Bottoming cycles are generating a real interest in a world where resources are becoming scarcer and the environmental footprint of power plants is becoming more controlled. With this in mind, reduction of flue gas temperature, power generation boost, and even production of heat for cogeneration application is very attractive and it becomes necessary to quantify how much can really be extracted from a simple cycle to be converted to a combined configuration.
Supercritical CO2 is becoming an ideal working fluid primarily due to two factors. First, turbomachines are being designed to be significantly more compact. Second, the fluid operates at a high thermal efficiency in the cycles. These two factors create an increased interest in its various applications. Evaluating the option of combined gas and supercritical CO2 cycles for different gas turbine sizes, gas turbine exhaust gas temperatures and configurations of bottoming cycle type becomes an essential step toward creating guidelines for the question, “how much more can I get with what I have?” Read More
When people design turbomachines, whether it be a turbine, compressor, blower or fan, they need to find the optimal design based on their criterion under certain constraints.
With AxSTREAM®, people are given several options for their design criteria, which provides flexibility. With that being said, we often get asked what their differences are and here is a brief explanation addressing just that.
The design criteria menu includes power, internal total-to-static efficiency, internal total-to-total efficiency, polytropic efficiency, diagram total-to-static efficiency, and diagram total-to-total efficiency as shown in Figure 1
Power and efficiency are related, but not always the same thing, especially when the boundary conditions are not fixed as design parameters. In AxSTREAM’s Preliminary Design Module, the user can set boundary conditions such as pressure at inlet and outlet, inlet total temperature, etc., as a range instead of a specific value. Along with other parameters, the solver generates hundreds or even thousands of solutions within the range.
This being my last post for 2017, I wanted to do a short review of what we have been discussing this year. During the beginning of the year, I decided to focus on the 3D analyses and capabilities that were implemented in our AxCFD and AxSTRESS modules for fluid and structural dynamics. With that in mind, my posts were tailored towards such, highlighting the importance of the right turbulence modelling for correct flow prediction. Among other topics, we studied the key factors that lead to resonance, the importance of not neglecting the energy transfer between fluid and structure, and the great advantage that increasing computing capacity offers to engineers in order to understand turbomachinery in depth. However, no matter how great the benefits are, the approximations and errors from CFD can still lead to high uncertainty. Together, we identified the most important factors, from boundary conditions all the way to mesh generation and simulation of cooling flows, and we put an emphasis on the necessary development of uncertainty quantification models. This 3D module related topic finished with an extensive article on fatigue in turbomachinery which plays a crucial role in the failure of the machine, and was the cause for many accidents in the past.
The second part of my posts focused on different industries that rely on turbomachinery as we tried to identify the challenges that they face. Being fascinated by the space industry along with the increasing interest of the global market for launching more rockets for different purposes, I started this chapter with the description of a liquid rocket propulsion system and how this can be designed or optimized using the AxSTREAM platform. Moving a step closer to earth, next I focused on the aerospace industry and the necessity for robust aircraft engines that are optimized, highly efficient, and absolutely safe. One of the articles that I enjoyed the most referred to helicopters and the constant threats that could affect the engine performance, the overall operation and the safety of the passengers. Dust, salt and ice are only a few of the elements that could affect the operation of the rotating components of the helicopter engine, which allows us understand how delicate this sophisticated and versatile aircraft is. Read More
I just received a question from a consulting company asking for our help: “What is the effect of the gap between the rotor blades and the casing on the performance of the machine?” To answer this question you need to have the right tools and the right experience. At SoftInWay we have both and this is why our customer are satisfied by the speed and quality of our services.
To go back to the question, blade tip losses represent a major efficiency penalty in a turbine rotor. These losses are presently controlled by maintaining close tolerances on tip clearances. Tip leakage resulting by gaps between the blade tip and the casing can account for about 1/3 of the total losses in a turbine stage. The reason is mainly the offloading of the tip since the leaking fluid is not exerting a force on the blade, as well as the generation of complicated flow further downstream due to the leakage vortex.
Supersonic axial turbines have attracted interest in the industry since the 1950s due to the high power they provide, allowing a reduction in the number of low-pressure stages, and thus leading to lighter turbines as well as lower manufacturing and operational costs. Due to these valuable features, supersonic axial turbines are currently widely used in different power generation and mechanical drive fields such as rocket engine turbopumps [1, 2, 3, 4], control stages in high pressure multi-stage steam turbines, standalone single stage and 2-row velocity compound steam turbines [5, 6], ORC turbo-generator including geothermal binary power stations [7, 8, 9, 10], turbochargers for large diesel engines  and other applications. Therefore it is not forgotten, but instead a very important field in turbomachinery when highest specific power, compactness, low weight, low cost and ease of maintenance are dominant requirements. Especially nowadays, when development of small capacity reusable low-cost rocket launchers, compact and powerful waste heat recovery (WHR) units in the automotive industry, distributed power generation, and other fields are in extreme demand.
Typically, supersonic turbine consists of supersonic nozzles with a subsonic inlet and one or two rows of rotating blades. The turbine usually has partial arc admission. The total flow could go through either a single partial arc or several ones. The latter is typical for a steam turbine control stage or standalone applications. The inlet manifold or nozzles chests, as well as exhaust duct, are critical parts of the turbine as well. Due to the very frequent application of partial admission, it is not possible to implement any significant reaction degree. Thus, this kind of turbine is almost always an impulse type. However, some reaction degree could still be applied to full admission turbines. The influence of the rotor blades profile designed for high reaction degree on rotor-stator supersonic interaction and turbine performance is not well studied at the moment.
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.
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). Read More
Steam turbine technology has advanced significantly since it was first developed by Sir Charles Parson in 1884 . 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
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