• Advanced engine flows and combustion

      Peng, Zhijun; Megaritis, Thanos; Sung, Chih-Jen; Yaga, Minoru; Hellier, Paul; Tian, Guohong; University of Bedfordshire; Brunel University; University of Connecticut; University of the Ryukyus; et al. (Hindawi, 2017-08-07)
      The transport sector accounts for a significant part of carbon emissions worldwide, and so the need to mitigate the greenhouse effect of CO2 from fossil fuel combustion, and to reduce vehicle exhaust emissions has been the primary driver for developing cleaner and more efficient vehicle powertrains, and environmentally friendly fuels.  As alternatives to combustion engines have yet to overcome technical challenges to attain significant utilisation in the transport sector, piston-driven internal combustion engines and gas turbine aero-engines remain very attractive powertrain options due to their high thermal efficiency. Meanwhile, since the introduction of various emissions standards, that have forced the employment of various aftertreatment systems, the evolution of combustion process has been significant. Advanced combustion strategies have attempted to find in-chamber approaches to either meet these emission standards fully and thus avoid the need to use aftertreatment, or at the very least, to lower the performance demands required from aftertreatment systems and thus reducing their cost and complexity. While the main focus of combustion system development has been recently to lower emissions of CO2, there is also significant interest to lower nitric oxides (NOx) and particulate matter (PM) emissions and other harmful emissions.
    • A closed-loop reciprocity calibration method for massive MIMO in terrestrial broadcasting systems

      Luo, Hua; Zhang, Yue; Huan, Li-Ke; Cosmas, John; Aggoun, Amar; University of Bedfordshire; Brunel University; Cobham Wireless (IEEE, 2016-09-22)
      Massive multi-input multioutput (MIMO) is believed to be an effective technique for future terrestrial broadcasting systems. Reciprocity calibration is one of the major practical challenges for massive MIMO systems operating in time-division duplexing mode. A new closed-loop reciprocity calibration method is investigated in this paper which can support online calibration with a higher accuracy compared to the existing methods. In the first part of the proposed method, an optimized relative calibration is introduced using the same structure of traditional relative calibration, but with less impaired hardware in the reference radio chain. In the second part, a test device (TD)-based calibration is proposed which makes online calibration possible. An experiment setup is built for the measurement of the base station hardware impairments and TD-based calibration implementation. Simulation results and the error vector magnitude of UE received signal after calibration show that the performance of our proposed method is improved significantly compared to the existing relative calibration methods.
    • Minimal mean-square error for 3D MIMO beamforming weighting

      Xu, C.; Cosmas, John; Zhang, Yue; Brunel University; University of Bedfordshire (Institution of Engineering and Technology, 2016-11-24)
      The 3D MIMO beamforming system needs a weighting method to determine the direction of beam whist reducing the interference for other beam areas operating at the same carrier frequency. The challenge is to determine the weights of the 3D MIMO beams to direct each beam towards its cluster of user terminals while placing its nulls at undesired user directions to minimise undesired interference. Therefore, the signal-to-interference-plus-noise ratio should be increased while the interference from the side lobes of the other beams reduced. A weight determining method is presented that constructs horizontal and vertical array weights, respectively, by minimising the mean-square error between the array pattern vector and the unit vector, where the unit vector expresses the desired direction for the array pattern and zero vector expresses the undesired direction. Since the rectangular planar array can be viewed as M linear arrays of N elements, the weight of the M–Nth element can be obtained based on the horizontal and vertical array weights.