Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
  1. Home
  2. Browse subjects
  3. Mathematics
  4. Fluid dynamics and solid mechanics
  5. Content listing

Refine search

Refine search

Access:
Content type:
Publication date:
Apply   From and To filters
Subject: Show more
Journals Show more
Publishers: Show more
Societies Show more
Series: Show more
Collections: Show more

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
Cancel
×

7861 results in Fluid dynamics and solid mechanics

Page 84 of 394

  • 2 - Project Delivery and Supporting Methodologies

  • Manoj Kumar Lal
  • Book:
    Knowledge Driven Development
    Published online:
    20 October 2018
    Print publication:
    08 March 2018, pp 30-45

  • Summary

    This chapter introduces the reader to the technology and domain aspects of an industry and then details it for IT industry. It elaborates the knowledge-based nature of the IT industry and the factors limiting its natural movement to a process-based industry. It then provides a general overview of the IT project delivery. It also lists project delivery methodologies, standards, guidelines and frameworks used in various scenarios to manage the IT projects.

    IT Industry from Technology and Domain Perspective

    Information Technology (IT) is nascent industry. Let us look at various industries and their ages:

  • 1. Agriculture (12,000 years old)

  • 2. Modern Banking Industry (700 years old)

  • 3. Modern Insurance Industry (350 years old)

  • 4. Modern Pharmaceutical Industry (200 years old)

  • 5. Modern Steel Industry (150 years old)

  • 6. Information Technology (60 years old)

    • Industries have matured with research and experience contributing to enrichment of knowledge in the industry. Old industries have matured enough to have standard and well-defined processes for delivery of various projects. Also, their processes are not generic but detailed and prescriptive. For example, in an agricultural plantation, separation between two plants is well-defined and prescriptive. It primarily depends on plant types. But this would not have been the case a few thousand years ago. At that time the practice must have gone through several phases of research and experience to come to the optimal separation between two plants. Agriculture, then, could be termed as a knowledge-based industry but now it is a process-based industry.

    One of the greatest revolutions in the maturity of industries has been the Industrial Revolution where mechanisation proved immensely beneficial for mass production. Another revolution of a similar or even greater scale is being brought about by the introduction of computers (including smartphones and apps), which has started impacting every aspect of our lives. Computers have brought in the IT industry to the fore.

    The IT industry itself is a new industry and is barely 60 years old. IT is a support industry which assists other industries. Predominantly, IT automates manual, repeatable processes via software.

    An industry has two components at the highest level. These are the implements and the way of using the implements effectively. The implement in IT is ‘technology’ and the knowledge about how to effectively use an implement is ‘domain’.

  • 10 - Enabling DevOps

  • Manoj Kumar Lal
  • Book:
    Knowledge Driven Development
    Published online:
    20 October 2018
    Print publication:
    08 March 2018, pp 198-202

  • Summary

    DevOps is becoming an increasingly popular concept, trying to optimise the product lifecycle, rather than the product development lifecycle, where the development team and service management team are seamlessly integrated. This chapter tries to understand the details of DevOps, issues in the product lifecycle it is trying to solve and how KDD supports DevOps.

    What Is DevOps

    Before the advent of the personal computers, it was almost the same team that used to develop the software and maintain it, until its decommissioning. The advent of personal computers and other advancements in technology led to the specialisation of skills and the formation of development, quality assurance and service management teams. The IT organisation structure has been based on functional specialisations since then. This structure initiated the development of the popular handover-takeover mechanism to pass information from one team to the other. This mechanism has limited effectiveness, as during handover-takeover, it is practically impossible to pass on all the knowledge that is gathered by one team to the other. Often, the functional splitting of the project acts as a barrier to the work rather than being the enabler of project delivery as widely observed in Waterfall methodology.

    The Agile methodology has brought significant improvement in the way software is developed. For example, Agile forms a joint project development team which is co-located and works together for the success of the project. Formal handover-takeover is no longer needed as the teams are physically or virtually co-located. This has made a working style possible that was difficult to imagine a couple of years ago. For example, testers in a Sprint team do not usually raise a defect. They come to developers, discuss and resolve the issue directly, saving a lot of effort in this way.

    However, Agile, like Waterfall, focuses on project delivery. Agile hands over the project knowledge in the form of minimal documents and Waterfall hands over exhaustive documentation that may not be kept updated to service management team. Neither of these two scenarios are ideal from the service management team perspective. There is a need to look into optimising the entire lifecycle of the product and not only product development. In the product lifecycle, the three most important stakeholders are:

  • • Development team (includes business analysts), which develops the product.

  • • Quality Assurance team, which ensures the product is as per the specifications of the customer.

  • DYNAMIC RELATIONSHIP BETWEEN THE MUTUAL INTERFERENCE AND GESTATION DELAYS OF A HYBRID TRITROPHIC FOOD CHAIN MODEL

  • Part of
  • RASHMI AGRAWAL, DEBALDEV JANA, RANJIT KUMAR UPADHYAY, V. SREE HARI RAO
  • Journal:
    The ANZIAM Journal / Volume 59 / Issue 3 / January 2018
    Published online by Cambridge University Press:
    26 February 2018, pp. 370-401
    • Article
      • You have access
    • PDF
    • Export citation

  • We have proposed a three-species hybrid food chain model with multiple time delays. The interaction between the prey and the middle predator follows Holling type (HT) II functional response, while the interaction between the top predator and its only food, the middle predator, is taken as a general functional response with the mutual interference schemes, such as Crowley–Martin (CM), Beddington–DeAngelis (BD) and Hassell–Varley (HV) functional responses. We analyse the model system which employs HT II and CM functional responses, and discuss the local and global stability analyses of the coexisting equilibrium solution. The effect of gestation delay on both the middle and top predator has been studied. The dynamics of model systems are affected by both factors: gestation delay and the form of functional responses considered. The theoretical results are supported by appropriate numerical simulations, and bifurcation diagrams are obtained for biologically feasible parameter values. It is interesting from the application point of view to show how an individual delay changes the dynamics of the model system depending on the form of functional response.

  • AN APPROPRIATE APPROACH TO PRICING EUROPEAN-STYLE OPTIONS WITH THE ADOMIAN DECOMPOSITION METHOD

  • Part of
  • ZIWIE KE, JOANNA GOARD, SONG-PING ZHU
  • Journal:
    The ANZIAM Journal / Volume 59 / Issue 3 / January 2018
    Published online by Cambridge University Press:
    26 February 2018, pp. 349-369
    • Article
      • You have access
    • PDF
    • Export citation

  • We study the numerical Adomian decomposition method for the pricing of European options under the well-known Black–Scholes model. However, because of the nondifferentiability of the pay-off function for such options, applying the Adomian decomposition method to the Black–Scholes model is not straightforward. Previous works on this assume that the pay-off function is differentiable or is approximated by a continuous estimation. Upon showing that these approximations lead to incorrect results, we provide a proper approach, in which the singular point is relocated to infinity through a coordinate transformation. Further, we show that our technique can be extended to pricing digital options and European options under the Vasicek interest rate model, in both of which the pay-off functions are singular. Numerical results show that our approach overcomes the difficulty of directly dealing with the singularity within the Adomian decomposition method and gives very accurate results.

  • CALCULATION OF CRITICAL PARAMETERS FOR SPONTANEOUS COMBUSTION FOR SOME COMPLEX GEOMETRIES USING AN INDIRECT NUMERICAL METHOD

  • Part of
  • QUANBING LUO, DONG LIANG, TING REN, JIAN ZHANG
  • Journal:
    The ANZIAM Journal / Volume 59 / Issue 3 / January 2018
    Published online by Cambridge University Press:
    26 February 2018, pp. 402-412
    • Article
      • You have access
    • PDF
    • Export citation

  • In the theory of spontaneous combustion, identifying the critical value of the Frank-Kamenetskii parameter corresponds to solving a bifurcation point problem. There are two different numerical methods used to solve this problem—the direct and indirect numerical methods. The latter finds the bifurcation point by solving a partial differential equation (PDE) problem. This is a better method to find the bifurcation point for complex geometries. This paper improves the indirect numerical method by combining the grid-domain extension method with the matrix equation computation method. We calculate the critical parameters of the Frank-Kamenetskii equation for some complex geometries using the indirect numerical method. Our results show that both the curve of the outer boundary and the height of the geometries have an effect on the values of the critical Frank-Kamenetskii parameter, however, they have little effect on the critical dimensionless temperature.

  • ON A CELL DIVISION EQUATION WITH A LINEAR GROWTH RATE

  • Part of
  • B. VAN BRUNT, A. ALMALKI, T. LYNCH, A. ZAIDI
  • Journal:
    The ANZIAM Journal / Volume 59 / Issue 3 / January 2018
    Published online by Cambridge University Press:
    26 February 2018, pp. 293-312
    • Article
      • You have access
    • PDF
    • Export citation

  • We consider an initial–boundary value problem that involves a partial differential equation with a functional term. The problem is motivated by a cell division model for size structured cell cohorts in which growth and division occur. Although much is known about the large time asymptotic behaviour of solutions to these problems for constant growth rates, general solution techniques are rare. We analyse the case where the growth rate is linear and the division rate is a monomial, and we develop a method to determine the general solution for a general class of initial data. The large time dynamics of solutions for this case are significantly different from the constant growth rate case. We show that solutions approach a time-dependent attracting solution that is periodic in the time variable.

Page 84 of 394