Investigation and optimisation of commercial refrigeration cycles using the natural refrigerant CO2

This thesis was submitted for the degree of Doctor of Engineering and awarded by Brunel University.With tighter regulations on the use of Hydroflurocarbons (HFCs) due to their high GWP (Global Warming Potential), many supermarket operators are looking for alternative refrigerants. To contribute to this, the objectives of this thesis are to investigate the practicality, environmental benefits and economic viability of an all-CO2 transcritical refrigeration system suitable for small supermarkets. Whilst the environmental benefits of using CO2 as a refrigerant are clear, there is rather limited practical and technical knowledge on the design and operation of these systems. In this work, simulation models of a transcritical ‘booster’ CO2 refrigeration system have been developed to investigate and evaluate its performance against that of a traditional HFC system. The models were verified using test results from an experimental CO2 system built at Brunel University. To evaluate the performance of the CO2 refrigeration system in the field, energy data from a real supermarket employing a HFC refrigeration system was used for energy simulations. The results demonstrate that the annual energy consumption of the CO2 refrigeration system in a small supermarket in Northern Ireland would be equivalent to that of a typical HFC refrigeration system. However, the low GWP of CO2 will result in a 50% reduction in the combined direct and indirect CO2 emissions over the operational life of the system assuming an annual leakage rate of 15%. Northern Ireland has a high number of small supermarkets due to its rural population, approximately 615. The CO2 system presented in this research could replace the existing R404A systems in these small supermarkets resulting in emissions reduction of up to 188,752 tCO2e. This research has developed selection techniques and criteria to be considered by supermarket designers and operators when developing national strategies for the eventual phase-out of HFC refrigerants in all supermarket sizes. The validated simulation models developed in this research combined with the detailed geographical and refrigeration load ratio analysis presented, will provide valuable information that will assist system designers and operators in the efficient design and optimisation of CO2 technology for small supermarkets.This study was funded by the Engineering and Physical Sciences Research Council and Shilliday Refrigeration

Music Information Retrieval in Live Coding: A Theoretical Framework

The work presented in this article has been partly conducted while the first author was at Georgia Tech from 2015–2017 with the support of the School of Music, the Center for Music Technology and Women in Music Tech at Georgia Tech. Another part of this research has been conducted while the first author was at Queen Mary University of London from 2017–2019 with the support of the AudioCommons project, funded by the European Commission through the Horizon 2020 programme, research and innovation grant 688382. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Music information retrieval (MIR) has a great potential in musical live coding because it can help the musician–programmer to make musical decisions based on audio content analysis and explore new sonorities by means of MIR techniques. The use of real-time MIR techniques can be computationally demanding and thus they have been rarely used in live coding; when they have been used, it has been with a focus on low-level feature extraction. This article surveys and discusses the potential of MIR applied to live coding at a higher musical level. We propose a conceptual framework of three categories: (1) audio repurposing, (2) audio rewiring, and (3) audio remixing. We explored the three categories in live performance through an application programming interface library written in SuperCollider, MIRLC. We found that it is still a technical challenge to use high-level features in real time, yet using rhythmic and tonal properties (midlevel features) in combination with text-based information (e.g., tags) helps to achieve a closer perceptual level centered on pitch and rhythm when using MIR in live coding. We discuss challenges and future directions of utilizing MIR approaches in the computer music field

Time-Dependent 3D Magnetohydrodynamic Pulsar Magnetospheres: Oblique Rotators

The current state of the art in pulsar magnetosphere modeling assumes the force-free limit of magnetospheric plasma. This limit retains only partial information about plasma velocity and neglects plasma inertia and temperature. We carried out time-dependent 3D relativistic magnetohydrodynamic (MHD) simulations of oblique pulsar magnetospheres that improve upon force-free by retaining the full plasma velocity information and capturing plasma heating in strong current layers. We find rather low levels of magnetospheric dissipation, with less than 10% of pulsar spindown energy dissipated within a few light cylinder radii, and the MHD spindown that is consistent with that in force-free. While oblique magnetospheres are qualitatively similar to the rotating split-monopole force-free solution at large radii, we find substantial quantitative differences with the split-monopole, e.g., the luminosity of the pulsar wind is more equatorially concentrated than the split-monopole at high obliquities, and the flow velocity is modified by the emergence of reconnection flow directed into the current sheet.Comment: 5 pages, 3 figures, MNRAS, accepted. Movies are available at http://youtu.be/cjua6XhhNL4 and http://youtu.be/dUR2Lx1JGRM and can be also downloaded from the ancillary files sectio

A Neural Attention Model for Abstractive Sentence Summarization

Summarization based on text extraction is inherently limited, but generation-style abstractive methods have proven challenging to build. In this work, we propose a fully data-driven approach to abstractive sentence summarization. Our method utilizes a local attention-based model that generates each word of the summary conditioned on the input sentence. While the model is structurally simple, it can easily be trained end-to-end and scales to a large amount of training data. The model shows significant performance gains on the DUC-2004 shared task compared with several strong baselines.Comment: Proceedings of EMNLP 201

Retrieve and Refine: Improved Sequence Generation Models For Dialogue

Sequence generation models for dialogue are known to have several problems: they tend to produce short, generic sentences that are uninformative and unengaging. Retrieval models on the other hand can surface interesting responses, but are restricted to the given retrieval set leading to erroneous replies that cannot be tuned to the specific context. In this work we develop a model that combines the two approaches to avoid both their deficiencies: first retrieve a response and then refine it -- the final sequence generator treating the retrieval as additional context. We show on the recent CONVAI2 challenge task our approach produces responses superior to both standard retrieval and generation models in human evaluations