A second-order dynamic subgrid-scale stress model

A second-order dynamic model based on the general relation between the subgrid-scale stress and the velocity gradient tensors was proposed. A priori test of the second-order model was made using moderate resolution direct numerical simulation date at high Reynolds number ( Taylor microscale Reynolds number R-lambda = 102 similar to 216) for homogeneous, isotropic forced flow, decaying flow, and homogeneous rotating flow. Numerical testing shows that the second-order dynamic model significantly improves the correlation coefficient when compared to the first-order dynamic models.

An overview of sequential Monte Carlo methods for parameter estimation in general state-space models

Nonlinear non-Gaussian state-space models arise in numerous applications in control and signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters, are numerical techniques based on Importance Sampling for solving the optimal state estimation problem. The task of calibrating the state-space model is an important problem frequently faced by practitioners and the observed data may be used to estimate the parameters of the model. The aim of this paper is to present a comprehensive overview of SMC methods that have been proposed for this task accompanied with a discussion of their advantages and limitations.

A Behavioral Foundation for Models of Evolutionary Drift

Binmore and Samuelson (1999) have shown that perturbations (drift) are crucial to study the stability properties of Nash equilibria. We contribute to this literature by providing a behavioural foundation for models of evolutionary drift. In particular, this article introduces a microeconomic model of drift based on the similarity theory developed by Tversky (1977), Kahneman and Tversky (1979) and Rubinstein (1988),(1998). An innovation with respect to those works is that we deal with similarity relations that are derived from the perception that each agent has about how well he is playing the game. In addition, the similarity relations are adapted to a dynamic setting. We obtain different models of drift depending on how we model the agent´s assessment of his behaviour in the game. The examples of the ultimatum game and the chain-store game are used to show the conditions for each model to stabilize elements in the component of Nash equilibria that are not subgame- perfect. It is also shown how some models approximate the laboratory data about those games while others match the data.

Reference Points Based on Dynamic Optimisation: A Versatil Algorithm for Mixed Fishery Management with Bio-economic Agestructured Models

Single-species management objectives may not be consistent within mixed fisheries. They may lead species to unsafe situations, promote discarding of over-quota and/or misreporting of catches. We provide an algorithm for characterising bio-economic reference points for a mixed fishery as the steady-state solution of a dynamic optimal management problem. The optimisation problem takes into account: i) that species are fishing simultaneously in unselective fishing operations and ii)intertemporal discounting and fleet costs to relate reference points to discounted economic profits along optimal trajectories. We illustrate how the algorithm can be implemented by applying it to the European Northern Stock of Hake (Merluccius merluccius), where fleets also capture Northern megrim (Lepidorhombus whiffiagonis) and Northern anglerfish (Lophius piscatorius and Lophius budegassa). We find that optimal mixed management leads to a target reference point that is quite similar to the 2/3 of the Fmsy single-species (hake) target. Mixed management is superior to singlespecies management because it leads the fishery to higher discounted profits with higher long-term SSB for all species. We calculate that the losses due to the use of the Fmsy single-species (hake) target in this mixed fishery account for 11.4% of total discounted profits.

Chemical equilibrium analysis and CFD simulation of coal combustion and NOx formation

A full two-fluid model of reacting gas-particle flows with an algebraic unified second-order moment (AUSM) turbulence-chemistry model is used to simulate Beijing coal combustion and NOx formation. The sub-models are the k-epsilon-kp two-phase turbulence model, the EBU-Arrhenius volatile and CO combustion model, the six-flux radiation model, coal devolatilization model and char combustion model. The blocking effect on NOx formation is discussed. In addition, the chemical equilibrium analysis is used to predict NOx concentration at different temperature. Results of CID simulation and chemical equilibrium analysis show that, optimizing air dynamic parameters can delay the NOx formation and decrease NOx emission, but it is effective only in a restricted range. In order to decrease NOx emission near to zero, the re-burning or other chemical methods must be used.

Comparisons of static, quasi-static and dynamic 3D porous media scale network models for two-phase immiscible flow in porous media

A dynamic 3D pore-scale network model is formulated for investigating the effect of interfacial tension and oil-water viscosity during chemical flooding. The model takes into account both viscous and capillary forces in analyzing the impact of chemical properties on flow behavior or displacement configuration, while the static model with conventional invasion percolation algorithm incorporates the capillary pressure only. From comparisons of simulation results from these models. it indicates that the static pore scale network model can be used successfully when the capillary number is low. With the capillary increases due to the enhancement of water viscosity or decrease of interfacial tension, only the quasi-static and dynamic model can give insight into the displacement mechanisms.

Failure detectors and communication efficiency in the crash and general omisión failure models

122 p.

A Dynamic Model For Ice-Induced Vibration Of Structures

A dynamic model for the ice-induced vibration (IIV) of structures is developed in the present study. Ice properties have been taken into account, such as the discrete failure, the dependence of the crushing strength on the ice velocity, and the randomness of ice failure. The most important prediction of the model is to capture the resonant frequency lock-in, which is analog to that in the vortex-induced vibration. Based on the model, the mechanism of resonant IIV is discussed. It is found that the dependence of the ice crushing strength on the ice velocity plays an important role in the resonant frequency lock-in of IIV. In addition, an intermittent stochastic resonant vibration is simulated from the model. These predictions are supported by the laboratory and field observations reported. The present model is more productive than the previous models of IIV.

Robot navigation in dense crowds : statistical models and experimental studies of human robot cooperation

This thesis explores the problem of mobile robot navigation in dense human crowds. We begin by considering a fundamental impediment to classical motion planning algorithms called the freezing robot problem: once the environment surpasses a certain level of complexity, the planner decides that all forward paths are unsafe, and the robot freezes in place (or performs unnecessary maneuvers) to avoid collisions. Since a feasible path typically exists, this behavior is suboptimal. Existing approaches have focused on reducing predictive uncertainty by employing higher fidelity individual dynamics models or heuristically limiting the individual predictive covariance to prevent overcautious navigation. We demonstrate that both the individual prediction and the individual predictive uncertainty have little to do with this undesirable navigation behavior. Additionally, we provide evidence that dynamic agents are able to navigate in dense crowds by engaging in joint collision avoidance, cooperatively making room to create feasible trajectories. We accordingly develop interacting Gaussian processes, a prediction density that captures cooperative collision avoidance, and a "multiple goal" extension that models the goal driven nature of human decision making. Navigation naturally emerges as a statistic of this distribution.

Most importantly, we empirically validate our models in the Chandler dining hall at Caltech during peak hours, and in the process, carry out the first extensive quantitative study of robot navigation in dense human crowds (collecting data on 488 runs). The multiple goal interacting Gaussian processes algorithm performs comparably with human teleoperators in crowd densities nearing 1 person/m², while a state of the art noncooperative planner exhibits unsafe behavior more than 3 times as often as the multiple goal extension, and twice as often as the basic interacting Gaussian process approach. Furthermore, a reactive planner based on the widely used dynamic window approach proves insufficient for crowd densities above 0.55 people/m². We also show that our noncooperative planner or our reactive planner capture the salient characteristics of nearly any dynamic navigation algorithm. For inclusive validation purposes, we show that either our non-interacting planner or our reactive planner captures the salient characteristics of nearly any existing dynamic navigation algorithm. Based on these experimental results and theoretical observations, we conclude that a cooperation model is critical for safe and efficient robot navigation in dense human crowds.

Finally, we produce a large database of ground truth pedestrian crowd data. We make this ground truth database publicly available for further scientific study of crowd prediction models, learning from demonstration algorithms, and human robot interaction models in general.