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Research project reports pertaining to physics, space sciences, computer systems, information processing, and control systems
Fractured systems in the subsurface play a role in many natural and engineered applications such as geologic carbon sequestration, hydraulic fracturing and underground nuclear test detection. Structural information (fracture size, orientation, etc.) plays a key role in governing the dominant physics for these systems but can only be known statistically. Traditional modeling approaches either ignore or idealize structural information at these larger scales because we lack a computational framework that utilizes it in its entirety. The work presented here integrates computational physics, machine learning and graph theory to make a paradigm shift from computationally intensive high-fidelity models to coarse-scale graphs without loss of critical structural information. We exploit the underlying discrete structure of fracture networks in systems considering flow through fractures and fracture propagation. We demonstrate that compact graph representations require significantly fewer degrees of freedom (dof) to capture micro-fracture information and further accelerate these models with Machine Learning.
Computing plays an essential role in all aspects of high energy physics. As computational technology evolves rapidly in new directions, and data throughput and volume continue to follow a steep trend-line, it is important for the HEP community to develop an effective response to a series of expected challenges. In order to help shape the desired response, the HEP Forum for Computational Excellence (HEP-FCE) initiated a roadmap planning activity with two key overlapping drivers -- 1) software effectiveness, and 2) infrastructure and expertise advancement. The HEP-FCE formed three working groups, 1) Applications Software, 2) Software Libraries and Tools, and 3) Systems (including systems software), to provide an overview of the current status of HEP computing and to present findings and opportunities for the desired HEP computational roadmap. The final versions of the reports are combined in this document, and are presented along with introductory material.
In this research, we investigated two approaches to detect job anoma- lies and/or contention for large scale computing efforts: 1. Preemptive job scheduling using binomial classification long short-term memory networks 2. Forecasting intra-node computing loads from the active jobs and addi- tional job(s) For approach 1, we achieved a 14% improvement in computational resources utilization and an overall classification accuracy of 85% on real tasks executed in a High Energy Physics computing workflow. For this paper, we present the preliminary results used in second approach.
Summary Important computational physics problems are often large‐scale in nature, and it is highly desirable to have robust and high performing computational frameworks that can quickly address these problems. However, it is no trivial task to determine whether a computational framework is performing efficiently or is scalable. The aim of this paper is to present various strategies for better understanding the performance of any parallel computational frameworks for solving PDEs. Important performance issues that negatively impact time‐to‐solution are discussed, and we propose a performance spectrum analysis that can enhance one's understanding of critical aforementioned performance issues. As proof of concept, we examine commonly used finite element simulation packages and software and apply the performance spectrum to quickly analyze the performance and scalability across various hardware platforms, software implementations, and numerical discretizations. It is shown that the proposed performance spectrum is a versatile performance model that is not only extendable to more complex PDEs such as hydrostatic ice sheet flow equations but also useful for understanding hardware performance in a massively parallel computing environment. Potential applications and future extensions of this work are also discussed.
The advent of high performance computing has changed the way scientists and engineers solve problems. Scientists previously would make a tremendous effort to simplify a complex problem to it’s essence by reducing geometry, neglecting changes and only preserving first-order effects. Now, scientist are able to leave the details in the problem and computationally arrive at a result. To develop the computational physics programs used by scientists, developers make assumptions and use approximation to reduce complex, continuous calculus to algebraic expressions which computers are able to solve.
SANDAL is a particle-mesh simulation mini-app. The simple simulated physics propagates a set of particles in a constant, 2D wind field with Gaussian turbulence. SANDAL demonstrates the feasibility of formulating a computational physics problem using an alternative, modern design. Specifically, it is implemented using relational tables and queries, rather than array-based data model. It also is implemented with functional language design and cloud deployment, Scala and Apache Spark, rather than Fortran and MPI+X.
SANDAL is a particle-mesh simulation mini-app. The simple simulated physics propagates a set of particles in a constant, 2D wind field with Gaussian turbulence. SANDAL demonstrates the feasibility of formulating a computational physics problem using an alternative, modern design. Specifically, it is implemented using relational tables and queries, rather than array-based data model. It also is implemented with functional language design and cloud deployment, Scala and Apache Spark, rather than Fortran and MPI+X.
The inversion of the Laplace-Beltrami operator and the computation of the Hodge decomposition of a tangential vector field on smooth surfaces arise as computational tasks in many areas of science, from computer graphics to machine learning to computational physics. In this work, we present a high-order accurate pseudo-spectral approach, applicable to closed surfaces of genus one in three-dimensional space, with a view toward applications in plasma physics and fluid dynamics.
Cloud Computing has become the ubiquitous approach to our Big Data challenge. However, one will quickly discover that moving (a.k.a. forklifting) existing on-premise data analytics solutions to the Cloud doesn’t always translate to costing saving and performance boost. The Cloud’s elasticity, its availability, and its wide selection of computing options and selections of costing models making Cloud an attractive environment to tackle our Big Data challenge. The fact is Cloud, on its own, is not the silver bullet to our daunting challenge need for analyze and derive scientific inferences through vast collections of multi-sensor measurements. We would like to have all scientific data in one easy to access environment, but getting the world of scientific data in one analytic system is immensely difficult to achieve. This paper describes the data analytics web architecture NASA is developing by infusing instances of Integrated Data Analytics systems next to the data. The goal is to minimize unnecessary data movement through collection of data access and analytics webservices for researchers to interact with and analyze measurements without have to download data to their local computer. These services are RESTful and provisioned by the data centers with the help from subject matter and science experts. These services encapsulate the physical computing infrastructure, which could local computing cluster, on-premise or public Cloud environment.
The Fortran mimetic abstraction language ("Formal") is a domain-specific language (DSL) embedded in Fortran 202Y [1]. Formal provides novel software abstractions for simulating phenomena governed by the partial differential equations (PDEs) of vector and tensor calculus. Such equations model an extremely broad set of physical phenomena, ranging from atmospheric winds to light propagation. Formal's data structures and algorithms mimic in form and behavior continuous functions and operators. Formal supports these mathematical constructs using mimetic discretizations that define a discrete calculus satisfying various tensor calculus theorems, thereby ensuring high-fidelity representations of the physics being modeled. [2] Formal 0.1.0 also lays a foundation for the future use of Fortran 202Y type-safe templates to facilitate the formal verification of tensor contractions in computational physics and artificial intelligence [3]. [1] "Fortran 202Y" is Fortran standard committee's informal designation for the next Fortran revision, which will likely be "Fortran 2028". [2] Corbino, J. and Castillo, J. (2020) Journal of Computational and Applied Mathematics, https://doi.org/10.1016/j.cam.2019.06.042. [3] Haveraaen, M., Järvi, J., & Rouson, D. (2019). Reflecting on Generics for Fortran. https://j3-fortran.org/doc/year/19/19-188.pdf.
Radiative and atomic processes in plasmas play a critical role in a wide variety of high energy density laboratory plasma (HEDLP) experiments. The emission, absorption, and transport of radiation can strongly affect the overall energetics and evolution of such plasmas. In addition, radiation-based diagnostics – including imaging, spectroscopy, and absolute flux measurements – are widely used to determine key features of HEDLPs. To advance our understanding of HEDLP science, it is vital to have high-fidelity computational physics tools that have well-tested radiation physics modeling, and that are readily accessible to researchers in the HEDLP community. Simulations play an extremely important role for planning and designing the experiments, as well as for post-experiment data analysis. Prism Computational Sciences develops software that is used by National Laboratories and universities (including five members of LaserNetUS network). Prominent examples of such research efforts include z-pinch and short-pulse laser experiments designed to study the basic physics of photoionized plasmas and photoionization fronts, as well as their application to astrophysical plasmas. The main effort was dedicated to the development of non-equilibrium equation-of-state (EOS) models within the HELIOS-CR code, a hydrodynamics code with inline collisional-radiative atomic kinetics. Gas cell experiments on Z and Omega demonstrated the importance of non-equilibrium effects on atomic kinetics in photoionized plasmas. Recent proof-of-principle experiments on Omega EP confirmed the advantages of using a short-pulse laser to create an intense radiation drive, leading to additional experiments being proposed. Photoionization front experiments at LLE also emphasizes the importance of radiation and atomic physics. In both studies, HELIOS-CR simulations played a crucial role in computing non-equilibrium opacities and ionization distributions. A newly developed non-LTE EOS model will help addressing possible non-equilibrium effects, on for example specific heat, and their influence in plasma evolution. Prism also implemented support for open-source atomic data generated by the Flexible Atomic Code. This allows researchers to generate custom atomic tables and use them within the complex framework of simulation tools developed by Prism. The ability to use open-source atomic data would be extremely valuable for hydrodynamics and spectroscopic simulations that include high-Z materials, e.g., picosecond x-ray pulse generation experiments. Support for new atomic structures was fully implemented, and the data can be used by all simulation tools developed at Prism: radiation-hydrodynamics, imaging and spectroscopy, EOS and opacity. The development resulted in a significant fidelity enhancement to the simulations tools developed by Prism that are currently used in other cutting-edge experiments including: opacity measurement experiments performed to both understand the basic radiative and atomic properties of plasmas as well as provide data for more accurately modeling the internal structure of the Sun and other stars, high-intensity short-pulse laser experiments performed to develop short-wavelength light sources for use as backlighters and to investigate fast ignition concepts for inertial fusion energy; capsule implosion experiments designed to develop inertial fusion as an energy source, etc.
For computational physics simulations, code verification plays a major role in establishing the credibility of the results by assessing the correctness of the implementation of the underlying numerical methods. In computational electromagnetics, surface integral equations, such as the method-of-moments implementation of the magnetic-field integral equation, are frequently used to solve Maxwell's equations on the surfaces of electromagnetic scatterers. These electromagnetic surface integral equations yield many code-verification challenges due to the various sources of numerical error and their possible interactions. In this paper, we provide approaches to separately measure the numerical errors arising from these different error sources. We demonstrate the effectiveness of these approaches for cases with and without coding errors.
PROTEUS is a zero-power research reactor based on a cylindrical graphite annulus with a central cylindrical cavity. The graphite annulus remains basically the same for all experimental programs, but the contents of the central cavity are changed according to the type of reactor being investigated. Through most of its service history, PROTEUS has represented light-water reactors, but from 1992 to 1996 PROTEUS was configured as a pebble-bed reactor (PBR) critical facility and designated as HTR-PROTEUS. The nomenclature was used to indicate that this series consisted of High Temperature Reactor experiments performed in the PROTEUS assembly. During this period, seventeen critical configurations were assembled and various reactor physics experiments were conducted. These experiments included measurements of criticality, differential and integral control rod and safety rod worths, kinetics, reaction rates, water ingress effects, and small sample reactivity effects (Ref. 3). HTR-PROTEUS was constructed, and the experimental program was conducted, for the purpose of providing experimental benchmark data for assessment of reactor physics computer codes. Considerable effort was devoted to benchmark calculations as a part of the HTR-PROTEUS program. References 1 and 2 provide detailed data for use in constructing models for codes to be assessed. Reference 3 is a comprehensive summary of the HTR-PROTEUS experiments and the associated benchmark program. This document draws freely from these references. Only Cores 9 and 10 are evaluated in this benchmark report due to similarities in their construction. The other core configurations of the HTR-PROTEUS program are evaluated in their respective reports as outlined in Section 1.0. Cores 9 and 10 were evaluated and determined to be acceptable benchmark experiments.
PROTEUS is a zero-power research reactor based on a cylindrical graphite annulus with a central cylindrical cavity. The graphite annulus remains basically the same for all experimental programs, but the contents of the central cavity are changed according to the type of reactor being investigated. Through most of its service history, PROTEUS has represented light-water reactors, but from 1992 to 1996 PROTEUS was configured as a pebble-bed reactor (PBR) critical facility and designated as HTR-PROTEUS. The nomenclature was used to indicate that this series consisted of High Temperature Reactor experiments performed in the PROTEUS assembly. During this period, seventeen critical configurations were assembled and various reactor physics experiments were conducted. These experiments included measurements of criticality, differential and integral control rod and safety rod worths, kinetics, reaction rates, water ingress effects, and small sample reactivity effects (Ref. 3). HTR-PROTEUS was constructed, and the experimental program was conducted, for the purpose of providing experimental benchmark data for assessment of reactor physics computer codes. Considerable effort was devoted to benchmark calculations as a part of the HTR-PROTEUS program. References 1 and 2 provide detailed data for use in constructing models for codes to be assessed. Reference 3 is a comprehensive summary of the HTR-PROTEUS experiments and the associated benchmark program. This document draws freely from these references. Only Cores 9 and 10 are evaluated in this benchmark report due to similarities in their construction. The other core configurations of the HTR-PROTEUS program are evaluated in their respective reports as outlined in Section 1.0. Cores 9 and 10 were evaluated and determined to be acceptable benchmark experiments.
In the laser powder bed fusion additive manufacturing process, the presence of porosity may result in cracks and significantly affects the part performance. A comprehensive understanding of the melt pool process dynamics and porosity evolution can help to improve build quality. In this study, a novel multi-physics computational fluid dynamics (CFD) model has been applied to investigate the fluid dynamics in melt pools and resultant pore defects. To accurately capture the melting and solidification process, major process physics, such as the surface tension, evaporation as well as laser multi-reflection, have been considered in the model. A discrete element method is utilized to model the generation of powder spreading upon build plate by additional numerical simulations. Multiple single track experiments have been performed to obtain the melt pool shape and cross-sectional dimension information. The predicted melt pool dimensions were found to have a reasonable agreement with experimental measurements, e.g., the errors are in the range of 1.3 to 10.6% for melt pool width, while they are between 1.4 and 15.9% for melt depth. Pores are captured by both CFD simulation and x-ray computed tomography measurement for the case with a laser power of 350 W and laser speed of 100 mm/s. The formation of keyholes maybe related to the melt pool front wall angle, and it is found that the front wall angle increases with the increase in laser line energy density. In addition, a larger laser power or smaller scanning speed can help to generate keyhole-induced pores; they also contribute to produce larger sized pores.
DIANA, a digital-analog simulation program for IBM 1620 2 computer, simulates physical systems by solving the simultaneous differential equations describing the systems. It expands and optimizes the input-output capabilities, permits additional flexibility in midstream program alternation, and minimizes the computational time.
Bison is a computational physics code that uses the finite element method to model the thermo-mechanical response of nuclear fuel. Since Bison is used to inform high-consequence decisions, it is important that its computational results are reliable and predictive. One important step in assessing the reliability and predictive capabilities of a simulation tool is the verification process, which quantifies numerical errors in a discrete solution relative to the exact solution of the mathematical model. One step in the verification process—called code verification—ensures that the implemented numerical algorithm is a faithful representation of the underlying mathematical model, including partial differential or integral equations, initial and boundary conditions, and auxiliary relationships. In this paper, the code verification process is applied to spatiotemporal heat conduction problems in Bison. Simultaneous refinement of the discretization in space and time is employed to reveal any potential mistakes in the numerical algorithms for the interactions between the spatial and temporal components of the solution. For each verification problem, the correct spatial and temporal order of accuracy is demonstrated for both first- and second-order accurate finite elements and a variety of time-integration schemes. Furthermore, these results provide strong evidence that the Bison numerical algorithm for solving spatiotemporal problems reliably represents the underlying mathematical model in MOOSE. The selected test problems can also be used in other simulation tools that numerically solve for conduction or diffusion.