Abstract:The modern “energetic‐on‐a‐chip” trend envisages reducing size and cost while increasing safety and maintaining the performance of energetic articles. However, the fabrication of reactive structures at micro‐ and nanoscales remains a challenge due to the spatial limitations of traditional tools and technologies. These mature techniques, such as melt casting or slurry curing, represent the formative approach to design as distinct from the emerging additive manufacturing (3D printing). The present review discuss… Show more
“…In both examples, it is noteworthy that there are no observable stitching boundaries or awkward image transitions. Using this facility, combined with the on-going micro-scale additive manufacturing (or micro 3D printing) techniques 13 , 21 , 27 , 66 , the present controlled micro-morphology generation technique can lead to the realization of microstructure-engineered materials, which is being pursued by the authors in ongoing work. Figure 12 ( a ) A graded microstructure generated by linearly interpolating two global morphology parameters.…”
Section: Resultsmentioning
confidence: 99%
“…(b) A layout of global morphology parameters (left) and the corresponding GAN-Synµ S. Foreground regions were "painted" with 1 in panel (a) while the background was painted with 0 . www.nature.com/scientificreports/ with the on-going micro-scale additive manufacturing (or micro 3D printing) techniques 13,21,27,66 , the present controlled micro-morphology generation technique can lead to the realization of microstructure-engineered materials, which is being pursued by the authors in ongoing work.…”
Section: Simulations Of Reactive Dynamics In Real and Synthetic Micromentioning
confidence: 99%
“…These models are used to close the macro-scale governing equations in a multi-scale predictive framework. The work in this paper presents a deep learning approach to generate ensembles of synthetic microstructures that can be used for simulations; the methodology also allows for designing new microstructures, paving the way for materials-by-design of energetic materials 13 , 27 . Simulations on an ensemble of microstructures also facilitate uncertainty quantification (UQ) due to microstructural variability 24 i.e.…”
Section: Introductionmentioning
confidence: 99%
“…The frontier in computational energetic materials research is to develop predictive multi-scale models to guide the design process of novel materials with tailored performance via microstructural control 12,13 . Predictive frameworks of energetic material response to loads are a matter of concerted current development by several groups worldwide [14][15][16][17][18][19][20] ; such capabilities are needed to establish structure-property-performance (S-P-P) linkages as necessary precursors to materials-by-design of heterogeneous materials 12,21,22 .…”
the sensitivity of heterogeneous energetic (He) materials (propellants, explosives, and pyrotechnics) is critically dependent on their microstructure. initiation of chemical reactions occurs at hot spots due to energy localization at sites of porosities and other defects. emerging multi-scale predictive models of He response to loads account for the physics at the meso-scale, i.e. at the scale of statistically representative clusters of particles and other features in the microstructure. Meso-scale physics is infused in machine-learned closure models informed by resolved meso-scale simulations. Since microstructures are stochastic, ensembles of meso-scale simulations are required to quantify hot spot ignition and growth and to develop models for microstructure-dependent energy deposition rates. We propose utilizing generative adversarial networks (GAn) to spawn ensembles of synthetic heterogeneous energetic material microstructures. the method generates qualitatively and quantitatively realistic microstructures by learning from images of He microstructures. We show that the proposed GAn method also permits the generation of new morphologies, where the porosity distribution can be controlled and spatially manipulated. Such control paves the way for the design of novel microstructures to engineer He materials for targeted performance in a materials-by-design framework.
“…In both examples, it is noteworthy that there are no observable stitching boundaries or awkward image transitions. Using this facility, combined with the on-going micro-scale additive manufacturing (or micro 3D printing) techniques 13 , 21 , 27 , 66 , the present controlled micro-morphology generation technique can lead to the realization of microstructure-engineered materials, which is being pursued by the authors in ongoing work. Figure 12 ( a ) A graded microstructure generated by linearly interpolating two global morphology parameters.…”
Section: Resultsmentioning
confidence: 99%
“…(b) A layout of global morphology parameters (left) and the corresponding GAN-Synµ S. Foreground regions were "painted" with 1 in panel (a) while the background was painted with 0 . www.nature.com/scientificreports/ with the on-going micro-scale additive manufacturing (or micro 3D printing) techniques 13,21,27,66 , the present controlled micro-morphology generation technique can lead to the realization of microstructure-engineered materials, which is being pursued by the authors in ongoing work.…”
Section: Simulations Of Reactive Dynamics In Real and Synthetic Micromentioning
confidence: 99%
“…These models are used to close the macro-scale governing equations in a multi-scale predictive framework. The work in this paper presents a deep learning approach to generate ensembles of synthetic microstructures that can be used for simulations; the methodology also allows for designing new microstructures, paving the way for materials-by-design of energetic materials 13 , 27 . Simulations on an ensemble of microstructures also facilitate uncertainty quantification (UQ) due to microstructural variability 24 i.e.…”
Section: Introductionmentioning
confidence: 99%
“…The frontier in computational energetic materials research is to develop predictive multi-scale models to guide the design process of novel materials with tailored performance via microstructural control 12,13 . Predictive frameworks of energetic material response to loads are a matter of concerted current development by several groups worldwide [14][15][16][17][18][19][20] ; such capabilities are needed to establish structure-property-performance (S-P-P) linkages as necessary precursors to materials-by-design of heterogeneous materials 12,21,22 .…”
the sensitivity of heterogeneous energetic (He) materials (propellants, explosives, and pyrotechnics) is critically dependent on their microstructure. initiation of chemical reactions occurs at hot spots due to energy localization at sites of porosities and other defects. emerging multi-scale predictive models of He response to loads account for the physics at the meso-scale, i.e. at the scale of statistically representative clusters of particles and other features in the microstructure. Meso-scale physics is infused in machine-learned closure models informed by resolved meso-scale simulations. Since microstructures are stochastic, ensembles of meso-scale simulations are required to quantify hot spot ignition and growth and to develop models for microstructure-dependent energy deposition rates. We propose utilizing generative adversarial networks (GAn) to spawn ensembles of synthetic heterogeneous energetic material microstructures. the method generates qualitatively and quantitatively realistic microstructures by learning from images of He microstructures. We show that the proposed GAn method also permits the generation of new morphologies, where the porosity distribution can be controlled and spatially manipulated. Such control paves the way for the design of novel microstructures to engineer He materials for targeted performance in a materials-by-design framework.
“…Currently, AM is most often used in the medical industry [ 1 , 2 , 3 ], and its rapid growth is taking place during the COVID-19 [ 4 ] pandemic to produce all possible elements used to combat the pandemic and support the production of life-saving components [ 5 ]. A wide range of applications can be observed in industries such as stomatology [ 6 , 7 ], power engineering [ 8 ], aerospace industry [ 9 ] and even the automotive industry [ 10 , 11 ]. AM methods used to manufacture real 3D objects using the layer-by-layer technique use a few types of building materials, namely metals, polymers, ceramics and glass [ 12 , 13 ].…”
This paper describes a novel method for the experimental validation of numerically optimised turbomachinery components. In the field of additive manufacturing, numerical models still need to be improved, especially with the experimental data. The paper presents the operational characteristics of a compressor wheel, measured during experimental research. The validation process included conducting a computational flow analysis and experimental tests of two compressor wheels: The aluminium wheel and the 3D printed wheel (made of a polymer material). The chosen manufacturing technology and the results obtained made it possible to determine the speed range in which the operation of the tested machine is stable. In addition, dynamic destructive tests were performed on the polymer disc and their results were compared with the results of the strength analysis. The tests were carried out at high rotational speeds (up to 120,000 rpm). The results of the research described above have proven the utility of this technology in the research and development of high-speed turbomachines operating at speeds up to 90,000 rpm. The research results obtained show that the technology used is suitable for multi-variant optimization of the tested machine part. This work has also contributed to the further development of numerical models.
The development of 3D printing technology provides a new idea for the application of energy‐containing materials as functional reactive materials, which helps Al–F high‐energy materials to achieve customized performance and structure, so that energy transmit can be targeted. A high fuel‐load ink (up to 90 wt%) based on Al‐polytetrafluoroethylene (PTFE) is designed and fabricated using fluororubber (FR) as a binder. The content of FR is directly related to the mechanical properties of ink, and the customized structure from the uniform and stable deposition can be obtained by adjusting the content of FR (10–20 wt%). Under the display of high‐speed photography, the ink shows a strong stability in combustion performance, which is conducive to realize the performance customization of the ink in the application. To better understand the combustion mechanism, the thermal properties and combustion process are analyzed in brief through thermal analysis and the combustion snapshots.
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