CERES :: Browsing by Author "Jenkins, Karl W."

Browsing by Author "Jenkins, Karl W."

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Open Access
An academic review: applications of data mining techniques in finance industry
(2017-05-31) Jadhav, Swati; He, Hongmei; Jenkins, Karl W.
With the development of Internet techniques, data volumes are doubling every two years, faster than predicted by Moore’s Law. Big Data Analytics becomes particularly important for enterprise business. Modern computational technologies will provide effective tools to help understand hugely accumulated data and leverage this information to get insights into the finance industry. In order to get actionable insights into the business, data has become most valuable asset of financial organisations, as there are no physical products in finance industry to manufacture. This is where data mining techniques come to their rescue by allowing access to the right information at the right time. These techniques are used by the finance industry in various areas such as fraud detection, intelligent forecasting, credit rating, loan management, customer profiling, money laundering, marketing and prediction of price movements to name a few. This work aims to survey the research on data mining techniques applied to the finance industry from 2010 to 2015.The review finds that Stock prediction and Credit rating have received most attention of researchers, compared to Loan prediction, Money Laundering and Time Series prediction. Due to the dynamics, uncertainty and variety of data, nonlinear mapping techniques have been deeply studied than linear techniques. Also it has been proved that hybrid methods are more accurate in prediction, closely followed by Neural Network technique. This survey could provide a clue of applications of data mining techniques for finance industry, and a summary of methodologies for researchers in this area. Especially, it could provide a good vision of Data Mining Techniques in computational finance for beginners who want to work in the field of computational finance.
Open Access
Advanced numerical methods for dissipative and non-dissipative relativistic hydrodynamics
(Cranfield University, 2020-05) Townsend, Jamie F.; Konozsy, Laszlo Z.; Jenkins, Karl W.
High-energy physical phenomena such as astrophysical events and heavy-ion collisions contain a hydrodynamic aspect in which a branch of fluid dynamics called relativistic hydrodynamics (RHD) is required for its mathematical description. The resulting equations must be, more often than not, solved numerically for scientists to ascertain useful information regarding the fluid system in question. This thesis describes and presents a twodimensional computational fluid dynamics (CFD) solver for dissipative and non-dissipative relativistic hydrodynamics, i.e. in the presence and absence of physically resolved viscosity and heat conduction. The solver is based on a finite volume, Godunov-type, HighResolution Shock-Capturing (HRSC) framework, containing a plethora of numerical implementations such as high-order Weighted-Essentially Non-Oscillatory (WENO) spatial reconstruction, approximate Riemann solvers and a third-order Total Variation Diminishing (TVD) Runge–Kutta method. The base numerical solver for the solution of non-dissipative RHD is extensively tested using a series of one-dimensional test cases, namely, a smooth flow problem and shock-tube configurations as well as the two-dimensional vortex sheet and Riemann problem test cases. For the case of non-dissipative relativistic hydrodynamics the relativistic CFD solver is found to perform well in terms of the orders of accuracy achieved and its ability to resolve shock wave patterns. Numerical pathologies have been identified when the relativistic HLLC Riemann solver is used in multi-dimensions for problems exhibiting strong shock waves. This is attributed to the so-called Carbuncle problem which is shown to occur because of pressure differencing within the process of restoring the missing contact discontinuity of its predecessor, the HLL Riemann solver. To avoid this numerical pathology and improve the robustness of numerical solutions that make use of the HLLC Riemann solver, the development of a rotated-hybrid Riemann solver arising from the hybridisation of the HLL and HLLC (or Rusanov and HLLC) approximate Riemann solvers is presented. A standalone application of the HLLC Riemann solver can produce spurious numerical artefacts when it is employed in conjunction with Godunov-type high-order methods in the presence of discontinuities. It has been found that a rotated-hybrid Riemann solver with the proposed HLL/HLLC (Rusanov/HLLC) scheme could overcome the difficulty of the spurious numerical artefacts and presents a robust solution for the Carbuncle problem. The proposed rotated-hybrid Riemann solver provides sufficient numerical dissipation to capture the behaviour of strong shock waves for relativistic hydrodynamics. Therefore, focus is placed on two benchmark test cases (odd-even decoupling and double-Mach reflection problems) and the investigation of two astrophysical phenomena, the relativistic Richtmyer– Meshkov instability and the propagation of a relativistic jet. In all presented test cases, the Carbuncle problem is shown to be eliminated by employing the proposed rotated-hybrid Riemann solver. This strategy is problem-independent, straightforward to implement and provides a consistent robust numerical solution when combined with Godunov-type highorder schemes for relativistic hydrodynamics...[cont.]
Open Access
Advancing aviation safety through machine learning and psychophysiological data: a systematic review
(IEEE, 2024-01-03) Alreshidi, Ibrahim; Moulitsas, Irene; Jenkins, Karl W.
In the aviation industry, safety remains vital, often compromised by pilot errors attributed to factors such as workload, fatigue, stress, and emotional disturbances. To address these challenges, recent research has increasingly leveraged psychophysiological data and machine learning techniques, offering the potential to enhance safety by understanding pilot behavior. This systematic literature review rigorously follows a widely accepted methodology, scrutinizing 80 peer-reviewed studies out of 3352 studies from five key electronic databases. The paper focuses on behavioral aspects, data types, preprocessing techniques, machine learning models, and performance metrics used in existing studies. It reveals that the majority of research disproportionately concentrates on workload and fatigue, leaving behavioral aspects like emotional responses and attention dynamics less explored. Machine learning models such as tree-based and support vector machines are most commonly employed, but the utilization of advanced techniques like deep learning remains limited. Traditional preprocessing techniques dominate the landscape, urging the need for advanced methods. Data imbalance and its impact on model performance is identified as a critical, under-researched area. The review uncovers significant methodological gaps, including the unexplored influence of preprocessing on model efficacy, lack of diversification in data collection environments, and limited focus on model explainability. The paper concludes by advocating for targeted future research to address these gaps, thereby promoting both methodological innovation and a more comprehensive understanding of pilot behavior.
Open Access
Analysing the sentiment of air-traveller: a comparative analysis
(IJCTE, 2022-03-31) Homaid, Mohammed Salih A; Bisandu, Desmond Bala; Moulitsas, Irene; Jenkins, Karl W.
Airport service quality is considered to be an indicator of passenger satisfaction. However, assessing this by conventional methods requires continuous observation and monitoring. Therefore, during the past few years, the use of machine learning techniques for this purpose has attracted considerable attention for analysing the sentiment of the air traveller. A sentiment analysis system for textual data analytics leverages the natural language processing and machine learning techniques in order to determine whether a piece of writing is positive, negative or neutral. Numerous methods exist for estimating sentiments which include lexical-based methodologies and directed artificial intelligence strategies. Despite the wide use and ubiquity of certain strategies, it remains unclear which is the best strategy for recognising the intensity of the sentiments of a message. It is necessary to compare these techniques in order to understand their advantages, disadvantages and limitations. In this paper, we compared the Valence Aware Dictionary and sentiment Reasoner, a sentiment analysis technique specifically attuned and well known for performing good on social media data, with the conventional machine learning techniques of handling the textual data by converting it into numerical form. We used the review data obtained from the SKYTRAX website for each airport. The machine learning algorithms evaluated in this paper are VADER sentiment and logistic regression. The term frequency-inverse document frequency is used in order to convert the textual review data into the resulting numerical columns. This was formulated as a classification problem, whereby the prediction of the algorithm was compared with the actual recommendation of the passenger in the dataset. The results were analysed according to the accuracy, precision, recall and F1-score. From the analysis of the results, we observed that logistic regression outperformed the VADER sentiment analysis.
Open Access
Application of central-weighted essentially non-oscillatory finite-volume interface-capturing schemes for modeling cavitation induced by an underwater explosion
(MDPI, 2024-01-29) Adebayo, Ebenezer Mayowa; Tsoutsanis, Panagiotis; Jenkins, Karl W.
Cavitation resulting from underwater explosions in compressible multiphase or multicomponent flows presents significant challenges due to the dynamic nature of shock–cavitation–structure interactions, as well as the complex and discontinuous nature of the involved interfaces. Achieving accurate resolution of interfaces between different phases or components, in the presence of shocks, cavitating regions, and structural interactions, is crucial for modeling such problems. Furthermore, pressure convergence in simulations involving shock–cavitation–structure interactions requires accurate algorithms. In this research paper, we employ the diffuse interface method, also known as the interface-capturing scheme, to investigate cavitation in various underwater explosion test cases near different surfaces: a free surface and a rigid surface. The simulations are conducted using the unstructured compressible Navier–Stokes (UCNS3D) finite-volume framework employing central-weighted essentially non-oscillatory (CWENO) reconstruction schemes, utilizing the five-equation diffuse interface family of methods. Quantitative comparisons are made between the performance of both models. Additionally, we examine the effects of cavitation as a secondary loading source on structures, and evaluate the ability of the CWENO schemes to accurately capture and resolve material interfaces between fluids with minimal numerical dissipation or smearing. The results are compared with existing high-order methods and experimental data, where possible, to demonstrate the robustness of the CWENO schemes in simulating cavitation bubble dynamics, as well as their limitations within the current implementation of interface capturing.
Open Access
An automatic image analysis methodology for the measurement of droplet size distributions in liquid–liquid dispersion: round object detection
(ACTA Press, 2018-11-08) Gawryszewski, K.; Rana, Zeeshan; Jenkins, Karl W.; Ioannou, Phivos; Okonkwo, D.
This article presents an efficient and economical automatic image analysis technique for the droplet characterization in a liquid–liquid dispersion. The methodology employs a combination of the Satoshi Suzuki's [Topological structural analysis of digitized binary images by border following. Comput Vis Graph Image Process. 1985;30:32–46] find contours algorithm and the method of minimal enclosing circle identification, proposed by Emo Welzl [Smallest enclosing disks (balls and ellipsoids). Berlin, Heidelberg: Springer; 1991. p. 359–370. chapter 24], to achieve the objectives. The round object detection algorithm has been designed for the identification and verification of correct droplets in the mixture which helped to increase the accuracy of automatic detection. Tests have been performed on various sets of images obtained during several emulsification processes and contain examples of droplets which differ in size, density, volume and appearance etc. An effective communication between the two methodologies and newly introduced algorithms demonstrated an accuracy of 90% or above in the measurement of droplet size distribution and Sauter mean diameters through an automatic vision-based system.
Open Access
A comparative analysis of different hydrogen production methods and their environmental impact
(MDPI, 2023-11-29) Nnabuife, Somtochukwu Godfrey; Darko, Caleb Kwasi; Obiako, Precious Chineze; Kuang, Boyu; Sun, Xiaoxiao; Jenkins, Karl W.
This study emphasises the growing relevance of hydrogen as a green energy source in meeting the growing need for sustainable energy solutions. It foregrounds the importance of assessing the environmental consequences of hydrogen-generating processes for their long-term viability. The article compares several hydrogen production processes in terms of scalability, cost-effectiveness, and technical improvements. It also investigates the environmental effects of each approach, considering crucial elements such as greenhouse gas emissions, water use, land needs, and waste creation. Different industrial techniques have distinct environmental consequences. While steam methane reforming is cost-effective and has a high production capacity, it is coupled with large carbon emissions. Electrolysis, a technology that uses renewable resources, is appealing but requires a lot of energy. Thermochemical and biomass gasification processes show promise for long-term hydrogen generation, but further technological advancement is required. The research investigates techniques for improving the environmental friendliness of hydrogen generation through the use of renewable energy sources. Its ultimate purpose is to offer readers a thorough awareness of the environmental effects of various hydrogen generation strategies, allowing them to make educated judgements about ecologically friendly ways. It can ease the transition to a cleaner hydrogen-powered economy by considering both technological feasibility and environmental issues, enabling a more ecologically conscious and climate-friendly energy landscape.
Open Access
Complete body aerodynamic study of three vehicles
(SAE International, 2017-03-28) Simmonds, Nicholas; Pitman, John; Tsoutsanis, Panagiotis; Jenkins, Karl W.; Gaylard, Adrian; Jansen, Wilko
Cooling drag has traditionally proven to be a difficult flow phenomenon to predict using computational fluid dynamics. With the advent of grille shutter systems, the need to accurately pre-dict this quantity during vehicle development has become more pressing. A comprehensive study is presented in the paper of three automotive models with different cool-ing drag deltas using the commercial CFD solver STARCCM+. The notchback DrivAer model with under-hood cooling provides a popular academic benchmark alongside two fully-engineered production cars; a large saloon (Jaguar XJ) and an SUV (Land Rover Range Rover). Previous studies detail the differences in the flow field; highlighting the interaction between the exiting under-hood cooling flow, and the wheel and base wakes for open and closed grilles. In this study three levels of spatial discretization were used for each vehicle in order to study the importance of accurately capturing the base wake on the absolute and cooling delta drag values and the cooling air mass flow rates. This study is performed using three steady-state RANS solvers (k-ɛ realizable, k-ω SST and Spalart-Allmaras), and the unsteady k-ω SST Detached-Eddy-Simulation. Results show that it is very important to capture both separation and large wake structures in order to recover physically realistic behavior. The RANS models perform well (within 0.005 Cd, 5 counts) on saloon based models, with the k-ɛ realizable model displaying mesh independence. For the SUV model the RANS models predict the correct cooling deltas; however, only the k-ω SST model gives accurate absolute values, with those for k-e realizable and Spalart-Allmaras 22 and 18 counts too high, respectively. The k-ω SST model on the finest mesh contains oscillations in the flow field, particularly in the wake, which are attributable to the unsteady nature of the flow. When averaging the steady-state simulations over 1000 iterations the resulting wake structure is shown to be in close agreement to the unsteady Detached-Eddy-Simulations. The DES model confirms that the variance in the residuals for the k-w SST was indicative of flow unsteadiness.
Open Access
A comprehensive analysis of machine learning and deep learning models for identifying pilots’ mental states from imbalanced physiological data
(AIAA, 2023-06-08) Alreshidi, Ibrahim; Yadav, Satendra; Moulitsas, Irene; Jenkins, Karl W.
This study focuses on identifying pilots' mental states linked to attention-related human performance-limiting states (AHPLS) using a publicly released, imbalanced physiological dataset. The research integrates electroencephalography (EEG) with non-brain signals, such as electrocardiogram (ECG), galvanic skin response (GSR), and respiration, to create a deep learning architecture that combines one-dimensional Convolutional Neural Network (1D-CNN) and Long Short-Term Memory (LSTM) models. Addressing the data imbalance challenge, the study employs resampling techniques, specifically downsampling with cosine similarity and oversampling using Synthetic Minority Over-sampling Technique (SMOTE), to produce balanced datasets for enhanced model performance. An extensive evaluation of various machine learning and deep learning models, including XGBoost, AdaBoost, Random Forest (RF), Feed-Forward Neural Network (FFNN), standalone 1D-CNN, and standalone LSTM, is conducted to determine their efficacy in detecting pilots' mental states. The results contribute to the development of efficient mental state detection systems, highlighting the XGBoost algorithm and the proposed 1D-CNN+LSTM model as the most promising solutions for improving safety and performance in aviation and other industries where monitoring mental states is essential.
Open Access
Computational engineering analysis of external geometrical modifications on the MQ-1 unmanned combat aerial vehicle
(Elsevier, 2020-03-17) Bagul, Prakash; Rana, Zeeshan A.; Jenkins, Karl W.; Könözsy, László Z.
This paper focuses on the effects of external geometrical modifications on the aerodynamic characteristics of the MQ-1 predator Unmanned Combat Aerial Vehicle (UCAV) using computational fluid dynamics. The investigations are performed for 16 flight conditions at an altitude of 7.6 km and at a constant speed of 56.32 m/s. Two models are analysed, namely the baseline model and the model with external geometrical modifications installed on it. Both the models are investigated for various angles of attack from −4° to 16°, angles of bank from 0° to 6° and angles of yaw from 0° to 4°. Due to the unavailability of any experimental (wind tunnel or flight test) data for this UCAV in the literature, a thorough verification of calculations process is presented to demonstrate confidence level in the numerical simulations. The analysis quantifies the loss of lift and increase in drag for the modified version of the MQ-1 predator UCAV along with the identification of stall conditions. Local improvement (in drag) of up to 96% has been obtained by relocating external modifications, whereas global drag force reduction of roughly 0.5% is observed. The effects of external geometrical modifications on the control surfaces indicate the blanking phenomenon and reduction in forces on the control surfaces that can reduce the aerodynamic performance of the UCAV.
Open Access
Data mining in computational finance
(2017-12) Jadhav, Swati; Jenkins, Karl W.; He, Hongmei
Computational finance is a relatively new discipline whose birth can be traced back to early 1950s. Its major objective is to develop and study practical models focusing on techniques that apply directly to financial analyses. The large number of decisions and computationally intensive problems involved in this discipline make data mining and machine learning models an integral part to improve, automate, and expand the current processes. One of the objectives of this research is to present a state-of-the-art of the data mining and machine learning techniques applied in the core areas of computational finance. Next, detailed analysis of public and private finance datasets is performed in an attempt to find interesting facts from data and draw conclusions regarding the usefulness of features within the datasets. Credit risk evaluation is one of the crucial modern concerns in this field. Credit scoring is essentially a classification problem where models are built using the information about past applicants to categorise new applicants as ‘creditworthy’ or ‘non-creditworthy’. We appraise the performance of a few classical machine learning algorithms for the problem of credit scoring. Typically, credit scoring databases are large and characterised by redundant and irrelevant features, making the classification task more computationally-demanding. Feature selection is the process of selecting an optimal subset of relevant features. We propose an improved information-gain directed wrapper feature selection method using genetic algorithms and successfully evaluate its effectiveness against baseline and generic wrapper methods using three benchmark datasets. One of the tasks of financial analysts is to estimate a company’s worth. In the last piece of work, this study predicts the growth rate for earnings of companies using three machine learning techniques. We employed the technique of lagged features, which allowed varying amounts of recent history to be brought into the prediction task, and transformed the time series forecasting problem into a supervised learning problem. This work was applied on a private time series dataset.
Open Access
A dataset for autonomous aircraft refueling on the ground (AGR)
(IEEE, 2023-09-01) Kuang, Boyu; Barnes, Stuart; Tang, Gilbert; Jenkins, Karl W.
Automatic aircraft ground refueling (AAGR) can improve the safety, efficiency, and cost-effectiveness of aircraft ground refueling (AGR), a critical and frequent operation on almost all aircraft. Recent AAGR relies on machine vision, artificial intelligence, and robotics to implement automation. An essential step for automation is AGR scene recognition, which can support further component detection, tracking, process monitoring, and environmental awareness. As in many practical and commercial applications, aircraft refueling data is usually confidential, and no standardized workflow or definition is available. These are the prerequisites and critical challenges to deploying and benefitting advanced data-driven AGR. This study presents a dataset (the AGR Dataset) for AGR scene recognition using image crawling, augmentation, and classification, which has been made available to the community. The AGR dataset crawled over 3k images from 13 databases (over 26k images after augmentation), and different aircraft, illumination, and environmental conditions were included. The ground-truth labeling is conducted manually using a proposed tree-formed decision workflow and six specific AGR tags. Various professionals have independently reviewed the AGR dataset to keep it no-bias. This study proposes the first aircraft refueling image dataset, and an image labeling software with a UI to automate the labeling workflow.
Open Access
Effects of channel surface finish on blood flow in microfluidic devices
(Springer Science Business Media, 2010-01-12T00:00:00Z) Prentner, S.; Allen, David M.; Larcombe, L. D.; Marson, Silvia; Jenkins, Karl W.; Saumer, M.
The behaviour of blood flow in relation to microchannel surface roughness has been investigated. Special attention was focused on the techniques used to fabricate the microchannels and on the apparent viscosity of the blood as it flowed through these microchannels. For the experimental comparison of smooth and rough surface channels, each channel was designed to be 10mm long and rectangular in cross-section with aspect ratios of â ¥100:1 for channel heights of 50 and 100Î¼m. Polycarbonate was used as the material for the device construction. The shims, which created the heights of the channels, were made of polyethylene terephthalate. Surface roughnesses of the channels were varied from Rz of 60nm to 1.8Î¼m. Whole horse blood and filtered water were used as the test fluids and differential pressures ranged from 200 to 5000Pa. The defibrinated horse blood was treated further to prevent coagulation. The results indicate that a surface roughness above an unknown value lowers the apparent viscosity of blood dramatically due to boundary effects. Furthermore, the roughness seemed to influence both water and whole blood almost equally. A set of design rules for channel fabrication is also presented in accordance with the experiments performed.
Open Access
Effects of initial radius on the propagation of premixed flame kernals in a turbulent environment
(AIP, 2006-05-12) Klein, M.; Chakraborty, N.; Jenkins, Karl W.; Cant, R. S.
The effects of mean curvature on the propagation of turbulent premixed flames have been investigated using three-dimensional direct numerical simulations (DNS) with single step Arrhenius-type chemistry in the thin reaction zones regime. A number of spherical flame kernels with different initial radius have been studied under identical conditions of turbulence and thermochemistry. A statistically planar turbulent back-to-back flame has been simulated as a special case of a spherical kernel in the limit of infinite kernel radius. Statistical analysis in terms of standard and joint probability density functions (pdfs) clearly indicates that the mean curvature of the flame kernel configuration has a major influence on the propagation behavior of the flame. For the planar flame configuration the density-weighted displacement speed is found to be fairly constant throughout the flame brush, in good agreement with previous DNS results. By contrast, for the flame kernel configuration the density-weighted displacement speed is found to vary strongly through the flame brush, changing from values on the order of the corresponding laminar flame speed near the fresh gas side to considerably smaller values near the burned gas side. The joint pdfs of displacement speed and its components with curvature are extensively studied, allowing for an explanation of the observed phenomena in terms of local flame geometry and its interaction with the turbulent flow fiel
Open Access
Effects of swirl on intermittency characteristics in non-premixed flames
(Taylor & Francis, 2012-05-31T00:00:00Z) Ranga Dinesh, K. K. J.; Jenkins, Karl W.; Kirkpatrick, M. P.; Malalasekera, W.
Swirl effects on velocity, mixture fraction, and temperature intermittency have been analyzed for turbulent methane flames using large eddy simulation (LES). The LES solves the filtered governing equations on a structured Cartesian grid using a finite volume method, with turbulence and combustion modeling based on the localized dynamic Smagorinsky and the steady laminar flamelet models, respectively. Probability density function (PDF) distributions demonstrate a Gaussian shape closer to the centerline region of the flame and a delta function at the far radial position. However, non-Gaussian PDFs are observed for velocity and mixture fraction on the centerline in a region where center jet precession occurs. Non-Gaussian behavior is also observed for the temperature PDFs close to the centerline region of the flame. Due to the occurrence of recirculation zones, the variation from turbulent to nonturbulent flow is more rapid for the velocity than the mixture fraction and therefore indicates how rapidly turbulence affects the molecular transport in these regions of the flame
Open Access
An empirical evaluation of generative adversarial nets in synthesizing X-ray chest images
(IEEE, 2022-12-12) Belmekki, Zakariae; Li, Jun; Jenkins, Karl W.; Reuter, Patrick; Gómez Jáuregui, David Antonio
In the last decade, Generative Adversarial Nets (GAN) have become a subject of growing interest in multiple research fields. In this paper, we focus on applications in the medical field by attempting to generate realistic X-ray chest images. A heuristic approach is adopted to perform an extensive evaluation of different architecture configurations and optimization algorithms and we propose an optimal configuration of the baseline Deep Convolutional GAN (DCGAN) based on empirical findings. Additionally, we highlight the technical limitations of GAN and provide an analysis of the high memory requirements, which we reduce by a range of 1.2-7 percent by removing unnecessary layers. Finally, we verify that the loss of the discriminator can be used as an assessment metric.
Open Access
Evaluating cascade correlation neural networks for surrogate modelling needs and enhancing the Nimrod/O toolkit for multi-objective optimisation
(Cranfield University, 2011-03) Riley, Mike J. W.; Jenkins, Karl W.
Engineering design often requires the optimisation of multiple objectives, and becomes significantly more difficult and time consuming when the response surfaces are multimodal, rather than unimodal. A surrogate model, also known as a metamodel, can be used to replace expensive computer simulations, accelerating single and multi-objective optimisation and the exploration of new design concepts. The main research focus of this work is to investigate the use of a neural network surrogate model to improve optimisation of multimodal surfaces. Several significant contributions derive from evaluating the Cascade Correlation neural network as the basis of a surrogate model. The contributions to the neural network community ultimately outnumber those to the optimisation community. The effects of training this surrogate on multimodal test functions are explored. The Cascade Correlation neural network is shown to map poorly such response surfaces. A hypothesis for this weakness is formulated and tested. A new subdivision technique is created that addresses this problem; however, this new technique requires excessively large datasets upon which to train. The primary conclusion of this work is that Cascade Correlation neural networks form an unreliable basis for a surrogate model, despite successes reported in the literature. A further contribution of this work is the enhancement of an open source optimisation toolkit, achieved by the first integration of a truly multi-objective optimisation algorithm.
Open Access
Evaluation of the SU2 open-source code for a hypersonic flow at mach number 5
(Miskolci Egyetemi Kiadó, 2022-11-10) Yeap, Jia-Ming; Rana, Zeeshan A.; Könözsy, László Z.; Jenkins, Karl W.
This paper presents the evaluation of the Stanford University Unstructured (SU2) open-source computational software package for a high Mach number 5 flow. The test case selected is an impinging shock wave turbulent boundary layer interaction (SWTBLI) on a flat plate where the experimental data of Sch¨ulein et al. [27] is used for validation purposes. Two turbulence models, the Spalart–Allmaras (SA) and the k-ω Shear Stress Transport (SST) within the SU2 code are evaluated in this study. Flow parameters, such as skin friction, wall pressure distribution and boundary layer profiles are compared with experimental values. The results demonstrate the performance of the SU2 code at a high Mach number flow and highlight its limitations in predicting fluid flow physics. At higher shock generator angles, the discrepancy between experimental and CFD data is more significant. Within the interaction and flow separation zones, a smaller separation bubble and delayed separation are predicted by the SA model while the k-ω SST model predicts early separation. Both models are able to predict wall pressure distribution correctly within the experimental values. However, discrepancies were observed in the prediction of skin friction due to the inability of the models to capture the boundary layer recovery after shock impingement.
Open Access
External Intermittency Simulation in Turbulent Round Jets
(Springer Science Business Media, 2012-09-30T00:00:00Z) Gilliland, T.; Ranga Dinesh, K. K. J.; Fairweather, M.; Falle, S. A. E. G.; Jenkins, Karl W.; Savill, Mark A.
Abstract to study passive scalar mixing and intermittency in turbulent round jets. Both simulation techniques are applied to the case of a low Reynolds number jet with Re between time-averaged results for the scalar field of the low Re case demonstrate reasonable agreement between the DNS and LES, and with experimental data and the predictions of other authors. Scalar probability density functions (pdfs) for this jet derived from the simulations are also in reasonable accord, although the DNS results demonstrate the more rapid influence of scalar intermittency with radial distance in the jet. This is reflected in derived intermittency profiles, with LES generally giving profiles that are too broad compared to equivalent DNS results, with too low a rate of decay with radial distance. In contrast, good agreement is in general found between LES predictions and experimental data for the mixing field, scalar pdfs and external intermittency in the high Reynolds number jet. Overall, the work described indicates that improved sub-grid scale modelling for use with LES may be beneficialDirect numerical and large eddy simulation (DNS and LES) are applied= 2,400, whilst LES is also used to predict a high Re = 68,000 flow. Comparison
Open Access
A generalised and low-dissipative multi-directional characteristics-based scheme with inclusion of the local Riemann problem investigating incompressible flows without free-surfaces,
(Elsevier, 2019-01-21) Teschner, Tom-Robin; Könözsy, László Z.; Jenkins, Karl W.
In the present study, we develop a generalised Godunov-type multi-directional characteristics-based (MCB) scheme which is applicable to any hyperbolic system for modelling incompressible flows. We further extend the MCB scheme to include the solution of the local Riemann problem which leads to a hybrid mathematical treatment of the system of equations. We employ the proposed scheme to hyperbolic-type incompressible flow solvers and apply it to the Artificial Compressibility (AC) and Fractional-Step, Artificial Compressibility with Pressure Projection (FSAC-PP) method. In this work, we show that the MCB scheme may improve the accuracy and convergence properties over the classical single-directional characteristics-based (SCB) and non-characteristic treatments. The inclusion of a Riemann solver in conjunction with the MCB scheme is capable of reducing the number of iterations up to a factor of 4.7 times compared to a solution when a Riemann solver is not included. Furthermore, we found that both the AC and FSAC-PP method showed similar levels of accuracy while the FSAC-PP method converged up to 5.8 times faster than the AC method for steady state flows. Independent of the characteristics- and Riemann solver-based treatment of all primitive variables, we found that the FSAC-PP method is 7–200 times faster than the AC method per pseudo-time step for unsteady flows. We investigate low- and high-Reynolds number problems for well-established validation benchmark test cases focusing on a flow inside of a lid driven cavity, evolution of the Taylor–Green vortex and forced separated flow over a backward-facing step. In addition to this, comparisons between a central difference scheme with artificial dissipation and a low-dissipative interpolation scheme have been performed. The results show that the latter approach may not provide enough numerical dissipation to develop the flow at high-Reynolds numbers. We found that the inclusion of a Riemann solver is able to overcome this shortcoming. Overall, the proposed generalised Godunov-type MCB scheme provides an accurate numerical treatment with improved convergence properties for hyperbolic-type incompressible flow solvers.