Design of Robust PTFE Faced Bearings for Performance and Reliability in Large Rotating Machinery

In this thesis a Finite Element Modelling (FEM) approach is proposed for modelling the visco-plastic creep effects that a PTFE-faced thrust bearing would undergo during normal operating conditions. A thermal elastic hydrodynamic lubrication (TEHL) model is developed, which uses the Reynolds equations for a fluid film, coupled with Hertzian contact theory and the energy equation to predict the pressure and film thickness on the PTFE face as well as the temperature and thickness of the fluid. These variables are then used with a Norton creep function to predict the secondary creep effects on the PTFE surface. This change in PTFE thickness due to visco-plastic effects are taken into account within the film thickness equations and the effects over a bearing’s operational life span studied. The Norton creep function is obtained from experimental creep results conducted on filled PTFE samples at the University of Leeds. This experimental method allowed for the displacement of the samples to be recorded over a 7 day test period at a representative range of pressure and temperature conditions and a Norton creep function to be obtained from the results. The Norton creep function was then included within the simulations to allow for the visco-plastic creep effects to be studied. Results obtained showed that whilst the secondary creep had a small effect on the pressure profile of the pad, the peak pressure was significantly affected during the duration of the pads operational life. It was concluded that this was due to the pad deformation changing, meaning a smaller peak pressure was observed, but the tilt angle did not change greatly, meaning the profile did not change. The pad and film thickness also changed as time passed, with the pad getting thinner in areas of high pressure and temperature, due to the secondary creep effects. It was also observed that the peak temperature of the pad also decreased as time passed, due to the film thickness increase in areas of high temperature

Deep Learning with Dynamically Weighted Loss Function for Sensor-Based Prognostics and Health Management

Deep learning has been employed to prognostic and health management of automotive and aerospace with promising results. Literature in this area has revealed that most contributions regarding deep learning is largely focused on the model’s architecture. However, contributions regarding improvement of different aspects in deep learning, such as custom loss function for prognostic and health management are scarce. There is therefore an opportunity to improve upon the effectiveness of deep learning for the system’s prognostics and diagnostics without modifying the models’ architecture. To address this gap, the use of two different dynamically weighted loss functions, a newly proposed weighting mechanism and a focal loss function for prognostics and diagnostics task are investigated. A dynamically weighted loss function is expected to modify the learning process by augmenting the loss function with a weight value corresponding to the learning error of each data instance. The objective is to force deep learning models to focus on those instances where larger learning errors occur in order to improve their performance. The two loss functions used are evaluated using four popular deep learning architectures, namely, deep feedforward neural network, one-dimensional convolutional neural network, bidirectional gated recurrent unit and bidirectional long short-term memory on the commercial modular aero-propulsion system simulation data from NASA and air pressure system failure data for Scania trucks. Experimental results show that dynamically-weighted loss functions helps us achieve significant improvement for remaining useful life prediction and fault detection rate over non-weighted loss function predictions

Seal Failure In Aerospace Applications -- Creating A Global Open-source Database

Seal failure in aerospace applications-creating a global open-source database Seals are essential components used in numerous applications across engineering. A seal is a component that impedes the flow of fluid through a given system. The word 'impede' holds emphasis as there is no such thing as a zero-leakage seal. All seals leak, even if it is as low as 1 mm3/year and referred to as "emission". [1] Although seals serve many purposes in aerospace applications, their main purposes are to restrict leakage out of a system and to prevent contaminants from entering a system-that is, keeping fluids in and keeping debris out. When selecting a seal for a specific application , many criteria require consideration. These include installation and assembly, temperature and pressure, contact and non-contact configurations , wear, and rotational and surface speeds. However, the perfect seal does not exist and the specific requirements crucial for optimum performance in a specific application must be considered when selecting the seal

Towards a More Reliable Interpretation of Machine Learning Outputs for Safety-Critical Systems Using Feature Importance Fusion

When machine learning supports decision-making in safety-critical systems, it is important to verify and understand the reasons why a particular output is produced. Although feature importance calculation approaches assist in interpretation, there is a lack of consensus regarding how features' importance is quantified, which makes the explanations offered for the outcomes mostly unreliable. A possible solution to address the lack of agreement is to combine the results from multiple feature importance quantifiers to reduce the variance of estimates. Our hypothesis is that this will lead to more robust and trustworthy interpretations of the contribution of each feature to machine learning predictions. To assist test this hypothesis, we propose an extensible Framework divided in four main parts: (i) traditional data pre-processing and preparation for predictive machine learning models; (ii) predictive machine learning; (iii) feature importance quantification and (iv) feature importance decision fusion using an ensemble strategy. We also introduce a novel fusion metric and compare it to the state-of-the-art. Our approach is tested on synthetic data, where the ground truth is known. We compare different fusion approaches and their results for both training and test sets. We also investigate how different characteristics within the datasets affect the feature importance ensembles studied. Results show that our feature importance ensemble Framework overall produces 15% less feature importance error compared to existing methods. Additionally, results reveal that different levels of noise in the datasets do not affect the feature importance ensembles' ability to accurately quantify feature importance, whereas the feature importance quantification error increases with the number of features and number of orthogonal informative features

The chemical and computational biology of inflammation

Non-communicable diseases (NCD) such as cancer, heart disease and cerebrovascular injury are dependent on or aggravated by inflammation. Their prevention and treatment is arguably one of the greatest challenges to medicine in the 21st century. The pleiotropic, proinflammatory cytokine; interleukin-l beta (IL-l~) is a primary, causative messenger of inflammation. Lipopolysaccharide (LPS) induction ofIL-l~ expression via toll-like receptor 4 (TLR4) in myeloid cells is a robust experimental model of inflammation and is driven in large part via p38-MAPK and NF-KB signaling networks. The control of signaling networks involved in IL-l~ expression is distributed and highly complex, so to perturb intracellular networks effectively it is often necessary to modulate several steps simultaneously. However, the number of possible permutations for intervention leads to a combinatorial explosion in the experiments that would have to be performed in a complete analysis. We used a multi-objective evolutionary algorithm (EA) to optimise reagent combinations from a dynamic chemical library of 33 compounds with established or predicted targets in the regulatory network controlling IL-l ~ expression. The EA converged on excellent solutions within 11 generations during which we studied just 550 combinations out of the potential search space of - 9 billion. The top five reagents with the greatest contribution to combinatorial effects throughout the EA were then optimised pair- wise with respect to their concentrations, using an adaptive, dose matrix search protocol. A p38a MAPK inhibitor (30 ± 10% inhibition alone) with either an inhibitor of IKB kinase (12 ± 9 % inhibition alone) or a chelator of poorly liganded iron (19 ± 8 % inhibition alone) yielded synergistic inhibition (59 ± 5 % and 59 ± 4 % respectively, n=7, p≥O.04 for both combinations, tested by one way ANOVA with Tukey's multiple test correction) of macrophage IL-l~ expression. Utilising the above data, in conjunction with the literature, an LPS-directed transcriptional map of IL-l ~ expression was constructed. Transcription factors (TF) targeted by the signaling networks coalesce at precise nucleotide binding elements within the IL-l~ regulatory DNA. Constitutive binding of PU.l and C/EBr-~ TF's are obligate for IL-l~ expression. The findings in this thesis suggest that PU.l and C/EBP-~ TF's form scaffolds facilitating dynamic control exerted by other TF's, as exemplified by c-Jun. Similarly, evidence is emerging that epigenetic factors, such as the hetero-euchromatin balance, are also important in the relative transcriptional efficacy in different cell types. Evolutionary searches provide a powerful and general approach to the discovery of novel combinations of pharmacological agents with potentially greater therapeutic indices than those of single drugs. Similarly, construction of signaling network maps aid the elucidation of pharmacological mechanism and are mandatory precursors to the development of dynamic models. The symbiosis of both approaches has provided further insight into the mechanisms responsible for IL-lβ expression, and reported here provide a - platform for further developments in understanding NCD's dependent on or aggravated by inflammation.EThOS - Electronic Theses Online ServiceBBSRCEPSRCGBUnited Kingdo

Experimental study of radial lip seals with different sleeve coatings

Radial Lip Seals with pre-loaded garter springs of 8.5oz, 12.5oz and 14oz are tested with 4 different sleeves to investigate various seal-sleeve combinations. To find optimum sleeve coating and seal combinations, leakage and performance is investigated on an experimental test bench. This paper analyses a) Performance of various seal-shaft combinations, b) performance of varying seal pre-loaded garter springs, c) exploring the importance of a coating by comparing a stainless steel shaft to the 3 other coatings and d) integrity of sleeves after testing. Results indicate that the tungsten carbide coated sleeve outperforms the chrome oxide, hard chrome and stainless steel sleeves in terms of leakage. Chrome oxide is second in performance, third is hard chrome and the stainless steel sleeve leaked the most. Further, the tungsten carbide, chrome oxide and hard chrome sleeves are all surface finished to the desirable roughness, Ra = 0.2-0.4 µm while the stainless steel sleeve is not. This shows that surface coatings are significant; Vickers hardness and surface roughness of a sleeve are important factors to consider in the design process, necessary for efficient sealing. The 14oz spring, in all cases, exhibits higher leakage than the 12.5oz and 8.5oz springs. This indicates that the higher load spring results in higher wear and therefore, higher leakage. Optimum performance and lowest leakage is seen in the 12.5oz spring seal

Recurrent Posterior Reversible Encephalopathy Syndrome Potentially Related to AIDS and End-Stage Renal Disease: A Case Report and Review of the Literature

Posterior reversible encephalopathy syndrome (PRES) is a clinicoradiological syndrome that is characterized by clinical features including headache, altered mental status, cortical blindness, seizures, and other focal neurological signs as well as subcortical edema without infarction on neuroimaging. Under the umbrella of hypertensive encephalopathy, PRES is defined by reversible cerebral edema due to dysfunction of the cerebrovascular blood-brain barrier unit. The pathophysiology of PRES is thought to result from abnormalities in the transmembrane flow of intravascular fluid and proteins caused by two phenomena: one, cerebral autoregulatory failure and two, loss of integrity of the blood-brain barrier. PRES is not a common disease in patients with human immunodeficiency virus (HIV) and AIDS with only three previously reported cases. Both the HIV and end-stage renal disease appear to further compromise the blood brain barrier. Although uncommon, PRES recurrence has been described. To the best of our knowledge, this is the first report demonstrating recurrent PRES in a HIV patient on hemodialysis for end-stage renal disease

An Experimental Study of Contact Temperatures at Sealing Interface against Varying Shaft Surfaces

Increased temperatures at the sealing interface between the seal and shaft can reduce the working life of a seal through elastomer aging, swelling and increased friction. Degradation of the seal due to increased temperatures can cause premature failure, wear and leakage. There is no such thing as a perfect seal; each application has requirements to cater to the needs of each system. For radial oil seals in helicopter gearboxes, the contact temperatures at the sealing interface are a critical parameter to consider. In this manuscript, investigating the factors that influence the temperatures at the contact interface shed light on the operating parameters that cause an increase in contact temperatures. Four varying shaft coatings are tested against three seal spring loads for a range of sliding velocities between 5-25 ms −1 to reproduce conditions of the gearbox. The study reveals an optimum seal spring of 12 oz, with a circumferential load of 3.34 N for lowest temperatures at the interface. Higher springs of 14 oz and lower springs of 8.5 oz both cause increased temperatures at the interface. Additionally, the need for surface coatings on the shaft is re-enforced through experimental evidence demonstrated by comparing temperatures reached between a plain stainless steel shaft and three surface coated shafts. Chrome plating shafts are undesirable due to the 'polishing' in effect they experience. The results of this study build on this by showing that chrome plated shafts have higher temperatures at the interface, aggravating any wear or polishing in of that surface. Contact temperatures with Tungsten carbide and Chrome oxide coatings remain within the expected temperature rise. Lastly, microscopically 'rougher' surfaces result in increased temperatures in contrast to surface coatings within the specified range of roughness as provided by DIN 3760/61/ISO 6194

Machine learning to determine the main factors affecting creep rates in laser powder bed fusion

There is an increasing need for the use of additive manufacturing (AM) to produce improved critical application engineering components. However, the materials manufactured using AM perform well below their traditionally manufactured counterparts, particularly for creep and fatigue. Research has shown that this difference in performance is due to the complex relationships between AM process parameters which affect the material microstructure and consequently the mechanical performance as well. Therefore, it is necessary to understand the impact of different AM build parameters on the mechanical performance of parts. Machine learning (ML) models are able to find hidden relationships in data using iterative statistical analyses and have the potential to develop process–structure–property–performance relationships for manufacturing processes, including AM. The aim of this work is to apply ML techniques to materials testing data in order to understand the effect of AM process parameters on the creep rate of additively built nickel-based superalloy and to predict the creep rate of the material from these process parameters. In this work, the predictive capabilities of ML and its ability to develop process–structure–property relationships are applied to the creep properties of laser powder bed fused alloy 718. The input data for the ML model included the Laser Powder Bed Fusion (LPBF) build parameters used—build orientation, scan strategy and number of lasers—and geometrical material descriptors which were extracted from optical microscope porosity images using image analysis techniques. The ML model was used to predict the minimum creep rate of the Laser Powder Bed Fused alloy 718 samples, which had been creep tested at 650∘C and 600MPa. The ML model was also used to identify the most relevant material descriptors affecting the minimum creep rate of the material (determined by using an ensemble feature importance framework). The creep rate was accurately predicted with a percentage error of 1.40 % in the best case. The most important material descriptors were found to be part density, number of pores, build orientation and scan strategy. These findings show the applicability and potential of using ML to determine and predict the mechanical properties of materials fabricated via different manufacturing processes, and to find process–structure–property relationships in AM. This increases the readiness of AM for use in critical applications