Search NASA⌕ Search

SEARCH · Search NASA

Results for “Process parameters”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,765 records · Page 98
All sourcesNASA NTRSDOE OSTI
NASA NTRS1975

Stochastic simulation of vertically nonhomogeneous gusts

The small-scale horizontal gust structure of detailed wind profiles along the vertical in the first 20 km of the atmosphere is a vertically nonhomogeneous process. A linear stochastic model was developed based on the process covariance function. This model was formulated through the use of a scaling hypothesis which transforms the nonhomogeneous gust process into a nondimensional gust process which is homogeneous in a nondimensional gust height coordinate. The velocity scaling parameter for the gust process is the gust standard deviation, and the length scale used to nondimensionalize the altitude is the vertical space lag associated with the first zero of the gust covariance function. State space theory was used to derive a digital filter from the model, which can be readily used to simulate gusts for space vehicle design applications.

Fichtl, G. H.↗
NASA NTRS2003

On the Confidence Limit of Hilbert Spectrum

Confidence limit is a routine requirement for Fourier spectral analysis. But this confidence limit is established based on ergodic theory: For stationary process, temporal average equals the ensemble average. Therefore, one can divide the data into n-sections and treat each section as independent realization. Most natural processes in general, and climate data in particular, are not stationary; therefore, there is a need for the Hilbert Spectral analysis for such processes. Here ergodic theory is no longer applicable. We propose to use various adjustable parameters in the shifting processes of the Empirical Mode Decomposition (EMD) method to obtain an ensemble of Intrinsic Mode Function 0 sets. Based on such an ensemble, we introduce a statistical measure in. a form of confidence limits for the Intrinsic Mode Functions, and consequently, the Hilbert spectra. The criterion of selecting the various adjustable parameters is based on the orthogonality test of the resulting M F sets. Length-of-day data from 1962 to 2001 will be used to illustrate this new approach. Its implication in climate data analysis will also be discussed.

Huang, Norden↗
NASA NTRS1975

Anelastic deformation of boron fibers

The flexural deformation behavior of vapor-deposited boron fibers was examined from 100 to 1100 K by stress-relaxation and internal friction techniques. Only strong thermally-activated anelasticity was observed with no evidence of plasticity up to surface strains of 0.006. The parameters governing the relaxation processes within the anelastic spectra of as-received and annealed fibers were determined. These parameters were correlated with X-ray structure studies to develop preliminary models for the sources of boron's anelasticity. The large relaxation strengths of the dominant Ia processes coupled with their relaxation times and energies suggest a sliding mechanism between certain basic structural subunits common to both the beta-rhombohedral and vapor-deposited boron structures.

Dicarlo, J. A.↗
NASA NTRS1992

Fuzzy simulation in concurrent engineering

Concurrent engineering is becoming a very important practice in manufacturing. A problem in concurrent engineering is the uncertainty associated with the values of the input variables and operating conditions. The problem discussed in this paper concerns the simulation of processes where the raw materials and the operational parameters possess fuzzy characteristics. The processing of fuzzy input information is performed by the vertex method and the commercial simulation packages POLYMATH and GEMS. The examples are presented to illustrate the usefulness of the method in the simulation of chemical engineering processes.

Kraslawski, A.↗
DOE OSTI2018

Automated correlative segmentation of large Transmission X-ray Microscopy (TXM) tomograms using deep learning

Highlights: • A unique automated segmentation approach for large 3D nanotomography datasets obtained by Transmission X-ray Microscopy (TXM) in an Al-Cu alloy. • Automated segmentation using a Convolutional Neural Network (CNN) architecture based on a deep learning approach was employed. • This extremely versatile technique is capable of emulating the manual segmentation process effectively. • Quantitative comparison between manual segmentation and CNN architecture established the accuracy of this automated technique. • Its ability to robustly process ultra-large volumes of data in relatively small time frames can exponentially accelerate tomographic data analysis. - Abstract: A unique correlative approach for automated segmentation of large 3D nanotomography datasets obtained using Transmission X-ray Microscopy (TXM) in an Al-Cu alloy has been introduced. Automated segmentation using a Convolutional Neural Network (CNN) architecture based on a deep learning approach was employed. This extremely versatile technique is capable of emulating the manual segmentation process effectively. Coupling this technique with post-scanning SEM imaging ensured precise estimation of 3D morphological parameters from nanotomography. The segmentation process as well as subsequent analysis was expedited by several orders of magnitude. Quantitative comparison between segmentation performed manually and using the CNN architecture established the accuracy of this automated technique. Its ability to robustly process ultra-large volumes of data in relatively small time frames can exponentially accelerate tomographic data analysis, possibly opening up novel avenues for performing 4D characterization experiments with finer time steps.

36 MATERIALS SCIENCE↗
NASA NTRS1990

Applications of ISES for instrument science

It is often the case that some instruments being used for geophysical measurements cannot measure some parameters that are important for processing the data obtained using the instrument. However, the parameters of interest may be measured by other instruments and these data made available to the operators of the first instrument. Processing the data immediately after it is acquired is useful in directing the operation of the same or different instrument or in providing a quick look data set to users on the ground. The four applications which are considered are: the decision to acquire data due to some important occurrence detected by Eos instruments; the decision not to acquire data at a scheduled time and/or location; the decision to acquire additional data to improve data quality; and combining data from several sources to enhance data quality. General examples are presented, which may or may not apply directly to Eos instruments on the various platforms.

Grant, William B.↗
NASA NTRS2005

Overview of Icing Physics Relevant to Scaling

An understanding of icing physics is required for the development of both scaling methods and ice-accretion prediction codes. This paper gives an overview of our present understanding of the important physical processes and the associated similarity parameters that determine the shape of Appendix C ice accretions. For many years it has been recognized that ice accretion processes depend on flow effects over the model, on droplet trajectories, on the rate of water collection and time of exposure, and, for glaze ice, on a heat balance. For scaling applications, equations describing these events have been based on analyses at the stagnation line of the model and have resulted in the identification of several non-dimensional similarity parameters. The parameters include the modified inertia parameter of the water drop, the accumulation parameter and the freezing fraction. Other parameters dealing with the leading edge heat balance have also been used for convenience. By equating scale expressions for these parameters to the values to be simulated a set of equations is produced which can be solved for the scale test conditions. Studies in the past few years have shown that at least one parameter in addition to those mentioned above is needed to describe surface-water effects, and some of the traditional parameters may not be as significant as once thought. Insight into the importance of each parameter, and the physical processes it represents, can be made by viewing whether ice shapes change, and the extent of the change, when each parameter is varied. Experimental evidence is presented to establish the importance of each of the traditionally used parameters and to identify the possible form of a new similarity parameter to be used for scaling.

Anderson, David N.↗
NASA NTRS2017

Balloon Thermal Model Design Parameters and Sensitivities

This presentation describes the thought process for determining balloon thermal model design parameters, including environmental parameters taken form NASA's top-of-atmosphere (TOA) database, and shows the sensitivity of an example model's key temperature results to those input parameters.

Balloon Thermal Model Design Paremeters and Sensit↗
NASA NTRS1994

Characterization of Hybrid Rocket Internal Heat Flux and HTPB Fuel Pyrolysis

None given. From statement of problem:The broad nature of this work has been to extend our understanding of the nature of the conventional or classical hybrid rocket combustion process (fuel decomposition, heat and mass transfer, and chemical reaction), and the critical engineering parameters that define this process.

rocket↗
DOE OSTI2022

Inferring topological transitions in pattern-forming processes with self-supervised learning

Abstract The identification of transitions in pattern-forming processes are critical to understand and fabricate microstructurally precise materials in many application domains. While supervised methods can be useful to identify transition regimes, they need labels, which require prior knowledge of order parameters or relevant microstructures describing these transitions. Instead, we develop a self-supervised, neural-network-based approach that does not require predefined labels about microstructure classes to predict process parameters from observed microstructures. We show that assessing the difficulty of solving this inverse problem can be used to uncover microstructural transitions. We demonstrate our approach by automatically discovering microstructural transitions in two distinct pattern-forming processes: the spinodal decomposition of a two-phase mixture and the formation of binary-alloy microstructures during physical vapor deposition of thin films. This approach opens a path forward for discovering unseen or hard-to-discern transitions and ultimately controlling complex pattern-forming processes.

Abram, Marcin↗
DOE OSTI2018

Mathematical Modeling of Nonstationary Separation Processes in a Cascade of Gas Centrifuges for Separation of Tungsten Isotopes

We have developed and program-realized a mathematical model of nonstationary separation processes proceeding in cascades of gas centrifuges for separation of multicomponent isotope mixtures. With the help of this model we have calculated the parameters of nonstationary separation processes proceeding in the cascades of gas centrifuges for tungsten isotope separation. It has been shown that the model adequately describes the nonstationary processes in the cascade and is suitable for calculating their parameters during separation of multicomponent isotope mixtures.

42 ENGINEERING↗
DOE OSTI2018

On Coarse Grid Correction Methods in Krylov Subspaces

Two approaches using coarse grid correction in the course of a certain Krylov iterative process are presented. The aim of the correction is to accelerate the iterations. These approaches are based on an approximation of the function sought for by simple basis functions having finite supports. Additional acceleration can be achieved if the iterative process is restarted and the approximate solution is refined. In this case, the resulting process turns out to be a two-level preconditioned method. The influence of different parameters of the iterative process on its convergence is demonstrated by numerical results.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1985

Terrestrial photovoltaic cell process testing

The paper examines critical test parameters, criteria for selecting appropriate tests, and the use of statistical controls and test patterns to enhance PV-cell process test results. The coverage of critical test parameters is evaluated by examining available test methods and then screening these methods by considering the ability to measure those critical parameters which are most affected by the generic process, the cost of the test equipment and test performance, and the feasibility for process testing.

Burger, D. R.↗
DOE OSTI2021

Multiparametric Programming in Process Systems Engineering: Recent Developments and Path Forward

The inevitable presence of uncertain parameters in critical applications of process optimization can lead to undesirable or infeasible solutions. For this reason, optimization under parametric uncertainty was, and continues to be a core area of research within Process Systems Engineering. Multiparametric programming is a strategy that offers a holistic perspective for the solution of this class of mathematical programming problems. Specifically, multiparametric programming theory enables the derivation of the optimal solution as a function of the uncertain parameters, explicitly revealing the impact of uncertainty in optimal decision-making. By taking advantage of such a relationship, new breakthroughs in the solution of challenging formulations with uncertainty have been created. Apart from that, researchers have utilized multiparametric programming techniques to solve deterministic classes of problems, by treating specific elements of the optimization program as uncertain parameters. In the past years, there has been a significant number of publications in the literature involving multiparametric programming. The present review article covers recent theoretical, algorithmic, and application developments in multiparametric programming. Additionally, several areas for potential contributions in this field are discussed, highlighting the benefits of multiparametric programming in future research efforts.

Pappas, Iosif↗
DOE OSTI2024

Molten Salt Electrodeposition: Review

Molten salt electrodeposition is the process of producing impressively dense deposits of refractory metals using the electrolysis of molten salts. However, predicting which electrochemical parameters and setup will best control different kinds of deposition (density, homogeneity, etc.) is an ongoing challenge, due to our limited understanding of the properties and mechanisms that drive molten salt electrodeposition. Because these advancements have been made rapidly and in different arenas, it is worth taking the time to stop and assess the progress of the field as a whole. These advancements have increasing relevance for the energy sector, the development of space materials and engineering applications. In this review, we assess four critical facets of this field: (1) how the current understanding of process variables enhances the electrodeposition of various molten salts and the quality of the resulting product; (2) how the electrochemical setup and the process parameters (e.g., cell reactions) are known to impact the electrodeposition of different metal coatings and refractory-metal coatings; (3) the benefits and drawbacks of non-aqueous molten salt electrodeposition, and (4) promising future avenues of research. The aim of this work is to enhance our understanding of the many procedures and variables that have been developed to date. The expectation is that this review will act as a stimulant, motivating scientists to delve further into the investigation of refractory-metal alloys by utilizing molten salt electrodeposition.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗