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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 1,729 records · Page 96
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Build optimization of fiber-reinforced additively manufactured components

Additive manufacturing (AM) is enabling the fabrication of artifacts with unprecedented geometric and material complexity. The focus of this paper is on the build the optimization of short fiber reinforced polymers (SFRP) AM components. Specifically, we consider optimization of the build direction, topology, and fiber orientation of SFRP components. All three factors have a significant impact on the functional performance of the printed part. While significant progress has been made on optimizing these independently, the objective of this paper is to consider all three factors simultaneously and explore their interdependency, within the context of thermal applications. Towards this end, the underlying design parameters are identified, appropriate sensitivity equations are derived, and a formal optimization problem is posed as an extension to the popular Solid Isotropic Material with Penalization (SIMP). Results from several numerical experiments are presented, highlighting the impact of build direction, topology, and fiber orientation on the performance of SFRP components.

42 ENGINEERING↗
DOE OSTI2026

Computationally inexpensive part-scale thermal history of additive friction-stir deposition

This study presents an analytical model for steady-state power generation and tool heat loss in additive friction-stir deposition (AFSD), developed to enable part-scale thermal simulation while remaining computationally inexpensive. The model predicts total generated power, yielding 3.7–4.7 kW across deposition temperature setpoints of 400–460 °C for the deposition of AA6061 with a Be-Cu tool. This corresponds to 90–95% of the reported spindle power. Tool heat loss is experimentally determined by calibrating a steady-state energy balance between the generated power, the substrate-deposition thermal gradient, and a temperature dependent tool heat loss term: q tool (T) = a + b (T - 400°C) with a = 2.7 x 10 6 Wm -2 and b = 9.5 x 10 3 Wm -2 K -1 . The calibration indicates that about 69% of the generated heat is conducted into the tool for this configuration, which is much higher than previously reported. The calibrated heat-source is implemented in finite element software (Adamantine) to simulate the transient thermal history of a 100 cm 3 representative build in 8 min on a standard desktop (at 0.635 mm build-height resolution). For the first three layers, the substrate temperatures between simulation and experiment are within 10% mean absolute percentage error. Sensitivity analysis indicates that uncertainties in average deposition temperature and deformation localization (stir-zone geometry, depth, and spatial dependance of strain-rate and flow stress) dominate model variance, motivating additional experimental verification.

Additive Friction-Stir Deposition↗
DOE OSTI2019

Compact feature-aware Hermite-style high-order surface reconstruction

High-order surface reconstruction is now a commonly used technique for mesh generation and adaptation, geometric and physical modeling, and high-order numerical methods for solving partial differential equations (PDEs). However, surface reconstruction from a relatively coarse mesh remained a challenging problem, especially for surfaces with sharp features. Here in this paper, we introduce a new method to address this challenge by improving the previous state of the art, including continuous moving frames (CMF) and weighted averaging of local fittings (WALF) (Eng Comput 28 (2012)), in two aspects. First, we significantly improve the robustness of reconstruction from coarse meshes using a Hermite-style least squares approximation to incorporate normals of the surface and tangents of the feature curves. Second, we ensure both G 0 continuity and high-order accuracy of the reconstruction near sharp features by generating parametric surface elements with an iterative feature-aware parameterization. We present the theoretical framework of our method and compare it against point-based methods in terms of accuracy and stability. We demonstrate the use of the proposed technique in generating high-order meshes for finite element methods, and show that it enables nearly identical solutions as using the meshes generated from exact geometry, while allowing additional flexibility.

42 ENGINEERING↗
DOE OSTI2024

Reinforcement learning for block decomposition of planar CAD models

Abstract The problem of hexahedral mesh generation of general CAD models has vexed researchers for over 3 decades and analysts often spend more than 50% of the design-analysis cycle time decomposing complex models into simpler blocks meshable by existing techniques. The decomposed blocks are required for generating good quality meshes (tilings of quadrilaterals or hexahedra) suitable for numerical simulations of physical systems governed by conservation laws. We present a novel AI-assisted method for decomposing (segmenting) planar CAD (computer-aided design) models into well shaped rectangular blocks. Even though the simple examples presented here can also be meshed using many conventional methods, we believe this work is proof-of-principle of a AI-based decomposition method that can eventually be generalized to complex 2D and 3D CAD models. Our method uses reinforcement learning to train an agent to perform a series of optimal cuts on the CAD model that result in a good quality block decomposition. We show that the agent quickly learns an effective strategy for picking the location and direction of the cuts and maximizing its rewards. This paper is the first successful demonstration of an agent autonomously learning how to perform this block decomposition task effectively, thereby holding the promise of a viable method to automate this challenging process for more complex cases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Implementation of a thermomechanical model for the simulation of selective laser melting

Selective laser melting (SLM) is an additive manufacturing process in which multiple, successive layers of metal powders are heated via laser in order to build a part. Modeling of SLM requires consideration of both heat transfer and solid mechanics. The present work describes continuum modeling of SLM as envisioned for eventual support of part-scale modeling of this fabrication process to determine end-state information such as residual stresses and distortion. The determination of the evolving temperatures is dependent on the material, the state of the material (powder or solid), the specified heating, and the configuration. Similarly, the current configuration is dependent on the temperatures, the powder-solid state, and the constitutive models. A multi-physics numerical formulation is required to solve such problems. This article describes the problem formulation, numerical method, and constitutive parameters necessary to solve such a problem. Additionally, various verification and example problems are simulated in the parallel, multi-physics finite element code Diablo, and the results presented herein.

42 ENGINEERING↗
DOE OSTI2023

Language model-accelerated deep symbolic optimization

Symbolic optimization methods have been used to solve varied challenging and relevant problems such as symbolic regression and neural architecture search. However, the current state of the art typically learns each problem from scratch and is unable to leverage pre-existing knowledge and datasets that are available for many applications. Here, inspired by the similarity between sequence representations learned in natural language processing and the formulation of symbolic optimization as a discrete sequence optimization problem, we propose language model-accelerated deep symbolic optimization (LA-DSO), a method that leverages language models to learn symbolic optimization solutions more efficiently. We demonstrate LA-DSO in two tasks: symbolic regression, which allows us to perform extensive experimentation due to its low computation requirements, and computational antibody optimization, which shows that our proposal accelerates learning in challenging real-world problems.

97 MATHEMATICS AND COMPUTING↗