The demands of the plant construction and energy sector after a shorter response time for questions upon newly defined operating points of a turbomachine train are one of the biggest challenges in the service business. This becomes particularly obvious if the future points can only be realized by redesigning the flow-relevant components. Often, it is necessary to have more time to check the dynamic behavior of the train, than in the development of the appropriate revamp measures for the core machine itself.
In addition to the different utilization rates of the affected departments, the causes of the delays often lie in the lack of interface quality between the design/ calculation and train integration team. On top of that, a certain amount of time will be required by manufacturers of the critical components such as gearboxes or drives to perform a lateral check. This lateral check is not only mandatory, in case of a component modification such as changing the transmission ratio or upgrading the drive, but it is also necessary if the coupling between the train components must be changed to ensure torsional stability.
Back when the California high-speed rail project was announced, Elon Musk (CEO of SpaceX and Tesla Inc. and perhaps the most admired tech leader of present day) was not only disappointed with this project, but also introduced an alternative to this system called the Hyperloop in 2012. Since the abstract of this project was introduced, many engineers around the world have started to evaluate the feasibility of this “5th Mode of Transportation”.
The general idea for the Hyperloop consists of a passenger pod operating within a low-pressure environment suspended by air bearings. At the realistic speeds estimated by NASA of 620 mph, the pod will be operating in the transonic region. While Japan’s mag-lev bullet train has succeeded at achieving speeds of up to 374 mph, the scale and complexity of a ground transportation system rising above 600 mph bring to surface an unusual number of engineering challenges. As well, brand new designs such as the one proposed by Musk have a certain amount of risk involved due to this technology inherently having no previous run history on a large scale.
Operation of most liquid-propellant rocket engines, first introduced by Robert Goddard in 1926- is simple. Initially, a fuel and an oxidizer are pumped into a combustion chamber, where they burn to create hot gases of high pressure and high speed. Next, the gases are further accelerated through a nozzle before leaving the engine. Nowadays, liquid propellant propulsion systems still form the back-bone of the majority of space rockets allowing humanity to expand its presence into space. However, one of the big problems in a liquid-propellant rocket engine is cooling the combustion chamber and nozzle, so the cryogenic liquids are first circulated around the super-heated parts to bring the temperature down.
With the blast of the French nuclear power plant a few weeks ago, safety of nuclear power plant designs has fallen under more scrutiny. Although according to sources the blast took place in the turbine hall and no nuclear leak was found, this event has brought more attention to improved design and operation standards.
Following the incident earlier this month Toshiba, a Japanese multinational company, announced the resignation of its chairman following a $6.3 billion loss in their nuclear sector –also withdrawing from the nuclear business. The two back to back events have highlighted the main two problems of nuclear power: high cost and environmental/safety concerns. Said to be a green technology, nuclear power raises concerns with potential nuclear meltdown and risk of safety from toxic waste, accompanying the fact that building a new plant cost around $5,000.00 per kilowatt of capacity with around 6 years of lead time. Each dollar invested on a nuclear power plant has about 2-10 less carbon savings and is 20-40 times slower compared to other alternatives. Yes, evidently nuclear power is found to be very reliable, enabling consistent baseload energy production at any time of day and night. Though, it has been questioned whether this reliability is worth the high cost of nuclear production, in fact all nuclear plants are still operating with 100% subsidized.
The Rotor-Dynamic System of a typical turbomachine consists of rotors, bearings and support structures. The aim of the designer undertaking analysis is to understand the dynamics of the rotating component and its implication. Today the industry practices and specifications rely heavily on the accuracy of rotor-dynamic simulated predictions to progressively reduce empirical iterations and save valuable time (as repeated direct measurements are always not feasible). Be it a centrifugal pump or compressor, steam or gas turbine, motor or generator, the lateral rotor-dynamic behavior is the most critical aspect in determining the reliability and operability. Such analytical predictions are often tackled using computer models and accuracy in representing the physical system is of paramount importance. Prior to analysis it is necessary to create a detailed model, and hence element such as cylindrical, conical , inner bore fillet/chamfer, groove/jut, disk / blade root and shroud, copy/mirror option, bearing element and position definition are built. Stations (rather than nodes) having six DOF (degrees of freedom) are used to model rotor-dynamic systems. Typically for lateral critical analysis each station has four DOF, two each translational and rotational (angular). Decoupled analysis followed by coupled lateral, torsional and axial vibration makes prediction realistic and comprehensive. The mathematical model has four essential components, i.e. rotating shafts with distributed mass and elasticity, disks, bearing and inevitable synchronous imbalance excitation. Components such as impellers, wheels, collars, balance rings, couplings – short axial length and large diameter either keyed or integral on shaft are best modelled as lumped mass. Bearings, dampers, seals, supports, and fluid-induced forces can be simulated with their respective characteristics. Bearing forces are linearized using dynamic stiffness and damping coefficients and together with foundation complete the bearing model. The governing equation of motion for MDOF system require determination of roots (Eigenvalue) and Eigen Vectors. Lateral analyses – such as static deflection and bearing loads, critical speed analysis, critical speed map, unbalance response analysis, whirl speed and stability analysis, torsional modal and time transient analyses are then performed.
Centrifugal and axial compressors must operate within certain parameters dictated by both the constraints of the given application as well as a number of mechanical factors. In general, integrated control systems allow compressors to navigate dynamically within their stable operating range. Typical operating ranges for compressors are represented on a plot of volumetric flow rate versus compression ratio. Compressors have a wide number of applications, from household vacuum cleaners to large 500 MW gas turbine units. Compression ratios found in refrigeration applications are typically around 10:1, while in air conditioners they are usually between 3:1 and 4:1. Of course, multiple compressors can be arranged in series to increase the ratio dramatically to upwards of 40:1 in gas turbine engines. While compressors in different applications range dramatically in their pressure ratios, similar incidents require engineers to carefully evaluate what is the proper operating range for the particular compressor design.
For intensive applications of centrifugal and axial compressors, the phenomenon of surge resides as one of the limiting boundary conditions for the operation of the turbomachine. Essentially, surge is regarded as the phenomena when the energy contained in the gas being compressed exceeds the energy imparted by the rotating blades of the compressor. As a result of the energetic gas overcoming the backpressure, a rapid flow reversal will occur as the gas expands back through the compressor. Once this gas expands back through to the suction of the compressor, the operation of the compressor returns back to normal. However, if preventative measures are not taken by the appropriate controls system or any implemented mechanical interruptions, the compressor will return to a state of surge. This cyclic event is referred to as surge cycling and can result in serious damage to the rotor seals, rotor bearings, driver mechanisms, and overall cycle operation.
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.