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A study of cultural models in automotive HMI : framework for accommodating cultural influenceKhan, Tawhid January 2016 (has links)
This doctoral innovation report presents a research study examining the importance of understanding automotive users’ cultural values and their individual preferences for HMI features and functionalities. The goal of this research was to explore how a cultural model can be applied in the development of automotive HMI solutions and future design localisation. To meet this goal, it was necessary to (1) identify the characteristics of the Hofstede cultural model; (2) identify the differences in cultural values using the model; (3) identify differences in HMI design preferences, usability and task performances across automotive user groups; (4) identify the potential success of a culturally adapted automotive HMI solution in automotive user acceptance and satisfaction. To explore the differences between users from two cultural regions, India and the UK, a series of user-centered HMI evaluation studies are conducted in which participants from each cultural region evaluate representative HMI samples. The outcomes of the user studies generate good quality data about automotive users’ cultural values and its relationship with vehicle user interface usability, task performances, and their feature preferences. The results are used in the development of a conceptual culturally adapted HMI design solution. This conceptual design is evaluated during the application phase of the research in order to explore whether such a design solution has a greater level of learnability and usability compared to the conventional solution when evaluated by Indian drivers. The results are also analysed to identify specific cultural traits that may influence the intention to use such solution in emerging markets like India. The outcome of the study shows different cultural groups have different behavioural tendencies and performances while using vehicle HMI solutions and have differences in expectations in design, suggesting an influence of culture on the perception of vehicle user interface technology. The analysis also highlights a preference for the culturally adapted automotive HMI solution when Indian drivers are provided with a choice between this and a non-adapted conventional solution. This leads to the conclusion that an understanding of cultural biases can influence design localisation and, as such, culturally-generated theories and recommendations can be applied as a basis for future automotive HMI design and development.
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Effective vehicle attribute delivery at Jaguar Land Rover : innovation reportDavis, Martin January 2017 (has links)
The objective of this research is to enhance the effectiveness of the new product development at Jaguar Land Rover by attending to the incongruities between the perceived organisational culture and the ecosystem in which the teams operate. The inquiry is aligned with both Donaldson's structural contingency theory (2001) and Syed's (2015) reflections regarding psychological alignment. The contention of the research is that an alternative operating model enables the teams to thrive and relish the uncertain, complex environment in which they now operate and hence improve their satisfaction and wellbeing whilst delivering increased value for both the business and the consumers (Davis, 2016a). This paper describes the rationale and the approach taken to embed an operating model which liberates the capabilities of the 'knowledge worker' community (Drucker, 1999) as opposed to the apparent Scientific Management (Taylor, 1914) bureaucratic efficiency model, which had previously been relevant. The research methodology incorporates Research Oriented Action Research (Eden and Huxham, 1996) in order to accommodate the unknowable outcomes and embedded paradoxes. The incorporation of a neurological metaphor attends to the innate human behaviours and social dynamics, whilst Dissipative Structure Theory (Prigogine and Allen, 1982) and the concept of panarchy (Garmestani et al., 2008) expands the traditional hierarchical perspective. The joint inquiry undertaken during an internship at Airbus Defence and Space corroborated the notion that an agile operating model could be realised for the creation of complex systems with significant hardware content and long lead times. The investigation also merges the constructive lean/agile values and principles from other sectors that are facing similar disruption in their ecosystems. The adoption of the principles that support self-determination (Ryan and Deci, 2000) result in reframing the participants' beliefs or "theories of action" (Argyris, 1995) by revising their experiences, hence a reduction in the observable stress and a verifiable increase in the delivery of valuable outcomes. The implications of the research spans both academic interest and real world utility regarding the co-creation of valuable knowledge through the alignment of the social dynamics of the participants and the methodology for progressing volatile problem situations with the ecosystems in which they find themselves.
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Consumer adoption of fuel cell vehicles : lessons from historical innovations and early adopters of battery electric vehiclesHardman, Scott John January 2016 (has links)
Fuel cell vehicles (FCVs), are one possible solution to address transportation-related climate change, urban air pollution and fossil fuel resource depletion. To solve these issues they need to displace internal combustion engine vehicles (ICEVs), the aim of this thesis is to understand whether FCVs can achieve this. First case studies of successful historical innovations are explored. Second the consumer adoption of battery electric vehicles (BEVs) is studied in detail by using questionnaire surveys and in-depth interviews. Finally, consumer attitudes and perceptions towards FCVs are investigated by conducting in-depth interviews and a FCV trial. From all of these results this thesis finds that FCVs have fewer benefits as perceived by consumers compared to BEVs and ICEVs. This means that consumers may preferentially adopt BEVs and will not be attracted to FCVs. This thesis makes recommendations on how to improve the attributes of FCVs so that they have more benefits for consumers. These efforts would increase the likelihood of consumers adopting FCVs. However, this thesis suggests that the adoption of FCVs still looks unlikely and that fuel cell (FC) stakeholders should seek to concentrate their efforts towards applications of FCs that have viable market entry potential.
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The separation of paraffin waxes in theory and practiceSawyer, Montagu Frederick January 1939 (has links)
This study considers the fractional melting process for the separation of paraffin waxes of different melting points, with the ultimates object of applying if possible. Its results contributed to the literature to experiemnts relating to the fractional melting of oil-free wax and two useful pieces of apparatus have been designed.
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Finite element analysis of aircraft tyresBehroozi, Mohammad H. January 2014 (has links)
In this thesis, the Finite Element (FE) Analysis of aircraft tyres is presented. The modelling and simulations of detailed construction of tyre enable tyre manufacturers to evaluate new designs and development before a prototype is fabricated, and aim to reduce the research costs and efforts to optimise the current tyre design of tyres. The material properties is key in the FE modelling and analysis, a number of sample from the rubber compounds and reinforcement were used to characterise the elastic, hyperelastic and viscoelastic behaviour of material. In this research, two aircraft tyres were employed for correlation study to a number of design checks and standard tests such as profile growth, sidewall deformation, footprint, contact pressure, and load-deformation data. The burst simulation was carried out to investigate when and where the failure in tyre occurs and compares it with what happens in real-world burst test as an important procedure in tyre safety certification by aviation authorities. As a result, the virtual testing would shorten the design procedure by checking the design parameters in advance of tyre prototyping. Moreover, the FE parameters such as mesh size and tyre geometry are investigated for optimisation of the runtime and accuracy and improvements in the FE results. A number of simulations were run to determine generated forces and moments across the contact patch using a steady-state approach in presence of air as the tyre inflator to obtain a higher accuracy in prediction of the vertical stiffness and footprint area. In addition, the tyre was freely rolled on the runway using an explicit approach to investigate the energy dissipation and heat build-up per tyre rotation due to the tyre viscoelasticity. Finally, TAIS (Tyre Analysis Interface System) development is explained in response to the design requirements from Dunlop Aircraft Tyres.
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Particle matter emission control and related issues for diesel enginesZhang, Jun January 2011 (has links)
The particulate matter is probably one of the most critical issues of the diesel engine emissions. After years of research, people begin to understand it further, with respect to its impact on the environment and human health, its formation mechanism inside and outside the diesel engine, and most importantly, the methods to control its formation. This study has focused on the particulate matter emission control in diesel engines. It started with the application of two closely coupled Diesel Particle Filters (DPFs), consisting of an assistant DPF and a main standard honeycomb DPF, to replace the standard Diesel Oxidation Catalyst (DOC) + DPF system, commonly applied on diesel engines these days. This new after-treatment system showed a great potential to be adopted in the future, not only for the tighter emission regulations but also for the great cost reduction of the total after-treatment system by the reduction of the main DPF’s size and weight and more options of other cheaper materials. The back pressure problem revealed in the experiment was then studied through a one dimensional model simulation and its influence to the engine performance was discussed. Fuel injection parameters which are related with the engine performance and emissions were also investigated in the study. After careful calibrations, a single injection induced Partially Charge Compression Ignition (PCCI) combustion was achieved, in which mode, NOx and smoke emissions were reduced by more than 80% compared with using the normal multiple injections. Following that, the non-volatile particulate emissions under the pilot injection’s impacts were studied and the key parameters of the pilot control such as fuel quantity and injection timing were examined. The particulate number concentrations and size distributions were investigated under different engine operation conditions and the results illustrated how a pilot injection alongside a main injection could influence particulates and what these effects were closely related to. The research continued with the investigation of bio-fuels on the particle emission reduction, when 10% alternative diesel fuel blends (Rapeseed Methyl Ester (RME) and Gas-to-Liquid (GTL)) were used. They indicated that without any modification to the engine, adding selected alternative fuels, even at a low percentage, could result in a noticeable reduction of the particle numbers, both in the total and the non-volatile parts; however, the number of nucleation mode particles could increase in certain cases. It was also revealed that the engine suffered very high numbers of the nucleation mode particulates during warming up.
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The application of X-ray computed tomography in aerospace industry : innovation reportKourra, Nadia January 2016 (has links)
In the 2015 report of ‘The aerospace industry: statistics and policy’, UK Government presents the aerospace industry as “phenomenal success story” with “tremendous opportunities for growth” (Rhodes, et al., 2015). The success of this sector depends on high efficiency and productivity levels while maintaining quality and satisfying market demands which request aircraft to stay safely in service for longer with reduced maintenance budgets. One of the strategic objectives of the aerospace companies is continuous improvement of the technologies and engineering capabilities. X-ray Computed Tomography (CT) is a growing Non-Destructive Evaluation (NDE) method with various applications in several sectors of industry. CT collects numerous radiographs that are then reconstructed to create a 3D model of the examined object. The results demonstrate the outer and inner structure of the part including any defects, altered densities and hidden constructions in the case of Additive Layer Manufacturing (ALM) parts. Product development in the aerospace industry is a challenging task with significant risks that are handled by complex processes for quality control. The product development steps in this industry follow the products from concept to manufacturing and from service to disposal. This project examines the capabilities and limitations of CT in order to identify potential applications in this sector by considering all of the stages of development. Several case studies demonstrate its application in the research and development phase of composite design and machining selection as well as in the production phase with metrological and non-destructive evaluation applications. Finally, the application of CT in failure investigations and forensic examinations, close to the end of the life and disposal of the product was also considered. The results of these investigations demonstrate the possibilities of this technology as well as its limitations and led the sponsoring company to purchase a digital radiography system with CT capabilities. The presented investigations answer the research question of ‘How can CT be applicable in aerospace industry?’ by identifying the product development phases where CT is applicable. The developed innovative methods provide CT inspections and measurements while reducing human error. They identify the capabilities and limitations of this technology and develop improved scanning methods and standard operating procedures. This report summarises the results of these investigations that clearly demonstrate the potential applications of this technology as well as their limitations while it also introduces and demonstrates innovative methods to overcome these limitations. The innovation of this project is in the novel methods that allow this technology to be used in this industrial sector and provide the required results that are unobtainable with other NDT methods.
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Machine learning techniques to estimate the dynamics of a slung load multirotor UAV systemVargas Moreno, Aldo Enrique January 2017 (has links)
This thesis addresses the question of designing robust and flexible controllers to enable autonomous operation of a multirotor UAV with an attached slung load for general cargo transport. This is achieved by following an experimental approach; real flight data from a slung load multirotor coupled system is used as experience, allowing for a computer software to estimate the pose of the slung in order to propose a swing-free controller that will dampen the oscillations of the slung load when the multirotor is following a desired flight trajectory. The thesis presents the reader with a methodology describing the development path from vehicle design and modelling over slung load state estimators to controller synthesis. Attaching a load via a cable to the underside of the aircraft alters the mass distribution of the combined "airborne entity" in a highly dynamic fashion. The load will be subject to inertial, gravitational and unsteady aerodynamic forces which are transmitted to the aircraft via the cable, providing another source of external force to the multirotor platform and thus altering the flight dynamic response characteristics of the vehicle. Similarly the load relies on the forces transmitted by the multirotor to alter its state, which is much more difficult to control. The principle research hypothesis of this thesis is that the dynamics of the coupled system can be identified by applying Machine Learning techniques. One of the major contributions of this thesis is the estimator that uses real flight data to train an unstructured black-box algorithm that can output the position vector of the load using the vehicle pose and pilot pseudo-controls as input. Experimental results show very accurate position estimation of the load using the machine learning estimator when comparing it with a motion tracking system (~2% offset). Another contribution lies in the avionics solution created for data collection, algorithm execution and control of multirotor UAVs, experimental results show successful autonomous flight with a range of algorithms and applications. Finally, to enable flight capabilities of a multirotor with slung load, a control system is developed that dampens the oscillations of the load; the controller uses a feedback approach to simultaneously prevent exciting swing and to actively dampen swing in the slung load. The methods and algorithms developed in this thesis are validated by flight testing.
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Innovative solutions for automotive embedded software development : innovation reportMouzakitis, Alexandros January 2016 (has links)
Embedded software is shaping and influencing our world and it is unimaginable to realise day to day life without it. Since the introduction of the first Electronic Control Unit (ECU) in the 1970s, the automotive industry has seen a substantial increase of embedded software in vehicles. The use of embedded software in the automotive industry has led to a significant increase in the number and complexity of different vehicle systems, features and functions. This level of complexity drives premium vehicles with no fewer than 70 ECUs interconnected by more than five on-board network systems such as Controller Area Network (CAN), Local Interconnect Network (LIN), Media Oriented Systems Transport (MOST), FlexRay and Ethernet. In a typical automotive development process, the main challenge for the engineers is to uncover as many failure modes and/or software defects as possible during the early stages of the vehicle programme. During the early phases of the development, failure modes and/or software defects are difficult to uncover but easy and inexpensive to fix. During the latter phases of the development, failure modes and/or software defects are easy to uncover since the final product has been built. At this stage, failure modes and/or software defects are hard and expensive to fix as changes required in the embedded software. The aim of this research was to develop and deploy innovative solutions in order to shift failure modes and/or software defects detection early in automotive product development. The initial research work was conducted through an analysis of failure modes and/or software defects found during a typical Jaguar Land Rover (JLR) vehicle programme development. This preliminary work also then focused on supplier base capability for automotive embedded software development. The research findings from the internal and external analysis, together with the literature review on best practice have driven the development of four solutions. A process called Model-based Product Engineering (MBPE) was created and deployed within JLR. The MBPE process brings together model-based development and other development processes in a standardised form. A new generic Design Verification Interface (DVI) for test exchange and traceability across all MBPE process levels was developed. The generic DVI eliminates or reduces redundant efforts of re-writing test cases and test scripts for automated testing. A semi-formal Standardised Design Verification Method (SDVM) was developed for defining test cases for all vehicle systems in a common template. The SDVM presents test cases as machine readable data and allows auto-generation of test scripts suitable for automated testing. An end-to-end solution called Platform Independent Test System was developed in order to integrate the MBPE, DVI and SDVM solutions. The proposed PITS supports all levels of system abstraction from the test case definition phase to the execution of automated scripts in both offline and real-time test environments. Evaluation results have demonstrated a significant shift in the detection of failure modes and/or software defects towards the early phases of the product development. An early detection of more than 50% of failure modes and/or software defects was achieved. This is a substantial change from the previous state where embedded software validation was conducted only after supplier software release. Furthermore, results have shown a 40% reduction in engineering effort for test scripts creation and a five to tenfold reduction in engineering time for automated testing.
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A new machine learning based method for multi-GNSS data quality assurance and multipath detectionQuan, Yiming January 2017 (has links)
Global Navigation Satellite Systems (GNSS) based high-precision positioning techniques have been widely used in geodesy, attitude determination, engineering survey and agricultural applications. With the modernisation of GNSS, the number of visible satellites and new signals are increasing. Multi-constellation and multi-frequency data provide users with more observations, and hence increase redundancy in parameter estimation. However, increased number of satellites may increase the chance of multipath errors, especially in difficult environments. Therefore, this thesis aims at characterising the measurement signal quality of all available and new signals of multi-GNSS (GPS, GLONASS, Galileo, BDS, and QZSS) with real data. Also, a new multipath detection model based on machine learning methods is developed. The measurement noise levels in all currently available signals have been studied by investigating their double difference (DD) carrier phase residuals. The positioning precision, accuracy, and ambiguity success rate have been assessed using the selected individual GNSS constellations and their selected combinations with static and kinematic antennas in low multipath and severe multipath environments. The statistical results show the residuals vary from 0.5 mm to 2 mm with different signals and models of receivers. Short baseline tests show that in ideal conditions (i.e., a low multipath environment), using a single GNSS constellation (GPS, GLONASS, Galileo, or BDS) or their combinations can usually achieve millimetre-level precision and centimetre-level accuracy with almost 100% ambiguity success rates, regardless if the rover antenna is static or kinematic. In realistic condition (i.e. a severe multipath environment) the positioning precision and accuracy reduce to the centimetre level or even worse with a kinematic antenna. Multipath effect is one of the major error sources in GNSS positioning. Most of the currently available multipath detection and mitigation methods are based on stochastic modelling, advanced techniques in data processing, spatial geometry modelling, and special hardware designs. A new machine learning based multipath detection model is developed for undifferenced measurements (a single receiver approach). The approach is based on the fact that the multipath signature can be found in the multipath contaminated time series, and the features of multipath signature can be learned and identified by machine learning methods. The proposed model has been trained and validated with simulated data in an urban canyon environment with different satellite geometries. Moreover, the model has been trained and tested with real kinematic LoS and multipath data collected with a rotating arm rig in a multipath environment, and tested with multipath data collected near solar panels and near a building. The model has been tested using all available GNSS signals. The results show the model can achieve accuracy of 80%-90% with the simulated GNSS (GPS, Galileo, and BDS) data, and accuracy of 65%-70% with the real data collected using rotating arm rig on GPS L1 and GLONASS L1 signals. Real data collected near solar panels and near a building show that the well-trained model can achieve accuracy of about 60% in completely different multipath environments. The test results show the model was not well trained on GLONASS L2 and BDS data due to their carrier multipath errors are close to their carrier measurement error in ideal environment (low multipath environment).
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