Development of a numerical optimization approach to ventilation system design to control airborne contaminant dispersion and occupant comfort

Airflow, contaminant and temperature during heating and ventilation in a model room represented by a square cavity with inlet and outlet ports, has been studied. The aim of this work is concerned with the development and implementation of a practical and robust optimization scheme based on the combination of Genetic algorithm and response surface methodology (RSM) with the aim of assisting hospital ward designers and managers /operators to enhance infection control (i.e. reduce the risk of airborne transmission) without compromising patient comfort and environmental impact

Aerodynamic Drag Reduction of Emergency Response Vehicles

This paper presents the first experimental and computational investigation into the aerodynamics of emergency response vehicles and focusses on reducing the additional drag that results from the customary practice of adding light-bars onto the vehicles’ roofs. A series of wind tunnel experiments demonstrate the significant increase in drag that results from the light bars and show these can be minimized by reducing the flow separation caused by them. Simple potential improvements in the aerodynamic design of the light bars are investigated by combining Computational Fluid Dynamics (CFD) with Design of Experiments and metamodelling methods. An aerofoil-based roof design concept is shown to reduce the overall aerodynamic drag by up to 20% and an analysis of its effect on overall fuel consumption indicates that it offers a significant opportunity for improving the fuel economy and reducing emissions from emergency response vehicles. These benefits are now being realised by the UK’s ambulance service

Maximum energy conversion from human motion using piezoelectric flex transducer: A multi-level surrogate modeling strategy

Conventional engineering design optimization requires a large amount of expensive experimental tests from prototypes or computer simulations, which may result in an inefficient and unaffordable design process. In order to overcome these disadvantages, a surrogate model may be used to replace the prototype tests. To construct a surrogate model of sufficient accuracy from limited number of tests/simulations, a multi-level surrogate modeling strategy is introduced in this article. First, a chosen number of points determined by optimal Latin Hypercube Design of Experiments are used to generate global-level surrogate models with genetic programming and the fitness landscape can be explored by genetic algorithms for near-optimal solutions. Local-level surrogate models are constructed then from the extended-optimal Latin Hypercube samples in the vicinity of global optimum on the basis of a much smaller number of chosen points. As a result, an improved optimal design is achieved. The efficiency of this strategy is demonstrated by the parametric optimization design of a piezoelectric flex transducer energy harvester. The optimal design is verified by finite element simulations and the results show that the proposed multi-level surrogate modeling strategy has the advantages of faster convergence and more efficiency in comparison with the conventional single-single level surrogate modeling technique

Application of topology optimisation to the design of steel I-section webs

This research focuses on the application of structural topology optimisation the design of steel I-section beam web openings. A topology optimisation study is performed for the first time on the web of a steel I-section beam. A beam web design is then proposed based on the results of the topology optimisation study. A nonlinear finite element analysis technique is employed to determine the load carrying performance of the optimised beam in comparison to the conventional, widely used, cellular type beam. It is found that the optimised beam over performs in terms of load carrying capacities and stress intensities. Barriers to the implementation of the topology optimisation technique to the routine design of beam web are highlighted. In detail, a parametric topology optimisation study is conducted in order to determine the optimum opening topology for the wide range of beam cross sections that are found in practice. Thereafter, a generalised optimum web opening configuration is suggested based on the results of the parametric study. It is anticipated that a shape optimisation study will be required to maximise the efficiency of beams with this optimum web opening configuration

The use of glycerol and cooking oil in masonry unit production

This paper presents an investigation into the development of a novel glycerol-bound masonry unit and the use of a multi-parameter mathematical modelling technique in evaluating the possibility to reduce the amount of sample produced with a comparable result obtained. Initially, an experimental optimisation was carried out to identify the best performance sample in term of compressive strength. The binder used for this stage is the blend of clean cooking oil and pure glycerol incorporating with secondary aggregates include incinerator bottom ash and pulverised fuel ash. Additional samples were made using natural aggregates and waste binders (waste cooking oil and waste glycerol) to evaluate their performance in relation to masonry products found in the UK in terms of compressive strength and water absorption properties. These properties are good indicators of overall performance of masonry units. In addition, modelling techniques were also used to evaluate the laboratory testing strategy to suggest the reduction in number of samples being produced in future experimental studies. Results from the study demonstrated that the novel masonry units could be produced with properties that were at least equivalent to those of currently used in the UK and modelling technique could reduce up to 90% of sample for the problem with three parameters

Multi-objective optimization of the ventilation system design in a two-bed hospital ward with an emphasis on infection control

Airflow, contaminant concentration and temperature distribution in a two-bed hospital ward represented by simple model room with inlet and outlet vents, have been studied. Our work is concerned with the development and implementation of a practical and robust response surface based multi-objective optimization scheme, with the aim of assisting hospital ward designers and managers /operators to enhance infection control (i.e. reduce the risk of airborne transmission) without compromising patient comfort and environmental impac

Meta-model based optimization of building thermal performance incorporating local comfort analysis

Good thermal control of the built environment is necessary to ensure comfort for occupants without excessive energy use. This is a particular concern in hospital environments where comfort needs may be more stringent and system design and control options are constrained by the need to consider infection control and clinical needs. In this study a building thermal optimisation methodology with the two contrasting objectives of optimizing thermal comfort and minimizing energy use is developed and applied to a generic "small room" model, representative of a typical single-bed hospital room. This model incorporates both a mechanical HVAC system and radiator heating, with selected design and operation parameters of both systems taken as design variables. The simulation is undertaken at a high level of detail, utilizing conflated dynamic thermal modelling (DTM) and computational fluid dynamics (CFD) to evaluate spatial variation in thermal conditions throughout the room. A moving least squares regression (MLSR) meta-model is applied to the problem and shown to model the problem to a very high degree of accuracy with a smaller sample set than required for complex methods such as neural networks and support vector regression. A parametric study shows how the room optimum changes when considering spatial variation of thermal comfort within the room, and variation in the time period of each optimization. As well as their own conclusions these analyses demonstrate the value of the "one sample many optimizations" approach, as they are all performed using only very few sample sets

Optimisation of Important Factors Influencing Spring-back after Sheet Metal Forming

Fabricating parts such as structural members of automobiles are achieved by using sheet metal forming technique. Light weight high strength materials are preferable to minimise the fuel consumption and therefore reducing the emissions. However, fabricating such materials are usually associated with some defects. One of the most announced phenomenon is called Spring-back; the deviation in specimen geometry subsequent to forming due to elastic recovery. The aim of the current research is to assess the effect of main forming process parameters on the Spring-back level. The U-drawing process was studied in this work, since this process supports more complex sheet metal forming operations. A reliable test rig for such operation was created and 3D finite element model was developed to simulate the process. The simulations for four different material were validated. An acceptable agreement between the simulations and experiments results were obtained, however, the high strength galvanised steel shows disagreement results. Parametric study was concocted to evaluate the influence of several parameters on the Spring-back magnitude. Finally, an optimisation method was developed to find the optimum parameters combination to reduce Spring-back level and successfully utilised to optimize the process parameters for the different materials studied

The use of optimisation for enhancing the development of a novel sustainable masonry unit.

This paper examines whether it is possible to utilise optimisation techniques to enhance the efficiency of the experiment-based development of a new product. Conventionally, a laboratory-based experimental investigation requires to check all combinations of all independent parameters describing the product; hence the optimum levels of the tested parameters is obtained. As a result there may be many thousands of samples produced to optimise these parameters; this is a time consuming, costly and laborious process. Instead, it is suggested that an empirical model is established and validated using the results of the laboratory investigation making it suitable for optimisation. In this paper the empirical model describes the compressive strength of a new masonry unit (bitublock) in terms of two parameters that are the proportion of the coarse aggregate and the compaction pressure. The expression for the compressive strength obtained from all experimental data was optimised showing a similar result to that from an experimental investigation. A much reduced set of experimental data was then used to establish further empirical models. The predictions from these models were compared with the output from the full set of experimental data to assess the accuracy of these empirical models and arrive at recommendations for the reduction of an experimental programme for future studies. From the experimental investigation alone, the optimal compressive strength (35.82 MPa) of the new unit was obtained from a mix containing 30% coarse aggregate compacted at 24 MPa. Empirical modelling using only half of the original experimental data predicted that a coarse aggregate content of 31.95% and a compaction pressure of 21.73 MPa would provide an optimum compressive strength of 35.95 MPa. This shows that the use of optimisation techniques can improve the efficiency and economy of laboratory investigations and also has a potential to provide economies during future scaling-up and manufacturing processes, e.g. in this case, by reducing compaction pressures