This file was created by the Typo3 extension sevenpack version 0.7.16 --- Timezone: CEST Creation date: 2021-10-24 Creation time: 00-48-59 --- Number of references 16 article Noack_25082020 Journal of Fluid Mechanics 2020 897 27 46 An artificial intelligence (AI) control system is developed to maximize the mixingrate of a turbulent jet. This system comprises of six independently operated unsteadyminijet actuators, two hot-wire sensors placed in the jet and genetic programmingfor the unsupervised learning of a near-optimal control law. The ansatz of thislaw includes multi-frequency open-loop forcing, sensor feedback and nonlinearcombinations thereof. Mixing performance is quantified by the decay rate of thecentreline mean velocity of the jet. Intriguingly, the learning process of AI controldiscovers the classical forcings, i.e. axisymmetric, helical and flapping achievable fromconventional control techniques, one by one in the order of increased performance,and finally converges to a hitherto unexplored forcing. Careful examination of thecontrol landscape unveils typical control laws, generated in the learning process, andtheir evolutions. The best AI forcing produces a complex turbulent flow structurethat is characterized by periodically generated mushroom structures, helical motionand an oscillating jet column, all enhancing the mixing rate and vastly outperformingothers. Being never reported before, this flow structure is examined in variousaspects, including the velocity spectra, mean and fluctuating velocity fields and theirdownstream evolution, and flow visualization images in three orthogonal planes, allcompared with other classical flow structures. Along with the knowledge of theminijet-produced flow and its effect on the initial condition of the main jet, theseaspects cast valuable insight into the physics behind the highly effective mixing ofthis newly found flow structure. The results point to the great potential of AI inconquering the vast opportunity space of control laws for many actuators and sensorsand in optimizing turbulence.� mixing enhancement, jets, turbulence control https://www.cambridge.org/core/journals/journal-of-fluid-mechanics/article/artificial-intelligence-control-of-a-turbulent-jet/FD1BA8BD9F20C797DC6FFBAE0173187B 0022-1120 https://doi.org/10.1017/jfm.2020.392 Y.Zhou D.Fan B.Zhang R.Li B. R.Noack article Noack12062020 Experiments in Fluids 2020 61 152 13 A fast triple-parameter extremum seeking method is applied for jet control based on the pioneering work of Gelbert et al.(J Process Control 22(4):700, 2012). The simultaneous adaptation of three input parameters takes less time than the singleinputadaptation of each parameter combined. The key enablers are phase-shifted sinusoids for the input each of which isevaluated by an extended Kalman filter (EKF). An acceleration of the adaption is obtained by a combined EKF couplingthe output to all inputs. The method is illustrated for an analytical optimization problem and experimentally demonstratedfor a turbulent jet mixing control. The considered Reynolds numbers ReD based on the jet exit diameter and velocity are5700, 8000 and 13,300. The main jet is manipulated by a pulsed radially injected minijet which is varied by a mass flowcontroller and an electromagnetic valve up to high frequencies. The mixing performance is characterized by the centerlinejet decay rate and monitored by a hot-wire sensor five diameters downstream at the end of the potential core. The proposedtriple-parameter extremum seeking method optimizes the actuation mass flow ratio, frequency and duty cycle. The decayrate increases 11-fold from the unforced reference value of 0.05 to the optimal actuation level of 0.56. The reproducibilityis demonstrated with various initial actuation parameters. Moreover, the adaptive control robustly tracks the optimal openloopactuation for varying ReD ; the optimal decay rate remains unchanged given the mass flow rate, frequency and dutycycle are optimized. The unforced and actuated flow are investigated with hot wires and visualizations. The three-input ESsignificantly outperforms a two-parameter optimization for the same configuration in multiple respects (Wu et al. in AIAAJ 56(4):1463, 2018): First, the jet decay rate is 8% faster. Second, the convergence time for three parameters is only 25% ofthe adaptation period of two parameters when ReD is varied. Finally, the current steady-state error is 45% less than that ofthe two-parameter optimization. We expect the proposed triple-parameter extremum seeking to be applicable for a largerange of flow control experiments.� https://link.springer.com/article/10.1007/s00348-020-02953-3 14321114,07234864 https://doi.org/10.1007/s00348-020-02953-3 D.Fan Y.Zhou B. R.Noack article Noack21122020 Physics of Fluids 2020 32 125117 1-18 A machine learning control (MLC) is proposed based on the explorative gradient method (EGM) for the optimization and sensitivity analysis of actuation parameters. This technique is applied to reduce the drag of a square-back Ahmed body at a Reynolds number Re = 1.7 × 105. The MLC system consists of pulsed blowing along the periphery of the base, 25 pressure taps distributed on the vertical base of the body, and an EGM controller for unsupervised searching for the best control law. The parameter search space contains the excitation frequency fe, duty cycle α, and flow rate blowing coefficient Cm. It is demonstrated that the MLC may cut short the searching process significantly, requiring only about 100 test runs and achieving 13% base pressure recovery with a drag reduction of 11%. Extensive flow measurements are performed with and without control to understand the underlying flow physics. The converged control law achieves fluidic boat tailing and, meanwhile, eliminates the wake bistability. Such simultaneous achievements have never been reported before. A machine-learned response model is proposed to link the control parameters with the cost function. A sensitivity analysis based on this model unveils that the control performance is sensitive to fe and α but less so to Cm. The result suggests that a small sacrifice on performance will give a huge return on actuation power saving, which may provide important guidance on future drag reduction studies as well as engineering applications. https://aip.scitation.org/doi/full/10.1063/5.0033156 1070-6631 https://doi.org/10.1063/5.0033156 B.Zhang Y.Zhou Y.Fan B. R.Noack article Noack_21042020 Physical Review Fluids 2020 5 4 31 As pioneered by Donzis and Maqui [J. Fluid Mech. 797, 181 (2016)] and Khurshid and Donzis [Phys. Fluids 31, 015103 (2019)], the compressible isotropic turbulence in thermal nonequilibrium is drawing attention in the fluid dynamics community. In the present study, the vibrational rate and the dissipation or production of vibrational energy fluctuation of compressible isotropic turbulence with solenoidal forcing in vibrational nonequilibrium are investigated using numerical simulations. The turbulent Mach number (Mt) is set to be 0.44 and 1.09, and the Taylor Reynolds number (Reλ) equals 98.58 approximately. The focus is on the effect of the normalized vibrational relaxation time (Kτ) and characteristic temperature (θv) on statistical features of the vibrational rate and the dissipation or production components of vibrational energy fluctuation. When Kτ is small (≲1.0), the average of normalized vibrational rate conditioned on the normalized dilatation is positive in the compression region and negative in the expansion region, enhanced with increase of compression and expansion level, respectively. It indicates that, on average, the energy transfers from translational-rotational to vibrational modes in the compression region and in the inverse direction in the expansion region. The conditional average of the normalized vibrational rate reduces with increase of Kτ, and is insensitive to θv. The joint probability density functions (PDFs) and conditional PDFs of the normalized vibrational rate are also analyzed, to reveal the effect of compression and expansion motions on the internal energy exchange between the translational-rotational and vibrational modes. The dissipation or production of vibrational energy fluctuation can result from effects of dilatation, thermal diffusion, and vibrational relaxation. The dissipation component due to thermal diffusion always weakens the vibrational energy fluctuation in both compression and expansion regions for the weakly and highly compressible turbulence, but its effect is insignificant compared to the other two components. For the weakly compressible turbulence, the dissipation or production of vibrational energy fluctuation mainly comes from effects of dilatation and vibrational relaxation when Kτ is small (≲1.0), and the vibrational relaxation component loses its significance with increasing of Kτ. For the highly compressible turbulence, both the dilatation and vibrational relaxation effects play an important role in the dissipation and production of vibrational energy fluctuation, and their effects are significantly different from that of the weakly compressible turbulence. The conditional average, joint PDFs, and conditional PDFs of each dissipation or production component are calculated and analyzed in detail to reveal their effects on the vibrational energy fluctuation in different compression and expansion regions. Furthermore, the bulk viscosity effect on the compression or expansion motion in flow field, the vibrational rate, and the dissipation or production components of vibrational energy fluctuation is discussed briefly. 044602 https://journals.aps.org/prfluids/abstract/10.1103/PhysRevFluids.5.044602 2469-990X 10.1103/PhysRevFluids.5.044602 Q.Zheng J.Wang B. R.Noack H.Li M.Wan J.Chen article Zhang2019 Combustion Science and Technology 2019 191 1520 - 1540 This work assesses the effect of using different closure concepts for the spatially filtered mean reaction rate on the resolved spectral response of turbulent heat release in large eddy simulations (LESs). Two well-known combustion models, the turbulent flame speed closure (TFC) and the dynamically thickened flame (DTF) models have been applied to a premixed turbulent jet flame with otherwise identical numerical setups. Although the flame front is artificially thickened in the DTF model, it reproduces a thinner flame and, hence, stronger flame–turbulence interactions compared to the TFC model. As the time-averaged quantities from both methods are comparable, the DTF approach shows overall higher fluctuations of local and integral heat release rates in the spectral domain compared to the TFC model, particularly in the high-frequency range. A better agreement with measured sound pressure density is observed for TFC in the low-frequency range and for DTF in the high-frequency range. TFC simulations with different source formulations, that is, ω˙¯¯c∝c˜(1−c˜) and ω˙¯¯c∝∣∣∇c˜∣∣, showed comparable flame thicknesses and spectra of heat release, but the averaged flow quantities calculated with ω˙¯¯c∝∣∣∇c˜∣∣, however, deviate largely from measured data for the current setup. In the second step, the same formulations for the mean rate are applied to an excited plane-jet flame (two-dimensional (2D)) using equidistant grid cells and forced inflow conditions, thereby excluding the influence of varying grid resolution and broadband turbulent fluctuations. This setup is specifically tailored for a detailed analysis of flame response to flow unsteadiness and grid resolution. The formulation of the reaction rate according to the TFC approach again results in a considerably thicker flame compared to results obtained from the DTF model and direct numerical simulation, even on a sufficiently fine mesh. Therefore, the DTF formulation of the reaction rate shows overall stronger responses of heat release rates to forced fluctuations than the TFC formulation. Differences are smaller in the low-frequency range, indicating a stronger damping of heat release fluctuations with increasing frequency for the TFC formulation. Coarsening the grid leads to a much stronger damping of heat release fluctuations in the DTF formulation compared with the TFC formulation, so that the benefit of the DTF formulation decreases with decreasing grid resolution. This reflects the different sensitivity behavior of these models with respect to unsteady flows and grid resolutions, which is of great importance for computing thermoacoustic problems with LES, for example, combustion noise. Large eddy simulation, turbulent premixed flame, turbulent flame speed, thickened flame approach, OpenFOAM https://doi.org/10.1080/00102202.2018.1558218 English 10.1080/00102202.2018.1558218 F.Zhang T.Zirwes P.Habisreuther H.Bockhorn T.Dimosthenis H.Nawroth C. O.Paschereit article Zanuy2017 Eisenbahntechnische Rundschau 2017 5 5 78 - 81 In diesem Artikel wird die Entwicklung einer Berechnungsformel für den aerodynamischen Widerstand von Containerzügen vorgestellt, in Abhängigkeit von Zuglänge, Abstand der auf dem Zug verladenen Container und Zuggeschwindigkeit. Hierzu wurden ein CFD-Modell (computational fluid dynamics model) erstellt und Windkanalversuche ausgewertet. Bei einem mit 100 km/h fahrenden Containerzug der Deutschen Bahn AG ist der Luftwiderstand für 44 % des Energieverbrauchs der Zugfahrt verantwortlich. Der Abstand zwischen den Containern spielt dabei eine große Rolle. Anders als bei Hochgeschwindigkeitszügen, die aerodynamisch optimiert sind, und bei Sattelschleppern im Straßen-Fernverkehr, bei denen der Energieverbrauch durch aerodynamische Formgebung reduziert werden konnte, fand dieses Thema im Güterverkehr auf der Schiene bisher wenig Beachtung. http://www.eurailpress.de/archiv/fachartikelarchiv/ergebnisliste/artikelansicht.html?tx_it24archiv_list%5Barticle%5D=13361&tx_it24archiv_list%5Baction%5D=show&tx_it24archiv_list%5Bcontroller%5D=Article&cHash=6a63329fb4be304879af99d684076628 ETR A. C.Zanuy C. N.Nayeri O. S.Pérez incollection Nayeri2016b 2016 79 133-141 Dillmann, A. and Orellano, A. Springer International Publishing Lecture Notes in Applied and Computational Mechanics The Aerodynamics of Heavy Vehicles III English 978-3-319-20121-4 10.1007/978-3-319-20122-1_8 C. N.Nayeri C.Strangfeld C.Zellmann M.Schober A.Tietze C. O.Paschereit inproceedings Scarpato2016 ASME 2016 6 4B, Cobustion, Fuels and Emissions Lean premix technology is widely spread in gas turbine combustion systems, allowing modern power plants to fulfill very stringent emission targets. These systems are however also prone to thermoacoustic instabilities, which can limit the engine operating window. The thermoacoustic analysis of a combustor is thus a key element in its development process. GT24/GT26 reheat combustion system feature a unique technology where fuel is injected into a hot gas stream from a first combustor and auto-ignites in a sequential combustion chamber. Recently, a methodology was successfully developed and validated to analyze the dynamic response of an auto-ignition flame and to extract the Flame Transfer Function using unsteady Large-Eddy Simulations (LES) [GT2015-42622]. The flame was assumed to behave as a Single Input Single Output (SISO) system. The analysis qualitatively highlighted the important role of temperature and equivalence ratio fluctuations, but it was not possible to separate these effects from velocity perturbations. This is the main target of the present work: the flame is treated as a multi-parameter system, and compressible LES are conducted to extract the frequency-dependent flame transfer function. The simulations are forced with uncorrelated broadband signals in order to efficiently calculate the dynamic response over the frequency range of interest. The methodology introduced in this work will help to define stable operation concepts for gas turbines. http://proceedings.asmedigitalcollection.asme.org/proceeding.aspx?articleid=2555105 ASME Proceedings Seoul, Korea Turbo Expo 2016: Turbomachinery Technical Conference and Exposition June 13–17, 2016 978-0-7918-4976-7 10.1115/GT2016-57699 A.Scarpato L.Zander R.Kulkarni B.Schuermans inproceedings Geiser2014a 2014 AIAA paper no. 2015-2476 AIAA Aviation, 13th AIAA/CEAS Aeroacoustics Conference, 16-20 June 2014, Atlanta, Georgia, USA 978-1-62410-285-1 10.2514/6.2014-2476 G.Geiser H.Nawroth A.Hosseinzadeh F.Zhang H.Bockhorn P.Habisreuther J.Janicka C. O.Paschereit W.Schroeder article zhang2013 Acta Acustica united with Acustica 2013 99 6 940-951 10.3813/AAA.918673 F.Zhang P.Habisreuther H.Bockhorn H.Nawroth C. O.Paschereit conference nawroth2013f 2013 Euromech Colloquium 546: Combustion Dynamics and Combustion Noise, May 13-16, 2013, Menaggio, Italy G.Geiser A.Hosseinzadeh H.Nawroth F.Zhang J.Schröder J.Janicka C. O.Paschereit P.Habisreuther H.Bockhorn inproceedings nawroth2013c 2013 AIAA paper 2013-2459 American Institute of Aeronautics and Astronautics 43rd AIAA Fluid Dynamics Conference, June 24-27, 2013, San Diego, California, USA 978-1-62410-214-1 10.2514/6.2013-2459 H.Nawroth C. O.Paschereit F.Zhang P.Habisreuther H.Bockhorn inproceedings nawroth2012a 2012 AIAA paper 2012-3072 http://arc.aiaa.org/doi/abs/10.2514/6.2012-3072 American Institute of Aeronautics and Astronautics 42nd AIAA Fluid Dynamics Conference, June 25-28, 2012, New Orleans, Louisiana, USA 10.2514/6.2012-3072 H.Nawroth A.Saurabh C. 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