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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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ERF is a next-generation regional atmospheric modeling code for simulation mesoscale atmospheric flows. Its capabilities include the ability to model advection, diffusion, turbulence, terrain and moisture physics.
A theoretical and experimental investigation is described on multiple pure tone noise. Based on a two-dimensional and inviscid flow model, an analysis is developed to predict the generation and subsequent evolution of multiple pure tone noise from prescribed blade-to-blade nonuniformities in the rotor geometry. The results show that even small nonuniformities within manufacturing tolerances can cause a significant amount of multiple pure tone noise. Among the different kinds of nonuniformities investigated, errors in blade spacings are a weaker generator of multiple pure tone noise than errors in blade stagger or blade contours. Experimental investigations of the effects of the rotor relative Mach number, incidence angle and length of the inlet duct on the evolution of the multiple pure tone noise are conducted with a known distribution of rotor nonuniformities. The model fan is operated, in Freon 12, in a closed loop acoustical facility.
Program descriptions, flow charts, and program listings for the SIMWEST model generation program, the simulation program, the file maintenance program, and the printer plotter program are given. For Vol 2, see .
This report describes the first study leading to the application of Particle Image Velocimetry to diagnose rotor type flows in the NASA Ames 80x120-foot wind tunnel facility. The necessary steps towards the implementation of PIV to study rotor type flows were first investigated in the Army Rotor Hover Chamber. The model problem consisted of the three-dimensional flow generated by a two-blade rotor at different wake ages.
A Zero-D cycle simulation of the GE90-94B high bypass turbofan engine has been achieved utilizing mini-maps generated from a high-fidelity simulation. The simulation utilizes the Numerical Propulsion System Simulation (NPSS) thermodynamic cycle modeling system coupled to a high-fidelity full-engine model represented by a set of coupled 3D computational fluid dynamic (CFD) component models. Boundary conditions from the balanced, steady state cycle model are used to define component boundary conditions in the full-engine model. Operating characteristics of the 3D component models are integrated into the cycle model via partial performance maps generated from the CFD flow solutions using one-dimensional mean line turbomachinery programs. This paper highlights the generation of the high-pressure compressor, booster, and fan partial performance maps, as well as turbine maps for the high pressure and low pressure turbine. These are actually "mini-maps" in the sense that they are developed only for a narrow operating range of the component. Results are compared between actual cycle data at a take-off condition and the comparable condition utilizing these mini-maps. The mini-maps are also presented with comparison to actual component data where possible.
Experience has suggested that pressure maintenance in hydraulically fractured reservoirs via lower, more sustained production drawdowns may offer improved cumulative recovery and overall resource extraction efficiency compared to more rapid drawdown approaches aimed at generating high initial production. However, given the inherent variability of oil and natural gas markets, operators pursue production strategies that maximize profitability over resource extraction efficiency. This study focuses on evaluating the implications of contrasting pressure drawdown strategies on the long-term production and resulting economics for a real, producing unconventional gas well in the Marcellus Shale of the Appalachian Basin using a techno-economic analysis approach. Our research combines elements of well-specific horizontal well design, production forecasting, equipment sizing and capital cost estimation, operating cost estimation, and revenue and tax calculations. Gas production forecast outlook scenarios were generated under varying pressure drawdowns using two approaches: 1) a novel physics-informed machine learning workflow and 2) traditional reservoir simulation. A discounted cash flow model was used to evaluate the resulting economic implications for each drawdown scenario—generating output for exploring the coupled effect of factors like the timing and volume of gas production, prevailing economic and market conditions for natural gas, and overall estimated ultimate recovery on profitability metrics such as internal rate of return and net present value. Results show that there is potential to maximize the cumulative gas produced in the specific case study well by employing a lower pressure drawdown. Conversely, the greatest profitability is achieved using rapid drawdown as signified by a small, specific subset of our outlook scenarios. On an averaging basis, we find that the combinations of highest cumulative producing and most profitable scenarios occur under lower drawdowns with long (>40 years) producing timeframes, but require higher relative gas price and lower discounting considerations. Further, the machine learning predictive outlooking capability proved effective for enabling rapid generation of a multitude of scenario forecasts. As a result, a variety of prominent example cases could be generated to strike the balance of greater productivity and economic return given their associated producing features and economic conditions when compared to similar producing scenarios—critical insight that offers improved decision support for unconventional oil and gas operations.
Turbulent reacting flow systems are often modeled with coupled time-dependent partial differential equations (PDEs). Solving such equations can easily tax the world's largest supercomputers. One pragmatic strategy for attacking such problems is to split the PDEs into components that can more easily be solved in isolation. This generic operator-splitting strategy leads to a set of ordinary differential equations (ODEs) that need to be solved as part of an "outer-loop" time-stepping approach. In many combustion applications, the ODEs to be solved can be very stiff, exhibiting timescales that span many orders of magnitude. The SUNDIALS library provides a plethora of robust time integration algorithms for solving these ODEs on exascale-capable computing hardware, yet for many complex applications (such multicomponent fuels or emissions predictions), the chemical models remain too complex to solve using reasonable resources. The Quasi-Steady State Approximation (QSSA) can be an effective tool for reducing the size and stiffness of the simulations. In this talk, I will discuss the use of the SUDIALS library of ODE solvers together with automatic code generation tools to solve complex turbulent reacting flow problems using QSSA models.
A prediction scheme was established to compute sound pressure levels in the interior of a simplified cabin model of the second generation Large Civil Tiltrotor (LCTR2) during cruise conditions, while being excited by turbulent boundary layer flow over the fuselage, or by tiltrotor blade loading and thickness noise. Finite element models of the cabin structure, interior acoustic space, and acoustically absorbent (poro-elastic) materials in the fuselage were generated and combined into a coupled structural-acoustic model. Fluctuating power spectral densities were computed according to the Efimtsov turbulent boundary layer excitation model. Noise associated with the tiltrotor blades was predicted in the time domain as fluctuating surface pressures and converted to power spectral densities at the fuselage skin finite element nodes. A hybrid finite element (FE) approach was used to compute the low frequency acoustic cabin response over the frequency range 6-141 Hz with a 1 Hz bandwidth, and the Statistical Energy Analysis (SEA) approach was used to predict the interior noise for the 125-8000 Hz one-third octave bands.
The paper presents a model of two-dimensional cavity flow in which the shear layer over the cavity is represented by discrete rectilinear vortices which are free to move as the flow progresses. Although the model is initially started impulsively, the computation is continued until a statistically steady flow is attained. The broadband noise generation of the cavity is calculated by first running the model until the steady state is reached and then computing a stationary record of far-field density fluctuation through an equation that is suitable for calculating the quadrupole noise generation by the model. The discrete time series obtained can be analyzed by ordinary digital spectral techniques to determine the spectra and overall levels of the noise in airframe noise testing of real aircraft.
Structured grid solvers such as NASA's OVERFLOW compressible Navier-Stokes flow solver can generate large data files that contain convergence histories for flow equation residuals, turbulence model equation residuals, component forces and moments, and component relative motion dynamics variables. Most of today's large-scale problems can extend to hundreds of grids, and over 100 million grid points. However, due to the lack of efficient tools, only a small fraction of information contained in these files is analyzed. OVERSMART (OVERFLOW Solution Monitoring And Reporting Tool) provides a comprehensive report of solution convergence of flow computations over large, complex grid systems. It produces a one-page executive summary of the behavior of flow equation residuals, turbulence model equation residuals, and component forces and moments. Under the automatic option, a matrix of commonly viewed plots such as residual histograms, composite residuals, sub-iteration bar graphs, and component forces and moments is automatically generated. Specific plots required by the user can also be prescribed via a command file or a graphical user interface. Output is directed to the user s computer screen and/or to an html file for archival purposes. The current implementation has been targeted for the OVERFLOW flow solver, which is used to obtain a flow solution on structured overset grids. The OVERSMART framework allows easy extension to other flow solvers.
Icing calculations were performed for a NACA 0012 swept wing tip using LEWICE3D Version 3.48 coupled with the ANSYS CFX flow solver. The calculated ice shapes were compared to experimental data generated in the NASA Glenn Icing Research Tunnel (IRT). The IRT tests were designed to test the performance of the LEWICE3D ice void density model which was developed to improve the prediction of swept wing ice shapes. Icing tests were performed for a range of temperatures at two different droplet inertia parameters and two different sweep angles. The predicted mass agreed well with the experiment with an average difference of 12%. The LEWICE3D ice void density model under-predicted void density by an average of 30% for the large inertia parameter cases and by 63% for the small inertia parameter cases. This under-prediction in void density resulted in an over-prediction of ice area by an average of 115%. The LEWICE3D ice void density model produced a larger average area difference with experiment than the standard LEWICE density model, which doesn't account for the voids in the swept wing ice shape, (115% and 75% respectively) but it produced ice shapes which were deemed more appropriate because they were conservative (larger than experiment). Major contributors to the overly conservative ice shape predictions were deficiencies in the leading edge heat transfer and the sensitivity of the void ice density model to the particle inertia parameter. The scallop features present on the ice shapes were thought to generate interstitial flow and horse shoe vortices which enhance the leading edge heat transfer. A set of changes to improve the leading edge heat transfer and the void density model were tested. The changes improved the ice shape predictions considerably. More work needs to be done to evaluate the performance of these modifications for a wider range of geometries and icing conditions
Icing calculations were performed for a NACA 0012 swept wing tip using LEWICE3D Version 3.48 coupled with the ANSYS CFX flow solver. The calculated ice shapes were compared to experimental data generated in the NASA Glenn Icing Research Tunnel (IRT). The IRT tests were designed to test the performance of the LEWICE3D ice void density model which was developed to improve the prediction of swept wing ice shapes. Icing tests were performed for a range of temperatures at two different droplet inertia parameters and two different sweep angles. The predicted mass agreed well with the experiment with an average difference of 12%. The LEWICE3D ice void density model under-predicted void density by an average of 30% for the large inertia parameter cases and by 63% for the small inertia parameter cases. This under-prediction in void density resulted in an over-prediction of ice area by an average of 115%. The LEWICE3D ice void density model produced a larger average area difference with experiment than the standard LEWICE density model, which doesn't account for the voids in the swept wing ice shape, (115% and 75% respectively) but it produced ice shapes which were deemed more appropriate because they were conservative (larger than experiment). Major contributors to the overly conservative ice shape predictions were deficiencies in the leading edge heat transfer and the sensitivity of the void ice density model to the particle inertia parameter. The scallop features present on the ice shapes were thought to generate interstitial flow and horse shoe vortices which enhance the leading edge heat transfer. A set of changes to improve the leading edge heat transfer and the void density model were tested. The changes improved the ice shape predictions considerably. More work needs to be done to evaluate the performance of these modifications for a wider range of geometries and icing conditions.
Serial femtosecond crystallography (SFX) is a powerful technique that exploits X-ray free-electron lasers to determine the structure of macromolecules at room temperature. Despite the impressive exposition of structural details with this novel crystallographic approach, the methods currently available to introduce crystals into the path of the X-ray beam sometimes exhibit serious drawbacks. Samples requiring liquid injection of crystal slurries consume large quantities of crystals (at times up to a gram of protein per data set), may not be compatible with vacuum configurations on beamlines or provide a high background due to additional sheathing liquids present during the injection. Proposed and characterized here is the use of an immiscible inert oil phase to supplement the flow of sample in a hybrid microfluidic 3D-printed co-flow device. Co-flow generation is reported with sample and oil phases flowing in parallel, resulting in stable injection conditions for two different resin materials experimentally. A numerical model is presented that adequately predicts these flow-rate conditions. The co-flow generating devices reduce crystal clogging effects, have the potential to conserve protein crystal samples up to 95% and will allow degradation-free light-induced time-resolved SFX.
The loss of the Space Shuttle Challenger was caused by the failure of the aft joint O-ring seals in its right solid rocket booster. It has been suggested by several sources that wind conditions through a reduction in temperature of the right solid rocket booster caused by the wind blowing across the cold external tank, played a role in the O-ring failure. To check the plausibility of the wind theory, an experiment was carried out in a water towing tank to visualize the flow past a two-dimensional model representing a cross section of the Space Shuttle launch configuration. The periodic formation of vortices was found to characterize the wake generated by the model. It is suggested that this organized motion in the flow is the dominant mechanism that accomplishes heat transfer from the external tank to the right solid rocket booster. Flow visualization results consisting of photographs that show instantaneous streamline patterns of the flow are presented.
Artificial intelligence (AI) generative models, such as generative adversarial networks (GANs), variational autoencoders, and normalizing flows, have been widely used and studied as efficient alternatives for traditional scientific simulations. However, they have several drawbacks, including training instability and inability to cover the entire data distribution, especially for regions where data are rare. This is particularly challenging for whole-event, full-detector simulations in high-energy heavy-ion experiments, such as sPHENIX at the Relativistic Heavy Ion Collider and Large Hadron Collider experiments, where thousands of particles are produced per event and interact with the detector. This work investigates the effectiveness of denoising diffusion probabilistic models (DDPMs) as an AI-based generative surrogate model for the sPHENIX experiment that includes the heavy-ion event generation and response of the entire calorimeter stack. DDPM performance in sPHENIX simulation data is compared with a popular rival, GANs. Results show that both DDPMs and GANs can reproduce the data distribution where the examples are abundant (low-to-medium calorimeter energies). Nonetheless, DDPMs significantly outperform GANs, especially in high-energy regions where data are rare. Additionally, DDPMs exhibit superior stability compared to GANs. The results are consistent between both central and peripheral centrality heavy-ion collision events. Moreover, DDPMs offer a substantial speedup of approximately a factor of 100 compared to the traditional Geant4 simulation method.
This paper summarizes an automated framework for generating high fidelity fluid data and provides example cases of four generalized and simplified (cosine-based) roughness geometries.
Improved capabilities of earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. At the NASA Earth eXchange (NEX), we build deep learning methods to learn from cross sensor satellite-based Earth observations for generating new datasets with efficient processing techniques. Using current generation geostationary satellites on NEX, we present an interchangeable set of machine models to perform spectral adjustment, physical model emulation, LEO-GEO emulation, and optical flow. These tools are used to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate land surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.
Extreme wind-wave conditions can create dangerous or damaging environmental conditions for sea-based structures and watercrafts. However, the model of consistent, coupled wind-wave flow conditions is computational expensive, even for simple flow scenarios. This work examines the use of diffusion-based generative artificial intelligence tools to accelerate the generation of these two-phase flows and to characterize probabilities of various states to help with the generation of extreme patterns.