Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202

Incorporating user preferences in multi-objective feature selection in software product lines using multi-criteria decision analysis

Software Product Lines Engineering has created various tools that assist with the standardisation in the design and implementation of clusters of equivalent software systems with an explicit representation of variability choices in the form of Feature Models, making the selection of the most ideal software product a Feature Selection problem. With the increase in the number of properties, the problem needs to be defined as a multi-objective optimisation where objectives are considered independently one from another with the goal of finding and providing decision-makers a large and diverse set of non-dominated solutions/products. Following the optimisation, decision-makers define their own (often complex) preferences on how does the ideal software product look like. Then, they select the unique solution that matches their preferences the most and discard the rest of the solutions—sometimes with the help of some Multi-Criteria Decision Analysis technique. In this work, we study the usability and the performance of incorporating preferences of decision-makers by carrying-out Multi-Criteria Decision Analysis directly within the multi-objective optimisation to increase the chances of finding more solutions that match preferences of the decision-makers the most and avoid wasting execution time searching for non-dominated solutions that are poor with respect to decision-makers’ preferences

Machine Learning in Aerodynamic Shape Optimization

Machine learning (ML) has been increasingly used to aid aerodynamic shape optimization (ASO), thanks to the availability of aerodynamic data and continued developments in deep learning. We review the applications of ML in ASO to date and provide a perspective on the state-of-the-art and future directions. We first introduce conventional ASO and current challenges. Next, we introduce ML fundamentals and detail ML algorithms that have been successful in ASO. Then, we review ML applications to ASO addressing three aspects: compact geometric design space, fast aerodynamic analysis, and efficient optimization architecture. In addition to providing a comprehensive summary of the research, we comment on the practicality and effectiveness of the developed methods. We show how cutting-edge ML approaches can benefit ASO and address challenging demands, such as interactive design optimization. Practical large-scale design optimizations remain a challenge because of the high cost of ML training. Further research on coupling ML model construction with prior experience and knowledge, such as physics-informed ML, is recommended to solve large-scale ASO problems

Index System Reduction Method Based on the Index Similarity

Multi-attribute decision making (MADM) always suffers from the result inconsistency and computational complexity problem, due to numbers of redundant and relational attributes (indexes) of the initial evaluation index system. Therefore, this paper studies the index system (IS) reduction problem through selecting the most representative indicator from each index subsystem after the IS structure partition. First, we propose and demonstrate the Index Subsystem Judgement theorem to improve the efficiency of the classic system structure partition algorithm. Second, an algorithm of index system reduction based on the index similarity (ISRS) is put forward. The ISRS is able to reduce the index quantity while still keeping the index meaning. Third, we define the direction loss rate to measure the evaluation ability loss of the IS during reduction. The algorithm is tested for a synthetic dataset to compare the proposed ISRS with different index reduction algorithms, followed by an extensive experimentation with a real-world financial dataset. Experiment results illustrate that our proposed method is able to obtain more accessible and available reduction results in practice

Uncertainty modeling : fundamental concepts and models

This book series represents a commendable effort in compiling the latest developments on three important Engineering subjects: discrete modeling, inverse methods, and uncertainty structural integrity. Although academic publications on these subjects are plenty, this book series may be the first time that these modern topics are compiled together, grouped in volumes, and made available for the community. The application of numerical or analytical techniques to model complex Engineering problems, fed by experimental data, usually translated in the form of stochastic information collected from the problem in hand, is much closer to real-world situations than the conventional solution of PDEs. Moreover, inverse problems are becoming almost as common as direct problems, given the need in the industry to maintain current processes working efficiently, as well as to create new solutions based on the immense amount of information available digitally these days. On top of all this, deterministic analysis is slowly giving space to statistically driven structural analysis, delivering upper and lower bound solutions which help immensely the analyst in the decisionmaking process. All these trends have been topics of investigation for decades, and in recent years the application of these methods in the industry proves that they have achieved the necessary maturity to be definitely incorporated into the roster of modern Engineering tools. The present book series fulfills its role by collecting and organizing these topics, found otherwise scattered in the literature and not always accessible to industry. Moreover, many of the chapters compiled in these books present ongoing research topics conducted by capable fellows from academia and research institutes. They contain novel contributions to several investigation fields and constitute therefore a useful source of bibliographical reference and results repository. The Latin American Journal of Solids and Structures (LAJSS) is honored in supporting the publication of this book series, for it contributes academically and carries technologically significant content in the field of structural mechanics

A Reduced Order Modeling Methodology for the Multidisciplinary Design Analysis of Hypersonic Aerial Systems

Recent years have seen a significant increase in the demand for an advance and diverse fleet of hypersonic aerial systems. As computational power has increased, high-fidelity physics-based numerical analyses have emerged as feasible alternatives to physical experimentation, especially during early design phases. Due to the complexity of the underlying physics that govern hypersonic aerodynamics, these numerical tools can be very costly and not practical for systems engineering tasks that require many queries. To overcome these challenges, Reduced Order Models (ROMs) have been implemented that are capable of replacing expensive numerical analyses with inexpensive field surrogate models that can accurately predict aerodynamic flow features. This dissertation puts forth a methodology for the development of accurate, efficient, data-driven ROMs capable of predicting complex off-body hypersonic flow features. This methodology uses both linear and nonlinear Dimensionality Reduction (DR) to reduce high-dimensional aerodynamic field data into low-dimensional representations that faithfully represent the original data set. After this reduction, state-of-the-art surrogate modeling techniques are used to map parametric design inputs into this low-dimensional space to enable predictions. Manifold Alignment (MA), has also been implemented to enable models to leverage data from multiple fidelity sources. The performance of this method is evaluated experimentally using a number of test problems that exhibit a range of size and feature complexity. It is observed in many of these experiments that nonlinear ROMs outperform equivalent linear ROMs when the underlying fields exhibit complex discontinuous behavior. Furthermore, nonlinear ROMs consistently reduce data to lower dimensional forms than equivalent linear models, which results in nonlinear ROMs having lower evaluation costs and being more resilient to the “curse of dimensionality” then their linear counterparts. Similar trends are observed with multi-fidelity ROMs. When implemented into a coupled analysis, ROMs trained using the proposed methodology are able to achieve superior performance to state-of-the-at scalar models when predicting integrated force coefficients. Moreover, the proposed ROMs offer the novel capability of providing parametric flow-field data within a coupled analysis, which enables more sophisticated assessments of system-level performance, objectives, and constraints.Ph.D

Evolutionary Algorithms in Engineering Design Optimization

Evolutionary algorithms (EAs) are population-based global optimizers, which, due to their characteristics, have allowed us to solve, in a straightforward way, many real world optimization problems in the last three decades, particularly in engineering fields. Their main advantages are the following: they do not require any requisite to the objective/fitness evaluation function (continuity, derivability, convexity, etc.); they are not limited by the appearance of discrete and/or mixed variables or by the requirement of uncertainty quantification in the search. Moreover, they can deal with more than one objective function simultaneously through the use of evolutionary multi-objective optimization algorithms. This set of advantages, and the continuously increased computing capability of modern computers, has enhanced their application in research and industry. From the application point of view, in this Special Issue, all engineering fields are welcomed, such as aerospace and aeronautical, biomedical, civil, chemical and materials science, electronic and telecommunications, energy and electrical, manufacturing, logistics and transportation, mechanical, naval architecture, reliability, robotics, structural, etc. Within the EA field, the integration of innovative and improvement aspects in the algorithms for solving real world engineering design problems, in the abovementioned application fields, are welcomed and encouraged, such as the following: parallel EAs, surrogate modelling, hybridization with other optimization techniques, multi-objective and many-objective optimization, etc

Data-Driven Geometric Design Space Exploration and Design Synthesis

A design space is the space of all potential design candidates. While the design space can be of any kind, this work focuses on exploring geometric design spaces, where geometric parameters are used to represent designs and will largely affect a given design's functionality or performance (e.g., airfoil, hull, and car body designs). By exploring the design space, we evaluate different design choices and look for desired solutions. However, a design space may have unnecessarily high dimensionality and implicit boundaries, which makes it difficult to explore. Also, if we synthesize new designs by randomly sampling design variables in the high-dimensional design space, there is high chance that the designs are not feasible, as there is correlation between feasible design variables. This dissertation introduces ways of capturing a compact representation (which we call a latent space) that describes the variability of designs, so that we can synthesize designs and explore design options using this compact representation instead of the original high-dimensional design variables. The main research question answered by this dissertation is: how does one effectively learn this compact representation from data and efficiently explore this latent space so that we can quickly find desired design solutions? The word "quickly" here means to eliminate or reduce the iterative ideation, prototyping, and evaluation steps in a conventional design process. This also reduces human intervention, and hence facilitates design automation. This work bridges the gap between machine learning and geometric design in engineering. It contributes new pieces of knowledge within two topics: design space exploration and design synthesis. Specifically, the main contributions are: 1. A method for measuring the intrinsic complexity of a design space based on design data manifolds. 2. Machine learning models that incorporate prior knowledge from the domain of design to improve latent space exploration and design synthesis quality. 3. New design space exploration tools that expand the design space and search for desired designs in an unbounded space. 4. Geometrical design space benchmarks with controllable complexity for testing data-driven design space exploration and design synthesis

Aerodynamic and cost modelling for aircraft in a multi-disciplinary design context.

A challenge for the scientific community is to adapt to and exploit the trend towards greater multidisciplinary focus in research and technology. This work is concerned with multi-disciplinary design for whole aircraft configuration, including aero performance and financial considerations jointly for an aircraft program. A Multi-Disciplinary (MD) approach is required to increase the robustness of the preliminary design data and to realise the overall aircraft performance objectives within the required timescales. A pre-requisite for such an approach is the existence of efficient and fully integrated processes. For this purpose an automatic aero high-speed analysis framework has been developed and integrated using a commercial integration/building environment. Starting from the geometry input, it automatically generates aero data for loads in a timescale consistent with level requirement, which can afterwards be integrated into the overall multi-disciplinary process. A 3D Aero-solution chain has been implemented as a high-speed aerodynamic evaluation capability, and although there is not yet a complementary fully automated Aerodynamic design process, two integrated systems to perform multi-objective optimisation have been developed using different optimisation approaches. In addition to achieving good aircraft performance, reducing cost may be essential for manufacturer survival in today's competitive market. There is thus a strong need to understand the cost associated with different competing concepts and this could be addressed by incorporating cost estimation in the design process along with other analyses to achieve economic and efficient aircraft. For this reason a pre-existing cost model has been examined, tested, improved, and new features added. Afterwards, the cost suite has been integrated using an integration framework and automatically linked with external domains, providing a capability to take input from other domain tool sets. In this way the cost model could be implemented in a multi-disciplinary process allowing a trade-off between weight, aero performance and cost. Additionally, studies have been performed that link aerodynamic characteristics with cost figures and reinforce the importance of considering aerodynamic, structural and cost disciplines simultaneously. The proposed work therefore offers a strong basis for further development. The modularity of the aero optimisation framework already allows the application of such techniques to real engineering test cases, and, in future, could be combined with the 3D aero solution chain developed. In order to further reduce design wall-clock time the present multi- level parallelisation could also be deployed within a more rapid multi-fidelity approach. Finally the 3D aero-solution chain could be improved by directly incorporating a module to generate aero data for performance, and linking this to the cost suite informed by the same geometrical variables.Engineering and Physical Sciences (EPSRC)PhD in Aerospac