Building an empirically robust framework for corporate brand communications using action research

This thesis aims to develop a more robust framework for understanding the management processes involved in Corporate Brand Communications. A review of the literature on corporate branding shows a growing body of conceptual work, but also highlights that much of the recent work in the field has not focused on the underlying processes involved in managing a corporate brand. There is, therefore, a clear need to understand how a corporate brand is defined, developed and communicated. This international study adopts a Participatory Action Research approach, grounded in Intervention Theory (Argyris, 1973), to develop an intervention framework based on the concept of privileged access' (Torbet, 1991). This methodological framework is tested on a pilot study and then adopted for the study of three separate organisations in the UK, France and the Netherlands to answer three distinct, but related Research Questions. Based upon the findings emerging from these studies, the researcher identifies a series of 'emergent management stages', and uses this empirical evidence to develop a new 'Six Conventions' framework for understanding the processes of nurturing and managing a corporate brand. The study makes an explicit contribution to the field by helping to 'join up' many of the existing, disparate conceptual models. It makes a further significant contribution by grounding the 'Six Conventions' framework in rich empirical data in a way that operationalizes the inherent management processes in a new and more robust manner than previous studies. These findings offer both new insight to academics, and a set of guiding principles and practices for managers engaged in managing brands at an organisational level, fulfilling the requirements of Participatory Action Research to generate both Propositional and Practical knowledge. A further methodological contribution is provided by demonstration of the potential that participatory approaches, utilising the concept of this privileged access', offer in contrast to traditional case research. This leads to the development of a new process to guide effective intervention studies of management processes.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A Study of Nanoclay Reinforcement of Biocomposites Made by Liquid Composite Molding

Liquid composite molding (LCM) processes are widely used to manufacture composite parts for the automotive industry. An appropriate selection of the materials and proper optimization of the manufacturing parameters are keys to produce parts with improved mechanical properties. This paper reports on a study of biobased composites reinforced with nanoclay particles. A soy-based unsaturated polyester resin was used as synthetic matrix, and glass and flax fiber fabrics were used as reinforcement. This paper aims to improve mechanical and flammability properties of reinforced composites by introducing nanoclay particles in the unsaturated polyester resin. Four different mixing techniques were investigated to improve the dispersion of nanoclay particles in the bioresin in order to obtain intercalated or exfoliated structures. An experimental study was carried out to define the adequate parameter combinations between vacuum pressure, filling time, and resin viscosity. Two manufacturing methods were investigated and compared: RTM and SCRIMP. Mechanical properties, such as flexural modulus and ultimate strength, were evaluated and compared for conventional glass fiber composites (GFC) and flax fiber biocomposites (GFBiores-C). Finally, smoke density analysis was performed to demonstrate the effects and advantages of using an environment-friendly resin combined with nanoclay particles

Impact of demand side response on a commercial retail refrigeration system

The UK National Grid has placed increased emphasis on the development of Demand Side Response (DSR) tariff mechanisms to manage load at peak times. Refrigeration systems, along with HVAC, are estimated to consume 14% of the UK’s electricity and could have a significant role for DSR application. However, characterized by relatively low individual electrical loads and massive asset numbers, multiple low power refrigerators need aggregation for inclusion in these tariffs. In this paper, the impact of the Demand Side Response (DSR) control mechanisms on food retailing refrigeration systems is investigated. The experiments are conducted in a test-rig built to resemble a typical small supermarket store. The paper demonstrates how the temperature and pressure profiles of the system, the active power and the drawn current of the compressors are affected following a rapid shut down and subsequent return to normal operation as a response to a DSR event. Moreover, risks and challenges associated with primary and secondary Firm Frequency Response (FFR) mechanisms, where the load is rapidly shed at high speed in response to changes in grid frequency, is considered. For instance, measurements are included that show a significant increase in peak inrush currents of approx. 30% when the system returns to normal operation at the end of a DSR event. Consideration of how high inrush currents after a DSR event can produce voltage fluctuations of the supply and we assess risks to the local power supply system

Nemesyst: A Hybrid Parallelism Deep Learning-Based Framework Applied for Internet of Things Enabled Food Retailing Refrigeration Systems

Deep Learning has attracted considerable attention across multiple application domains, including computer vision, signal processing and natural language processing. Although quite a few single node deep learning frameworks exist, such as tensorflow, pytorch and keras, we still lack a complete process- ing structure that can accommodate large scale data processing, version control, and deployment, all while staying agnostic of any specific single node framework. To bridge this gap, this paper proposes a new, higher level framework, i.e. Nemesyst, which uses databases along with model sequentialisation to allow processes to be fed unique and transformed data at the point of need. This facilitates near real-time application and makes models available for further training or use at any node that has access to the database simultaneously. Nemesyst is well suited as an application framework for internet of things aggregated control systems, deploying deep learning techniques to optimise individual machines in massive networks. To demonstrate this framework, we adopted a case study in a novel domain; deploying deep learning to optimise the high speed control of electrical power consumed by a massive internet of things network of retail refrigeration systems in proportion to load available on the UK Na- tional Grid (a demand side response). The case study demonstrated for the first time in such a setting how deep learning models, such as Recurrent Neural Networks (vanilla and Long-Short-Term Memory) and Generative Adversarial Networks paired with Nemesyst, achieve compelling performance, whilst still being malleable to future adjustments as both the data and requirements inevitably change over time

Thermoplastic RTM: Impact Properties of Anionically Polymerised Polyamide 6 Composites for Structural Automotive Parts

This study investigates the impact behaviour and post-impact performance of polyamide-6 glass fibre reinforced composites, manufactured by thermoplastic resin transfer moulding. Impact test samples were extracted from quasi-isotropic laminates using two different glass fibre sizings, both with a fibre volume fraction of approximately 52%. A previous study showed that one of these sizings enhanced the interfacial strength and Mode I fracture toughness; however, the effects of the sizing on out-of-plane impact is of greater significance in terms of automotive applications. A drop-weight impact tester was used to determine out-of-plane impact performance for both sizings in terms of impact load-induced and energy returned from the striker. High-speed video of the impact response was simultaneously captured. Testing was carried out at three impact energy levels: two sub-penetration and one full penetration. The impact damage area was observed, and the post-damage compression properties of samples were measured to determine the reduction in their strength and stiffness. Results showed that the use of different sizing technologies had little effect on the post-impact compressive properties and that penetration led to only a 29% drop in compression strength. Overall, the outcomes of this work demonstrate the potential of these materials in automotive applications

Facilitating static firm frequency response with aggregated networks of commercial food refrigeration systems

Aggregated electrical loads from massive numbers of distributed retail refrigeration systems could have a significant role in frequency balancing services. To date, no study has realised effective engineering applications of static firm frequency response to these aggregated networks. Here, the authors present a novel and validated approach that enables large scale control of distributed retail refrigeration assets. The authors show a validated model that simulates the operation of retail refrigerators comprising centralised compressor packs feeding multiple in-store display cases. The model was used to determine an optimal control strategy that both minimised the engineering risk to the pack during shut down and potential impacts to food safety. The authors show that following a load shedding frequency response trigger the pack should be allowed to maintain operation but with increased suction pressure set-point. This reduces compressor load whilst enabling a continuous flow of refrigerant to food cases. In addition, the authors simulated an aggregated response of up to three hundred compressor packs (over 2 MW capacity), with refrigeration cases on hysteresis and modulation control. Hysteresis control, compared to modulation, led to undesired load oscillations when the system recovers after a frequency balancing event. Transient responses of the system during the event showed significant fluctuations of active power when compressor network responds to both primary and secondary parts of a frequency balancing event. Enabling frequency response within this system is demonstrated by linking the aggregated refrigeration loads with a simplified power grid model that simulates a power loss incident

Aggregated power profile of a large network of refrigeration compressors following FFR DSR events

Refrigeration systems and HVAC are estimated to consume approximately 14% of the UK’s electricity and could make a significant contribution towards the application of DSR. In this paper, active power profiles of single and multi-pack refrigeration systems responding DSR events are experimentally investigated. Further, a large population of 300 packs (approx. 1.5 MW capacity) is simulated to investigate the potential of delivering DSR using a network of refrigeration compressors, in common with commercial retail refrigeration systems. Two scenarios of responding to DSR are adopted for the studies viz. with and without applying a suction pressure offset after an initial 30 second shut-down of the compressors. The experiments are conducted at the Refrigeration Research Centre at University of Lincoln. Simulations of the active power profile for the compressors following triggered DSR events are realized based on a previously reported model of the thermodynamic properties of the refrigeration system. A Simulink model of a three phase power supply system is used to determine the impact of compressor operation on the power system performance, and in particular, on the line voltage of the local power supply system. The authors demonstrate how the active power and the drawn current of the multi-pack refrigeration system are affected following a rapid shut down and subsequent return to operation. Specifically, it is shown that there is a significant increase in power consumption post DSR, approximately two times higher than during normal operation, particularly when many packs of compressors are synchronized post DSR event, which can have a significant effect on the line voltage of the power supply

Power and Energy Analysis for a Commercial Retail Refrigeration System Responding to a Static Demand Side Response

The paper considers the impact of Demand Side Response events on supply power profile and energy efficiency of widely distributed aggregated loads applied across commercial refrigeration systems. Responses to secondary grid frequency static DSR events are investigated. Experimental trials are conducted on a system of refrigerators representing a small retail store, and subsequently on the refrigerators of an operational superstore in the UK. Energy consumption and energy savings during 3 hours of operation, pre and post-secondary DSR, are discussed. In addition, a simultaneous secondary DSR event is realised across three operational retail stores located in different geographical regions of the UK. A Simulink model for a 3Φ power network is used to investigate the impact of a synchronised return to normal operation of the aggregated refrigeration systems post DSR on the local power network. Results show ~1% drop in line voltage due to the synchronised return to operation. An analysis of energy consumption shows that DSR events can facilitate energy savings of between 3.8% and 9.3% compared to normal operation. This is a result of the refrigerators operating more efficiently during and shortly after the DSR. The use of aggregated refrigeration loads can contribute to the necessary load-shed by 97.3% at the beginning of DSR and 27% during 30 minutes DSR, based on a simultaneous DSR event carried out on three retail stores

The Hyper Suprime-Cam Software Pipeline

In this paper, we describe the optical imaging data processing pipeline developed for the Subaru Telescope's Hyper Suprime-Cam (HSC) instrument. The HSC Pipeline builds on the prototype pipeline being developed by the Large Synoptic Survey Telescope's Data Management system, adding customizations for HSC, large-scale processing capabilities, and novel algorithms that have since been reincorporated into the LSST codebase. While designed primarily to reduce HSC Subaru Strategic Program (SSP) data, it is also the recommended pipeline for reducing general-observer HSC data. The HSC pipeline includes high level processing steps that generate coadded images and science-ready catalogs as well as low-level detrending and image characterizations.Comment: 39 pages, 21 figures, 2 tables. Submitted to Publications of the Astronomical Society of Japa