasing defect size and radial load for both co- and counter-rotation. The amplitudes of counter-rotation are larger than those of co-rotation for inner race and outer race, and the amplitude of inner race defect are larger than that of outer race defect for the same defect size or same radial load. This work reveals the SR variation of bearing vibration with localized surface defects under different defect sizes and radial loads, and accurately describes the dynamic characteristics of inter-shaft bearing with localized defects. The efforts of this study open a door to adopt a dynamic model in the future to evaluate and monitor the health condition of inter-shaft bearings in an aeroengine or other rotating machinery.

Surface engineering is of great importance to rationally change the catalyst's surface structure and provide insights into the surface reactivity when developing high-efficiency and earth-abundant catalysts for electrocatalytic water splitting. Herein, nitrogen-doped carbon (NC) nanoparticle-decorated MoS2 is successfully constructed via a surface carbonization strategy simply by sintering MoS2 microspheres and dicyandiamide (DCDA), which generates tiny NC nanoparticles on the MoS2 surface. The presence of NC nanoparticles on MoS2 not only facilitates the charge transfer and reaction kinetics but also promotes the hydrogen adsorption during the electrocatalytic hydrogen evolution reaction (HER) process, thereby endowing the catalyst with a much lower overpotential and a higher current density in comparison to pristine MoS2. This work indicates that the surface carbonization strategy provides a promising way for designing highly active electrocatalysts in terms of surface structure engineering.

The cavitation phenomenon in journal bearing and journal whirl are considered to evaluate the static and dynamic characteristics of journal bearing, to be more consistent with the actual operating conditions of journal bearing. With a 3D CFD method, the cavitation and journal whirl are simulated by adopting mesh deformation technique and mixture phase-change model. By comparing the results with those of the traditional Reynolds equation, we observe that first, the phenomena of cavitation and journal whirl are very consistent with the actual operating conditions of journal bearing. Moreover, for single-phase and two-phase flow models, the distribution features of oil film pressure are similar in convergent wedge zone while largely different in divergent part of the gap. The oil film pressure and vapor phase volume fraction notably vary on different whirl positions of the journal. We also observe that as whirl frequency increases, the oil film force gradually increases and the stability of journal bearing decreases rapidly before reaching a plateau. The efforts of this paper provide an efficient method to accurately evaluate the static and dynamic characteristics of journal bearing and offer a useful insight into the accurate analysis and design of journal bearing.

For unacceptable computational efficiency and accuracy on the probabilistic analysis of multi-component system with multi-failure modes, this paper proposed multi-extremum response surface method (MERSM). MERSM model was established based on quadratic polynomial function by taking extremum response surface model as the sub-model of multi-response surface method. The dynamic probabilistic analysis of an aeroengine turbine blisk with two components, and their reliability of deformation and stress failures was obtained, based on thermal-structural coupling technique, by considering the nonlinearity of material parameters and the transients of gas flow, gas temperature and rotational speed. The results show that the comprehensive reliability of structure is 0.9904 when the allowable deformations and stresses of blade and disk are 4.78×10–3 m and 1.41×109 Pa, and 1.64×10–3 m and 1.04×109 Pa, respectively. Besides, gas temperature and rotating speed severely influence the comprehensive reliability of system. Through the comparison of methods, it is shown that the MERSM holds higher computational precision and speed in the probabilistic analysis of turbine blisk, and MERSM computational precision satisfies the requirement of engineering design. The efforts of this study address the difficulties on transients and multiple models coupling for the dynamic probabilistic analysis of multi-component system with multi-failure modes.

To improve the efficiency and accuracy of transient probabilistic analysis of flexible multibody systems, a dynamic fuzzy neural network method-based distributed collaborative strategy is proposed by integrating extremum response surface method and fuzzy neural network. Distributed collaborative dynamic fuzzy neural network method is mathematically modeled and derived by considering the high nonlinearity, strong coupling, and multicomponent characteristics of a flexible multibody system. The proposed method is demonstrated to perform the transient probabilistic analysis of a two-link flexible robot manipulator. We obtain the distributional characteristics, reliability degree, and sensitivity degree of robot manipulator, which are useful for the effective design of robot manipulator. By comparing the full-scale method, extremum response surface method, dynamic fuzzy neural network method, and distributed collaborative dynamic fuzzy neural network method, we find that the distributed collaborative dynamic fuzzy neural network method can be used to perform the transient probabilistic analysis of the robot manipulator and improve the computational efficiency while maintaining a good accuracy. Moreover, this study offers a useful insight for the reliability-based design optimization of flexible multibody systems, and enriches the field of mechanical reliability theory as well.

To efficiently extract the features of aeroengine intershaft bearing faults with weak signal, the variational mode decomposition (VMD) method based on the tolerant adaptive genetic algorithm (TAGA) (TAGA-VMD) is proposed by introducing the idea of tolerance into the traditional adaptive genetic algorithm in this paper. In this method, the tolerant genetic algorithm was adopted to find the optimum empirical parameters K and α of VMD. A fault simulation experiment system of intershaft bearings was built for the inner ring fault and outer ring fault of bearings to verify the proposed TAGA-VMD method. The results show that the proposed method can effectively extract the fault feature frequency of intershaft bearings, and the error between the extracted fault feature frequency and the theoretical value of fault frequency is less than 0.1%. The efforts of this study provide one promising fault feature extraction approach for aeroengine intershaft bearing fault diagnosis with weak signal.

Because people desire a high quality of life, health is a vital standard of living factor that is attracting considerable attention. Thus, the development of methods that enable rapid and real-time evaluation and monitoring of the human health status has been crucial. In this study, we systematically reviewed the techniques of data mining and machine learning (ML) for wearable health monitoring (WHM) and their applications, including conventional ML methods (artificial neural networks, the Kriging model, support vector machines, and principal component analysis) and the latest advance in deep learning (DL) algorithms for WHM; specifically, the advantages of the DL-based approaches over the traditional ML methods were analyzed in line with metrics associated with data feature extraction and identification performances. Moreover, to attain an intuitive insight, this study further reviewed the developments on the classifier performance with regard to detection, monitoring, identification, and accuracy. Finally, with regard to the characteristics of time series data acquired using health condition monitoring through sensors, recommendations and advices are provided to apply DL methods to human body evaluation in specific fields. Moreover, future research trends required to improve the capability of DL algorithms further are offered.

Fatigue assessment for gas turbine blades under combined high and low fatigue cyclic loading is very difficult. In this paper, we propose a probabilistic approach based on a substructure-based kriging surrogate model (SKM) that embeds substructure simulation into a kriging surrogate model (KSM). A distributed collaborative SKM (DCSKM) approach is, then, proposed based on the combination of a distributed collaborative strategy (DC) with SKM. Low-cycle fatigue (LCF) life and high-cycle fatigue (HCF) life of turbine blades are predicted with DCSKM. Based on the simulation data, the combined high and low cycle fatigue (CCF) life and damage assessment are performed with respect to the linear cumulative damage by DCSKM. Further, the relationships between the number of applied cycles and CCF reliability R with survival probabilities P = 0.5 , 0.9 and 0.95, for a confidence level of 0.95, are fitted. Finally, the DCSKM is compared with the Monte Carlo method (MCM) and response surface method (RSM). It is found that (1) the CCF reliability of turbine blades decreases with increasing survival probability for the same applied cycle and decreases with increasing applied cycles under the same survival probability; (2) LCF holds a significant influence on the CCF damage of gas turbine blades; (3) the proposed DCSKM is found to be an available probabilistic analysis approach for the CCF assessment of turbine blades.

The heat load caused by windage heating effect of labyrinth seals seriously influences the sealing of shaft air flow. To reveal the windage heating effect of labyrinth seals to reasonably d...

An optical fiber magnetic field sensor based on serial-tilted-tapered fiber (STTF) integrated with magnetic fluid is proposed. The compact STTF structure consists of two closely tilted-tapered fibers with a length of approximately 836 µm, which results in stronger mode coupling. The transmission characteristics of the proposed sensor under different magnetic field intensities (MFIs) have been studied. The results show that the proposed structure has an outstanding response to MFI and that the highest sensitivity is 32.67 pm/Oe in wavelength and 0.0336 dB/Oe in transmission in the range of 0–75 Oe. The minimum resolution of the proposed sensor is up to 0.6734 Oe. These types of sensors have great potential application in weak magnetic field measurements due to their compact structure and good sensing performance.

Pneumatic soft actuators (PSAs) are components that produce predesigned motion or force in different end-use devices. PSAs are lightweight, flexible, and compatible in human-machine interaction. The use of PSAs in compression therapy has proven promising in proactive pressure delivery with a wide range of dosages for treatment of chronic venous insufficiency and lymphedema. However, effective design and control of PSAs for dynamic pressure delivery have not been fully elaborated. The purpose of this study is to explore interactive working mechanisms between a PSA and lower limbs through establishing fluid-structure coupling models, an intermittent pneumatic compression (IPC) testing system, and conducting experimental validation. The developed IPC testing system consisted of a PSA unit (multichambered bladders laminated with an external textile shell), a pneumatic controller, and various real-time pressure monitoring sensors and accessory elements. The established coupling model characterized the dynamic response process with varying design parameters of the PSA unit, and demonstrated that the design of initial thickness, stiffness, and air mass flow of the PSA, as well as stiffness of limb tissues of the users, influenced PSA-lower limb interactions and resultant pressure dosages. The simulated results presented a favorable agreement with the experimental data collected by the IPC testing system. This study enhanced understanding of PSA-lower limb interactive working mechanisms and provided an evidence-based technical guidance for functional design of PSA. These results contribute to improving the efficacy of dynamic compression therapy for promotion of venous hemodynamics and user compliance in practice.

Efficient analytical model directly enhances the reliability evaluation of flexible mechanism under operation. In this paper, genetic algorithm-based extremum neural network (GA-ENN) is developed as reliability model by introducing the thoughts of extremum and genetic algorithm (GA) into artificial neural network to address the key problems comprising transient response and modeling precision in the dynamic reliability analysis of flexible mechanism in a time domain. The thought of extremum is adopted to simplify transient response process as one extremum value to the difficulty of dynamic reliability analysis induced by transient process response, and the GA is applied to find the optimal model parameters of reliability model. The dynamic reliability analysis of two-link flexible robot manipulator (TFRM) (a typical flexible mechanism) was implemented based on the GA-ENN method, regarding the input random variables of material density, elastic modulus, section sizes of components, and the output response of components’ deformations. From the analysis, the comprehensive reliability of the TFRM is 0.951 when the allowable deformation is 1.8 × 10−2 m. Besides, the maximum deformations of the two components follow the normal distributions with the means of 1.45 × 10−2 m and 1.69 × 10−2 m and the standard variances of 6.77 × 10−4 m and 4.08 × 10−4 m, respectively. Through the comparison of methods, it is illustrated that the developed GA-ENN improves the simulation efficiency and modeling accuracy by overcoming the problems of transient response and model parameter optimization in the dynamic reliability analysis of TFRM.

Deformation is one important failure mode of turbine blades. The quality of a model seriously influences the reliability optimization of turbine blades in turbo machines. To improve the reliability optimization of turbine blades, this paper proposes a novel machine learning-based reliability optimization approach, named improved support vector regression (SR) model (ISRM) method, by fusing artificial bee colony (ABC), traditional SR model, and multipopulation genetic algorithm (MPGA). In this proposed method, the ABC algorithm was applied to find the optimal parameters in the SR model to establish accurate ISRM, following the thought of the surrogate model method and the randomness of constraints. Then the reliability optimization model and procedure with the ISRM method were resolved by the MPGA. Regarding many design parameters (i.e., rotor speed, temperature, and aerodynamic pressure), design objective (i.e., blade deformation), and the randomness constraints of reliability degree and boundary conditions, we performed the reliability optimization of a turbine blade deformation. From the optimization results, we find that the turbine blade deformation is reduced by 0.09329 mm, and the ISRM learning method can improve the reliability optimization design of complex structures with the emphasis on modeling precision and optimization efficiency. The works of this paper provide a machine learning-based reliability optimization approach for the reliability optimization of complex structures and enrich and develop mechanical reliability theory and methods.

Creep/fatigue accelerated failures of K403 superalloy turbine blades are inspected, and the damage behaviors are revealed and failure modes are determined in laboratory. It is concluded that: (1) significant discrepancies of fractographies and cross-sectional microstructures are interpreted by qualitative and quantitative investigations; (2) the observed morphologies are mainly attributed to the dendrite separation and γ' phase rafting behaviors, together with the development of ‘void migration mechanism’, i.e., grain interior → grain boundary → sub grain boundary; (3) the failure of turbine blade changes from the mixed to the intergranular mode, which is essentially controlled by the contribution proportion of creep damage and fatigue damage.

This paper proposes a new type of negative dislocated seal (NDS) based on the dislocated bearing theory to investigate the influential parameters of static rotor eccentricity and dislocated ratio on the static and rotordynamic characteristics of an example seal solved with computational fluid dynamics (CFD) the flow field. The rotordynamic characteristics of the NDS were investigated in respect of the effects of rotor whirling frequency on response force, stiffness coefficients, damping coefficients and rotor system stability, by multifrequency elliptical orbit rotor whirling model. Based on the studies, we reached the following conclusions. The circumferential pressure distribution of NDS and conventional labyrinth seal (LS) presents sine periodic variation approximately, while relative to the LS, the NDS has two divergent wedge gaps and reduces the hydrodynamic pressure effects, then the circumferential pressure difference and tangential force on rotor surface decreases by about 40 %~190 %. The leakage of the NDS linearly increases with the rising eccentricity ratio and dislocation ratio approximately, and is larger (about 0.9 %~1.5 %) than the LS. The direct stiffness coefficients of the two seals increase with the rise of rotor whirling frequency, while the absolute values of both the cross-coupled stiffness coefficients and the damping coefficients decrease with raising rotor whirling frequency. Compared with the LS, the NDS has smaller (about 28.8 %~206.2 %) cross-coupled stiffness coefficient, larger (about 26.15 %~60.39 %) effective damping coefficient, and good stability of rotor system. This study has developed a novel seal structure to improve the seal performance of turbomachinery such as aeroengine.

Inter-shaft bearing as a key component of turbomachinery is a major source of catastrophic accidents. Due to the requirement of high sampling frequency and high sensitivity to impact signals, AE (Acoustic Emission) signals are widely applied to monitor and diagnose inter-shaft bearing faults. With respect to the nonstationary and nonlinear of inter-shaft bearing AE signals, this paper presents a novel fault diagnosis method of inter-shaft bearing called the multi-domain entropy-random forest (MDERF) method by fusing multi-domain entropy and random forest. Firstly, the simulation test of inter-shaft bearing faults is conducted to simulate the typical fault modes of inter-shaft bearing and collect the data of AE signals. Secondly, multi-domain entropy is proposed as a feature extraction approach to extract the four entropies of AE signal. Finally, the samples in the built set are divided into two subsets to train and establish the random forest model of bearing fault diagnosis, respectively. The effectiveness and generalization ability of the developed model are verified based on the other experimental data. The proposed fault diagnosis method is validated to hold good generalization ability and high diagnostic accuracy (~0.9375) without over-fitting phenomenon in the fault diagnosis of bearing shaft.

A numerical simulation-based probabilistic analysis methodology, substructure-based distributed collaborative probabilistic analysis method (SDCPAM), is proposed for accurate and efficient fatigue prognosis based on distributed collaborative response surface method (DCRSM) and substructure analysis method. This paper focuses on the low-cycle fatigue (LCF) damage assessment for turbine blade–disk system. Based on the established probabilistic strain–life models and fatigue reliability model for tandem system, the LCF damage principle of turbine blade–disk system is proposed and integrated with SDCPAM. Following that, the LCF life prediction of the turbine blade–disk is completed, and probabilistic sensitivity analyses of blade and disk to the LCF life of the turbine blade–disk system are achieved. According to the above efforts, the feasibility and effectiveness of SDCPAM is verified. Finally, the LCF damage assessment for the blade–disk system is accomplished, and the influences of applied cycle and reliability level on the LCF damage are investigated. Through the comparisons of the proposed with traditional fatigue reliability model, it is illustrated that the proposed fatigue reliability model is reasonable. The results show that blade and disk almost have the same great influence on the blade–disk LCF life. In addition, applied cycles under normal and lognormal distributions produce the same LCF damage reliability that decreases with increasing applied cycle and reliability level. The efforts of this study indicate the reasonability of the proposed method and models in describing the LCF damage reliability of the blade–disk system, and enrich the reliability theory and method for the complex structure with multi-component and multi-failure mode.

The safety and reliability of any complex mechanical structures are critical to ensure that they can function properly. Therefore, we need to thoroughly evaluate their reliability by performing dynamic probabilistic analyses, including the reliability and sensitivity analyses, which take the variation in the input variables into consideration. The typical approach is by performing the Monte Carlo (MC) simulation, which requires thousands of runs and could be computationally intractable. An efficient and accurate surrogate model can help reduce the computational burden in these analyses. To further reduce the computational complexity, we model only the extremum values, instead of modeling all the output responses within the time domain of interest. The developed surrogate model is called the improved Kriging (IK) algorithm with extremum response surface method (ERSM), or the IK-ERSM model. Compared to the previously developed QP-ERSM, which uses the quadratic polynomial (QP) model, the improved Kriging can better model the nonlinearity within the system. To build the IK model, we employ the genetic algorithm (GA) method to find the Kriging hyperparameters θ, by solving the maximum likelihood equation (MLE). This model shows a good accuracy, with a testing error of less than 1%. The effectiveness of the developed IK-ERSM model is demonstrated to perform the reliability and sensitivity analyses of the compressor blisk radial deformation. For the direct simulation, we consider the fluid–structure coupling of the system, for a more realistic analysis. The results show that the compressor blisk has a reliability degree of 0.9984 when the allowable value of the compressor blisk radial deformation is 1.60 × 10 − 3 m. From the sensitivity analysis results, we identify that the angular speed has the highest impact on the output response, followed by the inlet velocity and material density. Through the validation process, we see that the developed IK-ERSM model has a better overall performance than the QP-ERSM and K-ERSM models, in terms of the fitting times and testing errors. With these results, the IK-ERSM is demonstrated to be efficient and accurate in structural dynamic probabilistic analysis. This study provide a useful insight for the dynamic probabilistic design of complex structure and enrich mechanical reliability theory.