Abstract

The global drive toward renewable energy is imposing challenging operating requirements on power turbines. Flexible load-leveling applications must accept more frequent and demanding start-stop cycles. Full transient analyses are too computationally expensive for real-time simulation across all operating regimes so monitoring relies on sparse physical measurements. Alone, these sparse data lack the fidelity for real-time prediction of a complex thermal field. A new hybrid methodology is proposed, coupling data across a range of fidelities to bridge the limitations in the individual analyses. Combining several fidelity methods in parallel: low-order models, corrected by real-time physical measurements, are calibrated with high-fidelity simulations. The multifaceted hybrid approach enables the real-time speed of low-order analysis at high resolution. This paper series develops the critical enabling features of the hybrid method. Fast three-dimensional thermal simulation is fundamental to the methodology. Low-order network models enable the real-time thermal calculation of regions inaccessible to monitoring and facilitate clearance and stress simulation necessary for flexible turbine operation. A novel automated construction method is presented, allowing complex full turbine thermal networks to be built with ease. Developed in Tensorflow, the thermal networks directly support graphics processing unit acceleration and neural network integration for seamless data transfer within the hybrid system. Capturing flow and material physics from high fidelity data, the hybrid network method demonstrates comparable accuracy at greatly reduced computational cost. The method is validated using real-machine data capturing a period of flexible transient operation.

References

1.
Greis
,
J.
,
Gobrecht
,
E.
, and
Wendt
,
S.
,
2012
, “
Flexible and Economical Operation of Power Plants—25 Years of Expertise
,”
ASME
Paper No. GT2012-68716.10.1115/GT2012-68716
2.
Della Villa
,
S.
,
2018
, “
Energy Innovation: A Focus on Power Generation Data Capture and Analytics in a Competitive Market
,”
ASME
Paper No. GT2018-75030.10.1115/GT2018-75030
3.
Baker
,
M.
, and
Rosic
,
B.
,
2024
, “
The Hybrid Pathway to Flexible Power Turbines, Part I: Novel Autoencoder Methods for the Automated Optimization of Thermal Probes and Fast Sparse Data Reconstruction, Enabling Real-Time Thermal Analysis
,”
ASME J. Eng. Gas Turbines Power
,
146
(
3
), p.
031020
.10.1115/1.4063581
4.
Baker
,
M.
, and
Rosic
,
B.
,
2024
, “
The Hybrid Pathway to Flexible Power Turbines, Part II: Fast Data Transfer Methods Between Varying Fidelity Simulations, to Enable Efficient Conjugate Thermal Field Prediction
,”
ASME J. Eng. Gas Turbines Power
,
146
(
3
), p.
031021
.10.1115/1.4063588
5.
Baker
,
M.
,
2022
, “
Turbines for Flexible Power Plant Operation
,” DPhil thesis,
University of Oxford, Oxford
, UK.
6.
Künzi
,
R.
,
2015
, “
Thermal Design of Power Electronic Circuits
,” R. Bailey, ed., Proceedings of the CAS-CERN Accelerator School: Power Converters, Baden, Switzerland, May 7–14, No.
CERN-2015-003
.10.5170/CERN-2015-003.311
7.
Lin
,
X.
,
Perez
,
H.
,
Siegel
,
J.
,
Stefanopoulou
,
A.
,
Li
,
Y.
,
Anderson
,
R.
,
Ding
,
Y.
, and
Castanier
,
M.
,
2013
, “
Online Parameterization of Lumped Thermal Dynamics in Cylindrical Lithium Ion Batteries for Core Temperature Estimation and Health Monitoring
,”
IEEE Trans. Control Syst. Technol.
,
21
(
5
), pp.
1745
1755
.10.1109/TCST.2012.2217143
8.
Sequeira
,
S.
,
Bennion
,
K.
,
Cousineau
,
J.
,
Narumanchi
,
S.
,
Moreno
,
G.
,
Kumar
,
S.
, and
Joshi
,
Y.
,
2022
, “
Validation and Parametric Investigations of an Internal Permanent Magnet Motor Using a Lumped Parameter Thermal Model
,”
ASME J. Electron. Packag.
,
144
(
2
), p.
021114
.10.1115/1.4053121
9.
Ghahfarokhi
,
P.
,
Kallaste
,
A.
,
Belahcen
,
A.
, and
Vaimann
,
T.
,
2020
, “
Analytical Thermal Model and Flow Network Analysis Suitable for Open Self‐Ventilated Machines
,”
IET Electr. Power Appl.
,
14
(
6
), pp.
929
936
.10.1049/iet-epa.2019.1020
10.
Giuntini
,
S.
,
Andreini
,
A.
,
Facchini
,
B.
,
Mantero
,
M.
,
Pirotta
,
M.
,
Olmes
,
S.
, and
Zierer
,
T.
,
2017
, “
Transient Thermal Modelling of Whole GT Engine With a Partly Coupled FEM-Fluid Network Approach
,”
ASME
Paper No. GT2017-64512.10.1115/GT2017-64512
11.
Spelling
,
J.
,
Jöcker
,
M.
, and
Martin
,
A.
,
2012
, “
Thermal Modeling of a Solar Steam Turbine With a Focus on Start-Up Time Reduction
,”
ASME J. Eng. Gas Turbines Power
,
134
(
1
), p.
013001
.10.1115/1.4004148
12.
Topel
,
M.
,
Spelling
,
J.
,
Jöcker
,
M.
, and
Laumert
,
B.
,
2014
, “
Geometric Modularity in the Thermal Modeling of Solar Steam Turbines
,”
Energy Procedia
,
49
, pp.
1737
1746
.10.1016/j.egypro.2014.03.184
13.
Topel
,
M.
,
Genrup
,
M.
,
Jöcker
,
M.
,
Spelling
,
J.
, and
Laumert
,
B.
,
2015
, “
Operational Improvements for Startup Time Reduction in Solar Steam Turbines
,”
ASME J. Eng. Gas Turbines Power
,
137
(
4
), p.
042604
.10.1115/1.4028661
14.
Topel
,
M.
, and
Laumert
,
B.
,
2018
, “
Improving Concentrating Solar Power Plant Performance by Increasing Steam Turbine Flexibility at Start-Up
,”
Sol. Energy
,
165
, pp.
10
18
.10.1016/j.solener.2018.02.036
15.
Murray
,
A.
,
Ireland
,
P.
, and
Romero
,
E.
,
2021
, “
An Experimentally Validated Low-Order Model of the Thermal Response of Double-Wall Effusion Cooling Systems for High-Pressure Turbine Blades
,”
ASME J. Turbomach.
,
143
(
11
), p.
111015
.10.1115/1.4050976
16.
Incropera
,
F.
,
2007
,
Fundamentals of Heat and Mass Transfer
, 6th ed.,
Wiley
,
Hoboken, NJ
.
17.
Codecasa
,
L.
, and
Rencz
,
M.
,
2019
,
Thermal and Electro-Thermal System Simulation
,
MDPI—Multidisciplinary Digital Publishing Institute
, Basel, Switzerland.
18.
Milan
,
H.
, and
Gebremedhin
,
K.
,
2016
, “
Tetrahedral Node for Transmission-Line Modeling (TLM) Applied to Bio-Heat Transfer
,”
Comput. Biol. Med.
,
79
, pp.
243
249
.10.1016/j.compbiomed.2016.10.023
19.
Schlömer
,
N.
,
2021
, “
Meshio: Tools for Mesh Files
,” Zenodo, Berlin, Germany, accessed Dec. 31, 2019, https://github.com/nschloe/meshio
20.
Abadi
,
M.
,
Agarwal
,
A.
,
Barham
,
P.
,
Brevdo
,
E.
,
Chen
,
Z.
,
Citro
,
C.
,
Corrado
,
G. S.
, et al.,
2015
, “
TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems
,”
Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation 2016
, Savannah, GA, Nov. 2–4, pp.
265
283
.https://dl.acm.org/doi/10.5555/3026877.3026899
21.
Ahrens
,
J.
,
Geveci
,
B.
, and
Law
,
C.
,
2005
, ParaView: An End-User Tool for Large Data Visualization, Paraview, New York.
22.
Cormen
,
T.
,
2009
,
Introduction to Algorithms (Electronic Resource)
, 3rd ed.,
MIT Press
,
Cambridge, UK
.
23.
Developers o
f Thermal Analysis Kit (TAK),
2000
, “
Thermal Network Modeling Handbook
,” K&K Associates, Westminster, CO, Report No. NAS 9-10435, Version: 97‐003.
24.
He
,
L.
, and
Oldfield
,
M.
,
2011
, “
Unsteady Conjugate Heat Transfer Modeling
,” ASME
J. Turbomach.
,
133
(
3
), p.
031022
.10.1115/1.4001245
25.
Łuczyński
,
P.
, Toebben, D., Wirsum, M., Mohr, W. F. D., and Helbig, K.,
2018
, “
Unsteady Conjugate Heat Transfer Investigation of a Multistage Steam Turbine in Warm-Keeping Operation With Hot Air
,”
ASME J. Eng. Gas Turbines Power
, 141(1), 011005.10.1115/1.4040823
26.
Giles
,
M.
,
1997
, “
Stability Analysis of Numerical Interface Conditions in Fluid-Structure Thermal Analysis
,”
Int. J. Numer. Methods Fluids
,
25
(
4
), pp.
421
436
.10.1002/(SICI)1097-0363(19970830)25:4<421::AID-FLD557>3.0.CO;2-J
27.
Carslaw
,
H.
, and
Jaeger
,
J.
,
1947
,
Conduction of Heat in Solids
,
Clarendon Press
,
Oxford
, UK.
28.
Goyal
,
V.
,
Xu
,
M.
,
Kapat
,
J.
, and
Vesely
,
L.
,
2020
, “
Prediction of Gas Turbine Performance Using Machine Learning Methods
,”
ASME
Paper No. GT2020-15232.10.1115/GT2020-15232
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