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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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At least 181 records · Page 10

Abell 548: An X-ray and optical analysis of substructure

X-ray observations of Abell 548 reveal that the hot gas in this cluster is at least as complex as the galaxy distribution. Our ROSAT Position Sensitive Proportional Counter (PSPC) image is used in conjunction with optical data from the Minnesota plate-scanning project and redshift data in the literature to investigate the degree of substructure in the intracluster medium and the galaxy distribution. A548 has several X-ray components: hot gas associated with clumps of galaxies, individual sources, and a newly discovered diffuse component. This diffuse component may be promordial gas falling into the cluster for the first time. The optical data suggest that this cluster consists of four major components, not three (as indicated when velocity data are ignored). Simple models of the orbital dynamics suggest that the cluster may not yet have reached its turnaround radius.

Davis, David S.↗

Parametric studies of stitching effectiveness for preventing substructure disbond

A methodology is desired that will allow a designer to select appropriate amounts of through-thickness reinforcement needed to meet design requirements. The goal is to use a relatively simple analysis to minimize the amount of testing that needs to be performed, and to make test results from simple configurations applicable to more general structures. Using this methodology one should be able to optimize the selection of stitching materials, the weight of the yarn, and the stitching density. The analysis approach is to treat substructure disbond as a crack propagation problem. In this approach, the stitches have little influence until a delamination begins to grow. Once the delamination reaches, or extends beyond a stitch, the stitch serves to reduce the strain-energy-release-rate (G) at the crack tip for a given applied load. The reduced G can then be compared to the unstitched materials toughness to predict the load required to further extend the crack. The current model treats the stitch as a simple spring which responds to displacements in the vertical (through-thickness) direction. In concept, this approach is similar to that proposed by other authors. Test results indicate that the model should be refined to include the shearing stiffness of the stitch. The strain-energy-release-rate calculations are performed using a code which uses interconnected higher-order plates to model built-up composite cross-sections. When plates are stacked vertically, the interfacial tractions between the plates can be computed. The plate differential equations are solved in closed-form. The code, called SUBLAM, was developed as part of this section in one dimension. Because of this limitation, rows of stitches are treated as a two-dimensional sheet. The spring stiffness of a row of stitches can be estimated from the stitch material, weight, and density. As a practical and conservative approach, we can assume that the stitch is bonded until a crack passes the stitch location. After the crack passes, it is fully bonded. A series of tests were performed to exercise this methodology and incorporated an attached flange such that the sudden change in thickness initiated a delamination. The analysis was used to estimate the materials' critical G from that of the unstitched specimens. With this data, a prediction was made for the load required to delaminate the stitched specimens. Using the methodology, design charts have been created for simplified geometries. These charts give stitch force, along with G(sub 1) and G(sub 2) as as function of the stitch spring stiffness. Using the charts, it should be possible to determine the stitch spring stiffness and strength required to reduce the G to a desired level. From these parameters, the actual stitching material, weight, and density can be computed.

Flanagan, Gerry↗

Development of a Probabilistic Component Mode Synthesis Method for the Analysis of Non-Deterministic Substructures

Standard methods of structural dynamic analysis assume that the structural characteristics are deterministic. Recognizing that these characteristics are actually statistical in nature, researchers have recently developed a variety of methods that use this information to determine probabilities of a desired response characteristic, such as natural frequency, without using expensive Monte Carlo simulations. One of the problems in these methods is correctly identifying the statistical properties of primitive variables such as geometry, stiffness, and mass. This paper presents a method where the measured dynamic properties of substructures are used instead as the random variables. The residual flexibility method of component mode synthesis is combined with the probabilistic methods to determine the cumulative distribution function of the system eigenvalues. A simple cantilever beam test problem is presented that illustrates the theory.

Brown, Andrew M.↗

Convergence of a Substructuring Method with LaGrange Multipliers

We analyze the convergence of a substructuring iterative method with Lagrange multipliers, proposed recently by Farhat and Roux. The method decomposes finite element discretization of an elliptic boundary value problem into Neumann problems on the subdomains and a coarse problem for the subdomain nullspace components. For linear conforming elements and preconditioning by the Dirichlet problems on the subdomains, we prove the asymptotic bound on the condition number C(1 + log(H/h))(sup gamma), gamma = 2 or 3, where h is the characteristic element size and H is the subdomain size.

Mandel, Jan↗

Probabilistic Component Mode Synthesis of Nondeterministic Substructures

Standard methods of structural dynamic analysis assume that the structural characteristics are deterministic. Recognizing that these characteristics are actually statistical in nature researchers have recently developed a variety of methods that use this information to determine probabilities of a desired response characteristic, such as natural frequency, without using expensive Monte Carlo simulations. One of the problems in these methods is correctly identifying the statistical properties of primitive variables such as geometry, stiffness, and mass. We present a method where the measured dynamic properties of substructures are used instead as the random variables. The residual flexibility method of component mode synthesis is combined with the probabilistic methods to determine the cumulative distribution function of the system eigenvalues. A simple cantilever beam test problem is presented that illustrates the theory.

Brown, Andrew M.↗

Simulating and Synthesizing Substructures Using Neural Network and Genetic Algorithms

The feasibility of simulating and synthesizing substructures by computational neural network models is illustrated by investigating a statically indeterminate beam, using both a 1-D and a 2-D plane stress modelling. The beam can be decomposed into two cantilevers with free-end loads. By training neural networks to simulate the cantilever responses to different loads, the original beam problem can be solved as a match-up between two subsystems under compatible interface conditions. The genetic algorithms are successfully used to solve the match-up problem. Simulated results are found in good agreement with the analytical or FEM solutions.

Liu, Youhua↗

The Substructure of a Flux Transfer Event Observed by the MMS Spacecraft

On 15 August 2015, MMS (Magnetospheric Multiscale mission), skimming the dusk magnetopause, detected an isolated region of an increased magnetic strength and bipolar Bn, indicating a flux transfer event (FTE). The four spacecraft in a tetrahedron allowed for investigations of the shape and motion of the FTE. In particular, high-resolution particle data facilitated our exploration of FTE substructures and their magnetic connectivity inside and surrounding the FTE. Combined field and plasma observations suggest that the core fields are open, magnetically connected to the northern magnetosphere from which high-energy particles leak; ion "D" distributions characterize the axis of flux ropes that carry old-opened field lines; counter streaming electrons superposed by parallel-heated components populate the periphery surrounding the FTE; and the interface between the core and draped regions contains a separatrix of newlyopened magnetic field lines that emanate from the X line above the FTE.

Hwang, K.-J.↗

Do graph neural networks learn traditional jet substructure?

At the CERN LHC, the task of jet tagging, whose goal is to infer the origin of a jet given a set of final-state particles, is dominated by machine learning methods. Graph neural networks have been used to address this task by treating jets as point clouds with underlying, learnable, edge connections between the particles inside. We explore the decision-making process for one such state-of-the-art network, ParticleNet, by looking for relevant edge connections identified using the layerwise-relevance propagation technique. As the model is trained, we observe changes in the distribution of relevant edges connecting different intermediate clusters of particles, known as subjets. The resulting distribution of subjet connections is different for signal jets originating from top quarks, whose subjets typically correspond to its three decay products, and background jets originating from lighter quarks and gluons. This behavior indicates that the model is using traditional jet substructure observables, such as the number of prongs -- energetic particle clusters -- within a jet, when identifying jets.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Heavy flavour jet substructure

We present a comprehensive study of energy correlation functions and jet angularities for heavy-flavour QCD jets. In particular, we discuss the possibility of using these observables to expose the dead cone effect, i.e. the suppression of collinear QCD radiation around massive quarks, and to investigate the sensitivity of different observable definitions to the presence of quark masses. Our calculations are presented as all-order resummed predictions at next-to-leading-logarithmic accuracy, matched to (partial) fixed-order results to obtain a better description of the transition around the dead cone threshold. We also compare our analytic results with Pythia, Herwig and Sherpa Monte Carlo predictions to estimate the impact of non-perturbative contributions such as hadronisation, underlying events and B-hadron decays.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Receptance coupling substructure analysis and chatter frequency-informed machine learning for milling stability

This paper describes a milling stability identification approach that simultaneously considers: physics-based models for the tool tip frequency response functions and stability predictions; the binary result from a milling test (automatically labeled as stable or unstable based on frequency content); chatter frequency when an unstable result is obtained; and user risk tolerance. The algorithm applies probabilistic Bayesian machine learning with adaptive, parallelized Markov Chain Monte Carlo sampling to update the probability of stability with each milling test. Furthermore, the result is a robust solution for rapid convergence to optimized milling parameters for maximum metal removal rate using all available information.

42 ENGINEERING↗