How Fidelity CFD Improves Hydraulic Turbine Performance Prediction

Francis-99 Hydraulic Turbine (Trivedi, 2026)

Accurately predicting the performance of hydraulic turbines requires more than a steady-state efficiency estimate. Engineers also need insight into how the machine behaves away from its best efficiency point, where unsteady flow structures, rotor-stator interaction, and draft-tube instabilities can affect both performance and stability. This is where a high-fidelity computational fluid dynamics (CFD) workflow becomes especially valuable.

This blog summarizes a detailed simulation study of the Francis-99 benchmark case using Fidelity CFD software, showcasing how GPU-accelerated steady and unsteady CFD can be used to predict the hill chart, evaluate part-load behavior, and identify a turbulence model that accurately predicts flow behavior for reliable hydraulic turbine analysis.

Building a Simulation Workflow for Complex Hydraulic Turbine Flows

The Francis-99 turbine is a well-known benchmark case with experimental data available from workshop studies for validation against the CFD simulation results. The simulation uses a mixed meshing strategy to balance topological control and geometric flexibility. Structured meshes generated with Fidelity Autogrid are applied to the guide vanes and runner, where periodicity, blade shape, and high mesh quality are critical.

Unstructured meshes generated with Fidelity ANSA are used for the inlet spiral and draft tube, where geometry is more complex and a structured topology is less practical. This combination allows the solver to maintain accuracy in the blade passages while keeping the workflow manageable in the surrounding stationary components.

The steady-state setup includes mass-flow inlet conditions, mixing planes at rotor-stator interfaces, periodic boundaries, and an outlet opening to accommodate backflow at selected operating points.

From Hill Chart Prediction to Part-Load Physics

For steady-state simulation, the workflow is used to compute the hill diagram across multiple guide vane angles and rotational speeds. The results capture both the location of the best efficiency point and the shape of the efficiency isocurves in strong agreement with the available experimental data.

A slight overprediction of efficiency is observed, which can be attributed to disk-friction losses not included in the computational model. From an engineering standpoint, this is an important result: it shows that CFD can reliably reproduce global turbine performance trends while also clarifying which modeling assumptions influence the final comparison.

Under part-load operating conditions, the flow becomes increasingly unsteady, and the limitations of a steady-state approach become more apparent. As a result, transient simulations are required to accurately capture the complex flow structures and associated performance characteristics in these regimes.

Capturing Rotor-Stator Interaction and Draft-Tube Instabilities

To analyze part-load behavior, the simulation is extended to a full-wheel model with all guide vanes included so that rotor-stator interaction can be resolved directly. A sliding grid is used between rotating and stationary domains, and the transient setup is evaluated with multiple time steps.

This comparison shows that although a coarse time step may capture some dominant frequencies, it fails to resolve the full range of flow physics. In this case, the one-degree time step resolves more unsteady flow features and avoids the artificial frequency behavior seen with coarser settings.

The study also compares three turbulence models—shear stress transport (SST), explicit algebraic Reynolds stress model (EARSM), and scale-adaptive simulation (SAS). While SST provides a strong baseline and the EARSM improves anisotropy representation, SAS is the only model that accurately captures large-scale unsteadiness, such as the vortex rope and its frequency in the draft tube.

Probe-based frequency analysis, along with Q-criterion and swirling-velocity visualizations, shows that SAS better resolves the coherent unsteady structure expected under partial-load conditions.

How This Workflow Supports Turbomachinery Design and Analysis

This benchmark demonstrates a practical CFD workflow that engineers can use to accurately predict both turbine performance and the underlying flow structures, including challenging off-design operating conditions. The study highlights how key modeling decisions — such as mesh strategy, turbulence-model choice, etc. — directly influence the ability to capture not only global performance metrics but also the complex flow phenomena responsible for efficiency losses, instabilities, and part-load behavior.

In this study, steady-state operating points were solved in minutes on the GPU, and even the unsteady Reynolds-Averaged Navier-Stokes (URANS) simulations—covering 40 full runner revolutions—were completed in about 10 hours, showing that both steady and unsteady hydraulic turbine analyses can fit within practical engineering timelines.

Frequently Asked Questions

⇒ How do you decide whether to use Fidelity Flow or Fidelity Charles Solver for a turbomachinery simulation?
Use Fidelity Flow when you need rapid, structured, GPU-enabled CFD for early design exploration, performance mapping, and efficient turnaround. Use Fidelity Charles when the problem involves complex off-design physics, strong unsteadiness, or separated flow that cannot be resolved with sufficient fidelity using RANS or URANS approaches.

⇒ Why use a mixed structured–unstructured workflow in turbomachinery CFD?
A mixed workflow helps balance mesh quality, geometric flexibility, and simulation cost. Structured meshes are well-suited for blade passages where topological control and periodicity are important, while unstructured meshes are more practical for geometrically complex regions such as the inlet spiral and draft tube.

⇒ Why is full-wheel meshing necessary for off-design rotor-stator interaction analysis?
Full-wheel meshing is needed when the goal is to resolve rotor-stator interaction, because reduced periodic sectors may suppress important circumferential interactions. In this case, the full runner and all guide vanes were meshed, increasing the total mesh size to about 37 million cells.

⇒ How do SST, EARSM, and SAS differ for turbomachinery off-design simulations?
SST is the standard baseline turbulence model and is widely used for steady-state performance prediction. EARSM improves the representation of turbulence anisotropy, which can be important in complex turbomachinery flows. SAS extends SST by enabling the resolution of large-scale unsteady structures, making it more suitable for capturing phenomena such as vortex rope behavior.

⇒ How does time-step size affect rotor-stator interaction capture in unsteady URANS?
Time-step size directly affects how well transient blade-row interactions are resolved. In this case, a five-degree time step was too coarse and introduced aliasing, whereas a one-degree time step resolved more of the unsteady content and was chosen to capture both rotor-stator interaction and low-frequency flow phenomena.

Reference

Trivedi, C. (2026). Francis-99 workshop 1: Steady state operation [Data set]. Francis-99 (V1). DataverseNO. https://doi.org/doi:10.18710/HKQ2RF

Watch the on-demand webinar Advanced CFD for Francis Turbines: Hill Chart Prediction and Part-Load Analysis by Margarita Campos to see the complete Francis turbine CFD workflow in action—from hill chart validation and transient setup to probe-based frequency analysis and draft-tube visualization.