531 research outputs found

    CFD Modelling of the Mixture Preparation in a Modern Gasoline Direct Injection Engine and Correlations with Experimental PN Emissions

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    A detailed 3D CFD analysis of a modern gasoline direct injection (GDI) engine is carried out to reveal the connections between pre-combustion mixture indicators and PN emissions. Firstly, a novel calibration methodology is introduced to accurately predict the widely used characteristics of the high-pressure fuel spray. The methodology utilised the Siemens STAR-CD 3D CFD software environment and employed a combination of statistical and optimization methods supported by experimental data. The calibration process identified dominant factors influencing spray properties and established their optimal levels. The two most used models for fuel atomisation were investigated. The Kelvin–Helmholtz/Rayleigh–Taylor (KH–RT) and Reitz–Diwakar (RD) break-up models were calibrated in conjunction with the Rosin–Rammler (RR) mono-modal droplet size distribution. RD outperformed KH–RT in terms of prediction when comparing numerical spray tip penetration and droplet size characteristics to the experimental counterparts. Then, the modelling protocol incorporated droplet-wall interaction models and a multi-component surrogate fuel blend model. The comprehensive digital model was validated using published data and applied to a modern small-capacity GDI engine. The study explored various engine operating conditions and highlights the contribution of fuel mal-distribution and liquid film retention at spark timing to Particle Number (PN) emissions. Finally, a novel surrogate model was developed to predict the engine-out PN. An extensive CFD analysis was conducted considering part-load operating conditions and variations of engine control variables. The PN surrogate model was developed using an Elastic Net (EN) regression technique, establishing relationships between experimental PN emission levels and modelled, pre-combustion, air-fuel mixture quality indicators. The approach enabled the reliable prediction of engine sooting tendencies without relying on complex measurements of combustion characteristics. These research efforts aim to enhance engine efficiency, reduce emissions, and contribute to the development of a reliable and cost-effective digital toolset for engine development and diagnostics

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    UMSL Bulletin 2022-2023

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    The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp

    Learning and Control of Dynamical Systems

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    Despite the remarkable success of machine learning in various domains in recent years, our understanding of its fundamental limitations remains incomplete. This knowledge gap poses a grand challenge when deploying machine learning methods in critical decision-making tasks, where incorrect decisions can have catastrophic consequences. To effectively utilize these learning-based methods in such contexts, it is crucial to explicitly characterize their performance. Over the years, significant research efforts have been dedicated to learning and control of dynamical systems where the underlying dynamics are unknown or only partially known a priori, and must be inferred from collected data. However, much of these classical results have focused on asymptotic guarantees, providing limited insights into the amount of data required to achieve desired control performance while satisfying operational constraints such as safety and stability, especially in the presence of statistical noise. In this thesis, we study the statistical complexity of learning and control of unknown dynamical systems. By utilizing recent advances in statistical learning theory, high-dimensional statistics, and control theoretic tools, we aim to establish a fundamental understanding of the number of samples required to achieve desired (i) accuracy in learning the unknown dynamics, (ii) performance in the control of the underlying system, and (iii) satisfaction of the operational constraints such as safety and stability. We provide finite-sample guarantees for these objectives and propose efficient learning and control algorithms that achieve the desired performance at these statistical limits in various dynamical systems. Our investigation covers a broad range of dynamical systems, starting from fully observable linear dynamical systems to partially observable linear dynamical systems, and ultimately, nonlinear systems. We deploy our learning and control algorithms in various adaptive control tasks in real-world control systems and demonstrate their strong empirical performance along with their learning, robustness, and stability guarantees. In particular, we implement one of our proposed methods, Fourier Adaptive Learning and Control (FALCON), on an experimental aerodynamic testbed under extreme turbulent flow dynamics in a wind tunnel. The results show that FALCON achieves state-of-the-art stabilization performance and consistently outperforms conventional and other learning-based methods by at least 37%, despite using 8 times less data. The superior performance of FALCON arises from its physically and theoretically accurate modeling of the underlying nonlinear turbulent dynamics, which yields rigorous finite-sample learning and performance guarantees. These findings underscore the importance of characterizing the statistical complexity of learning and control of unknown dynamical systems.</p

    Insights on Learning Tractable Probabilistic Graphical Models

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    Computational modelling and optimal control of interacting particle systems: connecting dynamic density functional theory and PDE-constrained optimization

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    Processes that can be described by systems of interacting particles are ubiquitous in nature, society, and industry, ranging from animal flocking, the spread of diseases, and formation of opinions to nano-filtration, brewing, and printing. In real-world applications it is often relevant to not only model a process of interest, but to also optimize it in order to achieve a desired outcome with minimal resources, such as time, money, or energy. Mathematically, the dynamics of interacting particle systems can be described using Dynamic Density Functional Theory (DDFT). The resulting models are nonlinear, nonlocal partial differential equations (PDEs) that include convolution integral terms. Such terms also enter the naturally arising no-flux boundary conditions. Due to the nonlocal, nonlinear nature of such problems they are challenging both to analyse and solve numerically. In order to optimize processes that are modelled by PDEs, one can apply tools from PDE-constrained optimization. The aim here is to drive a quantity of interest towards a target state by varying a control variable. This is constrained by a PDE describing the process of interest, in which the control enters as a model parameter. Such problems can be tackled by deriving and solving the (first-order) optimality system, which couples the PDE model with a second PDE and an algebraic equation. Solving such a system numerically is challenging, since large matrices arise in its discretization, for which efficient solution strategies have to be found. Most work in PDE-constrained optimization addresses problems in which the control is applied linearly, and which are constrained by local, often linear PDEs, since introducing nonlinearity significantly increases the complexity in both the analysis and numerical solution of the optimization problem. However, in order to optimize real-world processes described by nonlinear, nonlocal DDFT models, one has to develop an optimal control framework for such models. The aim is to drive the particles to some desired distribution by applying control either linearly, through a particle source, or bilinearly, though an advective field. The optimization process is constrained by the DDFT model that describes how the particles move under the influence of advection, diffusion, external forces, and particle–particle interactions. In order to tackle this, the (first-order) optimality system is derived, which, since it involves nonlinear (integro-)PDEs that are coupled nonlocally in space and time, is significantly harder than in the standard case. Novel numerical methods are developed, effectively combining pseudospectral methods and iterative solvers, to efficiently and accurately solve such a system. In a next step this framework is extended so that it can capture and optimize industrially relevant processes, such as brewing and nano-filtration. In order to do so, extensions to both the DDFT model and the numerical method are made. Firstly, since industrial processes often involve tubes, funnels, channels, or tanks of various shapes, the PDE model itself, as well as the optimization problem, need to be solved on complicated domains. This is achieved by developing a novel spectral element approach that is compatible with both the PDE solver and the optimal control framework. Secondly, many industrial processes, such as nano-filtration, involve more than one type of particle. Therefore, the DDFT model is extended to describe multiple particle species. Finally, depending on the application of interest, additional physical effects need to be included in the model. In this thesis, to model sedimentation processes in brewing, the model is modified to capture volume exclusion effects

    Spherical and Hyperbolic Toric Topology-Based Codes On Graph Embedding for Ising MRF Models: Classical and Quantum Topology Machine Learning

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    The paper introduces the application of information geometry to describe the ground states of Ising models by utilizing parity-check matrices of cyclic and quasi-cyclic codes on toric and spherical topologies. The approach establishes a connection between machine learning and error-correcting coding. This proposed approach has implications for the development of new embedding methods based on trapping sets. Statistical physics and number geometry applied for optimize error-correcting codes, leading to these embedding and sparse factorization methods. The paper establishes a direct connection between DNN architecture and error-correcting coding by demonstrating how state-of-the-art architectures (ChordMixer, Mega, Mega-chunk, CDIL, ...) from the long-range arena can be equivalent to of block and convolutional LDPC codes (Cage-graph, Repeat Accumulate). QC codes correspond to certain types of chemical elements, with the carbon element being represented by the mixed automorphism Shu-Lin-Fossorier QC-LDPC code. The connections between Belief Propagation and the Permanent, Bethe-Permanent, Nishimori Temperature, and Bethe-Hessian Matrix are elaborated upon in detail. The Quantum Approximate Optimization Algorithm (QAOA) used in the Sherrington-Kirkpatrick Ising model can be seen as analogous to the back-propagation loss function landscape in training DNNs. This similarity creates a comparable problem with TS pseudo-codeword, resembling the belief propagation method. Additionally, the layer depth in QAOA correlates to the number of decoding belief propagation iterations in the Wiberg decoding tree. Overall, this work has the potential to advance multiple fields, from Information Theory, DNN architecture design (sparse and structured prior graph topology), efficient hardware design for Quantum and Classical DPU/TPU (graph, quantize and shift register architect.) to Materials Science and beyond.Comment: 71 pages, 42 Figures, 1 Table, 1 Appendix. arXiv admin note: text overlap with arXiv:2109.08184 by other author

    Essays on monetary policy

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    This is a summary of the four chapters that comprise this D.Phil. thesis.1 This thesis examines two major aspects of policy. The first two chapters examine monetary policy communication. The second two examine the causes and consequences of a time-varying reaction function of the central bank. 1. Central Bank Communication and Higher Moments In this first chapter, I investigate which parts of central bank communication affect the higher moments of expectations embedded in financial market pricing. Much of the literature on central bank communication has focused on how communication impacts the conditional expected mean of future policy. But this chapter asks how central bank communication affects the second and third moments of the financial market’s perceived distribution of future policy decisions. I use high frequency changes in option-prices around Bank of England communications to show that communication affects higher moments of the distribution of expectations. I find that the relevant communication in the case of the Bank of England is primarily confined to the information contained in the Q&A and Statement, rather than the longer Inflation Report. 2. Mark My Words: The Transmission of Central Bank Communication to the General Public via the Print Media In the second chapter, jointly with James Brookes, I ask how central banks can change their communication in order to receive greater newspaper coverage, if that is indeed an objective of theirs. We use computational linguistics combined with an event-study methodology to measure the extent of news coverage a central bank communication receives, and the textual features that might cause a communication to be more (or less) likely to be considered newsworthy. We consider the case of the Bank of England, and estimate the relationship between news coverage and central bank communication implied by our model. We find that the interaction between the state of the economy and the way in which the Bank of England writes its communication is important for determining news coverage. We provide concrete suggestions for ways in which central bank communication can increase its news coverage by improving readability in line with our results. 3. Uncertainty and Time-varying Monetary Policy In the third chapter, together with Michael McMahon, I investigate the links between uncertainty and the reaction function of the Federal Reserve. US macroeconomic evidence points to higher economic volatility being positively correlated with more aggressive monetary policy responses. This represents a challenge for “good policy” explanations of the Great Moderation which map a more aggressive monetary response to reduced volatility. While some models of monetary policy under uncertainty can match this comovement qualitatively, these models do not, on their own, account for the reaction-function changes quantitatively for reasonable changes in uncertainty. We present a number of alternative sources of uncertainty that we believe should be more prevalent in the literature on monetary policy. 4. The Element(s) of Surprise In the final chapter, together with Michael McMahon, I analyse the implications for monetary surprises of time-varying reaction functions. Monetary policy surprises are driven by several separate forces. We argue that many of the surprises in monetary policy instruments are driven by unexpected changes in the reaction function of policymakers. We show that these reaction function surprises are fundamentally different from monetary policy shocks in their effect on the economy, are likely endogenous to the state, and unable to removed using current orthogonalisation procedures. As a result monetary policy surprises should not be used to measure the effect of a monetary policy “shock” to the economy. We find evidence for reaction function surprises in the features of the high frequency asset price surprise data and in analysing the text of a major US economic forecaster. Further, we show that periods in which an estimated macro model suggests policymakers have switched reaction functions provide the majority of variation in monetary policy surprises
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