The processes of power plant design, enlargement, and redesign must consider certain factors, such as technological scheme, basic cycle parameters, equipment configuration, and fuel type. These factors have long reached beyond the scope of the technical and physical, and must consider economic criteria. Economic indicators are fundamental when selecting a specific solution. Therefore, even at the initial stages of a project, engineering problems should be considered in parallel with the assessment of economic efficiency. In addition, a power plant is a very complex entity, and introductory capital costs cannot be the only economic criteria considered. The economic indexes over the entire lifecycle of the plant must be accounted for.
The modern world has seen extensive investment in the field of cost estimation. The approximate estimation of cost and economic efficiency of a power plant, however, is a complicated and time-consuming process. It demands a high level of knowledge and information.
In order to simplify this process, and make it available for the engineering community, SoftInWay, a leading turbomachinery solutions provider, developed the new AxCYCLE Module for Economic Analysis. This webinar will demonstrate the module and discuss its extensive capabilities and applications.
We look forward to a great webinar and your challenging questions. Please register ahead of time and if you have any specific questions, let us know during the registration so that we can try to incorporate the answers into our presentation.
It is common knowledge that CFD analyses are more of a “see you tomorrow” affair than an “I’ll grab a coffee and I’ll be back”.
Although the fairly recent developments in electronics allow for more computing power while being more affordable, it can still take a significant amount of time to run a good CFD case.
One of the main advantages of running CFD is that there is no need to have an actual, manufactured prototype in order to run an experiment. Prototypes have been known to be mainly restricted to companies/individuals that had manufacturing capabilities and quite a lot of money on their hands. However, with recent advancements like 3D printing, this prototyping is not only possible but is also relatively fast (and getting faster everyday with new techniques being developed).
It comes to a point where it is worth evaluating, qualitatively, each method, however different they actually are.
Although CFD is an extremely common practice in modern day engineering and is immensely useful, it tends to sometimes completely replace actual prototyping and this can create some issues… Indeed, CFD is neither an exact science nor it is always “cheap” (some complex problems can easily cost several thousand dollars in computing costs) but either way it sure has its perks. These two arguments are unfortunately largely part of a general misconception of CFD that decision makers and the younger generation of engineers are often victims of. When managers are given the choice between purchasing a software that can supposedly simulate any physical problem (CFD case) and a machine that can physically build components (manufacturing case) the upfront cost strongly leads these decision makers to adopt the first option.
However, CFD does not always suffice. Results of CFD analyses are influenced by numerical and modeling errors, unknown boundary conditions or geometry and more. Refining your mesh is becoming easier and ultimately leads to reduced numerical errors while, at the same time, increasing your calculation time. Modeling errors can come from misuse or inaccuracy of certain models when trying to simulate real, complex physics like turbulence. And so on to the point that different codes and even different engineers can find some minor discrepancies in the final results of the same case.
This means that less experienced engineers tend to over-trust their results, thinking of CFD as the universal answer to every physical problem. To place (smartly) more confidence in CFD results the codes should be calibrated and corrected based on experimental results that do require prototyping at some point unless a product is wrongly put on the market without proper physical testing – which can happen, unfortunately. Comparing both an original and an optimized geometry in CFD is perfectly possible and realistic but as for any solver a baseline should be created. One cannot simply say he has improved the efficiency of a machine by 2% if the original machine was not analyzed beforehand.
Calibration of the CFD models is based on available data from experiments and this data is often very limited compared to the results that CFD can provide. While a physical test would provide values like power as well as some pressures and temperatures in most cases, CFD analyses can go way beyond this by providing parameters distributions, flow recirculation areas, representation of the boundary layer appearing on the surfaces, etc. that allow getting a good understanding of what is happening to the flow within the machine, which is something that definitely cannot be appreciated in most experimental runs. Beside the mentioned disadvantages that 3D printing has, an important one that is shared with CFD is that the time needed to build a geometry strongly depends on its size. However, CFD can deal with the repetition of an element in a row fairly accurately while the entire wheel has to be manufactured to be analyzed. This sort of restrains rapid prototyping to smaller machines, at the moment.
For these reasons and despite all these “warnings”, CFD remains and will remain an essential engineering tool that provides a good comparison of cases rather than a truly accurate representation of the reality we live in. As a conclusion, CFD still continues to evolve with the recent technological developments and should be supplemented with experimental testing instead of substituting it.
Companies utilize different principles to design new turbomachinery. A design exercise is an extremely complex task and requires knowledge of many design trade-offs. This article is intended to reveal preliminary design philosophy and clarify some mysteries in this fast solution method.
Let’s define a few terms first. Boundary conditions (BCs) are the inlet and outlet states of a working fluid. Design inputs are small number of variables that are necessary to begin the design exercise. SoftInWay identifies BCs, design mass flow rate, rotational speed, and a few dimensions as the design inputs. The Preliminary design is a tool for quickly assessing design outputs giving many sets of design inputs. The algorithm utilized in the Preliminary design tool is an inverse solver. Inverse solution in this context implies finding geometry of interest knowing a very few design inputs.
How stuff works? The whole process comes down to estimating losses in each component and then calculating fluid states and component geometry applying simple kinematics and conservation equations. Calculated geometry and states are used to find real losses from loss models. This loss model results are compared with the guessed values and the algorithm repeats until they agree. In a practical implementation, however, the solution scheme will be more comprehensive but underlying principle remains the same — design output heavily relies on the models.
Loss models are extremely important and they determine the range of applicability for an industrial code. The models are collective work of many scientists and designers. Usually, they are some empirical correlations serving large family of components and predicting real machine performance quite well. Can we trust the results? That raises a lot of concerns and skepticism. The predictions are as good as the models that describe the physical processes. Verification and validation plays vital role in the developing of the code. The industry trend is to rely on published scientific data as a first iteration and calibrate models while working on real projects. Range of applicability is determined for each empirical correlation. For example, the veteran of compressor design Ronald Aungier shows that his loss model with respect to return channel in centrifugal stage has good agreement with experiment (Figure 1). Therefore, Aungier’s model can be used for similar machines.
Figure 1 — Loss in optimized return system design
Preliminary design space study — know your limits! When an aerodynamicist is given specification on a new piece of machinery, he/she does not know anything about all the details of the design. Preliminary design can quickly show achievable performance for the machine, estimate critical relationship between design inputs and outputs, and facilitate in determining trends and trade-offs. Design space is a set of many preliminary designs. Because inverse solver is fast, a designer can generate thousands of designs in the matter of eye blink. Moreover, set of mathematical statements and state-of-the art aerodynamic reasoning allows outputting three dimensional geometry for each preliminary design with properly sized components. Ultimately, exploring the design space will eliminate costly mistakes prior to detailed design is carried on.
Myths and misconceptions about preliminary design. Inverse solver does not solve potential flow problem. Inverse task does not perform boundary layer analysis. Preliminary design is not a Navier-Stoks solver. Inverse design is not a table look-up but utilizes empirical loss model in the tested and verified domain. At the same time, preliminary design is not a blade-to-blade analysis tool. Preliminary design is a good starting point for further detailed design and analysis including blade profiling, performance map generation, impeller design, structural analysis, and CFD. All the above can be accomplished within one integrated design environment such AxSTREAM.
Our next webinar will be held on February 26th and cover the best industry practices when it comes to power plant redesign. The constant increase of global energy consumption and rising cost of fuel require higher energy generating capacity with a simultaneous improvement of the efficiency of energy conversion processes. The greatest effect of improving the performance of existing power plants and other energy systems can be obtained by modifying the thermodynamic cycles of these plants.
The 43rd Turbomachinery and 30th Pump Symposia are quickly approaching. The 3-day event will begin on September 22nd in Houston, Texas at the George R. Brown Convention Center. The Symposia are organized in order to promote professional development, technology transfer, peer networking and information exchange among industry professionals. The event serves as a premier training and networking opportunity for professionals in both the turbomachinery and pump industries.
Let’s face it, we know the operations of our gas turbines can’t all be perfect, and we’ll run through calculations, feasibility studies and more to pinpoint the exact cause. But before all of that is accomplished, you should keep a list in the back of your mind of what might be causing your loss in performance, based on common factors that affect gas turbine efficiency and more.