Development of simplified models for crashworthiness analysis.

Simplified modeling generates a great deal of interest in the area of crashworthiness analysis. Modeling methods used to create simplified computer models for crashworthiness have been well developed. In advanced simplified models, researchers develop simplified elements that can correctly predict structure\u27s crash behavior based on the existing collapse theories. These developed simplified elements then are applied to develop the simplified models. Nevertheless, most of the exiting collapse theories are regarding the thin-walled box section beams. However, in addition to the box section member, the channel section member is another popular member and is widely used in engineering for architectural structures, vehicles, and etc. Therefore, to simplify the thin-walled channel section beams, new collapse theory is required to predict the crash behavior for such beams. This topic is the focus of this dissertation. This dissertation develops a mathematical model to predict the crash behavior of the thin-walled channel section beams based on their real collapse mechanisms. The derived math formulae are verified through several basic applications. After that, both the existing collapse theories and the developed collapse theory regarding the thin-walled channel section beams are applied to simplify the detailed truck chassis model. The developed simplified model is used for crashworthiness analysis and the results are compared to those from the detailed model. The developed theory and the modeling method are then validated through the comparison. Additionally, in developing the simplified truck chassis model, the cross members that were modeled using coarse shell elements in previous simplified models are remodeled using simple elements. Two of the simplified modeling methods, the superelement method and the equivalent beam method, are utilized to generate the simplified models for the cross members of the truck chassis model. The principle of both methods is to use simple elements to transfer the original members\u27 mass and stiffness matrices. The equivalent beam method is recommended after comparison of the results of the crashworthiness analyses of each method. The primary contributions of this work are first, the derivation of crash theory that can predict the crash behavior of thin-walled channel section beams. The second is the use of equivalent beams to simplify the cross members within truck chassis models. Finally, a simplified modeling methodology is presented and evaluated. All the theory and modeling method developed in this work are applied for creating simplified models. Both the simplified and detailed models are used for crashworthiness analyses, results show that the errors caused by the simplified models are fewer than 10% and the simplified models only take less than 10% of the computer time of the corresponding detailed models

Preliminary Investigation into Modeling The Damage to Carbon Fibre Composites Due to the Thermo-electric Effects of a Lightning Strikes

The impact of a lightning strike causes a short high electrical current burst through Carbon Fibre Composites (CFC). Due to the electrical properties of CFC the large current leads to a rapid heating of the surrounding impact area which degrades and damages the CFC. It is therefore necessary to study in detail the thermal response and possible degradation processes caused to CFC. The degradation takes place in two ways, firstly via direct mechanical fracture due to the thermal expansion of the CFC and secondly via thermo-chemical processes (phase change and pyrolysis) at high temperatures. The main objective of this work is to construct a numerical model of the major physical processes involved, and to understand the correlation between the damage mechanisms and the damage witnessed in modern CFC. For this work we are only considering the thermo-chemical degradation of CFC. Bespoke numerical models have been constructed to predict the extent of the damage caused by the two thermo-chemical processes separately (e.g. a model for phase change and a model for pyrolysis). The numerical model predictions have then been verified experimental by decoupling of the damage mechanisms, e.g. the real Joule heating from a lightning strike is replaced by a high power laser beam acting on composite surface. This was done to simplify the physical processes which occur when a sample is damaged. The experimentally damaged samples were then investigated using X-ray tomography to determine the physical extent of the damage. The experimental results are then compared with the numerical predictions by considering the physical extent of the polymer removal. The extent of polymer removal predicted by the numerical model, solving for pyrolysis, gave a reasonable agreement with the damage seen in the experimental sample. Furthermore the numerical model predicts that the damage caused by polymer phase change has a minimal contribution to the overall extent of the damage

TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks

We present a framework for specifying, training, evaluating, and deploying machine learning models. Our focus is on simplifying cutting edge machine learning for practitioners in order to bring such technologies into production. Recognizing the fast evolution of the field of deep learning, we make no attempt to capture the design space of all possible model architectures in a domain- specific language (DSL) or similar configuration language. We allow users to write code to define their models, but provide abstractions that guide develop- ers to write models in ways conducive to productionization. We also provide a unifying Estimator interface, making it possible to write downstream infrastructure (e.g. distributed training, hyperparameter tuning) independent of the model implementation. We balance the competing demands for flexibility and simplicity by offering APIs at different levels of abstraction, making common model architectures available out of the box, while providing a library of utilities designed to speed up experimentation with model architectures. To make out of the box models flexible and usable across a wide range of problems, these canned Estimators are parameterized not only over traditional hyperparameters, but also using feature columns, a declarative specification describing how to interpret input data. We discuss our experience in using this framework in re- search and production environments, and show the impact on code health, maintainability, and development speed.Comment: 8 pages, Appeared at KDD 2017, August 13--17, 2017, Halifax, NS, Canad