[:en]Blade Shape Optimization[:]

An Example of Design of Experiment Study Methods in AxSTREAM

One of the most challenging tasks during turbomachinery design is the definition of aerodynamic shape of the blades, taking into account the complicated flow phenomena and the effect that the shape will have to other disciplines of the design. The rapid increase of computational resources along with the development of CFD has led to a big interference of optimization methods and numerical simulations as part of the design process. There are two main categories in which optimization methods fall: the stochastic models and the gradient-based models. The first family of models focuses on finding the optimum design, while the second uses the gradient information to lead the optimization. Apart from the optimization algorithms, there are several techniques that help designers understand the dependence of design parameters towards others and extract meaningful information for the design. First, the design of experiment approach (DoE) consists of the design of any task that aims to describe or explain the variation of information for conditions that are hypothesized to reflect the variation. Next, we have the surrogate models that are used instead of the optimization algorithms to generate a model that is as accurate as possible while using as few simulation evaluations as possible with low computational cost. The most common surrogate models used for turbomachinery design are the Response Surface Method, the Kriging Model and the Artificial Neural Networks. Last, data mining approaches have recently become very popular as they allow engineers to look for patterns in large data sets to extract information and transform it into an understandable structure for further use.

As far as the aerodynamic design optimization methods is concerned, they can be grouped into inverse and direct designs. Inverse methods rely on definition of pressure distribution and they iterate along blade shape, changing to develop a final profile shape. The computational cost is low and such methods can be combined with an optimization method in an efficient design process. However, the biggest disadvantages lies on the fact that this approach is strongly dependent on the experience of the designer. Young engineers may fail to define a pressure distribution that performs well in design and off-design conditions. In addition, with the inverse method approach the user cannot account for geometric and mechanical constraints.

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Importance of Preliminary Design for Centrifugal Compressors

Preliminary Design in AxSTREAM

Centrifugal compressors span a number of applications including oil compression systems, gas shift systems, HVAC, refrigeration, and turbochargers. It works by using energy from the flow to raise pressure, using gas to enter the primary suction eye (impeller). As the impeller rotates, the blades on the impeller push the gas outwards from the center to the open end of impeller to form a compression. Compressors are commonly used for combustion air supplies on cooling and drying systems. In HVAC system application, fans produce air movement to the space that is being conditioned. As a key component of an energy cycle, design/performance requirement must be met. While a design can easily be scaled from an existing design through appropriate parameters, a tailored design from scratch to confirm with design requirement for the specific cycle would give a better match and improve overall cycle performance.

There are variants of non-aerodynamic constraints in centrifugal compressor design practice, from frame size to durability and ultimately cost. An optimized impeller design should also ensure that aerodynamic problems associated with the all compressor components are minimized. With all of these (aerodynamic and non-aerodynamic) design constraints, there is no better way to optimize your compressor design than starting from the preliminary step, making sure that your compressor meets your criteria from a one dimensional basis ( a step that is often overlooked in practice).
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[:en]SoftInWay’s Role in Meeting the Future Needs of Steam Turbine Industry[:]

[:en]Steam turbine technology has advanced significantly since it was first developed by Sir Charles Parson in 1884 [1]. The concept of impulse steam turbines was first demonstrated by Karl Gustaf Patrik de Laval in 1887. A pressure compounded steam turbine based on in de laval principle was developed by Auguste Rateau in 1896. Westinghouse was one of the earliest licensee for manufacturing steam turbines obtained from Sir Charles Parson and became one of the earliest Original Equipment Manufacturers (OEM) in power generation and transmission.

Over the years, as steam turbine technology advanced, the design principles were based on either impulse type or reaction type with reaction type being more efficient. Though impulse was not as efficient as reaction type, it gained popularity due to lower cost and compact size. With advances in design and optimization methods being employed, the efficiency levels between these two types are not very distant, ranging between 2 – 5% based on the size and application. Read More

An Insight into Organic Rankine Cycle Design

[:en]Nowadays, organic Rankine cycles (ORCs) are a widely studied technology. Currently, several research and academic institutions are focused on the design, optimization, and dynamic simulation of this kind of system. Regarding the numerical analysis of an ORC, several steps are required to select the optimal working fluid and the best cycle configuration, taking into account not only nominal performance indexes, but also economic aspects, off-design efficiency, the dynamic behaviour of the plant, and the plant volume or weight.

To begin, a detailed description of the heat source and heat sink, evaluation of all the technical constraints (component selection or plant layout), and both environmental and safety issues is needed. The most significant stage of the design is definitely the correct choice with both working fluid and cycle configuration. Making the wrong choice at this stage will result in poor cycle performance. A huge number of possible working fluids can be selected for ORC systems, which is one of the major advantages of these systems since they can be suitable for almost every heat source but, on the other hand, it makes the resolution of the optimization problem inevitably more complicated. Read More