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Fast Free Fermion Compiler (F3C) is an application-specific quantum circuit compiler for time-evolution circuits of spin Hamiltonian systems that can be mapped to free fermions. F3C is the Matlab software version and the related F3C++ is the C++ software version.
SolVap_StarTer is an open-source solvent evaporation GUI. The software combines previously published (peer-reviewed scientific journals) evaporation and crystallization physics into an easy-to-use graphical interface. The software will allow the user to specify a coating composition and drying environment, and then predict the evolution of the film as it dries. Potential applications include tracking the change in multi-solvent composition and solute concentration in solution-processed perovskites films, predicting the point of crystallization, and calculating equivalent processing parameters for transferring a process between different coating and drying methods. Github repo: https://github.com/NatLabRockies/SolVap Streamlit app: https://solvap.streamlit.app/
We demonstrate that highly accurate joint redshift–stellar mass probability distribution functions (PDFs) can be obtained using the Random Forest (RF) machine learning (ML) algorithm, even with few photometric bands available. As an example, we use the Dark Energy Survey (DES), combined with the COSMOS2015 catalogue for redshifts and stellar masses. We build two ML models: one containing deep photometry in the griz bands, and the second reflecting the photometric scatter present in the main DES survey, with carefully constructed representative training data in each case. We validate our joint PDFs for 10 699 test galaxies by utilizing the copula probability integral transform and the Kendall distribution function, and their univariate counterparts to validate the marginals. Benchmarked against a basic set-up of the template-fitting code bagpipes, our ML-based method outperforms template fitting on all of our predefined performance metrics. In addition to accuracy, the RF is extremely fast, able to compute joint PDFs for a million galaxies in just under 6 min with consumer computer hardware. Such speed enables PDFs to be derived in real time within analysis codes, solving potential storage issues. As part of this work we have developed galpro 1, a highly intuitive and efficient python package to rapidly generate multivariate PDFs on-the-fly. galpro is documented and available for researchers to use in their cosmology and galaxy evolution studies.
A primary advantage to using reduced complexity climate models (RCMs) has been their ability to quickly conduct probabilistic climate projections, a key component of uncertainty quantification in many impact studies and multisector systems. Providing frameworks for such analyses has been a target of several RCMs used in studies of the future co-evolution of the human and Earth systems. In this paper, we present Matilda, an open-science R software package that facilitates probabilistic climate projection analysis, implemented here using the Hector simple climate model in a seamless and easily applied framework. The primary goal of Matilda is to provide the user with a turn-key method to build parameter sets from literature-based prior distributions, run Hector iteratively to produce perturbed parameter ensembles (PPEs), weight ensembles for realism against observed historical climate data, and compute probabilistic projections for different climate variables. This workflow gives the user the ability to explore viable parameter space and propagate uncertainty to model ensembles with just a few lines of code. The package provides significant freedom to select different scoring criteria and algorithms to weight ensemble members, as well as the flexibility to implement custom criteria. Additionally, the architecture of the package simplifies the process of building and analyzing PPEs without requiring significant programming expertise, to accommodate diverse use cases. We present a case study that provides illustrative results of a probabilistic analysis of mean global surface temperature as an example of the software application.
Argonne Training Program in Extreme Scale Computing (ATPESC) was started by the Argonne National Laboratory with the objective of expanding the ranks of better prepared users of high performance computing (HPC) machines. One of the unique aspects of the program was inclusion of software engineering and community codes track. The inclusion was motivated by the observation that the projects with a good scientific and software process were better able to meet their scientific goals. In this paper we present our experience of running the software track from the beginning of the program until now. We discuss the motivations, the reception, and the evolution of the track over the years. We welcome discussion and input from the community to enhance the track in ATPESC, and also to facilitate inclusion of similar tracks in other HPC oriented training programs.
This milestone report aims to identify and assess the cluster dynamics (CD) modeling requirements within the Department of Energy's Office of Nuclear Energy (DOE-NE) Advanced Materials and Manufacturing Technologies (AMMT) program and to communicate these needs to the DOE-NE Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. The goal is to ensure NEAMS is well-informed about the CD modeling requirements to support AMMT's mission of accelerating the development, qualification, demonstration, and deployment of advanced structural materials and manufacturing for nuclear energy applications. CD modeling is an essential tool for predicting the degradation of structural materials under irradiation, which is a key component of AMMT's accelerated qualification process. The AMMT program focuses on both additively manufactured and wrought structural alloys, such as laser powder-bed fusion 316H austenitic stainless steel, alloy 709, Haynes 244, and alloy 617. These materials require a generalized CD modeling framework to facilitate rapid model development and computational simulation. A flexible, generalized CD software, similar to the Multiphysics Object-Oriented Simulation Environment (MOOSE) finite element framework, would enable modeling of various cluster types, including defect clusters, defect-solute clusters, and multicomponent clusters, incorporating thermodynamics and kinetics parameters. Radiation effects, microstructural feature evolution, and multi-dimensional modeling are critical considerations for the CD model. The usability of the CD code should allow for easy modification and coupling with MOOSE-based simulations. Additionally, the software should adhere to Nuclear Quality Assurance-1 standards, include a testing suite for verification and validation, and be version-controlled within a national laboratory-managed Git repository. Benchmark problems are needed to assess code predictions and performance.
Here, we present MESA-Web,a cloud resource with an online interface to the Modules for Experiments in Stellar Astrophysics (MESA) software instrument. MESA-Web allows learners to evolve stellar models without the need to download and install MESA. Since being released in 2015, MESA-Web has delivered over 17,000 calculations to over 2,200 unique learners and currently performs about 11 jobs per day. MESA-Web can be used as an educational tool for stars in the classroom or for scientific investigations. We report on new capabilities of MESA-Web introduced since its 2015 release including learner-supplied nuclear reaction rates, custom stopping conditions, and an expanded selection of input parameters. To foster collaboration we have created a Zenodo MESA-Web community hub, where instructors can openly share examples of using MESA-Web in the classroom. We discuss two examples in the current community hub. The first example is a lesson module on Red Giant Branch stars that includes a suite of exercises designed to fit a range of learners and a Jupyter workbook for additional analysis. The second example is lesson materials for an upper-level Astronomy majors course in Stars and Radiation that includes an assignment verifying some of the expected trends that are presented in a popular stellar physics textbook.
The article is focused on the problem of generating a uniform temperature field on the inner surface of shell metal moulds. Such moulds are used e.g. in the automotive industry for artificial leather production. To produce artificial leather with uniform surface structure and colour shade the temperature on the inner surface of the mould has to be as homogeneous as possible. The heating of the mould is realized by infrared heaters located above the outer mould surface. The conceived mathematical model allows us to optimize the locations of infrared heaters over the mould, so that approximately uniform heat radiation intensity is generated. A version of differential evolution algorithm programmed in Matlab development environment was created by the authors for the optimization process. For temperate calculations software system ANSYS was used. A practical example of optimization of heaters locations and calculation of the temperature of the mould is included at the end of the article.
The strategic and economic impacts of high performance computing (HPC) systems, over the last several decades, have enabled dramatic improvements in manufacturing, design, development, and research, across almost every sector of the U.S. economy, including genetic analysis for agriculture and medicine, oil exploration, electronics, aircraft, and automotive design, and of course national defense technologies. HPC has repeatedly compounded reductions in time-to-solution and time-to-market in each of these areas. In recognition of these strategic and economic impacts, the Department of Energy (DOE) created the Exascale Computing Project (ECP) as a broad-based technology project focusing on simultaneously co-evolving high-performance computing architecture, system software, and application software. While many details of the exascale architectures are undefined, communicating the form and direction of this co-evolution of advances, in each of these three components, across the project, presents a need for a common language through which these advances can be shared. In this document we present a series of abstract representations designed to allow application developers to focus on the aspects of the machine that are important or relevant to performance and code structure. These abstract machine models (AMMs) describe the proposed architectures at the component and system level and are intended as communication aids between application developers and hardware architects during the co-design process. In addition to providing a common language for communication, the constraints of protecting the intellectual property of ECP’s vendor partners requires that the architectural advances be presented in an abstract sense, rather than exposing the specifics of an individual vendor’s designs, while still providing enough detail such that application developers will be able to reason about the performance trade-offs in the design space of their applications.
In late 2019, INL and ANL agreed to jointly develop the reactor physics code named Griffin based on the integration of the two code suites, MAMMOTH/Rattlesnake (INL) and MC2 - 3/PROTEUS (ANL). Griffin is being developed based on the MOOSE framework and MOOSE quality assurance procedures. This decision was made to be able to allow DOE-NE to efficiently invest funding to this area and to provide effective and timely support for existing and potential users; the latter includes industry and government organizations who are developing various types of advanced reactors in the near and long term. Since MAMMOTH/Rattlesnake has been developed based on the MOOSE framework, the INL/ANL Griffin development team agreed to build Griffin beginning with a merger of MAMMOTH and Rattlesnake into a single code and moving forward by implementing capabilities from the PROTEUS suite into Griffin. Moving forward, both ANL and INL efforts are equally invested in the Griffin project, with management support, to provide an advanced reactor multiphysics tool to assist in reactor design, optimization, and safety analysis. Much work remains in moving Griffin forward to migrate PROTEUS capabilities and to optimize performance to meet user needs. The main objective of this work is to assess the current status of Griffin capabilities in terms of performance and accuracy, to determine priorities for PROTEUS migration, and to identify capabilities and features to improve for supporting the code integration effort. For this assessment, the Empire micro reactor problem that was developed in the ARPA-E MEITNER program was selected as an advance reactor concept of interest to the technical community. The Empire reactor problem was expanded from its original incomplete specification to be a small heat-pipe-cooled micro reactor core with ~113 cm radius and 70 cm in height, composed of 18 fuel assemblies, 12 control drums, and beryllium radial and axial reflectors. In the current model, using 5 cm axial reflectors specified in the original Empire assembly model, more than 10% of neutrons leak axially and through the empty center safety hole, as well as through heat pipe channels in fuel assembly elements that extend through the top reflector region. Several calculation models of the core were defined for systematic assessment, including 2-D and 3-D fuel assemblies and whole cores with cylindrical boundaries. Cross sections were generated using Serpent 2, and meshes were produced using the Argonne mesh tool or the INL neutronics meshing tools combined with CUBIT. Cross sections and meshes were converted to the ISOXML and Exodus formats, respectively, so that Griffin and PROTEUS could use consistent data for solving the reactor problems. With the prepared cross sections and meshes, PROTEUS was run first to ensure that all input data were correctly generated and input options in terms of angle, mesh, and energy group were accurately determined. Comparisons against Serpent 2 solutions were made in terms of eigenvalue and pin power. The same calculations and comparisons were then conducted using Griffin. For the fuel assembly and whole core problems, the PROTEUS eigenvalues agreed well with reference Serpent 2 solutions within 100 and 30 pcm, respectively, and pin power differences relative to Serpent 2 were overall less than 2.2% and RMS 0.8% for the whole core models. This indicated that all input data were properly prepared. Using the same data, Griffin was run selecting the SAAF-CFEM SN solver with Legendre-Gaussian quadrature and NDA and DSA for acceleration. It was found that the SAAF-CFEM solver of Griffin required finer meshes to achieve eigenvalue and pin power solutions in good agreement with Serpent 2, consequently requiring more memory requirement and longer computation time. On the other hand, the SPH-Diffusion 2-D core calculations performed using Griffin were able to recover the exact eigenvalue from the reference Serpent 2 solutions, resulting in a pin-power distribution with an RMS of 0.6% and maximum absolute difference of less than 1.4%. The runtimes for SPH-Diffusion for the 2-D core were less than 3 minutes on 40 cores. During this evolution of this evaluation, many updates were made in Griffin by the Griffin development team of INL (focusing on software updates) and ANL (reviewing and supporting software updates) to complete this assessment. Observations from the code assessment are presented in the conclusion section of this report, followed by a discussion of recommendations for future work.
In this work, we investigated the evolution in bunch length of beams through the CEBAF injector for 8?770 fC charge per bunch. Using the software General Particle Tracer (GPT), we have simulated beams through the beamline of the CEBAF injector to predict bunch lengths at the location of a beam chopper for comparison with measurements and to validate the model. We performed these simulations with the existing injector using a 130 kV gun voltage. The mea- surements have been done using chopper phase scanning technique for two injector laser drive frequencies 499 MHz and 249.5 MHz. Acknowledgement This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under contract DE-AC05-06OR23177.
Communication with stakeholders, regulatory agencies, and the public is an essential part of implementing different remediation and monitoring activities, and developing site closure strategies at contaminated sites. Modeling of contaminant plume evolution plays a critical role in estimating the benefit, cost, and risk of particular options. At the same time, effective visualization of monitoring data and modeling results are particularly important for conveying the significance of the results and observations. In this paper, we present the results of the Advanced Simulation Capability for Environmental Management (ASCEM) project, including the discussion of the capabilities of newly developed ASCEM software package, along with its application to the F-Area Seepage Basins located in the U.S. Department of Energy Savannah River Site (SRS). ASCEM software includes state-of-the-art numerical methods for simulating complex flow and reactive transport, as well as various toolsets such as a graphical user interface (GUI), visualization, data management, uncertainty quantification, and parameter estimation. Using this software, we have developed an advanced visualization of tritium plume migration coupled with a data management system, and simulated a three-dimensional model of flow and plume evolution on a high-performance computing platform. We evaluated the effect of engineered flow barriers on a nonreactive tritium plume, through advanced plume visualization and modeling of tritium plume migration. In addition, we developed a geochemical reaction network to describe complex geochemical processes at the site, and evaluated the impact of coupled hydrological and geochemical heterogeneity. These results are expected to support SRS’s monitoring activities and operational decisions.
Communication with stakeholders, regulatory agencies, and the public is an essential part of implementing different remediation and monitoring activities, and developing site closure strategies at contaminated sites. Modeling of contaminant plume evolution plays a critical role in estimating the benefit, cost, and risk of particular options. At the same time, effective visualization of monitoring data and modeling results are particularly important for conveying the significance of the results and observations. In this paper, we present the results of the Advanced Simulation Capability for Environmental Management (ASCEM) project, including the discussion of the capabilities of newly developed ASCEM software package, along the its application to the F-Area Seepage Basins located in the U.S. Department of Energy Savannah River Site (SRS). ASCEM software includes state-of-the-art numerical methods for simulating complex flow and reactive transport, as well as various tool-sets such as a graphical user interface (GUI), visualization, data management, uncertainty quantification, and parameter estimation. Using this software, we have developed an advanced visualization of tritium plume migration coupled with a data management system, and simulated a three-dimensional model of flow and plume evolution on a high-performance computing platform. We evaluated the effect of engineered flow barriers on a nonreactive tritium plume, through advanced plume visualization and modeling of tritium plume migration. In addition, we developed a geochemical reaction network to describe complex geochemical processes at the site, and evaluated the impact of coupled hydrological and geochemical heterogeneity. These results are expected to support SRS's monitoring activities and operational decisions. (authors)
The texture evolution during high-temperature rolling (800 deg. C) of two model zirconium-niobium alloys was studied using electron backscatter diffraction. The aim was to decouple the relative importance of deformation and phase transformation on the final texture in an effort to elucidate the origin of the strong α-transverse basal texture commonly found in the hot-rolled product. The analysis uses a reconstruction software based on the Burgers relation to reconstruct the high-temperature β orientations, with the indexed α as input. In the low niobium content alloy, the texture evolution of the {11 2-bar 0}<10 1-bar 0> component was consistent with deformation in both α and β phases, along with a variant selection mechanism that occurred during the β → α phase transformation. Variant selection was observed to depend on the orientation of the deformed prior β grains. In the high niobium content alloy, a lower α volume fraction during hot rolling gave rise to different β textures and promoted the formation of a Goss component. This contributed to the {11 2-bar 0} <10 1-bar 0> component upon cooling, with variant selection enhanced by anisotropic grain breakup. (authors)
Many galaxies display clear bulges and discs, and understanding how these components form is a vital step towards understanding how the galaxy has evolved into what we see today. The BUDDI-MaNGA project aims to study galaxy evolution and morphological transformations through the star-formation histories of the bulges and discs. We have applied our buddi software to galaxies from the MaNGA Survey in the SDSS DR15 in order to isolate their bulge and disc spectra, from which we derived their stellar populations. To date, this work provides the largest sample of clean bulge and disc spectra extracted from IFU datacubes using the galaxies light profile information, and will form the basis for a series of papers aiming to answer open questions on how galaxies have formed and evolved, and the role of their individual structures. Here this paper presents an introduction to the project, including an overview of these fits, a characterization of the sample, and a series of tests on the fits to ensure reliability.
Stochastic changes in DNA methylation (i.e., spontaneous epimutations) contribute to methylome diversity in plants. Here, we describe AlphaBeta, a computational method for estimating the precise rate of such stochastic events using pedigree-based DNA methylation data as input. We demonstrate how AlphaBeta can be employed to study transgenerationally heritable epimutations in clonal or sexually derived mutation accumulation lines, as well as somatic epimutations in long-lived perennials. Application of our method to published and new data reveals that spontaneous epimutations accumulate neutrally at the genome-wide scale, originate mainly during somatic development and that they can be used as a molecular clock for age-dating trees.
The Campaign Management project at the National Ignition Facility is embarking on a major redesign of the software used by experimentalists to setup experiments on the NIF. The current editor was architected over a decade ago and optimized for a different purpose than supporting production science campaigns. It has a steep learning curve and lacks many desirable features. Furthermore, its underlying technology stack and architecture do not support the kind of routine capability evolution that represents current and future growth needs of the Program. The principal stakeholders are the physicists who are users of the editing application, but the entire integrated suite of Campaign Management software has a broad customer base spanning experimentalists, NIF sub-system subject matter experts, planners, and shot operations staff. A recent DOE-mandated review of NIF activity, conducted with the goal of improving the shot rate at NIF, identified the experiment editor as a clear barrier to a more productive and efficient experiment lifecycle, exposing unnecessary levels of detail, lacking adequate automation, and lacking adequate integration with related activities and data sources in what should be a shared data environment.
The upcoming high-luminosity run of the CERN Large Hadron Collider (HL-LHC) will yield an unprecedented volume of data. In order to process this data, the ATLAS collaboration is evolving its offline software to be able to use heterogeneous resources such as graphical processing units (GPUs) and field-programmable gate arrays (FPGAs). To reduce conversion overheads, the event data model (EDM) should be compatible with the requirements of these resources. While the ATLAS EDM has long allowed representing data as a structure of arrays, further evolution of the EDM can enable more efficient sharing of data between CPU and GPU resources. Some of this work will be summarized here, including extensions to allow controlling how memory for event data is allocated and the implementation of jagged vectors.