Multi-Dimensional Coupling CFD Method for Shrouded Turbines

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 [2]. 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. [1] 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. [2] 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 [2] demonstrate that the proposed models and methods will contribute to pursue deeper understanding and better design methods of shrouded axial turbines.

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Figure 1: (a) Schematic of full 3D computation; (b) Schematic of multi-dimensional coupling simulation. [2]
 Check out AxSTREAM CFD for your designing needs!

References:

[1] “The Importance of Shroud Leakage Modeling in Multistage Turbine Flow Calculations”, Budimir Rosic, John D. Denton, and Graham Pullan, Journal of Turbomachinery, Vol 128, pp. 699-707, October 2006

[2] “Shroud leakage flow models and a multi-dimensional coupling CFD (computational fluid dynamics) method for shrouded turbines”, Zhengping Zou, Jingyuan Liu, Weihao Zhang, and Peng Wang, Energy journal, Vol 103, pp. 410-249

Feasibility of Mixed Flow Compressors in Aero Engines

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.

Figure 1 - Mixed Flow Compressor Arrangement in AxSTREAM
Figure 1 – Mixed Flow Compressor Arrangement in AxSTREAM

Since the advent of more advanced Unmanned Air Vehicles (UAVs) in the 1990’s, successful integration of gas turbines into these aircrafts required high performance and lower cross-sectional areas. These requirements facilitated the introduction of mixed flow compressors as a strategic alternative. In order to analyze the feasibility of these types of compressors for aero engines, several tactics must be put in place to ensure the design is both effective and reliable. With the use of a structured database and various analysis methods, the designer can ensure an accurate study of this proposed alternative for smaller gas turbines. Design of Experiment (DoE) methods study the effect that multiple variables have on the outcome of the system simultaneously. Multiple parameters must be considered before considering this mixed flow arrangement as a feasible design. The engineer must look at the variation of the pressure ratio and flow coefficient with the meridional exit flow angle. As well, studies on the effects that different pressure ratios, meridional exit flow angles, and power variations have on the mass flow rate of the system are crucial to the design. All of these simultaneous parameters and objectives must be analyzed within a proper database to guarantee an optimized design. To learn more about the DoE optimization methods seen on SoftInWay’s AxSTREAM platform please follow this link: http://www.softinway.com/software-functions/optimization-doe/

 

References:

http://www.nal.res.in/cfdupload/Udocument/15.PDF

https://etd.lib.metu.edu.tr/upload/12611105/index.pdf

 

Exchanging Steam for SCO2

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.

Getting back to the basics, it is important to establish what supercritical CO2 is. SCO2 is a fluid state of carbon dioxide where it is held at or above its critical temperature and critical pressure. When carbon dioxide is heated above its critical temperature and compressed above its critical pressure, the fluid inherits both liquid and gaseous phase properties. SCO2 has many unique properties that allow the fluid to dissolve materials like a liquid but at the same time flow like a gas. It also carries the advantage of being non-toxic, non-flammable and environmentally friendly.

Supercritical CO2 is believed to improve the efficiency of thermal power plants that utilize coal, natural gas,  solar, geothermal or nuclear energy. At its supercritical state, carbon dioxide is able to generate a higher amount of electricity from the same fuel compared to a steam power plant. Accordingly , it will drop down carbon dioxide & greenhouse gas emissions as well as operating cost. The use of carbon dioxide as a working fluid also allows for the usage of smaller and more economically feasible machines. Supercritical carbon dioxide is twice as dense as steam, thus easier to compress. With this in mind, smaller components can be used, for example, to decrease the turbine size compared to a steam generating power cycle, resulting in lower costs. Although an economically feasible SCO2 plant has yet to exist due to the early stage of technology and the still high research and development costs, we may be able to expect one in the near future as it is beneficial both economically as well as environmentally compared to a traditional steam power cycle.

Optimize your SCO2 cycle and component design using AxCYCLE and AxSTREAM!

References:
http://energy.gov/under-secretary-science-and-energy/articles/doe-announces-80-million-investment-build-supercritical
https://www.scientificamerican.com/article/can-carbon-dioxide-replace-steam-to-generate-power/

An Introduction to Cavitation in Hydro Turbomachinery

A major concern for pump system engineers over the last fifty years has been caviation. Cavitation is defined as the formation of vapor bubbles in low pressure regions within a flow. Generally, this phenomenon occurs when the pressure value within the flow-path of the pump becomes lower than the vapor pressure; which is defined as the pressure exerted by a vapor in thermodynamic equilibrium conditions with its liquid at a specified temperature. Normally, this happens when the pressure at the suction of the pump is insufficient, in formulas NPSHa ≤ NPSHr, where the net positive suction head is the difference between the fluid pressure and the vapor pressure at the pump suction and the “a” and “r” stand respectively for the values available in the system and required by the system to avoid cavitation in the pump.

The manifestation of cavitation causes the generation of gas bubbles in zones where the pressure gets below the vapor pressure corresponding to that fluid temperature. When the liquid moves towards the outlet of the pump, the pressure rises and the bubbles implode creating major shock waves and causing vibration and mechanical damage by eroding the metal surfaces. This also causes performance degradation, noise and vibration, which can lead to complete failure. Often a first sign of a problem is vibration, which also has an impact on pump components such as the shaft, bearings and seals.

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The Economics of Power Generation

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Implementation feasibility of power plant design relies heavily on the economic benefits. More often than not, newer technology cannot be implemented due to high cost of electric generation which would not be acceptable in the market since energy is a price sensitive commodity. Sometimes while deciding on a design to choose, we are given a choice between a high initial equipment cost and efficiency versus a lower capital cost with lower efficiency. The designer must be able to choose which design would fit best with their needs and goals.

While running a power generation plant, there are three types of cost that need to be taken into consideration: capital cost, operational cost and financing cost. With point one and two to being of higher priority.

Capital cost generally covers the cost of land, construction, equipment and so on. In other words, capital cost includes all costs in the initial phase of building the plant itself. Capital costs varies from time to time, and from one location to another. Largely, it is a function of labor costs, material costs and regulatory cost –which all is dependent on investment time and the availability of resources as well as the administrative regulation that governs the area. For example, building a power plant in an engineering hot spot like Texas would be much easier then it would be in a residential area such as near a neighborhood in California due to environmental laws as well as construction regulations. Consequently, the time needed to build a plant of the same size in both cases could be significantly different, thus making a noticeable gap in the capital cost. In common practices, capital costs are not necessarily paid in advance as cash, rather sometimes in debt and equity. This fact brings us to financing cost, which would be the cost of paying off the capital expenditure for a period of time.

In practice power plants take into consideration three main things while calculating for operating cost: fuel, labor and maintenance. With that being said, there are many other aspects to consider that could vary based on each individual designs. Operational cost usually varies with the capacity of the plant or with plant operations. In most cases, fuel cost dominates the marginal cost of a conservative power plant, say fossil-fuel, whereas newer technology such as biomass or geothermal, the cost of fuel is generally “free” though higher capital cost. The trade-off between operating and capital cost investment should be taken into consideration while designing a power plant.

For more information and to calculate your power plant costs, check out AxCYCLE Economics!

Reference:

  1. https://www.e-education.psu.edu/eme801/node/530

Mesh Generation Characteristics for an Accurate Turbomachinery Design

This post will examine the meshing requirements for an accurate analysis of flow characteristics in terms of turbomachinery applications, based on Marco Stelldinger et al study [1]. Computational Fluid Dynamics (CFD) are widely used for the analysis and the design of turbomachinery blade rows.  A well-established method is the application of semi-unstructured meshes, which uses a combination of structured meshes in the radial direction and unstructured meshes in the axial as well as the tangential direction. Stelldinger’s paper presents a library for turbomachinery meshing, which enables the generation of semi-unstructured meshes for turbomachinery blade passages, including cavities, fillets and varying clearance sizes. The focus lies on the generation of a mesh that represents the real geometry as accurately as possible, while the mesh quality is preserved.

The above was achieved by using two different approaches. The first approach divides the blade passage into four parts. Inside of these parts, a structured grid is generated by solving a system of elliptic partial differential equations. The second approach is based on the domain being split into fourteen blocks. It has benefits concerning computational time towards the first one, because of a faster generation procedure as well as a faster performance of the inverse mapping.

Mesh View
Figure 1 Mesh View

Another key aspect in mesh generation is the improvement of the mesh quality applying suitable methods. Since mesh smoothing algorithms have been shown to be effective in improving the mesh quality, two smoothing algorithms, a constrained Laplace smoothing and an optimization-based smoothing were presented. Both algorithms showed benefits concerning the achieved mesh quality compared to the standard Laplace smoothing, while the computational time is longer. For the investigated turbomachinery meshes the constrained Laplace smoothing is exposed as the most feasible choice, because of a suitable combination of mesh quality and computational time.

Several methods for the modelling of fillets between blade and the casing were also presented. The methods provide meshes with different qualities, that results into different convergence rates and residuals. Furthermore, the axisymmetric surfaces are dependent on the axial position that enables the modelling of clearances with a variable size. CFD simulations for a variable stator vane with a constant clearance size between blade and inner casing as well as with a variable clearance size were performed. The results show a different flow behavior near the clearance. This emphasizes the requirement of an accurate representation of the real geometry for CFD simulations of turbomachinery flows.

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Figure 2: AxCFD mesh view

The AxCFD module of the AxSTREAM platform allows the user to employ an automatic turbomachinery-specific, structured hexagonal meshing by customization in the setup period. Different types of mesh generation are available and can be refined in each direction. Take some time to use AxSTREAM and enjoy the design process!

A Look into Combined Cycle Power Plants – Problems, Advantages and Applications.

urs Combined Cycle Power Plants are among the most common type of power generation cycle. Demand of CCP application has risen across board due to the rising energy demand (and consumption) as well as growing environmental awareness. Combined cycle is a matured energy that has been proven to generate much lower CO2 (and other environmental footprints) compared to a traditional fossil fuel steam or gas turbine power generation cycle Consequently, this application is often looked as a “better” substitute compared to other a fossil fuel technologies. That being said, CCP is still a temporary alternative to substitute SPP since although CCP generally is more environmentally friendly, CCP process still requires the combustion of fossil fuel (though at a significantly lower degree compared to SPP) for initial heat/energy source.

The application takes two kinds of thermodynamic cycle in assembly to work together from the same heat source. Fluid Air and fuel enters a gas turbine cycle (Joule or Brayton) to generate electricity, waste heat/energy from working fluid will then be extracted then go through a Heat Recovery Steam Generator and towards steam turbine cycle (Rankine) to generate extra electricity. The main advantage of this cycle combination is the improvement of overall net efficiency (around 50-60% higher compared to each cycle alone), thus, lower fuel expenses. With that being said, net efficiency of a CCP is often inflated especially on systems which use a low-temperature waste heat.

There are two configurations of a combined cycle power plant – single-shaft and multi-shaft. The first configuration has one gas turbine and one steam turbine coupled to one generator and one heat recovery steam generator. A multi-shaft has one large steam turbine, condenser and heat sink for up to three gas turbines — each gas turbine and each steam turbine also has its own generator. Each configuration comes with its own advantages and disadvantages, for example single shaft design has a slightly smaller initial cost and smaller footprint whereas multi-shaft is found to be more economical in the long run due to the number of gas turbine to operate in conjunctions. Though overall it’s hard to say which configuration is best to be applied, judgement should be based on needs and consideration of the designer since each wins and losses in different categories.
Design the optimal combined cycle for your application using AxCYCLE!

Reference:
http://arthur.shumwaysmith.com/life/content/the_problems_with_combined_heat_and_power_chp_critique_part_3

Achieving Successful 3-Dimensional Hand Tracking Using Quasi-Random Sequences

With the advent of emerging technologies in the space of human-computer interaction (HCI), a prevalent challenge has been finding methods that can accurately represent these motions in real time.  Applications using RGB-D cameras to track movements for consumer-based systems has already been employed by Microsoft in the space of tracking silhouette movements in video games as well as app navigation in the Microsoft Kinect system.  However, tracking methods must evolve in order to successfully represent the complexity of human hand motion.  The two main categories of 3D hand articulation tracking methods consist of appearance-based and model-based tracking.  Appearance-based tracking methods are efficient in the limited space of comparing the present model to a number of already defined hand configurations.  Model-based tracking methods allow the computational configuration to explore a continuous space in which the hand motions are optimized at a high dimensional space in near real time.

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Figure 1 – 256 Points from a Pseudorandom Number Source (Left) Compared to a Quasi-Random Low-Discrepancy Source (Right)

If the computer tracks the human wrist with six degrees of freedom and the other joints accordingly, the ensuing dimensional analysis occurs at a high dimensional space.  A saddle joint (2 DOF) at the base of the each finger plus the additional hinge joints (1 DOF each) at the middle of the finger describes each finger with four degrees of freedom.  In turn, the problem of tracking the articulation of a single hand is performed in a dimensional space of 27.  This highly dimensional problem formulation requires an optimization technique specific to the problem that can provide a uniform coverage of the sampled space.  Quasi-random sequences are known to exhibit a more uniform coverage of a high dimensional compared to random samples taken from a uniform distribution.  The Sobol sequence, developed by Russian mathematician Ilya Sobol, describes a quasi-random low-discrepancy sequence that more evenly distributes a number of points in a higher dimensional space.  Figure 1 represents the distribution discrepancy between a pseudorandom number generation and a quasi-random low-discrepancy Sobol sequence generation.

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Figure 2 – High Dimensional Design Space with Given Constraints from Preliminary Design Module in AxSTREAM™

Clearly described in the figure, it is possible to visualize how the quasi-random distribution would employ a better system for tracking hand articulations on 27-dimensional space with much fewer missteps. This particular technology will continue to evolve as the steps of the process are improved. The quasi-random sampling presents a candidate solution in the parametric space of hand configurations, and objectively creates iterations for each frame in which these points are captured. Although the commercial application of this technology still seems rather futuristic, the ability to interact with a computer system by using a number of hand gestures has seen massive improvement in the past years. This technology could potentially represent the next big advancement for upcoming interactive computer systems. Aside from the applications displayed in this article, SoftInWay has been using this technology in order to optimize the highly dimensional system seen in the preliminary design of turbomachines. The solution generator in AxSTREAM® uses a quasi-random search algorithm to successfully distribute a high dimensional system characterized by geometry limits, performance bounds, and different flow conditions. To learn more about the preliminary design module for applications in any turbomachinery platform follow the link – http://www.softinway.com/software-functions/preliminary-design/

Reference:

http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Oikonomidis_Evolutionary_Quasi-random_Search_2014_CVPR_paper.pdf 

Enhanced Design Capabilities Using CFD

The use of computational fluid dynamics (CFD) in turbomachinery design is getting more and more popular given the increased computational resources. For the design process, however, there is no need for extensive CFD capabilities as the effort is put on minimizing engineering time while obtaining a design which is about 90% optimized. Here we are presenting two cases where CFD is used to derive significant information for pump design.

First, the influence of the blade shape on the parameters of the single blade hydrodynamic pump was studied by Knížat et al [1]. The investigation of the pump properties was carried out experimentally with a support of CFD methods. The accuracy of applied steady-state calculations was satisfactory for the process of design of a single blade pump, because of the good agreement between measured and calculated power curves.

For the CFD the Menter SST (shear stress transport) model of turbulence was chosen. This model effectively combines robustness and accuracy of the k-ω model in regions close to the wall with the model k-ε working better in a free stream away from the wall. These improvements make the SST model more accurate and reliable compared with the standard k-ω model. The CFD calculations served for the estimation of pump power curves. The specific energy, torque and hydraulic efficiency were evaluated for each flow rate.

This studied showed that the position of the best efficiency point is sensitive on the blade shape. Thus, it is necessary to form the blade more carefully than in a case of a classical multi-blade pump. It also follows from the calculations that the pump flow is non-symmetrical and it may cause increased dynamical load of the shaft.

In a second study conducted by Yang et al, a double volute centrifugal pump with relative low efficiency and high vibration was redesigned to improve the efficiency and reduce the unsteady radial forces with the aid of unsteady CFD analysis. The concept of entropy generation rate was proposed to evaluate the magnitude and distribution of the loss generation inside the pump. It was found that the wall frictions, wakes downstream the blade TE, flow separation near hub on pressure surface side, and mixing loss in volute are the four main sources leading to significant entropy generation in baseline pump. In the redesigned model, the entropy generation near the hub on pressure surface side was diminished and the loss in the volute was also reduced, while the loss generated by wall friction was increased with the blade number increasing. In general, the entropy generation rate was a useful technique to identify the loss sources and it is really helpful for the redesign and optimization of pumps. The local Euler head distribution (LEHD) obtained in viscous flow was proposed to evaluate the flow on constant span stream surfaces from the hub to shroud. It was found that Kutta condition was not necessarily satisfied at blade leading edge in viscous flow. A two-step-form LEHD was recommended to suppress flow separation and secondary flow near the hub on pressure side of the blade in a centrifugal impeller. The impeller was redesigned with two-step-form LEHD, and the splitter blades were added to improve hydraulic performance and to reduce unsteady radial forces.

The use of CFD integrated in a streamline engineering platform like AxSTREAM would be a valuable tool for every engineer. Try AxSTREAM and AxCFD to conduct your own research and lead to significant outcomes related to turbomachinery design, analysis and optimization!

 

[1] Impeller design of a single blade hydrodynamic pump, Knížat,B. and Csuka,Z. and Hyriak,M., AIP Conference Proceedings, Volume 1768, 016

[2] Computational fluid dynamics- based pump redesign to improve efficiency and decrease unsteady radial forces. Yan, P., Chu, N., Wu, D., Cao, L., Yang, S., & Wu, P. (2017).  Journal of Fluids Engineering, Transactions of the ASME, 139(1)

Can 1D Tools be Used to Design an HVAC System?

The heating, ventilation, and air-conditioning (HVAC) system is arguably the most complex system that is installed in a house and it is responsible for a substantial amount of the total house energy used. A right-sized HVAC system will provide the desired comfort and will run efficiently. Right-sizing of a HVAC system is the selection of equipment and the designing of the air distribution system to meet the accurate predicted heating and cooling loads of the house. Rightsizing the HVAC system begins with an accurate understanding of the heating and cooling loads on a space, however, a full HVAC design involves more than just the load estimate calculation as this is only the first step of the iterative HVAC design procedure. Heating and cooling loads are dependent on the building location, sighting, and the construction of the house, whereas the equipment selection and the air distribution design are dependent upon the loads and each other.

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Figure 1: A 3D model representing the HVAC          ducting in a building.
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Figure 2: Ducts layout for HVAC

The initial design iteration starts with tools such as AxCYCLE™, the thermodynamic design, analysis and optimization tool offered by SoftInWay Inc., based on which the initial specification for the equipment selection is obtained. Furthermore, it is more important to study the distribution of the air into the building to further refine the design iterations. Figure 1 shows the ducting layout in a typical building. The ducting design is a major activity not only from the point of pressure loss but also to ensure the air flows into the room effectively.

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Figure 3: Design and Analysis of the                            HVAC ducts in AxSTREAM® NET.

Figure 2 shows the ducting layout with the air discharge ports. The branching of the duct and the location of the discharge ports affect the cooling in the room which needs to be analyzed in detail. However, to begin the initial design process, it is more appropriate to use a 1D tool such as AxSTREAM® NET which can be used to model the flow through the duct and estimate the pressure loss and cooling effectiveness based on whether the thermodynamic cycle can be further fine-tuned iteratively before any detailed 2D/3D analysis is performed.

To learn more about how AxSTREAM® NET can be used for HVAC design and analysis, please write to info@softinway.com.