Variational methods in elasticity and plasticity

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Multibody Systems Approach To Vehicle Dynamics

Dynamic simulation patterns and spatiotemporal analysis of land-use/land-cover changes in the Wuhan metropolitan area, China

An integrated approach of logistic-MCE-CA-Markov to predict the land use structure and their micro-spatial characteristics analysis in Wuhan metropolitan area, Central China

Hard landings account for a large proportion of aircraft accidents and are generally judged by the intuition of pilots. For this reason, there are frequent false judgments that lead to unnecessary and costly ground inspections. False judgments can be reduced significantly if detailed load information is available, such as strains or loads on critical areas of aircraft structures. This study used an artificial neural network (ANN) to develop a numerical model that can estimate the maximum strains for areas of interest and landing loads from basic flight parameters. The results can be used to provide the required detailed load information. An efficient and accurate landing simulation model was constructed and used to build reliable datasets for training. Basic flight parameters from immediately after touchdown were used as input data for training, and the corresponding maximum values of strains and landing loads were obtained from the landing simulation model as target data for training. This information was used to train the ANNs with the Levenberg–Marquardt backpropagation algorithm. A performance evaluation using test data confirmed that the trained ANNs can successfully estimate strains and landing loads with sufficient accuracy.

Estimation of Maximum Strains and Loads in Aircraft Landing Using Artificial Neural Network

In this work, an efficient aircraft landing simulation strategy is proposed to develop an efficient and reliable hard-landing monitoring procedure. Landing stage is the most dangerous moment during operation cycle of aircraft and it may cause structural damage when hard-landing occurs. Therefore, the occurrence of hard-landing should be reported accurately to guarantee the structural integrity of aircraft. In order to accurately determine whether hard-landing occurs or not from given landing conditions, full nonlinear structural dynamic simulation can be performed, but this approach is highly timeconsuming. Thus, a more efficient approach for aircraft landing simulation which uses a hierarchical aircraft landing model and an extended inertia relief technique is proposed. The proposed aircraft landing model is composed of a multi-body dynamics model equipped with landing gear and tire models to extract the impact force and inertia force at touch-down and a linear dynamic structural model with an extended inertia relief method to analyze the structural response subject to the prescribed rigid body motion and the forces extracted from the multi-body dynamics model. The numerical examples show the efficiency and practical advantages of the proposed landing model as an essential component of aircraft hard-landing monitoring procedure.

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Hard-landing Simulation by a Hierarchical Aircraft Landing Model and an Extended Inertia Relief Technique

Additive manufacturing (AM) technology is increasingly being used in the aerospace industry due to its advantages for aerospace components such as reduction of weight. A deep understanding of the behavior and properties of additively manufactured materials or parts is required to effectively carry out the certification process which is inevitable for aerospace components. However, since AM has so many parameters that affect the performance of products, the help of high-fidelity process simulation techniques is essential to fully analyze and understand their effects. In this research, we propose a new method to effectively implement the thermal analysis for process simulations of laser powder-bed fusion technique, a representative AM technique for metal materials, using existing commercial finite element analysis software. Thermal analysis for simulations of AM process is performed and the melt pool size is compared with test results to verify the accuracy of the simulation. In AM process simulations, material properties may vary significantly with temperature, and they are also dependent on the temperature history of the material because whether the current state is a powder or solid state is determined by the maximum temperature value in the past temperature history. Therefore, in this paper, user-defined subroutines and field variables are implemented so that the temperature history of each integration point for the finite element analysis can be properly tracked and appropriate material properties can be assigned accordingly. Using the proposed methods, thermal analysis for AM process simulations can be performed successfully with good accuracy compared with the existing test results.

Seon Ho Jeong

Papers

Hard-landing Simulation by a Hierarchical Aircraft Landing Model and an Extended Inertia Relief Technique

Estimation of Maximum Strains and Loads in Aircraft Landing Using Artificial Neural Network

Thermal Analysis for Simulation of Metal Additive Manufacturing Process Considering Temperature- and History-Dependent Material Properties

Development of Load Spectrum Generation Technique Using Artificial Neural Network

Development of Automatic Hard Landing Detection Model Using Autoencoder