ORCID Profile
0000-0001-5596-5473
Current Organisations
University of South Australia
,
South Australian Institute of Technology
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In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Optimisation | Operations Research | Applied Mathematics | Applied Statistics | Numerical and Computational Mathematics | Applied Mathematics not elsewhere classified
Energy Conservation and Efficiency in Transport | Energy Systems Analysis | Expanding Knowledge in the Mathematical Sciences |
Publisher: IOP Publishing
Date: 14-03-2018
Publisher: MDPI AG
Date: 14-02-2020
DOI: 10.3390/NANO10020330
Abstract: A facile method for the preparation of microwave absorbers with low density, high microwave absorptivity, and broad band is of paramount importance to the progress in practical application. Herein, commonly-used metal organic frameworks (MOFs) prepared just by mechanical stirring in methanol at room temperature were chosen as sacrificial templates to synthesize porous carbon composites with tunable dielectric and magnetic properties. With the replacement of Co atoms on the surface of zeolitic imidazolate framework-67 (ZIF-67) by Zn atoms, a Co-doped porous carbon composite with a low-dielectric amorphous carbon/Zn shell was constructed after annealing, leading to excellent impedance matching condition. Consequently, the as-obtained composite (Co/C@C-800) shows marvelous microwave absorption properties with an absorption capacity of −43.97 dB and a corresponding effective absorption bandwidth of 4.1 GHz, far exceeding that of the traditional porous carbon and composites directly derived from ZIF-67. The results provide a convenient way to modify MOFs for enhanced microwave absorption materials from the synergy of dielectric and magnetic losses.
Publisher: MDPI AG
Date: 12-12-2014
Publisher: SAE International
Date: 13-05-2022
DOI: 10.4271/2022-01-5038
Publisher: Wiley
Date: 21-06-2018
DOI: 10.1002/PAT.4368
Publisher: Wiley
Date: 11-07-2018
DOI: 10.1002/PAT.4400
Publisher: Elsevier BV
Date: 10-2017
Publisher: Elsevier BV
Date: 10-2022
Publisher: Modelling and Simulation Society of Australia and New Zealand
Date: 12-2019
Publisher: Springer London
Date: 1995
Publisher: Elsevier BV
Date: 05-2018
Publisher: IEEE
Date: 07-2015
Publisher: Australian Mathematical Publishing Association, Inc.
Date: 13-01-2008
Publisher: Birkhäuser Boston
Date: 2023
Publisher: Informa UK Limited
Date: 06-2012
Publisher: Australian Mathematical Publishing Association, Inc.
Date: 27-05-2014
Publisher: Institution of Engineering and Technology (IET)
Date: 05-2015
Publisher: Elsevier BV
Date: 11-2009
Publisher: Elsevier BV
Date: 06-2020
Publisher: Elsevier BV
Date: 11-2015
Publisher: Springer Science and Business Media LLC
Date: 26-04-2019
Publisher: Australian Mathematical Publishing Association, Inc.
Date: 26-10-2010
Publisher: Institute for Operations Research and the Management Sciences (INFORMS)
Date: 03-2023
Abstract: We propose an analytic solution to the problem of finding optimal driving strategies that minimize total tractive energy consumption for a fleet of trains traveling on the same track in the same direction subject to clearance-time equality constraints that ensure safe separation and compress the headway timespan. We assume the track is ided into sections by a set of trackside signals at fixed positions. For each intermediate signal there is an associated signal segment consisting of the two adjacent sections. Successive trains are safely separated only if the leading train leaves the signal segment before the following train enters. Although the fleet can be safely separated by a complete set of clearance times and associated clearance-time inequality constraints the problem of finding optimal schedules with safe separation rapidly becomes intractable as the number of trains and signals increases. The main difficulty is in distinguishing between active equality constraints and inactive inequality constraints. The curse of dimensionality means it is not feasible to check every different combination of active constraints, find the optimal strategies for each train, optimize the corresponding prescribed times and calculate the cost. Nevertheless we can formulate and solve an alternative problem with active clearance-time equality constraints for successive trains defined at selected signals. We show that this problem can be formulated as an unconstrained convex optimization and propose a solution algorithm that finds the optimal schedule and the associated optimal strategies for each train. Finally we find optimal schedules for a case study using realistic parameters on a busy metropolitan line.
Publisher: Oxford University Press (OUP)
Date: 1997
Publisher: IEEE
Date: 11-2022
Publisher: Australian Mathematical Publishing Association, Inc.
Date: 17-07-2022
DOI: 10.21914/ANZIAMJ.V63.17209
Abstract: TTG Energymiser is an in-cab system that provides real-time driving advice to train drivers with the aim of reducing energy use subject to meeting the train schedule. A survey of the efficacy of Energymiser has been undertaken, to provide evidence for marketing claims. Results from 23 different trials are analysed, where 16 of the trials were on passenger routes and 7 were on freight routes. Each trial consists of many trips, with Energymiser activated for around half, and yields an estimate of the change in energy use when Energymiser is used. A Bayesian hierarchical model is fitted to the 16 estimates from passenger routes and provides an estimate of the mean saving and the standard deviation of in idual trials about the mean. The mean saving is 7.2% and the standard deviation of in idual trials is estimated as 3.3%. The corresponding mean and standard deviation for freight routes are 8.4% and 5.8%, respectively. References A. Albrecht, P. Howlett, P. Pudney, X. Vu, and P. Zhou. The key principles of optimal train control—Part 2: Existence of an optimal strategy, the local energy minimization principle, uniqueness, computational technique. Transport. Res. B: Method. 94 (2016), pp. 509–538. doi: 10.1016/j.trb.2015.07.024. A. Albrecht, P. Howlett, P. Pudney, X. Vu, and P. Zhou. The key principles of optimal train control—Part 1: Formulation of the model, strategies of optimal type, evolutionary lines, location of optimal switching points. Transport. Res. B: Method. 94 (2016), pp. 482–508. doi: 10.1016/j.trb.2015.07.023. A. Gelman, J. B. Carlin, H. S. Stern, D. B. Dunson, A. Vehtari, and D. B. Rubin. Bayesian Data Analysis. Chapman and Hall/CRC Press, 2012. doi: 10.1201/b16018 C. Röver. Bayesian random-effects meta-analysis using the bayesmeta R package. J. Stat. Software 93.6 (2020), pp. 1–51. doi: 10.18637/jss.v093.i06
Publisher: Elsevier BV
Date: 04-1994
Publisher: International Academy Publishing (IAP)
Date: 05-2014
Publisher: Australian Mathematical Publishing Association, Inc.
Date: 27-08-2016
Publisher: Springer London
Date: 1995
Publisher: SAGE Publications
Date: 09-2013
Abstract: In Australia, and elsewhere, the movement of trains on long-haul rail networks is usually planned in advance. Typically, a train plan is developed to confirm that the required train movements and track maintenance activities can occur. The plan specifies when track segments will be occupied by particular trains and maintenance activities. On the day of operation, a train controller monitors and controls the movement of trains and maintenance crews, and updates the train plan in response to unplanned disruptions. It can be difficult to predict how good a plan will be in practice. The main performance indicator for a train service should be reliability – the proportion of trains running the service that complete at or before the scheduled time. We define the robustness of a planned train service to be the expected reliability. The robustness of in idual train services and for a train plan as a whole can be estimated by simulating the train plan many times with random, but realistic, perturbations to train departure times and segment durations, and then analysing the distributions of arrival times. This process can also be used to set arrival times that will achieve a desired level of robustness for each train service.
Publisher: Cambridge University Press (CUP)
Date: 07-2016
DOI: 10.1017/S1446181116000092
Abstract: In this paper, we show that the cost of an optimal train journey on level track over a fixed distance is a strictly decreasing and strictly convex function of journey time. The precise structure of the cost–time curves for in idual trains is an important consideration in the design of energy-efficient timetables on complex rail networks. The development of optimal timetables for busy metropolitan lines can be considered as a two-stage process. The first stage seeks to find optimal transit times for each in idual journey segment subject to the usual trip-time, dwell-time, headway and connection constraints in such a way that the total energy consumption over all proposed journeys is minimized. The second stage adjusts the arrival and departure times for each journey while preserving the in idual segment times and the overall journey times, in order to best synchronize the collective movement of trains through the network and thereby maximize recovery of energy from regenerative braking. The precise nature of the cost–time curve is a critical component in the first stage of the optimization.
Publisher: IEEE
Date: 12-2015
Publisher: Australian Mathematical Publishing Association, Inc.
Date: 16-08-2016
Publisher: IOP Publishing
Date: 24-04-2018
Publisher: Elsevier BV
Date: 06-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: IEEE
Date: 1990
Publisher: Elsevier BV
Date: 03-2019
Publisher: Modelling and Simulation Society of Australia and New Zealand
Date: 08-2023
Publisher: Springer Science and Business Media LLC
Date: 18-11-2018
Publisher: Springer Science and Business Media LLC
Date: 03-03-2018
Publisher: IOP Publishing
Date: 09-02-2018
Publisher: Springer Science and Business Media LLC
Date: 13-07-2018
Publisher: Australian Mathematical Publishing Association, Inc.
Date: 21-06-2010
Publisher: Elsevier
Date: 2002
Publisher: Elsevier BV
Date: 05-2017
Publisher: Australian Mathematical Publishing Association, Inc.
Date: 22-03-2018
Publisher: Springer London
Date: 1995
Publisher: Wiley
Date: 31-08-2018
DOI: 10.1002/PAT.4145
Publisher: Springer London
Date: 1995
Publisher: Springer London
Date: 1995
Publisher: Elsevier BV
Date: 03-2021
Publisher: Springer London
Date: 1995
Publisher: Springer London
Date: 1995
Publisher: Springer London
Date: 1995
Publisher: Elsevier BV
Date: 06-2023
Publisher: Springer US
Date: 2001
Publisher: Springer London
Date: 1995
Publisher: Springer Science and Business Media LLC
Date: 2002
Publisher: Springer London
Date: 1995
Publisher: Elsevier BV
Date: 09-2023
Publisher: Elsevier BV
Date: 04-2018
Publisher: Springer Science and Business Media LLC
Date: 08-08-2019
Publisher: Elsevier BV
Date: 12-2016
Publisher: Elsevier BV
Date: 05-2016
Publisher: Oxford University Press (OUP)
Date: 03-1999
Publisher: Elsevier BV
Date: 10-2013
Publisher: Modelling and Simulation Society of Australia and New Zealand
Date: 16-04-0004
Publisher: IEEE
Date: 10-2014
Publisher: Informa UK Limited
Date: 02-2013
DOI: 10.1057/JORS.2012.42
Publisher: Springer London
Date: 1995
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Springer London
Date: 1995
Publisher: Springer London
Date: 1995
Publisher: Springer London
Date: 1995
Publisher: IEEE
Date: 2007
Publisher: Elsevier BV
Date: 12-2016
Publisher: Informa UK Limited
Date: 03-2010
Publisher: Elsevier BV
Date: 12-2016
Publisher: IEEE
Date: 06-2011
Publisher: Cambridge University Press (CUP)
Date: 07-1994
DOI: 10.1017/S0334270000010225
Abstract: How should a vehicle he driven to minimise fuel consumption? In this paper we consider the case where a train is to be driven along a straight, level track, but where speed limits may apply over parts of the track. The journey is to be completed within a specified time using as little fuel as possible. For a journey without speed limits, the optimal driving strategy typically requires full power, speed holding, coasting and full braking, in that order. The holding speed and braking speed can be determined from the vehicle characteristics and the time available to complete the journey. If the vehicle has discrete control settings, the holding phase should be approximated by alternate coast and power phases between two critical speeds. For a journey with speed limits, a similar strategy applies. For each given journey time there is a unique holding speed. On intervals of track where the speed limit is below the desired holding speed, the speed must be held at the limit. If braking is necessary on an interval, the speed at which braking commences is determined in part by the holding speed for the interval. For vehicles with discrete control, speed-holding is approximated by alternate coast and power phases between two critical speeds, or between a lower critical speed and the speed limit.
Publisher: Australian Mathematical Publishing Association, Inc.
Date: 05-10-2016
Publisher: Elsevier BV
Date: 03-2018
Publisher: Institution of Engineering and Technology (IET)
Date: 05-2016
Publisher: Elsevier BV
Date: 08-2023
Publisher: Modelling and Simulation Society of Australia and New Zealand
Date: 08-2023
Publisher: Springer International Publishing
Date: 2023
Start Date: 12-2015
End Date: 12-2018
Amount: $430,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 11-2011
End Date: 06-2016
Amount: $540,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 07-2021
End Date: 06-2024
Amount: $285,638.00
Funder: Australian Research Council
View Funded Activity