Fakultät für Maschinenbau
https://www.repo.uni-hannover.de/handle/123456789/6
Frei zugängliche Publikationen aus der Fakultät für Maschinenbau2019-05-24T18:25:36ZA Novel Ultrasonic Cavitation Peening Approach Assisted by Water Jet
https://www.repo.uni-hannover.de/handle/123456789/4881
A Novel Ultrasonic Cavitation Peening Approach Assisted by Water Jet
Bai, Fushi; Wang, Liang; Saalbach, Kai-Alexander; Twiefel, Jens
Ultrasonic cavitation peening is an environmentally friendly technology to improve surface properties. In the traditional ultrasonic cavitation peening process, specimens have to be immersed in a liquid and temperature control is required, which limits the wide usage of this technology due to the geometry and complicated setup. In order to improve this process, water is slowly jetted (75 mL/min) into the gap between the sonotrode tip and specimen surface. The water jet makes the gap full of water. Thus, cavitation bubbles can be generated in the gap as the traditional ultrasonic cavitation peening process. In this case, the water container and temperature control are no longer necessary. The goal of this contribution is to evaluate the treatment effectiveness of this novel approach by the impact loads, the volume loss, the surface roughness, the microhardness and the microstructure of the specimen surface. The results indicate that a higher input power is beneficial and there would be an optimal gap width for this novel ultrasonic cavitation peening process.
2018-01-01T00:00:00ZComputational Machine Learning Representation for the Flexoelectricity Effect in Truncated Pyramid Structures
https://www.repo.uni-hannover.de/handle/123456789/4811
Computational Machine Learning Representation for the Flexoelectricity Effect in Truncated Pyramid Structures
Hamdia, Khader M.; Ghasemi, Hamid; Zhuang, Xiaoying; Alajlan, Naif; Rabczuk, Timon
In this study, machine learning representation is introduced to evaluate the flexoelectricity effect in truncated pyramid nanostructure under compression. A Non-Uniform Rational B-spline (NURBS) based IGA formulation is employed to model the flexoelectricity. We investigate 2D system with an isotropic linear elastic material under plane strain conditions discretized by 45×30 grid of B-spline elements. Six input parameters are selected to construct a deep neural network (DNN) model. They are the Young's modulus, two dielectric permittivity constants, the longitudinal and transversal flexoelectric coefficients and the order of the shape function. The outputs of interest are the strain in the stress direction and the electric potential due flexoelectricity. The dataset are generated from the forward analysis of the flexoelectric model. 80% of the dataset is used for training purpose while the remaining is used for validation by checking the mean squared error. In addition to the input and output layers, the developed DNN model is composed of four hidden layers. The results showed high predictions capabilities of the proposed method with much lower computational time in comparison to the numerical model.
2019-01-01T00:00:00ZThe Prediction of Self-Healing Capacity of Bacteria-Based Concrete Using Machine Learning Approaches
https://www.repo.uni-hannover.de/handle/123456789/4809
The Prediction of Self-Healing Capacity of Bacteria-Based Concrete Using Machine Learning Approaches
Zhuang, Xiaoying; Zhou, Shuai
Advances in machine learning (ML) methods are important in industrial engineering and attract great attention in recent years. However, a comprehensive comparative study of the most advanced ML algorithms is lacking. Six integrated ML approaches for the crack repairing capacity of the bacteria-based self-healing concrete are proposed and compared. Six ML algorithms, including the Support Vector Regression (SVR), Decision Tree Regression (DTR), Gradient Boosting Regression (GBR), Artificial Neural Network (ANN), Bayesian Ridge Regression (BRR) and Kernel Ridge Regression (KRR), are adopted for the relationship modeling to predict crack closure percentage (CCP). Particle Swarm Optimization (PSO) is used for the hyper-parameters tuning. The importance of parameters is analyzed. It is demonstrated that integrated ML approaches have great potential to predict the CCP, and PSO is efficient in the hyper-parameter tuning. This research provides useful information for the design of the bacteria-based self-healing concrete and can contribute to the design in the rest of industrial engineering
2019-01-01T00:00:00ZA Nonlocal Operator Method for Partial Differential Equations with Application to Electromagnetic Waveguide Problem
https://www.repo.uni-hannover.de/handle/123456789/4808
A Nonlocal Operator Method for Partial Differential Equations with Application to Electromagnetic Waveguide Problem
Rabczuk, Timon; Ren, Huilong; Zhuang, Xiaoying
A novel nonlocal operator theory based on the variational principle is proposed for the solution of partial differential equations. Common differential operators as well as the variational forms are defined within the context of nonlocal operators. The present nonlocal formulation allows the assembling of the tangent stiffness matrix with ease and simplicity, which is necessary for the eigenvalue analysis such as the waveguide problem. The present formulation is applied to solve the differential electromagnetic vector wave equations based on electric fields. The governing equations are converted into nonlocal integral form. An hourglass energy functional is introduced for the elimination of zero-energy modes. Finally, the proposed method is validated by testing three classical benchmark problems.
2019-01-01T00:00:00Z