The concept of using gas turbines to power a car is not new. In fact, for many decades now, various car manufacturers have experimented with the idea of using either axial or radial gas turbines as the main propulsion of concept vehicles. In the 50’s and 60’s it was Fiat and Chrysler who introduced such concept cars. In those cases, the gas turbine was directly powering the wheels for propulsion. Toyota followed the same concept in the 80’s (Figure 1) . Their concept car utilized a radial turbine in order to propel the vehicle using an advanced electronically controlled transmission system.The main advantage of a gas turbine compared with conventional reciprocating (or even rotary) car engines is the fact that it has a much higher power-to-weight ratio. This means that for the same engine weight, a gas turbine is able to deliver much higher power output. This is why aviation was one of the biggest adopters of this technology.
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.
All centrifugal compressor designers want to achieve the highest efficiency as well as wide operating range. With this in mind, the inlet guide vane (IGV) is a convenient and economic option for various applications.
IGVs are a series of blades circumferentially arranged at the inlet of compressor, driven electronically or pneumatically.By adjusting the orientation of IGVs, the air flow enters the impeller at a different direction therefore changing the flow behavior while affecting the passing mass flow rate (throttling). This can effectively reduce the power consumption to increase the compressor’s overall efficiency while avoiding surge to provide a better off design working range.
The designer needs to optimize blade profile and positioning of the IGV for efficient operation of a compressor, which can be a tedious job if one does not have a handy tool. Figure 1 shows an example of IGV working on different angles.
In AxSTREAM, people are able to add IGV component before the centrifugal compressor impeller which can provide different ways to edit its profile such as: 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.