Accurate Particle Image Velocimetry Research Articles

Comparison of particle image velocimetry and the underlying agents dynamics in collectively moving self propelled particles

Collective migration of cells is a fundamental behavior in biology. For the quantitative understanding of collective cell migration, live-cell imaging techniques have been used using e.g., phase contrast or fluorescence images. Particle tracking velocimetry (PTV) is a common recipe to quantify cell motility with those image data. However, the precise tracking of cells is not always feasible. Particle image velocimetry (PIV) is an alternative to PTV, corresponding to Eulerian picture of fluid dynamics, which derives the average velocity vector of an aggregate of cells. However, the accuracy of PIV in capturing the underlying cell motility and what values of the parameters should be chosen is not necessarily well characterized, especially for cells that do not adhere to a viscous flow. Here, we investigate the accuracy of PIV by generating images of simulated cells by the Vicsek model using trajectory data of agents at different noise levels. It was found, using an alignment score, that the direction of the PIV vectors coincides with the direction of nearby agents with appropriate choices of PIV parameters. PIV is found to accurately measure the underlying motion of individual agents for a wide range of noise level, and its condition is addressed.

The frost heave characteristics of a coarse-grained volcanic soil quantified by particle image velocimetry

The increasing use of the seasonally frozen and permafrost regions for civil engineering constructions and the effects of global warming on these regions have stimulated research on the behaviors of frozen soils. In the present study, the frost heave characteristics of a coarse-grained soil with volcanic nature was experimentally investigated. A large soil tank model was established in laboratory for this purpose. The effects of temperature boundary, external water supply, and water transfer type on the frost heave characteristics of the volcanic soil were studied, through a series of frost heave tests. The particle image velocimetry (PIV) technique was used to quantify the full field deformation of the soil specimen. The results suggest that temperature gradient inside the soil specimen is the driving force for the migration of pore water and vapor. The largest increment in water content generally agrees well with the frost penetration depth. The contribution of vapor to the frost heave of the soil specimen is typically small. The applied seeding method, selected subset size, image-object space calibration, and calculation processes ensured accurate PIV results. Discussions regarding the presented experimental investigation and the employment of PIV technique for quantifying frozen soil deformation are summarized. These findings and discussions can provide valuable insights into the frost heave behavior of the studied soil in particular, as well as promote the application of PIV for frozen soil engineering.

Correlation-based algorithms for accurate PIV measurement near the slip boundary

Accurate particle image velocimetry (PIV) measurement near the wall is of great significance in many fields. However, it is challenging for conventional PIV algorithms to deal with the near-wall flow, especially under the slip boundary condition. In general, the conventional window correlation method cannot accurately calculate the flow velocity at any location that is less than half the size of the interrogation window away from the boundary. For steady or periodic flow, the single-pixel ensemble correlation method can estimate the velocity very near the wall, but numerous image pairs are required, which comes at a great computational cost. In this paper, a new method based on window deformation is proposed to estimate the velocity profile of near-wall flows. Furthermore, a multi-pixel ensemble correlation method is proposed based on the single-pixel method, which improves accuracy and significantly reduces the computational cost relative to the single-pixel method. Both methods are validated by synthetic particle images and experiments. The present work extends the PIV methodology for accurately measuring near-wall flows, especially under the slip boundary condition, which will benefit research on the boundary layer, drag reduction, microfluidics, etc.

Particle Image Velocimetry in a High-Speed Short-Duration Turbine Rig

AbstractThis article presents the implementation of a particle image velocimetry (PIV) into the high-speed short-duration rotating turbine facility of the von Karman Institute. The advantage of PIV as a whole-field measurement is emphasized in such circumstances for which the use of optical technique can drastically reduce the number of tests and the need for multiple intrusive expensive probes, ultimately lowering the testing cost. Practical solutions were demonstrated that address various challenges for the effective application of PIV. An endoscope delivered the laser sheet to the region of interest and a plano-concave window provided optical access for the measurement in the annular test section. Sub-micron scale oil droplets were seeded into the main flow through custom-made probes located upstream of the nozzle guide vane and into a pipeline supplying rim seal purge flow. A high-speed laser system and a high-speed camera were synchronized at 1 kHz sampling rate. Complementary measurements and dedicated image processing were performed to ensure the synchronization of the PIV images with the rotor position that was monitored through an encoder. The region of interest was the blade-to-blade plane at the 58% span turbine exit on a rectangular field of view covering approximately one rotor pitch and 0.15 rotor axial chord from the rotor trailing edge. Phase-locked-average velocity fields are obtained from PIV and compared against steady-state Reynolds-averaged Navier–Stokes (RANS) simulations along with four-hole probe measurement results. Together with uncertainty analysis, the results demonstrate the promising robustness and accuracy of PIV. A practical guideline for PIV application in such kinds of turbine test rigs is provided as a conclusion of the paper.

On the Accuracy of Particle Image Velocimetry with Citizen Videos—Five Typical Case Studies

The application of image velocimetry to measure surface streamflow velocities requires meticulous preparation, including surveying and securing both the existence of floating features on the water surface, and, as in every hydrometry method, appropriate hydraulic conditions (e.g., uniform flow, turbulent velocity profile, etc.). Though these requirements can be easily satisfied when all stages involved in image velocimetry are prepared and executed by specialists, this is not guaranteed when the video footage is recorded by citizens. This kind of spontaneously obtained data are frequently the only available information of extreme flood events; therefore, and despite their non-scientific origin and standardization, these data are very important for hydrology. In this study, we evaluate image velocimetry under a variety of conditions, including conditions resembling citizen videos. Furthermore, we conclude on the manual analysis as a means of verification of the accuracy of the velocity estimations. An interesting finding from the case study with non-uniform flow conditions was that the surface velocities occurring at the middle section of the river, estimated using large-scale particle image velocimetry algorithms, exhibited a significant error, whereas the manual estimation was more accurate. This finding calls for further investigation and a more careful approach in similar conditions.

Accurate PIV measurement on slip boundary using single-pixel algorithm

To accurately measure the near-wall flow by particle image velocimetry (PIV) is a big challenge, especially for the slip boundary condition. Apart from high-precision measurements, an appropriate PIV algorithm is important to resolve the near-wall velocity profile. In our study, the single-pixel algorithm is employed to calculate the near-wall flow, which is demonstrated to be capable of accurately resolving the flow velocity near the slip boundary condition. Based on synthetic particle images, the advantages of the single-pixel algorithm are manifested in comparison with the conventional window-correlation algorithm. In particular, the single-pixel algorithm has higher spatial resolution and accuracy, and lower systematic error and random error for the case of the slip boundary condition. Furthermore, for experimental verification, micro-PIV measurements are conducted over a liquid–gas interface, and the single-pixel algorithm is successfully applied to the calculation of near-wall velocity under the slip boundary condition, especially negative slip velocity. The current work demonstrates the advantages of the single-pixel algorithm in analyzing complex flows under the slip boundary condition, such as in drag reduction, wall skin-friction evaluation, and near-wall vortex structure measurement.

Experimental Validation of Enhanced Magnetic Resonance Imaging (EMRI) Using Particle Image Velocimetry (PIV).

Flow-sensitive four-dimensional Cardiovascular Magnetic Resonance Imaging (4D Flow CMR) has increasingly been utilised to characterise patients' blood flow, in association with patiens' state of health and disease, even though spatial and temporal resolutions still constitute a limit. Computational fluid dynamics (CFD) is a powerful tool that could expand these information and, if integrated with experimentally-obtained velocity fields, would enable to derive a large variety of the flow descriptors of interest. However, the accuracy of the flow parameters is highly influenced by the quality of the input data such as the anatomical model and boundary conditions typically derived from medical images including 4D Flow CMR. We previously proposed a novel approach in which 4D Flow CMR and CFD velocity fields are integrated to obtain an Enhanced 4D Flow CMR (EMRI), allowing to overcome the spatial-resolution limitation of 4D Flow CMR, and enable an accurate quantification of flow. In this paper, the proposed approach is validated in a U bend channel, an idealised model of the human aortic arch. The flow patterns were studied with 4D Flow CMR, CFD and EMRI, and compared with high resolution 2D PIV experiments obtained in pulsatile conditions. The main strengths and limitations of 4D Flow CMR and CFD were illustrated by exploiting the accuracy of PIV by comparing against PIV velocity fields. EMRI flow patterns showed a better qualitative and quantitative agreement with PIV results than the other techniques. EMRI enables to overcome the experimental limitations of MRI-based velocity measurements and the modelling simplifications of CFD, allowing an accurate prediction of complex flow patterns observed experimentally, while satisfying mass and momentum balance equations.

A hybrid phase boundary detection technique for two-phase-flow PIV measurements

Particle Image Velocimetry (PIV) measurement accuracy is lower along the phase boundaries of two-phase-flows, because the interrogation windows contain information from both phases. Different seeding density, background intensity, velocity magnitude and flow direction conditions often exist across the boundary, and the cross-correlation-based PIV algorithm selects only the highest correlation peak. The highest correlation peak is either influenced by the wrong phase (across the boundary), or the correctly calculated displacement is erroneously detected as an outlier at a later stage and is subsequently replaced. Phase-separated PIV measurements minimize this problem, and increase accuracy along the boundary by treating each phase separately. This type of measurement requires for each time step; (i) the accurate detection of the phase boundary in consecutive frames, (ii) generation of dynamic phase masks, (iii) an accurate PIV evaluation of each phase and (iv) recombination of the flow fields. In this article, we focus on the first step and test a hybrid phase boundary detection (PBD) technique in three different two-phase-flow configurations which manifest different challenges: The first configuration is the mixing of two liquids in a magnetic micromixer, the second is a combustion experiment where a turbulent, pre-mixed, low-swirl, lifted flame is investigated, and the third is a bubble column reactor where air bubbles are rising in a water tank. The PBD implementation uses a three-step procedure: approximate global thresholding, local Otsu thresholding, and discrimination of image gradients. Comparison of results with and without the use of PBD and phase separation indicate that there are significant measurement accuracy improvements along the boundary.

Inferring Surface Flow Velocities in Sediment-Laden Alaskan Rivers from Optical Image Sequences Acquired from a Helicopter

The remote, inaccessible location of many rivers in Alaska creates a compelling need for remote sensing approaches to streamflow monitoring. Motivated by this objective, we evaluated the potential to infer flow velocities from optical image sequences acquired from a helicopter deployed above two large, sediment-laden rivers. Rather than artificial seeding, we used an ensemble correlation particle image velocimetry (PIV) algorithm to track the movement of boil vortices that upwell suspended sediment and produce a visible contrast at the water surface. This study introduced a general, modular workflow for image preparation (stabilization and geo-referencing), preprocessing (filtering and contrast enhancement), analysis (PIV), and postprocessing (scaling PIV output and assessing accuracy via comparison to field measurements). Applying this method to images acquired with a digital mapping camera and an inexpensive video camera highlighted the importance of image enhancement and the need to resample the data to an appropriate, coarser pixel size and a lower frame rate. We also developed a Parameter Optimization for PIV (POP) framework to guide selection of the interrogation area (IA) and frame rate for a particular application. POP results indicated that the performance of the PIV algorithm was highly robust and that relatively large IAs (64–320 pixels) and modest frame rates (0.5–2 Hz) yielded strong agreement ( R 2 > 0.9 ) between remotely sensed velocities and field measurements. Similarly, analysis of the sensitivity of PIV accuracy to image sequence duration showed that dwell times as short as 16 s would be sufficient at a frame rate of 1 Hz and could be cut in half if the frame rate were doubled. The results of this investigation indicate that helicopter-based remote sensing of velocities in sediment-laden rivers could contribute to noncontact streamgaging programs and enable reach-scale mapping of flow fields.

An improved particle image velocimetry method for efficient flow analyses

This study proposes a spatiotemporally Adaptive Search Area (SA) size selecting algorithm for Particle image velocimetry (PIV), ASAPiv. The presented method releases the constraint of conventionally used static SAs, offering a significant computational performance increase through optimizing the dimensions of the SA according to the local flow conditions in a transient manner. The algorithm is implemented as a part of a new PIV framework, developed within the MATLAB environment. The most relevant steps of PIV and the related methods are reviewed, starting from image pre-processing up to the post-processing of raw PIV results. The performance of the proposed algorithm and the PIV tool in general is demonstrated through three examples of different nature, including a synthetic image sequence, a conventional, laser illuminated PIV case and a large-scale, field application. The dynamic alteration of the SAs is found to be consistent with the prevailing flow conditions, while the accuracy of PIV in general is maintained. Total calculation times with static and dynamic SAs are compared. The benchmark cases highlight the relevance of adaptive SAs in cases of spatiotemporally varied flow conditions, where significant (up to 900%) computational performance increase is achieved. In case of unidirectional, steady flow conditions the method offers moderate speed-up compared to the employment of static SA sizes.

Detection of Passive Scalar Interface Directly from PIV Particle Images in Inhomogeneous Turbulent Flows

This article proposes a technique to estimate the cross-sectional scalar interface (outer boundary) in an inhomogeneous turbulent flow from a conditioned particle image velocimetry (PIV) experiment, which is suitable for medium to high Reynolds numbers. The scalar interface is estimated directly by using conditioned PIV particle images which have distinguishably high particle seeding density in the area of interest, whereas conventionally in water based experiments, scalar interface is often determined from planar laser induced fluorescence (PLIF) or equivalent dye images. By comparing quantities in the vicinity of this scalar interface, it also shows that in terms of separate turbulent and non-turbulent regions, this technique could also replace the function of PLIF images in water experiments, with slightly lower spatial resolution. At the same time, if velocity information is also required simultaneously then the cost of a separate camera-laser system can be saved. The effect of particle field inhomogeneity on the PIV accuracy can be well reduced to an insignificant level by an image local normalisation treatment. This article shows that the interfacial layer could be detected fairly accurately by enhancing the particle images by wavelet based thresholding methods. The degree of detection accuracy is quantified by synthetic particle image analyses, where a scalar interface can be artificially pre-defined. The proposed technique is tested in two water based experiments but is expected to be particularly useful in gas-phase based experiments or some combustion applications, where liquid-phase dye cannot be applied.

Influence of the velocity vector base relocation to the center of mass of the interrogation area on PIV accuracy

This paper is aimed at modification of calculation algorithm used in data processing from PIV (Particle Image Velocimetry) method. The modification of standard Multi-step correlation algorithm is based on imaging the centre of mass of the interrogation area to define the initial point of the respective vector, instead of the geometrical centre. This paper describes the principle of initial point-vector assignment, the corresponding data processing methodology including the test track analysis. Both approaches are compared within the framework of accuracy in the conclusion. The accuracy test is performed using synthetic and real data.

Image-preprocessing method for near-wall particle image velocimetry (PIV) image interrogation with very large in-plane displacement

Accurate particle image velocimetry (PIV) measurements very near the wall are still a great challenge. The problem is compounded by the very large in-plane displacement on PIV images commonly encountered in measurements in hypersonic boundary layers. An improved image-preprocessing method is presented in this paper which expands the traditional window deformation iterative multigrid scheme to PIV images with very large displacement. Before the interrogation, stationary artificial particles of uniform size are added homogeneously in the wall region. The mean squares of the intensities of signals in the flow and in the wall region are postulated to be equal when half the initial interrogation window overlaps the wall region. The initial estimation near the wall is then smoothed by data from both sides of the shear layer to reduce the large random uncertainties. Interrogations in the following iterative steps then converge to the correct results to provide accurate predictions for particle tracking velocimetries. Significant improvement is seen in Monte Carlo simulations and experimental tests. The algorithm successfully extracted the small flow structures of the second-mode wave in the hypersonic boundary layer from PIV images with low signal-noise-ratios when the traditional method was not successful.

Particle Image Velocimetry near Interfaces: A Moving Future

Particle Image Velocimetry (PIV) has become a standardized measurement technique in the field of experimental research both in academic and industrial environments with applications covering the breadth of fluid dynamics. The metrology’s success must be ascribed to its non-intrusive nature together with its intrinsic simplicity and ability to retrieve vast amounts of instantaneous spatial velocity information (order of giga- to terabytes) relative to the short operation time (order of seconds). While the majority of PIV image processing related studies have been aimed so far at improving the accuracy of the fluid velocities extraction, PIV image analysis involving arbitrarily moving bodies has received limited to no attention. Notwithstanding, it is expected that great advances can be made in the field of fluid-structure interactions by improving the technique’s measurement capabilities as a direct result of enhanced image analyses. Interfaces are encountered in many engineering applications. Flow over moving or stationary rigid surfaces (boundary layers, turbomachinery, aerofoils), deformable surfaces (pulmonary and arterial flows, morphing wings, flexing membranes), interfacial/multiphase flows (bubbles, waves, free surface turbulence) are all examples of fluid-structure-related problems where the primary concerns are either the transport of momentum across or near the surface, the interactive coupling between fluid motion and surface deformation, or both. Although Computational Fluid Dynamics (CFD) has made considerable progress over the last decades, the inherent modelling of the fluid-structure interactions remains at the forefront of CFD development necessitating high resolution and reliable experimental verification. From both an experimental and image analysis point of view, it is considered a worldwide challenge to obtain reliable, accurate PIV velocity measurements with sufficient resolution near dynamic surfaces. This fundamental limitation of PIV has driven typical experiments to be restricted to fields of view, free of interfaces or other boundaries. The inhibition to characterize the coupling between boundary motion and fluid forces consequently hampers a proper understanding of the underlying physics. This presents a very stringent limitation in the study of aero- or hydro-elastic effects. Being scientifically important, future PIV development should therefore drive towards ameliorating the measurement capability close to dynamic interfaces in spite of the accompanying difficulties. Particle Image Velocimetry allows the measurement of flow velocity of air or water by injecting microseed particles. Being illuminated, these tracer particles reflect the light which is recorded by a specialised camera. A spatial section of the investigated flow is illuminated at two sequential time instances. High-energy light sources, typically lasers, are capable of light pulse durations in the order of nano-seconds and ensure sharp tracer images. Comparison of the two image recordings then enables the calculation of the displacement of the particles’ images and thus the 2D (or 3D depending on the camera arrangement) velocity components of the flow in which they are transported. Nowadays, the acronym PIV embodies a collection of imaging methodologies but all are based on the original operational principle.

Coupled human erythrocyte velocity field and aggregation measurements at physiological haematocrit levels

Simultaneous measurement of erythrocyte (RBC) velocity fields and aggregation properties has been successfully performed using an optical shearing microscope and Particle Image Velocimetry (PIV). Blood at 45% haematocrit was sheared at rates of 5.4⩽γ˙⩽252s-1 and imaged using a high speed camera. The images were then processed to yield aggregation indices and flow velocities. Negligible levels of aggregation were observed for γ˙⩾54.0s-1, while high levels of aggregation and network formation occurred for γ˙⩽11.7s-1. The results illustrate that the velocity measurements are dependent on the extent of RBC aggregation. High levels of network formation cause the velocities at γ˙⩾5.4s-1 to deviate markedly from the expected solid body rotation profile. The effect of aggregation level on the PIV accuracy was assessed by monitoring the two-dimensional (2D) correlation coefficients. Lower levels of aggregation result in poorer image correlation, from which it can be inferred that PIV accuracy is reduced. Moreover, aggregation is time-dependent, and consequently PIV accuracy may decrease during recording as the cells break up. It is therefore recommended that aggregation and its effects are taken into account in future when undertaking blood flow studies using PIV. The simplicity of the technique, which requires no lasers, filters, or special pretreatments, demonstrates the potential wide-spread applicability of the data acquisition system for accurate blood flow PIV and aggregation measurement.

Influence of Blade Outlet Angle on Inner Flow Field of Centrifugal Pump Transporting Salt Aqueous Solution

During transportation of salt aqueous solutions with centrifugal pump, crystallization phenomenon is frequently encountered. For this kind of two-phase flow, it is difficult to be accurately modeled since there are various medium properties and phase change characteristics. In view of experiment, several problems are hampering the implementation of precise measurement. Influences of blade outlet angle and medium temperature on crystallization rate were studied. Sodium sulfate solution was applied to simulate practical fluid in chemical industry. Particle image velocimetry(PIV) was employed to measure velocity distributions in rotating impeller. Crystallization processes in three impellers with different blade outlet angles were investigated. Relations among crystallization and flow parameters such as temperature and velocity were obtained. With the same blade wrap angle, when blade outlet angle is larger, diffusion of single flow passage gets stronger, relative velocity at blade outlet decreases and large scale vortex tends to appear near the blade working surface. For the impact of volume effect of particle phase on fluid viscosity, both liquid and solid phase velocities decrease with continual forming and growing of crystal particles. Velocity of solid phase is greater than that of liquid phase and its direction leans more closely to blade working surface. Solid particles tend to move towards blade working surface, as is more obvious in the impeller with large blade outlet angle. Therefore, collision between solid particles with stern part of blade working surface is more intensive in impeller with large blade outlet angle. Concerning transportation of salt aqueous solution, accurate PIV measurement is conducted in centrifugal impellers with different blade outlet angles. The results are useful and instructive in relevant engineering design and operation.

Evaluation of aero-optical distortion effects in PIV

Aero-optical distortion effects on the accuracy of particle image velocimetry (PIV) are investigated. When the illuminated particles are observed through a medium that is optically inhomogeneous due to flow compressibility, the resulting particle image pattern is subjected to deformation and blur. In relation to PIV two forms of error can be identified: position error and velocity error. In this paper a model is presented that describes these errors and particle image blur in relation to the refractive index field of the flow. In the case of 2D flows the model equations can be simplified and, furthermore, the background oriented schlieren technique (BOS) can be applied as a means to assess and correct for the optical error in PIV. The model describing the optical distortion is validated by both computer simulation and real experiments of 2D flows. Two flow features are considered: one with optical distortion normal to the velocity (shear layer) and one with optical distortion in the direction of the flow (expansion fan). Both simulation and experiments demonstrate that the major source for the velocity error is the second derivative of the refractive index in the direction of the velocity vector. The aero-optical distortion effect is less critical for shearing interfaces in comparison with compression/expansion fronts, the most critical case being represented by shock waves. Based on the results from the simulated experiments, it is concluded that for the 2D flow case the BOS method allows a measurement of the mean velocity error in PIV and can reduce it to a large extent.

A simple model for the effect of peak-locking on the accuracy of boundary layer turbulence statistics in digital PIV

A simple model was constructed to study the effect of peak-locking on the accuracy of particle image velocimetry (PIV) turbulence statistics. A crucial parameter is the ratio between the root-mean- ...

PIV VERIFICATION OF GREENHOUSE VENTILATION AIR FLOWS TO EVALUATE CFD ACCURACY

A computational fluid dynamics (CFD) model was developed to investigate the natural ventilation of greenhouses. Before investigating its accuracy, a wind tunnel and particle image velocimetry (PIV) test was initially conducted to find the best experimental conditions and improve the PIV accuracy. After many trials of the PIV tests, good results were obtained in the PIV accuracy test, and the errors were shown to be mostly in the .2.1% range, with a maximum of 5.4%. A single-span, wide-span greenhouse, one of the most popular types of multi-span greenhouses in Korea, was used to get qualitative and quantitative airflow distribution in the greenhouse using PIV and CFD. To improve the CFD accuracy, the PIV- and CFD-computed airflows in the greenhouse were compared, particularly on the distribution, local air velocity, and turbulent intensity in the greenhouse. The quality of the mesh density and the design of the boundary condition, especially that of the wind velocity and turbulence profiles, were found to be very important for getting accurate results. Assuming the PIV results were accurate, the most accurate CFD results were obtained when using renormalization-group (RNG) k-. and Reynolds stress turbulence numerical models. The errors of the CFD-computed air velocity when using the RNG k-. and Reynolds stress models were -7.9% and -7.6%, respectively. However, the average errors of turbulent intensity when the RNG k-. and Reynolds stress turbulence numerical models were used were -37.5% and -43.2%, respectively.

A WIND TUNNEL STUDY OF NATURAL VENTILATION FOR MULTI–SPAN GREENHOUSE SCALE MODELS USING TWO–DIMENSIONAL PARTICLE IMAGE VELOCIMETRY (PIV)

A twodimensional particle image velocimetry (PIV) system installed in a largesized wind tunnel was used toexamine the natural ventilation of fully open roof and Venlotype multispan greenhouses. Since the PIV system has rarelybeen used for largescale, lowturbulence airflow conditions, the PIV system was investigated to improve its performanceand accuracy. The PIV accuracy tests were conducted without any greenhouse models in the wind tunnel because it was verydifficult to insert the anemometer sensors in the smallscale greenhouse models, and it was also assumed that the sensor itselfcould affect natural airflow patterns inside the models. Good results were obtained in the PIV accuracy test, and the errorswere shown to be mostly in the .5% range. Additionally, the natural airflow in 2 to 10span greenhouses was investigatedusing the PIV system. The PIV results indicated that the PIV technique was an effective method for evaluating the relativeperformance of alternative designs of greenhouse and other structures with turbulent interior air distributions.