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Title: | Data-driven Modeling for Fluid Dynamics and Control |
Authors: | |
Advisors: | |
Contributors: | Mechanical and Aerospace Engineering Department |
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Subjects: | |
Issue Date: | 2020 |
Publisher: | Princeton, NJ : Princeton University |
Abstract: | One of the most critical tasks in fluid dynamics and control is to build simple, low-order, and accurate models. The models are essential for understanding dynamics and control. However, in many cases, the models are either unknown or too complicated to be useful. As an example, fluid flows are governed by Navier-Stokes equations (NSE), which remain intractable for real-time applications. Meanwhile, with increasing computational power and advances in experimental and numerical methods, researchers have access to much more data about dynamical systems. For instance, computational fluid dynamics (CFD) produces tons of data, but the data have not been fully utilized. Data-driven modeling addresses these challenges by learning dynamical system models from data. This thesis focuses on data-driven modeling methods for applications in fluid dynamics and control. First, we propose an evaluation criterion to quantify the accuracy of dynamic mode decomposition (DMD), a data-driven algorithm for extracting spatial and temporal features about dynamical systems from data. DMD is a numerical approximation to the linear Koopman operator associated with a dynamical system. By exploiting this connection, the accuracy criterion is purely data-driven and physically meaningful. It also applies to other variants of DMD algorithms and assists in model selection. Second, fast algorithms are developed for online dynamic mode decomposition (ODMD). Given real-time measurement about a dynamical system, this algorithm efficiently updates an adaptive model upon each new snapshot. It reduces both the computational time and memory requirements by order of magnitudes compared with existing methods. ODMD algorithm can be modified to gradually forget old data, which enables faster tracking of dynamics. ODMD also extends to both linear and nonlinear system identification, where control is included. Finally, we study the input-output response of a separated flow past a flat plate. The analysis is based on the frequency-domain transfer function of the linearized NSE about the mean flow. The control input is body forcing, and the output is the flow field. This analysis sheds light on the optimal control placement and reveals that the trailing edge separation bubble is most sensitive to streamwise body force (control) in upstream of the separation point. |
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Alternate format: | The Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog: |
Type of Material: | Academic dissertations (Ph.D.) |
Language: | en |
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File | Description | Size | Format | |
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Zhang_princeton_0181D_13281.pdf | 11.46 MB | Adobe PDF |
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Data-driven model development and identification of dynamical systems public deposited.
In recent years, data-driven model discovery has become increasingly popular due to rapid advances in computational power, and data processing and storage procedures. This has fostered the development of new algorithms to identify complex systems from data. However, the performance and robustness of the present techniques significantly deteriorate when the data is contaminated with noise. This dissertation considers modern sparse regression techniques to robustly recover governing equations of nonlinear dynamical systems from noisy state measurements. Comprised in three main chapters, we investigate convex ℓ 1 -regularized least squares methods, denoising strategies to enhance the performance and accuracy of identification algorithms, and non-convex optimization procedures for dynamical system identification. We begin by exploring an iteratively reweighted version of l1-regularized least squares to mitigate noise effects on measurements and conclude that a reweighted approach enhances the accuracy of the dynamical identification process. We also propose a method to recover dynamical constraints given by implicit functions of the state variables. Next, we compare and assess local and global measurement denoising strategies as well as model selection techniques as a pre-processing step to improve the robustness and performance of sparse identification algorithms. We empirically prove that global methods outperform local methods, and that Pareto curves generally yield better regularization parameters than generalized cross-validation. Finally, we present a promising non-convex formulation and suitable optimization algorithms for sparse dynamical system identification that avoids errors arising from numerical differentiation of noisy data. We conclude by discussing potential improvements for non-convex dynamical system identification approaches and provide further research directions.
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50 Topic Ideas To Kickstart Your Research Project
If you’re just starting out exploring data science-related topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research by providing a hearty list of data science and analytics-related research ideas , including examples from recent studies.
PS – This is just the start…
We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . These topic ideas provided here are intentionally broad and generic , so keep in mind that you will need to develop them further. Nevertheless, they should inspire some ideas for your project.
To develop a suitable research topic, you’ll need to identify a clear and convincing research gap , and a viable plan to fill that gap. If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, consider our 1-on-1 coaching service .
While the ideas we’ve presented above are a decent starting point for finding a research topic, they are fairly generic and non-specific. So, it helps to look at actual studies in the data science and analytics space to see how this all comes together in practice.
Below, we’ve included a selection of recent studies to help refine your thinking. These are actual studies, so they can provide some useful insight as to what a research topic looks like in practice.
As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest. In the video below, we explore some other important things you’ll need to consider when crafting your research topic.
If you’re still unsure about how to find a quality research topic, check out our Research Topic Kickstarter service, which is the perfect starting point for developing a unique, well-justified research topic.
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Author: | |
Title: | Data-driven modelling of soil properties and behaviours with geotechnical applications |
Advisors: | Yin, Zhen-yu (CEE) Yin, Jian-hua (CEE) |
Degree: | Ph.D. |
Year: | 2022 |
Award: | FCE Awards for Outstanding PhD Theses (2022/23) PolyU PhD Thesis Award - Merit Award (2023) |
Subject: | Soil mechanics Soil mechanics -- Data processing Hong Kong Polytechnic University -- Dissertations |
Department: | Department of Civil and Environmental Engineering |
Pages: | xvii, 218 pages : color illustrations |
Language: | English |
Abstract: | Understanding soil properties and behaviours are fundamental to geotechnical design. Myriad empirical and analytical models have been proposed for prediction accordingly but they tend to be site-specific and increasing parameters need to be calibrated for constitutive models. With the increasing data in the geotechnical domain, machine learning (ML) has emerged as a new methodology to directly learn from raw data to identify soil properties and behaviours. Its applicability has been proved to be promising because of its versatility and strong fitting capability. Nevertheless, the current ML-based data-driven models still exhibited limitations including lack of interpretability, dependency on numerous high-quality data and poor generalization ability, thus they are still far away from application to engineering practice. To this end, this study aims to elaborate data-driven models for predicting soil properties and mechanical behaviours merely based on their micro computed-tomography (µCT) images, as well as facilitate their applications in geotechnical engineering. First, a set of ML-assisted algorithms is developed for automatically reconstructing three-dimensional real particles from µCT images and subsequently identifying their particle size and morphology. Bayesian inference is incorporated into the ML algorithms for enhancing the interpretability of the data-driven model. Then, a multi-fidelity residual neural network incorporating Bayesian uncertainty is proposed to leverage existing knowledge and limited high-quality data for modelling mechanical behaviours of soils. In this context, a multi-scale data-driven model is proposed from the identification of particle size and morphology to the prediction of their mechanical responses together with fabric evolution. Finally, the developed data-driven models are integrated with finite element code for modelling boundary value problems and the results are compared with conventional numerical modelling methods and measurements for the validation. The proposed data-driven modelling methods are successfully used to predict various soil properties such as compressibility, creep, strength and permeability, behaviours such as anisotropy and dilatancy and boundary value problems. |
Rights: | All rights reserved |
Access: | open access |
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Since its inception, the field of heterogeneous catalysis has evolved to address the needs of the ever-growing human population. Necessity, after all, fosters innovation. Today, the world faces numerous challenges related to anthropogenic climate change, and that has necessitated, among other things, a search for new catalysts that can enable renewable energy conversion and storage, sustainable food and chemicals production, and a reduction in carbon emissions. This search has led to the emergence of many promising classes of materials, each having a unique set of catalytic properties. Among such candidate materials, high-entropy alloys (HEAs) have very recently shown the potential to be a new catalyst design paradigm. HEAs are multimetallic, disordered alloys containing more than four elements and, as a result, possess a higher configurational entropy, which gives them considerable stability. They have many conceivable benefits over conventional bimetallic alloy catalysts—greater site heterogeneity, larger design space, and higher stability, among others. Consequently, there is a need to explore their application in a wide range of thermal and electrocatalytic reaction systems so that their potential can be realized.
In the past few decades, first principles-based approaches involving Density Functional Theory (DFT) calculations have proven to be effective in probing catalytic mechanisms at the atomic scale. Fundamental insights from first principles studies have also led to a detailed understanding of reactivity and stability trends for bimetallic alloy catalysts. However, the express application of first principles approaches to study HEA catalysts remains a challenge, due to the large computational cost incurred in performing DFT calculations for disordered alloys, which can be represented by millions of different configurations. A combination of first principles approaches and computationally efficient machine learning (ML) approaches can, however, potentially overcome this limitation.
In this thesis, combined workflows involving first principles and machine learning-based approaches are developed. To map catalyst structure to properties graph convolutional network (GCN) models are developed and trained on DFT-predicted target properties such as formation energies, surface energies, and adsorption energies. Further, the Monte Carlo dropout method is integrated into GCN models to provide uncertainty quantification, and these models are in turn used in active learning workflows that involve iterative model retraining to both improve model predictions and optimize the target property value. Dimensionality reduction methods, such as principal components analysis (PCA) and Diffusion Maps (DMaps), are used to glean physicochemical insights from the parameterization of the GCN.
These workflows are applied to the analysis of binary, ternary, and quaternary alloy catalysts, and a series of fundamental insights regarding their stability are elucidated. In particular, the origin and stability of “Pt skins” that form on Pt-based bimetallic alloys such as Pt 3 Ni in the context of the oxygen reduction reaction (ORR) are investigated using a rigorous surface thermodynamic framework. The active learning workflow enables the study of Pt skin formation on stepped facets of Pt 3 Ni (with a complex, low-symmetry geometry), and this analysis reveals a hitherto undiscovered relationship between surface coordination and surface segregation. In another study, an active learning workflow is used to identify the most stable bulk composition in the Pd-Pt-Sn ternary alloy system using a combination of exhaustively sampled binary alloy data and prudently sampled ternary alloy data. Lastly, a new GCN model architecture, called SlabGCN, is introduced to predict the sulfur poisoning characteristics of quaternary alloy catalysts, and to find an optimal sulfur tolerant composition.
On another front, the electrocatalytic activity of quinary HEAs towards the ORR is investigated by performing DFT calculations on HEA structures generated using the High-Entropy Alloy Toolbox (HEAT), an in-house code developed for the high-throughput generation and analysis of disordered alloy structures with stability constraints (such as Pt skin formation). DFT-predicted adsorption energies of key ORR intermediates are further deconvoluted into ligand, strain, and surface relaxation effects, and the influence of the number of Pt skins on these effects is expounded. A Sabatier volcano analysis is performed to calculate the ORR activities of selected HEA compositions, and correspondence between theoretical predictions and experimental results is established, to pave the way for rational design of HEA catalysts for oxygen reduction.
In summary, this thesis examines stability and reactivity trends of a multitude of alloy catalysts, from conventional bimetallic alloys to high-entropy alloys, using a combination of first principles approaches (involving Density Functional Theory calculations) and machine learning approaches comprising graph convolutional network models.
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decade, this thesis studies the length to which we can push various business oper-ations with new technologies, in our theoretical understanding and practical per-formance alike. Towards this goal, this thesis develops data-driven decision-making methods for a selection of challenging emerging problems in supply chain and other business operations.
Data powers insights, decision and actions, and we are only scratching the surface of the value that can be created, captured and redistributed through data-driven decision making. Description Thesis: S.M. in Management Studies, Massachusetts Institute of Technology, Sloan School of Management, 2017.
Abstract. This thesis aims to advance the theory and practice of data-driven dynamic decision making, by synergizing ideas from machine learning and operations research. Throughout this thesis, we focus on three aspects: (i) developing new, practical algorithms that systematically empower data-driven dynamic decision making, (ii) identifying ...
Data-DrivenDynamicDecisionMaking: Algorithms,Structures,and ComplexityAnalysis by Yunzong Xu SubmittedtotheInstituteforData,Systems,andSociety onMay5,2023 ...
This thesis takes a multi-perspective view toward several important decision support problems encountered in various application domains and attempts to enhance existing data-driven techniques for better decision support. 6 In summary, all three essays are related to the overarching theme of the thesis.
Data Driven Computing is a new eld of computational analysis which uses provided data to directly produce predictive outcomes. This thesis rst establishes de nitions of Data-Driven solvers and working examples of static mechanics problems to demonstrate e cacy. Signi cant extensions are
Master Thesis project: FROM CHAOS TO ORDER: A study on how data-driven development can help improve decision-making 2 Contact information Author: ... Keywords: Data-driven development, Agile methodologies, Waterfall model, Continuous Integration, Continuous Deployment, A/B Testing, Test Driven Development, Product ...
Articles report data-driven decision-making with the aid of information and communication technology. Studies report data-driven decision-making and its impact on education quality in developing countries. Exclusion Articles do not discuss data-driven decision-making technologies in countries other than developing ones.
(Talouselämä, 2013). Data utilization is shaping how organizations ought to do business and even survive (Hurwitz, 2013). Digital transformation has forced most organizations to operate in data-driven ways and data-driven decisions have widely been argued to lead to higher performance and sometimes even in competitive
Data Driven Computing is a new field of computational analysis which uses provided data to directly produce predictive outcomes. This thesis first establishes definitions of Data-Driven solvers and working examples of static mechanics problems to demonstrate efficacy. Significant extensions are then explored to both accommodate noisy data sets ...
The central goal of this thesis is to bridge the divide between theoretical linguistics—the scien-tific inquiry of language—and applied data-driven statistical language processing, to provide deeper insight into data and to build more powerful, robust models. To corroborate the practi-
Abstract: Data-driven decision-making has become an essential part of modern life by virtue of the rapid growth in data, the massive improvements in computing power, and great progress in academic research. The range of techniques used fall broadly on the spectrum that varies from model-based to applied, depending on the problem complexity and ...
This thesis topic develops an online, data-driven predictive model for dynamic obstacles, accounting for measurement noise and low-frequency data rates. First inspired by singular spectrum analysis (SSA), a time-series forecast technique, obstacle models characterized by linear recurrence relationships are extracted from real-time position ...
This is often referred to as data-based decision-making, data-driven decision-making or data-informed decision-making, which can be defined as the process of 'systematically analyzing existing data sources within the school, applying the outcomes of analyses in order to innovate teaching, curricula, and school performance, and, implementing ...
Data-driven modeling addresses these challenges by learning dynamical system models from data. This thesis focuses on data-driven modeling methods for applications in fluid dynamics and control. First, we propose an evaluation criterion to quantify the accuracy of dynamic mode decomposition (DMD), a data-driven algorithm for extracting spatial ...
In recent years, data-driven model discovery has become increasingly popular due to rapid advances in computational power, and data processing and storage procedures. This has fostered the development of new algorithms to identify complex systems from data.
If you're just starting out exploring data science-related topics for your dissertation, thesis or research project, you've come to the right place. In this post, we'll help kickstart your research by providing a hearty list of data science and analytics-related research ideas, including examples from recent studies.. PS - This is just the start…
Thesis: S.M. in Management Studies, Massachusetts Institute of Technology, Sloan School of Management, 2017. Cataloged from PDF version of thesis. Includes bibliographical references (pages ix-xii). ... an obligatory first step is to make decisions more data-driven, and less guided by intuition. While the positive effects of data-driven ...
To explore these questions, we examine several fields and describe an historical progression in knowledge production. We believe that in these contexts large scale data collection and analysis represent the next step - going beyond the capabilities of todays simulation models with an empirical data-collection driven approach.
Abstract. Encouraged by the plethora of advances in artificial intelligence (AI) in the past decade, this thesis studies the length to which we can push various business operations with new technologies, in our theoretical understanding and practical performance alike. Towards this goal, this thesis develops data-driven decision-making methods ...
an extremely large data set is required for training. A good compromise between the analytical and model-free approaches may come in the form of empirical data-fitting. Using data-driven dynamics, collected data is fit using simpler mathematical models such as n-th degree multivariable polynomials. A famous example is Pacejka's empirical ...
PolyU PhD Thesis Award - Merit Award (2023) Subject: Soil mechanics Soil mechanics -- Data processing Hong Kong Polytechnic University -- Dissertations: ... The proposed data-driven modelling methods are successfully used to predict various soil properties such as compressibility, creep, strength and permeability, behaviours such as anisotropy ...
Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, September, 2020. ... .Retail operations have experienced a transformational change in the past decade with the advent and adoption of data-driven approaches to drive decision making. Granular data collection has enabled firms to make ...
In summary, this thesis examines stability and reactivity trends of a multitude of alloy catalysts, from conventional bimetallic alloys to high-entropy alloys, using a combination of first principles approaches (involving Density Functional Theory calculations) and machine learning approaches comprising graph convolutional network models.