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Results for “experimental fluid mechanics”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Reinforcement learning for bluff body active flow control in experiments and simulations

Significance Reinforcement learning (RL) has been applied effectively in games and robotic manipulation. We demonstrate the effectiveness of RL in experimental fluid mechanics by applying it to reduce the drag of circular cylinders in turbulent flow, a canonical fluid–structure interaction problem. Although physics agnostic, RL managed to reduce the drag by 30 % or reach another specified optimum point very quickly. Following this discovery, we used high-fidelity simulations to probe the underlying physical mechanisms so that the discovered control techniques can be generalized to other similar flow problems. More broadly, RL-guided active control can lead to efficient exploration of additional flow-control strategies in experimental fluid mechanics, potentially paving the way for accelerating scientific discovery and different designs in flow-related engineering problems.

42 ENGINEERING↗
NASA NTRS1972

Additional research on instabilities in atmospheric flow systems associated with clear air turbulence

Analytical and experimental fluid mechanics studies were conducted to investigate instabilities in atmospheric flow systems associated with clear air turbulence. The experimental portion of the program was conducted using an open water channel which allows investigation of flows having wide ranges of shear and density stratification. The program was primarily directed toward studies of the stability of straight, stratified shear flows with particular emphasis on the effects of velocity profile on stability; on studies of three-dimensional effects on the breakdown region in shear layers; on the the interaction of shear flows with long-wave length internal waves; and on the stability of shear flows consisting of adjacent stable layers. The results of these studies were used to evaluate methods used in analyses of CAT encounters in the atmosphere involving wave-induced shear layer instabilities of the Kelvin-Helmholta type. A computer program was developed for predicting shear-layer instability and CAT induced by mountain waves. This technique predicts specific altitudes and locations where CAT would be expected.

Stoeffler, R. C.↗
DOE OSTI2021

Particle image velocimetry analysis with simultaneous uncertainty quantification using Bayesian neural networks

Particle image velocimetry (PIV) is an effective tool in experimental fluid mechanics for extracting flow fields from images. Recently, convolutional neural networks (CNNs) have been used to perform PIV analysis with accuracy on par with classical methods. Here we extend the use of CNNs to analyze PIV data while providing simultaneous uncertainty quantification on the inferred flow field. The method we apply in this paper is a Bayesian convolutional neural network (BCNN) which learns distributions of the CNN weights through variational Bayes. In order to demonstrate the utility of BCNNs for the PIV task, we compare the performance of three distinct BCNN models with simple architectures. The first network estimates flow velocity from image interrogation regions only. Our second model learns to infer velocity from both the image interrogation regions and interrogation region cross-correlation maps. Finally, our best performing network infers velocities from interrogation region cross-correlation maps only. We find that BCNNs using interrogation region cross-correlation maps as inputs perform better than those using interrogation windows only as inputs and discuss reasons why this may be the case. Additionally, we test the best performing BCNN on a full synthetic test image pair and a real image pair from the 1st International PIV Challenge. We show that ~98% of true particle displacements from the full synthetic image pair can be captured within the BCNN's 95% confidence intervals, and that the BCNN's performance on the real image pair is quantitatively similar to that of algorithms tested in the 1st International PIV Challenge. Finally, we show that BCNNs can be generalized to be used with multi-pass PIV algorithms with a moderate loss in accuracy, which may be overcome by future work on finetuning and training schemes. So to our knowledge, this is the first use of Bayesian neural networks to perform PIV.

47 OTHER INSTRUMENTATION↗
NASA NTRS1978

Effects of grazing flow on the steady-state flow resistance and acoustic impedance of thin porous-faced liners

The effects of grazing flow on the steady state flow resistance and acoustic impedance of seven Feltmetal and three Rigimesh thin porous faced liners were studied. The steady-state flow resistance of the ten specimens was measured using standard fluid mechanical experimental techniques. The acoustic impedance was measured using the two microphone method. The principal findings of the study are that the effects of grazing flow were measured and found to be small; small differences were measured between steady-state and acoustic resistance, and a semi-empirical model was derived that correlated the steady-state resistance data of the seven Feltmetal liners and the face sheet reactance of both the Feltmetal and Rigimesh liners.

Hersh, A. S.↗
NASA NTRS1989

Simulation of turbomachinery flows

Significant advancements have been made in the last five years in the ability to model turbomachinery flows of engineering interest. This advancement can be directly attributed to the second generation of supercomputers like the Cray XMP and Cray 2 and advanced instrumentation techniques. Early on, the National Aeronautics and Space Administration Lewis Research Center recognized the potential gains in turbomachinery performance and life that could be achieved by taking advantage of this technology and instituted a comprehensive research program in turbomachinery flow modeling. This activity combined the areas of fluid flow analysis, computational fluid dynamics, and experimental fluid mechanics. As a result of this activity, Lewis has become an internationally recognized leader in turbomachinery flow modeling. Many of the research activities conducted under this program are utilized by industry. The presentation gives an overview of this program and provides sample illustration of simulation performed to date.

Adamczyk, John J.↗
NASA NTRS1987

Experimental studies in fluid mechanics and materials science using acoustic levitation

Ground-based and short-duration low gravity experiments have been carried out with the use of ultrasonic levitators to study the dynamics of freely suspended liquid drops under the influence of predominantly capillary and acoustic radiation forces. Some of the effects of the levitating field on the shape as well as the fluid flow fields within the drop have been determined. The development and refinement of measurement techniques using levitated drops with size on the order of 2 mm in diameter have yielded methods having direct application to experiments in microgravity. In addition, containerless melting, undercooling, and freezing of organic materials as well as low melting metals have provided experimental data and observations on the application of acoustic positioning techniques to materials studies.

Trinh, E. H.↗
NASA NTRS1972

The turbulent boundary layer on a porous plate: An experimental study of the fluid mechanics for adverse free stream pressure gradients

An experimental investigation of transpired turbulent boundary layers in zero and adverse pressure gradients has been carried out. Profiles of: (1) the mean velocity, (2) the three intensities of the turbulent fluctuations, and (3) the Reynolds stress were obtained by hot-wire anemometry. The friction coefficients were measured by using an integrated form of the boundary layer equation to extrapolate the measured shear stress profiles to the wall.

Anderson, P. S.↗
NASA NTRS1991

The NASA Lewis Research Center Internal Fluid Mechanics Facility

An experimental facility specifically designed to investigate internal fluid duct flows is described. It is built in a modular fashion so that a variety of internal flow test hardware can be installed in the facility with minimal facility reconfiguration. The facility and test hardware interfaces are discussed along with design constraints of future test hardware. The plenum flow conditioning approach is also detailed. Available instrumentation and data acquisition capabilities are discussed. The incoming flow quality was documented over the current facility operating range. The incoming flow produces well behaved turbulent boundary layers with a uniform core. For the calibration duct used, the boundary layers approached 10 percent of the duct radius. Freestream turbulence levels at the various operating conditions varied from 0.64 to 0.69 percent of the average freestream velocity.

Porro, A. R.↗
NASA NTRS1992

An Experimental Study of the Fluid Mechanics Associated With Porous Walls

The fluid mechanics of air exiting from a porous material is investigated. The experiments are filter rating dependent, as porous walls with filter ratings differing by about three orders of magnitude are studied. The flow behavior is investigated for its spatial and temporal stability. The results from the investigation are related to jet behavior in at least one of the following categories: (1) jet coalescence effects with increasing flow rate; (2) jet field decay with increasing distance from the porous wall; (3) jet field temporal turbulence characteristics; and (4) single jet turbulence characteristics. The measurements show that coalescence effects cause jet development, and this development stage can be traced by measuring the pseudoturbulence (spatial velocity variations) at any flow rate. The pseudoturbulence variation with increasing mass flow reveals an initial increasing trend followed by a leveling trend, both of which are directly proportional to the filter rating. A critical velocity begins this leveling trend and represents the onset of fully developed jetting action in the flow field. A correlation is developed to predict the onset of fully developed jets in the flow emerging from a porous wall. The data further show that the fully developed jet dimensions are independent of the filter rating, thus providing a length scale for this type of flow field (1 mm). Individual jet characteristics provide another unifying trend with similar velocity decay behavior with distance; however, the respective turbulence magnitudes show vast differences between jets from the same sample. Measurements of the flow decay with distance from the porous wall show that the higher spatial frequency components of the jet field dissipate faster than the lower frequency components. Flow turbulence intensity measurements show an out of phase behavior with the velocity field and are generally found to increase as the distance from the wall is increased.

N Ramachandran↗
NASA NTRS1992

An experimental study of the fluid mechanics associated with porous walls

The fluid mechanics associated with the blowing phenomenon from porous walls is measured and characterized. The measurements indicate that the flow exiting a porous wall exhibits a lumpy velocity profile caused by the coalescence effects of smaller jets emerging from the surface. The velocity variations are spatially stable and prevail even at low flow rates. The intensity of this pseudoturbulence is found to be directly proportional to the filter rating of the porous wall and to increase linearly with the mean velocity. Beyond a critical mean velocity, the pseudoturbulence intensity shows a leveling trend with increase in the mean velocity. This critical velocity varies inversely as the filter rating and represents the onset of fully developed jetting action in the flow field. Based on the data, a more appropriate length scale for the flow field is proposed and a correlation is developed that can be used to predict the onset of fully developed jets in the flow emerging from a porous wall.

Ramachandran, N.↗
NASA NTRS2004

Collaborative Aerospace Research and Fellowship Program at NASA Glenn Research Center

During the summer of 2004, a 10-week activity for university faculty entitled the NASA-OAI Collaborative Aerospace Research and Fellowship Program (CFP) was conducted at the NASA Glenn Research Center in collaboration with the Ohio Aerospace Institute (OAI). This is a companion program to the highly successful NASA Faculty Fellowship Program and its predecessor, the NASA-ASEE Summer Faculty Fellowship Program that operated for 38 years at Glenn. The objectives of CFP parallel those of its companion, viz., (1) to further the professional knowledge of qualified engineering and science faculty,(2) to stimulate an exchange of ideas between teaching participants and employees of NASA, (3) to enrich and refresh the research and teaching activities of participants institutions, and (4) to contribute to the research objectives of Glenn. However, CFP, unlike the NASA program, permits faculty to be in residence for more than two summers and does not limit participation to United States citizens. Selected fellows spend 10 weeks at Glenn working on research problems in collaboration with NASA colleagues and participating in related activities of the NASA-ASEE program. This year's program began officially on June 1, 2004 and continued through August 7, 2004. Several fellows had program dates that differed from the official dates because university schedules vary and because some of the summer research projects warranted a time extension beyond the 10 weeks for satisfactory completion of the work. The stipend paid to the fellows was $1200 per week and a relocation allowance of $1000 was paid to those living outside a 50-mile radius of the Center. In post-program surveys from this and previous years, the faculty cited numerous instances where participation in the program has led to new courses, new research projects, new laboratory experiments, and grants from NASA to continue the work initiated during the summer. Many of the fellows mentioned amplifying material, both in undergraduate and graduate courses, on the basis of the summer s experience at Glenn. A number of 2004 fellows indicated that proposals to NASA will grow out of their summer research projects. In addition, some journal articles and NASA publications will result from this past summer s activities. Fellows from past summers continue to send reprints of articles that resulted from work initiated at Glenn. This report is intended primarily to summarize the research activities comprising the 2004 CFP Program at Glenn. Particular research studies include: 1) Development of an Imaging-Based, Computational Fluid Dynamics Tool to Assess Fluid Mechanics in Experimental Models that Simulate Blood Vessels; 2) Analysis of Nanomaterials Produced from Precursors; and 3) LEO Propagation Analysis Tool.

Heyward, Ann O.↗
DOE OSTI2021

Flow over an espresso cup: inferring 3-D velocity and pressure fields from tomographic background oriented Schlieren via physics-informed neural networks

Tomographic background oriented Schlieren (Tomo-BOS) imaging measures density or temperature fields in three dimensions using multiple camera BOS projections, and is particularly useful for instantaneous flow visualizations of complex fluid dynamics problems. We propose a new method based on physics-informed neural networks (PINNs) to infer the full continuous three-dimensional (3-D) velocity and pressure fields from snapshots of 3-D temperature fields obtained by Tomo-BOS imaging. The PINNs seamlessly integrate the underlying physics of the observed fluid flow and the visualization data, hence enabling the inference of latent quantities using limited experimental data. In this hidden fluid mechanics paradigm, we train the neural network by minimizing a loss function composed of a data mismatch term and residual terms associated with the coupled Navier–Stokes and heat transfer equations. We first quantify the accuracy of the proposed method based on a two-dimensional synthetic data set for buoyancy-driven flow, and subsequently apply it to the Tomo-BOS data set, where we are able to infer the instantaneous velocity and pressure fields of the flow over an espresso cup based only on the temperature field provided by the Tomo-BOS imaging. Moreover, we conduct an independent PIV experiment to validate the PINN inference for the unsteady velocity field at a centre plane. To explain the observed flow physics, we also perform systematic PINN simulations at different Reynolds and Richardson numbers and quantify the variations in velocity and pressure fields. Furthermore, the results in this paper indicate that the proposed deep learning technique can become a promising direction in experimental fluid mechanics.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1972

The fluid mechanics of thrombus formation

Experimental data are presented for the growth of thrombi (blood clots) in a stagnation point flow of fresh blood. Thrombus shape, size and structure are shown to depend on local flow conditions. The evolution of a thrombus is described in terms of a physical model that includes platelet diffusion, a platelet aggregation mechanism, and diffusion and convection of the chemical species responsible for aggregation. Diffusion-controlled and convection-controlled regimes are defined by flow parameters and thrombus location, and the characteristic growth pattern in each regime is explained. Quantitative comparisons with an approximate theoretical model are presented, and a more general model is formulated.

Source record↗
DOE OSTI2021

Modelling a compressible packed bed flow-through washing and deacetylation reactor for corn stover pretreatment

The deacetylation process is a key step in the lignocellulosic biomass pretreatment process that currently is done in batch reactors. A flow-through diffuser type reactor is a promising approach to make the process more efficient and economically favorable. A washing step before deacetylation can further reduce chemical usage and increase the process efficiency. However, the highly compacted biomass particles can cause significant pressure-drop for fluid flow through the bed, which in the worst case can cause self-enhancing pressure-drop leading to reactor blocking. Detailed understanding of the fundamentals of flow through a compressible packed bed is necessary to scale up the washing and deacetylation process. This work closely examines the classical Kozeny Carman equation and proposes modifications to accurately predict the pressure-drop at different compaction states. Here, a compressible packed bed model is further developed based on the interaction between solid mechanics and fluid dynamics. Experimental measurements of water flow through a packed bed of corn stover show good agreement with the developed model. Corn stover washing process scale up simulations show that smaller reactor diameter, higher friction lining material, lower process throughput, and higher process temperature is favorable in stabilizing the bed from extensive compression when the bed height is fixed.

09 BIOMASS FUELS↗
DOE OSTI2026

Learning turbulent flows with generative models for super resolution and sparse flow reconstruction

Neural operators are promising surrogates for dynamical systems but when trained with standard L 2 losses they tend to oversmooth fine-scale turbulent structures. Here, we show that combining operator learning with generative modeling overcomes this limitation. We consider three practical turbulent-flow challenges where conventional neural operators fail: spatio-temporal super-resolution, forecasting, and sparse flow reconstruction. For Schlieren jet super-resolution, an adversarially trained neural operator (adv-NO) reduces the energy-spectrum error by 15 × while preserving sharp gradients at neural operator-like inference cost. For 3D homogeneous isotropic turbulence, adv-NO trained on only 160 timesteps from a single trajectory forecasts accurately for five eddy-turnover times and offers 114 × wall-clock speed-up at inference than the baseline diffusion-based forecasters, enabling near-real-time rollouts. For reconstructing cylinder wake flows from highly sparse Particle Tracking Velocimetry-like inputs, a conditional generative model infers full 3D velocity and pressure fields with correct phase alignment and statistics. These advances enable accurate reconstruction and forecasting at low compute cost, bringing near-real-time analysis and control within reach in experimental and computational fluid mechanics.

Fluid dynamics↗