Uncertainties In Mechanical Parameters Research Articles

Probabilistic evaluation method for the stability of large underground cavern considering the uncertainty of rock mass mechanical parameters: A case study of Baihetan underground powerhouse project

For deep large underground cavern projects under complex geological conditions, the determination of rock mass mechanical parameters and stability analysis is fraught with a great deal of uncertainty. This paper proposes a set of methods for estimating rock mass mechanical parameters and probabilistic stability assessment suitable for construction characteristics of large underground caverns. On the one hand, according to the Hoek-Brown criterion and Monte Carlo simulation, a dynamic estimation method for rock mass mechanical parameters is proposed by using Rock Mass Rating (RMR), the uniaxial compressive strength (UCS) of intact rock, and the material constant (mb) as input parameters. On the other hand, considering the uncertainty of rock mass mechanical parameters, a probabilistic evaluation method applicable to the unloading response characteristics of rock mass (including displacement and fracturing zones) around the large cavern is put forward, combining the point estimation method and numerical simulation. The proposed methods were applied to an underground powerhouse, with an excavation span of 34 m and height of 88.7 m, at the Baihetan hydropower station in the southwest of China. Based on extensive field investigations and tests, the probability distributions of key rock mass mechanical parameters were obtained. Furthermore, the high sensitivity of input parameters in the H-B criterion for estimating rock mass mechanical parameters were revealed. Through dynamic simulation, the probability distributions of the displacement and fracturing zones in surrounding rock during the layer-by-layer excavation of the cavern were presented. Comprehensive on-site tests showed that the simulated results were in basic agreement with the actual unloading response behavior during the cavern excavation, validating the correctness and applicability of the proposed methods. The study provides a relatively comprehensive approach for estimating rock mass mechanical parameters and stability evaluation in similar underground engineering projects, and also has guiding significance for predicting and preventing engineering rock mass hazards.

Analysis and optimization of repetitive positioning precision for kinematic couplings of opto-mechanical components considering uncertainty

Kinematic coupling, as an efficient form of connection, is frequently employed for the repeated assembly and accurate positioning of replaceable opto-mechanical assemblies within high-power solid-state laser systems. During the kinematic coupling process, uncertainties in the mechanical behavior within the contact region often hinder the attainment of consistent positioning precision, potentially leading to an inability to meet operational requirements. This study aims to enhance the repetitive positioning precision of kinematic couplings. A predictive model for repetitive positioning precision is established, accounting for uncertainties in contact region forces. Based on this model, an optimization design of kinematic couplings is conducted to mitigate these challenges. Initially, accounting for uncertainties in mechanical parameters within the contact region, we formulate a static analysis computational model to elucidate the probabilistic distribution of these parameters. Subsequently, employing Hertz contact theory and Holm-Archard wear theory, we quantify the elastic deformation and equivalent wear for each contact region through the calculation of normal pressures. Building upon this foundation, we establish a predictive model for repetitive positioning precision. Finally, optimization design is carried out on the friction coefficient of contact surfaces and the structural dimensions of positioning elements. The proposed optimization design is validated through experimental verification. The findings demonstrate that optimization of structural dimensions and contact surface friction coefficients resulted in enhanced repetitive positioning precision of 35.9 %, effectively ameliorating the assembly quality. This study contributes a quantifiable analytical approach to the prediction and optimization design of repetitive positioning precision in kinematic couplings.

Robust sliding mode hybrid controller applied to a wind energy conversion system based on a doubly-fed induction generator to overcome mechanical parameters uncertainties

Robust sliding mode hybrid controller applied to a wind energy conversion system based on a doubly-fed induction generator to overcome mechanical parameters uncertainties

A mathematical-mechanical hybrid driven approach for determining the deformation monitoring indexes of concrete dam

This paper aims at quantifying the uncertainty of mechanical parameters of the concrete dam based on prototype measured data, and puts forward a novel method for determining deformation monitoring indexes based on the quantification results of the uncertainty of deformation causes. Firstly, the deformation components are separated by the HST model for deformation of concrete dam. Secondly, on the basis of the separated hydraulic component and numerical simulation, the posterior distribution of main mechanical parameters involved in the finite element model (FEM) is updated by utilizing Markov chain Monte Carlo sampling method under Bayesian framework. Meanwhile, in order to improve the computational efficiency of the parameter calibration procedure, this study adopts a surrogate model based on the multi-layer perceptron algorithm to replace the finite element calculation. The training set of the surrogate model is generated by Latin hypercube sampling in the sample space and the optimum sample size is discussed. Thirdly, based on the probability inversion analysis results of mechanical parameters, the uncertainty quantification of hydraulic component is realized by forward analysis based on FEM. The uncertainty of temperature and aging component is characterized by confidence interval method. Then, the deformation monitoring indexes are established by integrating the uncertainty quantization results of three components. Finally, the feasibility of the proposed method is verified by using the long-term deformation monitoring data of Jinping-I arch dam. The results show that the monitoring indexes determined by this method is more sensitive to the abnormal measurement due to the uncertainty quantification of hydraulic component.

Assessment of probability of failure on rainfall-induced shallow landslides at slope scale using a physical-based model and fuzzy point estimate method

Shallow hillslope failure caused by rainfall is characterized by complex soil hydrology and mechanical behavior. It is important to understand the hydraulic behavior of hillslopes and quantify the effect of the uncertainty of mechanical parameters on hillslope stability for forewarning and hillslope management. Intra-hole deformation and displacement were record for the hillslope of the Babaoliao collapse site in the Chiayi County, as a case study. The fuzzy point estimation method and physical-based model were combined with the local factor of safety (LFS) theory to calculate the internal local factor of safety of the hillslope. A reliability analysis was then performed to determine the failure probability at different depths. Historical rainfall events were used to validate the model and predict the development of the failure probability for different rainfall patterns with the same warning rainfall. The results revealed that the failure probability model could effectively predict the area of hillslope instability and its changes over time and space. Different rainfall patterns affected the infiltration flux, leading to the difference in hillslope failure time. The delayed rainfall pattern had a significant impact on the time of slope instability, and shallow collapse was most likely to occur earlier. This study can be used as a reference for developing future hillslope warnings.

Updating the models and uncertainty of mechanical parameters for rock tunnels using Bayesian inference

Rock mechanical parameters and their uncertainties are critical to rock stability analysis, engineering design, and safe construction in rock mechanics and engineering. The back analysis is widely adopted in rock engineering to determine the mechanical parameters of the surrounding rock mass, but this does not consider the uncertainty. This problem is addressed here by the proposed approach by developing a system of Bayesian inferences for updating mechanical parameters and their statistical properties using monitored field data, then integrating the monitored data, prior knowledge of geotechnical parameters, and a mechanical model of a rock tunnel using Markov chain Monte Carlo (MCMC) simulation. The proposed approach is illustrated by a circular tunnel with an analytical solution, which was then applied to an experimental tunnel in Goupitan Hydropower Station, China. The mechanical properties and strength parameters of the surrounding rock mass were modeled as random variables. The displacement was predicted with the aid of the parameters updated by Bayesian inferences and agreed closely with monitored displacements. It indicates that Bayesian inferences combined the monitored data into the tunnel model to update its parameters dynamically. Further study indicated that the performance of Bayesian inferences is improved greatly by regularly supplementing field monitoring data. Bayesian inference is a significant and new approach for determining the mechanical parameters of the surrounding rock mass in a tunnel model and contributes to safe construction in rock engineering.

Uncertainty and Sensitivity Analysis of In-Situ Stress in Deep Inclined Strata

In the development process of oil and gas field, the dip angles generally exist in deep strata, and the variability and dispersion in the mechanics parameters of the formation make the computation model of in situ stress for a gentle structure no longer applicable. In light of the fact that the dip angle was large and the geological structure was complex in Sichuan basin and Xinjiang basin, uncertainty of mechanical parameters and tectonic stress coefficients of the formation were analyzed by the Monte Carlo method based on acoustic logging data and stratigraphic dipmeter log data. Taking dip angle and dip direction into account, the uncertainty calculation method for in situ stress in deep inclined strata was proposed. Multi-factor analysis was used to analyze the sensitivity of various factors affecting the in situ stress in deep inclined strata. The results show that, the horizontal in situ stress calculation model adopted in this paper is more suitable for structurally intense areas. The horizontal stress confidence interval is more significant for oil and gas field engineering than single principal stress value. For PY gas field and TLM oil field with different geological conditions and rocks mechanical parameters of formation, the dip angle and effective stress coefficient are the most sensitive to the horizontal in situ stress.

RISE-based adaptive control for EICoSI exoskeleton to assist knee joint mobility

Exoskeleton devices are used to assist joint motion of subjects suffering from mobility deficiencies. Controlling an exoskeleton subjects to high nonlinearities, which are mainly due to the mechanical coupling, external disturbances, parameter uncertainties, and modeling errors. Keeping in view the requirement of a relatively accurate movement tracking while reducing the disturbances effects, there is a need for a robust controller. In this paper, an adaptive RISE (Robust Integral of Sign Error) controller is developed and implemented on the EICoSI (Exoskeleton Intelligently Communicating and Sensitive to Intention) knee exoskeleton. RISE has an advantage over standard controllers that it achieves semi-global asymptotic tracking even in the presence of unstructured disturbances. But to achieve this tracking, high control gains are required. Thus, to limit such high gains, RISE control strategy is combined with an adaptive controller, which has the advantage of improving the tracking performance while reducing the eventual overshoots. The stability of the coupled human/ exoskeleton system is analyzed based on Lyapunov theory and the system has shown semi-global asymptotic stability. The adaptive RISE controller gives better SNR (signal to noise ratio) by 11% and 2% as compared with adaptive and RISE controller respectively. In terms of tracking error, the adaptive RISE controller shows 9% more RMSE than adaptive controller but 41% less when compared with RISE controller Three experimental scenarios are analyzed to validate the proposed controller, namely (i) external disturbances, that could come from the ground during walking; (ii) induced payload, that could come from the resistive/assistive torque from the muscles and (iii) payload with external disturbances. The system is found to be robust and efficient in tracking the reference trajectories while maintaining limited error and high signal to noise ratio.

An innovative computationally efficient hydromechanical coupling approach for fault reactivation in geological subsurface utilization

Estimating the efficiency and sustainability of geological subsurface utilization, i.e., Carbon Capture and Storage (CCS) requires an integrated risk assessment approach, considering the occurring coupled processes, beside others, the potential reactivation of existing faults. In this context, hydraulic and mechanical parameter uncertainties as well as different injection rates have to be considered and quantified to elaborate reliable environmental impact assessments. Consequently, the required sensitivity analyses consume significant computational time due to the high number of realizations that have to be carried out. Due to the high computational costs of two-way coupled simulations in large-scale 3D multiphase fluid flow systems, these are not applicable for the purpose of uncertainty and risk assessments.Hence, an innovative semi-analytical hydromechanical coupling approach for hydraulic fault reactivation will be introduced. This approach determines the void ratio evolution in representative fault elements using one preliminary base simulation, considering one model geometry and one set of hydromechanical parameters. The void ratio development is then approximated and related to one reference pressure at the base of the fault. The parametrization of the resulting functions is then directly implemented into a multiphase fluid flow simulator to carry out the semi-analytical coupling for the simulation of hydromechanical processes. Hereby, the iterative parameter exchange between the multiphase and mechanical simulators is omitted, since the update of porosity and permeability is controlled by one reference pore pressure at the fault base. The suggested procedure is capable to reduce the computational time required by coupled hydromechanical simulations of a multitude of injection rates by a factor of up to 15.

Design of a fractional order adaptive controller for velocity control of a permanent magnet synchronous motor

Purpose – The purpose of this paper is to present a two-loop approach for velocity control of a permanent magnet synchronous motor (PMSM) under mechanical uncertainties. Design/methodology/approach – The inner loop calculates the two-axis stator reference voltages through a feedback linearization method. The outer loop employs an RST control structure to compute the q-axis stator reference current. To increase the robustness of the proposed method, the RST controller parameters are adapted through a fractional order model reference adaptive system (FO-MRAS). The fractional order gradient and Lyapunov methods are utilized as adaptation mechanisms. Findings – The effect of the fractional order derivative in the load disturbance rejection, transient response speed and the robustness is verified through computer simulations. The simulation results show the effectiveness of the proposed method against the external torque and mechanical parameters uncertainties. Originality/value – The proposed FO-MRAS based on Lyapunov adaptation mechanism is proposed for the first time. Moreover, application of the FO-MRAS for velocity control of PMSM is presented for the first time.

Parameter Uncertainty in Shallow Rainfall-triggered Landslide Modeling at Basin Scale: A Probabilistic Approach

This study proposes a methodology to account for the uncertainty of hydrological and mechanical parameters in coupled distributed hydrological-stability models for shallow landslide assessment. A probabilistic approach was implemented in an existing eco-hydrological and landslide model by randomizing soil cohesion, friction angle and soil retention parameters. The model estimates the probability of failure through an assumed theoretical Factor of Safety (FS) distribution, conditioned on soil moisture content. The time-dependent and spatially distributed FS statistics are approximated by the First Order Second Moment (FOSM) method. The model was applied to the Rio Mameyes Basin, located in the Luquillo Experimental Forest in Puerto Rico.

Robust nonlinear receding-horizon control of induction motors

Nonlinear robust receding-horizon control is designed and applied to fifth-order model of induction motor in cascade structure. The controller is based on a finite horizon continuous time minimization of the predicted tracking errors and no online optimization is needed. The initial system is decomposed into two sub-systems (mechanical and electromagnetic sub-systems) in cascade form. An integral action is incorporated in external loop to increase the robustness of the controller with respect to unknown time-varying load torque and mechanical parameters uncertainties. The control uses only measurement of the rotor speed and stator currents. The rotor flux is estimated by Kalman filter. The proposed nonlinear controller permits to achieve asymptotic speed and flux tracking in presence of mechanical parameters uncertainties, unknown variable load torque and resistances variations. In addition, it assures asymptotic decoupling of the speed and flux subsystems. The controller is applied, via simulation, to a benchmark example.

Performance Analysis and Dynamic Characteristics of Hysteresis-Based Indirect Field-Oriented Control of Induction Motordrives

In this paper, the dynamic properties of an induction motor drive using an indirect field control method (IFCM) with a constant frequency hysteresis current controller (CFHCC) are discussed. A comparison between space vector modulation techniques with proportional integral (PI) controller or with a CFHCC is then done. In the first place, the paper presents a theoretical analysis of the whole system consisting of an induction motor with a frequency converter, the principles of scalar and vector control methods.The stability analysis and optimal performance of the proposed control scheme with CFHCC under no-load conditions or mechanical load disturbances and parameter uncertainties are also discussed via experimental and simulation examples. The effect of setting the range of the modulation frequency on these different load curves is also explained.Finally, the advantages of using the IFCM with the hysteresis current controller as compared to the IFCM with a classic PI controller are mentioned.

Adaptive Nonlinear Control of Induction Motors Through AC/DC/AC Converters

AbstractThe problem of controlling induction motors, together with associated AC/DC rectifiers and DC/AC inverters, is addressed. The control objectives are threefold: (i) the motor speed should track any reference signal despite mechanical parameter uncertainties and variations; (ii) the DC Link voltage must be tightly regulated; and (iii) the power factor correction (PFC) w.r.t. the power supply net must be performed in a satisfactory way. First, a nonlinear model of the whole controlled system is developed within the Park coordinates. Then, a multi‐loop nonlinear adaptive controller is synthesized using the backstepping design technique. A formal analysis based on Lyapunov stability and average theory is carried out to exhibit the control system performances. In addition to closed‐loop global asymptotic stability, it is proven that all control objectives (motor speed tracking, rotor flux regulation, DC link voltage regulation and unitary power factor) are asymptotically achieved, up to small but unavoidable harmonic errors (ripples).

Adaptive-backstepping-based design of a nonlinear position controller for an IPMSM servo drive

This paper presents the design of a novel nonlinear position controller for an interior permanent-magnet synchronous motor (IPMSM) servo drive. The motor model equations provide the basis for the proposed controller, which is designed with the adaptive backstepping technique. Various system uncertainties, particularly mechanical-parameter uncertainties, are incorporated in the design of the controller. Using Lyapunov's stability theory, it is verified that the control variables are asymptotically stable. The complete drive system is then simulated with MATLAB/Simulink software. Performance of the proposed adaptive-backstepping-based nonlinear position controller (ABNPC) is investigated under different dynamic operating conditions such as step changes in command positions, step changes in load, and parameter variations. The results indicate that the proposed ABNPC-based IPMSM drive could be a candidate for real-time industrial servo motor drive applications.

Position control of linear induction motor using an adaptive fuzzy integral: Back stepping controller

In this paper the position control of a linear induction motor using adaptive fuzzy back stepping design with integral action is proposed. First, the indirect field oriented control for LIM is derived. Then, an integral back stepping design for indirect field oriented control of LIM is proposed to compensate the uncertainties which occur in the control. Finally, the fuzzy integral-back stepping controller is investigated, where a simple fuzzy inference mechanism is used to achieve a position tracking objective under the mechanical parameters uncertainties. The effectiveness of the proposed control scheme is verified by numerical simulation. The numerical validation results of the proposed scheme have presented good performances compared to the conventional integral back stepping control.

Analysis of Piezoresistance Gauges for Stress in Divergent Flow Fields

An analytical procedure is presented for interpreting resistance histories from three piezoresistance gauges in a plane at a “point” to uniquely determine the normal stress at that point for nonuniaxial strain loading. The response of the elements can be plastic, and the analysis is valid for unloading and reloading. The only restrictions are that the elements must be aligned with the principal directions of the flow field, and the coupling between matrix strains and gauge strains must be specified. The gauges' piezoresistance properties, constitutive properties, and the coupling relations form a set of highly nonlinear simultaneous equations that can be solved for the normal stress and in-plane strains. A computer program named PIEZOR3 performs this task. Finite-element calculations are used to generate resistance histories from which the original stresses and strains can be computed with PIEZOR3. The computational examples demonstrate the accuracy of the procedure and quantify the uncertainties in the resolved stresses arising from uncertainties in mechanical, piezoresistance, and strain-coupling parameters.