Publications

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This report provides detailed documentation of the algorithms that where developed and implemented in the Plato software over the course of the Optimization-based Design for Manufacturing LDRD project.

The typical topology optimization workflow uses a design domain that does not change during the optimization process. Consequently, features of the design domain, such as the location of loads and constraints, must be determined in advance and are not optimizable. A method is proposed herein that allows the design domain to be optimized along with the topology. This approach uses topology and shape derivatives to guide nested optimizers to the optimal topology and design domain. The details of the method are discussed, and examples are provided that demonstrate the utility of this approach.

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The final review for the FY20 Advanced Simulation and Computing (ASC) Integrated Codes (IC) L2 Milestone #7181 was conducted on August 31, 2020 at Sandia National Laboratories in Albuquerque, New Mexico. The review panel unanimously agreed that the milestone has been successfully completed. Roshan Quadros (1543) led the milestone team and various members from the team presented the results. The review panel was comprised of staff from Sandia National Laboratories Albuquerque and California that are involved with computational engineering modeling and analysis. The panel consisted of experts in the fields of solid modeling, discretization, meshing, simulation workflows, and computational analysis including personnel Brett Clark (1543, Chair); Jay Foulk (8363); Jackie Moore (1553); Ron Kensek (1341); Ed Hoffman (8753); Dan Ibanez (1443). The presentation documented the technical approach of the team and summarized the results with sufficient detail to demonstrate both the value and the completion of the milestone. A separate SAND report was also generated with more detail to supplement the presentation. The purpose of the milestone was to advance capabilities for automatically finding, displaying, and resolving geometric overlaps in CAD models.

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Structural dynamic testing is a common method for determining if the design of a component of a system will mechanically fail when deployed into its field environment. To satisfy the test's goal, the mechanical stresses must be replicated. Structural dynamic testing is commonly executed on a shaker table or a shock apparatus such as a drop table or a resonant plate. These apparatus impart a force or load on the component through a test fixture that connects the unit under test to the apparatus. Because the test fixture is directly connected to the unit under test, the fixture modifies the structural dynamics of the system, thus varying the locations and relative levels of stress on the unit under test. This may lead to a false positive or negative indication if the unit under test will fail in its field environment depending on the environment and the test fixture. This body of research utilizes topology optimization using the Plato software to design a test fixture that attaches to the unit under test that matches the dynamic impedance of the next level of assembly. The optimization's objective function is the difference between the field configuration and the laboratory configuration's frequency response functions. It was found that this objective function had many local minima and posed difficulties in converging to an acceptable solution. A case study is presented that uses this objective function and although the results are not perfect, they are quantifiably better than the current method of using a sufficiently stiff fixture.

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Additive manufacturing offers unprecedented opportunities to design complex structures optimized for performance envelopes inaccessible under conventional manufacturing constraints. Additive processes also promote realization of engineered materials with microstructures and properties that are impossible via traditional synthesis techniques. Enthused by these capabilities, optimization design tools have experienced a recent revival. The current capabilities of additive processes and optimization tools are summarized briefly, while an emerging opportunity is discussed to achieve a holistic design paradigm whereby computational tools are integrated with stochastic process and material awareness to enable the concurrent optimization of design topologies, material constructs and fabrication processes.

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