### 21982 results for qubit oscillator frequency

Contributors: Fang Yuan, Daotong Chong, Quanbin Zhao, Weixiong Chen, Junjie Yan

Date: 2016-07-01

The condensation **oscillation** of submerged steam was investigated theoretically and experimentally at the condensation **oscillation** regime. It was found that pressure **oscillation** **frequency** was consistent with the bubble **oscillating** **frequency** and there was a quasi-steady stage when bubble diameters remained constant. A thermal-hydraulic model for the condensation **oscillation** regime was proposed based on potential flow theory, taking into account the effects of interface condensation and translatory flow. Theoretical derivations indicated that **oscillation** **frequencies** were mainly determined by bubble diameters and translatory velocity. A force balance model was applied to the calculation of bubble diameters at quasi-steady stage, and the **oscillation** **frequencies** were predicted with the calculated diameters. Theoretical analysis and experimental results turned out that **oscillation** **frequencies** at the condensation **oscillation** regime decreased with the increasing steam mass flux and pool temperature. The predicted **frequencies** corresponded to the experimental data well with the discrepancies of ±21.7%....Dominant **frequencies** of 10mm nozzle.
...Condensation regime map by Cho et al. [1] (C–chugging, TC—transitional region from chugging to CO, CO—condensation **oscillation**, SC—stable condensation, BCO—bubble condensation **oscillation**, IOC—interfacial **oscillation** condensation).
...Condensation **oscillation**...**Frequencies** at different test conditions—250kgm−2s−1.
...**Frequency**...Prediction accuracy of simultaneous equations for **oscillation** **frequency**.
...**Frequencies** at different test conditions—300kgm−2s−1.
... The condensation **oscillation** of submerged steam was investigated theoretically and experimentally at the condensation **oscillation** regime. It was found that pressure **oscillation** **frequency** was consistent with the bubble **oscillating** **frequency** and there was a quasi-steady stage when bubble diameters remained constant. A thermal-hydraulic model for the condensation **oscillation** regime was proposed based on potential flow theory, taking into account the effects of interface condensation and translatory flow. Theoretical derivations indicated that **oscillation** **frequencies** were mainly determined by bubble diameters and translatory velocity. A force balance model was applied to the calculation of bubble diameters at quasi-steady stage, and the **oscillation** **frequencies** were predicted with the calculated diameters. Theoretical analysis and experimental results turned out that **oscillation** **frequencies** at the condensation **oscillation** regime decreased with the increasing steam mass flux and pool temperature. The predicted **frequencies** corresponded to the experimental data well with the discrepancies of ±21.7%.

Files:

Contributors: Xiufeng Cao, Qing Ai, Chang-Pu Sun, Franco Nori

Date: 2012-01-09

**Qubit**...We propose a strategy to demonstrate the transition from the quantum Zeno effect (QZE) to the anti-Zeno effect (AZE) using a superconducting **qubit** coupled to a transmission line cavity, by varying the central **frequency** of the cavity mode. Our results are obtained without the rotating wave approximation (RWA), and the initial state (a dressed state) is easy to prepare. Moreover, we find that in the presence of both qubitʼs intrinsic bath and the cavity bath, the emergence of the QZE and the AZE behaviors relies not only on the match between the **qubit** energy-level-spacing and the central **frequency** of the cavity mode, but also on the coupling strength between the **qubit** and the cavity mode....(Color online.) Contour plots of the normalized decay rate γ(τ)/γ0 of the **qubit** only in the cavity bath, versus the time interval τ between successive measurements, and the central **frequency** ωcav of the cavity mode. (a) The width of the cavity **frequency** is λ=10−4Δ, and accordingly the cavity quality factor Q=104. (b) The width of the cavity **frequency** λ=5×10−3Δ, corresponding to the cavity quality factor Q=2×103. The region 1⩽γ(τ)/γ0⩽1.05 is shown as light magenta. The QZE region corresponds to γ(τ)/γ01. Evidently, a transition from the QZE to the AZE is observed by varying the central **frequency** of the cavity mode at finite τ (τ>0.6Δ−1 when Q=104, and τ>2.6Δ−1 when Q=2×103).
...(Color online.) Time dependence of the probability for the **qubit** at its excited state. In the resonant case, the parameters are ωcav=Δ=100g and τ=0.1g−1. In the detuning case, the cavity mode **frequency** is varied to ωcav=80g. Note that the successive measurements slow down the decay rate of excited state in the resonant case, which is the QZE. While in the detuning case, the measurements speed up the **qubit** decay rate, which is the AZE.
...The normalized effective decay rate γ(τ)/γ0 of the **qubit** for two quality factors Q when τ=5Δ−1, in the presence of both the cavity bath and the low-**frequency** qubitʼs intrinsic bath.
...(Color online.) (a) Sketch of a **qubit** with the spontaneous dissipation rate γ coupled to a cavity with the loss rate κ via a coupling strength g. (b) and (c) schematically show the bath density spectrum of the **qubit** environment: (b) the Ohmic qubitʼs intrinsic bath (green dashed) and the Lorentzian cavity bath (red solid), (c) the low-**frequency** qubitʼs intrinsic bath (green dashed) and the Lorentzian cavity bath (red solid).
...(Color online.) (a) Superconducting circuit model of a **frequency**-tunable transmission line resonator, which is archived by changing the boundary condition, coupled with a **qubit**. (b) Superconducting circuit model (1) of the effective tunable inductors, which are consisted of a series array of SQUIDs (2).
... We propose a strategy to demonstrate the transition from the quantum Zeno effect (QZE) to the anti-Zeno effect (AZE) using a superconducting **qubit** coupled to a transmission line cavity, by varying the central **frequency** of the cavity mode. Our results are obtained without the rotating wave approximation (RWA), and the initial state (a dressed state) is easy to prepare. Moreover, we find that in the presence of both qubitʼs intrinsic bath and the cavity bath, the emergence of the QZE and the AZE behaviors relies not only on the match between the **qubit** energy-level-spacing and the central **frequency** of the cavity mode, but also on the coupling strength between the **qubit** and the cavity mode.

Files:

Contributors: Rina Zelmann, Maeike Zijlmans, Julia Jacobs, Claude-E. Châtillon, Jean Gotman

Date: 2009-08-01

High **Frequency** **Oscillations**...High **Frequency** **Oscillations** (HFOs), including Ripples (80–250Hz) and Fast Ripples (250–500Hz), can be recorded from intracranial macroelectrodes in patients with intractable epilepsy. We implemented a procedure to establish the duration for which a stable measurement of rate of HFOs is achieved. ... High **Frequency** **Oscillations** (HFOs), including Ripples (80–250Hz) and Fast Ripples (250–500Hz), can be recorded from intracranial macroelectrodes in patients with intractable epilepsy. We implemented a procedure to establish the duration for which a stable measurement of rate of HFOs is achieved.

Files:

Contributors: Hongjing Ma, Weiqing Liu, Ye Wu, Meng Zhan, Jinghua Xiao

Date: 2014-08-01

Spatial **frequencies** distributions...Ragged **oscillation** death...The phase synchronization domains (areas enclosed by the red lines) and the OD regions (black areas) in the parameter space of ε-δω for a ring of coupled Rossler systems with different **frequency** distributions: (a) G={1,2,3,4,5,6,7,8}, (b) G={1,4,3,6,2,8,5,7}, and (c) G={1,2,3,6,8,4,7,5}. N=8. The ragged OD sates are clear in (b) and (c) within a certain interval of δω indicated by two vertical dashed lines. In all three insets, the values of ωj are plotted for given ω0=0 and δω=N. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
...The bifurcation diagram and the largest Lyapunov exponent λ of the coupled Rossler **oscillators** versus the coupling strength ε with the same spatial arrangement of natural **frequencies** as in Fig. 1(a)–(c), respectively for δω=0.58. The bifurcation diagram is realized by the soft of XPPAUT [33] where the black dots are fixed points and the red dots are the maximum and minimum values of x1 for the stable periodic solution while the blue dots means the max/min values of x1 for the unstable periodical states. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
...The critical curves of OD domain from analysis in N coupled Landau–Stuart **oscillators** for different N’s: (a) N=2, (b) N=3, and (c)–(e) N=4 for G={1,2,3,4},G={1,2,4,3}, and G={1,3,2,4}, respectively. The ragged OD domain is clear in (d). The numerical results with points within the domains perfectly verify the analytical results.
...The OD regions in the parameter space of ε-δω for a ring of coupled Rossler systems with different **frequency** distributions: (a) G={1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30}, (b) G={26,16,25,18,5,14,10,4,6,7,21,12,23,8,1,15,9,29,28,11,2,20,27,30,3,13,17,22,24,19}, and (c) G={19,22,18,13,10,28,7,15,17,8,30,12,26,11,20,9,27,21,25,6,29,1,23,5,3,24,16,14,4,2}. N=30. In all three insets, the values of ωj are plotted for given ω0=0 and δω=N.
...Coupled nonidentical **oscillators**...In this paper, the effect of spatial **frequencies** distributions on the **oscillation** death in a ring of coupled nonidentical **oscillators** is studied. We find that the rearrangement of the spatial **frequencies** may deform the domain of **oscillation** death and give rise to a ragged **oscillation** death in some parameter spaces. The usual critical curves with shape V in the parameter space of **frequency**-mismatch vs coupling-strength may become the shape W (or even shape WV). This phenomenon has been not only numerically observed in coupled nonidentical nonlinear systems, but also well supported by our theoretical analysis. ... In this paper, the effect of spatial **frequencies** distributions on the **oscillation** death in a ring of coupled nonidentical **oscillators** is studied. We find that the rearrangement of the spatial **frequencies** may deform the domain of **oscillation** death and give rise to a ragged **oscillation** death in some parameter spaces. The usual critical curves with shape V in the parameter space of **frequency**-mismatch vs coupling-strength may become the shape W (or even shape WV). This phenomenon has been not only numerically observed in coupled nonidentical nonlinear systems, but also well supported by our theoretical analysis.

Files:

Contributors: Catherine Oikonomou, Frederick R Cross

Date: 2010-12-01

A phase-locking model for entrainment of peripheral **oscillators** to the cyclin–CDK **oscillator**. (a) Molecular mechanism of the Cdc14 release **oscillator**. The mitotic phosphatase Cdc14 is activated upon release from sequestration in the nucleolus. This release is controlled by a negative feedback loop in which Cdc14 release, promoted by the polo kinase Cdc5, activates APC-Cdh1, which then promotes Cdc5 degradation, allowing Cdc14 resequestration. This negative-feedback **oscillator** is entrained to the cyclin–CDK cycle at multiple points: by cyclin–CDK promotion of CDC5 transcription and Cdc5 kinase activation, and by cyclin–CDK inhibition of Cdh1 activity. (b) Schematic of multiple peripheral **oscillators** coupled to the CDK **oscillator** in budding yeast. As described above, coupling entrains such peripheral **oscillators** to cell cycle progression; peripheral **oscillators** also feed back on the cyclin–CDK **oscillator** itself. For example, major genes in the periodic transcription program include most cyclins, CDC20, and CDC5; Cdc14 directly promotes establishment of the low-cyclin–CDK positive feedback loop by activating Cdh1 and Sic1 as well as more indirectly antagonizing cyclin–CDK activity by dephosphorylating cyclin–CDK targets; the centrosome and budding cycles could communicate with the cyclin–CDK cycle via the spindle integrity and morphogenesis checkpoints. (c) **Oscillator** coupling ensures once-per-cell-cycle occurrence of events. Three hypothetical **oscillators** are shown: a master cycle in black, a faster peripheral cycle in blue, and a slower peripheral cycle in red. In the absence of phase-locking (top), the **oscillators** trigger events (colored circles) without a coherent phase relationship. In the presence of **oscillator** coupling (bottom), the peripheral **oscillators** are slowed or accelerated within their critical periods to produce a locked phase relationship, with events occurring once and only once within each master cycle.
...The cell cycle **oscillator**, based on a core negative feedback loop and modified extensively by positive feedback, cycles with a **frequency** that is regulated by environmental and developmental programs to encompass a wide range of cell cycle times. We discuss how positive feedback allows **frequency** tuning, how size and morphogenetic checkpoints regulate **oscillator** **frequency**, and how extrinsic **oscillators** such as the circadian clock gate cell cycle **frequency**. The master cell cycle regulatory **oscillator** in turn controls the **frequency** of peripheral **oscillators** controlling essential events. A recently proposed phase-locking model accounts for this coupling....Positive and negative feedback loops in the cyclin–CDK **oscillator**. (a) Inset: a negative feedback loop which can give rise to **oscillations**. Such an **oscillator** is thought to form the core of eukaryotic cell cycles, with cyclin–Cyclin Dependent Kinase (cyclin–CDK) acting as activator, Anaphase Promoting Complex-Cdc20 (APC-Cdc20) acting as repressor, and non-linearity in APC-Cdc20 activation preventing the system from settling into a steady state. Below is shown the cyclin–CDK machinery in eukaryotic cell cycles. CDKs, present throughout the cell cycle, require the binding of a cyclin subunit for activity. These cyclin partners can also determine the localization of the complex and its specificity for targets. At the beginning of the cell cycle, cyclin–CDK activity is low, and ramps up over most of the cycle. Early cyclins trigger production of later cyclins and these later cyclins then turn off the earlier cyclins, so that control is passed from one set of cyclin–CDKs to the next. The last set of cyclins to be activated, the G2/M-phase cyclins, initiate mitosis, and also initiate their own destruction by activating the APC-Cdc20 negative feedback loop. APC-Cdc20 targets the G2/M-phase cyclins for destruction, resetting the cell to a low-CDK activity state, ready for the next cycle. (b) Positive feedback is added to the **oscillator** in multiple ways. Left: a highly conserved but non-essential mechanism consists of ‘handoff’ of cyclin proteolysis from APC-Cdc20 to APC-Cdh1. Cdh1 is a relative of Cdc20 which activates the APC late in mitosis and into the ensuing G1. Cdh1 is inhibited by cyclin–CDK activity, resulting in mutual inhibition (which is logically equivalent to positive feedback). Middle: antagonism between cyclin–CDK and stoichiometric CDK inhibitors (CKIs) results in positive feedback. These loops stabilize high- and low-CDK activity states. Right: a double positive feedback loop comprising CDK-mediated inhibition of the Wee1 kinase (which inhibits CDK) and activation of the Cdc25 phosphatase (which activates CDK by removing the phosphorylation added by Wee1) is proposed to stabilize intermediate CDK activity found in mid-cycle, and an alternative stable state of high mitotic CDK activity.
... The cell cycle **oscillator**, based on a core negative feedback loop and modified extensively by positive feedback, cycles with a **frequency** that is regulated by environmental and developmental programs to encompass a wide range of cell cycle times. We discuss how positive feedback allows **frequency** tuning, how size and morphogenetic checkpoints regulate **oscillator** **frequency**, and how extrinsic **oscillators** such as the circadian clock gate cell cycle **frequency**. The master cell cycle regulatory **oscillator** in turn controls the **frequency** of peripheral **oscillators** controlling essential events. A recently proposed phase-locking model accounts for this coupling.

Files:

Contributors: Feng Liu, JiaFu Wang, Wei Wang

Date: 1999-05-31

(a) The SNR vs noise intensity D for fs=30,15, and 100 Hz, respectively. (b) The mean synaptic input Isyn(t) vs time for fs=30 Hz and D=0.15 and 6, respectively. (c) The SNR for various **frequencies** for the cases of D=0.5 and 5, respectively, in the case of I0i=0.8 and I1=0.11, and Jij∈[−4,20]. (d) The SNR vs signal **frequency** for D=0.5 and 5, respectively, for the case of I0i∈[0,1] and I1=0.072.
...Intrinsic **oscillations**...The 40 Hz **oscillation**...The **frequency** sensitivity...The **frequency** fi and the corresponding height H of the main peak in PSD of Isyn(t) vs (a) A for the case of I0i∈[0,3.5]; (b) M in the case of Jij∈[−5,10].
...The phenomena of **frequency** sensitivity in weak signal detection and the 40 Hz **oscillation** in a neuronal network have been interpreted based on the intrinsic **oscillations** of the system. There exists a most sensitive **frequency** range of 20–60 Hz, over which the signal-to-noise ratio has a large value. This results from the resonance between the subthreshold intrinsic **oscillation** and the periodic signal. The network can exhibit the synchronous 40 Hz **oscillation** only with constant bias, which is due to the intrinsic features of neurons and long-range interactions between them....I0i∈[0,2] and Jij∈[−1,10]. (a) The spatiotemporal firing pattern is plotted by recording the firing time tni defined by Xi(tni)>0 and Xi(tni−)**frequency** fi and the corresponding height H of the main peak in PSD of Isyn(t) for different coupling strength.
... The phenomena of **frequency** sensitivity in weak signal detection and the 40 Hz **oscillation** in a neuronal network have been interpreted based on the intrinsic **oscillations** of the system. There exists a most sensitive **frequency** range of 20–60 Hz, over which the signal-to-noise ratio has a large value. This results from the resonance between the subthreshold intrinsic **oscillation** and the periodic signal. The network can exhibit the synchronous 40 Hz **oscillation** only with constant bias, which is due to the intrinsic features of neurons and long-range interactions between them.

Files:

Contributors: S.N. Shevchenko, S. Ashhab, Franco Nori

Date: 2010-07-01

A transition between energy levels at an avoided crossing is known as a Landau–Zener transition. When a two-level system (TLS) is subject to periodic driving with sufficiently large amplitude, a sequence of transitions occurs. The phase accumulated between transitions (commonly known as the Stückelberg phase) may result in constructive or destructive interference. Accordingly, the physical observables of the system exhibit periodic dependence on the various system parameters. This phenomenon is often referred to as Landau–Zener–Stückelberg (LZS) interferometry. Phenomena related to LZS interferometry occur in a variety of physical systems. In particular, recent experiments on LZS interferometry in superconducting TLSs (**qubits**) have demonstrated the potential for using this kind of interferometry as an effective tool for obtaining the parameters characterizing the TLS as well as its interaction with the control fields and with the environment. Furthermore, strong driving could allow for fast and reliable control of the quantum system. Here we review recent experimental results on LZS interferometry, and we present related theory....(Color online) Same as in Fig. 6 (i.e. LZS interferometry with low-**frequency** driving), but including the effects of decoherence. The time averaged upper level occupation probability P+¯ was obtained numerically from the Bloch equations with the Hamiltonian (1). The dephasing time T2 is given by ωT2/(2π)=0.1 in (a), 1 in (b), 5 in (c) and T2=2T1 in (d). The relaxation time is given by ωT1/(2π)=10.
...(Color online) Same as in Fig. 7 (i.e. LZS interferometry with high-**frequency** driving), but including the effects of decoherence. The time-averaged upper diabatic state occupation probability P¯up is obtained numerically by solving the Bloch equations with the Hamiltonian (1). The dephasing time T2 is given by ωT2/(2π)=0.1 in (a), 0.5 in (b), 1 in (c) and T2=2T1 in (d). The relaxation time is given by ωT1/(2π)=103.
...Superconducting **qubits**...Stückelberg **oscillations**...(Color online) (a) Energy levels E versus the bias ε. The two solid curves (red and blue) represent the adiabatic energy levels, E±, which display avoided crossing with energy splitting Δ. The dashed lines show the crossing diabatic energy levels E↑,↓, corresponding to the diabatic states φ↑ and φ↓. (b) The bias ε represents the driving signal, and it **oscillates** between εmin=ε0−A and εmax=ε0+A with a sinusoidal time dependence: ε(t)=ε0+Asinωt.
...Parameters used in different experiments studying LZS interferometry: tunneling amplitude Δ, maximal driving amplitude Amax, and driving **frequency** ω in the units GHz×2π, minimal adiabaticity parameter δmin=Δ2/(4ωAmax), and maximal LZ probability PLZmax=exp(−2πδmin).
... A transition between energy levels at an avoided crossing is known as a Landau–Zener transition. When a two-level system (TLS) is subject to periodic driving with sufficiently large amplitude, a sequence of transitions occurs. The phase accumulated between transitions (commonly known as the Stückelberg phase) may result in constructive or destructive interference. Accordingly, the physical observables of the system exhibit periodic dependence on the various system parameters. This phenomenon is often referred to as Landau–Zener–Stückelberg (LZS) interferometry. Phenomena related to LZS interferometry occur in a variety of physical systems. In particular, recent experiments on LZS interferometry in superconducting TLSs (**qubits**) have demonstrated the potential for using this kind of interferometry as an effective tool for obtaining the parameters characterizing the TLS as well as its interaction with the control fields and with the environment. Furthermore, strong driving could allow for fast and reliable control of the quantum system. Here we review recent experimental results on LZS interferometry, and we present related theory.

Files:

Contributors: Tadashi Watanabe

Date: 2008-01-21

Variation of **frequency** shift due to the amplitude and the rotation rate.
...Conditions for zero **frequency** shift and zero pressure difference. Broken lines indicate linear fitting lines through the origin.
...Free-decay **oscillations** and rotations of a levitated liquid droplet are simulated numerically, and the **frequency** shift of drop-shape **oscillations** is studied. It is shown for an **oscillating**-rotating liquid droplet that the **oscillation** **frequency** decreases as the amplitude of drop-shape **oscillations** increases, while it increases as the rotation rate increases. The pressure difference between the equator and the pole of the droplet is found to correspond to the **frequency** shift. It is also found that the relation between the amplitude and the rotation rate is linear both for zero **frequency** shift and for zero pressure difference....**Oscillation**...**Frequency** shift ... Free-decay **oscillations** and rotations of a levitated liquid droplet are simulated numerically, and the **frequency** shift of drop-shape **oscillations** is studied. It is shown for an **oscillating**-rotating liquid droplet that the **oscillation** **frequency** decreases as the amplitude of drop-shape **oscillations** increases, while it increases as the rotation rate increases. The pressure difference between the equator and the pole of the droplet is found to correspond to the **frequency** shift. It is also found that the relation between the amplitude and the rotation rate is linear both for zero **frequency** shift and for zero pressure difference.

Files:

Contributors: Niko Bako, Adrijan Baric

Date: 2013-12-01

**Oscillator**...Block scheme of the **oscillator**.
...Reference current and the **oscillator** **frequency** variations as a function of supply voltage and temperature obtained by simulations. (a) Reference current variation for typical (TT), slow (SS) and fast (FF) process corners with respect to the reference current at room temperature. (b) **Frequency** variation for typical, slow and fast corners with a supply voltage as a parameter with respect to **frequency** at room temperature.
...A low-power, 3.82MHz **oscillator** based on a feedback loop is presented. The **oscillator** does not need a stable current reference to obtain a stable **frequency** independent of voltage and temperature variations because of the usage of negative feedback. The **frequency** variation, in the temperature range from −20°C to 80°C, is±0.6% and it depends only on the temperature coefficient of the resistor R, while the reference current variations are −11%/+25% in the same temperature range. The **oscillator** power consumption is 5.1μW and the active area is 0.09mm2. The proposed **oscillator** is implemented in a 0.18μm CMOS process and the simulation results are shown....The **oscillator** layout.
...Supply voltage compensated **frequency**...Simulated **oscillator** output.
...Temperature compensated **frequency** ... A low-power, 3.82MHz **oscillator** based on a feedback loop is presented. The **oscillator** does not need a stable current reference to obtain a stable **frequency** independent of voltage and temperature variations because of the usage of negative feedback. The **frequency** variation, in the temperature range from −20°C to 80°C, is±0.6% and it depends only on the temperature coefficient of the resistor R, while the reference current variations are −11%/+25% in the same temperature range. The **oscillator** power consumption is 5.1μW and the active area is 0.09mm2. The proposed **oscillator** is implemented in a 0.18μm CMOS process and the simulation results are shown.

Files:

Contributors: Weixiong Chen, Quanbin Zhao, Yingchun Wang, Palash Kumar Sen, Daotong Chong, Junjie Yan

Date: 2016-09-01

Submerged steam jet condensation is widely applied in various fields because of its high heat transfer efficiency. Condensation **oscillation** is a major character of submerged steam turbulent jet, and it significantly affects the design and safe operation of industrial equipment. This study is designed to reveal the mechanism of the low-**frequency** pressure **oscillation** of steam turbulent jet condensation and determine its affected region. First, pressure **oscillation** signals with low **frequency** are discovered in the downstream flow field through **oscillation** **frequency** spectrogram and power analysis. The **oscillation** **frequency** is even lower than the first dominant **frequency**. Moreover, the critical positions, where the low-**frequency** pressure **oscillation** signals appear, move downstream gradually with radial distance and water temperature. However, these signals are little affected by the steam mass flux. Then, the regions with low-**frequency** pressure **oscillation** occurring are identified experimentally. The affected width of the low-**frequency** pressure **oscillation** is similar to the turbulent jet width. Turbulent jet theory and the experiment results collectively indicate that the low-**frequency** pressure **oscillation** is generated by turbulent jet vortexes in the jet wake region. Finally, the angular coefficients of the low-**frequency** affected width are obtained under different water temperatures. Angular coefficients, ranging from 0.2268 to 0.2887, decrease with water temperature under test conditions....**Frequency** spectrograms distribution along the axial direction (R/D=2).
...**Frequency** spectrograms of condensation **oscillation** [21].
...**Frequency** spectrograms under radial position of R/D=3.0 and R/D=4.0.
...Half affected width of pressure **oscillation**.
...Pressure **oscillation**...**Oscillation** power axial distribution for low **frequency** region.
... Submerged steam jet condensation is widely applied in various fields because of its high heat transfer efficiency. Condensation **oscillation** is a major character of submerged steam turbulent jet, and it significantly affects the design and safe operation of industrial equipment. This study is designed to reveal the mechanism of the low-**frequency** pressure **oscillation** of steam turbulent jet condensation and determine its affected region. First, pressure **oscillation** signals with low **frequency** are discovered in the downstream flow field through **oscillation** **frequency** spectrogram and power analysis. The **oscillation** **frequency** is even lower than the first dominant **frequency**. Moreover, the critical positions, where the low-**frequency** pressure **oscillation** signals appear, move downstream gradually with radial distance and water temperature. However, these signals are little affected by the steam mass flux. Then, the regions with low-**frequency** pressure **oscillation** occurring are identified experimentally. The affected width of the low-**frequency** pressure **oscillation** is similar to the turbulent jet width. Turbulent jet theory and the experiment results collectively indicate that the low-**frequency** pressure **oscillation** is generated by turbulent jet vortexes in the jet wake region. Finally, the angular coefficients of the low-**frequency** affected width are obtained under different water temperatures. Angular coefficients, ranging from 0.2268 to 0.2887, decrease with water temperature under test conditions.

Files: