- Email: [email protected]
Preliminary verification of the open-source CFD solver SU2 for radial-inflow turbine applications
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ScienceDirect ScienceDirect Energy Procedia 00 (2017) 000–000 Energy Procedia 00 (2017) 000–000 Energy Procedia 129 Energy Procedia 00(2017) (2017)1071–1077 000–000
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
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IV IV International International Seminar Seminar on on ORC ORC Power Power Systems, Systems, ORC2017 ORC2017 13-15 September 2017, Milano, 13-15 September 2017, Milano, Italy Italy
Preliminary verification of the CFD solver SU2 Preliminary ofSymposium the open-source open-source CFD and solver SU2 for for The verification 15th International on District Heating Cooling radial-inflow radial-inflow turbine turbine applications applications b b b Assessing the a,∗ feasibilityVitale of using the heat demand-outdoor Joshua Joshua A. A. Keep Keepa,∗,, Salvatore Salvatore Vitaleb ,, Matteo Matteo Pini Pinib ,, Matteo Matteo Burigana Buriganab School a of Mechanical & Mining Engineering temperature function for long-term district heat demand forecast School of Mechanical & Mining Engineering a a
The University of Queensland The University of Queensland St Lucia Australia a,b,c a a 4072, c c Stb Lucia 4072, Australia b & Power b Propulsion Propulsion & Power Delft University of Technology University of Technology a IN+ Center for Innovation, Technology andKluyverweg Policy Delft Research Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal 1, 2629- Instituto HS Delft,Superior The Netherlands Kluyverweg 1, 2629 HS Delft, The Netherlands b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France
I. Andrić
*, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre
Abstract Abstract There is a need for reliable CFD tools for design and performance prediction of Organic Rankine Cycle turbines. The open-source Abstract There is a need for reliable CFD tools for design and performance prediction of Organic Rankine Cycle turbines. The open-source solver SU2 has gained recognition in the ORC community for the possibility to efficiently perform analysis and design of devices solver SU2 has gained recognition in the ORC community for the possibility to efficiently perform analysis and design of devices operating organic flows.are This paper presents a preliminary verification of the tool SU2 for the simulation of or-the District with heating networks commonly addressed in the literature as one of open-source the most effective solutions for decreasing operating with organic flows. This paper presents a preliminary verification of the open-source tool SU2 for the simulation of organic flows in radial inflow turbines. A comparison is performed against the well established commercial ANSYS CFX solver greenhouse emissions from the Abuilding sector. These systems require highestablished investments which are ANSYS returnedCFX through thefor heat ganic flows ingas radial inflow turbines. comparison is performed against the well commercial solver for two exemplary test-cases. Results show that the solvers predict quantitatively and qualitatively similar fluid-dynamic performance sales. Due totest-cases. the changed climate and building renovation policies, heat demand the future could decrease, two exemplary Results showconditions that the solvers predict quantitatively and qualitatively similarinfluid-dynamic performance and flow features. and flow features. prolonging the investment return period. The main scope of this paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat demand c 2017 Authors. Published by Elsevier Ltd. © 2017 The TheThe cforecast. 2017 The Authors. by Elsevier districtPublished of Alvalade, locatedLtd. in Lisbon (Portugal), was used as a case study. The district is consisted of 665 Peer-review under Peer-review underresponsibility responsibilityof ofthe thescientific scientificcommittee committeeof ofthe theIV IVInternational InternationalSeminar Seminaron onORC ORCPower PowerSystems. Systems. Peer-review under responsibility of the scientific of the Three IV International Seminar on ORC Power Systems. buildings that vary in both construction periodcommittee and typology. weather scenarios (low, medium, high) and three district renovationORC, scenarios were developed intermediate, deep). To estimate the error, obtained heat demand values were Keywords: SU2, Mixing-plane, Radial(shallow, Inflow Turbine, CFD Keywords: ORC, SU2, Mixing-plane, Radial Inflow Turbine, CFD compared with results from a dynamic heat demand model, previously developed and validated by the authors. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). 1. Introduction value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the 1.The Introduction decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and The in is plants renovation scenarios considered).power On thegeneration other hand,(DPG), functionwhereby interceptelectricity increased for 7.8-12.7%by persmaller decade power (depending onin The interest interest in decentralized decentralized power generation (DPG), whereby electricity is produced produced by smaller power plants inthe the same location as the demand, has recently increased. With respect to the traditional centralized power generation coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and the same location as the demand, has recently increased. With respect to the traditional centralized power generation paradigm, DPG has shown advantages for reducing carbon-dioxide emissions. Firstly, it avoids network distribution improve the accuracy of heat demand estimations. paradigm, DPG has shown advantages for reducing carbon-dioxide emissions. Firstly, it avoids network distribution
losses. losses. Second, Second, it it promotes promotes the the exploitation exploitation of of diverse diverse renewable renewable energy energy sources. sources. Lastly, Lastly, it it allows allows cogeneration. cogeneration. ©Among 2017 Thethe Authors. Published by Elsevier Ltd. various technologies that are used for DPG, ORC turbogenerators are arguably one of most Among the various technologies that are used for DPG, ORC turbogenerators are arguably one of the the most Peer-review under responsibility of the Scientific Committee ofFor Thethis 15thpower International Symposium on District Heating that, and repromising[1], particularly, for applications below 100kW. capacity, recent work has outlined promising[1], particularly, for applications below 100kW. For this power capacity, recent work has outlined that, reCooling. the cycle configuration, the use of a single stage radial inflow turbine (RIT) provides the best performance[2]. gardless gardless of of the cycle configuration, the use of a single stage radial inflow turbine (RIT) provides the best performance[2]. Keywords: Heat demand; Forecast; Climate change ∗ ∗
Corresponding author. Tel.: +61 0490 400 157. Corresponding author. Tel.: +61 0490 400 157. E-mail address: [email protected] E-mail address: [email protected]
c 2017 The Authors. Published by Elsevier Ltd. 1876-6102 c ©2017 1876-6102 1876-6102 2017The TheAuthors. Authors.Published PublishedbybyElsevier ElsevierLtd. Ltd. Peer-review under responsibility of the scientific committee of the IV International Seminar on ORC Power Systems. Peer-review under responsibility of of thethe scientific committee of the IV International Seminar on ORC Power Systems. Peer-review under responsibility Scientific Committee of The 15th International Symposium on District Heating and Cooling.
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the IV International Seminar on ORC Power Systems. 10.1016/j.egypro.2017.09.130
Joshua A. Keep et al. / Energy Procedia 129 (2017) 1071–1077 J.A. Keep et al. / Energy Procedia 00 (2017) 000–000
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As documented, the fluid-dynamic design of a RIT is a challenging task[3], especially for ORC applications, whereby design practices and experimental information are much more limited. Reliable steady-state computational fluid dynamic (CFD) simulations and optimization are therefore key tools to design high-efficient RITs for ORC applications. The open-source CFD platform SU2 has recently gained recognition within the ORC community [4–6]. With respect to other CFD tools, SU2 has been developed specifically for solving constrained shape optimization problem via adjoint methods [7]. Recent effort has been devoted to the extension of the solver for turbomachinery calculations with complex thermodynamic models [4,8]. A flux-conservative mixing-plane is now available to simulate multi-stage turbomachinery. Nonetheless, a verification of the implementation has not been performed. This paper presents three-dimensional turbomachinery calculations obtained with SU2 on single-stage RITs. To verify the SU2 results, a comparison is performed against the well established commercial ANSYS CFX[9] CFD tool for two applications. The first test-case features a radial inflow gas turbine for an auxiliary power unit (APU) application, for which experimental data is available. For the second test-case, the SU2 solver is benchmarked by simulating a supersonic single-stage RIT for high-temperature ORC applications, currently under development at the Propulsion & Power Lab of TU Delft [10]. 2. Methodology Steady-state turbomachinery simulations are performed using the Reynolds-averaged Navier-Stokes (RANS) equations closed with SST k-ω turbulence model of Menter [11]. Stator and rotor domains are coupled with a mixing-plane interface[12], and the rotor solution is computed in a rotating reference frame. Second order discretization schemes are applied in both cases. For further details regarding the numerical algorithms implemented in the two CFD tools, the interested reader is referred to[4,7–9]. Geometry is represented as a single stator and rotor blade passage without diffuser or tip clearance. Hexahedral meshes are generated in an automated manner using ANSYS TurboGrid. Near wall refinements are made using automated scaling for y+ based on estimated stage Reynolds number. Mesh statistics for the respective turbines are summarised in Table. 1. Table 1: Mesh statistics
Turbine
Stator Nodes [x1000]
Rotor Nodes [x1000]
y+ [-]
APU ORC
552 1000
687 500
<1.0 stator <1.0 ; rotor <5.0
3. Results and Discussion 3.1. APU Turbine A well documented example of a conventional RIT is the 100 kW APU turbine described in [13]. Nominal boundary conditions based on the test-rig conditions described in [13] are listed in Table 2. The inlet turbulence intensity and the inlet turbulent-laminar viscosity ratio are set to 5 % and 100 %, respectively. To determine a suitable mesh size, a preliminary grid convergence study monitoring the total to static stage efficiency was performed. It was observed that there was no change in performance estimation for meshes of approximately 1.2 million and 6 million nodes, thus the smaller mesh was selected for the comparison study. The the two solvers are initially compared at the nominal conditions listed in Table 2. Results are summarised in Table 3. Solvers are further compared for Mach triangles in Fig. 1. Further to quantitiative comparisons, qualitative comparisons are made for both Mach number and entropy in Fig. 3 and 4 respectively. Fig. 3 shows close qualitative correlation of the predicted flow fields particularly for the
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Table 2: APU turbine boundary conditions.
Fluid
EoS
Shaft speed [RPM]
Ptot,in [kPa]
T tot,in [K]
Ps,out [kPa]
Air
Ideal Gas (γ=1.4)
71700
413.6
477.6
66.71
Table 3: Performance summary and loss breakdown for APU turbine at nominal shaft speed.
Solver
ηt,s
Mass flow rate [kg/s]
Stator KE [%]
Stator Ptot [%]
Rotor Ptot [%]
CFX SU2
90.96 89.95
0.359 0.357
9.00 10.60
12.33 15.25
61.17 68.0
(a) Stator outlet .
(b) Rotor outlet .
Fig. 2: APU Turbine total to static efficiency at design pressure ratio for 80 % - 110 % nominal shaft speed estimated with CFX and SU2, and predicted with experiments.
Fig. 1: Mach triangles for design speed, APU turbine.
rotor. A key difference however appears in the stator outlet region where the solution obtained from SU2 shows a more pronounced wake. The same trend can be observed in the entropy contours in Fig.4, where it is shown that higher entropy generation occurs in the stator wake. This leads to an overall higher entropy value at the rotor inlet for the SU2 prediction. Although this is a purely qualitative comparison, the highlighted entropy difference may explain the lower efficiency value predicted by SU2. Next, the two solvers are compared with experimental data for total to static efficiency at off-design conditions. For off-design considerations the rotational speed is varied from 80% to 110% of the nominal value with pressure ratio set as the design value. Results are illustrated in Fig.2. Both solvers predict higher efficiencies through the operating range. Furthermore, the efficiency peak is estimated at lower rotational speed. One causal factor for the discrepancy between numerical and experimental results is the tip-leakage flow, which is not modelled in numerical calculations and generally causes an efficiency decay at on and off-design conditions. A further potential causal factor for this discrepancy is the use of C p evaluated at room temperature for the gas model in the numerical calculations.
Joshua A. Keep et al. / Energy Procedia 129 (2017) 1071–1077 J.A. Keep et al. / Energy Procedia 00 (2017) 000–000
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(a) CFX .
(b) SU2 .
Fig. 3: Contour plots for relative Mach number at design speed, APU turbine.
(a) CFX .
(b) SU2 .
Fig. 4: Contour plots for static entropy normalised by CFX inlet values, APU turbine.
Note that when comparing experimental and numerical observations for global performance parameters it is crucial to know the location of pressure and temperature measurements [14]. For the present APU turbine comparison, the locations of total and static pressure measurements downstream of the rotor were not disclosed [13], however appropriate values were selected from a prior numerical study [14]. Further to uncertainty in measurement locations, the geometry is not fully replicated, namely the interface between stator and rotor which includes a scalloped back face in the original geometry as shown in images presented by Jones [13]. The impact of such simplifications should be investigated if RANS simulations are to be matched to experimental data. 3.2. ORC Turbine A single-stage RIT for high-temperature ORC applications with 12 kW shaft power has been designed for the ORCHID facility [10] at TU Delft. A preliminary design of this turbine is investigated as the present test case for an ORC turbine. The full geometry of the turbine is described in a companion paper.
Joshua A. Keep et al. / Energy Procedia 129 (2017) 1071–1077 J.A. Keep et al. / Energy Procedia 00 (2017) 000–000
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Boundary conditions are listed in Table 4. As with the previous test case, the inlet turbulence intensity and the inlet turbulent-laminar viscosity ratio are set to 5 % and 100 % respectively. After performing a mesh sensitivity study, a mesh of 1.5 million nodes was selected. Fluid properties are modelled using the Peng-Robinson equation of state for both solvers, with parameters for the equation obtained from REFPROP [17]. The Peng-Robinson implementation for SU2 is detailed in a prior publication [4]. Table 4: ORCHID turbine boundary conditions.
Fluid
EoS
Shaft speed [RPM]
Ptot,in [kPa]
T tot,in [K]
Ps,out [kPa]
Siloxane MM
Peng-Robinson
98119
1809.3
573.16
44.3
Table 5 shows that the predicted mass-flow rate, inter-stage static pressure (Pint ) and absolute Mach number (Mstat,out ) are within a few percent for the two solvers. Further to bulk performance, pressure distribution on the stator blades is investigated in Fig. 5. A key difference in the pressure distributions, shown in Fig. 5, is the suction side of the blade trailing edge where CFX predicts shocks. Table 5: Summary of ORCHID results.
Solver
m ˙ [kg/s]
Pint [kPa]
Mstat,out [-]
CFX SU2 ∆
0.137 0.132 3.6%
150.36 148.37 1.3%
2.04 2.04 -
Fig. 5: Stator blade pressure distributions, ORC turbine.
As can be observed in Fig. 6, both solvers qualitatively predict similar Mach number distribution. The spanwise Mach number distribution correlates qualitatively well along the expansion. As noted for the blade pressure
Joshua A. Keep et al. / Energy Procedia 129 (2017) 1071–1077 J.A. Keep et al. / Energy Procedia 00 (2017) 000–000
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(a) CFX .
(b) SU2 .
Fig. 6: Contour plots for relative Mach number, ORC turbine.
(a) CFX .
(b) SU2 .
Fig. 7: Contour plots for static entropy normalised by CFX inlet values, ORC turbine.
distribution, a key difference between solvers in Fig. 6 is the shocks predicted by CFX. This difference can likely be attributed to the boundary condition implementations of the respective solvers. The same considerations for Mach number hold for the entropy distribution, depicted in Fig. 7. 4. Conclusion This work presents preliminary verification of the SU2 open-source solver against the well-established ANSYS CFX for the simulation of air-based and ORC RIT’s. For the first test case, the APU turbine, the two solvers show quantitative performance parameters within a few percent of each other at design and off-design conditions. For the ORC turbine, a good qualitative and quantitative match in performance and 3D flow features is found for design
Joshua A. Keep et al. / Energy Procedia 129 (2017) 1071–1077 J.A. Keep et al. / Energy Procedia 00 (2017) 000–000
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conditions. The close agreement between the solvers suggests that SU2 is a capable tool for the analysis and design of such turbines. Future work should focus on validating SU2 with more accurate experimental data. Such data should prescribe exact locations of pressure and temperature measurements. Ideally such data would also contain rotor exit traces of static and total pressure. Acknowledgements This research was performed as part of the Australian SolarThermal Research Initiative (ASTRI), a project supported by the Australian Government. The authors greatly acknowledge the Dutch Technology Foundation STW and the partner Robert Bosch GmbH (grant number 13385) and DANA Holding Corporation (grant number 12171) for funding this work. Joshua Keep also wishes to personally acknowledge the support of the Australian Government Research Training Program Scholarship as well as the UQ Graduate School International Travel Award (GSITA). Special thanks to Mostafa Odabaee for supplying the meshes used as the basis for the APU turbine study, and to Vicente Molla for his contribution in the development of the mixing-plane interface in SU2. References [1] Colonna, P., Casati, E., Trapp, C., Mathijssen, T., Larjola, J., Turunen-Saaresti, T., et al. Organic rankine cycle power systems: From the concept to current technology, applications, and an outlook to the future. ASME J Eng Gas Turbines Power 2015;137(10). [2] Bahamonde, S., Pini, M., De Servi, C., Rubino, A., Colonna, P.. Method for the preliminary fluid dynamic design of high-temperature mini-orc turbines. ASME J Eng Gas Turbines Power 2017;. [3] Whitfield, A., Baines, N.C.. Design of radial turbomachines. New York, NY (USA); John Wiley and Sons Inc.; 1990. [4] Vitale, S., Gori, G., Pini, M., Guardone, A., Economon, T.D., Palacios, F., et al. Extension of the su2 open source cfd code to the simulation of turbulent flows of fuids modelled with complex thermophysical laws. In: 22nd AIAA Computational Fluid Dynamics Conference. 2015, p. 2760. [5] Pini, M., Vitale, S., Colonna, P., Gori, G., Guardone, A., Economon, T., et al. Su2: the open-source software for non-ideal compressible flows. IOP Journal of Physics: Conference Series NICFD 2016 for propulsion and power, Varenna, Italy, () 20-21 October 2016 2016;. [6] Gori, G., Molesini, P., Persico, G., Guardone, A.. Non-ideal compressible-fluid dynamics on fast-response pressure probes for unsteady flow measurements in turbomachinery. IOP Journal of Physics: Conference Series NICFD 2016 for propulsion and power, Varenna, Italy, () 20-21 October 2016 2016;. [7] Economon, T.D., Palacios, F., Copeland, S.R., Lukaczyk, T.W., Alonso, J.J.. Su2: An open-source suite for multiphysics simulation and design. AIAA Journal 2015;54(3):828–846. [8] Vitale, S., Albring, T., Pini, M., Gauger, N., Colonna, P.. Fully turbulent discrete adjoint solver for non-ideal compressible flow applications. ready for submission to the GPPS Journal 2017;. [9] ANSYS, I.. Ansys cfx-pre user’s guide. Release 15.0; 2013,. [10] Head, A.J., De Servi, C., Casati, E., Pini, M., Colonna, P.. Preliminary design of the orchid: A facility for studying non-ideal compressible fluid dynamics and testing orc expanders. In: ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers; 2016, p. V003T25A001–V003T25A001. [11] Menter, F.R.. Two-equation eddy-viscosity turbulence models for engineering applications. AIAA journal 1994;32(8):1598–1605. [12] Saxer, A.P., Giles, M.B.. Quasi-three-dimensional nonreflecting boundary conditions for euler equations calculations. Journal of Propulsion and Power 1993;9(2):263–271. [13] Jones, A.. Design and test of a small, high pressure ratio radial turbine. Journal of Turbomachinery 1996;118:363. [14] Sauret, E.. Open design of high pressure ratio radial-inflow turbine for academic validation. In: ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers; 2012, p. 3183–3197. [15] Zangeneh-Kazemi, M., Dawes, W., Hawthorne, W.. Three dimensional flow in radial-inflow turbines. In: ASME 1988 International Gas Turbine and Aeroengine Congress and Exposition. American Society of Mechanical Engineers; 1988, p. V001T01A046–V001T01A046. [16] Bosdas, I., Mansour, M., Kalfas, A.I., Abhari, R.S.. Experimental methods for performance and reliability of steam and gas turbines. In: Proceedings of the 1st Global Power and Propulsion Forum GPPF 2017 Jan 16-18, 2017, Zurich, Switzerland. GPPF-2017-88; 2017,. [17] Lemmon, E., Huber, M., McLinden, M.. Refprop: Reference fluid thermodynamic and transport properties. NIST standard reference database 2007;23(8.0).
Availableonline onlineatatwww.sciencedirect.com www.sciencedirect.com Available
ScienceDirect ScienceDirect Energy Procedia 00 (2017) 000–000 Energy Procedia 00 (2017) 000–000 Energy Procedia 129 Energy Procedia 00(2017) (2017)1071–1077 000–000
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
www.elsevier.com/locate/procedia
IV IV International International Seminar Seminar on on ORC ORC Power Power Systems, Systems, ORC2017 ORC2017 13-15 September 2017, Milano, 13-15 September 2017, Milano, Italy Italy
Preliminary verification of the CFD solver SU2 Preliminary ofSymposium the open-source open-source CFD and solver SU2 for for The verification 15th International on District Heating Cooling radial-inflow radial-inflow turbine turbine applications applications b b b Assessing the a,∗ feasibilityVitale of using the heat demand-outdoor Joshua Joshua A. A. Keep Keepa,∗,, Salvatore Salvatore Vitaleb ,, Matteo Matteo Pini Pinib ,, Matteo Matteo Burigana Buriganab School a of Mechanical & Mining Engineering temperature function for long-term district heat demand forecast School of Mechanical & Mining Engineering a a
The University of Queensland The University of Queensland St Lucia Australia a,b,c a a 4072, c c Stb Lucia 4072, Australia b & Power b Propulsion Propulsion & Power Delft University of Technology University of Technology a IN+ Center for Innovation, Technology andKluyverweg Policy Delft Research Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal 1, 2629- Instituto HS Delft,Superior The Netherlands Kluyverweg 1, 2629 HS Delft, The Netherlands b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France
I. Andrić
*, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre
Abstract Abstract There is a need for reliable CFD tools for design and performance prediction of Organic Rankine Cycle turbines. The open-source Abstract There is a need for reliable CFD tools for design and performance prediction of Organic Rankine Cycle turbines. The open-source solver SU2 has gained recognition in the ORC community for the possibility to efficiently perform analysis and design of devices solver SU2 has gained recognition in the ORC community for the possibility to efficiently perform analysis and design of devices operating organic flows.are This paper presents a preliminary verification of the tool SU2 for the simulation of or-the District with heating networks commonly addressed in the literature as one of open-source the most effective solutions for decreasing operating with organic flows. This paper presents a preliminary verification of the open-source tool SU2 for the simulation of organic flows in radial inflow turbines. A comparison is performed against the well established commercial ANSYS CFX solver greenhouse emissions from the Abuilding sector. These systems require highestablished investments which are ANSYS returnedCFX through thefor heat ganic flows ingas radial inflow turbines. comparison is performed against the well commercial solver for two exemplary test-cases. Results show that the solvers predict quantitatively and qualitatively similar fluid-dynamic performance sales. Due totest-cases. the changed climate and building renovation policies, heat demand the future could decrease, two exemplary Results showconditions that the solvers predict quantitatively and qualitatively similarinfluid-dynamic performance and flow features. and flow features. prolonging the investment return period. The main scope of this paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat demand c 2017 Authors. Published by Elsevier Ltd. © 2017 The TheThe cforecast. 2017 The Authors. by Elsevier districtPublished of Alvalade, locatedLtd. in Lisbon (Portugal), was used as a case study. The district is consisted of 665 Peer-review under Peer-review underresponsibility responsibilityof ofthe thescientific scientificcommittee committeeof ofthe theIV IVInternational InternationalSeminar Seminaron onORC ORCPower PowerSystems. Systems. Peer-review under responsibility of the scientific of the Three IV International Seminar on ORC Power Systems. buildings that vary in both construction periodcommittee and typology. weather scenarios (low, medium, high) and three district renovationORC, scenarios were developed intermediate, deep). To estimate the error, obtained heat demand values were Keywords: SU2, Mixing-plane, Radial(shallow, Inflow Turbine, CFD Keywords: ORC, SU2, Mixing-plane, Radial Inflow Turbine, CFD compared with results from a dynamic heat demand model, previously developed and validated by the authors. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). 1. Introduction value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the 1.The Introduction decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and The in is plants renovation scenarios considered).power On thegeneration other hand,(DPG), functionwhereby interceptelectricity increased for 7.8-12.7%by persmaller decade power (depending onin The interest interest in decentralized decentralized power generation (DPG), whereby electricity is produced produced by smaller power plants inthe the same location as the demand, has recently increased. With respect to the traditional centralized power generation coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and the same location as the demand, has recently increased. With respect to the traditional centralized power generation paradigm, DPG has shown advantages for reducing carbon-dioxide emissions. Firstly, it avoids network distribution improve the accuracy of heat demand estimations. paradigm, DPG has shown advantages for reducing carbon-dioxide emissions. Firstly, it avoids network distribution
losses. losses. Second, Second, it it promotes promotes the the exploitation exploitation of of diverse diverse renewable renewable energy energy sources. sources. Lastly, Lastly, it it allows allows cogeneration. cogeneration. ©Among 2017 Thethe Authors. Published by Elsevier Ltd. various technologies that are used for DPG, ORC turbogenerators are arguably one of most Among the various technologies that are used for DPG, ORC turbogenerators are arguably one of the the most Peer-review under responsibility of the Scientific Committee ofFor Thethis 15thpower International Symposium on District Heating that, and repromising[1], particularly, for applications below 100kW. capacity, recent work has outlined promising[1], particularly, for applications below 100kW. For this power capacity, recent work has outlined that, reCooling. the cycle configuration, the use of a single stage radial inflow turbine (RIT) provides the best performance[2]. gardless gardless of of the cycle configuration, the use of a single stage radial inflow turbine (RIT) provides the best performance[2]. Keywords: Heat demand; Forecast; Climate change ∗ ∗
Corresponding author. Tel.: +61 0490 400 157. Corresponding author. Tel.: +61 0490 400 157. E-mail address: [email protected] E-mail address: [email protected]
c 2017 The Authors. Published by Elsevier Ltd. 1876-6102 c ©2017 1876-6102 1876-6102 2017The TheAuthors. Authors.Published PublishedbybyElsevier ElsevierLtd. Ltd. Peer-review under responsibility of the scientific committee of the IV International Seminar on ORC Power Systems. Peer-review under responsibility of of thethe scientific committee of the IV International Seminar on ORC Power Systems. Peer-review under responsibility Scientific Committee of The 15th International Symposium on District Heating and Cooling.
1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the IV International Seminar on ORC Power Systems. 10.1016/j.egypro.2017.09.130
Joshua A. Keep et al. / Energy Procedia 129 (2017) 1071–1077 J.A. Keep et al. / Energy Procedia 00 (2017) 000–000
1072 2
As documented, the fluid-dynamic design of a RIT is a challenging task[3], especially for ORC applications, whereby design practices and experimental information are much more limited. Reliable steady-state computational fluid dynamic (CFD) simulations and optimization are therefore key tools to design high-efficient RITs for ORC applications. The open-source CFD platform SU2 has recently gained recognition within the ORC community [4–6]. With respect to other CFD tools, SU2 has been developed specifically for solving constrained shape optimization problem via adjoint methods [7]. Recent effort has been devoted to the extension of the solver for turbomachinery calculations with complex thermodynamic models [4,8]. A flux-conservative mixing-plane is now available to simulate multi-stage turbomachinery. Nonetheless, a verification of the implementation has not been performed. This paper presents three-dimensional turbomachinery calculations obtained with SU2 on single-stage RITs. To verify the SU2 results, a comparison is performed against the well established commercial ANSYS CFX[9] CFD tool for two applications. The first test-case features a radial inflow gas turbine for an auxiliary power unit (APU) application, for which experimental data is available. For the second test-case, the SU2 solver is benchmarked by simulating a supersonic single-stage RIT for high-temperature ORC applications, currently under development at the Propulsion & Power Lab of TU Delft [10]. 2. Methodology Steady-state turbomachinery simulations are performed using the Reynolds-averaged Navier-Stokes (RANS) equations closed with SST k-ω turbulence model of Menter [11]. Stator and rotor domains are coupled with a mixing-plane interface[12], and the rotor solution is computed in a rotating reference frame. Second order discretization schemes are applied in both cases. For further details regarding the numerical algorithms implemented in the two CFD tools, the interested reader is referred to[4,7–9]. Geometry is represented as a single stator and rotor blade passage without diffuser or tip clearance. Hexahedral meshes are generated in an automated manner using ANSYS TurboGrid. Near wall refinements are made using automated scaling for y+ based on estimated stage Reynolds number. Mesh statistics for the respective turbines are summarised in Table. 1. Table 1: Mesh statistics
Turbine
Stator Nodes [x1000]
Rotor Nodes [x1000]
y+ [-]
APU ORC
552 1000
687 500
<1.0 stator <1.0 ; rotor <5.0
3. Results and Discussion 3.1. APU Turbine A well documented example of a conventional RIT is the 100 kW APU turbine described in [13]. Nominal boundary conditions based on the test-rig conditions described in [13] are listed in Table 2. The inlet turbulence intensity and the inlet turbulent-laminar viscosity ratio are set to 5 % and 100 %, respectively. To determine a suitable mesh size, a preliminary grid convergence study monitoring the total to static stage efficiency was performed. It was observed that there was no change in performance estimation for meshes of approximately 1.2 million and 6 million nodes, thus the smaller mesh was selected for the comparison study. The the two solvers are initially compared at the nominal conditions listed in Table 2. Results are summarised in Table 3. Solvers are further compared for Mach triangles in Fig. 1. Further to quantitiative comparisons, qualitative comparisons are made for both Mach number and entropy in Fig. 3 and 4 respectively. Fig. 3 shows close qualitative correlation of the predicted flow fields particularly for the
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Table 2: APU turbine boundary conditions.
Fluid
EoS
Shaft speed [RPM]
Ptot,in [kPa]
T tot,in [K]
Ps,out [kPa]
Air
Ideal Gas (γ=1.4)
71700
413.6
477.6
66.71
Table 3: Performance summary and loss breakdown for APU turbine at nominal shaft speed.
Solver
ηt,s
Mass flow rate [kg/s]
Stator KE [%]
Stator Ptot [%]
Rotor Ptot [%]
CFX SU2
90.96 89.95
0.359 0.357
9.00 10.60
12.33 15.25
61.17 68.0
(a) Stator outlet .
(b) Rotor outlet .
Fig. 2: APU Turbine total to static efficiency at design pressure ratio for 80 % - 110 % nominal shaft speed estimated with CFX and SU2, and predicted with experiments.
Fig. 1: Mach triangles for design speed, APU turbine.
rotor. A key difference however appears in the stator outlet region where the solution obtained from SU2 shows a more pronounced wake. The same trend can be observed in the entropy contours in Fig.4, where it is shown that higher entropy generation occurs in the stator wake. This leads to an overall higher entropy value at the rotor inlet for the SU2 prediction. Although this is a purely qualitative comparison, the highlighted entropy difference may explain the lower efficiency value predicted by SU2. Next, the two solvers are compared with experimental data for total to static efficiency at off-design conditions. For off-design considerations the rotational speed is varied from 80% to 110% of the nominal value with pressure ratio set as the design value. Results are illustrated in Fig.2. Both solvers predict higher efficiencies through the operating range. Furthermore, the efficiency peak is estimated at lower rotational speed. One causal factor for the discrepancy between numerical and experimental results is the tip-leakage flow, which is not modelled in numerical calculations and generally causes an efficiency decay at on and off-design conditions. A further potential causal factor for this discrepancy is the use of C p evaluated at room temperature for the gas model in the numerical calculations.
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(a) CFX .
(b) SU2 .
Fig. 3: Contour plots for relative Mach number at design speed, APU turbine.
(a) CFX .
(b) SU2 .
Fig. 4: Contour plots for static entropy normalised by CFX inlet values, APU turbine.
Note that when comparing experimental and numerical observations for global performance parameters it is crucial to know the location of pressure and temperature measurements [14]. For the present APU turbine comparison, the locations of total and static pressure measurements downstream of the rotor were not disclosed [13], however appropriate values were selected from a prior numerical study [14]. Further to uncertainty in measurement locations, the geometry is not fully replicated, namely the interface between stator and rotor which includes a scalloped back face in the original geometry as shown in images presented by Jones [13]. The impact of such simplifications should be investigated if RANS simulations are to be matched to experimental data. 3.2. ORC Turbine A single-stage RIT for high-temperature ORC applications with 12 kW shaft power has been designed for the ORCHID facility [10] at TU Delft. A preliminary design of this turbine is investigated as the present test case for an ORC turbine. The full geometry of the turbine is described in a companion paper.
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Boundary conditions are listed in Table 4. As with the previous test case, the inlet turbulence intensity and the inlet turbulent-laminar viscosity ratio are set to 5 % and 100 % respectively. After performing a mesh sensitivity study, a mesh of 1.5 million nodes was selected. Fluid properties are modelled using the Peng-Robinson equation of state for both solvers, with parameters for the equation obtained from REFPROP [17]. The Peng-Robinson implementation for SU2 is detailed in a prior publication [4]. Table 4: ORCHID turbine boundary conditions.
Fluid
EoS
Shaft speed [RPM]
Ptot,in [kPa]
T tot,in [K]
Ps,out [kPa]
Siloxane MM
Peng-Robinson
98119
1809.3
573.16
44.3
Table 5 shows that the predicted mass-flow rate, inter-stage static pressure (Pint ) and absolute Mach number (Mstat,out ) are within a few percent for the two solvers. Further to bulk performance, pressure distribution on the stator blades is investigated in Fig. 5. A key difference in the pressure distributions, shown in Fig. 5, is the suction side of the blade trailing edge where CFX predicts shocks. Table 5: Summary of ORCHID results.
Solver
m ˙ [kg/s]
Pint [kPa]
Mstat,out [-]
CFX SU2 ∆
0.137 0.132 3.6%
150.36 148.37 1.3%
2.04 2.04 -
Fig. 5: Stator blade pressure distributions, ORC turbine.
As can be observed in Fig. 6, both solvers qualitatively predict similar Mach number distribution. The spanwise Mach number distribution correlates qualitatively well along the expansion. As noted for the blade pressure
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(a) CFX .
(b) SU2 .
Fig. 6: Contour plots for relative Mach number, ORC turbine.
(a) CFX .
(b) SU2 .
Fig. 7: Contour plots for static entropy normalised by CFX inlet values, ORC turbine.
distribution, a key difference between solvers in Fig. 6 is the shocks predicted by CFX. This difference can likely be attributed to the boundary condition implementations of the respective solvers. The same considerations for Mach number hold for the entropy distribution, depicted in Fig. 7. 4. Conclusion This work presents preliminary verification of the SU2 open-source solver against the well-established ANSYS CFX for the simulation of air-based and ORC RIT’s. For the first test case, the APU turbine, the two solvers show quantitative performance parameters within a few percent of each other at design and off-design conditions. For the ORC turbine, a good qualitative and quantitative match in performance and 3D flow features is found for design
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conditions. The close agreement between the solvers suggests that SU2 is a capable tool for the analysis and design of such turbines. Future work should focus on validating SU2 with more accurate experimental data. Such data should prescribe exact locations of pressure and temperature measurements. Ideally such data would also contain rotor exit traces of static and total pressure. Acknowledgements This research was performed as part of the Australian SolarThermal Research Initiative (ASTRI), a project supported by the Australian Government. The authors greatly acknowledge the Dutch Technology Foundation STW and the partner Robert Bosch GmbH (grant number 13385) and DANA Holding Corporation (grant number 12171) for funding this work. Joshua Keep also wishes to personally acknowledge the support of the Australian Government Research Training Program Scholarship as well as the UQ Graduate School International Travel Award (GSITA). Special thanks to Mostafa Odabaee for supplying the meshes used as the basis for the APU turbine study, and to Vicente Molla for his contribution in the development of the mixing-plane interface in SU2. References [1] Colonna, P., Casati, E., Trapp, C., Mathijssen, T., Larjola, J., Turunen-Saaresti, T., et al. Organic rankine cycle power systems: From the concept to current technology, applications, and an outlook to the future. ASME J Eng Gas Turbines Power 2015;137(10). [2] Bahamonde, S., Pini, M., De Servi, C., Rubino, A., Colonna, P.. Method for the preliminary fluid dynamic design of high-temperature mini-orc turbines. ASME J Eng Gas Turbines Power 2017;. [3] Whitfield, A., Baines, N.C.. Design of radial turbomachines. New York, NY (USA); John Wiley and Sons Inc.; 1990. [4] Vitale, S., Gori, G., Pini, M., Guardone, A., Economon, T.D., Palacios, F., et al. Extension of the su2 open source cfd code to the simulation of turbulent flows of fuids modelled with complex thermophysical laws. In: 22nd AIAA Computational Fluid Dynamics Conference. 2015, p. 2760. [5] Pini, M., Vitale, S., Colonna, P., Gori, G., Guardone, A., Economon, T., et al. Su2: the open-source software for non-ideal compressible flows. IOP Journal of Physics: Conference Series NICFD 2016 for propulsion and power, Varenna, Italy, () 20-21 October 2016 2016;. [6] Gori, G., Molesini, P., Persico, G., Guardone, A.. Non-ideal compressible-fluid dynamics on fast-response pressure probes for unsteady flow measurements in turbomachinery. IOP Journal of Physics: Conference Series NICFD 2016 for propulsion and power, Varenna, Italy, () 20-21 October 2016 2016;. [7] Economon, T.D., Palacios, F., Copeland, S.R., Lukaczyk, T.W., Alonso, J.J.. Su2: An open-source suite for multiphysics simulation and design. AIAA Journal 2015;54(3):828–846. [8] Vitale, S., Albring, T., Pini, M., Gauger, N., Colonna, P.. Fully turbulent discrete adjoint solver for non-ideal compressible flow applications. ready for submission to the GPPS Journal 2017;. [9] ANSYS, I.. Ansys cfx-pre user’s guide. Release 15.0; 2013,. [10] Head, A.J., De Servi, C., Casati, E., Pini, M., Colonna, P.. Preliminary design of the orchid: A facility for studying non-ideal compressible fluid dynamics and testing orc expanders. In: ASME Turbo Expo 2016: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers; 2016, p. V003T25A001–V003T25A001. [11] Menter, F.R.. Two-equation eddy-viscosity turbulence models for engineering applications. AIAA journal 1994;32(8):1598–1605. [12] Saxer, A.P., Giles, M.B.. Quasi-three-dimensional nonreflecting boundary conditions for euler equations calculations. Journal of Propulsion and Power 1993;9(2):263–271. [13] Jones, A.. Design and test of a small, high pressure ratio radial turbine. Journal of Turbomachinery 1996;118:363. [14] Sauret, E.. Open design of high pressure ratio radial-inflow turbine for academic validation. In: ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers; 2012, p. 3183–3197. [15] Zangeneh-Kazemi, M., Dawes, W., Hawthorne, W.. Three dimensional flow in radial-inflow turbines. In: ASME 1988 International Gas Turbine and Aeroengine Congress and Exposition. American Society of Mechanical Engineers; 1988, p. V001T01A046–V001T01A046. [16] Bosdas, I., Mansour, M., Kalfas, A.I., Abhari, R.S.. Experimental methods for performance and reliability of steam and gas turbines. In: Proceedings of the 1st Global Power and Propulsion Forum GPPF 2017 Jan 16-18, 2017, Zurich, Switzerland. GPPF-2017-88; 2017,. [17] Lemmon, E., Huber, M., McLinden, M.. Refprop: Reference fluid thermodynamic and transport properties. NIST standard reference database 2007;23(8.0).