168 research outputs found

    Toward smart and efficient scientific data management

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    Scientific research generates vast amounts of data, and the scale of data has significantly increased with advancements in scientific applications. To manage this data effectively, lossy data compression techniques are necessary to reduce storage and transmission costs. Nevertheless, the use of lossy compression introduces uncertainties related to its performance. This dissertation aims to answer key questions surrounding lossy data compression, such as how the performance changes, how much reduction can be achieved, and how to optimize these techniques for modern scientific data management workflows. One of the major challenges in adopting lossy compression techniques is the trade-off between data accuracy and compression performance, particularly the compression ratio. This trade-off is not well understood, leading to a trial-and-error approach in selecting appropriate setups. To address this, the dissertation analyzes and estimates the compression performance of two modern lossy compressors, SZ and ZFP, on HPC datasets at various error bounds. By predicting compression ratios based on intrinsic metrics collected under a given base error bound, the effectiveness of the estimation scheme is confirmed through evaluations using real HPC datasets. Furthermore, as scientific simulations scale up on HPC systems, the disparity between computation and input/output (I/O) becomes a significant challenge. To overcome this, error-bounded lossy compression has emerged as a solution to bridge the gap between computation and I/O. Nonetheless, the lack of understanding of compression performance hinders the wider adoption of lossy compression. The dissertation aims to address this challenge by examining the complex interaction between data, error bounds, and compression algorithms, providing insights into compression performance and its implications for scientific production. Lastly, the dissertation addresses the performance limitations of progressive data retrieval frameworks for post-hoc data analytics on full-resolution scientific simulation data. Existing frameworks suffer from over-pessimistic error control theory, leading to fetching more data than necessary for recomposition, resulting in additional I/O overhead. To enhance the performance of progressive retrieval, deep neural networks are leveraged to optimize the error control mechanism, reducing unnecessary data fetching and improving overall efficiency. By tackling these challenges and providing insights, this dissertation contributes to the advancement of scientific data management, lossy data compression techniques, and HPC progressive data retrieval frameworks. The findings and methodologies presented pave the way for more efficient and effective management of large-scale scientific data, facilitating enhanced scientific research and discovery. In future research, this dissertation highlights the importance of investigating the impact of lossy data compression on downstream analysis. On the one hand, more data reduction can be achieved under scenarios like image visualization where the error tolerance is very high, leading to less I/O and communication overhead. On the other hand, post-hoc calculations based on physical properties after compression may lead to misinterpretation, as the statistical information of such properties might be compromised during compression. Therefore, a comprehensive understanding of the impact of lossy data compression on each specific scenario is vital to ensure accurate analysis and interpretation of results

    Lattice Boltzmann method for warm fluid simulations of plasma wakefield acceleration

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    A comprehensive characterization of lattice Boltzmann (LB) schemes to perform warm fluid numerical simulations of particle wakefield acceleration (PWFA) processes is discussed in this paper. The LB schemes we develop hinge on the moment matching procedure, allowing the fluid description of a warm relativistic plasma wake generated by a driver pulse propagating in a neutral plasma. We focus on fluid models equations resulting from two popular closure assumptions of the relativistic kinetic equations, i.e., the local equilibrium and the warm plasma closure assumptions. The developed LB schemes can thus be used to disclose insights on the quantitative differences between the two closure approaches in the dynamics of PWFA processes. Comparisons between the proposed schemes and available analytical results are extensively addressed.Comment: 8 figure

    Sustainability in HPC: Vision and Opportunities

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    Tackling climate change by reducing and eventually eliminating carbon emissions is a significant milestone on the path toward establishing an environmentally sustainable society. As we transition into the exascale era, marked by an increasing demand and scale of HPC resources, the HPC community must embrace the challenge of reducing carbon emissions from designing and operating modern HPC systems. In this position paper, we describe challenges and highlight different opportunities that can aid HPC sites in reducing the carbon footprint of modern HPC systems.Comment: Accepted at the ACM Sustainable Supercomputing Workshop in conjunction with SC'2

    Development of a wide-spectrum thermochemical code with application to planar reacting and non-reacting shocks

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    Mención Internacional en el título de doctorThe recent scientific and technological advancements have underscored the critical necessity for reliable, robust, and efficient numerical codes capable of predicting the chemical composition and properties of complex mixtures at chemical equilibrium. In response to this demand, this thesis presents the development and validation of a novel open-source thermochemical code called Combustion Toolbox (CT). This tool is designed to determine the equilibrium state of multi-species mixtures in gaseous or pure condensed phases, including ions. The code incorporates a comprehensive suite of algorithms, ranging from fundamental chemical equilibrium problems to complex computations of steady shock and detonation waves in various flow configurations, as well as predictions of rocket engine performance. Implemented in MATLAB, CT is accompanied by a user-friendly graphical user interface, ensuring flexibility and accessibility for all users. Extensive validation demonstrates excellent agreement with established codes such as NASA’s CEA, Cantera within Caltech’s Shock and Detonation Toolbox, and the recent Thermochemical Equilibrium Abundances code. CT has been utilized in all of the studies presented in this thesis, demonstrating its reliability and versatility. The second part of the thesis delves into the theoretical analysis of reactive and nonreactive shocks propagating through non-homogeneous conditions. Conducting experiments and high-fidelity simulations in this field can be challenging and computationally expensive. In this context, linear interaction analysis has emerged as a valuable tool to evaluate the hydrodynamical aspects contributing to the amplification of disturbances across the shock. Two prominent cases are investigated. Firstly, the study focuses on detonations with inhomogeneities in the upstream fuel mass fraction. The findings reveal that, in most cases, the detonation propagation speed is higher than in equivalent homogeneous scenarios. Subsequently, the investigation shifts towards the interaction of hypersonic shocks with turbulent flows, incorporating the associated thermochemical effects in single-species diatomic gases. The analysis is further extended to multi-species mixtures using CT, with a particular emphasis on air. These studies demonstrate that thermochemical effects arising at hypersonic velocities significantly enhance turbulent fluctuations in the post-shock gas compared to the simplified thermochemical frozen gas assumption.Los avances científicos y tecnológicos recientes han destacado la necesidad crítica de contar con códigos numéricos fiables, robustos y eficientes capaces de predecir la composición química y las propiedades de mezclas complejas en equilibrio químico. En respuesta a esta demanda, esta tesis presenta el desarrollo y la validación de un novedoso código termoquímico de código abierto llamado Combustion Toolbox (CT). Esta herramienta permite determinar el estado de equilibrio de mezclas multiespecie en fases gaseosas o condensadas puras, incluyendo iones. El código incorpora una amplia gama de algoritmos, desde problemas fundamentales de equilibrio químico hasta complejos cálculos de ondas de choque y detonación estacionarias en varias configuraciones de flujo, así como predicciones del rendimiento de motores cohete. Implementado en MATLAB, CT cuenta con una interfaz gráfica de usuario fácil de usar, que garantiza flexibilidad y accesibilidad para todos los usuarios. Se ha realizado una extensa validación que demuestra una excelente concordancia con códigos establecidos como el CEA de la NASA, Cantera y Shock and Detonation Toolbox del Caltech, así como el reciente código Thermochemical Equilibrium Abundances. CT se ha utilizado en todos los estudios presentados en esta tesis, demonstrando su fiabilidad y versatilidad. En la segunda parte de la tesis, se analizan los choques reactivos y no reactivos que se propagan en condiciones no homogéneas. Realizar experimentos y simulaciones de alta fidelidad en este campo puede ser desafiante y costoso computacionalmente. En este contexto, el análisis de interacción lineal ha surgido como una herramienta valiosa para evaluar los aspectos hidrodinámicos que contribuyen a la amplificación de las perturbaciones a través del choque. Se investigan dos casos destacados. En primer lugar, el estudio se centra en las detonaciones con inhomogeneidades aguas arriba de la fracción másica del combustible. Los resultados indican que, en la mayoría de los casos, la velocidad de propagación de la detonación es mayor que en escenarios homogéneos equivalentes. Posteriormente, la investigación se centra en la interacción de choques hipersónicos con flujos turbulentos, incorporando los efectos termoquímicos asociados en gases diatómicos de una sola especie. El análisis se extiende además a mezclas multiespecie utilizando CT, con un énfasis particular en el aire. Estos estudios demuestran que los efectos termoquímicos que surgen a velocidades hipersónicas aumentan significativamente las fluctuaciones turbulentas en el gas posterior al choque en comparación con la aproximación de gas termoquímicamente congelado.Programa de Doctorado en Mecánica de Fluidos por la Universidad Carlos III de Madrid; la Universidad de Jaén; la Universidad de Zaragoza; la Universidad Nacional de Educación a Distancia; la Universidad Politécnica de Madrid y la Universidad Rovira iPresidente: Francisco José Higuera Antón.- Secretario: Carlos Manuel del Pino Peñas.- Vocal: Bruno Dene

    2022 Review of Data-Driven Plasma Science

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    Data-driven science and technology offer transformative tools and methods to science. This review article highlights the latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS), i.e., plasma science whose progress is driven strongly by data and data analyses. Plasma is considered to be the most ubiquitous form of observable matter in the universe. Data associated with plasmas can, therefore, cover extremely large spatial and temporal scales, and often provide essential information for other scientific disciplines. Thanks to the latest technological developments, plasma experiments, observations, and computation now produce a large amount of data that can no longer be analyzed or interpreted manually. This trend now necessitates a highly sophisticated use of high-performance computers for data analyses, making artificial intelligence and machine learning vital components of DDPS. This article contains seven primary sections, in addition to the introduction and summary. Following an overview of fundamental data-driven science, five other sections cover widely studied topics of plasma science and technologies, i.e., basic plasma physics and laboratory experiments, magnetic confinement fusion, inertial confinement fusion and high-energy-density physics, space and astronomical plasmas, and plasma technologies for industrial and other applications. The final section before the summary discusses plasma-related databases that could significantly contribute to DDPS. Each primary section starts with a brief introduction to the topic, discusses the state-of-the-art developments in the use of data and/or data-scientific approaches, and presents the summary and outlook. Despite the recent impressive signs of progress, the DDPS is still in its infancy. This article attempts to offer a broad perspective on the development of this field and identify where further innovations are required

    Machine Learning and Its Application to Reacting Flows

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    This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows. These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and “greener” combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation

    General Purpose Flow Visualization at the Exascale

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    Exascale computing, i.e., supercomputers that can perform 1018 math operations per second, provide significant opportunity for improving the computational sciences. That said, these machines can be difficult to use efficiently, due to their massive parallelism, due to the use of accelerators, and due to the diversity of accelerators used. All areas of the computational science stack need to be reconsidered to address these problems. With this dissertation, we consider flow visualization, which is critical for analyzing vector field data from simulations. We specifically consider flow visualization techniques that use particle advection, i.e., tracing particle trajectories, which presents performance and implementation challenges. The dissertation makes four primary contributions. First, it synthesizes previous work on particle advection performance and introduces a high-level analytical cost model. Second, it proposes an approach for performance portability across accelerators. Third, it studies expected speedups based on using accelerators, including the importance of factors such as duration, particle count, data set, and others. Finally, it proposes an exascale-capable particle advection system that addresses diversity in many dimensions, including accelerator type, parallelism approach, analysis use case, underlying vector field, and more

    Application of Crystal Engineering in Multicomponent Pharmaceutical Crystals: A Study of Theory and Practice

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    Multicomponent crystallization, a prominent strategy in crystal engineering, offers the ability to modify the physicochemical properties of crystals by introducing a secondary component to their lattice structure. Such multicomponent crystals have found widespread application in the pharmaceutical industry. This thesis explores the experimental screening, characterization, application, and theoretical prediction of multicomponent crystals of Active Pharmaceutical Ingredients (APIs). The first case study investigates a new solvate of Dasatinib which exhibits high instability at room temperature and transforms into a different polymorph upon desolvation. The crystal structure of this compound is obtained, revealing insights into its transient nature and the potential application of desolvation for particle size reduction. Another case study focuses on synthesizing a new cocrystal of zinc-phenylacetate (Zn-PA) with isonicotinamide (INAM). The resulting Zn-PA-INAM ionic cocrystal resolves the hydrophobicity issue of Zn-PA, enhancing solubility and dissolution rate. The crystal structure of Zn-PA-INAM, lattice energy comparison, and crystal morphology studies provide scientific explanations for these alterations. Additionally, this thesis proposes computational prediction strategies to discover new multicomponent crystals. Quantitative predictive approaches based on hydrogen bonding strength are investigated, employing DFT-derived electrostatic potential (ESP) maps, hydrogen bond energy (HBE) and propensity (HBP) calculations. We demonstrate the enhanced classification capability achieved by combining HBE and HBP through multivariate logistic regression. Expanding on cocrystal prediction strategies, we performed DFT calculations for a comprehensive database of 6,388 cocrystals from literature reports of both successful and unsuccessful experimental attempts. The extracted ESP surfaces were utilized to develop robust machine learning models that demonstrated exceptional discriminatory performance and achieved up to 94% accuracy on unseen test data. Lastly, an investigation is conducted on the crystal morphology of Rufinamide (RUF), utilizing temperature cycling, solvent screening, and additive selection to modify its thread-like morphology into a more isometric shape. The crystal structures of three RUF polymorphs are determined, and a connection between the microscopic structure and the macroscopic morphologies is established through face indexing. This thesis provides valuable insights into the application and systematic discovery of multicomponent crystals. By combining experimental screening, characterization, and predictive tools, it contributes to advancing the field’s understanding and utilization of multicomponent crystals

    Cyber-Human Systems, Space Technologies, and Threats

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    CYBER-HUMAN SYSTEMS, SPACE TECHNOLOGIES, AND THREATS is our eighth textbook in a series covering the world of UASs / CUAS/ UUVs / SPACE. Other textbooks in our series are Space Systems Emerging Technologies and Operations; Drone Delivery of CBNRECy – DEW Weapons: Emerging Threats of Mini-Weapons of Mass Destruction and Disruption (WMDD); Disruptive Technologies with applications in Airline, Marine, Defense Industries; Unmanned Vehicle Systems & Operations On Air, Sea, Land; Counter Unmanned Aircraft Systems Technologies and Operations; Unmanned Aircraft Systems in the Cyber Domain: Protecting USA’s Advanced Air Assets, 2nd edition; and Unmanned Aircraft Systems (UAS) in the Cyber Domain Protecting USA’s Advanced Air Assets, 1st edition. Our previous seven titles have received considerable global recognition in the field. (Nichols & Carter, 2022) (Nichols, et al., 2021) (Nichols R. K., et al., 2020) (Nichols R. , et al., 2020) (Nichols R. , et al., 2019) (Nichols R. K., 2018) (Nichols R. K., et al., 2022)https://newprairiepress.org/ebooks/1052/thumbnail.jp
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