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Text I

 

Catalytic effect of ash on char gasification has been investigated for several biomass samples. Kinetics of food waste char gasification did not draw the attention of researchers in this field. Since food waste has considerable ash content, its catalytic effect must be investigated. Results show that ash has a positive effect on char reactivity. Kinetic parameters have been calculated for different degrees of conversion. Values of kinetic parameters were found to be affected by the degree of conversion. Quantitative analysis of kinetic parameters dependency on sample conversion has been examined here. Quantifying the catalytic effect of ash on char kinetics will help assist improving gasifiers design with better controlled parameters for input and operational conditions, such as, operating temperature, gasification condition, gasifying support media, rate of feedstock to the gasifier. In conjunction with fluid dynamic simulations, improved expressions for reaction rates will help provide better estimate on char particles residence time in the reactor by providing an accurate conversion-time relationship.

Consequently, for a desired feed rate of feedstock into the reactor and for known gasifier operational conditions an accurate reactivity expression will lead to a close estimate of the gasifier size and configuration. If a constant reactivity value is used in reacting flow simulations for feedstock having time dependant reactivity, misleading information on char particles residence time will be obtained. This will consequently result in a departure gasifier size from the true design size and configuration. For example, if a constant reactivity value is used for chars having ash catalytic effect, such as the case examined here, the designed gasifier size will be over estimated since the reactivity of char was fond to increase with the degree of conversion.

(Ahmed I.I., Gupta A.K. Pyrolysis and gasification of food waste: Syngas characteristics and char gasification kinetics // Applied Energy 87 (2010), Pp. 101–108)

Text II

 

Several methodologies exist to control large teams of robots. One way of reducing the complexity of the controller is to require the team to conform to a geometric rigid virtual structure. Most of the recent works on stabilization and control of virtual structures model formations using formation graphs. Controllers guaranteeing local asymptotic stability of a given rigid formation can be derived using standard techniques such as input-output linearization, input-to-state stability, Lyapunov energy-type functions, and biologically-inspired artificial potential functions. Virtual structures unnecessarily constrain the problem, making this approach inappropriate for tasks in complex environments. Additionally, graph formulations and leader-follower architectures require identification and ordering of robots, which makes the overall architecture sensitive to failures.

The problem of controlling the trajectory of the group and shape of a large team of point robots is studied in (Belta &Kumar, 2004; Michael et al., 2006). The authors defined an abstraction of the team that has a product structure of the Euclidean group and a shape space, and is independent of the number of robots. The group captures the pose of an ellipse spanning the team with semi-axes given by the shape variables. The overall abstract description is invariant to robot permutations. In addition, the model and the formulation is invariant to left actions of the group. This description allows one to define and control the behavior of the abstract state or the abstract description of the team at a high level, with automatic generation of individual robot control laws based only on the feedback of this abstract state. However, the control laws do not account for the physical constraints of the robots and ignore inter-agent interactions.

Coverage control schemes proposed by (Cortes et al.,2004; Schwager et al., 2006) and their variants have a similar approach. They enable large groups of robots to use local information to distribute themselves so that a suitable integral over this distribution is maximized. However, this formulation does not lend itself to the control of the position and orientation of the overall team.

(Michael N., Kumar V. Planning and Control of Ensembles of Robots with Nonholonomic Constraints // The International Journal of Robotics Research, 2009, Issue 28, Pp. 962–975).

Text III

Boundary Conditions and Computational Grid. The inlet boundary condition was obtained by taking the circumferential average of a separate guide vane calculation, yielding an axisymmetric inlet flow [22]. This corresponds to a perfect distribution from the spiral casing and without any disturbance from the guide vane wakes. Wall-functions and rotating wall velocities were used at the walls, and at the outlet the homogeneous Neumann boundary condition was used for all quantities. Recirculatingflow was thus allowed at the outlet, and did occur. The turbulence quantities of the recirculating flow at the outlet are unknown, but to set a relevant turbulence level for the present case the back-flow values for k and _ were assumed to be similar to the average of those quantities at the inlet.

The background of this assumption is that the turbulence level is high already at the inlet due to the wakes of the stay vanes and the guide vanes. It is thus assumed that the increase in turbulence level is small compared with that at the inlet. It is further believed that the chosen values are of minor importance for the overall flow. For the pressure the homogeneous Neumann boundary condition is used at all boundaries. The computations are made for a complete runner with five blades. The computational domain is shown by Figure 1. A block-structured hexahedral wall-function grid was used, consisting of approximately 2 200 000 grid points.

(Martin Karlsson, Hakan Nilsson, and Jan-Olov Aidanpaa. Numerical Estimation of Torsional Dynamic Coefficients of a Hydraulic Turbine // International Journal of Rotating Machinery, 2009)

 

3. Переведите английские термины на русский язык:

feedback, resolution, welding, gas tourbine,test mode, gasifier, electrical generator, route, industrial robot, mechanized excavation, technology, earthmoving equipment, machines, Industrial Revolu­tion, trans­portation, railroad construction, steam excavator, back pressure.

 





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