Thermo-Mechanical Coupling for Ablation

In order to investigate the thermal stress and expansion as well as the associated strain effect on material properties caused by high temperature and large temperature gradient, a two-way thermo-mechanical coupling solver is developed. This solver integrates a new structural response module to the Kentucky Aerothermodynamics and Thermal response System (KATS) framework. The structural solver uses a finite volume approach to solve either hyperbolic equations for transient solid mechanics, or elliptic equations for static solid mechanics. Then, based on the same framework, a quasi-static approach is used to couple the structural response and thermal response to estimate the thermal expansion and stress within Thermal Protection System (TPS) materials. To better capture the thermal expansion and study its impacts on material properties such as conductivity and porosity, a moving mesh scheme is also developed and incorporated into the solver. Grid deformation is transferred among different modules in the form of variations of geometric parameters and strain effects. By doing so, a bi-direction information loop is formed to accomplish the two-way strong thermo-mechanical coupling. Results revealed that the thermal stress experienced during atmospheric re-entry concentrates in a banded area at the edge of the pyrolysis zone and its magnitude can be large enough to cause the failure of the TPS. In addition, thermal expansion causes the whole structure to deform and the changes in material properties. Results also indicated that the impacts coming from structural response should not be ignored in thermal response

Style Transfer in Text: Exploration and Evaluation

Style transfer is an important problem in natural language processing (NLP). However, the progress in language style transfer is lagged behind other domains, such as computer vision, mainly because of the lack of parallel data and principle evaluation metrics. In this paper, we propose to learn style transfer with non-parallel data. We explore two models to achieve this goal, and the key idea behind the proposed models is to learn separate content representations and style representations using adversarial networks. We also propose novel evaluation metrics which measure two aspects of style transfer: transfer strength and content preservation. We access our models and the evaluation metrics on two tasks: paper-news title transfer, and positive-negative review transfer. Results show that the proposed content preservation metric is highly correlate to human judgments, and the proposed models are able to generate sentences with higher style transfer strength and similar content preservation score comparing to auto-encoder.Comment: To appear in AAAI-1