It is very important to have Anti-Icing Systems for ground-based gas turbines located in humid climates (where air relative humidity can be more than 80% and dense fog can cause air temperatures to drop below 5 0C). Such climatic conditions lead to ice formation. This ice can plug the inlet filtration system causing a significant drop in pressure in the inlet system, which in turn leads to performance loss. In extreme cases, there is even a possibility that the ice pieces get ingested into the compressor (first blade stage) which may cause foreign object damage. Ice may also cause the disruption of compressor work because of excessive vibration, or surging by decreasing the inlet flow. The major factors that lead to the ice formation in gas turbines are ambient temperature, humidity and droplet size. So, under the climatic conditions which are prone to ice formation, an anti-icing system is employed which heats the inlet air before entering the compressor. Let us discuss some important aspects of Anti-Icing Systems.
The objective of an Anti-Icing System is to prevent or limit the ice formation in the gas turbine inlet path.
Gas Turbine Anti-Icing Systems (GT-AIS) can be categorized in two groups.
Inlet heating systems
Component heating systems
Inlet heating systems operate by transferring heat from a heat source (exhaust gases can be used) to the cold ambient air at the entrance of the gas turbine. If the temperature of inlet air raises sufficiently by this heat transfer, icing cannot form in the gas turbine intake.
AxCYCLE™ is a tool, which provides the flexibility and convenience to study various parameters and understand the performance of thermodynamic cycles.
Steam turbine seals are parts inserted between moving and stationary components, to reduce and prevent steam leakage and air leaking into the low pressure areas. The leakage can happen through vane, gland, and shaft, etc. To reduce leakage from those parts while guaranteeing smooth operation of a steam turbine, engineers have to design these seals, taking into account not only efficiency, but also mechanical strength, vibration and cost.
As an example, steam turbine flow path seals improve overall efficiency installing various types of shrouds, diaphragms, and end seals which prevent idle leaks of working steam in the cylinders. In steam turbines, labyrinth seals are widely used. Some labyrinth seals are also used with honeycomb inserts. It is believed that the use of such seals makes it possible to achieve a certain gain due to smaller leaks of working fluid and more reliable operation of the system under the conditions in which the rotor’s rotating parts may rub against the stator elements. However, we can only consider it as a successful design if the structures are compliant with the manufacturing capabilities and have good vibration stability.  Furthermore, seal leakage can significantly affect efficiencies. Better seals increase efficiencies but add extra cost to both manufacturing and maintenance, so the design needs to be done with the turbine flow path design. Although modeling the seals in 3D CFD is theoretically possible, the calculation resources and time are extremely demanding.
This important task can be completed very easily with AxSTREAM NETTM. AxSTREAM NETTM provides a flexible method to represent fluid path and solid structure as a set of 1D elements, which can be connected to each other to form a thermal-fluid network. For each fluid path section, the program calculates fluid flow parameters for inlet and outlet cross-sections, like velocity, density, temperature, mass flow rate, etc. Therefore, the leakage from the whole system can be modeled in this network, as shown in Figure 1.
The steam turbine is one of the most important power generating equipment items in use. Around half of the electricity generated worldwide comes from steam turbines. Steam turbines can be fueled by coal, nuclear energy, petroleum or natural gas, alternatively by biomass, solar energy or geothermal energy. Thus a large amount of fuel can be saved and CO2 emissions significantly reduced by optimizing key components of these widely used machines.
An important goal in steam turbine technology is to improve efficiency. The continuous flow of steam conditions is one of the commonly accepted efficiency contributor for steam power plants. The chart below shows expected improvement in thermal efficiency for USC double-reheat fossil-fuel power units compared to common supercritical-pressure ones, according to Hitachi.
This might seem like a strange question, but we get ask this a lot. The question takes the form of: Can the sales side do a proper preliminary design and select the optimal machine (turbine/compressor/pump)? Is it possible for the design and application task to be integrated in a way allowing the application team the autonomy to make decisions without going back to the engineering team every time they get an inquiry? After realizing how large of a pain point this is for our clients, we decided to solve this problem for a major turbine manufacturer in Asia and in the process, provided a time-saving solution to maximize the returns for all the stakeholders.
The challenge came with the different competencies of the sales and design team. The sales/application teams are not necessarily experts in design while designers cannot double as application engineers to meet the sales requirements.
In our efforts to solve this issue, we worked with this turbine manufacturer. We listed all of their current processes, limitation, requirements, constraints, and etc. to explore the many possible ways to resolve this pain point. In the end, there were two solutions; (1) Develop custom selection software, or (2) Leverage the AxSTREAM® platform using AxSTREAM ION™.
Developing Custom Selection Software: Developing a custom selection software specific to the manufacturer where their application team can choose the optimal turbine based on expected customer needs. Developing such a custom system requires bringing together the expertise of different teams from turbomachinery (such as aero-thermal and structural) to software developer, testing, etc. Developing such a one-off system also takes considerable time at considerable cost. This approach could solve the current problem, but with rapidly changing technologies and market requirements, this is not a viable long-term solution.
Leverage the AxSTREAM® Platform using AxSTREAM ION™: We evaluated the limitation and possibilities of utilizing our turbomachinery design platform AxSTREAM® to meet the requirement of sales/application engineering team for today’s needs and in the future. We found the organization had a greater advantage using this existing platform rather than investing in the short-term solution of developing a custom selection software. Many of the building blocks required for customization are already available to use via an interface a non-technical sales person could easily use. This platform was utilized for meeting the requirement of this turbine manufacturer saving time and cost while resolving a large pain-point for the organization.
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.