Stochastic Point Process Modeling for Engineering Applications

<p>Hawkes model or self-exciting point process model is a branching point process model. The model classifies the dataset of discrete events to background and offspring events. It has been used to study interconnected events in many fields, but relatively little work exists in applying these concepts to engineering problems. In our research, we use a self-exciting point process model for two engineering applications: (a) To identify secondary crashes from a given traffic data and (b) To quantify the agglomeration state and size of nanoparticles from computationally generated carbon nanotube microstructure using stochastic percolation model and experimentally generated titanium nanoparticle microstructures.</p> <p>We have developed a self-exciting temporal point process to analyze secondary crashes. The model is validated using data on secondary crashes from a data-intensive model from the literature. The model is used to analyze crash incidents on Interstate-4 (I-4) from 2017-2019.The model parameters are optimized using a maximum likelihood estimate. The model is then used to calculate the probability that a given crash from the dataset is a secondary crash. A non-stationary background rate with step and sinusoidal function is introduced to account for the periodic variation of traffic. The results from the model can potentially be utilized to improve the traffic safety especially for first responders.</p> <p>The same approach is used to study nanoparticle agglomeration. The spatial point process model is applied to assess the agglomeration state and size of titanium nanoparticle microstructures generated experimentally. This is accomplished by altering the duration of ultrasonic treatment on solutions with uniform concentrations to achieve varying levels of agglomeration. Similarly, the point process model is used to analyze computationally generated carbon nanotube microstructures, encompassing equiaxed and rope-like morphologies, created through a stochastic microstructure model. We extract nanoparticle locations from these micrographs and apply a point process model for a comprehensive analysis of nanoparticle agglomeration.</p> <p>As an extension of the agglomeration study, a stochastic percolation model was used to investigate the effects of electrical conductivity and resistance on layer-wise inkjet-printed carbon nanotube microstructures. The model was employed to generate microstructures, enabling a parametric exploration of the ink composition, alignment, and agglomeration of CNTs within printed structures affect the sheet resistance with respect to the number of printed layers. A high degree of CNT alignment hindered CNT network formation, resulting in higher resistivity, whereas the partial alignment lowered the network resistivity. In addition, we replicate the microstructure through experimental methods involving sessile drop and twin-line deposition techniques to quantify the resistivity of multilayer CNT structures.</p>

Name thesis:degree_name

Doctor of Philosophy in Aerospace Engineering

Level thesis:degree_level

Dissertation - Open Access

Discipline thesis:degree_discipline

Aerospace Engineering

Year

2023

Author dc:creator

• Motagi, Samarth

• Point-Process Model
• Epidemic-Type Aftershock Sequence
• Probability Density Function
• Maximum Likelihood Estimate
• Akaike Information Criterion
• Representative Area Element
• Structures and Materials

Repository record dc:identifier

https://commons.erau.edu/edt/774

OAI identifier oai:identifier

oai:commons.erau.edu:edt-1807