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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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Neutron Characterization for Additive Manufacturing

Oak Ridge National Laboratory (ORNL) is leveraging decades of experience in neutron characterization of advanced materials together with resources such as the Spallation Neutron Source (SNS) and the High Flux Isotope Reactor (HFIR) shown in Fig. 1 to solve challenging problems in additive manufacturing (AM). Additive manufacturing, or three-dimensional (3-D) printing, is a rapidly maturing technology wherein components are built by selectively adding feedstock material at locations specified by a computer model. The majority of these technologies use thermally driven phase change mechanisms to convert the feedstock into functioning material. As the molten material cools and solidifies, the component is subjected to significant thermal gradients, generating significant internal stresses throughout the part (Fig. 2). As layers are added, inherent residual stresses cause warping and distortions that lead to geometrical differences between the final part and the original computer generated design. This effect also limits geometries that can be fabricated using AM, such as thin-walled, high-aspect- ratio, and overhanging structures. Distortion may be minimized by intelligent toolpath planning or strategic placement of support structures, but these approaches are not well understood and often "Edisonian" in nature. Residual stresses can also impact component performance during operation. For example, in a thermally cycled environment such as a high-pressure turbine engine, residual stresses can cause components to distort unpredictably. Different thermal treatments on as-fabricated AM components have been used to minimize residual stress, but components still retain a nonhomogeneous stress state and/or demonstrate a relaxation-derived geometric distortion. Industry, federal laboratory, and university collaboration is needed to address these challenges and enable the U.S. to compete in the global market. Work is currently being conducted on AM technologies at the ORNL Manufacturing Demonstration Facility (MDF) sponsored by the DOE's Advanced Manufacturing Office. The MDF is focusing on R&D of both metal and polymer AM pertaining to in-situ process monitoring and closed-loop controls; implementation of advanced materials in AM technologies; and demonstration, characterization, and optimization of next-generation technologies. ORNL is working directly with industry partners to leverage world-leading facilities in fields such as high performance computing, advanced materials characterization, and neutron sciences to solve fundamental challenges in advanced manufacturing. Specifically, MDF is leveraging two of the world's most advanced neutron facilities, the HFIR and SNS, to characterize additive manufactured components.

Watkins, Thomas↗

Ordering Unstructured Meshes for Sparse Matrix Computations on Leading Parallel Systems

The ability of computers to solve hitherto intractable problems and simulate complex processes using mathematical models makes them an indispensable part of modern science and engineering. Computer simulations of large-scale realistic applications usually require solving a set of non-linear partial differential equations (PDES) over a finite region. For example, one thrust area in the DOE Grand Challenge projects is to design future accelerators such as the SpaHation Neutron Source (SNS). Our colleagues at SLAC need to model complex RFQ cavities with large aspect ratios. Unstructured grids are currently used to resolve the small features in a large computational domain; dynamic mesh adaptation will be added in the future for additional efficiency. The PDEs for electromagnetics are discretized by the FEM method, which leads to a generalized eigenvalue problem Kx = AMx, where K and M are the stiffness and mass matrices, and are very sparse. In a typical cavity model, the number of degrees of freedom is about one million. For such large eigenproblems, direct solution techniques quickly reach the memory limits. Instead, the most widely-used methods are Krylov subspace methods, such as Lanczos or Jacobi-Davidson. In all the Krylov-based algorithms, sparse matrix-vector multiplication (SPMV) must be performed repeatedly. Therefore, the efficiency of SPMV usually determines the eigensolver speed. SPMV is also one of the most heavily used kernels in large-scale numerical simulations.

Oliker, Leonid↗

Radioactivities and gamma-rays from supernovae

An account is given of the implications of several calculations relevant to the estimation of gamma-ray signals from various explosive astronomical phenomena. After discussing efforts to constrain the amounts of Ni-57 and Ti-44 produced in SN 1987A, attention is given to the production of Al-27 in massive stars and SNs. A 'delayed detonation' model of type Ia SNs is proposed, and the gamma-ray signal which may be expected when a bare white dwarf collapses directly into a neutron star is discussed.

Woosley, S. E.↗