An official website of the United States government
Microstructured materials that can selectively control the optical properties are crucial for the development of thermal management systems in aerospace and space applications. However, due to the vast design space available for microstructures with varying material, wavelength, and temperature conditions relevant to thermal radiation, the microstructure design optimization becomes a very time-intensive process and with results for specific and limited conditions. Here, we develop a deep neural network to emulate the outputs of finite-difference time-domain simulations (FDTD). The network we show is the foundation of a machine learning based approach to microstructure design optimization for thermal radiation control. Our neural network differentiates materials using discrete inputs derived from the materials’ complex refractive index, enabling the model to build relationships between the microtexture’s geometry, wavelength, and material. Thus, material selection does not constrain our network and it is capable of accurately extrapolating optical properties for microstructures of materials not included in the training process. Our surrogate deep neural network can synthetically simulate over 1,000,000 distinct combinations of geometry, wavelength, temperature, and material in less than a minute, representing a speed increase of over 8 orders of magnitude compared to typical FDTD simulations. This speed enables us to perform sweeping thermal-optical optimizations rapidly to design advanced passive cooling or heating systems. The deep learning-based approach enables complex thermal and optical studies that would be impossible with conventional simulations and our network design can be used to effectively replace optical simulations for other microstructures.
more »
« less
Cut‐Cell Microstructures for Two‐scale Structural Optimization
Abstract Two‐scale topology optimization, combined with the design of microstructure families with a broad range of effective material parameters, is widely used in many fabrication applications to achieve a target deformation behavior for a variety of objects. The main idea of this approach is to optimize the distribution of material properties in the object partitioned into relatively coarse cells, and then replace each cell with microstructure geometry that mimics these material properties. In this paper, we focus on adapting this approach to complex shapes in situations when preserving the shape's surface is essential. Our approach extends any regular (i.e. defined on a regular lattice grid) microstructure family to complex shapes, by enriching it with tiles adapted to the geometry of the cut‐cell. We propose a fully automated and robust pipeline based on this approach, and we show that the performance of the regular microstructure family is only minimally affected by our extension while allowing its use on 2D and 3D shapes of high complexity.
more »
« less
- PAR ID:
- 10529344
- Publisher / Repository:
- EG
- Date Published:
- Journal Name:
- Computer Graphics Forum
- ISSN:
- 0167-7055
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Traditionally, tissue-specific organoids are generated as 3D aggregates of stem cells embedded in Matrigel or hydrogels, and the aggregates eventually end up a spherical shape and suspended in the matrix. Lack of geometrical control of organoid formation makes these spherical organoids limited for modeling the tissues with complex shapes. To address this challenge, we developed a new method to generate 3D spatial-organized cardiac organoids from 2D micropatterned human induced pluripotent stem cell (hiPSC) colonies, instead of directly from 3D stem cell aggregates. This new approach opens the possibility to create cardiac organoids that are templated by 2D non-spherical geometries, which potentially provides us a deeper understanding of biophysical controls on developmental organogenesis. Here, we designed 2D geometrical templates with quadrilateral shapes and pentagram shapes that had same total area but different geometrical shapes. Using this templated substrate, we grew cardiac organoids from hiPSCs and collected a series of parameters to characterize morphological and functional properties of the cardiac organoids. In quadrilateral templates, we found that increasing the aspect ratio impaired cardiac tissue 3D self-assembly, but the elongated geometry improved the cardiac contractile functions. However, in pentagram templates, cardiac organoid structure and function were optimized with a specific geometry of an ideal star shape. This study will shed a light on “organogenesis-by-design” by increasing the intricacy of starting templates from external geometrical cues to improve the organoid morphogenesis and functionality.more » « less
-
Abstract Single point incremental forming (SPIF) is a flexible manufacturing process that has applications in industries ranging from biomedical to automotive. In addition to rapid prototyping, which requires easy adaptations in geometry or material for design changes, control of the final part properties is desired. One strategy that can be implemented is stress superposition, which is the application of additional stresses during an existing manufacturing process. Tensile and compressive stresses applied during SPIF showed significant effects on the resulting microstructure in stainless steel 304 truncated square pyramids. Specifically, the amount of martensitic transformation was increased through stress superposed incremental forming. Finite element analyses with advanced material modeling supported that the stress triaxiality had a larger effect than the Lode angle parameter on the phase transformation that occurred during deformation. By controlling the amount of tensile and compressive stresses superposed during incremental forming, the microstructure of the final component can be manipulated based on the intended application and desired final part properties.more » « less
-
Abstract Directional interactions that generate regular coordination geometries are a powerful means of guiding molecular and colloidal self-assembly, but implementing such high-level interactions with proteins remains challenging due to their complex shapes and intricate interface properties. Here we describe a modular approach to protein nanomaterial design inspired by the rich chemical diversity that can be generated from the small number of atomic valencies. We design protein building blocks using deep learning-based generative tools, incorporating regular coordination geometries and tailorable bonding interactions that enable the assembly of diverse closed and open architectures guided by simple geometric principles. Experimental characterization confirms the successful formation of more than 20 multicomponent polyhedral protein cages, two-dimensional arrays and three-dimensional protein lattices, with a high (10%–50%) success rate and electron microscopy data closely matching the corresponding design models. Due to modularity, individual building blocks can assemble with different partners to generate distinct regular assemblies, resulting in an economy of parts and enabling the construction of reconfigurable networks for designer nanomaterials.more » « less
-
Abstract 3D bioprinting has enabled the fabrication of tissue‐mimetic constructs with freeform designs that include living cells. In the development of new bioprinting techniques, the controlled use of diffusion has become an emerging strategy to tailor the properties and geometry of printed constructs. Specifically, the diffusion of molecules with specialized functions, including crosslinkers, catalysts, growth factors, or viscosity‐modulating agents, across the interface of printed constructs will directly affect material properties such as microstructure, stiffness, and biochemistry, all of which can impact cell phenotype. For example, diffusion‐induced gelation is employed to generate constructs with multiple materials, dynamic mechanical properties, and perfusable geometries. In general, these diffusion‐based bioprinting strategies can be categorized into those based on inward diffusion (i.e., into the printed ink from the surrounding air, solution, or support bath), outward diffusion (i.e., from the printed ink into the surroundings), or diffusion within the printed construct (i.e., from one zone to another). This review provides an overview of recent advances in diffusion‐based bioprinting strategies, discusses emerging methods to characterize and predict diffusion in bioprinting, and highlights promising next steps in applying diffusion‐based strategies to overcome current limitations in biofabrication.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1111/cgf.15139
