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pyDiSCaMB : enabling the use of multipolar scattering factors in Phenix
Multipolar scattering models, such as the transferable aspherical atom model, account for atomic chemical interactions and provide a more accurate representation of experimental data. However, the simpler independent atom model (IAM), which assumes non-interacting atoms, is the only model available in the most widely used macromolecular refinement programs. This is primarily because IAM offers a hard-to-beat combination of computational efficiency and modelling power at typical macromolecular resolutions. By contrast, more accurate multipolar modelling has historically been limited due to its computational cost and the absence of an interface between software capable of calculating structure factors and gradients based on multipolar models and software designed for macromolecular refinement. This work introduces pyDiSCaMB , a Python software package designed to integrate between the computational crystallography toolbox ( cctbx ) and the quantum crystallography library DiSCaMB ( Densities in Structural Chemistry and Molecular Biology ), thus enabling multipolar scattering models in Phenix 's toolkit. The implementation, features and capabilities of pyDiSCaMB are presented, the runtimes for the calculation of structure factor and target gradients with respect to atomic parameters are explored, and Fourier images of electrostatic potential, electron density and deformation maps are computed as illustrative examples. The pyDiSCaMB library will make multipolar modelling widely available to the structural biology community, potentially transforming refinement and model-building for both crystallography and cryogenic electron microscopy (cryoEM).
Serpentine Magnet Designs for the Interaction Region of the Electron-Ion Collider (EIC)
The Electron-Ion Collider (EIC), hosted by Brookhaven National Laboratory, is designed to deliver a peak luminosity of 1 × 10 34 cm −2 sec −1 . The interaction region (IR) of the EIC imposes several constraints in terms of field quality, aperture, and spatial layout, which necessitates the development of several unique superconducting serpentine direct wind magnets. These magnets are constructed using either a single strand or a small-diameter 6-around-1 NbTi cable, presenting unique challenges for design and optimization. This paper introduces a new computational code specifically developed to streamline and integrate the design process for these magnets, enabling faster design iterations while addressing their complex requirements. Here, in this paper, we first introduce the code, which builds on established electromagnetic fundamentals. The code incorporates tools for optimizing winding patterns and for correcting magnetic multipoles; additionally, it interfaces with established magnet design software. We also present the design of several serpentine magnets for the EIC IR, demonstrating the code’s capability to deliver precise and efficient solutions. These designs highlight the code’s ability to accelerate the development cycle, ensuring the serpentine magnets meet the demanding specifications of the EIC project.
Co-Designing to Create Sustainable Software: How to leverage a user-centered process to co-design sustainable software with users and stakeholders.
Abstract not provided.
RDPM: An Extensible Tool for Resilience Design Patterns Modelling
Resilience to faults, errors, and failures in extreme-scale high-performance computing (HPC) systems is a critical challenge. Resilience design patterns offer a new, structured hardware and software design approach for improving resilience. While prior work focused on developing performance, reliability, and availability models for resilience design patterns, this paper extends it by providing a Resilience Design Patterns Modeling (RDPM) tool which allows (1) exploring performance, reliability, and availability of each resilience design pattern, (2) offering customization of parameters to optimize performance, reliability, and availability, and (3) allowing investigation of trade-off models for combining multiple patterns for practical resilience solutions.
Pyomo.DoE 2.0: Improved Usability and Computational Efficiency for Science-Based Design of Experiments (SBDoE)
Conference paper explaining the new user interface and software design for the Pyomo.DoE and ParmEst packages. An example utilizing these packages will be presented, showcasing modeling capabilities, and capabilities for handling complex models that have been developed within the PrOMMiS and CCSI2 ecosystems.
Quantum Software Engineering (Dagstuhl Seminar 24512)
The Dagstuhl Seminar 24512 on "Quantum Software Engineering" was held from December 15 to 20, 2024. It brought together 26 participants from industry and academia from 13 different countries, including senior and junior researchers as well as practitioners in the field of Quantum Software Engineering. The aim of the seminar was to advance software engineering methods and tools for the engineering of hybrid quantum systems by promoting personal interaction and open discussion among researchers who are already working in this emerging area of knowledge. The first day of the seminar was devoted to the topic "When software engineering meets quantum mechanics", while the second day focused on "Quantum software engineering and its challenges." During both days, 16 invited presentations were given. The rest of the seminar was organized into three working groups to address the topics "Quantum Software Design, Modelling and Architecturing", "Adaptive Hybrid Quantum Systems", and "Quantum Software Quality Assurance". The seminar was a very fruitful experience for all participants both in terms of scientific outcomes and in terms of the personal relationships that were generated to jointly address future experiences.
Maritime Battery Electrification Simulator (MariBES) v1
MariBES is a Python-based software designed for calculating emissions and energy consumption in maritime transportation. This software is capable of performing calculations for multiple vessels, facilitating emission analysis at regional, national, and international scales. It also allows for the examination of energy consumption under various resource such as heavy fuel oil, diesel, and battery-electric, enabling the assessment of different decarbonization strategies in the maritime sector. MariBES utilizes public data on ship activities combined with detailed vessel specifications, significantly enhancing the accuracy of its simulations. This approach marks a considerable advancement over previous models that were constrained by limited spatial and temporal resolution. It features a temporal resolution based on 5-minute intervals and a spatial resolution using precise coordinates.
Optimizing Grain Boundary Structures with LAMMPS Using Evolutionary Algorithms
Grain boundary structure optimization is an important part of materials modeling. Current methods for grain boundary structure optimization involve inefficient, time-consuming processes that do not fully explore the interface parameter space. Evolutionary algorithms have recently been demonstrated to be effective at determining both stable and metastable grain boundary interface structures. In this work, we demonstrate the use of GBOpt, a grain boundary structure optimization software designed to use the Large-scale Atomic/Molecular Massively Parallel Simulation (LAMMPS) software to efficiently determine grain boundary structures. We demonstrate that a only a few manipulations, namely atom insertion, atom removal, and relative grain displacement, are sufficient to explore much of the grain boundary structure parameter space. The efficacy of this approach is demonstrated on an FCC Ni system, and a BCC Fe system. The computational cost is compared against the gamma-surface sampling approach to demonstrate performance improvement.
Component Modeling, Co-Optimization, and Trade-Space Evaluation (FY2021 Annual Progress Report)
This project is intended to support the development of new traction drive systems that meet the targets of 100 kW/L for power electronics and 50 kW/L for electric machines with reliable operation to 300,000 miles. To meet these goals, new designs must be identified that make use of state-of-the-art and next-generation electronic materials and design methods. Designs must exploit synergies between components, for example converters designed for high-frequency switching using wide band gap devices and ceramic capacitors. This project includes: (1) a survey of available technologies; (2) the development of design tools that consider the converter volume and performance; (3) exercising the design software to evaluate performance gaps and predict the impact of certain technologies and design approaches, i.e. GaN semiconductors, ceramic capacitors, and select topologies; and (4) building and testing hardware prototypes to validate models and concepts. Early instantiations of the design tools enable co-optimization of the power module and passive elements and provide some design guidance; later instantiations will enable the co-optimization of inverter and machine. Prototype testing begins with evaluation of simpler conversion topologies (i.e. the half-bridge boost converter) and progresses with fabrication of prototype inverter drives.
Electric Drive Technologies Research: ELT223 Component Modeling, Co-Optimization, and Trade-Space Evaluation Annual Report
This project is intended to support the development of new traction drive systems that meet the targets of 100 kW/L for power electronics and 50 kW/L for electric machines with reliable operation to 300,000 miles. To meet these goals, new designs must be identified that make use of state-of-the-art and next-generation electronic materials and design methods. Designs must exploit synergies between components, for example converters designed for high-frequency switching using wide band gap (WBG) devices and ceramic capacitors. This project included: (1) a survey of available technologies; (2) investigating new technologies, that for example, reduce volume of thermal management or magnetic components; (3) the development of computer aided design tools that consider the converter volume, reliability, and electrical performance; (4) exercising the design software to evaluate performance gaps and predict the impact of certain technologies and design approaches, i.e. GaN semiconductors, ceramic capacitors, ceramic thermal management components, and select topologies; (5) building and testing hardware prototypes to validate models and concepts. The design tools enable co-optimization of the power module and passive elements and provide some design guidance. At the end of the project, new advanced computing methods, such as machine learning approaches, were considered.
DUNE Data Management: Network Visualization Monitoring Software
Fermilab’s fagship Deep Underground Neutrino Experiment (DUNE) seeks to better understand the nature of neutrinos within the context of Leptogenesis, neutrino oscillations, multi-messenger Astronomy, and other scientifc phenomena. The experiment will send a beam of neutrinos from the Fermilab site in Illinois to the Sanford Underground Neutrino Facility (SURF) in South Dakota, generating petabytes of scientifc data. Given the high volume of data expected when measurements begin at the end of the decade, DUNE computing and the data management group must carefully monitor data transfers across the 15 remote storage sites and, more generally, the 36 global DUNE computing sites. This report will describe both the frontend and backend data monitoring software designed to analyze and visualize these data transfers. Specifc emphasis will be placed on the software’s setup, usage, and methods for future implementations. The full software code can be found under the DUNE/data-mgmt-testing GitHub repository.
Control Co-Design Studies for a 22 MW Semisubmersible Floating Wind Turbine Platform
We present a control co-design software framework that can be used to optimize floating wind turbines and their controllers. Because this framework has many options for design variables, constraints, and merit figures, along with modeling fidelity levels, we seek to demonstrate best practices for using the tool while designing a floating platform for the new 22 MW offshore reference wind turbine developed within the International Energy Agency Wind Technology Commercialization Programme 55 on Reference Wind Turbines and Farms. During these studies, we evaluate the use of different simulation fidelity levels, the effect of using different load cases for controller tuning, and the difference between sequential and simultaneous control co-design solutions. Based on these efforts, we suggest using an algorithm that performs an initial search of the design space before optimization. We find that solving smaller optimization problems, in a sequential manner, leads to more reliable outcomes in fewer iterations than larger, simultaneous control co-design solutions. However a simultaneous CCD solution produces a platform with a 2% lower mass than the sequential CCD outcome.
TrioSim: A Lightweight Simulator for Large-Scale DNN Workloads on Multi-GPU Systems
Deep Neural Networks (DNNs) have become increasingly capable of performing tasks ranging from image recognition to content generation. The training and inference of DNNs heavily rely on GPUs, as GPUs' massively parallel architecture delivers extremely high computing capability. With the growing complexity of DNNs and the size of training datasets, training DNNs with a large number of GPUs is becoming a prevalent strategy. Researchers have been exploring how to design software and hardware systems for GPU farms to achieve the best utilization, efficiency, and DNN accuracy during training or inference. However, when designing and deploying such systems, designers usually rely on testing on physical hardware platforms equipped with many GPUs, incurring high costs that are almost prohibitive for system designers to test different configurations and designs, even for highly resourceful companies. While an alternative solution is to test on GPU simulators, they are often too slow for these l
Control and Management of Multiple Converters in a Residential Smart Grid
Power electronic systems are becoming a staple building block for electric grid networks. However, these systems have been largely designed to grid integrate in ad hoc configurations with little or no coordination of control. This work proposes a central controller and power electronic system hardware and software design for a residential system (photovoltaic, energy storage and residential building). This system supports auto-integration, plug and play capabilities, and optimal energy management to meet different use cases. Modelling and multi-day testing of the system have been conducted in a controller hardware-in-the-loop (C-HIL) testbed. Multiple use cases and pricing options have been considered as part of the simulation and testing. Results are presented of these systems as a proof of principle.
Exploration of Potential Superconducting Multi-Mode Cavity Architectures for Quantum Computing
This thesis describes the investigation of superconducting multi-mode cavity architecture for superconducting transmon-based quantum computing. The dissertation highlights useful features of radio-frequency cavities used for quantum computing, with a brief discussion on the advantages of superconducting cavities. In the subsequent section, the concept of transmon is introduced and the mechanism of coupling with a cavity is analyzed. Once the foundations of the topic are laid down, the thesis focuses on optimizing a multi-mode superconducting rf cavity design originally developed for high-energy physics applications. Such section articulates in two main parts, the first part concerning the "bare" cavity remake via finite-elements eigenmode simulations using a computer-aided design software called CST Studio Suite®. In the second part, a transmon qubit is physically inserted into the modified cavity to assess the qubit-cavity coupling of the new design. In evaluating said coupling, two distinct analysis methods are used, namely the black-box quantization method and the energy participation ratio method, both implemented using Ansys® High-Frequency Electromagnetic-Field Simulator, or HFSS™. Results from the two evaluations, compared together, show that the optimized design meets the requisites to be used for quantum computing purposes.
BatteryPro: A Python Toolkit for Battery Data Analysis and Machine Learning Predictions
Analyzing battery test data for research & development can be time-consuming since battery tests often run on the order of months to years, generating large volumes of data. BatteryPro is a comprehensive Python package and software designed to facilitate advanced analysis and performance predictions for battery test data. Developed for battery researchers, it supports data types from widely used battery testing instruments, including MACCOR and Biologic cycling systems. The software provides a variety of tools for extracting and plotting key battery parameters such as time, voltage, capacity, current, and pressure. In addition to its extensive data analysis capabilities, BatteryPro features a dedicated machine learning module that employs a Bayesian Gaussian Mixture Model (GMM) to predict battery performance and degradation. Users can generate synthetic capacity fade data, calculate fade metrics, and leverage predictive models to forecast long-term battery behavior. The software's graphical user interface (GUI) enhances usability, allowing researchers to upload, merge, and analyze multiple data files with full customizability. The GUI also supports machine learning predictions, enabling users to fit models and make predictions based on selected data and parameters. BatteryPro is built using QtDesigner, scikit-learn, matplotlib, and pandas, ensuring a high level of customization, flexibility, and accuracy in battery data analysis. This tool aims to empower researchers with the ability to perform detailed battery analysis and make informed predictions, ultimately advancing the field of battery research.
Ascribe XR v0.1.0
Ascribe XR is an immersive visualization software designed for scientists and engineers working with 3D data sets. Its key features include interactive exploration, multi-user collaboration, and flexible data import capabilities, supporting various formats such as meshes, volumes, and terrain maps. The software utilizes Godot, OpenXR and PC-VR technology to provide an immersive experience. Ascribe XR is used for data analysis, visualization, and collaboration in various fields, enabling users to gain deeper insights into complex data sets. Its advantages over similar technologies include its flexibility, customizability, and ease of use. Ascribe XR's interactive and immersive environment facilitates collaboration and accelerates the discovery process. Compared to traditional 2D visualization tools, Ascribe XR offers a more engaging and intuitive experience, allowing users to explore complex data sets in a more natural and interactive way. Its ability to support multi-user collaboration and flexible data import capabilities make it a versatile tool for various applications. Overall, Ascribe XR provides a unique combination of features, usability, and performance, making it an attractive solution for scientists and engineers working with 3D data sets.