Hydraulic System Noise Prediction and Control
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This chapter contains sections titled: Introduction Positive Displacement Pump Flow Ripple and Noise Valve Noise Tuning of the Circuit to Avoid Resonant Conditions Fluid-Borne Noise Silencers or Pulsation Dampers Flexible Hose Vibration Isolation Acoustic Enclosures and Cladding Conclusions ReferencesKeywords:
Cladding (metalworking)
Positive displacement meter
Effects of ripple width in rippled-spectrum signals on ripple density resolution was investigated. Two measurement paradigms were tested: (i) ripple density resolution for discrimination between two rippled signals and (ii) discrimination between a rippled test signal and non-rippled reference signal. The ripple widths varied from 9% to 64% of the ripple frequency spacing. For both paradigms, the ripple density resolution increased with deceasing the ripple width. For discrimination between two rippled signals, the resolution was 8.1 ripples/oct for a ripple width of 64% and increased to 15.1 ripples/oct at the ripple width of 9%. For discrimination between a rippled test and non-rippled reference signal, the resolution was 9.3 ripples/oct at a ripple width of 64% and increased to 85 ripples/oct at a ripple width of 9%. Discrimination between two rippled signals is hypothesized to depend on ripple depth in the excitation pattern; the depth increases with narrowing the ripple width. Discrimination between a rippled test and non-rippled reference signal is hypothesized to depend on temporal processing; the effect of the ripple width appears due to increasing the ratio of the autocorrelated to uncorrelated components of the input signal with narrowing the ripples.
SIGNAL (programming language)
Uncorrelated
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The objective of the study was to better understand of contribution of excitation-pattern and temporal-processing mechanisms of frequency analysis to discrimination of complex-spectrum signals in various discrimination tasks. Using rippled-spectrum signals, the ripple depth thresholds were measured as functions of ripple density under conditions of rippled or non-rippled reference signals. With rippled reference signals, the ripple depth thresholds were as low as 0.11 at low ripple densities (2–3 cycles/oct) and rose to 1.0 at a ripple density of 8.9 cycles/oct. For non-rippled reference signals, ripple depth thresholds were nearly the same as for rippled reference signals at ripple densities of up to 7 cycles/oct; at ripple densities of 10 cycles/oct and higher, ripple depth thresholds rose slowly and reached 1.0 at a ripple density of 26 cycles/oct. The results hypothetically suggest contributions of the excitation-pattern processing and temporal-processing mechanisms of frequency analysis to discrimination of rippled signals. The excitation-pattern mechanism featured low depth thresholds at low ripple densities but could not function at ripple densities above 10 cycles/oct. The temporal-processing mechanism manifested at higher ripple densities and non-rippled reference stimuli.
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The goal of the study was to investigate how variations in ripple width influence the ripple density resolution. The influence of the ripple width was investigated with two experimental paradigms: (i) discrimination between a rippled test signal and a rippled reference signal with opposite ripple phases and (ii) discrimination between a rippled test signal and a flat reference signal. The ripple density resolution depended on the ripple width: the narrower the width, the higher the resolution. For distinguishing between two rippled signals, the resolution varied from 15.1 ripples/oct at a ripple width of 9% of the ripple frequency spacing to 8.1 ripples/oct at 64%. For distinguishing between a rippled test signal and a non-rippled reference signal, the resolution varied from 85 ripples/oct at a ripple width of 9% to 9.3 ripples/oct at a ripple width of 64%. For distinguishing between two rippled signals, the result can be explained by the increased ripple depth in the excitation pattern due to the widening of the inter-ripple gaps. For distinguishing between a rippled test signal and a non-rippled reference signal, the result can be explained by the increased ratio between the autocorrelated and uncorrelated components of the input signal.
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Forcing (mathematics)
Hysteresis
Wavenumber
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It is critical for buried target detection via ripple scattering to know the ripple structure, e.g., the ripple height and spatial wavelength. In the present paper, backscattering data from a 300-kHz system show that ripple wavelength and height can potentially be estimated from backscattering images. Motivated by the backscatter data, we have developed a time-domain numerical model to simulate scattering of high-frequency sound by a ripple field. This model treats small-scale scatterers as Lambertian scatterers distributed randomly on the large-scale ripple field. We have found that this approach characterizes the field data well. Numerical simulations are conducted to investigate the possibility of remotely sensing bottom ripple heights and wavelength.
Backscatter (email)
Ripple marks
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This paper analyzes the mechanism of ripple generation,summarizes characteristics of the ripple and its effects on load,and puts forward a discussion of methods for measuring ripple of DC stabilized power supply.
Ripple marks
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Ripple marks
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Signals with rippled frequency spectra are used to investigate capabilities of hearing to analyze and discriminate complex sounds. Till now, rippled signals with the difference between discriminated signals equal across the frequency band were exploited. In the present study, signals of different ripple densities in which ripple differences varied across the signal band were used. Ripple-density difference (RDD) thresholds increased with increasing the standard ripple density. At a standard ripple density of 2 to 10 ripples/oct, RDD threshold dependence on ripple density was identical for signal frequencies of 1 to 4 kHz: thresholds were from 0.06 ripples/oct at a standard density of 2 ripples/oct to 5–7 ripples/oct at a standard ripple density of 10 ripples/oct; RDD thresholds were not measurable at standard ripple densities above 10 ripples/oct and frequencies of 1 and 2 kHz. However, at a frequency of 4 kHz, RDD thresholds were measurable at standard ripple densities of 15 ripples/oct and higher. Hypothetically, at ripple densities of up to 10 ripples/oct, the signals were discriminated by the excitation-pattern mechanism; at ripple densities above 10 ripples/oct and a signal center frequency of 4 kHz, the signals were discriminated by the temporal-processing mechanism. [Work supported by Russian Science Foundation, Grant 16-15-10046.]
SIGNAL (programming language)
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Hysteresis
Ripple marks
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When a solid object or wheel is repeatedly dragged on a dry sandy surface, ripple patterns are formed. Although the conditions to form ripple patterns have been studied well, methods to eliminate the developed ripple patterns have not been understood thus far. Therefore, history-dependent stability of the ripple patterns formed on a sandy surface is investigated in this study. First, the ripple patterns are formed by sweeping the flat sandy surface with a flexible plow at a constant speed. Then, the sweeping speed is reduced, and the variation of ripple patterns is measured. As a result, we find that the ripple patterns show hysteresis. Specifically, the increase in amplitude of ripples is observed when the reduced velocity is close to the initial velocity forming the ripple pattern. In addition, splitting of ripples is found when the reduced velocity is further decreased. From a simple analysis of the plow's motion, we discuss the physical mechanism of the ripple splitting.
Hysteresis
Ripple marks
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