The following is a list of master's dissertations that were recently completed in this Department.
All these dissertations can be found at UPSpace, the University of Pretoria Institutional Repositotry.
The abstracts of older masters from the department are summarized in the following archive pages:
M Vermaak, "Experimental investigation of microchannel flow boiling heat transfer with non-uniform circumferential heat flux at various gravitational orientations"
Supervisor: Prof Jaco Dirker
Co-supervisor: Prof Josua P. Meyer
Co-supervisor: Prof Khellil Sefiane
Flow boiling of Perfluorohexane (FC-72) in rectangular microchannels with one-sided uniform heating was studied experimentally at different rotations (). Various rotational orientations were investigated ranging from = 0° (bottom-heating) to 90° (side-heating) in increments of 30° as well as 180° (top-heating).
The channels had a relatively high aspect ratio of 10 (5 mm x 0.5 mm), a hydraulic diameter of 909 m and a heated length of approximately 78 mm. Mass fluxes of 10 kg/m2s, 20 kg/m2s and 40 kg/m2s were considered at several heat flux values at a saturation temperature of 56°C. For these conditions, in-channel flow visualisations and heated surface temperature distributions were recorded; fluid temperature and pressure readings were taken, and heat transfer coefficients were determined from subcooled conditions, through the onset of nucleate boiling, to near dryout conditions within the channel.
A channel at a rotation of = 0° produced the optimal results. = 0° had the highest heat transfer coefficient at all mass flux and heat flux combinations tested and had the lowest cross-sectional temperature variation of all rotations, minimizing the probability of warping electronic components. = 0° was nucleate boiling dominated resulting in an improved heat transfer performance with an increase in heat flux. = 180° experienced heat transfer coefficients that were greater than = 30°, 60° and 90° at various qualities up to = 0.3 where the vapour slug became confined the heat transfer coefficient decreased rapidly. = 90° had the lowest heat transfer coefficients at most mass flux and heat flux test cases. = 0° had the highest pressure drop while = 180° had the lowest pressure drop.
D.G. Marx, "Towards a hybrid approach for diagnostics and prognostics of planetary gearboxes."
Supervisor: Prof P.S. Heyns
Co-supervisor: Dr S. Schmidt
The reliable operation of planetary gearboxes is critical for the sustained operation of many machines such as wind turbines and helicopter transmissions. Hybrid methods that make use of the respective advantages of physics-based and data-driven models can be valuable in addressing the unique challenges associated with the condition monitoring of planetary gearboxes.
In this dissertation, a hybrid framework for diagnostics and prognostics of planetary gearboxes is proposed. The proposed framework aims to diagnose and predict the root crack length in a planet gear tooth from accelerometer measurements. Physics-based and data-driven models are combined to exploit their respective advantages, and it is assumed that no failure data is available for training these models. Components required for the implementation of the proposed framework are studied separately and challenges associated with each component are discussed.
The proposed hybrid framework comprises a health state estimation and health state prediction part. In the health state estimation part of the proposed framework, the crack length is diagnosed from the measured vibration response. To do this, the following model components are implemented: A first finite element model is used to simulate the crack growth path in the planet gear tooth. Thereafter, a second finite element model is used to establish a relationship between the gearbox time varying mesh stiffness, and the crack length in the planet gear tooth. A lumped mass model is then used to model the vibration response of the gearbox housing subject to the gearbox time varying mesh stiffness excitation. The measurements from an accelerometer mounted on the gearbox housing are processed by computing the synchronous average. Finally, these model components are combined with an additional data-driven model for diagnosing the crack length from the measured vibration response through the solution of an inverse problem.
After the crack length is diagnosed through the health state estimation model, the Paris crack propagation law and Bayesian state estimation techniques are used to predict the remaining useful life of the gearbox.
To validate the proposed hybrid framework, an experimental setup is developed. The experimental setup allows for the measurement of the vibration response of a planetary gearbox with different tooth root crack lengths in the planet gear. However, challenges in reliably detecting the damage in the experimental setup lead to the use of simulated data for studying the respective components of the hybrid method.
Studies conducted using simulated data highlighted interesting challenges that need to be overcome before a hybrid diagnostics and prognostics framework for planetary gearboxes can be applied in practice.
RPJ Ludeke, "Towards a Deep Reinforcement Learning based approach for real-time decision making and resource allocation for Prognostics and Health Management applications"
Supervisor: Prof. P.S. Heyns
Industrial operational environments are stochastic and can have complex system dynamics which introduce multiple levels of uncertainty. This uncertainty leads to sub-optimal decision making and resource allocation. Digitalisation and automation of production equipment and the maintenance environment enable predictive maintenance, meaning that equipment can be stopped for maintenance at the optimal time. Resource constraints in maintenance capacity could however result in further undesired downtime if maintenance cannot be performed when scheduled.
In this article the applicability of using a Multi-Agent Deep Reinforcement Learning based approach for decision making is investigated to determine the optimal maintenance scheduling policy in a fleet of assets where there are maintenance resource constraints. By considering the underlying system dynamics of maintenance capacity, as well as the health state of individual assets, a near-optimal decision making policy is found that increases equipment availability while also maximising maintenance capacity.
The implemented solution is compared to a run-to-failure corrective maintenance strategy, a constant interval preventive maintenance strategy and a condition based predictive maintenance strategy. The proposed approach outperformed traditional maintenance strategies across several asset and operational maintenance performance metrics. It is concluded that Deep Reinforcement Learning based decision making for asset health management and resource allocation is more effective than human based decision making.
B.D. Collins, "Insights into the use of Linear Regression Techniques in Response Reconstruction"
Supervisor: Prof. P.S. Heyns
Co-supervisor: Prof. S. Kok
Response reconstruction is used to obtain accurate replication of vehicle structural responses
of field recorded measurements in a laboratory environment, a crucial step in the process of
Accelerated Destructive Testing (ADT). Response Reconstruction is cast as an inverse problem
whereby the desired input is inferred using the measured outputs of a system. ADT typically
involves large shock loadings resulting in a nonlinear response of the structure. A promising
linear regression technique known as Spanning Basis Transformation Regression (SBTR) in con-
junction with non-overlapping windows casts the low dimensional nonlinear problem as a high
dimensional linear problem. However, it is determined that the original implementation of SBTR
struggles to invert a broader class of sensor configurations. A new windowing method called
AntiDiagonal Averaging (ADA) is developed to overcome the shortcomings of the SBTR im-
plementation. ADA introduces overlaps within the predicted time signal windows and averages
them. The newly proposed method is tested on a numerical quarter car model and is shown to
successfully invert a broader range of sensor configurations as well as being capable of describing
nonlinearities in the system.
R. Balshaw, "Latent analysis of unsupervised latent variable models in fault diagnostics of rotating machinery under stationary and time-varying operating conditions"
Supervisor: Prof. P.S. Heyns
Co-supervisor: Prof. D.N. Wilke
Co-supervisor: Dr. S. Schmidt
Vibration-based condition monitoring is a key and crucial element for asset longevity and to avoid unexpected financial compromise. Currently, data-driven methodologies often require significant investments into data acquisition and a large amount of operational data for both healthy and unhealthy cases. The acquisition of unhealthy fault data is often financially infeasible and the result is that most methods detailed in literature are not suitable for critical industrial applications.
In this work, unsupervised latent variable models negate the requirement for asset fault data. These models operate by learning the representation of healthy data and utilise health indicators to track deviance from this representation. A variety of latent variable models are compared, namely: Principal Component Analysis, Variational Auto-Encoders and Generative Adversarial Network-based methods. This research investigated the relationship between time-series data and latent variable model design under the sensible notion of data interpretation, the influence of model complexity on result performance on different datasets and shows that the latent manifold, when untangled and traversed in a sensible manner, is indicative of damage.
Three latent health indicators are proposed in this work and utilised in conjunction with a proposed temporal preservation approach. The performance is compared over the different models. It was found that these latent health indicators can augment standard health indicators and benefit model performance. This allows one to compare the performance of different latent variable models, an approach that has not been realised in previous work as the interpretation of the latent manifold and the manifold response to anomalous instances had not been explored. If all aspects of a latent variable model are systematically investigated and compared, different models can be analysed on a consistent platform.
In the model analysis step, a latent variable model is used to evaluate the available data such that the health indicators used to infer the health state of an asset, are available for analysis and comparison. The datasets investigated in this work consist of stationary and time-varying operating conditions. The objective was to determine whether deep learning is comparable or on par with state-of-the-art signal processing techniques. The results showed that damage is detectable in both the input space and the latent space and can be trended to identify clear condition deviance points. This highlights that both spaces are indicative of damage when analysed in a sensible manner. A key take away from this work is that for data that contains impulsive components that manifest naturally and not due to the presence of a fault, the anomaly detection procedure may be limited by inherent assumptions made in model formulations concerning Gaussianity.
This work illustrates how the latent manifold is useful for the detection of anomalous instances, how one must consider a variety of latent-variable model types and how subtle changes to data processing can benefit model performance analysis substantially. For vibration-based condition monitoring, latent variable models offer significant improvements in fault diagnostics and reduce the requirement for expert knowledge. This can ultimately improve asset longevity and the investment required from businesses in asset maintenance.
Z. Dlamini, "Experimental Investigation of Film-Cooling Hole Performance"
Supervisor: Dr. G. Mahmood
Film cooling has, over the years, allowed for the operation of modern gas turbines at temperatures far exceeding the limits of the material properties of the turbine components. This has resulted in increased power output and efficiency of the gas turbines. But over 40+ years of research has not culminated in the goal of achieving ideal cooling films, such as from two-dimensional (2D) continuous slots.
This study employed a curvature in the forward diffuser section of the film cooling hole; these holes are referred to as cases 1 to 4 in this study. This was expected to improve the performance of the hole. The performance parameters investigated and reported were the discharge coefficient of the holes, the flowfield downstream of the hole exit trailing edge, the temperature field downstream of the hole exit trailing edge and the effectiveness.
The effects of pressure ratio, mainstream crossflow, compound angle, hole geometry, manufacturing method, 3D print build orientation, and inclination angle, on the discharge coefficient were investigated.
The effects of blowing ratio, hole geometry, compound angle, turbulence intensity and downstream distance from hole exit trailing edge, on the flowfield, temperature field and effectiveness were also investigated.
The hole geometries had a diameter of 8 mm and length to diameter ratio equals to 7.5. The compound angle was varied between zero (0) to sixty (60) degrees. The inclination angles of the holes were either thirty (30) and forty (40) degrees.
The effect of the compound angle, manufacturing method and 3D print build orientation was found to be negligible for the discharge coefficient. But the above parameters had a significant effect on the adiabatic film cooling effectiveness.
Cases 1 to 4 holes showed higher discharge coefficient values as compared to the cylindrical and the laidback fan-shaped holes. This was a result of the development of the flow inside the hole and the resulting exit coolant jet velocity profile and its interaction with the mainstream crossflow.
From the flow structure and temperature field measurements it was determined that employing the curvature and the lateral expansion of the cases 1 to 4 holes decreases the height and trajectory of the jet on exit. The decreased height is due to the decreased vertical momentum content of the coolant jet. The decreased trajectory positions the longitudinal vortices closer to the wall which results in better lateral spread of the coolant.
From the effectiveness measurements it was found that increasing the compound angle decreases the lateral averaged effectiveness. And a decrease in the lateral averaged effectiveness was observed as the blowing ratio was increased.
The case 2 hole geometry resulted in low jet height when in the mainstream, which means that it was closer to the surface that requires cooling. It also resulted in a relatively good lateral spread of the coolant on the surface. And it resulted in the highest laterally averaged effectiveness at most of the compound angles and blowing ratios tested.
M.K. Seal, "The prediction of condensation flow patterns by using artificial intelligence techniques"
Supervisor: Dr. M. Mehrabi
Co-supervisor: Prof J.P. Meyer
Multiphase flow provides a solution to the high heat flux and precision required by modern-day gadgets and heat transfer devices. An application of multiphase flow commonly used in industrial applications is the condensation of refrigerants in inclined tubes, where the prediction of multiphase flow patterns is fundamental to the successful design and subsequent optimization given that the performance of such thermo-hydraulic systems is strongly dependent on the local flow patterns – or flow regimes, which affect heat transfer efficiency and pressure gradients.
In this study, it is shown that with the use of visualization data and artificial neural networks (ANN), a machine can learn and subsequently classify the separate flow patterns of condensation of R-134a refrigerant in inclined smooth tubes with more than 98% accuracy. The study considers ten classes of flow pattern images acquired from previous experimental works covering a wide range of flow conditions and the full range of tube inclination angles. Two types of classifiers are considered, namely multilayer perceptron (MLP) and convolutional neural networks (CNN). Although not the focus of this paper, the use of a principal component analysis (PCA) allows feature dimensionality reduction, dataset visualization, as well as decreased associated computational cost when used together with MLP neural networks. The superior 2-dimensional spatial learning capability of convolutional neural networks allows improved image classification and generalization performance across all ten flow pattern classes. In either case, it is shown that the prediction can be performed sufficiently fast to enable real-time execution and analysis in two-phase flow systems. The analysis sequence leads to the development of an online tool for the classification of in-tube flow patterns in inclined tubes, with the goal that the features learned through visualization will be applicable to a broad range of flow conditions, fluids, tube geometries, and orientations, and even generalize well to predicting adiabatic and boiling two-phase flow patterns. The method is validated with the prediction of flow pattern images found in the existing literature.
C. Roosendaal, "Analysis of a novel low-cost solar concentrator using lunar flux mapping techniques and ray-tracing models"
Supervisor: Dr WG le Roux
Co-supervisor: Prof JP Meyer
Concentrated solar power is a growing but expensive alternative energy resource. One of the most common issues faced when it comes to solar dish design is the complex trade-off between cost and optical quality. A novel solar dish reflector setup that makes use of low-cost, commercial television satellite dishes to support aluminised plastic membranes in a multifaceted vacuum-membrane concentrator was investigated in this work. The design aims to reduce costs while maintaining high optical accuracy with the added benefit of optical adjustability. The flux distribution of the novel solar dish reflector setup had to be determined to make recommendations on the feasibility of the design. This research presents a method to determine the expected solar flux distribution from lunar tests using a Canon EOS 700D camera.
Experimental tests and different pollution treatment methods were conducted using lunar flux mapping techniques. A numerical model of the experimental setup, based on photogrammetry results of the membrane surface, was also developed in SolTrace to ascertain the sources of error and allow for further design improvements. Preliminary testing proved that JPEG image formats yielded insufficient accuracy in capturing the incident flux when compared to RAW images. Based on the flux ratio maps, the intercept factor for a large multifaceted dish setup was calculated as 88.6% for an aperture size of 0.25 m × 0.25 m, with a maximum solar flux of 1 395 kW/m2 for a 1 000 W/m2 test case.
The numerical model showed that the experimental setup had a total optical error of 17.5 mrad with a comparable intercept factor of 88.8%, which was mainly due to facet misalignment and not reflector surface inaccuracies. The results suggest that large performance improvements can be gained through a more accurate aiming strategy. It is recommended that more durable membrane material can be used, along with an automated vacuum control system that can account for membrane leaks and temperature swings during operation. Correlations between the optical behaviour and geometrical features of elliptically supported facets can be further investigated to develop a design tool to aid in the design and development of high-performance systems. Overall, the design proved to be a viable design alternative for point focus solar concentrators to reduce costs and maintain optical accuracy. The lunar flux mapping techniques proved effective and safe by using the incident light from the moon and standard camera equipment.
K.A Goddard, "Investigation of wind patterns on Marion Island using Computational Fluid Dynamics and measured data"
Supervisor: Prof K.J. Craig (supervisor)
Co-supervisor: Mrs. J. Schoombie
There have been countless research investigations taking place on Marion Island (MI), both ecological and geological, which have reached conclusions that must necessarily neglect the impacts of wind on the systems under study. Since only the dominant wind direction of the general atmospheric wind is known from weather and satellite data, not much can be said about local wind conditions at ground level. Therefore, a baseline Computational Fluid Dynamics (CFD) model has been developed for simulating wind patterns over Marion and Prince Edward Islands, a South African territory lying in the subantarctic Indian Ocean.
A review of the current state of the art of Computational Wind Engineering (CWE) revealed that large-scale Atmospheric Boundary Layer (ABL) simulations have been successfully performed before with varying degrees of success. With ANSYS Fluent chosen as the numerical solver, the Reynolds-Averaged Navier-Stokes (RANS) equations were set up to simulate a total of 16 wind flow headings approaching MI from each of the cardinal compass directions. The standard k-ε turbulence closure scheme with modified constants was used to numerically approximate the atmospheric turbulence. A strategy was devised for generating a reusable mesh system to simulate multiple climatic conditions and wind directions around MI.
In conjunction with the computational simulations, a wind measurement campaign was executed to install 17 wind data logging stations at key locations around MI. Raw data output from the stations were cleaned and converted into an easily accessible MySQL database format using the Python scripting language. The Marion Island Recorded Experimental Dataset (MIRED) database contains all wind measurements gathered over the span of two years. The decision was taken to focus on validating only three of the 16 cardinal wind directions against the measured wind data; North-Westerly, Westerly and South-Westerly winds.
An initial interrogation of the simulation results showed that island-to-island wake interactions could not be ignored as the turbulent stream from MI could definitely be intercepted by its neighbour under the right conditions, and vice versa. An underestimation of the true strength of the Coriolis effect led to larger wind deflection in the simulations than originally expected, thus resulting in the wind flow at surface levels having an entirely different heading to what was intended. The westerly and south- westerly wind validation cases did not seem too badly affected by the lapse in judgement but the north-westerly case suffered strong losses in accuracy.
Significant effort was put into quantifying the error present in the simulations. After a full validation exercise, it was finally resolved to apply a conservative uncertainty factor of 35 % when using these simulations to predict actual wind speed conditions. Similarly, the predicted wind direction can only be trusted within the bounds of a 35° prediction uncertainty. Under these circumstances, the baseline CFD model was successfully validated against the measured wind data and can thus be used in further research. In terms of post-processing, all the wind direction simulations have been combined into a single wind velocity map, generated by weighting each of the simulations by the frequency of wind prevalence measured in the corresponding wind sector. A second turbulence intensity combined map has been provided using similar techniques. These maps, as well as the individual wind maps showing all cardinal wind directions, are believed to be helpful to many future biological studies on MI as well as any possible forays into wind energy generation on the island.
Despite the encountered deficiencies, this project offers significant value to academia by providing a reliable method of predicting fine-scale wind patterns in a location previously devoid of any accurate data. Furthermore, it has highlighted where future CFD attempts can be improved in order to produce a compelling approximation of the realistic atmospheric phenomena occurring in the Marion Island territory. While error cannot be avoided when modelling such complex systems, it has been well quantified and discussed here so that any further research may make informed judgements in future studies.