S. Roy

Bio: S. Roy is an academic researcher from University of Iowa. The author has contributed to research in topics: Slosh dynamics & Mechanics. The author has an hindex of 5, co-authored 10 publications receiving 69 citations. Previous affiliations of S. Roy include Siksha O Anusandhan University & Indian Institute of Technology Madras.

Deep learning for synthetic microstructure generation in a materials-by-design framework for heterogeneous energetic materials.

Abstract: 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.

Deep learning for synthetic microstructure generation in a materials-by-design framework for heterogeneous energetic materials

Abstract: 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.

Modeling meso-scale energy localization in shocked HMX, Part II: training machine-learned surrogate models for void shape and void-void interaction effects

Abstract: Surrogate models for hotspot ignition and growth rates were presented in Part I, where the hotspots were formed by the collapse of single cylindrical voids. Such isolated cylindrical voids are idealizations of the void morphology in real meso-structures. This paper therefore investigates the effect of non-cylindrical void shapes and void-void interactions on hotspot ignition and growth. Surrogate models capturing these effects are constructed using a Bayesian Kriging approach. The training data for machine learning the surrogates are derived from reactive void collapse simulations spanning the parameter space of void aspect ratio (AR), void orientation ($\theta$), and void fraction ($\phi$). The resulting surrogate models portray strong dependence of the ignition and growth rates on void aspect ratio and orientation, particularly when they are oriented at acute angles with respect to the imposed shock. The surrogate models for void interaction effects show significant changes in hotspot ignition and growth rates as the void fraction increases. The paper elucidates the physics of hotspot evolution in void fields due to the creation and interaction of multiple hotspots. The results from this work will be useful not only for constructing meso-informed macro-scale models of HMX, but also for understanding the physics of void-void interactions and sensitivity due to void shape and orientation.

Structure–property linkage in shocked multi-material flows using a level-set-based Eulerian image-to-computation framework

Abstract: Morphology and dynamics at the mesoscale play crucial roles in the overall macro- or system-scale flow of heterogeneous materials. In a multi-scale framework, closure models upscale unresolved sub-grid (mesoscale) physics and therefore encapsulate structure–property (S–P) linkages to predict performance at the macroscale. This work establishes a route to S–P linkage, proceeding all the way from imaged microstructures to flow computations in one unified level-set-based framework. Level sets are used to: (1) define embedded geometries via image segmentation; (2) simulate the interaction of sharp immersed boundaries with the flow field; and (3) calculate morphological metrics to quantify structure. Mesoscale dynamics is computed to calculate sub-grid properties, i.e., closure models for momentum and energy equations. The S–P linkage is demonstrated for two types of multi-material flows: interaction of shocks with a cloud of particles and reactive meso-mechanics of pressed energetic materials. We also present an approach to connect local morphological characteristics in a microstructure containing topologically complex features with the shock response of imaged samples of such materials. This paves the way for using geometric machine learning techniques to associate imaged morphologies with their properties.

Structure–property–performance linkages for heterogenous energetic materials through multi-scale modeling

Abstract: In porous solid energetic materials, the mechanical processing technique (e.g. casting, pressing) creates defects such as voids, cracks, interfaces, and inclusions; these defects in the microstructure strongly influence the sensitivity of the material to imposed loading. Energy localization at defects causes hotspots; the ignition and growth of hotspots in the microstructure (i.e. the meso-scale) play a crucial role in the macroscale initiation of the material. Predictive models of shock response of energetic materials must connect the meso-scale heterogeneities (structure) and hotspot physics (properties) to macro-scale response (performance and safety). To achieve this structure–property–performance (S–P–P) linkage, SEM-imaged samples of neat pressed HMX are obtained, and morphometry is performed to quantify the microstructure. Since the microstructure is stochastic, the aleatory uncertainties in the morphological parameters are quantified. The link between the microstructure and the key meso-scale quantity of interest—the hotspot ignition and growth rates—is established using reactive meso-scale computations to construct meso-informed surrogate models for energy localization. The surrogate models are used to close homogenized macro-scale governing equations. The performance of the HE at the macro-scale, i.e. its sensitivity to shock loading, is measured via run-to-detonation distances (in Pop plots) and the critical energy required for initiation (in James plots). The predicted critical energy for the material is compared with experimental data. The methods established in this paper can be useful not only for establishing structure–property–performance (S–P–P) linkages for pressed energetic materials, but also for other heterogenous reactive composites such as propellants and plastic-bonded explosives.

Advanced Engineering Thermodynamics

Abstract: Thank you for reading advanced engineering thermodynamics. As you may know, people have look numerous times for their chosen novels like this advanced engineering thermodynamics, but end up in harmful downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they juggled with some harmful virus inside their computer. advanced engineering thermodynamics is available in our book collection an online access to it is set as public so you can get it instantly. Our book servers saves in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the advanced engineering thermodynamics is universally compatible with any devices to read.

Spatial tessellations. Concepts and Applications of Voronoi diagrams

Abstract: Definitions and basic properties of the Voronoi diagram generalizations of the Voronoi diagram algorithms for computing Voronoi diagrams poisson Voronoi diagrams spatial interpolation models of spatial processes point pattern analysis locational optimization through Voronoi diagrams.

Overview: Computer Vision and Machine Learning for Microstructural Characterization and Analysis

Abstract: Microstructural characterization and analysis is the foundation of microstructural science, connecting materials structure to composition, process history, and properties. Microstructural quantification traditionally involves a human deciding what to measure and then devising a method for doing so. However, recent advances in computer vision (CV) and machine learning (ML) offer new approaches for extracting information from microstructural images. This overview surveys CV methods for numerically encoding the visual information contained in a microstructural image using either feature-based representations or convolutional neural network (CNN) layers, which then provides input to supervised or unsupervised ML algorithms that find associations and trends in the high-dimensional image representation. CV/ML systems for microstructural characterization and analysis span the taxonomy of image analysis tasks, including image classification, semantic segmentation, object detection, and instance segmentation. These tools enable new approaches to microstructural analysis, including the development of new, rich visual metrics and the discovery of processing-microstructure-property relationships.

Effect of operating parameters and antioxidant additives with biodiesels to improve the performance and reducing the emissions in a compression ignition engine – A review

Abstract: It is an overwhelming argument that the use of biodiesel in diesel engine causes slight decrease in performance and reduction in exhaust emissions but at the expense of oxides of nitrogen (NOx) emission. In order to improve the performance without sacrificing the advantage in terms of exhaust emissions, it is essential to vary the engine operating parameters such as compression ratio (CR), injection pressure (IP) and injection timing (IT). Nowadays, treatment of biodiesel with antioxidant additive is a promising approach to reduce the NOx emission because it reduces the hydrogen free radicals, which is responsible for prompt NOx formation during combustion process. Hence, in the present review a detailed study has been carried out with the operating parameters and antioxidant additives used in biodiesel operated diesel engine so that its performance can be improved and exhaust emissions were reduced.

Recent Advances and Applications of Deep Learning Methods in Materials Science

Abstract: Deep learning (DL) is one of the fastest growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. Recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep-learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science. The application of DL methods in materials science presents an exciting avenue for future materials discovery and design.