Evolution of vortices in the wake of an ARJ21 airplane: Application of the lift-drag model

Wake separation is crucial to aircraft landing safety and is an important factor in airport operational efficiency. The near-ground evolution characteristics of wake vortices form the foundation of the wake separation system design. In this study, we analysed the near-ground evolution of vortices in the wake of a domestic aircraft ARJ21 initialised by the lift-drag model using large eddy simulations based on an adaptive mesh. Evolution of wake vortices formed by the main wing, flap and horizontal tail was discussed in detail. The horizontal tail vortices are the weakest and dissipate rapidly, whereas the flap vortices are the strongest and induce the tip vortex to merge with them. The horizontal tail and flap of an ARJ21 do not significantly influence the circulation evolution, height change and movement trajectory of the wake vortices. The far-field evolution of wake vortices can therefore be analysed using the conventional wake vortex model.     

基于多通道盲识别的自适应光学图像事后处理

为实时恢复天文或空间目标的湍流退化成像,提出一种适应大气湍流动态变化的多通道自适应光学图像恢复方法.以自适应光学校正后不同时刻的目标成像作为多个通道,建立求解系统点扩散函数的线性方程,根据解出的点扩散函数利用超拉普拉斯算法,求解待观测目标的估计值.结果表明:不同时刻的点扩散函数之间存在互质关系,满足多通道盲识别的理论要求.利用建立的线性方程求解出的点扩散函数与原点扩散函数的均方误差在10^-30~10^-27量级,采用超拉普拉斯算法恢复出的目标成像与原始目标之间的均方误差在10^-5~10^-4量级.本文研究为湍流退化图像的实时恢复提供了理论基础.     

Adaptive Catalytic Systems for Chemical Energy Conversion

The rapidly growing importance of green hydrogen and renewable carbon resources as essential feedstocks for sustainable chemical value chains opens room for disruptive innovations regarding chemical production processes. The fluctuation and variability associated with non-fossil energy and raw material supply holds many challenges for catalysts to cope with the resulting dynamics. However, many new opportunities also arise once catalyst design starts to aim at performance that is “adaptive” rather than “task-specific”. In this Scientific Perspective, we propose to define adaptivity in catalysis on the basis of three essential properties that are reversibility, rapidity, and robustness (R³ rule). Promising design strategies and selected examples are described to substantiate the scientific concept and to highlight its potential for chemical energy conversion.     

Adaptive multimode transmission in wireless sensor networks with energy harvesting

Limited energy has always been an important factor restricting the development of wireless sensor networks. The unbalanced energy consumption of nodes will accelerate the death of some nodes. To solve the above problems, an adaptive routing algorithm for energy collection sensor networks based on distributed energy saving clustering (DEEC) is proposed. In each hop of data transmission, the optimal mode is adaptively selected from four transmission modes: single-hop cooperative, multi-hop cooperative, single-hop non-cooperative and multi-hop non-cooperative, so as to reduce and balance the energy consumption of nodes. The performance of the proposed adaptive multi-mode transmission method and several benchmark schemes are evaluated and compared by computer simulation, where a few performance metrics such as the network lifetime and throughput are adopted. The results show that, the proposed method can effectively reduce the energy consumption of the network and prolong the network lifetime; it is superior to various benchmark schemes.     

A Cross-Entropy Approach to the Estimation of Generalized Linear Multilevel Models

In this article, we use the cross-entropy method for noisy optimization for fitting generalized linear multilevel models through maximum likelihood. We propose specifications of the instrumental distributions for positive and bounded parameters that improve the computational performance. We also introduce a new stopping criterion, which has the advantage of being problem-independent. In a second step we find, by means of extensive Monte Carlo experiments, the most suitable values of the input parameters of the algorithm. Finally, we compare the method to the benchmark estimation technique based on numerical integration. The cross-entropy approach turns out to be preferable from both the statistical and the computational point of view. In the last part of the article, the method is used to model the probability of firm exits in the healthcare industry in Italy. Supplemental materials are available online.     

Adaptive neuro-fuzzy interference system modelling for chlorpyrifos removal with walnut shell biochar

Accumulation of chlorpyrifos (CP), a pesticide, causes a significant environmental problem in food, surface/ground waters further to human health. The removal of the CP pollutant in surface/wastewater could be achieved by biochar due to the improved physical and chemical properties. In this work, the CP removal capacities of biochar samples derived from walnut shells at various temperatures from 450 to 900 °C were investigated. The experiments were performed as laboratory batch type study and the adsorption efficiency was determined at various conditions such as adsorbent dosage (10–500 mg/L), sorbate concentrations (100–1500 µg/L), contact time (0–300 min), initial pH (3–10), and the number of recycle.By subtracting the pyrolysis temperature from 450 °C to 900 °C, the surface areas were found to increase from 12.9 m²/g to 353.3 m²/g, respectively.The 143 experimental data were evaluated by a pair of kinetics and isotherm models and the Adaptive Neural Fuzzy Inference System (ANFIS). The developed ANFIS model was 98.56% successful in predicting the CP removal efficiency depending on the adsorption conditions. Walnut Shell Biochar (WSBC) can be applied for CP adsorption with 86.64% removal efficiency under optimum adsorption conditions (adsorbent = 250 µg/L, sorbate = 1000 µg/L, pH = 7.07 and contact time 15 min) thanks to its improved porosity. It was determined that the biochar samples could be reused 5 times. Equilibrium adsorption was observed to conform to the Langmuir isotherm, and the maximum adsorption capacity for WSBC@900 was 3.536 mg/g.     

Estimation of IIR Systems with Binary-Valued Observations*

Estimation and control problems with binary-valued observations exist widely in practical systems. However, most of the related works are devoted to finite impulse response (FIR for short) systems, and the theoretical problem of infinite impulse response (IIR for short) systems has been less explored. To study the estimation problems of IIR systems with binary-valued observations, the authors introduce a projected recursive estimation algorithm and analyse its global convergence properties, by using the stochastic Lyapunov function methods and the limit theory on double array martingales. It is shown that the estimation algorithm has similar convergence results as those for FIR systems under a weakest possible non-persistent excitation condition. Moreover, the upper bound for the accumulated regret of adaptive prediction is also established without resorting to any excitation condition.     

Viscous waves and wave-structure interaction in a tank using adapting quadtree grids

Viscous waves and waves over a submerged cylinder in a stationary tank are simulated using a volume-of-fluid numerical scheme on adaptive hierarchical grids. A high resolution interface-capturing method is used to advect the free surface interface and the Navier–Stokes equations are discretised using finite volumes with collocated primitive variables and solved using a Pressure Implicit with Splitting of Operators (PISO) algorithm. The cylinder is modelled by using the technique of Cartesian cut cells. Results of flow of a single fluid past a cylinder at Reynolds number Re=100 are presented and found to agree well with experimental and other numerical data. Viscous free surface waves in a tank are simulated using uniform and quadtree grids for Reynolds numbers in the range from 2 to 2000, and the results compared against analytical solutions where available. The quadtree-based results are of the same accuracy as those on the equivalent uniform grids, and retain a sharp interface at the free surface while leading to considerable savings in both storage and CPU requirements. The nonlinearity in the wave is investigated for a selection of initial wave amplitudes. A submerged cylinder is positioned in the tank and its influence on the waves as well as the hydrodynamic loading on the cylinder is investigated.     

Adaptive Control of Nonlinear Dynamical Systems Using a Model Reference Approach

In this paper we consider using a model reference adaptive control approach to control nonlinear systems. We consider the controller design and stability analysis associated with these type of adaptive systems. Then we discuss the use of model reference adaptive control algorithms to control systems which exhibit nonlinear dynamical behaviour using the example of a Duffing oscillator being controlled to follow a linear reference model. For this system we show that if the nonlinearity is small then standard linear model reference control can be applied. A second example, which is often found in synchronization applications, is when the nonlinearities in the plant and reference model are identical. Again we show that linear model reference adaptive control is sufficient to control the system. Finally we consider controlling more general nonlinear systems using adaptive feedback linearization to control scalar nonlinear systems. As an example we use the Lorenz and Chua systems with parameter values such that they both have chaotic dynamics. The Lorenz system is used as a reference model and a single coordinate from the Chua system is controlled to follow one of the Lorenz system coordinates.