Engineering Commons^™

Deep Learning Strategies For Pool Boiling Heat Flux Prediction Using Image Sequences, Connor Heo

Graduate Theses and Dissertations

The understanding of bubble dynamics during boiling is critical to the design of advanced heater surfaces to improve the boiling heat transfer. The stochastic bubble nucleation, growth, and coalescence processes have made it challenging to obtain mechanistic models that can predict boiling heat flux based on the bubble dynamics. Traditional boiling image analysis relies on the extraction of the dominant physical quantities from the images and is thus limited to the existing knowledge of these quantities. Recently, machine-learning-aided analysis has shown success in boiling crisis detection, heat flux prediction, real-time image analysis, etc., whereas most of the existing studies are …

Modeling The Mechanical Behavior And Shock Propagation Of Metallic And Nanocomposite Materials, Pouya Shojaeishahmirzadi

UNLV Theses, Dissertations, Professional Papers, and Capstones

Evaluating the materials properties under different loading conditions is critical in various industries. Compared to quasi-static loading, predicting the behavior of structures under dynamic loads is more challenging. In this work, we will address multiple problems with strain rates varying from quasi-static to hypervelocity conditions. Computer simulation is increasingly used in the design and evaluation phases to improve the efficiency, cost-effectiveness, and flexibility. However, verification and validation of each simulation is necessary. Experiments are performed in all topics and the computational models are validated by comparing with the experiments. One of the most common types of connections in structures is …

Numerical Modeling Of Advanced Propulsion Systems, Peetak P. Mitra

Doctoral Dissertations

Numerical modeling of advanced propulsion systems such as the Internal Combustion Engine (ICE) is of great interest to the community due to the magnitude of compute/algorithmic challenges. Fuel spray atomization, which determines the rate of fuel-air mixing, is a critical limiting process for the phenomena of combustion within ICEs. Fuel spray atomization has proven to be a formidable challenge for the state-of-the-art numerical models due to its highly transient, multi-scale, and multi-phase nature. Current models for primary atomization employ a high degree of empiricism in the form of model constants. This level of empiricism often reduces the art of predictive …

Reaction Wheels Fault Isolation Onboard 3-Axis Controlled Satellite Using Enhanced Random Forest With Multidomain Features, Mofiyinoluwa Oluwatobi Folami

Electronic Theses and Dissertations

With the increasing number of satellite launches throughout the years, it is only natural that an interest in the safety and monitoring of these systems would increase as well. However, as a system becomes more complex it becomes difficult to generate a high-fidelity model that accurately describes all the system components. With such constraints using data-driven approaches becomes a more feasible option. One of the most commonly used actuators in spacecraft is known as the reaction wheel. If these reaction wheels are not maintained or monitored, it could result in mission failure and unwarranted costs. That is why fault detection …

Laser Surface Treatment And Laser Powder Bed Fusion Additive Manufacturing Study Using Custom Designed 3d Printer And The Application Of Machine Learning In Materials Science, Hao Wen

LSU Doctoral Dissertations

Selective Laser Melting (SLM) is a laser powder bed fusion (L-PBF) based additive manufacturing (AM) method, which uses a laser beam to melt the selected areas of the metal powder bed. A customized SLM 3D printer that can handle a small quantity of metal powders was built in the lab to achieve versatile research purposes. The hardware design, electrical diagrams, and software functions are introduced in Chapter 2. Several laser surface engineering and SLM experiments were conducted using this customized machine which showed the functionality of the machine and some prospective fields that this machine can be utilized. Chapter 3 …

Mitigating Insider Threat Risks In Cyber-Physical Manufacturing Systems, Jinwoo Song

Dissertations - ALL

Cyber-Physical Manufacturing System (CPMS)—a next generation manufacturing system—seamlessly integrates digital and physical domains via the internet or computer networks. It will enable drastic improvements in production flexibility, capacity, and cost-efficiency. However, enlarged connectivity and accessibility from the integration can yield unintended security concerns. The major concern arises from cyber-physical attacks, which can cause damages to the physical domain while attacks originate in the digital domain. Especially, such attacks can be performed by insiders easily but in a more critical manner: Insider Threats.

Insiders can be defined as anyone who is or has been affiliated with a system. Insiders have knowledge …

Implementing A Data Acquisition System For The Training Of Cloud Coverage Neural Networks, Weston C. Montgomery

Master's Theses

Cal Poly is home to a solar farm designed to nominally generate 4.5 MW of electricity. The Gold Tree Solar Farm (GTSF) is currently the largest photovoltaic array in the California State University (CSU) system, and it was claimed to be able to produce approximately 11 GWh per year. These types of projections come from power generation models which have been developed to predict power production of these large solar fields. However, when it comes to near-term forecasting of power generation with variable sources such as wind and solar, there is definitely room for improvement.

The two primary factors that …

Towards Remote Gait Analysis: Combining Physics And Probabilistic Models For Estimating Human Joint Mechanics, Reed Donovan Gurchiek

Graduate College Dissertations and Theses

The connected health movement and remote patient monitoring promise to revolutionize patient care in multiple clinical contexts. In orthopedics, continuous monitoring of human joint and muscle tissue loading in free-living conditions will enable novel insight concerning musculoskeletal disease etiology. These developments are necessary for comprehensive patient characterization, progression monitoring, and personalized therapy. This vision has motivated many recent advances in wearable sensor-based algorithm development that aim to perform biomechanical analyses traditionally restricted to confined laboratory spaces. However, these techniques have not translated to practical deployment for remote monitoring. Several barriers to translation have been identified including complex sensor arrays. Thus, …

Prediction Of Tensile Behaviors Of L-Ded 316 Stainless Steel Parts Using Machine Learning, Israt Zarin Era

Graduate Theses, Dissertations, and Problem Reports

Directed energy deposition (DED) is a rising field in the arena of metal additive manufacturing and has extensive applications in aerospace, medical and rapid prototyping. The process parameters, such as laser power, scanning speed and specimen height, play a great deal in controlling and affecting the properties of DED fabricated parts. Nevertheless, both experimental and simulation methods have shown constraints and limited ability to generate accurate and efficient computational predictions on the correlations between the process parameters and the final part quality. In this work, a data driven machine learning model XGBoost has been built and applied to predict the …