Search results1 – 3 of 3
An alternative algorithm has been developed for computing the behaviour of flows within arbitrary ducts and channels. This technique requires a small number of downstream…
An alternative algorithm has been developed for computing the behaviour of flows within arbitrary ducts and channels. This technique requires a small number of downstream marches in the primary flow direction, employing, on each march, numerically efficient procedures originally developed for a single sweep non‐elliptic flow solver. The multiple sweeps allow the capture of effects such as upstream pressure influences and streamwise recirculation. The energy equation is also solved to allow for varying heat transfer between the fluid and the boundary walls. The numerical work is further complicated by considering flows within turning sections of ducts which demonstrate large transverse velocities and consequent distortion of the primary flow. The computations are validated by comparison with a number of fluid/heat transfer experiments. The majority of these are taken from studies of turning flows within circular arc ducts which display the various pressure and transverse flow phenomena for which this new algorithm was initially developed to represent.
The boiler pressure parts are a major asset of a power station, and the maintenance cost is often accountable for a huge portion of the annual budget. In the power…
The boiler pressure parts are a major asset of a power station, and the maintenance cost is often accountable for a huge portion of the annual budget. In the power generation industry, the outage costs due to loss of production, both planned or forced, are very significant and thus it is of interest to seek for a meaningful approach to the management of boiler pressure parts maintenance such that the enterprise performance is optimised. This paper aims to do this.
This paper proposes a framework that introduces the division of the enterprise objectives into the three decision dimensions. The framework is applied to the case of power station boiler pressure parts maintenance to optimise maintenance outages decisions for Loy Yang, a power station in Victoria, Australia.
The study finds that the framework provides meaningful approach to optimising maintenance decisions and is generic for application in different cases.
The paper provides a new insight and integrated approach to optimising asset maintenance for an enterprise with the use of a case study.
Maintenance is constantly challenged to increase productivity by maximizing up‐time and reliability while at the same time reducing maintenance expenditure and investment…
Maintenance is constantly challenged to increase productivity by maximizing up‐time and reliability while at the same time reducing maintenance expenditure and investment. Traditional reliability models are based on detailed statistical analysis of individual component failures. For complex machinery, especially involving many rotable parts, such analyses are difficult and time‐consuming. This paper aims to propose an alternative method for estimating and improving reliability.
The methodology is based on simulating the up‐time of the machine or process as a series of critical modules. Each module is characterized by an empirically derived failure distribution. The simulation model consists of a number of stages including operational up‐time, maintenance down‐time and a user‐interface allowing maintenance and replacement decisions.
Initial analysis performed on aircraft gas‐turbine data yielded an optimal combination of modules out of a pool of multiple spares, resulting in an increased machine up‐time of 16 percent.
The benefits of this methodology are that it is capable of providing reliability trends and forecasts in a short time frame and is based on available data. In addition, it takes into account the rotable nature of many components by tracking the life and service history of individual parts and allowing the user to simulate different combinations of rotables and operating scenarios. Importantly, as more data become available or as greater accuracy is demanded, the model or database can be updated or expanded, thereby approaching the results obtainable by pure statistical reliability analysis.
The model presented provides senior maintenance managers with a decision tool that optimizes the life cycle maintenance cost of complex machinery in a short time frame by taking into account the rotable nature of modules.