Time-domain dynamic constitutive model suitable for mucky soil site seismic response

Soil nonlinear behavior displays noticeable effects on the site seismic response.This study proposes a new functional expression of the skeleton curve to replace the hyperbolic skeleton curve.By integrating shear modulus and combining the dynamic skeleton curve and the damping degradation coefficient,the constitutive equation of the logarithmic dynamic skeleton can be obtained,which considers the damping effect in a soil dynamics problem.Based on the finite difference method and the multi-transmitting boundary condition,a 1D site seismic response analysis program called Soilresp1D has been developed herein and used to analyze the time-domain seismic response in three types of sites.At the same time,this study also provides numerical simulation results based on the hyperbolic constitutive model and the equivalent linear method.The results verify the rationality of the new soil dynamic constitutive model.It can analyze the mucky soil site nonlinear seismic response,reflecting the deformation characteristics and damping effect of the silty soil.The hysteresis loop area is more extensive,and the residual strain is evident.

Evaluation of unconstrained and constrained mathematical functions to model girth growth of rubber trees (Hevea brasiliensis) using young agemeasurements

No attempt has been made to date to model growth in girth of rubber tree （Hevea brasiliansis）. We evaluated the few widely used growth functions to identify the most parsimonious and biologically reasonable model for describing the girth growth of young rubber trees based on an incomplete set of young age measurements. Monthly data for girth of immature trees （age 2 to 12 yearsi from two locations were sub- jected to modelling. Re-parameterized, unconstrained and constrained growth functions,of Richards （RM）, Gompertz （GM） and the monomo- lecular ＇model ^（MM） were fitted to data. Duration of growth was the firsf constraint introduced. In the stagel We attempted a population aver- age （PA） model to capture the trend in growth. The best PA model was fitted as a subject specific （SS） model. We used appropriate error vari- ance-covariance structure to account for correlation due to repeated measurements over time. Unconstrainecl functions underestimated the asymptotic maximum that did not reflective carrying capacity of the locations. Underestimafions were attributed to the partial set＇ of meas- urements made during the early growth phase of the trees. MM proved superior to RM and GM. In the randomcoefficient models, both Gf and Go appeared to be influenced by tree level effects. Inclusion of diagonal definite positive matrix removed the correlation between random effects. The results were similar at both locations. In the overall assessment MM appeared as the candidate model for studying the girth-age relationships in Hevea trees. Based on the fitted model we conclude that, in Hevea trees, growth rate is maintained at maximum value at to, then decreases until the final state at dG/dt 〉 0, resulting in yield curve with no period of accelerating growth. One physiological explanation is that photosynthetic activity in Hevea trees decreases as girth increases and constructive metabolism is larger than destructive metabolism.

A TRUST REGION METHOD WITH A CONIC MODEL FOR NONLINEARLY CONSTRAINED OPTIMIZATION

Trust region methods are powerful and effective optimization methods. The conic model method is a new type of method with more information available at each iteration than standard quadratic-based methods. The advantages of the above two methods can be combined to form a more powerful method for constrained optimization. The trust region subproblem of our method is to minimize a conic function subject to the linearized constraints and trust region bound. At the same time, the new algorithm still possesses robust global properties. The global convergence of the new algorithm under standard conditions is established.

Synchrotron-Based Data-Constrained Modeling Analysis of Microscopic Mineral Distributions in Limestone

Three dimensional (3D) microscopic distributions of dolomite and calcite in a limestone sample have been analyzed with a data-constrained modeling (DCM) technique using synchrotron radiation-based multi-energy X-ray computed tomography (CT) data as constraints. In order to optimize the experimental parameters, X-ray CT simulations and DCM analysis of a numerical phantom consisting of calcite (CaCO3) and dolomite (CaMg(CO3)2) have been used to investigate the effects on the predicted results in relation to noise, X-ray energy and sample-to-detector distance (SDD). The simulation results indicate that the optimal X-ray energies are 25 and 35 keVs, and the SDD is 10 mm. The high resolution 3D distributions of mineral phases of a natural limestone have been obtained. The results are useful for quantitative understanding of mineral, porosity, and physical property distributions in relation to oil and gas reservoirs hosted in carbonate rocks, which account for more than half of the world’s conventional hydrocarbon resources. The case studied is also instructive for the applicability of the DCM methods for other types of composite materials with modest atomic number contrasts between the mineral phases.

Disturbance rejection tube model predictive levitation control of maglev trains

Magnetic levitation control technology plays a significant role in maglev trains.Designing a controller for the levitation system is challenging due to the strong nonlinearity,open-loop instability,and the need for fast response and security.In this paper,we propose a Disturbance-Observe-based Tube Model Predictive Levitation Control(DO-TMPLC)scheme combined with a feedback linearization strategy for the levitation system.The proposed strategy incorporates state constraints and control input constraints,i.e.,the air gap,the vertical velocity,and the current applied to the coil.A feedback linearization strategy is used to cancel the nonlinearity of the tracking error system.Then,a disturbance observer is implemented to actively compensate for disturbances while a TMPLC controller is employed to alleviate the remaining disturbances.Furthermore,we analyze the recursive feasibility and input-to-state stability of the closed-loop system.The simulation results indicate the efficacy of the proposed control strategy.

Fourth-Order Conservative Transport on Overset Grids Using Multi-Moment Constrained Finite Volume Scheme for Oceanic Modeling

With an increase in model resolution,compact high-order numerical advection scheme can improve its effectiveness and competitiveness in oceanic modeling due to its high accuracy and scalability on massive-processor computers.To provide high-quality numerical ocean simulation on overset grids,we tried a novel formulation of the fourth-order multi-moment constrained finite volume scheme to simulate continuous and discontinuous problems in the Cartesian coordinate.Utilizing some degrees of freedom over each cell and derivatives at the cell center,we obtained a two-dimensional(2D)cubic polynomial from which point values on the extended overlap can achieve fourth-order accuracy.However,this interpolation causes a lack of conservation because the flux between the regions are no longer equal;thus,a flux correction is implemented to ensure conservation.A couple of numerical experiments are presented to evaluate the numerical scheme,which confirms its approximately fourth-order accuracy in conservative transportation on overset grid.The test cases reveal that the scheme is effective to suppress numerical oscillation in discontinuous problems,which may be powerful for salinity advection computing with a sharp gradient.

Improved Differential Evolution with Shrinking Space Technique for Constrained Optimization

Most of the current evolutionary algorithms for constrained optimization algorithm are low computational efficiency. In order to improve efficiency, an improved differential evolution with shrinking space technique and adaptive trade-off model, named ATMDE, is proposed to solve constrained optimization problems. The proposed ATMDE algorithm employs an improved differential evolution as the search optimizer to generate new offspring individuals into evolutionary population. For the con- straints, the adaptive trade-off model as one of the most important constraint-handling techniques is employed to select better individuals to retain into the next population, which could effectively handle multiple constraints. Then the shrinking space technique is designed to shrink the search region according to feedback information in order to improve computational efficiency without losing accuracy. The improved DE algorithm introduces three different mutant strategies to generate different offspring into evo- lutionary population. Moreover, a new mutant strategy called ＂DE/rand/best/l＂ is constructed to generate new individuals according to the feasibility proportion ofcurrent population. Finally, the effectiveness of the pro- posed method is verified by a suite of benchmark functions and practical engineering problems. This research presents a constrained evolutionary algorithm with high efficiency and accuracy for constrained optimization problems.

Identification and nonlinear model predictive control of MIMO Hammerstein system with constraints

This work is concerned with identification and nonlinear predictive control method for MIMO Hammerstein systems with constraints. Firstly, an identification method based on steady-state responses and sub-model method is introduced to MIMO Hammerstein system. A modified version of artificial bee colony algorithm is proposed to improve the prediction ability of Hammerstein model. Next, a computationally efficient nonlinear model predictive control algorithm(MGPC) is developed to deal with constrained problem of MIMO system. The identification process and performance of MGPC are shown. Numerical results about a polymerization reactor validate the effectiveness of the proposed method and the comparisons show that MGPC has a better performance than QDMC and basic GPC.

Incorporating shape constraints in generalized additive modelling of the height-diameter relationship for Norway spruce

Background： Measurements of tree heights and diameters are essential in forest assessment and modelling. Tree heights are used for estimating timber volume, site index and other important variables related to forest growth and yield, succession and carbon budget models. However, the diameter at breast height （dbh） can be more accurately obtained and at lower cost, than total tree height. Hence, generalized height-diameter （h-d） models that predict tree height from dbh, age and other covariates are needed. For a more flexible but biologically plausible estimation of covariate effects we use shape constrained generalized additive models as an extension of existing h-d model approaches. We use causal site parameters such as index of aridity to enhance the generality and causality of the models and to enable predictions under projected changeable climatic conditions. Methods： We develop unconstrained generalized additive models （GAM） and shape constrained generalized additive models （SCAM） for investigating the possible effects of tree-specific parameters such as tree age, relative diameter at breast height, and site-specific parameters such as index of aridity and sum of daily mean temperature during vegetation period, on the h-d relationship of forests in Lower Saxony, Germany. Results： Some of the derived effects, e.g. effects of age, index of aridity and sum of daily mean temperature have significantly non-linear pattern. The need for using SCAM results from the fact that some of the model effects show partially implausible patterns especially at the boundaries of data ranges. The derived model predicts monotonically increasing levels of tree height with increasing age and temperature sum and decreasing aridity and social rank of a tree within a stand, The definition of constraints leads only to marginal or minor decline in the model statistics like AIC An observed structured spatial trend in tree height is modelled via 2-dimensional surface fitting. Conclusions： We demonstrate that the SCAM approach allows optimal regression modelling flexibility similar to the standard GAM but with the additional possibility of defining specific constraints for the model effects. The longitudinal character of the model allows for tree height imputation for the current status of forests but also for future tree height prediction.

Decentralized adaptive neural network sliding mode position/force control of constrained reconfigurable manipulators

A decentralized adaptive neural network sliding mode position/force control scheme is proposed for constrained reconfigurable manipulators. Different from the decentralized control strategy in multi-manipulator cooperation, the proposed decentralized position/force control scheme can be applied to series constrained reconfigurable manipulators. By multiplying each row of Jacobian matrix in the dynamics by contact force vector, the converted joint torque is obtained. Furthermore, using desired information of other joints instead of their actual values, the dynamics can be represented as a set of interconnected subsystems by model decomposition technique. An adaptive neural network controller is introduced to approximate the unknown dynamics of subsystem. The interconnection and the whole error term are removed by employing an adaptive sliding mode term. And then, the Lyapunov stability theory guarantees the stability of the closed-loop system. Finally, two reconfigurable manipulators with different configurations are employed to show the effectiveness of the proposed decentralized position/force control scheme.

Longitudinal height-diameter curves for Norway spruce, Scots pine and silver birch in Norway based on shape constraint additive regression models

Background： Generalized height-diameter curves based on a re-parameterized version of the Korf function for Norway spruce （Piceo abies （L.） Karst.）, Scots pine （Pinus sylvestris L.） and silver birch （Betula pendula Roth） in Norwa are presented. The Norwegian National Forest Inventory （NFI） is used as data base for estimating the model parameters. The derived models are developed to enable spatially explicit and site sensitive tree height imputatio in forest inventories as well as future tree height predictions in growth and yield scenario simulations. Methods： Generalized additive mixed models （gamm） are employed to detect and quantify potentially non-linear effects of predictor variables. In doing so the quadratic mean diameter serves as longitudinal covariate since stand ag as measured in the NFI, shows only a weak correlation with a stands developmental status in Norwegian forests. Additionally the models can be locally calibrated by predicting random effects if measured height-diameter pairs are available. Based on the model selection of non-constraint models, shape constraint additive models （scare） were fit tc incorporate expert knowledge and intrinsic relationships by enforcing certain effect patterns like monotonicity. Results： Model comparisons demonstrate that the shape constraints lead to only marginal differences in statistical characteristics but ensure reasonable model predictions. Under constant constraints the developed models predict increasing tree heights with decreasing altitude, increasing soil depth and increasing competition pressure of a tree. / two-dimensional spatially structured effect of UTM-coordinates accounts for the potential effects of large scale spatial correlated covariates, which were not at our disposal. The main result of modelling the spatially structured effect is lower tree height prediction for coastal sites and with increasing latitude. The quadratic mean diameter affects both the level and the slope of the height-diameter curve and both effects are positive. Conclusions： In this investigation it is assumed that model effects in additive modelling of height-diameter curves which are unfeasible and too wiggly from an expert point of view are a result of quantitatively or qualitatively limited data bases. However, this problem can be regarded not to be specific to our investigation but more general since growth and yield data that are balanced over the whole data range with respect to all combinations of predictor variables are exceptional cases. Hence, scare may provide methodological improvements in several applications by combining the flexibility of additive models with expert knowledge.

Inversion of time-domain airborne EM data with IP effect based on Pearson correlation constraints

Due to the induced polarization(IP)eff ect,the sign reversal often occurs in timedomain airborne electromagnetic(AEM)data.The inversions that do not consider IP eff ect cannot recover the true umderground electrical structures.In view of the fact that there are many parameters of airborne induced polarization data in time domain,and the sensitivity diff erence between parameters is large,which brings challenges to the stability and accuracy of the inversion.In this paper,we propose an inversion mehtod for time-domain AEM data with IP effect based on the Pearson correlation constraints.This method uses the Pearson correlation coeffi cient in statistics to characterize the correlation between the resistivity and the chargeability and constructs the Pearson correlation constraints for inverting the objective function to reduce the non uniqueness of inversion.To verify the eff ectiveness of this method,we perform both Occam’s inversion and Pearson correlation constrained inversion on the synthetic data.The experiments show that the Pearson correlation constrained inverison is more accurate and stable than the Occam’s inversion.Finally,we carried out the inversion to a survey dataset with and without IP eff ect.The results show that the data misfit and the continuity of the inverted section are greatly improved when the IP eff ect is considered.

A Dual-mode Nonlinear Model Predictive Control with the Enlarged Terminal Constraint Sets

Aiming at a class of nonlinear systems with multiple equilibrium points, we present a dual-mode model predictive control algorithm with extended terminal constraint set combined with control invariant set and gain schedule. Local LQR control laws and the corresponding maximum control invariant sets can be designed for finite equilibrium points. It is guaranteed that control invariant sets are overlapped each other. The union of the control invariant sets is treated as the terminal constraint set of predictive control. The feasibility and stability of the novel dual-mode model predictive control are investigated with both variable and fixed horizon. Because of the introduction of extended terminal constrained set, the feasibility of optimization can be guaranteed with short prediction horizon. In this way, the size of the optimization problem is reduced so it is computationally efficient. Finally, a simulation example illustrating the algorithm is presented.

Performance Analysis of GNSS/MIMU Tight Fusion Positioning Model with Complex Scene Feature Constraints

In order to meet the requirements of high-precision vehicle positioning in complex scenes,an observation noise adaptive robust GNSS/MIMU tight fusion model based on the gain matrix is proposed considering static zero speed,non-integrity,attitude,and odometer constraint models.In this model,the robust equivalent gain matrix is constructed by the IGG-Ⅲmethod to weaken the influence of gross error,and the on-line adaptive update of observation noise matrix is carried out according to the change of actual observation environment,so as to improve the solution performance of filtering system and realize high-precision position,attitude and velocity measurement when GNSS signal is unlocked.A real test on a road over 600 km demonstrates that,in about 100 km shaded environment,the fixed rate of GNSS ambiguity resolution in the shaded road is 10%higher than that of GNSS only ambiguity resolution.For all the test,the positioning accuracy can reach the centimeter level in an open environment,better than 0.6 m in the tree shaded environment,better than 1.5 m in the three-dimensional traffic environment,and can still maintain a positioning accuracy of 0.1 m within 10 s when the satellite is unlocked in the tunnel scene.The proposal and verification of the algorithm model show that low-cost MIMU equipment can still achieve high-precision positioning when there are scene feature constraints,which can meet the problem of high-precision vehicle navigation and location in the urban complex environment.

DYNAMIC INTERACTION OF PLANE WAVES WITH A UNILATERALLY FRICTIONALLY CONSTRAINED INCLUSION-TIME DOMAIN BOUNDARY ELEMENT ANALYSIS

A 2D time domain boundary element method(BEM)is developed to solve the transient scattering of plane waves by a unilaterally frictionally constrained inclusion.Coulomb friction is assumed along the contact interface.The incident wave is assumed strong enough so that localized slip and separation take place along the interface.The present problem is in effect a nonlinear boundary value problem since the mixed boundary conditions involve unknown intervals (slip,separation and stick regions).In order to determine the unknown intervals,an iterative technique is developed.As an example,we consider the scattering of a circular cylinder embedded in an infinite solid.

An Adaptive Nonhydrostatic Atmospheric Dynamical Core Using a Multi-Moment Constrained Finite Volume Method

An adaptive 2 D nonhydrostatic dynamical core is proposed by using the multi-moment constrained finite-volume(MCV) scheme and the Berger-Oliger adaptive mesh refinement(AMR) algorithm. The MCV scheme takes several pointwise values within each computational cell as the predicted variables to build high-order schemes based on single-cell reconstruction. Two types of moments, such as the volume-integrated average(VIA) and point value(PV), are defined as constraint conditions to derive the updating formulations of the unknowns, and the constraint condition on VIA guarantees the rigorous conservation of the proposed model. In this study, the MCV scheme is implemented on a height-based, terrainfollowing grid with variable resolution to solve the nonhydrostatic governing equations of atmospheric dynamics. The AMR grid of Berger-Oliger consists of several groups of blocks with different resolutions, where the MCV model developed on a fixed structured mesh can be used directly. Numerical formulations are designed to implement the coarsefine interpolation and the flux correction for properly exchanging the solution information among different blocks. Widely used benchmark tests are carried out to evaluate the proposed model. The numerical experiments on uniform and AMR grids indicate that the adaptive model has promising potential for improving computational efficiency without losing accuracy.

Time-Domain Protection for Transmission Lines Connected to Wind Power Plant based on Model Matching and Hausdorff Distance

The system impedance instability,high-order harmonics,and frequency offset are main fault characteristics of wind power system.Moreover,the measurement angle of faulty phase is affected by rotation speed frequency component,which causes traditional directional protections based on angle comparison between voltage and current to operate incorrectly.In this paper,a time-domain protection for connected to wind power plant based on model matching is proposed,which compares the calculated current and the measured current to identify internal faults and external faults.Under external faults,the calculated current and measured current waveform are quite similar because the protected transmission lines is equivalent to a lumped parameter model and the model itself is not damaged.However,the similarity of calculated current and measured current is quite low,due to destroyed integrity of model under internal faults.Additionally,Hausdorff distance is introduced to obtain the similarity of the calculated current and measured current.Since the proposed protection scheme is applied in time domain,it is independent from current frequency offsets of wind energy system,high-order harmonics,and system impedance variations.Comprehensive case studies are undertaken through Power Systems Computer Aided Design(PSCAD),while simulation results verify the accuracy and efficiency of the proposed approach in fault identification.

Constraining the Generalized and Superfluid Chaplygin Gas Models with the Sandage-Loeb Test

The Sandage Loeb （SL） test is a direct measurement of the cosmic expansion by probing the redshift drifts of quasi-stellar objects in the ＇redshift desert＇ of 2 〈 z 〈 5. In this work, we investigate its constraints on the unified dark energy and dark matter models including the generalized Chaplygin gas and the superfluid Chaplygin gas. In addition, type Ia supernovae （SNIa） data and the distance ratios derived from the cosmic microwave background radiation and baryon acoustic oscillation observations （CMB/BAO） are also used. We find that the mock SL data gives the tightest constraints on the model parameters and it can help to reduce the parameter regions allowed by the present SNIa＋CMB/BAO by about 75% when all datasets considered are combined. Thus the SL test is a worthy and long awaited measurement to probe effectively the cosmic expanding history and the properties of dark energy.

基于多项式混沌展开的电力系统概率可用输电能力评估

大规模开发和利用风能有利于实现电力系统清洁低碳转型,是实现国家“碳达峰、碳中和”战略目标的重要技术手段,但风电出力的强不确定性对电力系统区域间可用输电能力(available transfer capability,ATC)评估带来了全新的挑战,传统用于求解计及风电出力不确定性的概率ATC评估模型在计算效率和计算精度方面均存在一定的不足。为此,该文提出一种基于多项式混沌展开(polynomialchaos expansion,PCE)的电力系统概率ATC评估方法,该方法首先构建基于机会约束的电力系统概率ATC评估模型;然后,根据风电出力预测误差的概率分布特征,选择对应的正交多项式为基函数以近似风电出力预测误差及电力网络中与之相关联的其他随机变量;进一步,借助Galerkin投影和基于一阶矩、二阶矩的机会约束转化方法,将所构建的机会约束模型的概率约束转化为确定性约束,实现基于机会约束的概率ATC评估模型向易于求解的确定性优化模型的转化;进而,将概率ATC评估模型的求解问题转化为ATC的最优多项式逼近系数的求解问题,根据求得的最优多项式逼近系数和选取的基函数计算电力系统ATC的概率分布特征;最后,通过修改后的PJM-5节点测试系统、IEEE-118节点测试系统及吉林西部电网实际算例验证了所提基于多项式混沌展开的电力系统概率ATC评估方法的准确性和有效性。