The evolution of turbomachinery technology can be traced back several centuries and has resulted in the high efficiency turbomachines of today. Since the 1940s, turbomachinery development has been led mainly by gas turbine and aeroengine development, and the growth in power within the past 60 years has been dramatic. The development of numerical methods and the increasing computing capacity helped establish a strong design capability in the industry.
The first numerical methods related to turbomachinery were developed years before the use of digital computations. In 1951 Wu  introduced the blade-to-blade (S1) and hub-to-tip (S2) stream surfaces, which dominated the field until the 1980s when computer resources made it possible to account for 3D methods. The axisymmetric S2 calculations, also called “throughflow calculation” became the backbone of turbomachinery design, while the S1 calculation remains the basis for defining the detailed blade shape.
In my earlier blog titled “Optimizing the Cooling Holes in Gas Turbine Blades, I wrote about how optimizing the cooling flow through turbine blades is important considering both performance and reliability. The design process differs between different designers and depends on a number of factors including expertise, availability of design tools, statistical or empirical data, corporate procedure and so on. That being said, the ultimate goal is to provide a design which is considered optimal. Though the designer is often satisfied on completion of a design and when the machine is put into operation, there is always the feeling that we could have done better if there were more resources and time. Integrating the entire design process with multidisciplinary optimization provides a great opportunity to arrive at the optimal design rapidly with less manual intervention and effort.
Figure 1 shows the integrated approach to design a cooled gas turbine using multidisciplinary tools in an optimization environment. The flow path design starts from the conceptual stage to arrive at the optimal flow path geometry, accounting for a preliminary estimate of the cooling flow. Detailed design requires accurate estimation of the cooling flow considering the actual geometries and the material temperatures. Using ID head and flow simulation tools such as AxSTREAM® NET, the cooling flow can be modelled to produce the optimal geometric dimension in an iterative process to further fine tune the flow path performance. To meet the performance and reliability objectives, multidisciplinary optimization can be achieved via the integrated modules. The process when further integrated with a CAD package can help in generating the optimized geometry that can be taken for prototype development.
This post is based on DeLuca’s publication about fatigue phenomena in gas turbines . One of the most significant characteristics of a gas turbine is its durability. Especially for the aerospace industry where engines must meet not only propulsion but also safety requirements, the failure of gas turbine blades is a major concern. The “cyclic” loading of the components associated with generator excursions is one of the principal sources of degradation in turbomachinery. In addition, fatigue can be caused during the manufacturing of the components. There are three commonly recognized forms of fatigue: high cycle fatigue (HCF), low cycle fatigue (LCF) and thermal mechanical fatigue (TMF).The principal distinction between HCF and LCF is the region of the stress strain curve (Figure 1) where the repetitive application of the load (and resultant deformation or strain) is taking place.
HCF is metal fatigue that results from cracking or fracturing generally characterized by the failure of small cracks at stress levels substantially lower than stresses associated with steady loading. HCF occurs as a result from a combination of steady stress, vibratory stress and material imperfections . It is initiated by the formation of a small, often microscopic, crack. HCF is characterized by low amplitude high frequency elastic strains. An example of this would be an aerofoil subjected to repeated bending. One source of this bending occurs as a compressor or turbine blade passes behind a stator vane. When the blade emerges into the gas path it is bent by high velocity gas pressure. Changes in rotor speed change the frequency of blade loading. The excitation will, at some point, match the blade’s resonant frequency which will cause the amplitude of vibration to increase significantly.
An important first step in understanding the gas turbine design process is the knowledge of how individual components act given their particular boundary conditions. However, in order to effectively leverage these individual design processes, a basic knowledge of how these components interact with each other is essential to the overall performance of a gas turbine unit. The power and efficiency outputs of a gas turbine are the result of a complex interaction between different turbomachines and a combustion system. Therefore, performance metrics for a gas turbine are not only based on the respective performances of each turbine, compressor, and combustion system, but also on their interactions. The concept of component matching becomes crucial in understanding how to deal with these systems simultaneously.
In general, gas turbines for industrial applications consist of a compressor, a power turbine, and a gas generator turbine designed into one of two arrangements. The first arrangement invokes the use of the gas generator turbine to drive the air compressor, and a power turbine to load the generator on a separate shaft. This two-shaft arrangement allows the speed of the gas generator turbine to only depend on the load applied to the engine. On a single-shaft arrangement, the system obviously cannot exist at varied speeds and the power turbine coupled with the gas generator turbine would be responsible for driving both the generator and the compressor. A simplified diagram of each arrangement is displayed in Figures 1 and 2.
Heat recovery steam generators (HRSGs) are used in power generation to recover heat from hot flue gases (500-600 °C), usually originating from a gas turbine or diesel engine. The HRSG consists of the same heat transfer surfaces as other boilers, except for the furnace. Since no fuel is combusted in a HRSG, the HRSG have convention based evaporator surfaces, where water evaporates into steam. A HRSG can have a horizontal or vertical layout, depending on the available space. When designing a HRSG, the following issues should be considered:
The pinch-point of the evaporator and the approach temperature of the economizer
The pressure drop of the flue gas side of the boiler
Optimization of the heating surfaces
The pinch-point (the smallest temperature difference between the two streams in a system of heat exchangers) is found in the evaporator, and is usually 6-10 °C, which can be seen in Figure 2. To maximize the steam power of the boiler, the pinch-point must be chosen as small as possible. The approach temperature is the temperature difference of the input temperature in the evaporator and the output of the economizer. This is often 0-5 °C.
The increased use of CFD for turbomachinery design is an outcome of the increasing accuracy thanks to high computational resources. Although the benefits of such computations are strong, the approximations and errors derived from CFD could significantly affect the prediction of crucial parameters such as flow temperature and heat transfer. This article will present the challenges related to uncertainties in turbomachinery CFD, based on “Uncertainty Quantification in Computational Fluid Dynamics and Aircraft Engines” 
The exact definition of boundary conditions presents one of the biggest challenges in CFD and turbomachinery given the high accuracy needed to determine the distributions of the non-uniform conditions to which turbomachinery components are subjected .
Global warming is a very popular topic at the present time. With the upwards trend of clean technology and the realization that strict climate policy should be implemented, demand of renewable energy has sky-rocketed while conservative plant popularity continues to fall. Additionally, the number of coal power plants have significantly dropped since its peak era, as they are now known as the largest pollutant contribution, producing nitrogen, sulfur oxide and carbon dioxides.
Renewable energy comes from many sources: hydropower, wind power, geothermal energy, bioenergy and many more. The ability to replenish and have no limit on usage and application makes renewable energy implementation attractive. To make this even better, it also produces low emission. Theoretically, with the usage of renewable energy, human-kind should be able to meet their energy needs with minimal environmental damage. With growth rates ranging from 10% to 60% annually, renewable energy is getting cheaper through the technological improvements as well as market competition. In the end, the main goal is to maximize profit while minimizing our carbon footprint. Since the technology is relatively new, capital costs are still considerably higher compared to more traditional (–and naturally harmful) implementations. This begs the question of exactly how we maximize the economic potential of a renewable energy power generation plant.
Tip leakage is generated inevitably by the clearance between the rotating blades and the stationary casing of a turbine, and is responsible for both the aerodynamic losses in a turbine stage and the high heat-loads in the tip region . To decrease tip leakage and improve component performance, shroud seal structures have been widely applied to modern turbine components, especially to low pressure turbines, because of their advantage on both aerodynamic and structural features. However, due to the complexity of the shroud geometry, the flow structures and thermodynamic process in shroud can be extremely complicated, that is interactions of vortices, separations, jet flow, etc. Thus, because of the complex geometry of shrouds, as well as strong interactions between the tip leakage and main flow, it is not easy to draw a numerical simulation with satisfactory accuracy and time-costing in shrouded turbines. This begs the question of what should the compromise be between using simplified loss models and full 3D CFD analysis for leakage modelling?
In the main flow path of a turbine the flow will always be dominated by the blades shape, while for leakage cases the flow will be dominated by the motion and evolution of small eddies. Rosic et al.  reviewed the importance of shroud leakage modelling in multistage turbines. The comparison of measurements and 3D calculations shows that the flow in shrouded low aspect ratio turbines is dominated by shroud leakage. This is especially true as regards the loss distribution. The rotor shroud leakage flow greatly increases the secondary flow in the downstream stators and drives low energy fluid towards mid-span. It was pointed out that with very low values of shroud leakage the flow is reasonably well modelled by a simple 1D model of the leakage flow, using sources and sinks on the casing. However, for more representative real clearances, full 3D modelling of the seal and cavity flows is necessary in order to obtain reasonable agreement. Given that developing a simulation method with both high precision and fast solving speed is imperatively demanded for engineers to assess new designs, Zhengping Zou et al.  suggested that one of the potential approaches for solving the problem is a method that couples low dimensional models, 1D and 2D models, of the shroud flow with 3D (three-dimensional) simulations of the main flow passage. Specifically, some boundary source and boundary sink is set on the interface between the shroud and the main flow passage, and the source term and sink term are determined by the shroud leakage model. The schematic of this process is given in Fig. 1. The results of his study  demonstrate that the proposed models and methods will contribute to pursue deeper understanding and better design methods of shrouded axial turbines.
The term, “mixed flow compressor”, refers to a type of compressor that combines axial and radial flow paths. This phenomenon produces a fluid outflow angle somewhere between 0 and 90 degrees with respect to the inlet path. Referred to as the meridional exit angle, the angled outflow of this mixed flow configuration possesses the advantages of both axial and centrifugal compressors. Axial compressors can produce higher order efficiencies for gas engines, but they have relatively low-pressure ratios unless compounded into several stages. Centrifugal compressors can produce high-pressure ratios in a single stage, but they suffer from a drop in efficiency. The geometrical distinction of mixed flow compressors allows for higher efficiencies while maintaining a limited cross-sectional area. The trade-off for a mixed flow compressor when introduced to aero gas turbines is that there is an associated weight increase due to the longer impellers needed to cover this diagonal surface. However, when related to smaller gas turbines, the weight increase becomes less significant to the overall performance of the engine.
In recent days, many people find themselves spending time and resources on uncovering the best solution to optimize the power generation cycle. Until recently, 80% of power plants worldwide (whether fossil fuel, nuclear, or clean technology) used steam as its main working fluid and while it is still the most common option, today’s power plants are finding another fluid to use.
Although supercritical CO2 study began in the 1940’s, it was disregarded as an alternative fluid option because it was expensive to explore and steam was still perfectly reliable at the time. Nowadays due to increasing quantity and quality demand in power, researchers are looking into the possibility of replacing steam with supercritical carbon dioxide. The discover of this property, increases the incentive of exploring the technology further. This year, the US Department of Energy is awarding up to $80 million towards projects to build and operate a supercritical CO2 plant.