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A system and method for simulation of fluid flow. The system being configured to remove loops in a vortex filament in a simulation model and reconnect the filament. The system may also be configured to model fluid flow in relation to a moving object and to correct errors in surface vorticity.
This paper describes a finite volume procedure for network flow analysis in a thermofluid system. A flow network is defined as a group of inter-connected control volumes called “nodes” that are connected by “branches”. The mass and energy conservation equations are solved at the nodes and momentum conservation equations are solved at the branches. The flow network also includes solid nodes to account for fluid to solid heat transfer. The heat conduction equation is solved at the solid nodes in conjunction with the flow equations. The properties of a real fluid are calculated using a thermodynamic property program and used in the conservation equations. The system of equations describing the fluid-solid network is solved by a hybrid numerical method that is a combination of the Newton-Raphson and successive substitution method. This procedure has been incorporated into a general-purpose computer program, the Generalized Fluid System Simulation Program, GFSSP. This paper also presents the application and verification of the method by comparison with test data for several applications that include (a) internal flow in a rocket engine turbo-pump, (b) pressurization and loading of a cryogenic propellant tank, (c) fluid transient during sudden opening of valve for priming of an evacuated feed line, and (d) chill down of a cryogenic transfer line with phase change and two phase flows.
A pumped fluid heat rejection system (HRS) requires continuous flow of the working fluid to ensure that the components controlled by the HRS stay within their allowable temperature limits. An interruption of flow could result in violations of hardware qualification limits and mission failure. Some of these violations can happen within a few hours. Hence, quick detection of a flow fault to invoke mitigation measures is very critical. In typical pumped fluid HRS, dual or triple pumps are employed to switch the backup units in case the primary unit were to fail. The key metric for the selection of a fault detection system is that it should detect the fault much before the fault’s impact on the thermal health of the HRS controlled components is realized. A trade study was conducted to select such a system for the Europa Clipper Mission to Europa, a moon of Jupiter that is planned for a launch in 2023. Out of the several concepts studied, the most attractive one was a novel and simple concept that uses a low power film heater attached to a section of the HRS tubing. While the fluid flows at its nominal rate, the high thermal coupling of the flowing fluid leads to the tube being close to the fluid’s temperature. However, when the flow stops, the heater warms the small thermal mass of the tubing to a high temperature in a short span of time (~15 minutes). This large temperature rise would then imply that the flow must have stopped. This paper will describe the various concepts considered, the chosen concept, its implementation, and the results of developments tests to validate its performance.
Sensor determines fluid flow angles by means of a simple vane that positions itself in the direction of the flow. The vane rotates a small light-reflecting disc as it moves while the readout system uses two cyclically polarized light beams.
A flow cell system includes a vessel and a fluid located in the vessel. A fluid surface of the fluid can be vented to a first gas pressure. The fluid surface can have a first cross-sectional area. The flow cell system includes a conduit in fluid communication with the vessel and positioned downstream of the vessel. The conduit can have a region that includes one or more orifices and has a second cross-sectional area. The second cross-sectional area can be less than the first cross-sectional area. The one or more orifices can be vented to a second gas pressure. The second gas pressure can be equal to or greater than the first gas pressure. Methods for analyzing a process fluid can include characterizing the fluid in the conduit.
This paper describes a finite volume based numerical algorithm that allows multi-dimensional computation of fluid flow within a system level network flow analysis. There are several thermo-fluid engineering problems where higher fidelity solutions are needed that are not within the capacity of system level codes. The proposed algorithm will allow NASA's Generalized Fluid System Simulation Program (GFSSP) to perform multi-dimensional flow calculation within the framework of GFSSP s typical system level flow network consisting of fluid nodes and branches. The paper presents several classical two-dimensional fluid dynamics problems that have been solved by GFSSP's multi-dimensional flow solver. The numerical solutions are compared with the analytical and benchmark solution of Poiseulle, Couette and flow in a driven cavity.
A general purpose, one dimensional fluid flow code is currently being interfaced with the thermal analysis program Systems Improved Numerical Differencing Analyzer/Gaski (SINDA/G). The flow code, Generalized Fluid System Simulation Program (GFSSP), is capable of analyzing steady state and transient flow in a complex network. The flow code is capable of modeling several physical phenomena including compressibility effects, phase changes, body forces (such as gravity and centrifugal) and mixture thermodynamics for multiple species. The addition of GFSSP to SINDA/G provides a significant improvement in convective heat transfer modeling for SINDA/G. The interface development is conducted in multiple phases. This paper describes the first phase of the interface which allows for steady and quasi-steady (unsteady solid, steady fluid) conjugate heat transfer modeling.
Induced fluid flow - lift and drag theory of nonplanar lifting surfaces
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The flow experiment consisted of an oil tunnel, 76 x 76 mm in cross-section, packed with lucite spheres. The index of refraction of the working fluid and the spheres were matched such that the physical spheres invisible to the eye and camera. By seeding the oil and illuminating the packed bed with planar laser light sheet, aligned in the direction of the bulk flow, the system fluid dynamics becomes visible and the 2-D projection was recorded at right angles to the bulk flow. The planar light sheet was traversed from one side of the tunnel to the other providing a simulated 3-D image of the entire flow field. The boundary interface between the working fluid and the sphere rendered the sphere black permitting visualization of the exact locations of the circular interfaces in both the axial and transverse directions with direct visualization of the complex interstitial spaces between the spheres within the bed. Flows were observed near the surfaces of a plane and set of spheres as well as minor circles that appear with great circles and not always uniformly ordered. In addition to visualizing a very complex flow field, it was observed that flow channeling in the direction of the bulk flow occurs between sets of adjacent spheres. Still photographs and video recordings illustrating the flow phenomena will be presented.
A sample generator system includes a fluid source and a flow cell. The sample generator system includes a sampling system in communication with the flow cell to eject a fluid sample through an orifice in the flow cell. The sample generator system includes a gas pressure at the orifice in the flow cell greater than or equal to a fluid pressure at the orifice in the flow cell.
Here, this study focuses on design parameter sensitivity studies pertaining to several Once-Through Steam Generator (OTSG) model cases both with and without a riser using python and advanced risk assessment and optimization tool, i.e. Risk Analysis Virtual Environment (RAVEN) developed at Idaho National Laboratory (INL), to support a Small Modular Reactor (SMR) system. The presented Steam Generator (SG) python-based model is a mathematical representation of a steam-generating unit for a Pressurized Water Reactor (PWR)-type SMR system, including fluid flow and heat transfer equations, models, and correlations. Design studies involve changing the model’s input design parameters (e.g., temperature, pressure, mass flow rate) to observe the resulting effects on the output of the system, such as the Heat Transfer Coefficient (HTC), Reynolds number, Nusselt number, and heat transfer performance. Sensitivity studies analyze the degree to which system output and/or desired parameters (e.g., HTC or heat transfer performance) are sensitive to changes in the input parameters. By using RAVEN, detailed design parametric sensitivity studies. Six input parameters—namely, the pressure, temperature, and mass flow rate for the inlet of the primary-side (hot fluid) and secondary-side (cold fluid) of the SG—were randomly perturbed via RAVEN’s Monte Carlo Sampler module, using uniform distributions (i.e., ±1%, ±5% and ±10 % relative changes) for 600 samples. The analysis results give valuable insights into SG system performance, and provide justification for further research and development such as optimized sensor placement, design verification, validation, and optimization.
When test valve is actuated, piston is moved by pressurized fluid, and displacement is monitored by electro-optical tracking system and recorded by oscilloscope camera. Electro-optical monitor produces output voltage proportional to displacement of piston.
In this paper, we present an analysis using unsupervised machine learning (ML) to identify the key geologic factors that contribute to the geothermal production in the Brady geothermal field. Brady is a hydrothermal system in northwestern Nevada that supports both electricity production and direct use of hydrothermal fluids. Transmissive fluid flow pathways are relatively rare in the subsurface but are critical components of hydrothermal systems like Brady and many other types of fluid flow systems in fractured rock. The ML method, non-negative matrix factorization with k-means clustering (NMFk), is applied to a library of fourteen 3D geologic characteristics hypothesized to control hydrothermal circulation in the Brady geothermal field. Our results indicate the macro-scale faults and a local step-over in the fault system preferentially occur along with production wells when compared to injection wells and non-productive wells. We infer that these are the key geologic characteristics that control the through-going hydrothermal transmission pathways at Brady. Our results demonstrate 1) the specific geologic controls on the Brady hydrothermal system and 2) the efficacy of pairing ML techniques with 3D geologic characterization to enhance the understanding of subsurface processes.
One embodiment of the present invention provides a system that facilitates automatically generating a performance table for an object, wherein the object is subject to fluid flow. The system operates by first receiving a description of the object and testing parameters for the object. The system executes a flow solver using the testing parameters and the description of the object to produce an output. Next, the system determines if the output of the flow solver indicates negative density or pressure. If not, the system analyzes the output to determine if the output is converging. If converging, the system writes the output to the performance table for the object.
The measurement of shear stress developed as a fluid moves around a solid body is difficult to measure. Stresses at the fluid-solid interface are very small and the nature of the fluid flow is easily disturbed by introducing sensor components to the interface. To address these challenges, an array of direct and indirect techniques have been investigated with various advantages and challenges. Hot wire sensors and other indirect sensors all protrude significantly into the fluid flow. Microelectromechanical systems (MEMS) devices, although facilitating very accurate measurements, are not durable, are prone to contamination, and are difficult to implement into existing model geometries. One promising approach is the use of engineered surfaces that interact with fluid flow in a detectable manner. To this end, standard lithographic techniques have been utilized to generate elastomeric micropillar arrays of various lengths and diameters. Micropillars of controlled length and width were generated in polydimethylsiloxane (PDMS) elastomer using a soft-lithography technique. The 3D mold for micropillar replication was fabricated using laser ablative micromachining and contact lithography. Micropillar dimensions and mechanical properties were characterized and compared to shear sensing requirements. The results of this characterization as well as shear stress detection techniques will be discussed.
This paper introduces a new neural network architecture designed to forecast high-dimensional spatio-temporal data using only sparse measurements. The architecture uses a two-stage end-to-end framework that combines neural ordinary differential equations (NODEs) with vision transformers. Initially, our approach models the underlying dynamics of complex systems within a low-dimensional space; and then it reconstructs the corresponding high-dimensional spatial fields. Many traditional methods involve decoding high-dimensional spatial fields before modeling the dynamics, while some other methods use an encoder to transition from high-dimensional observations to a latent space for dynamic modeling. In contrast, our approach directly uses sparse measurements to model the dynamics, bypassing the need for an encoder. This direct approach simplifies the modeling process, reduces computational complexity, and enhances the efficiency and scalability of the method for large datasets. We demonstrate the effectiveness of our framework through applications to various spatio-temporal systems, including fluid flows and global weather patterns. Although sparse measurements have limitations, our experiments reveal that they are sufficient to forecast system dynamics accurately over long time horizons. Our results also indicate that the performance of our proposed method remains robust across different sensor placement strategies, with further improvements as the number of sensors increases. This robustness underscores the flexibility of our architecture, particularly in real-world scenarios where sensor data is often sparse and unevenly distributed.
Here, this study outlines the development and testing of a flow meter prototype specifically designed for use in nuclear reactor environments. The meter uses flow-induced vibration to measure flow rates and is designed for remote monitoring in harsh environments where radiation can damage electronics. The device generates a vibration signal produced by a feedback system when fluid flows out of an orifice and interacts with a downstream wedge. The frequency of the vibration signal corresponds directly to flow rate and velocity. The study investigates geometric parameters intrinsic to the prototype, such as orifice height, width, edge distance, and chamber length. Different configurations were also tested, including a bypass loop with variable cross-sections around the device. The investigation results reveal the impact of these geometric parameters on the vibration signal output, providing valuable insights for future design improvements and enhancing the effectiveness of flow metering systems used in nuclear reactor applications.