Flow field detection using acoustic travel time tomography

Acoustic travel-time tomography of the atmosphere is a nonlinear inverse problem which attempts to reconstruct temperature and wind velocity fields in the atmospheric surface layer using the dependence of sound speed on temperature and wind velocity fields along the propagation path. This paper presents a statistical-based acoustic travel-time tomography algorithm based on dual state-parameter unscented Kalman filter (UKF) which is capable of reconstructing and tracking, in time, temperature, and wind velocity fields (state variables) as well as the dynamic model parameters within a specified investigation area. An adaptive 3-D spatial-temporal autoregressive model is used to capture the state evolution in the UKF. The observations used in the dual state-parameter UKF process consist of the acoustic time of arrivals measured for every pair of transmitter/receiver nodes deployed in the investigation area. The proposed method is then applied to the data set collected at the Meteorological Observatory Lindenberg, Germany, as part of the STINHO experiment, and the reconstruction results are presented.

Acoustic travel time tomography will be applied on the atmosphere to directly provide spatially averaged values of meteorological quantities (temperature and wind field) which are needed for the evaluation of numerical models as well as to complete in situ point measurements and conventional studies of the atmospheric surface layer over natural surfaces. Sound waves propagate through the atmosphere with different sound velocities according to the distribution of air temperature and wind vector and become, therefore, an information carrier about the medium properties. In the presented study acoustic travel time data were measured between sources and receivers placed around an array of 200×260 m2 as initial values for the tomographic reconstruction. Thereby, a frequently applied procedure of ray tomography, the simultaneous iterative reconstruction technique, characterized by simple handling and small computational requirements, was used. Additionally, a special system of equations for the isolation of the temperature and wind influence on the sound speed was solved. To estimate the sound ray propagation and to simulate acoustic travel times during various meteorological conditions a ray tracing model based on a modified version of Snell’s law corresponding to the influence of wind vector will be introduced.

Acoustic travel-time tomography of the atmosphere is a nonlinear inverse problem which attemps to reconstruct temperature and wind velocity fields in the atmospheric surface layer (ASL) using the dependence of sound speed on temperature and wind velocity fields along the propagation path. A new statistical-based algorithm is introduced in this paper based on fixed-point unscented Kalman filter (UKF) which is capable of reconstructing and tracking temperature and wind velocity fields within a specified investigation area.

Acoustic energy propagates effectively to long ranges in the ocean interior because of the physical properties of the marine environment. Sound propagation in the ocean is relevant to a variety of studies in communication, climatology and marine biology. Examples drawn from ocean acoustics, therefore, are compelling to students with a variety of interests. The dependence of sound speed on depth results in a waveguide that permits the detection of acoustic energy at ranges, in some experiments, of thousands of kilometres. This effect serves as an illustration of Snell's law with a continuously variable index of refraction. Acoustic tomography also offers a means for imaging the ocean's thermal structure, because of the dependence of sound speed on temperature. The ability to perform acoustic thermometry for large transects of the ocean provides an effective means of studying climate change. This application in an area of substantial popular attention allows for an effective introduction to concepts in ray propagation. Aspects of computational ocean acoustics can be productive classroom examples in courses ranging from introductory physics to upper-division mathematical methods courses.

On the basis of the stream function field computed using a diagnostic model in the South China Sea in the summer 1998, the acoustic ray travel time tomography algorithm for reconstruction of two-dimensional flow field was tested by the numerical simulation in the region east to Vietnam. Error or noise contaminates the coefficients of both transfer matrix and measure data of the inverse equation of ocean acoustic tomography (OAT). Previous work on ill-posed problem in OAT commonly used Tikhonov regularization technology considering error only in observation data. Here, the truncated total least squares method (TTLS), which simultaneously considers error and noise on both sides, is applied for inverse solutions and its performances is compared with conventional Tikhonov method. The investigation suggests that the truncated total least squares method can substantially improve reconstruction of stream function field and is a promising alternative method for the solution of ocean acoustic tomography inverse problem.

Based on vortex theory, we experimentally and directly predict sound sources distributing in the flow field and determine the sound pressure level as a result of the spatial integration of sound sources. In employing this direct evaluation method for the aeroacoustic sound, the problem is that a large integration area is required to minimize errors caused by the sudden truncation of the integration area; we overcome it by adopting and applying a modified formula that neglects the quadrupole sound under the condition that the dipole sound is dominant at a low Mach number. Through the flow field measurement using a time-resolved particle image velocimetry (TR-PIV) technique, we will clearly demonstrate the feasibility of our method and the distribution of dipole sound sources in the vicinity of a body even if a comparatively small integration area must be taken. In this basic study, a circular cylinder with a diameter of 6.0 mm is used; the spatially integrated sound pressure is compared with the actual sound pressure which is measured with a microphone. Further, the sound sources evaluated using only the flow field are determined, which give us detailed information about the amplitude and phase of the sound source structure. This direct evaluation method for the dipole sound is applicable to a more complex body.

Experimental study on acoustic vector tomography of 2-D flow field in an experiment-scale furnace

The flow field associated with supersonic free jets can be understood better by using visualization techniques like schlieren in the laboratory. Supersonic jets are used in the fields of aerospace, refrigeration, and precision manufacturing. The supersonic free jets are normally characterized by a compressible flow field with complex shock structures at off-design conditions. Supersonic free jets from nozzles with exotic shapes like Elliptic Sharp Tipped Shallow (ESTS) lobed nozzle have three-dimensional flow fields that are difficult to completely understand using line-of-sight integrated techniques like schlieren. These nozzles present a challenge to the measurement of flow properties with novel flow diagnostic techniques. Density is one such flow property which is very informative yet difficult to obtain. The three-dimensional density fields can be obtained by the background-oriented schlieren (BOS) [1, 2]. Various nozzle configurations with non-axis symmetric profiles have been studied earlier [3] including the ESTS lobed nozzle [4, 5]. This is an attempt to capture the density flow field of an ESTS lobed nozzle free jet using BOS. In this study, a supersonic ESTS lobed nozzle with four lobes is operated to get a supersonic free jet. The density field of this jet is obtained at several azimuthal positions by turning the nozzle about its axis. The information from all the positions is used to reconstruct the three-dimensional flow field of the nozzle. From this density field, both the basic physics of the complex flow field can be understood as well as CFD modeling of the flow field can be validated.

An acoustic tomographic system has been used to estimate area averaged wind and temperature data within an area of 97 m × 12 m considering the dependence of sound speed on meteorological conditions To obtain information about vertical structure of meteorological data, eight sound sources and receivers were placed in two different heights above the ground (0.5 m and 2.7 m). Spatially, the acoustic measurements correspond to line integrated N2O concentration measurements (98 m) using FTIR-spectrometers Taking stability of atmospheric layering into account, acoustic tomographic measurements serve as basis for estimating vertical fluxes of momentum and sensible heat.

PurposePhysics-informed neural networks (PINNs) have become a new tendency in flow simulation, because of their self-advantage of integrating both physical and monitored information of fields in solving the Navier–Stokes equation and its variants. In view of the strengths of PINN, this study aims to investigate the impact of spatially embedded data distribution on the flow field results around the train in the crosswind environment reconstructed by PINN.Design/methodology/approachPINN can integrate data residuals with physical residuals into the loss function to train its parameters, allowing it to approximate the solution of the governing equations. In addition, with the aid of labelled training data, PINN can also incorporate the real site information of the flow field in model training. In light of this, the PINN model is adopted to reconstruct a two-dimensional time-averaged flow field around a train under crosswinds in the spatial domain with the aid of sparse flow field data, and the prediction results are compared with the reference results obtained from numerical modelling.FindingsThe prediction results from PINN results demonstrated a low discrepancy with those obtained from numerical simulations. The results of this study indicate that a threshold of the spatial embedded data density exists, in both the near wall and far wall areas on the train’s leeward side, as well as the near train surface area. In other words, a negative effect on the PINN reconstruction accuracy will emerge if the spatial embedded data density exceeds or slips below the threshold. Also, the optimum arrangement of the spatial embedded data in reconstructing the flow field of the train in crosswinds is obtained in this work.Originality/valueIn this work, a strategy of reconstructing the time-averaged flow field of the train under crosswind conditions is proposed based on the physics-informed data-driven method, which enhances the scope of neural network applications. In addition, for the flow field reconstruction, the effect of spatial embedded data arrangement in PINN is compared to improve its accuracy.

An exhaust diffuser determines the turbine outlet pressure by recovering kinetic energy. Conversely, the distributions of the total pressure and flow directions at the turbine exit affect the aerodynamic performance of the exhaust diffuser. As the output power increases gradually, the structure of the modern gas turbine becomes more compact. Consequently, the coupled effect of the flow in the last-stage turbine and the exhaust diffuser becomes increasingly obvious. Understanding the correlation between the flow field and the performance of the coupled system is of great significance. As a predictive regression algorithm, the Kriging method is widely used due to its high efficiency and unique mathematical characteristics. In this paper, computational fluid dynamics (CFD) numerical simulation is employed to investigate the interactions between the flow fields of the coupled system, and the corresponding datasets are obtained. Accordingly, the Kriging method is successfully employed to reconstruct the complex flow field, and a quantitative model describing the interaction between the two parts is established. This paper provides a detailed summary of the interaction between the flow field in the exhaust diffuser and the flow field at the outlet of the last-stage turbine. Through the prediction of the flow field, the conditions that induce the separation vortex on the casing of the diffuser are determined. Specifically, the slope of the total pressure change along the blade height near the casing is found to be k = −4.37.

The CFD numerical simulation was used to simulate the flow field and sound field of the intake system, combined with the acoustic finite element method. Based on the Lighthill acoustic analogy and the Möhring acoustic analogy method, the internal sound source distribution of the intake system, the intensity of the nozzle noise and the acoustic directivity were analyzed. Improvement measures were proposed from the perspective of noise transmission, and the influence of different wall thicknesses on the wall stimulated radiation noise, as well as the effects of attaching sound absorbing materials and spraying damping materials on wall radiation noise were studied.

This paper proposes a novel particle image velocimetry (PIV) technique to generate an instantaneous two-dimensional velocity field for sediment-laden fluid based on the optical flow algorithm of ultrasound imaging. In this paper, an ultrasonic PIV (UIV) system is constructed by integrating a medical ultrasound instrument and an ultrasonic particle image velocimetry algorithm. The medical ultrasound instrument with a phased sensor array is used to acquire acoustic echo signals of particles in water and generate two-dimensional underwater ultrasound images. Based on the optical flow field, the instantaneous velocity of the particles in water corresponding to the pixels in the ultrasonic particle images is derived from the grayscale change between adjacent images under the L-K local constraint based on the optical flow field, and finally, the two-dimensional flow field is obtained. Through multiple sets of experiments, the proposed algorithm is verified. The experimental results are compared with those of the conventional cross-correlation algorithms. The results show that the L-K optical flow method can not only obtain the underwater velocity field accurately, but also has the advantages of good smoothness and extensive suitability, especially for the flow field measurement in sediment-laden fluid than conventional algorithms.

In the field of fluid mechanics, inferring high-dimensional data from low-dimensional observations enables the reconstruction of the high-dimensional flow field, allowing for a more comprehensive understanding of the flow, which is of great importance for analyzing flow mechanisms and optimizing aerodynamic designs. However, the lack of theoretical methodologies makes reconstructing three-dimensional (3D) flow fields a challenging task. To overcome this difficulty, a diffusion transformer-based method is proposed in this paper for reconstructing two-dimensional (2D) flow fields into 3D flow fields. The method introduces plane position embedding to enable reconstruction from arbitrary 2D planes, significantly enhancing flexibility. Furthermore, a local attention mechanism based on windows and planes is introduced to replace the vanilla global attention mechanism, improving computational efficiency by approximately 25%–40%. The developed method is applied to the reconstruction of several 3D turbulent flow fields both with and without objects. The results show that the diffusion transformer-based method in this paper exhibits high accuracy in flow field reconstruction for all numerical experiments compared to other methods such as U-Net and generative adversarial network-based approaches and achieves an average structural similarity index measure improvement of 20%, with normalized root mean square error improvements exceeding 50% in some test cases, and due to its lower resource consumption, the method demonstrates tremendous potential for reconstructing complex 3D fluid flows.

Effective splitting of serpentine flow field for applications in large-scale flow batteries