A heat circulator pump is a centrifugal pump designed to circulate heated fluids in closed systems like boilers, hot water pumping, etc., or open systems like swimming pools. Such pumps are subjected to high-temperature fluids and low-pressure heads in the circulating region, relative to the system pressure. Some common applications where heat circulator pumps are employed include underfloor heating systems, boilers and hot water circulators in buildings, ventilation, air conditioners, heat recovery systems, industrial recovery systems, etc. 

If not designed efficiently, heat circulator pumps can be the biggest energy drains in a heating or cooling system. The EU has placed stringent requirements on the design of circulator pumps, in-line with the aspiration to transition to a low-carbon economy by 2050. The “Energy-related Products (ErP)” directive of 2009 established an ecodesign framework for the design and operation of many ErP products including heat circulator pumps. Under this directive, manufacturers are required to comply with the prescribed energy and resource efficiency standards in order for their products to be sold in the EU.

The efficiency of heat circulator pumps is rated based on their ‘Energy Efficiency Index (EEI)’. EEI is a measure of how much the power input of the pump is lower than the prescribed power input. For example, an EEI of 0.21 means that the pump only utilizes 21% of the threshold power input. Thus, the lower the EEI value, the better the efficiency rating of the circulator pump. KSB, a world leader in pump and valve manufacturing, has a range of high-efficiency products like the Calio (EEI ≤ 0.2) and Calio Z (EEI ≤ 0.23). Read on to learn how KSB continues to innovate on the ecodesign of heat circulator pumps by using a simulation-driven approach in SimScale.

KSB Heat Circulator Pump Design and Optimization: Project Objectives

EEI is governed by the average power consumption across the load profile, compared to the reference hydraulic power. Typically, power consumption at 4 weighted flow rates, as shown in the hydraulic curve below, is used to evaluate the EEI. The weighted flow rate, Q_100%, is taken as the flow rate where (Q x H) is maximum and that is extrapolated to get the weighted flow rates Q_75%, Q_50%, and Q_25%, and the corresponding Power ‘P’ at those flow rates. 

Using the control curve shown in green, the weighted average electrical input power is calculated (Eq. 1) and is then used to compute the EEI of the specified circulator (Eq. 2).

Eq. (1) P_el,avg = 0.06 x P_L,100% + 0.15 x P_L,75% + 0.35 x P_L,50% + 0.44 x P_L,25%   

Eq. (2) EEI = (P_el,avg / P_hyd,ref) x C                            

where: 

P_hyd,ref = reference power

C = calibration factor ~ 0.49

Currently, the pump rarely ever operates at the best efficiency point. Motor power is usually limited which shifts the Q_100% to the left, resulting in a new control curve (dashed green line in figure 1). This means that the final EEI is now dependent on the motor as well as other systems components, most of which get finalized only in the final stage of the production process. 

This is the precise problem that the turbomachinery expert at KSB Germany, Toni Klemm, was faced with. How does one quickly select an impeller design, subject to specific EEI requirements, at the last stage of the production cycle? Does one leave the impeller design until late in the production process, risking a longer time to market as well as higher prototyping costs?

SimScale, in partnership with Friendship Systems AG, the makers of CAD design and shape optimization software CAESES, provided a cost-effective, simulation-driven approach to solve KSB’s problem. A hydraulic toolchain was developed to create a surrogate model of the pump impeller, which can be queried to select the right design based on the system requirements before production. 

Overview of SimScale – CAESES Workflow

CAESES is a powerful CAD modeling and shape optimization software, which can be integrated with any simulation-driven optimization loop. Its dependency-based modeling approach is fully automated and it comes with inbuilt strategies for flexible parametric design and shape optimization. 

SimScale’s turbomachinery solver combines best-in-class CFD techniques with cloud computing to accelerate simulation-driven design and analysis of pumps and turbomachinery. The solver accuracy is close to 2% in comparison to test data and a designer can calculate an entire pump curve, by simultaneously running multiple simulations in the cloud, in 15 minutes. This is possible using input parameterization for fast design prototyping and CAD associativity for easy geometry variation. A simple application programming interface (API) enables the integration of the turbomachinery solver with third-party optimization and design of experiment (DoE) tools. 

In this case study, the parametric CAD geometry of the heat pump impeller was generated in CAESES, which was connected with SimScale via the API for running a DoE to evaluate the parametric hydraulic performance curves. The CFD-driven performance characteristics for different designs were fed back to CAESES for surrogate model creation and optimization. 

The CAESES – SimScale workflow can be summarized as:

DoE in SimScale: Simulation Setup and Results

14 design variables were chosen for CAD parameterization in CAESES. These include:

Number of blades

Meridional contours (3 parameters)

Blade angle distributions 
• 2 parameters for LE and TE blade angles
• 2 parameters for the hub to shroud variation of LE blade angle
• 6 parameters for shape control of beta functions between LE and TE

For each design variant, 3 flow rates needed to be run (0.7, 0.85, 1.1 x Q/Q opt). A simple python script enabled the transfer of Parasolid CAD geometry and simulation inputs from CAESES to SimScale’s turbomachinery solver, where geometry meshing and simulations were run. The CFD simulations assumed incompressible, steady state, fully turbulent flow across the pump impeller, and further input condition parameterization was employed to run all three flow rates per geometry variant together. This enables automatic calculation of the performance curve including the pressure head across the impeller, shaft power, and efficiency, which are sent back to CAESES. The flow around the impeller for changing the blade exit angle and the corresponding performance curves are shown in Figure 2.

A massive DoE comprising 377 design variants (900+ simulations in total) was run in parallel in SimScale to evaluate the hydraulic performance of each variant and send it back to CAESES. The DoE statistics are given below:

Surrogate Model Creation and Optimization in CAESES

The DoE results from SimScale include 9 output parameters (head, efficiency, and power for 3 flow rates) as shown in Figure 3. Using these, surrogate models were created in CAESES by leveraging the inbuilt RSMtools feature and response surfaces for each of them can be visualized.

Next Steps

Optimizations on the surrogate models for minimal EEI are being planned. This needs measured performance curves for the full pump configuration, which will be approximated from the impeller-only DoE results. Testing of the surrogate models is also in progress, where the average power consumption at the weighted flow rates now computed should lead to lower EEI.

Faster Innovation With Simulation-Driven Design in SimScale

Using cloud-native simulation in SimScale accelerates product innovation by opening up a vast design space that is otherwise not possible due to cost and time constraints. In this case study, we saw how KSB company combined the DoE results from SimScale with optimization strategies in CAESES to develop a novel methodology for the rapid selection of circulator pump impellers while adhering to EU’s ecodesign regulations. A turnaround of 1.5 days for a DoE of 300+ designs at a compute cost of $300 is the perfect motivation for companies to embed cloud-native simulations in their product development cycles, from conceptual design all the way to production.

Be sure to watch the on-demand webinar to hear the full story on heat circulator pump optimization from Toni Klemm (KSB) and Mattia Brenner (Friendship Systems AG).

The post Design and Optimization of KSB Heat Circulator Pump Using SimScale and CAESES appeared first on SimScale.

From a simulation perspective, steady-state methods like frozen rotor and mixing planes do not capture the true transient nature of such flows and are therefore less accurate in predicting the turbomachine’s performance, especially in off-design conditions. A full transient analysis that models the actual movement of the rotor and its interaction with stationary components becomes necessary in such situations. 

Despite the limitations of steady-state methods, engineers and designers still heavily rely on them, either reserving transient analysis only for final stage prototyping when it may be difficult to make design changes, or completely skipping it. This trend is primarily driven by the disproportionately high computational requirements, long turnaround times, and workflow nuances of 3D transient simulations in traditional CAE tools. A transient run for a single data point could typically take a few days on a desktop workstation.

To bridge this gap, SimScale has developed cloud-native transient capabilities within its proprietary solver for rotating machinery, which yields converged results for a single data point as well as a parametric sweep in under 4 hours. Our technology employs the sliding mesh technique in a robust binary-tree mesher and high-order accurate RANS solver, which can handle incompressible as well as compressible flow. In this article, we present how the newest addition to our rotating equipment simulation technology paves the way for fast and accurate transient analysis early in the design stages of digital prototyping.

Fast and Accurate 

As the pioneer of cloud-native simulation, SimScale continues to perfect its cloud computing algorithms that make it possible to run transient simulations in a fraction of the time taken by traditional CAE. For example, a full transient simulation for a centrifugal pump on a mesh size of nearly 0.6 million cells takes under 20 minutes to complete. The same simulation in traditional CAE tools would take at least 12 hours or even a couple of days. Combined with the parametric studies capability that we launched in 2021, it is also now possible to obtain performance curves with the full transient physics included in nearly the same time as a single data point run.

To establish the accuracy and reliability of the transient solution, we have validated the solver against a range of standard benchmark cases. Figure B below shows the comparison of power vs. flow rate for a centrifugal pump. The results from the transient analysis are a very good match with the experiment, closer than the steady-state MRF method. Mesh independence study for this case led to a mesh size of about 0.6 million cells and a full transient run for one flow rate took 18 minutes to complete. 

Intuitive and Accessible 

In traditional CFD software, getting the correct mesh arrangement across the sliding interface, and consequently, quality results from a transient analysis requires a high level of CFD expertise. Additionally, complex simulation workflows often frustrate designers and engineers, forcing them to devote more time to navigating software nuances than on iterating and perfecting their designs. SimScale is committed to breaking down the technical and economical barriers to advanced simulation. Our proprietary CFD technology for rotating machinery is built on the foundations of accessibility and ease of use, with the aim of enabling faster design iterations in a cost-effective manner. With the introduction of the advanced transient analysis capability, we continue to focus on features that allow greater automation and ease-of-use, including: 

• Robust binary tree mesher that automatically generates optimal mesh interfaces between the rotating and stationary components
• Workflow parity between steady-state and transient analysis —using the transient approach is as simple as turning on a switch
• Real time visualization of results in the built-in post-processor
• Intuitive user interface with physics-based, predefined inputs
• Browser-based simulation that can be accessed, and collaboratively worked on, from anywhere in the world

Advanced Transient Analysis for Rotating Equipment

With the addition of the transient analysis capability in SimScale, it is now possible to include comprehensive physics in rotating equipment CFD and predict their effect on the component’s performance and wear and tear. Cloud-native implementation means that transient simulations in SimScale are orders of magnitude faster than in on-premise software. 

Whether you are a pump engineer interested in analyzing vibrations due to flow pulsations, or a turbine designer optimizing the blade to reduce flow separation, cloud-native transient analysis capability offers you the advantage of super-fast design iterations, early in the design process and throughout the product’s life cycle. With SimScale, advanced transient analysis that was previously computationally expensive, time-consuming, or required expertise is now accessible via a browser, in a cloud-native platform that is scalable, easy to use, and cost-effective.

Learn more in our whitepaper: Simulating Turbomachinery Designs 10x Faster

Download Whitepaper

The post Cloud-Native Transient Analysis for Rotating Equipment appeared first on SimScale.

• How do we extract the last iota of efficiency from a pump or a turbine? 
• How do we optimize the operation and maintenance of critical rotating equipment without increasing costs? 
• How do we reduce time to market and stay ahead of the competition?

The answer is SimScale’s newest offering for rotating equipment simulations, which harnesses the power of the cloud to enable phenomenally fast and accurate simulations for flow and performance assessment of rotating equipment.

Fast and Accurate Simulation of Rotating Equipment

Consider the performance study of a centrifugal pump, which involves calculating the pump’s pressure head and efficiency for a range of outlet flow rates to generate what is known as the ‘pump curve’. Typically, a pump curve requires a minimum of six input data points and can take from days to months to produce in traditional software.

Our customer Benjamin van der Walt, from Hazleton Pumps, agrees. He says, “We averaged about 3-6 days to simulate 1 data point with AMI (transient simulation) and 9-15 hours with MRF (steady-state) on HPC. Generating a pump curve, with ideally 6 data points, can take up to a month”. With SimScale’s new proprietary rotating machinery technology, it is now possible to generate a pump curve for a medium-sized geometry within 15 minutes (See Figure 1).

The highly parallelized and cloud-optimized algorithms that form the backbone of our new technology enable users to make design decisions faster by running parametric studies at the same time as a single data point run.  

Solution accuracy is critical to the design and analysis of rotating equipment and cannot be compromised at any cost. Cognizant of this fact, we have tested and validated our proprietary technology for a variety of academic and industrial use cases to obtain solution accuracy within 1-5% of experimental data.

Figure (2) depicts the pump curve parametric sweep, which includes a mesh independence study, and the results obtained from SimScale are within a 2% error of experiment. The solver is backed by high-fidelity numerical models and robust mesh generation techniques that can handle a variety of flow physics through different types of rotating equipment.  

Accessible and Versatile Simulation

In rotating machinery simulations, it is common for traditional simulation tools to have turnaround times that run into days and also require a sizable investment in scalable hardware and data management processes. In most cases, the inherent complexity of rotating machinery simulation workflows can be quite frustrating for designers and engineers, resulting in a further increase in project lead times.

SimScale’s cloud-native simulation technology for rotating machinery not only reduces the simulation turnaround time to minutes but also drives the IT and hardware costs of companies to zero. We have invested heavily in automating and simplifying the simulation workflow so that rotating machinery designers and engineers do not get bogged down with the nuances of local software licensing and can instead devote their time to analyzing, iterating, and improving their designs.

Users can access the browser-based platform from anywhere in the world through a simple login, run multiple simulations simultaneously, and collaborate with team members on any projects they wish to share.

Cloud-Native Simulation for Rotating Equipment

SimScale is committed to making fast and accurate simulation accessible to all engineers working with rotating equipment and facilitating its adoption across applications and throughout the product’s lifecycle. We are confident that our proprietary technology for rotating machinery will help companies make significant savings in money and time, which they can channel back into doing what they do best – building better products faster.

Webinar: Pump Design Powered by the Cloud with Hazleton Pumps

Watch our on-demand webinar to see how simple it is to set up a simulation using our new technology for rotating machinery and get performance pump curves for all types of rotating equipment accurately and in minutes, not days:

Watch On-Demand Webinar

The post Rotating Equipment Design Powered by the Cloud appeared first on SimScale.

Matter: Matter is defined as a physical structure that has mass and volume in space. It comprises all forms of physical elements that exist in the form of atoms. Uranium, water, salt, and even a piece of wood, are all expressed as matter.

Phase: It is a distinctive form of matter with three types:

• Solid: Shape and volume are definite
• Liquid: Volume is definite, yet the shape belongs to the container
• Gas: Shape and volume belong to the entire structure of the container

Interface: In a system with different phases, a narrow and distinct region, known as the ‘phase interface’, emerges. The phases on either side of the interface have different chemical and physical properties, and mathematical expressions are required to model the transport of mass, momentum, and energy across the interface.

Some examples of multiphase flows include a mixture of liquid mercury and liquid water and phase-changing processes like the transition of ice into liquid water. The expression of multiphase fluid flow might be indicated as “number of phases” + “flow” in accordance with the total number of phases. For instance, the mixture of liquid water and liquid oil can be classed as a two-phase flow or a multiphase flow. On the upshot, multiphase flow is the interaction of more than one matter or phase of matter that exists simultaneously. Transportation of mass, momentum, and energy among phases—based on conservation laws—is examined through the simulation.

Multiphase Flow Models

For the purpose of carrying out a reliable numerical simulation, the mathematical model which describes the physics should be scrutinized. For instance, transition among phases such as condensation, requires an appropriate mathematical model, correlation, or theory which defines the condensation process as accurately as possible.

Generally, the modeling process of multiphase flow includes three main stages, which are the description of the physical process, the specification of the flow, and the determination of the suitable mathematical model.

Description of the Physical Process

In the case that fluid flow comprises more than one phase, the physical model should be defined properly to describe the governing process so as to herald fluid flow and even mathematical model. Some of the processes and models might be specified concerning multiphase flow, as illustrated in Figure 2.

Specification of the Fluid Flow Regime

There are various types of multiphase fluid flow in the literature that diversify in accordance with the physical process and properties of the problem, and these can be classified into three main fields:

Separated Phases: More than one immiscible fluid in continuous phases and separated by interface [1].

Mixed Phases: Presence of both separated and dispersed phases [2].

Dispersed Phases: Finite numbers of phases spread through the volume of continuous phases such as droplets, drops, particles, or bubbles [1].

Various types of fluid flow can be classified, as shown in Figure 3:

The Mathematical Model

The discrimination of phases in the numerical simulation relies on the rate of volume or mass. To determine an appropriate mathematical model for fluid flow, factors such as physical process and flow regime have to be described in advance. Several mathematical models have been developed in order to properly simulate fluid flow. The investigation of multiphase flow still has several hindrances due to complexities related to the mathematical models. However, the Navier-Stokes equations might be broadly used to examine multiphase flows, and the capability of hardware in conducting numerical studies, which are reliant on Navier-Stokes equations, is still far from an affirmative solution.

Over and above that, a challenging model—such as those related to turbulence, chemical reaction, or mass transfer—carries the problem to a further level of complexity. For this reason, generating both realistic and simpler models is the key factor for multiphase fluid flow simulations [1]. The most commonly used mathematical approaches—such as empirical correlations—might be conducted as follows [3]:

• Volume of Fluid (VOF): Separated flows, free surface flows
• Lagrangian Multiphase (LMP): Droplet flows, track individual point particles, particles do not interact
• Discrete Element Method (DEM): Particle flows, solve the trajectories of individual objects and their collisions, inside a continuous phase
• Eulerian Multiphase (EMO): Dispersed flow, particle flows, bubbly flows, boiling heat and mass transfers, interphase mass transfer
• Eulerian/Lagrangian Dispersed Phase Model (DPM): Particle-wall interaction is always considered, particle-particle is usually not
• Eulerian-Eulerian Model (EEM): Particle-wall interaction is considered, particle-particle is usually not
• Eulerian-Granular Model (EGM): Both particle-wall and particle-particle interaction are considered

Apart from the models above, further miscellaneous models can be found in the literature according to the type of fluid flow.

Application of Multiphase Flow

Many engineering problems depend on the numerical examination of fluid flow, which typically comprises more than one phase. Automotive, power generation, chemical industry, food industry, environment, and even medicine are some sectors that have been using numerical tools to predict the outcomes in advance. To learn more about multiphase simulation within SimScale, check out the latest features in this on-demand webinar.

The post Multiphase Flow in CFD: Basics and Modeling appeared first on SimScale.