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Exploiting Building Demand Flexibility Through Machine Learning For Building-To-Grid Integration, Hannah Charlene Fontenot
Exploiting Building Demand Flexibility Through Machine Learning For Building-To-Grid Integration, Hannah Charlene Fontenot
Dissertations - ALL
Demand flexibility – the ability to adjust a building's load profile across different timescales – is a key aspect of the ongoing effort to increase interconnectivity between buildings and the power grid. By harnessing their demand flexibility, buildings can provide significant benefits to the grid and bolster grid resilience and reliability. To facilitate the transition toward the "smart grid", new and intelligent control approaches are required that can seamlessly integrate building, occupant, and grid data and effectively control multiple building assets to provide grid services while maintaining occupants' required thermal comfort levels and reducing the building's overall energy consumption and …
Predictive Computational Materials Modeling With Machine Learning: Creating The Next Generation Of Atomistic Potential Using Neural Networks, Mashroor Shafat Nitol
Predictive Computational Materials Modeling With Machine Learning: Creating The Next Generation Of Atomistic Potential Using Neural Networks, Mashroor Shafat Nitol
Theses and Dissertations
Machine learning techniques using artificial neural networks (ANNs) have proven to be effective tools to rapidly mimic first principles calculations. These tools are capable of sub meV/atom accuracy while operating with linear scaling with respect to the system size. Here novel interatomic potentials are constructed based on the rapid artificial neural network (RANN) formalism. This approach generates precise force fields for various metals that have historically been difficult to describe at the atomic scale. These force fields can be utilized in molecular dynamics simulations to provide new physical insights. The RANN formalism, which is incorporated into a LAMMPS molecular dynamics …
A Machine Learning Method For The Prediction Of Melt Pool Geometries Created By Laser Powder Bed Fusion, Jonathan Ciaccio
A Machine Learning Method For The Prediction Of Melt Pool Geometries Created By Laser Powder Bed Fusion, Jonathan Ciaccio
University of New Orleans Theses and Dissertations
A machine learning model is created to predict melt pool geometries of Ti-6Al-4V alloy created by the laser powder bed fusion process. Data is collected through an extensive literature survey, using results from both experiments and CFD modeling. The model focuses on five key input parameters that influence melt pool geometries: laser power, scanning speed, spot size, powder density, and powder layer thickness. The two outputs of the model are melt pool width and melt pool depth. The model is trained and tested by using the k fold cross validation technique. Multiple regression models are then applied to find the …
Investigation Of The Prevalence Of Faults In The Heating, Ventilation, And Air-Conditioning Systems Of Commercial Buildings, Amir Ebrahimifakhar
Investigation Of The Prevalence Of Faults In The Heating, Ventilation, And Air-Conditioning Systems Of Commercial Buildings, Amir Ebrahimifakhar
Durham School of Architectural Engineering and Construction: Dissertations, Thesis, and Student Research
This dissertation describes a large-scale investigation of heating, ventilation, and air-conditioning (HVAC) fault prevalence in commercial buildings in the United States. A multi-year dataset with 36,556 pieces of HVAC equipment including air handling units (AHUs), air terminal units (ATUs), and packaged rooftop units (RTUs) was analyzed to determine values for several HVAC fault prevalence metrics. The primary source of data for this study comes from three commercial fault detection and diagnostics (FDD) providers. Since each FDD provider uses different terms to refer to the same fault in an HVAC system, a mapping function was created for each FDD provider’s dataset, …
Searching Extreme Mechanical Properties Using Active Machine Learning And Density Functional Theory, Joshua Ojih
Searching Extreme Mechanical Properties Using Active Machine Learning And Density Functional Theory, Joshua Ojih
Theses and Dissertations
Materials with extreme mechanical properties leads to future technological advancements. However, discovery of these materials is non-trivial. The use of machine learning (ML) techniques and density functional theory (DFT) calculation for structure properties prediction has helped to the discovery of novel materials over the past decade. ML techniques are highly efficient, but less accurate and density functional theory (DFT) calculation is highly accurate, but less efficient. We proposed a technique to combine ML methods and DFT calculations in discovering new materials with desired properties. This combination improves the search for materials because it combines the efficiency of ML and the …
Data And Sensor Fusion Using Fmg, Semg And Imu Sensors For Upper Limb Prosthesis Control, Jason S. Gharibo
Data And Sensor Fusion Using Fmg, Semg And Imu Sensors For Upper Limb Prosthesis Control, Jason S. Gharibo
Electronic Thesis and Dissertation Repository
Whether someone is born with a missing limb or an amputation occurs later in life, living with this disability can be extremely challenging. The robotic prosthetic devices available today are capable of giving users more functionality, but the methods available to control these prostheses restrict their use to simple actions, and are part of the reason why users often reject prosthetic technologies. Using multiple myography modalities has been a promising approach to address these control limitations; however, only two myography modalities have been rigorously tested so far, and while the results have shown improvements, they have not been robust enough …
Use Of Machine Learning For Automated Convergence Of Numerical Iterative Schemes, Leonardo A. Bueno-Benitez
Use Of Machine Learning For Automated Convergence Of Numerical Iterative Schemes, Leonardo A. Bueno-Benitez
Doctoral Dissertations and Master's Theses
Convergence of a numerical solution scheme occurs when a sequence of increasingly refined iterative solutions approaches a value consistent with the modeled phenomenon. Approximations using iterative schemes need to satisfy convergence criteria, such as reaching a specific error tolerance or number of iterations. The schemes often bypass the criteria or prematurely converge because of oscillations that may be inherent to the solution. Using a Support Vector Machines (SVM) machine learning approach, an algorithm is designed to use the source data to train a model to predict convergence in the solution process and stop unnecessary iterations. The discretization of the Navier …
A Dynamic Active Noise Control System For Live Music Attenuation, Elliot James Krueger
A Dynamic Active Noise Control System For Live Music Attenuation, Elliot James Krueger
Graduate Research Theses & Dissertations
This thesis proposes a system design that will be suitable for applying active noise control (ANC) effectively to live musical instruments. The design consists of three parts: a signal separation section, an instrument classification section, and the active noise control section. The signal separation section will split up the music signals. The instrument classification section will identify the signals, and the ANC section will attenuate the music signal based on the previous information from the other sections. The two instruments of focus will be the trombone and tuba for their low frequency and ability to be quite loud in a …
A Theory-Supported Machine Learning Model For The Prediction Of Melt Pool Geometry And Optimal Process Window In Metal Additive Manufacturing, Sina Tayebati
Graduate Research Theses & Dissertations
Direct Energy Deposition (DED) is an additive manufacturing (AM) process capable of producing complicate-shaped or functionally graded components, and it is getting intense attention as a revolutionary technology to satisfy high demand in manufacturing process for the aerospace, automotive, and medical industries. However, the repeatability in geometries and properties of fabricated products is one of the most challenging issues for the DED process to be fully utilized, requiring comprehensive understanding of effect of processing conditions on the properties of fabricated parts, and development of relations among those conditions and properties. That is the motivation of this research. In this study, …