Pseudo-timestepping and verification for automatic differentiation derived CFD codes

The derivation of adjoint CFD codes through automatic differentiation is discussed with focus on the verification of the resulting code. Two alternative time-stepping formulations are discussed and compared, and the inclusion of multi-grid acceleration in the AD-derived code is shown. The adjoint code is embedded in a one-shot design loop and results for an inverse design case are presented.     

Preparation and assembly of discrete adjoint CFD codes

A methodology for constructing the sensitivity of the incompressible Navier–Stokes equations is presented as the context for differentiating high-level Fortran source code using source-transformation automatic differentiation tools. The methodology aims to be scalable and to retain all the compile-time and run-time safety the language offers. The incompressible solver is presented as it is the standard kernel in industrial CFD software. To complement this paper, the software used for this work has been made available on www.gpde.net.     

Computational modelling of titanium particles in warm spray

A warm spray system has been computationally investigated by introducing a centrally located mixing chamber into a HVOF thermal spray gun. The effects of injecting a cooling gas on the gas and particle dynamics are examined. The gas phase model incorporates liquid fuel droplets which heat, evaporate and then exothermically combust with oxygen within the combustion chamber producing a realistic compressible, supersonic and turbulent jet. The titanium powder is tracked using the Lagrangian approach including particle heating, melting and solidification. The results present an insight into the complex interrelations between the gas and particle phases, and highlight the advantage of warm spray, especially for the deposition of oxygen sensitive materials such as titanium. This work also demonstrates the effectiveness of a computational approach in aiding the development of thermal spray devices.     

An efficient decoding technique for Huffman codes

We present a new data structure for Huffman coding in which in addition to sending symbols in order of their appearance in the Huffman tree one needs to send codes of all circular leaf nodes (nodes with two adjacent external nodes), the number of which is always bounded above by half the number of symbols. We decode the text by using the memory efficient data structure proposed by Chen et al. [Inform. Process. Lett. 69 (1999) 119-122].     

New modelling technique for aperiodic-sampling linear systems

A general input-output modelling technique for aperiodic-sampling linear systems has been developed. The procedure describes the dynamics of the system and includes the sequence of sampling periods among the variables to be handled. Some restrictive conditions on the sampling sequence are imposed in order to guarantee the validity of the model. The particularization to the periodic case represents an alternative to the classic methods of discretization of continuous systems without using the Z-transform. This kind of representation can be used largely for identification and control purposes.     

Frequency decomposition technique for large archivable databases

A frequency decomposition technique is suggested for very large archivable databases. Modern large databases intended for use in a highly interactive and responsive environment face serious performance and availability problems caused by prolonged archiving and back up procedures. The suggested technique solves the problem by splitting the original database into a collection of smaller independently archivable ones. The decomposition pattern is based on the frequency separation criterion and provides a flexible and economical way of achieving a needed degree of database recoverability and archivability. The principles of database frequency decomposition are presented and discussed in detail. A numerical algorithm is suggested to allocate resources needed for implementation of the frequency decomposition technique. The quantitative analysis of the algorithm is illustrated by a practical example of large file management system decomposition. This paper also presents an adaptive procedure that controls growth and dynamic restructuring of the decomposed structure. Necessary modifications in database logical organization and access mechanisms are considered as well.     

A shrinking technique for complex object decomposition

A shrinking technique is described for the segmentation of a 2D complex object, represented by a binary array, into compact subobjects. A novel feature of the technique is that it uses the distance values on the skeleton of the object to estimate the number of shrinking steps needed in order to decompose the object.     

A new implicit blending technique for volumetric modelling

Current implicit blending techniques are mostly designed for use in surface modelling, where only boundaries of the object defined by the implicit primitives are important. In contrast, in volumetric implicit modelling the interior of the object is also significant, which requires different and more suitable techniques for combining implicit primitives. In this paper, we first discuss irregularities that occur using the current techniques. Then, a new technique for blending implicit primitives, especially appropriate in volumetric modelling (e.g., cloud modelling), is introduced. It overcomes these abnormalities and gives us better results than current techniques.     

Data-driven process decomposition and robust online distributed modelling for large-scale processes

With the increasing attention of networked control, system decomposition and distributed models show significant importance in the implementation of model-based control strategy. In this paper, a data-driven system decomposition and online distributed subsystem modelling algorithm was proposed for large-scale chemical processes. The key controlled variables are first partitioned by affinity propagation clustering algorithm into several clusters. Each cluster can be regarded as a subsystem. Then the inputs of each subsystem are selected by offline canonical correlation analysis between all process variables and its controlled variables. Process decomposition is then realised after the screening of input and output variables. When the system decomposition is finished, the online subsystem modelling can be carried out by recursively block-wise renewing the samples. The proposed algorithm was applied in the Tennessee Eastman process and the validity was verified.     

An incremental node embedding technique for error correcting output codes

The error correcting output codes (ECOC) technique is a useful way to extend any binary classifier to the multiclass case. The design of an ECOC matrix usually considers an a priori fixed number of dichotomizers. We argue that the selection and number of dichotomizers must depend on the performance of the ensemble code in relation to the problem domain. In this paper, we present a novel approach that improves the performance of any initial output coding by extending it in a sub-optimal way. The proposed strategy creates the new dichotomizers by minimizing the confusion matrix among classes guided by a validation subset. A weighted methodology is proposed to take into account the different relevance of each dichotomizer. As a result, overfitting is avoided and small codes with good generalization performance are obtained. In the decoding step, we introduce a new strategy that follows the principle that positions coded with the symbol zero should have small influence in the results. We compare our strategy to other well-known ECOC strategies on the UCI database, and the results show it represents a significant improvement.     

Two-time-scale discrete-time modelling and decomposition of synchronous machines

In this paper a reduced-order model for control design applications of a seventh-order two-time-scale discrete-time model of synchronous machines is developed. The numerical simulation is performed on synchronous machines, and the simulation results obtained using the iterative separation of time-scales provide improved accuracy of the reduced-order models, compared with that of the same obtained via the classical quasi-steady-state (qss) technique.     

A data-driven kernel method assimilation technique for geophysical modelling

Incorporating the quantity and variety of observations in atmospheric and oceanographic assimilation and prediction models has become an increasingly complex task. Data assimilation allows for uneven spatial and temporal data distribution and redundancy to be addressed so that the models can ingest massive data sets. Traditional data assimilation methods introduce Kalman filters and variational approaches. This study introduces a family of algorithms, motivated by advances in machine learning. These algorithms provide an alternative approach to incorporating new observations into the analysis forecast cycle. The application of kernel methods to processing the states of a quasi-geostrophic numerical model is intended to demonstrate the feasibility of the method as a proof-of-concept. The speed, efficiency, accuracy and scalability in recovering unperturbed state trajectories establishes the viability of machine learning for data assimilation.     

A symbolic matrix decomposition algorithm for reduced order linear fractional transformation modelling

In this paper an algorithm that provides an equivalent, but of reduced order, representation for multivariate polynomial matrices is given. It combines ideas from computational symbolic algebra, polynomial/matrix algebraic manipulations and information logic. The algorithm is applied to the problem of finding minimal linear fractional transformation models. Statistical performance analysis of the algorithm reveals that it consistently outperforms currently available algorithms.     

An investigation of multiphase CFD modelling of a lateral sloshing tank

A near-resonant, sway-induced sloshing flow in a rectangular tank is used to compare a homogeneous and inhomogeneous multiphase approach for fluid density and viscosity in a commercial CFD code. Dimensional analysis of the relative motion between the phases suggests the application of an inhomogeneous multiphase model whereas previous published work has used the computationally cheaper homogeneous (or average property) approach. The comparison between the computational and experimental results shows that the homogeneous model tends to underestimate the experimental peak pressures by up to 50%. The inhomogeneous multiphase model gives good agreement with the experimental pressure data. Examination of the relative velocity at the fluid interface confirms that the inhomogeneous model is the appropriate model to use for the simulation of a violent sloshing flow.     

An automatic technique for optimizing Reed-Solomon codes to improve fault tolerance in memories

Modern SoC architectures manufactured at ever-decreasing geometries use multiple embedded memories. Error detection and correction codes are becoming increasingly important to improve the fault tolerance of embedded memories. This article focuses on automatically optimizing classical Reed-Solomon codes by selecting the appropriate code polynomial and set of used symbols.     

A decomposition approach to modelling high service time variability

The predictive ability of queueing network models can be greatly enhanced if these models include the effects of system characteristics such as high service time variability and simultaneous resource possession, which violate the assumptions required for their efficient exact solution. In this paper we present a new approximate solution technique for queueing networks that include Coxian servers to represent resources at which customers have high service time variability. Our approach is unique in several respects: it is based directly on the theory of near-complete decomposability, it is non-iterative (performance measures for the queueing network of interest are expressed as linear combinations of the performance measures of a set of separable queueing networks), and it is conceptually and computationally simple.     

Relax-and-fix decomposition technique for solving large scale grid-based location problems

Many problems in business, engineering, defence, resource exploitation, and even the medical sciences with location aspects can be expressed as grid-based location problems (GBLPs), modeled as integer linear programming problems. Such problems are often very computationally complex to solve. We develop a relax-and-fix-based decomposition approach to solve large-scale GBLPs, which we demonstrate will significantly reduce solution runtimes while not severely impacting optimality. We also introduce problem-specific logical restrictions, constraints that reduce the feasible region and the resulting branch-and-bound tree with minimal reductions in optimality.     

Nonlinear modelling and control of the flow over aerofoils using CFD simulations

A simulation based approach for nonlinear dynamical modelling and feedback control of the drag to lift ratio for aerofoils is investigated through case studies involving NACA 23012, ag13 and b737a aerofoils. The flow around the aerofoils is studied via numerical solutions of the 2D Navier–Stokes (NS) equations. A standard computational fluid dynamics (CFD) solver is extended to be able to measure desired feedback values and to apply a control input to the flow field. The proposed modelling and control approach is based on first determining the measurement points and injection points on the aerofoil for the control input. Then, to estimate the dynamical model, some input–output data are collected by injecting a chirp input flow to the field and saving the measurement data. Next a Hammerstein–Wiener (HW) type nonlinear dynamical model of the flow field is estimated using system identification. For control design, the nonlinear part of the model is eliminated by means of inverse functions, followed by the application of automated tuning methods to the linear part to obtain the closed-loop system. The results show that the designed feedback control system can reduce the drag to lift ratio considerably as compared to the unactuated case.