{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T02:56:23Z","timestamp":1775789783781,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T00:00:00Z","timestamp":1714089600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Performance adaptation is an effective way to improve the accuracy of gas turbine performance models. Although current performance adaptation methods, such as those using genetic algorithms or evolutionary computation to modify component characteristic maps, are useful for finding good solutions, they are essentially searching methods and suffer from long computation time. This paper presents a novel approach that can achieve good performance adaptation with low time complexity and without using any searching method. In this method, the actual component performance parameters are first estimated using engine measurements at different operating conditions. For each operating condition, some scaling factors are introduced and calculated to indicate the difference between the actual and predicted component performance parameters. Afterward, an interpolating algorithm is adopted to synthesize the scaling factors for modifying all major component maps. The adapted component maps are then able to make the engine model match all the gas path measurements and achieve the required accuracy of the engine performance model. The proposed approach has been tested with a model high-bypass turbofan engine using simulated data. The results show that the proposed performance adaptation approach can effectively improve the model\u2019s accuracy. Specifically, the prediction errors can be reduced from about 9% to about 0.6%. In addition, this approach has much less computational complexity compared to other optimization-based counterparts.<\/jats:p>","DOI":"10.3390\/sym16050522","type":"journal-article","created":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T08:18:27Z","timestamp":1714119507000},"page":"522","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Deterministic Calibration Method for the Thermodynamic Model of Gas Turbines"],"prefix":"10.3390","volume":"16","author":[{"given":"Zhen","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Energy and Power Engineer, Beihang University, Beijing 102206, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Energy and Power Engineer, Beihang University, Beijing 102206, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shubo","family":"Yang","sequence":"additional","affiliation":[{"name":"Institute for Aero Engine, Tsinghua University, Beijing 102202, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7764-0530","authenticated-orcid":false,"given":"Meiyin","family":"Zhu","sequence":"additional","affiliation":[{"name":"International Innovation Institute, Beihang University, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Miao, K., Wang, X., Zhu, M., Yang, S., Pei, X., and Jiang, Z. (2022). Transient Controller Design Based on Reinforcement Learning for a Turbofan Engine with Actuator Dynamics. Symmetry, 14.","DOI":"10.3390\/sym14040684"},{"key":"ref_2","first-page":"961","article-title":"Status, Challenges and Perspectives of Aero-Engine Simulation Technology","volume":"39","author":"Cao","year":"2018","journal-title":"J. Propuls. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1340","DOI":"10.1109\/TR.2018.2822702","article-title":"Performance-based gas turbine health monitoring, diagnostics, and prognostics: A survey","volume":"67","author":"Hanachi","year":"2018","journal-title":"IEEE Trans. Reliab."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Jiang, Z., Yang, S., Wang, X., and Long, Y. (2022). An Onboard Adaptive Model for Aero-Engine Performance Fast Estimation. Aerospace, 9.","DOI":"10.3390\/aerospace9120845"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"8704","DOI":"10.15282\/ijame.18.2.2021.08.0664","article-title":"The Use of Short-Term Compressed Air Supercharging in a Combustion Engine with Spark Ignition","volume":"18","author":"Mamala","year":"2021","journal-title":"Int. J. Automot. Mech. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Cruz-Manzo, S., Panov, V., and Zhang, Y. (2018). Gas path fault and degradation modelling in twin-shaft gas turbines. Machines, 6.","DOI":"10.3390\/machines6040043"},{"key":"ref_7","unstructured":"Panov, V. (2014, January 16\u201320). Auto-tuning of real-time dynamic gas turbine models. Turbo Expo: Power for Land, Sea, and Air. Proceedings of the ASME Turbo Expo 2014: Turbine Technical Conference and Exposition (GT2014-25606, V006T06A004), D\u00fcsseldorf, Germany."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1017\/aer.2017.37","article-title":"Reducing parametric uncertainty in limit-cycle oscillation computational models","volume":"121","author":"Hayes","year":"2017","journal-title":"Aeronaut. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1115\/1.2906157","article-title":"Adaptive Simulation of Gas Turbine Performance","volume":"112","author":"Stamatis","year":"1990","journal-title":"J. Eng. Gas Turbines Power"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"890","DOI":"10.2514\/3.23828","article-title":"Adaptive modeling of jet engine performance with application to condition monitoring","volume":"10","author":"Lambiris","year":"1994","journal-title":"J. Propuls. Power"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"635","DOI":"10.2514\/1.38823","article-title":"Multiple-point adaptive performance simulation tuned to aeroengine test-bed data","volume":"25","author":"Li","year":"2009","journal-title":"J. Propuls. Power"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1115\/1.4002620","article-title":"Nonlinear Multiple Points Gas Turbine Off-Design Performance Adaptation Using a Genetic Algorithm","volume":"133","author":"Li","year":"2011","journal-title":"J. Eng. Gas Turbines Power"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"031701","DOI":"10.1115\/1.4004395","article-title":"Improved Multiple Point Nonlinear Genetic Algorithm Based Performance Adaptation Using Least Square Method","volume":"134","author":"Li","year":"2012","journal-title":"J. Eng. Gas Turbines Power"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"071202","DOI":"10.1115\/1.4026548","article-title":"Turbojet engine performance tuning with a new map adaptation concept","volume":"136","author":"Benini","year":"2014","journal-title":"J. Eng. Gas Turbines Power"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1758","DOI":"10.1017\/aer.2017.96","article-title":"Non-linear model calibration for off-design performance prediction of gas turbines with experimental data","volume":"121","author":"Tsoutsanis","year":"2017","journal-title":"Aeronaut. J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1016\/j.apenergy.2014.08.115","article-title":"A component map tuning method for performance prediction and diagnostics of gas turbine compressors","volume":"135","author":"Tsoutsanis","year":"2014","journal-title":"Appl. Energy"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"36772","DOI":"10.1109\/ACCESS.2019.2905865","article-title":"Joint steady state and transient performance adaptation for aero engine mathematical model","volume":"7","author":"Pang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1016\/j.energy.2015.04.025","article-title":"Dynamic modeling of exergy efficiency of turboprop engine components using hybrid genetic algorithm-artificial neural networks","volume":"86","author":"Baklacioglu","year":"2015","journal-title":"Energy"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1515\/tjj-2015-0030","article-title":"Optimization of a Turboprop UAV for Maximum Loiter and Specific Power Using Genetic Algorithm","volume":"33","author":"Dinc","year":"2016","journal-title":"Int. J. Turbo Jet-Engines"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/s10479-005-3971-7","article-title":"Metaheuristics in combinatorial optimization","volume":"140","author":"Gendreau","year":"2005","journal-title":"Ann. Oper. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s11047-008-9098-4","article-title":"A survey on metaheuristics for stochastic combinatorial optimization","volume":"8","author":"Bianchi","year":"2009","journal-title":"Nat. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1115\/1.2136369","article-title":"An adaptation approach for gas turbine design-point performance simulation","volume":"128","author":"Li","year":"2006","journal-title":"J. Eng. Gas Turbines Power"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Roth, B., Doel, D.L., Mavris, D.N., and Beeson, D. (2003, January 16\u201319). High-accuracy matching of engine performance models to test data. Proceedings of the ASME Turbo Expo 2003: Power for Land, Sea and Air (GT2003-38784), Atlanta, GA, USA.","DOI":"10.1115\/GT2003-38784"},{"key":"ref_24","first-page":"398","article-title":"Iterative Solution of Nonlinear Equations in Several Variables","volume":"25","author":"Ortega","year":"1970","journal-title":"Math. Comput."},{"key":"ref_25","unstructured":"Kurzke, J., and Halliwell, I. (2018). Propulsion and Power: An Exploration of Gas Turbine Performance Modeling, Springer International Publishing."},{"key":"ref_26","unstructured":"Yang, S. (2024, January 01). GTML-E: Gas Turbine Modeling Library for Education. Available online: https:\/\/github.com\/xjysb\/GTML_E."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chapman, J.W., Lavelle, T.M., May, R., Litt, J.S., and Guo, T.-H. (2014, January 28\u201330). Propulsion System Simulation Using the Toolbox for the Modeling and Analysis of Thermodynamic Systems (T MATS). Proceedings of the 50th AIAA\/ASME\/SAE\/ASEE Joint Propulsion Conference (AIAA 2014-3929), Cleveland, OH, USA.","DOI":"10.2514\/6.2014-3929"},{"key":"ref_28","unstructured":"Visser, W.P., and Broomhead, M.J. (2000, January 8\u201311). GSP, a Generic Object-Oriented Gas Turbine Simulation Environment. Proceedings of the ASME Turbo Expo 2000: Power for Land, Sea, and Air Turbo (V001T01A002), Munich, Germany."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Dyson, R.J., and Doel, D.L. (1984, January 11\u201313). CF6-80 condition monitoring-the engine manufacturer\u2019s involvement in data acquisition and analysis. Proceedings of the 20th AIAA\/\/SAE\/ASEE Joint Propulsion Conference, Cincinnati, OH, USA.","DOI":"10.2514\/6.1984-1412"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/16\/5\/522\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:33:46Z","timestamp":1760106826000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/16\/5\/522"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,26]]},"references-count":29,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["sym16050522"],"URL":"https:\/\/doi.org\/10.3390\/sym16050522","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,26]]}}}