模拟跨声速湍流的线性和非线性涡粘性湍流模式

讨论应用线性和非线性涡粘性湍流模式计算跨声速湍流 .用于比较的 3个线性湍流模式是Shih和Lumley的k ε模式 ,CMOTT模式和SST模式 .2个非线性模式是Shih ,Zhu和Lumleyd的二阶模式 ,以及Shih等的三阶模式 .计算的典型流动是绕凸起的跨声速流动 ,该流动的特征是激波与边界层相互作用且伴有流动分离 .计算结果表明非线性湍流模式具有明显的优势 .     

Unified modeling and analysis of a proportional valve

Developments in nonlinear control theory have made it possible to design controllers for systems having non-smooth nonlinearities in their dynamics. Hydraulic systems that use inexpensive proportional valves are examples of such systems, where nonlinearities arise due to valve geometry and spool imperfections. Without a proper valve model, however, nonlinear analysis and control of these hydraulic systems is not possible.We have developed nonlinear equations for a generic proportional valve model and have used them to obtain simplified flow rate expressions under generally accepted assumptions. These equations relate a set of geometric spool properties and physical model parameters to the flow rate through the valve ports. The development focuses on obtaining a single set of flow rate equations applicable to critical center, overlapped, and underlapped proportional valves. These unified model equations are useful for simulation and nonlinear controller design. We have also demonstrated that the errors incurred when using the unified valve model are dependent on the damping coefficient alone and are less than 10% in the frequency range within which most valves are used.     

Spheroid-on-chip microfluidic technology for the evaluation of the impact of continuous flow on metastatic potential in cancer models in vitro

The majority of cancer deaths are linked to tumor spread, or metastasis, but 3D in vitro metastasis models relevant to the tumor microenvironment (including interstitial fluid flow) remain an area of unmet need. Microfluidics allows us to introduce controlled flow to an in vitro cancer model to better understand the relationship between flow and metastasis. Here, we report new hybrid spheroid-on-chip in vitro models for the impact of interstitial fluid flow on cancer spread. We designed a series of reusable glass microfluidic devices to contain one spheroid in a microwell under continuous perfusion culture. Spheroids derived from established cancer cell lines were perfused with complete media at a flow rate relevant to tumor interstitial fluid flow. Spheroid viability and migratory/invasive capabilities were maintained on-chip when compared to off-chip static conditions. Importantly, using flow conditions modeled in vitro, we are the first to report flow-induced secretion of pro-metastatic factors, in this case cytokines vascular endothelial growth factor and interleukin 6. In summary, we have developed a new, streamlined spheroid-on-chip in vitro model that represents a feasible in vitro alternative to conventional murine in vivo metastasis assays, including complex tumor environmental factors, such as interstitial fluid flow, extracellular matrices, and using 3D models to model nutrient and oxygen gradients. Our device, therefore, constitutes a robust alternative to in vivo early-metastasis models for determination of novel metastasis biomarkers as well as evaluation of therapeutically relevant molecular targets not possible in in vivo murine models.     

陆架陆坡区非线性内波生成和传播的数值模拟

内孤立波是陆架陆坡区一种常见的现象。本文借助于一个两层非静力近似模式（Gerkema,1996）模拟分析了内孤立波在陆架生成及传播特性,并与已有的观测资料进行了比较分析。结果表明,正压潮与地形相互作用激发生成内潮,内潮在向岸传播过程中,由于正压潮流及自身的非线性作用而发生分裂,受频散作用的影响进一步演化成内孤立子波列。而对于离岸传播的内潮,由于较强的频散效应,非线性与频散在较大尺度上达到平衡,从而使得波形呈现椭圆余旋波的特征。     

Parameter fault detection and estimation of a class of nonlinear systems using observers

The focus of this paper is on the detection and estimation of parameter faults in nonlinear systems with nonlinear fault distribution functions. The novelty of this contribution is that it handles the nonlinear fault distribution function; since such a fault distribution function depends not only on the inputs and outputs of the system but also on unmeasured states, it causes additional complexity in fault estimation. The proposed detection and estimation tool is based on the adaptive observer technique. Under the Lipschitz condition, a fault detection observer and adaptive diagnosis observer are proposed. Then, relaxation of the Lipschitz requirement is proposed and the necessary modification to the diagnostic tool is presented. Finally, the example of a one-wheel model with lumped friction is presented to illustrate the applicability of the proposed diagnosis method.     

交通流复杂动态特性的微观和宏观模式研究（英文）

本文在综合分析现有各种类型的交通流模型的基础上，提出更为符合实际的微观模型和宏观模型。具体地说，我们提出了一种全速度差车辆跟驰模型，一种考虑前车速度效应的元胞自动机模型，另一种能较好模拟同步流的元胞自动机模型，以及一种能体现交通流各向异性特征的速度梯度宏观连续模型。通过理论分析和数值模拟，研究了交通流中的各种非线性现象，并探讨了各种不同类型的模型之间的相互联系。     

Stochastic stability of center difference predictive filter

In this paper, based on Stirling’?s polynomial interpolation formula, the Second-order Central Difference Predictive Filter (CDPF2) is proposed for nonlinear estimation. To facilitate the new method, the algorithm flow of CDPF2 is given first. Then, the theoretical deductions demonstrate that the estimated accuracy of the model error and system state for the CDPF2 is higher than that of the conventional PF. In addition, the stochastic boundedness and the error behavior of CDPF2 is analyzed for general nonlinear systems in a stochastic framework. The theoretical analysis presents that the estimation error will remain bounded and the covariance will remain stable if the system?s initial estimation error, disturbing noise terms and model error are small enough, which is the core part of the CDPF2 theory. All of the results have been demonstrated by numerical simulations for a nonlinear example system.     

Viable immersion and invariance control for a class of nonlinear systems and its application to aero-engines

This paper presents a novel approach to stabilize a class of nonlinear systems with state constraints. The motivation behind this study is the need to develop a stabilizing state feedback controller that does not require the knowledge of Lyapunov function and can regulate the states to the equilibrium while meeting the constraints. By using an integration of two relatively new tools: immersion and invariance (I&I) theory and viability theory, a sufficient condition for stability and stabilizability of a general nonlinear affine system with state constraints is derived; Then, the related results are exploited to stabilize a class of nonlinear system in feedback form and with state constraints represented by inequalities and the viable I&I stabilizing state feedback controller is obtained constructively. Further, an application to a nonlinear aero-engine model with the temperature constraint is given to illustrate the applicability and the effectiveness of the proposed method. Finally, a comparative simulation is presented, highlighting the advantages of the viable I&I controller.     

Robust fault detection for a class of nonlinear time-delay systems

In this paper, the robust fault detection filter (RFDF) design problems are studied for nonlinear time-delay systems with unknown inputs. Firstly, a reference residual model is introduced to formulate the robust fault detection filter design problem as an H_∞ model-matching problem. Then appropriate input/output selection matrices are introduced to extend a performance index to the time-delay systems in time domain. The reference residual model designed according to the performance index is an optimal residual generator, which takes into account the robustness against disturbances and sensitivity to faults simultaneously. Applying robust H_∞ optimization control technique, the existence conditions of the robust fault detection filter for nonlinear time-delay systems with unknown inputs are presented in terms of linear matrix inequality (LMI) formulation, independently of time delay. An illustrative design example is used to demonstrate the validity and applicability of the proposed approach.     

Approximately bisimilar symbolic model for switched systems with unstable subsystems

This paper deals with approximate bisimulation for the switched nonlinear system with mode-dependent dwell time. A criterion for incremental stability is presented for this switched nonlinear system by constructing incremental Lyapunov-like functions. Then for the case that all the subsystems are linear, a more solvable criterion is provided in terms of linear matrix inequalities. A symbolic model which is approximately bisimilar to the original switched nonlinear system is developed by using the grid-based approach, and the bisimilar precision is also given. Numerical examples are provided to show the application of the proposed results.     

Identification of a nonlinear dynamic biological model using the dominant parameter selection method

The identification of nonlinear models sometimes encounters problems because of the limited amount of available measurements in combination with a large number of uncertain model parameters to be identified. E.g., the determination of the chemical composition of a lettuce crop is a rather expensive procedure; thus the number of experimental measurements is limited. As a result, the number of parameters of the dynamic model that can be successively identified is also limited, and the subset of the parameters to be identified must be chosen in a reasonable way.Parameter estimation for an extended nonlinear three-state model for lettuce growth in greenhouses is presented in this paper. The varying structural nitrogen concentration and water contents are the new elements included in the model. The dominant parameter selection (DPS) method was used to select a suitable set of identifiable parameters. The resulting calibrated model predicts quite well the experimental data which also include observations with severe nitrogen stress.     

Gradient-based neural networks for solving periodic Sylvester matrix equations

This paper considers neural network solutions of a category of matrix equation called periodic Sylvester matrix equation (PSME), which appear in the process of periodic system analysis and design. A linear gradient-based neural network (GNN) model aimed at solving the PSME is constructed, whose state is able to converge to the unknown matrix of the equation. In order to obtain a better convergence effect, the linear GNN model is extended to a nonlinear form through the intervention of appropriate activation functions, and its convergence is proved through theoretical derivation. Furthermore, the different convergence effects presented by the model with various activation functions are also explored and analyzed, for instance, the global exponential convergence and the global finite time convergence can be realized. Finally, the numerical examples are used to confirm the validity of the proposed GNN model for solving the PSME considered in this paper as well as the superiority in terms of the convergence effect presented by the model with different activation functions.     

Transient electro-osmotic flow in cylindrical microcapillaries containing salt-free medium

A theoretical study on the transient electro-osmotic flow through a cylindrical microcapillary containing a salt-free medium is presented for both constant surface charge density and constant surface potential. The exact analytical solutions for the electric potential distribution and the transient electro-osmotic flow velocity are derived by solving the nonlinear Poisson-Boltzmann equation and the Navier-Stokes equation. Based on these results, a systematic parametric study on the characteristics of the transient electro-osmotic flow is detailed. The general behavior of transient electro-osmotic flow in a cylindrical tube is similar to that observed in a microchannel containing an electrolyte solution. However, the steady-state electro-osmotic flow significantly deviates from the typical plug flow at higher surface charge and the rate of increase in the electro-osmotic mobility is strongly suppressed due to the effect of counterion condensation. In addition, the applicability limit of these solutions is also discussed.     

A novel learning algorithm of the neuro-fuzzy based Hammerstein–Wiener model corrupted by process noise

The Hammerstein–Wiener model is a nonlinear system with three blocks where a dynamic linear block is sandwiched between two static nonlinear blocks. For parameter learning of the Hammerstein–Wiener model, the synchronous parameter learning methods are proposed to learn the model parameters by constructing hybrid model of the three series block, such as over parameterization method, subspace method and maximum likelihood method. It should be pointed out that the aforementioned methods appeared the product term of model parameters in the process of parameter learning, and parameter separation method is further adopted to separate hybrid parameters, which increases the complexity of parameter learning. To address this issue, a novel three-stage parameter learning method of the neuro-fuzzy based Hammerstein–Wiener model corrupted by process noise using combined signals is developed in this paper. The combined signals are designed to completely separate the parameter learning issues of the static input nonlinear block, the linear dynamic block and the static output nonlinear block, which effectively simplifies the process of parameter learning of the Hammerstein–Wiener model. Parameter learning of the Hammerstein–Wiener model are summarized into the following three aspects: The first one is to learn the output static nonlinear block parameters using two sets of separable signals with different sizes. The second one is to estimate the linear dynamic block parameters by means of the correlation analysis method, the unmeasurable intermediate variable information problem is effectively handled. The final one is to determine the parameters of the static input nonlinear block and the moving average noise model using recursive extended least square scheme. The simulation results are presented to illustrate that the proposed learning approach yields high learning accuracy and good robustness for the Hammerstein–Wiener model corrupted by process noise.     

An algorithm for modeling the sinusoidal input/steady-state response behavior of nonlinear systems over a set of frequencies and amplitudes

An algorithm for constructing a black box model of the sinusoidal input/steady-state response behavior of nonlinear time-invariant systems over a set of frequencies and amplitudes is presented. It is assumed that the steady-state response is periodic of the same fundamental frequency as the excitation, and that the Fourier coefficients are continuous functions of amplitude and square-integrable functions of frequency. The algorithm converges, in a mean-square sense, to an exact representation of the first N harmonics of the steady-state response minus its d.c. component. The model constructed by the algorithm admits a relatively simple physical realization characterized by 2NM+1 linear dynamic elements, and N(2M+1)+1 nonlinear static elements. The underlying mathematical structure of the model is an orthogonal series expansion relative to time whose coefficients are themselves truncated orthogonal expansions relative to frequency. Here M, the number of harmonics used for frequency interpolation, is determined by the algorithm. Of the N(2M+1)+1 memoryless nonlinearities which characterize the model, N of these are specified ahead of time (Tchebysheff polynomials), and 2NM+1 are parameters which mold the representation to the specific system being modeled. Each of these functions of a single variable can be obtained in a pointwise manner directly from steady-state measurements. The algorithm was implemented on a digital computer, and forced versions of the classic equations of van der Pol and Duffing were run as examples. An additional analytic example of a frequency multiplier of prescribed bandwidth was also presented.     

Adaptive fuzzy tracking control for switched uncertain strict-feedback nonlinear systems

This paper concerns an adaptive fuzzy tracking control problem for a class of switched uncertain nonlinear systems in strict-feedback form via the modified backstepping technique. The unknown nonlinear functions are approximated by the generalized fuzzy hyperbolic model (GFHM). It is shown that if the designed parameters in the controller and adaptive laws are appropriately selected, then all closed-loop signals are bounded and the stability of the system can be kept under average dwell time methods. In the end, simulation studies are presented to illustrate the effectiveness of the proposed method.     

Bond graph fluid line models for inclusion with dynamic systems simulations

A procedure is presented whereby the control volume equations for one-dimensional, compressible gas dynamics are cast into first-order, state variable form. These equations are interpreted using causal bond graphs. The resulting bond graph is shown to reduce to the classic I-C chain under acoustic constraints and to a more recently developed model of low speed thermal energy transport subject to associated constraints.Through example it is demonstrated that the control volume bond graph is easily coupled to an overall system model and thus can be digitally simulated as part of the overall nonlinear state space representation. The result is that a very accurate gas dynamic model can be coupled with an overall dynamic system model without requiring a prohibitively large number of equations.     

Robust non-fragile proportional plus derivative state feedback control for a class of uncertain Takagi–Sugeno fuzzy singular systems

This paper proposes a novel robust non-fragile proportional plus derivative state feedback (PDSF) control scheme for a class of uncertain nonlinear singular systems. The Takagi–Sugeno (T–S) fuzzy model is employed to represent the nonlinear singular system with parameter uncertainties appearing not only in distinct state matrices, but also in distinct derivative matrices. By using the free-weighting matrix technique, some sufficient conditions, which guarantee the resulting closed-loop system to be normal and stable (NS), are presented. With these conditions, the problems of non-fragile PDSF controllers design with additive and multiplicative uncertainties are respectively solved in terms of linear matrix inequalities (LMIs), which can be conveniently solved via the convex optimization technique. Finally, two examples are provided to illustrate the validity of the presented results.     

电动汽车用外转子双凸极永磁电机建模与仿真

提出了一种新的建模方法,即在双凸极永磁电机电磁场有限元计算的基础上,利用非线性等效磁网络对双凸极永磁电机进行非线性建模与仿真。最后,用提出的非线性建模和仿真的计算方法,对一台三相12/8极双凸极永磁电机的相绕组电感和动态转矩进行了仿真。结果表明:相绕组电感和动态转矩的仿真与实验结果一致性较好。     

A novel reset control approach to leader-following consensus of second-order nonlinear multi-agent systems

This paper investigates the leader-following consensus problem of second-order nonlinear multi-agent systems with directed graph. A novel reset control approach is proposed for the aim of improving transient consensus performance, e.g., settling time. By introducing consensus error into reset conditions, the output of reset integrator will keep the same sign with the consensus error, thus, the desired states can be compensated preferentially and the system transient performance is improved accordingly. To appropriately describe the closed-loop system with reset-induced jump dynamics, a hybrid system model consisting of both flow dynamics and jump dynamics is constructed. Based on this model, and combined with backstepping method, Lyapunov-based consensus analysis is presented under hybrid system framework. Finally, a numerical example is provided to show the effectiveness of the obtained results.