What is an Integrated Coal Gasification Combined Cycle (IGCC) and What are the Advantages?

integrated-coal-gasification-combined-cycle-igcc
Source: http://www.slideshare.net/AbhijitPrasad4/integrated-gasification-combined-cycle-plant

Though fossil fueled power plants aren’t as commonly used anymore, coal fired power generation is still a major source of global electricity, making up about 25% of the market in total. Compared to other options in fossil fuel power generation, coal is found to be the most economical choice as well as a reliable option. Making demands that are heavily reliant on other fuels, such as oil-fired for example, slowly levers to coal power generation. The global reserve of coal can be found in abundance when compared to other energy sources (such as oil for example) as there is about 3 times more of it. Also, IGCC comes with an economic benefit as the price of coal has remained relatively constant, which results in a higher degree of confidence when relying on coal as an energy source in the future.

How Does an IGCC Work?

The system uses a high pressure gasifier to turn coal and other carbon based fuels such as high-sulfur coal, heavy petroleum residues and biomass into pressurized clean coal synthesis gas (also known as syngas). The solid coal is gas-fired to produce syngas by gasifying coal in a closed pressurized reactor with a shortage of oxygen to ensure that coal is broken down by the heat and pressure. Before going out of the system, the syngas runs through a pre-combustion separation process to remove impurities,  starting with water-gas-shift reaction to increase concentration of hydrogen and efficiency during combustion process, to a physical separation process (through variable methods). After that, a fairly pure syngas is used as a fuel in a combustion turbine that produces electricity. Waste heat contained in a gas turbine’s exhaust is used to produce steam from feed water that further turns a steam turbine to generate additional electricity.

What are the Advantages of IGCC?

IGCC is currently found to be the cleanest of coal technology with lower emission (especially for carbon dioxide by 10%) and is about 30-40 percent more efficient. Using syngas in gas turbines results in a higher output that is less dependent on temperature when compared with natural gas. Additionally, looking into the economic benefit of this technology, IGCC produces couple by-products, from chemicals to materials for industrial use that could be sold for side economic benefits.

References:

http://www.global-greenhouse-warming.com/integrated-gasification-combined-cycle-IGCC.html

https://en.wikipedia.org/wiki/Integrated_gasification_combined_cycle

 

 

Utilization of Supercritical CO2 Bottoming Cycles

In the ever-expanding market for waste-heat recovery methods, different approaches have been established in order to combat the latest environmental restrictions while achieving more attractive power plant efficiencies.  As gas turbine cycles continue to expand within the energy market, one particular technology has seen a significant upsurge due to a number of its beneficial contributions.  Supercritical CO2 (S-CO2) bottoming cycles have allowed low power units to utilize waste heat recovery economically.  For many years, the standard for increasing the efficiency level of a GTU (Gas Turbine Unit) was to set up a steam turbine Rankine cycle to recycle the gas turbine exhaust heat.  However, the scalability constraints of the steam system restrict its application to only units above 120MW.

sco2_cycle
Supercritical Co2 Cycle

HRSGs (Heat Recovery Steam Generators) are water-to-steam boilers which capture the waste heat exhaust of GTUs and convert this heat into energy in the form of high-pressure, high-temperature steam.  These systems can exist in a single or modular fashion depending on the scope of the project.  Modular HRSGs consist of any number of low pressure, intermediate pressure, and high pressure sections.  Each section allows for the extraction of gas turbine exhaust heat using separate steam drum and evaporator sections.  Even in a single pressure HRSG combined cycle, the immense amount of auxiliary equipment, the high installation costs, and the frequent maintenance necessary for such a system prevent them from providing viable heat recovery for low power GTUs.

With the introduction of a different fluid, gas turbines of small and medium size are able to utilize waste heat recovery.  Unlike steam, a supercritical CO2 system is designed to lie in the simply in the gaseous phase.  This single-phase fluid design removes the boiling process necessary for a steam system and therefore results in higher fluid temperatures and cycle efficiencies.  As well, the high energy density reduces the system component’s size and cost, and offers higher system efficiencies, reduced footprints, and significantly easier installation methods.  While all these advantages do exist within a supercritical CO2 system, working with a relatively new fluid presents different challenges that have not had the time and exposure with engineering experts as steam and gas systems have.  In particular, developing a turbine that will most efficiently run under this new fluid presents perhaps the tallest demand within the supercritical cycle. The task becomes to embrace these challenges for the benefit of higher efficiencies, lower O&M costs, and reduced greenhouse emissions.

For a more in-depth look at SoftInWay’s involvement in the S-CO2 sector, please follow this link or contact us for more information

 

References:

  1. http://www.echogen.com/documents/why-sco2-can-displace-steam.pdf
  2. http://www.softinway.com/wp-content/uploads/2015/12/IGTC2015-EvaluationOfGasTurbineExhaustHeatRecoveryUtilizingSCO2Cycle.pdf

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.

cfd
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/

The Economics of Power Generation

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Source

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.

mesh-view
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!

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.

scatter-plot
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)

Importance and Modelling of Internal Combustion Engine Cooling Systems

In an internal combustion engine, combustion of air and fuel takes place inside the engine cylinder and hot gases are generated with temperature of gases around 2300-2500°C which may result in not only burning of oil film between the moving parts, but also in seizing or welding of the stationery and moving components. This temperature must be reduced such that the engine works at top efficienc,  promoting high volumetric efficiency and ensuring better combustion without compromising the thermal efficiency due to overcooling. Most importantly, the engine needs to function both in the sense of mechanical operation and reliability. In short, cooling is a matter of equalization of internal temperature to prevent local overheating as well as to remove sufficient heat energy to maintain a practical overall working temperature.

It is also important to note that about 20-25% of the total heat generated is used for producing brake power (useful work). The cooling system should be designed to remove 30-35% of total heat and the remaining heat is lost in friction and carried away by exhaust gases.

The design of cooling systems depends on whether the engine is air cooled or liquid cooled. Air cooling is generally used in small engines wherein fins or extended surfaces are provided on the cylinder walls, cylinder head, and so on. Heat generated due to combustion in the engine cylinder will be conducted to the fins and when the air flows over the fins, heat will be dissipated to air. The amount of heat dissipated to air depends upon: Amount of air flowing through the fins, fin surface area and the thermal conductivity of metal used for fins.

In water cooling methods, cooling water jackets are provided around the cylinder, cylinder head, valve seats and so on. When the water is circulated through the jackets, it absorbs heat of combustion. This hot water will then be cooling in the radiator partially by a fan and partially by the flow developed by the forward motion of the vehicle. The cooled water is again recirculated through the water jackets either through a pump or thermos-siphon which is based on the principle of density difference in working fluid.

cooling-water-ports
Figure 1: Cooling water ports in an IC engine cylinder block

Figure 1. shows the cooling water jacket for an IC engine cylinder block. The engine cooling jacket is of complex shape and is influenced by many factors including the shape of the engine block and optimal temperature at which the engine runs. A large cooling jacket would be effective in transporting heat away from the cylinders, but makes the engine bulky and heavier. The cooling water jacket needs to be optimized considering both the cooling effectiveness and engine weight. Hence the flow through the cooling jacket needs to be optimized from the inlet to the outlet covering the lengthwise along the geometry as well as traversing from cylinder block to the head. The optimization is done with the objective of minimizing the fluid pressure loss between inlet and outlet and obtains even distribution of the flow to each cylinder in the engine block and uniform velocities along its flow.

The engine cooling jacket is of complex geometry and performing 3D simulation over this is quite a complex task involving generating the 3D geometry with all the intricate details and preparing the model for performing conjugate heat transfer analysis. As an initial step it is advisable to perform a simple 1D heat and flow network analysis to obtain the heat transfer distribution and data for creating the 3D model using commercial tools such as AxSTREAM NET™.

To know more about how AxSTREAM NET™ can simplify engine cooling system design and analysis, please write to info@softinway.com.