Vorticity-transport and unstructured RANS investigation of rotor-fuselage interactions

The prediction capabilities of unstructured primitive-variable and vorticity-transport-based Navier-Stokes solvers have been compared for rotorcraft-fuselage interaction. Their accuracies have been assessed using the NASA Langley ROBIN series of experiments. Correlation of steady pressure on the isolated fuselage delineates the differences between the viscous and inviscid solvers. The influence of the individual blade passage, model supports, and viscous effects on the unsteady pressure loading has been studied. Smoke visualization from the ROBIN experiment has been used to determine the ability of the codes to predict the wake geometry. The two computational methods are observed to provide similar results within the context of their physical assumptions and simplifications in the test configuration

Performance and wake analysis of rotors in axial flight using computational fluid dynamics

Flow field around rotors in axial flight is known to be complex especially in steep descent where the rotor is operating inside its own wake. It is often reported that, in this flight condition, the rotor is susceptible to severe wake interactions causing unsteady blade load, severe vibration, loss of performance, as well as poor control and handling. So far, there is little data from experimental and numerical analysis available for rotors in axial flight. In this paper, the steady Reynolds-Averaged Navier-Stokes Computational Fluid Dynamics solver Helicopter Multi-Block was used to predict the performance of rotors in axial flight. The main objective of this study was to improve the basic knowledge about the subject and to validate the flow solver used. The results obtained are presented in the form of surface pressure, rotor performance parameters, and vortex wake trajectories. The detailed velocity field of the tip vortex for a rotor in hover was also investigated, and a strong self-similarity of the swirl velocity profile was found. The predicted results obtained when compared with available experimental data showed a reasonably agreement for hover and descent rate, suggesting unsteady solution for rotors in vortex-ring state

Wake dynamics and rotor-fuselage aerodynamic interactions

The unsteady loads experienced by a helicopter are known to be strongly influenced by aerodynamic interactions between the rotor and fuselage; these unsteady loads can lead to deficiencies in handling qualities and unacceptable vibratory characteristics of the rotorcraft. This work uses a vorticity-based computational model to study the governing processes that underpin this aerodynamic interaction and aims to provide greater understanding of the wake dynamics in the presence of a fuselage, as well as an appreciation of how the geometry of the wake affects the loading on the fuselage. The well-known experiments using NASA's ROBIN fuselage are used to assess the accuracy of the computations. Comparisons of calculations against results from smoke visualization experiments are used to demonstrate the ability of the model to reproduce accurately the geometry of the rotor wake, and comparisons with inflow data from the experiments show the method to capture well the velocity field near to the rotor. The fuselage model is able to predict accurately the unsteady fuselage loading that is induced by blade passage and also by the inviscid interaction between the main rotor wake and fuselage

Advances in Rotor Performance and Turbulent Wake Simulation Using DES and Adaptive Mesh Refinement

Time-dependent Navier-Stokes simulations have been carried out for a rigid V22 rotor in hover, and a flexible UH-60A rotor in forward flight. Emphasis is placed on understanding and characterizing the effects of high-order spatial differencing, grid resolution, and Spalart-Allmaras (SA) detached eddy simulation (DES) in predicting the rotor figure of merit (FM) and resolving the turbulent rotor wake. The FM was accurately predicted within experimental error using SA-DES. Moreover, a new adaptive mesh refinement (AMR) procedure revealed a complex and more realistic turbulent rotor wake, including the formation of turbulent structures resembling vortical worms. Time-dependent flow visualization played a crucial role in understanding the physical mechanisms involved in these complex viscous flows. The predicted vortex core growth with wake age was in good agreement with experiment. High-resolution wakes for the UH-60A in forward flight exhibited complex turbulent interactions and turbulent worms, similar to the V22. The normal force and pitching moment coefficients were in good agreement with flight-test data

Implementation of the Semi Empirical Kinetic Soot Model within Chemistry Tabulation Framework for Efficient Emissions Predictions in Diesel Engines

Soot prediction for diesel engines is a very important aspect of internal combustion engine emissions research, especially nowadays with very strict emission norms. Computational Fluid Dynamics (CFD) is often used in this research and optimisation of CFD models in terms of a trade-off between accuracy and computational efficiency is essential. This is especially true in the industrial environment where good predictivity is necessary for engine optimisation, but computational power is limited. To investigate soot emissions for Diesel engines, in this work CFD is coupled with chemistry tabulation framework and semi-empirical soot model. The Flamelet Generated Manifold (FGM) combustion model precomputes chemistry using detailed calculations of the 0D homogeneous reactor and then stores the species mass fractions in the table, based on six look-up variables: pressure, temperature, mixture fraction, mixture fraction variance, progress variable and progress variable variance. Data is then retrieved during online CFD simulation, enabling fast execution times while keeping the accuracy of the direct chemistry calculation. In this work, the theory behind the model is discussed as well as implementation in commercial CFD code. Also, soot modelling in the framework of tabulated chemistry is investigated: mathematical model and implementation of the kinetic soot model on the tabulation side is described, and 0D simulation results are used for verification. Then, the model is validated using real-life engine geometry under different operating conditions, where better agreement with experimental measurements is achieved, compared to the standard implementation of the kinetic soot model on the CFD side

Data Driven Forecast for Fire

Being able to forecast the evolution of a fire is essential for fire safety design and fire response strategies. Despite advances in understanding fire dynamics and improvements in computational capability, the ability to predict the evolution of a fire remains limited due to large uncertainties associated to multiple scales and the non-linearities. The data-driven approach provides a viable technique from models corrected by observations. However, the complicated coupling between gaseous and condensed phases has, in the past, limited proper prediction with a positive leading time. This work proposes and investigates a series of approaches to data-driven hybrid modelling that integrate analytical and numerical descriptions to address the coupling effects. The data-driven hybrid model is developed for different scenarios covering various complexity and scales. Different approaches are evaluated to reflect the dominant physics; nevertheless, they are structured by differentiating the condensed and gas phases. The initial scenario corresponds to one-dimensional convective-diffusive droplet combustion in micro and normal gravity. Then, concurrent flame spread in micro and normal gravity where a two-dimensional boundary layer combustion approach is implemented. Finally, the Malveira fire test represents a large-scale, three-dimensional, travelling fire. Coefficients assimilated with their experimental observations are used to alter analytical formulations describing the gas and condensed phases. By separating the phases, the data-driven hybrid model can forecast various types of variables while reducing processing resources. Convergence of the assimilated coefficients is used as an indicator for an appropriate representation of the model and therefore is suitable for predictions. The proposed methodology still requires ongoing research, however. This work provides evidence for specific approaches and of areas where additional attention is necessary. It has become apparent that to adequately predict real-scale fire, it is necessary for more sophisticated explanations of heat and mass transfer and descriptions of the interactions between fire and its environment

Quasi second-order methods for PDE-constrained forward and inverse problems

La conception assistée par ordinateur (CAO), les effets visuels, la robotique et de nombreux autres domaines tels que la biologie computationnelle, le génie aérospatial, etc. reposent sur la résolution de problèmes mathématiques. Dans la plupart des cas, des méthodes de calcul sont utilisées pour résoudre ces problèmes. Le choix et la construction de la méthode de calcul ont un impact important sur les résultats et l'efficacité du calcul. La structure du problème peut être utilisée pour créer des méthodes, qui sont plus rapides et produisent des résultats qualitativement meilleurs que les méthodes qui n'utilisent pas la structure. Cette thèse présente trois articles avec trois nouvelles méthodes de calcul s'attaquant à des problèmes de simulation et d'optimisation contraints par des équations aux dérivées partielles (EDP). Dans le premier article, nous abordons le problème de la dissipation d'énergie des solveurs fluides courants dans les effets visuels. Les solveurs de fluides sont omniprésents dans la création d'effets dans les courts et longs métrages d'animation. Nous présentons un schéma d'intégration temporelle pour la dynamique des fluides incompressibles qui préserve mieux l'énergie comparé aux nombreuses méthodes précédentes. La méthode présentée présente une faible surcharge et peut être intégrée à un large éventail de méthodes existantes. L'amélioration de la conservation de l'énergie permet la création d'animations nettement plus dynamiques. Nous abordons ensuite la conception computationelle dont le but est d'exploiter l'outils computationnel dans le but d'améliorer le processus de conception. Plus précisément, nous examinons l'analyse de sensibilité, qui calcule les sensibilités du résultat de la simulation par rapport aux paramètres de conception afin d'optimiser automatiquement la conception. Dans ce contexte, nous présentons une méthode efficace de calcul de la direction de recherche de Gauss-Newton, en tirant parti des solveurs linéaires directs épars modernes. Notre méthode réduit considérablement le coût de calcul du processus d'optimisation pour une certaine classe de problèmes de conception inverse. Enfin, nous examinons l'optimisation de la topologie à l'aide de techniques d'apprentissage automatique. Nous posons deux questions : Pouvons-nous faire de l'optimisation topologique sans maillage et pouvons-nous apprendre un espace de solutions d'optimisation topologique. Nous appliquons des représentations neuronales implicites et obtenons des résultats structurellement sensibles pour l'optimisation topologique sans maillage en guidant le réseau neuronal pendant le processus d'optimisation et en adaptant les méthodes d'optimisation topologique par éléments finis. Notre méthode produit une représentation continue du champ de densité. De plus, nous présentons des espaces de solution appris en utilisant la représentation neuronale implicite.Computer-aided design (CAD), visual effects, robotics and many other fields such as computational biology, aerospace engineering etc. rely on the solution of mathematical problems. In most cases, computational methods are used to solve these problems. The choice and construction of the computational method has large impact on the results and the computational efficiency. The structure of the problem can be used to create methods, that are faster and produce qualitatively better results than methods that do not use the structure. This thesis presents three articles with three new computational methods tackling partial differential equation (PDE) constrained simulation and optimization problems. In the first article, we tackle the problem of energy dissipation of common fluid solvers in visual effects. Fluid solvers are ubiquitously used to create effects in animated shorts and feature films. We present a time integration scheme for incompressible fluid dynamics which preserves energy better than many previous methods. The presented method has low overhead and can be integrated into a wide range of existing methods. The improved energy conservation leads to noticeably more dynamic animations. We then move on to computational design whose goal is to harnesses computational techniques for the design process. Specifically, we look at sensitivity analysis, which computes the sensitivities of the simulation result with respect to the design parameters to automatically optimize the design. In this context, we present an efficient way to compute the Gauss-Newton search direction, leveraging modern sparse direct linear solvers. Our method reduces the computational cost of the optimization process greatly for a certain class of inverse design problems. Finally, we look at topology optimization using machine learning techniques. We ask two questions: Can we do mesh-free topology optimization and can we learn a space of topology optimization solutions. We apply implicit neural representations and obtain structurally sensible results for mesh-free topology optimization by guiding the neural network during optimization process and adapting methods from finite element based topology optimization. Our method produces a continuous representation of the density field. Additionally, we present learned solution spaces using the implicit neural representation