arXiv subjects

Robert Tempke

Publications and source records attributed to Robert Tempke.

4 recordsLinked to original sources

arXiv2021

A Machine Learning Model for Predicting Progressive Crack Extension based on DCPD Fatigue Data

Time history data collected from a Direct Current Potential Drop (DCPD) fatigue experiment at a range of temperatures was used to train a Bidirectional Long-Short Term Memory Neural Network (BiLSTM) model. The model was trained on high sampling rate experimental data from crack initiation up through the Paris regime. The BiLSTM model was able to predict the progressive crack extension at intermediate temperatures and stress intensities. The model was able to reproduce crack jumps and overall crack progression. The BiLSTM model demonstrated the potential to be used as a tool for future investigation into fundamental mechanisms such as high-temperature oxidation and new damage models.

physics.app-ph↗

arXiv2020

Machine Learning Approach for Transforming Scattering Parameters to Complex Permittivity

This study investigates the application of an artificial neural network to predict the complex dielectric properties of granular catalysts commonly used in microwave reaction chemistry. The study utilizes finite element electromagnetic simulations and two-dimensional convolutional neural networks to solve for a large solution space of varying dielectrics. This convolutional neural network was trained using a supervised learning approach and a common backpropagation. The frequency range of interest was between 0.1 to 13.5 GHz with the real part of the dielectric constants ranging from 1 to 100 and the imaginary part ranging from 0.0 to 0.2. The network was double validated using experimental data collected from a coaxial airline. The model was demonstrated to convert either experimental or computational derived scattering parameter to complex permittivities. Moreover, the model eliminates the need for iterative solutions that often have difficulty with the piecewise continuous nature of frequency dependent scattering parameters.

physics.app-ph↗

arXiv2020

Dielectric Measurement of Powdery Materials using a Coaxial Transmission Line

The following study investigates the use of a coaxial transmission line for determining the properties of powdery dielectric materials (1-10GHz). Four powdery materials with dielectric constants ranging from 3.5 to 70 (SiO$_2$, Al$_2$O$_3$, CeO$_2$, and TiO$_2$) were experimentally investigated at varying volume loading fractions. Powder particles were mixed with a paraffin matrix and properties of the powder were analyzed using ten mixing equations to verify their accuracy. These powder-paraffin composites were also modeled at varying volume loadings for comparison with experimental data to gain a better understanding of the interactions between the different phases. The optimal volume loading fraction was determined to be 10% for all powders tested. A metric for selecting the most well-suited mixing equation was proposed that involved taking the ratio of the particle dielectric to that of the matrix. This study ultimately provides guidance for experimentally measuring the dielectric properties of unknown powdery materials that have application for new devices that utilized powder-based dielectric materials.

physics.app-ph↗

arXiv2019

GHz Dielectric Measurement of Powdery TiO

This study experimentally determines the dielectric properties of a powdery titanium (II) oxide (TiO) material within the microwave range (0.1-13.5 GHz). The properties were determined using a coaxial airline method using a TiO/paraffin mixture at several loading fractions. A permittivity of 60 for volume fraction below 30% and 100 for volume fraction above were measured.

physics.app-ph↗