Modeling the effect of anisotropic pressure on tokamak plasmas normal modes and continuum using fluid approaches

Extending the ideal MHD stability code MISHKA, a new code, MISHKA-A, is developed to study the impact of pressure anisotropy on plasma stability. Based on full anisotropic equilibrium and geometry, the code can provide normal mode analysis with three fluid closure models: the single adiabatic model (SA), the double adiabatic model (CGL) and the incompressible model. A study on the plasma continuous spectrum shows that in low beta, large aspect ratio plasma, the main impact of anisotropy lies in the modification of the BAE gap and the sound frequency, if the q profile is conserved. The SA model preserves the BAE gap structure as ideal MHD, while in CGL the lowest frequency branch does not touch zero frequency at the resonant flux surface where $m+nq=0$, inducing a gap at very low frequency. Also, the BAE gap frequency with bi-Maxwellian distribution in both model becomes higher if $p_\perp > p_\parallel$ with a q profile dependency. As a benchmark of the code, we study the m/n=1/1 internal kink mode. Numerical calculation of the marginal stability boundary with bi-Maxwellian distribution shows a good agreement with the generalized incompressible Bussac criterion [A. B. Mikhailovskii, Sov. J. Plasma Phys 9, 190 (1983)]: the mode is stabilized(destabilized) if $p_\parallel < p_\perp (p_\parallel > p_\perp)$

Analysing the impact of anisotropy pressure on tokamak equilibria

Neutral beam injection or ion cyclotron resonance heating induces pressure anisotropy. The axisymmetric plasma equilibrium code HELENA has been upgraded to include anisotropy and toroidal flow. With both analytical and numerical methods, we have studied the determinant factors in anisotropic equilibria and their impact on flux surfaces, magnetic axis shift, the displacement of pressures and density contours from flux surface. With $p_\parallel/p_\perp \approx 1.5$, $p_\perp$ can vary 20% on $s=0.5$ flux surface, in a MAST like equilibrium. We have also re-evaluated the widely applied approximation to anisotropy in which $p^*=(p_\parallel + p_\perp)/2$, the average of parallel and perpendicular pressure, is taken as the approximate isotropic pressure. We find the reconstructions of the same MAST discharge with $p_\parallel/p_\perp \approx 1.25$, using isotropic and anisotropic model respectively, to have a 3% difference in toroidal field but a 66% difference in poloidal current

Doctor of Philosophy

dissertationPartial differential equations (PDEs) are widely used in science and engineering to model phenomena such as sound, heat, and electrostatics. In many practical science and engineering applications, the solutions of PDEs require the tessellation of computational domains into unstructured meshes and entail computationally expensive and time-consuming processes. Therefore, efficient and fast PDE solving techniques on unstructured meshes are important in these applications. Relative to CPUs, the faster growth curves in the speed and greater power efficiency of the SIMD streaming processors, such as GPUs, have gained them an increasingly important role in the high-performance computing area. Combining suitable parallel algorithms and these streaming processors, we can develop very efficient numerical solvers of PDEs. The contributions of this dissertation are twofold: proposal of two general strategies to design efficient PDE solvers on GPUs and the specific applications of these strategies to solve different types of PDEs. Specifically, this dissertation consists of four parts. First, we describe the general strategies, the domain decomposition strategy and the hybrid gathering strategy. Next, we introduce a parallel algorithm for solving the eikonal equation on fully unstructured meshes efficiently. Third, we present the algorithms and data structures necessary to move the entire FEM pipeline to the GPU. Fourth, we propose a parallel algorithm for solving the levelset equation on fully unstructured 2D or 3D meshes or manifolds. This algorithm combines a narrowband scheme with domain decomposition for efficient levelset equation solving

Aircraft engine performance and integration in a flying wing aircraft conceptual design

The increasing demand of more economical and environmentally friendly aero engines leads to the proposal of a new concept – geared turbofan. In this thesis, the characteristics of this kind of engine and relevant considerations of integration on a flying wing aircraft were studied. The studies can be divided into four levels: GTF-11 engine modelling and performance simulation; aircraft performance calculation; nacelle design and aerodynamic performance evaluation; preliminary engine installation. Firstly, a geared concept engine model was constructed using TURBOMATCH software. Based on parametric analysis and SFC target, the main cycle parameters were selected. Then, the maximum take-off thrust was verified and corrected from 195.56kN to 212kN to meet the requirements of take-off field length and second segment climb. Besides, the engine performance at offdesign points was simulated for aircraft performance calculation. Secondly, an aircraft performance model was developed and the performance of FW-11 was calculated on the basis of GTF-11 simulation results. Then, the effect of GTF-11 characteristics performance on aircraft performance was evaluated. A comparison between GTF-11 and conventional turbofan, RB211- 524B4, indicated that the aircraft can achieve a 13.1% improvement in fuel efficiency by using the new concept engine. Thirdly, a nacelle was designed for GTF-11 based on NACA 1-series and empirical methods while the nacelle dimensions of conventional turbofan RB211-525B4 were obtained by measure approach. Then, the installation thrust losses caused by nacelle drags of the two engines were evaluated using ESDU 81024a. The results showed that the nacelle drags account for about 4.08% and 3.09% of net thrust for GTF-11 and RB211-525B4, respectively. Finally, the considerations of engine installation on a flying wing aircraft were discussed and a preliminary disposition of GTF-11 on FW-11 was presented

Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification

Implicit discourse relation classification is of great challenge due to the lack of connectives as strong linguistic cues, which motivates the use of annotated implicit connectives to improve the recognition. We propose a feature imitation framework in which an implicit relation network is driven to learn from another neural network with access to connectives, and thus encouraged to extract similarly salient features for accurate classification. We develop an adversarial model to enable an adaptive imitation scheme through competition between the implicit network and a rival feature discriminator. Our method effectively transfers discriminability of connectives to the implicit features, and achieves state-of-the-art performance on the PDTB benchmark.Comment: To appear in ACL201