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At least 163 records · Page 9

Dynamics in Layer Models of Solid Flame Propagation

Self-propagating high-temperature synthesis (SHS) is a process in which combustion waves, e.g., "solid flames", which are considered here, are employed to synthesize desired materials. Like many other systems, SHS is a pattern forming system. The problem of describing experimentally observed patterns and of predicting new, as yet unobserved, patterns continues to attract the attention of scientists and mathematicians due to the fundamental significance of the phenomena in combustion in particular, and in nonlinear science in general. Here, we analyze the dynamics of solid flame propagation in a 2D region by considering the region to be composed of parallel, identical layers aligned along the direction of propagation and having thermal contact. Each layer is then described by wave propagation in 1D, with the transverse Laplacian replaced by a term describing heat exchange between neighboring layers. This configuration is the simplest model of a 2D system because it accounts, in a simple way, for the principal feature of the problem, i.e., heat exchange between neighbors in the transverse direction. For simplicity, we describe the situation for two layers. Because the layers are identical, uniformly propagating waves in each layer must be identical, independent of the heat exchange rate alpha. When the Zeldovich number Z exceeds a critical value Z(sub c), which depends on alpha, uniformly propagating waves become unstable. The stability diagram for the two coupled layers reproduces that for the full 2D problem after appropriate identification of parameters in the two problems. Depending on parameter values, we determine three different steady-state dynamical behaviors (though additional behaviors are also expected to occur). The three behaviors are: (i) waves in each layer which pulsate in phase as they propagate, so that together they form a single pulsating propagating wave; (ii) the waves in each layer are no longer identical, and antiphase pulsations occur, with the two waves alternately advancing and receding as they propagate. This wave is the analog of the spinning wave on the surface of a circular cylinder; (iii) finally, there is a region of bistability between the in phase and antiphase waves. with each having its own domain of attraction, so that which of the two behaviors occur depends on the condition of initiation of the wave. The results of our computations indicate a qualitative similarity in the behavior of combustion waves in the layer model and in the full 2D model. Specifically, due to the similarity between the small alpha wave behavior in the layer model and the large diameter behavior in the model of waves on the surface of a cylinder, we are able to predict the behavior of the mean velocity for the waves on the cylinder, where computations of the full problem can be rather difficult. We also compute solutions for three or more layers. The results of our computations prompt us to predict that, while planar uniformly propagating waves are unstable, the wave will be quasiplanar, i.e., the resulting spinning waves have very low amplitude hot spots, and travel with the velocity close to that of the uniformly propagating wave. Such waves may be difficult to distinguish from uniformly propagating waves in experiments. We also find that for both the layer model and full 2D problem, steady-state time-dependent waves, e.g., pulsating and spinning wave, have a conserved quantity H which characterizes the excess energy in the wave, just as in the case of uniformly propagating waves. The quantity H, which is generated by dissipation, does not vary in time and is proportional to the diffusivity and caloricity of the system, and inversely proportional to the mean wave velocity.

Aldushin, A. P.↗

Fractal Variation with Changing Line Length: A Potential Problem for Planetary Lava Flow Identification

Fractals are objects that are generally self similar at all scales. Coastlines, mountains, river systems, planetary orbits and some mathematical objects are all examples of fractals. Bruno et al. used the structured walk model of Richardson to establish that lava flows are fractals and that lava flow morphology could be determined by looking at the fractal dimension of flow margins. They determined that Hawaiian a.a flows have fractal dimensions that range from 1.05 to 1.09 and that the pahoehoe lava flows have a fractal dimension from 1.13 to 1.23. We have analyzed a number of natural and simulated lava flow margins and find that the fractal dimension varies according to the number and length of rod lengths used in the structured walk method. The potential variation we find in our analyses is sufficiently large so that unambiguous determination of lava flow morphology is problematic for some flows. We suggest that the structured walk method can provide meaningful fractal dimensions if rod lengths employed in the analysis provide a best-fit residual of greater than 0.98, as opposed to the 0.95 cutoff used in previous studies. We also find that the use of more than 4 rod lengths per analysis also reduces ambiguity in the results.

Hudson, Richard K.↗

A Recipe for Streamlining Mission Management

This paper describes a project's design and implementation for streamlining mission management with knowledge capture processes across multiple organizations of a NASA directorate. Thc project's focus is on standardizing processes and reports; enabling secure information access and case of maintenance; automating and tracking appropriate workflow rules through process mapping; and infusing new technologies. This paper will describe a small team's experiences using XML technologies through an enhanced vendor suite of applications integrated on Windows-based platforms called the Wallops Integrated Scheduling and Document Management System (WISDMS). This paper describes our results using this system in a variety of endeavors, including providing range project scheduling and resource management for a Range and Mission Management Office; implementing an automated Customer Feedback system for a directorate; streamlining mission status reporting across a directorate; and initiating a document management, configuration management and portal access system for a Range Safety Office's programs. The end result is a reduction of the knowledge gap through better integration and distribution of information, improved process performance, automated metric gathering, and quicker identification of problem areas and issues. However, the real proof of the pudding comes through overcoming the user's reluctance to replace familiar, seasoned processes with new technology ingredients blended with automated procedures in an untested recipe. This paper shares some of the team's observations that led to better implementation techniques, as well as an IS0 9001 Best Practices citation. This project has provided a unique opportunity to advance NASA's competency in new technologies, as well as to strategically implement them within an organizational structure, while wetting the appetite for continued improvements in mission management.

Mitchell, Andrew E.↗

XML Translator for Interface Descriptions

A computer program defines an XML schema for specifying the interface to a generic FPGA from the perspective of software that will interact with the device. This XML interface description is then translated into header files for C, Verilog, and VHDL. User interface definition input is checked via both the provided XML schema and the translator module to ensure consistency and accuracy. Currently, programming used on both sides of an interface is inconsistent. This makes it hard to find and fix errors. By using a common schema, both sides are forced to use the same structure by using the same framework and toolset. This makes for easy identification of problems, which leads to the ability to formulate a solution. The toolset contains constants that allow a programmer to use each register, and to access each field in the register. Once programming is complete, the translator is run as part of the make process, which ensures that whenever an interface is changed, all of the code that uses the header files describing it is recompiled.

Boroson, Elizabeth R.↗

Digital system identification and its application to digital flight control

On-line system identification of linear discrete systems for implementation in a digital adaptive flight controller is considered by the conventional extended Kalman filter and a decoupling process in which the linear state estimation problem and the linear parameter identification problem are each treated separately and alternately. Input requirements for parameter identifiability are established using the standard conditions of observability for a time variant system. Experimental results for simulated linearized lateral aircraft motion are included along with the effect of different initialization and updating procedures for the priming trajectory used by the filter.

Kotob, S.↗

Application of statistical process control and process capability analysis procedures in orbiter processing activities at the Kennedy Space Center

Successful ground processing at KSC requires that flight hardware and ground support equipment conform to specifications at tens of thousands of checkpoints. Knowledge of conformance is an essential requirement for launch. That knowledge of conformance at every requisite point does not, however, enable identification of past problems with equipment, or potential problem areas. This paper describes how the introduction of Statistical Process Control and Process Capability Analysis identification procedures into existing shuttle processing procedures can enable identification of potential problem areas and candidates for improvements to increase processing performance measures. Results of a case study describing application of the analysis procedures to Thermal Protection System processing are used to illustrate the benefits of the approaches described in the paper.

Safford, Robert R.↗

Cluster Analysis of Combined EDS and EBSD Data to Solve Ambiguous Phase Identifications

A common problem in analytical scanning electron microscopy (SEM) using electron backscatter diffraction (EBSD) is the differentiation of phases with distinct chemistry but the same or very similar crystal structure. X-ray energy dispersive spectroscopy (EDS) is useful to help differentiate these phases of similar crystal structures but different elemental makeups. However, open, automated, and unbiased methods of differentiating phases of similar EBSD responses based on their EDS response are lacking. This paper describes a simple data analytics-based method, using a combination of singular value decomposition and cluster analysis, to merge simultaneously acquired EDS + EBSD information and automatically determine phases from both their crystal and elemental data. I use hexagonal TiB 2 ceramic contaminated with multiple crystallographically ambiguous but chemically distinct cubic phases to illustrate the method. Code, in the form of a Python 3 Jupyter Notebook, and the necessary data to replicate the analysis are provided as Supplementary material.

47 OTHER INSTRUMENTATION↗

Distributed parameter system coupled ARMA expansion identification and adaptive parallel IIR filtering - A unified problem statement

A novel interconnection of distributed parameter system (DPS) identification and adaptive filtering is presented, which culminates in a common statement of coupled autoregressive, moving-average expansion or parallel infinite impulse response configuration adaptive parameterization. The common restricted complexity filter objectives are seen as similar to the reduced-order requirements of the DPS expansion description. The interconnection presents the possibility of an exchange of problem formulations and solution approaches not yet easily addressed in the common finite dimensional lumped-parameter system context. It is concluded that the shared problems raised are nevertheless many and difficult.

Johnson, C. R., Jr.↗

Cloud identification for ERBE radiative flux retrieval

A maximum likelihood estimation (MLE) technique to the problem of cloud identification using coarse resolution broadband satellite data is developed and tested using simulated satellite observations. The results suggest that, in the determination of cloud conditions for the inversion of satellite-measured radiances to fluxes, the MLE method is an improvement over a Lambertian earth assumption and the clear/cloud threshold used in the inversion of Nimbus 3 and Nimbus 7 data. The use of the MLE method in the operational processing of Earth Radiation Budget Experiment scanner data is considered.

Wielicki, Bruce A.↗

The reduced order model problem in distributed parameter systems adaptive identification and control

The basic assumption that a large space structure can be decoupled preceding the application of reduced order active control was considered and alternative solutions to the control of such structures (in contrast to the strict modal control) were investigated. The transfer function matrix from the actuators to the sensors was deemed to be a reasonable candidate. More refined models from multivariable systems theory were studied and recent results in the multivariable control field were compared with respect to theoretical deficiencies and likely problems in application to large space structures.

Johnson, C. R., Jr.↗

Affinity propagation clustering of full-field, high-spatial-dimensional measurements for robust output-only modal identification: A proof-of-concept study

Determination of the model order is a challenging problem in system identification, especially in output-only or operational modal identification where some modes are weakly excited. While existing methods such as the stabilization diagram method (spectral information) are effective, they do not scale to high-dimensional data, which is usually needed for high-fidelity characterization of structural dynamics and has been made available in the emerging full-field measurement techniques using optical methods such as photogrammetry and laser vibrometers. In this proof-of-concept study we present a new non-parametric, data-driven approach for robust output-only identification of high-spatial-dimensional modal parameters of basic structures by efficiently processing and interactively exploiting the full-field measurement (i.e., very dense spatial measurement points). Specifically, we first over-estimate the system model once, producing a pool of candidate modes associated with their modal frequencies and full-field, high-spatial-dimensional mode shapes. This is accomplished by a data-driven method termed affinity propagation clustering (APC), where the active clusters, which are the active modes in our formulations, emerge from the "message-passing" procedure and does not require a pre-determination of the cluster number (mode or model order). Next, rather than using the spectral information to distinguish the physical and spurious modes in the stabilization diagram method, we exploit and visualize the spatial, full-field mode shape associated with each candidate mode to do so. We conduct extensive experiments on basic structural models with comparisons to a few existing methods. The results indicate that the new method is computationally efficient for identifying high-spatial-dimensional modal parameters, and robust to identify weak modes by exploiting the full-field measurement. We also discuss its applicability and limitations for structures with complex geometry (shapes).

42 ENGINEERING↗

Robust A-Optimal Experimental Design for Sensor Placement in Bayesian Linear Inverse Problems

Optimal design of experiments for Bayesian inverse problems has recently gained wide popularity and attracted much attention, especially in the computational science and Bayesian inversion communities. An optimal design maximizes a predefined utility function that is formulated in terms of the elements of an inverse problem, an example being optimal sensor placement for parameter identification. The state-of-the-art algorithmic approaches following this simple formulation generally overlook misspecification of the elements of the inverse problem, such as the prior or the measurement uncertainties. This work presents an efficient algorithmic approach for designing optimal experimental design schemes for Bayesian linear inverse problems such that the optimal design is robust to misspecification of elements of the inverse problem. Specifically, we consider a worst-case scenario approach for the uncertain or misspecified parameters, formulate robust objectives, and propose an algorithmic approach for optimizing such objectives. Furthermore, both relaxation and stochastic solution approaches are discussed with detailed analysis and insight into the interpretation of the problem and the proposed algorithmic approach. Extensive numerical experiments to validate and analyze the proposed approach are carried out for sensor placement in a parameter identification problem.

Bayesian inverse problems↗

A Benchmark Problem for Development of Autonomous Structural Modal Identification

This paper summarizes modal identification results obtained using an autonomous version of the Eigensystem Realization Algorithm on a dynamically complex, laboratory structure. The benchmark problem uses 48 of 768 free-decay responses measured in a complete modal survey test. The true modal parameters of the structure are well known from two previous, independent investigations. Without user involvement, the autonomous data analysis identified 24 to 33 structural modes with good to excellent accuracy in 62 seconds of CPU time (on a DEC Alpha 4000 computer). The modal identification technique described in the paper is the baseline algorithm for NASA's Autonomous Dynamics Determination (ADD) experiment scheduled to fly on International Space Station assembly flights in 1997-1999.

Pappa, Richard S.↗

Numerical Experimentation with Maximum Likelihood Identification in Static Distributed Systems

Many important issues in the control of large space structures are intimately related to the fundamental problem of parameter identification. One might also ask how well this identification process can be carried out in the presence of noisy data since no sensor system is perfect. With these considerations in mind the algorithms herein are designed to treat both the case of uncertainties in the modeling and uncertainties in the data. The analytical aspects of maximum likelihood identification are considered in some detail in another paper. The questions relevant to the implementation of these schemes are dealt with, particularly as they apply to models of large space structures. The emphasis is on the influence of the infinite dimensional character of the problem on finite dimensional implementations of the algorithms. Those areas of current and future analysis are highlighted which indicate the interplay between error analysis and possible truncations of the state and parameter spaces.

Scheid, R. E., Jr.↗

Implementation of an algorithm for cylindrical object identification using range data

One of the problems in 3-D object identification and localization is addressed. In robotic and navigation applications the vision system must be able to distinguish cylindrical or spherical objects as well as those of other geometric shapes. An algorithm was developed to identify cylindrical objects in an image when range data is used. The algorithm incorporates the Hough transform for line detection using edge points which emerge from a Sobel mask. Slices of the data are examined to locate arcs of circles using the normal equations of an over-determined linear system. Current efforts are devoted to testing the computer implementation of the algorithm. Refinements are expected to continue in order to accommodate cylinders in various positions. A technique is sought which is robust in the presence of noise and partial occlusions.

Bozeman, Sylvia T.↗

WeakIdent: Weak formulation for identifying differential equation using narrow-fit and trimming

Data-driven identification of differential equations is an interesting but challenging problem, especially when the given data are corrupted by noise. When the governing differential equation is a linear combination of various differential terms, the identification problem can be formulated as solving a linear system, with the feature matrix consisting of linear and nonlinear terms multiplied by a coefficient vector. This product is equal to the time derivative term, and thus generates dynamical behaviors. The goal is to identify the correct terms that form the equation to capture the dynamics of the given data. We propose a general and robust framework to recover differential equations using a weak formulation with two new mechanisms, narrow-fit and trimming, for both ordinary and partial differential equations (ODEs and PDEs). The weak formulation facilitates an efficient and robust way to handle noise, and two new mechanisms, narrow-fit and trimming, improve the coefficient support and value recoveries respectively. For each sparsity level, Subspace Pursuit is utilized to find an initial set of support from the large dictionary. Then, we focus on highly dynamic regions (rows of the feature matrix), and error normalize the feature matrix in the narrow-fit step. The support is further updated via trimming the terms that contribute the least. Finally, the support set of features with the smallest Cross-Validation error is chosen as the result. A comprehensive set of numerical experiments are presented for both systems of ODEs and PDEs with various noise levels. The proposed method gives a robust recovery of the coefficients, and a significant denoising effect which can handle up to 100% noise-to-signal ratio for some equations. We compare the proposed method with several state-of-the-art algorithms for the recovery of differential equations.

97 MATHEMATICS AND COMPUTING↗

Domain Aware Deep-learning Algorithms Integrated with Scientific-computing Technologies (DADAIST)

This technical report summarized the contribution of the DADAIST project funded by the Data Model Convergence Initiative via the Laboratory Directed Research and Development (LDRD) investments at Pacific Northwest National Laboratory (PNNL). Specifically, we report the development of the NeuroMANCER (Neural Modules with Adaptive Nonlinear Constraints and Efficient Regularizations), a new open-source Scientific Machine Learning library for formulating and solving parametric constrained optimization problems, physics-informed system identification, and parametric optimal control problems. NeuroMANCER is using differentiable programming to combine modern data-driven models and optimization modeling language into a coherent algorithmic and software framework. NeuroMANCER is a Pytorch-based framework and adopts much of its philosophy focused on research and development, rapid prototyping, and streamlined deployment. Strong emphasis is given to extensibility, interoperability with the PyTorch ecosystem, and quick adaptability to custom domain problems. Neuromancer repository contains a comprehensive library of differentiable modules, including custom activation functions, matrix factorizations, deep learning architectures, neural differential equations, differential equation solvers, implicit layers such as iterative solvers, high-level API for symbolic expressions, API for modeling and control of dynamical systems, and extensive set of tutorial code examples in the form of python scripts and jupyter notebooks.

97 MATHEMATICS AND COMPUTING↗