ARL-TR-7871 ● NOV 2016

US Army Research Laboratory

Computational Fluid Dynamic (CFD) Study of 

an Articulating Turbine Blade Cascade

by Richard Blocher, Luis Bravo, Anindya Ghoshal, 

Muthuvel Murugan, and Michael Walock 

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ARL-TR-7871 ● NOV 2016

US Army Research Laboratory

Computational Fluid Dynamic (CFD) Study of 

an Articulating Turbine Blade Cascade

by Luis Bravo, Anindya Ghoshal, Muthuvel Murugan, and 

Michael Walock

Vehicle Technology Directorate, ARL

Richard Blocher

Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, 

Tennessee

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1. REPORT DATE (DD-MM-YYYY)

November 2016

2. REPORT TYPE

Technical Report

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1 June–31 August 2016

4. TITLE AND SUBTITLE

Computational Fluid Dynamic (CFD) Study of an Articulating Turbine Blade 

Cascade

5a. CONTRACT NUMBER

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

6. AUTHOR(S)

Richard Blocher, Luis Bravo, Anindya Ghoshal, Muthuvel Murugan, and 

Michael Walock

5d. PROJECT NUMBER

1120-1120-99

5e. TASK NUMBER

5f. WORK UNIT NUMBER

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US Army Research Laboratory

ATTN: RDRL-VTP

Aberdeen Proving Ground, MD 21005-5066

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ARL-TR-7871

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13. SUPPLEMENTARY NOTES

14. ABSTRACT

This technical report presents some preliminary work toward a project that seeks to develop a shape memory alloy actuator for 

rotor and stator blades in aircraft turbine engines that will increase efficiency and extend flight ranges. A major step toward 

the project’s completion is the development of a model that simulates both the fluid-structure interaction in the engine and the 

mechanical behavior of the shape memory alloy actuator. A simple computational fluid dynamic (CFD) case with 2 pairs of 

actuated blades is presented as a proof-of-concept. Immediate future work will involve altering the case setups for CFD 

simulation to more accurately represent the fluid-structure interaction in a real engine with articulating turbine blades.

15. SUBJECT TERMS

computational fluid dynamic, CFD, fluid structure interaction, shape memory alloy, SMA

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OF ABSTRACT

SAR

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Richard Blocher

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410-278-9719

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Contents

List of Figures iv

Student Bio v

Acknowledgments vi

1. Introduction 1

2. Methods, Assumptions, and Procedures 3

3. Preliminary Results and Discussion 5

4. Conclusions and Future Work 6

5. References 7

List of Symbols, Abbreviations, and Acronyms 8

Distribution List 9
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List of Figures

Fig. 1 Cutaway image of typical rotorcraft engine...........................................1

Fig. 2 SMA phase hysteresis............................................................................2

Fig. 3 Refined mesh for 2 rotor-2 stator setup .................................................4

Fig. 4 Case setup for CFD simulation..............................................................5

Fig. 5 Velocity fields at 3 points during articulation........................................5
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Student Bio

Impact of High Performance Computing (HPC) Internship 

Program Research Experience

Just before participating in the HPC Internship Program, I completed my bachelor’s 

degree at The Ohio State University (OSU). I began my PhD program in Materials 

Science at OSU in Fall 2016, just after completing my research experience at the 

US Army Research Laboratory (ARL). My mentorship at ARL gave me the basic 

understanding of computational fluid dynamics (a subject I had never encountered 

before) required to successfully navigate this research project. The research 

experience gained over the summer, as well as the availability of HPC systems for 

use in computational work, have significantly facilitated the beginning of my 

graduate career.
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vi

Acknowledgments

This research was sponsored by the US Army Research Laboratory and took place 

within its Vehicle Technology Directorate. I would like to acknowledge Luis 

Bravo, Anindya Ghoshal, and Mutheval Murugan for their mentorship throughout 

the internship experience. I would also like to thank the Department of Defense’s 

High Performance Computing centers for the continued use of the Excalibur 

supercomputer. Finally, I would not have had this experience without my graduate 

advisor at The Ohio State University, Peter Anderson.
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1. Introduction

Efficiency is among the most important considerations in the design of aircraft 

turbine engines. Modern internal combustion turbine engines have improved 

significantly in the past few decades; efforts to increase their efficiency have 

included design innovations to increase the usable air pressure at combustion, 

which is limited by compressed stall. Such design improvements include the 

capability of newer turbine blades to have fluid run through them during use1

—a

feature which many newer engines include. A cutaway view of a typical rotorcraft 

engine is shown in Fig. 1. Although there have been many innovations,

opportunities exist for improvements that are largely unexplored. The present 

initiative aims to enhance the efficiency of aircraft turbine engines through the 

articulation of turbine blades during flight. The blade pitch chosen for all rotor and 

stator blades in existing turbine engines is fixed, even though aircraft are operated 

through a wide range of conditions. Different flight velocities and altitudes, for 

example, influence air pressures and temperatures, which change the flow behavior 

of the air. If it were possible to rotate turbine blades to a new angle-of-attack during 

flight, these changes in the viscous properties of air could be compensated for so

that optimal fluid-flow behavior is maintained.2

Fig. 1 Cutaway image of typical rotorcraft engine2

There are several candidate mechanisms and materials that could be used to achieve 

turbine blade articulation, but many of these are not plausible. For example, if each 

blade were individually motorized, the required power and extra weight would 

negate the benefit of the articulation capability. Piezoelectric materials, which 

change shape with an applied electric charge, are another option;
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however, weight would become a concern because of the relatively low-power 

density of piezoelectric materials. Shape memory alloys are an option with very 

high-power density, and they can be operated without the use of motors or hydraulic 

fluid.

Shape memory alloys (SMAs) compensate for changes in shape through a phase 

change between austenite and martensite rather than through the creation of 

dislocations for strains up to around 6%. This phase change can be influenced by 

either a change in temperature or an applied stress. If a piece of shape memory alloy

in the martensite phase is heated to above its transformation temperature, it will 

undergo a phase change to austenite; the resulting shape change can be used to do 

mechanical work on the SMAs’ surroundings. Figure 2 illustrates the phase 

composition versus temperature behavior of SMAs.

Fig. 2 SMA phase hysteresis3

SMAs for the articulation of turbine blades are promising, but there are several 

challenges associated with them that need to be overcome. For example, the 

temperature of the air inside the hot section of turbine engines exceeds 1000 °C, 

while the transformation temperatures of existing SMAs are between 100 and 

200 °C.

3 There are various possible solutions to this challenge, such as running 

coolant across the SMA, housing the SMA itself in a cooler part of the engine, and 

developing new SMAs with higher phase transition temperatures.

The long-term goal of the project is to develop a working prototype of an actuating 

turbine blade, but a computational approach will help to answer design questions 

and concerns without being too costly. Therefore, a computational model of an 

SMA actuator is an intermediate goal. This model will be developed in the 

following 3 general steps:
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1) Develop a computational fluid dynamic (CFD) model of the fluidstructure interaction between the airflow and the articulating airfoils,

2) Develop a finite-element model of the SMA mechanism, and

3) Interface results from 2 models to achieve a comprehensive model of 

the actuated turbine blades during engine operation.

Steps 1 and 2 are being worked on concurrently, and the focus of this writing is the 

first work toward Step 1.

2. Methods, Assumptions, and Procedures

CFD simulations were carried out on a High Performance Computing (HPC)

Excalibur, a Cray XC40 System, located at the US Army Research Laboratory’s

Department of Defense Supercomputing Resource Center.

4 The software chosen 

for simulation on Excalibur was Converge, which features a fully compressible 

Navier Stokes equation formulation, fluid-structure interaction modeling for 

immersed objects, and a parallelized solver with Message-Passing-Interface (MPI)

protocols for efficient running on distributed memory architectures (i.e., HPC 

Excalibur).

Converge also uses a feature called Adaptive Mesh Refinement (AMR) to 

significantly cut down on computational time. AMR coarsens the cell size in areas 

where there is little activity. For the simulations carried out in this experiment, the 

mesh was refined by velocity, meaning the difference in air velocity from one cell 

to adjacent cells determined how refined the cell size needed to be in that region. 

See Fig. 3 for an example of what an AMR result for this project looked like.
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Fig. 3 Refined mesh for 2 rotor-2 stator setup

Figure 4 depicts the case setup used for the results shown in this technical report. 

The case setup consisted of 2 pairs of blades: 1 pair of rotors and 1 pair of stators. 

The inflow of air was fixed at 85 m/s, and the walls were defined as inviscid walls. 

The rotors did not move in the radial direction for this simulation (that motion 

would be in the up direction in Fig. 4), but the blades were articulated 

simultaneously in a sinusoidal pattern about the leading edge of the blades with an 

articulation magnitude of 10°. The AMR parameters for the simulation give a 

maximum cell size of a cube 2 cm on a side, and the smallest cell size was 1/8 cm 

in each dimension. The cord length of the blades shown was about 22 mm. The 

maximum number of cells allowed by the simulation was 2 million; the actual 

number of cells during the simulation never exceeded approximately 72,000.
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Fig. 4 Case setup for CFD simulation

3. Preliminary Results and Discussion

Figure 5 shows flow velocity fields at 3 extreme points during blade articulation: 

10° above normal, no articulation, and 10° below normal. The simulation depicted 

used an articulation period of 0.5 s; however, simulations were also performed for 

an articulation period of 0.25 s. Those results were indistinguishable from the ones 

shown in Fig. 5. Air flow behavior changes depending on the angle of the blades;

the flow between the right pair of blades (the rotors) slows when the air is not 

directed in between them by the stator blades. Of course, in a real engine, there 

would be an entire ring of blades, so one would expect the boundary layer 

transitions to be different.

Fig. 5 Velocity fields at 3 points during articulation
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4. Conclusions and Future Work

The results gathered thus far serve as a proof-of-concept that the articulation of 

turbine blades does in fact have a visible effect on the boundary layer behavior in 

the airflow across the blades. The tools are now in place to develop more rigorous 

case setups. For example, the number of rotor and stator blades will be increased, 

with the eventual goal of simulating an entire ring of blades, as in a real turbine 

engine. The nondimensional governing parameters of the simulation, such as 

Reynold’s number and y+ value, will be rigorously scrutinized to ensure the 

validity of future results. In addition, various points and patterns of articulation will 

be studied. The aerodynamic center and center of gravity of the blades, for example, 

will be tested as points of articulation. The effect of manipulating the phase 

difference between the 2 sets of blades will also be simulated.

Converge is capable of outputting pressure values at each point on the surfaces 

immersed in the CFD simulation. Future simulations will fetch these values, which 

will in turn be used to develop an expression for the moment on some articulating 

mechanism attached to the blade as a function of angle of attack and rate of change 

of angle of attack. This expression will be used in a finite-element simulation of the 

SMA actuator, resulting in a quasi-fully-coupled simulation of the complete 

actuating mechanism in flight.
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5. References

1. Wilson MA. United States patent US 6,105,359 A. 2000.

2. Murugan M, Booth D, Ghoshal A, Thurman D, Kerner K. Concept study for 

adaptive gas turbine rotor blade. The International Journal of Engineering and 

Science. 2015;4(9):10–17.

3. Müller C. A novel shape memory plate osteosynthesis for noninvasive 

modulation of fixation stiffness in a rabbit tibia osteotomy model. BioMed 

Research International. 2015.

4. Department of Defense High Performance Computing. Unclassified systems

[accessed 2013 Aug 28]. https://centers.hpc.mil/systems/unclassified.html.
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List of Symbols, Abbreviations, and Acronyms

AMR Adaptive Mesh Refinement

CFD computational fluid dynamic

HPC High Performance Computing 

MPI Message-Passing-Interface

SMA DOD Supercomputing Resource Center
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1 DEFENSE TECHNICAL

(PDF) INFORMATION CTR

DTIC OCA

2 DIRECTOR

(PDF) US ARMY RESEARCH LAB

RDRL CIO L

IMAL HRA MAIL & RECORDS

MGMT

1 GOVT PRINTG OFC

(PDF) A MALHOTRA

6 DIR USARL

(PDF) RDRL VT

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W WINNER

RDRL VTP

L BRAVO

A GHOSHAL

M MURUGAN

M WALOCK
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