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The essential idea of this algorithm is to compute the angular distribution of a vector quantity, then create an informative image. In our example, we will compute the angular distribution of linear momentum from an xRage simulation of an exploding shaped charge. We will then explore one possible method for adding information to the resulting image.
This report documents the FY21 progress and achievements made in the computational analyses of the water-based NSTF. Both system-level and high-fidelity Computational Fluid Dynamics (CFD) analyses were performed to gain a complete understanding of the complex flow and heat transfer phenomena in natural convection systems. The progress on the waterbased NSTF experimental testing is summarized in a companion report (ANL-ART-230). As a continuation of progress from previous years, in FY21 the RELAP5 model of the NSTF was first updated to include a representative heated cavity that incorporated convection and conduction means of heat transfer. Two different cavity models were developed, one that models the radiation heat exchange among all surfaces in one enclosure (Cavity Model 1), and another that employs multiple radiation enclosures with each considering the surfaces at one axial level (Cavity Model 2). This updated RELAP5 deck was benchmarked with single-phase test data to tune form loss coefficients of the elbows and tees, along with heat transfer coefficients within the cavity and off the external insulation panels. This tuned model was then found to accurately predict fluid temperatures within 2.5% and both system and riser flow rates within 6% during single-phase, steady-state simulations. For transient simulations, there is a slight over prediction of the flow rate, fluid temperatures, and heater temperatures during the heat-up period, which indicate that the transient heat loss may be underestimated. Following, the capabilities of RELAP5-3D were examined, which feature a conduction enclosure model that is not present in the current version of RELAP5-MOD3.3 used for all previous NSTF simulations. As an initial comparison of the two codes, single-phase simulations were run that used similar input decks. The results indicate that the single-phase flow predicted by the two codes was very similar, however RELAP5-3D appears to predict a higher system flow during the two-phase transient when compared to the flow rates predicted by RELAP5-MOD3.3. Furthermore, RELAP5-3D run did not capture the density wave oscillations or geysering phenomena, which are large oscillation flow instabilities observed in the experimental facility. To further the prediction capabilities for capturing two-phase instabilities, Options 55, 57, 58, 61, which are implemented by default in RELAP5-MOD3.3 but not in RELAP5-3D, were investigated. When enabling Option 61, RELAP5-3D agrees well with MOD3.3 in predicting the total system flow, while other options have negligible effect on the predicted total system flow by RELAP5-3D. However, none of these options enables RELAP5-3D to produce the same flow instabilities. For future steps, efforts are currently underway to investigate RELAP5-3D’s different behavior in predicting low-pressure two-phase instabilities. STAR-CCM+ was used to perform CFD analysis of the water-based test facility and provide simulation results for comparisons to experimental data. Efforts first focused on describing the characteristics of flow within the heated cavity. A study was performed on whether possible leakage flow between the two sides of the cavity could substantially impact and explain the results. An additional second assumption was examined which defined adiabatic side walls, so physical insulation structures were added to the models to allow conduction across this outer insulation. The results suggest that potential porosity of the insulation could be a major factor in the temperature distribution within the cavity and should be considered in any studies where it is desirable to know the cavity wall temperatures to higher accuracy.
The HPC landscape is changing and we’re headed toward an era where compute specialization will be prevalent. There are opportunities for co-design that can influence this future. Technological heterogeneity will be a major challenge unless we shift our approach to developing the computational tools for astrophysics. Parthenon provides convenient functionality and a bright future for block structured AMR applications. Phoebus is a new (soon-to-be) open source code for relativistic astro that promises excellent performance, portability, and unique physics capabilities.
Wire-arc additive manufacturing (WAAM) has demonstrated its unique capability of producing large-size alloy components with a significantly reduced fabrication time and enhanced geometry design freedom. In this project, the team has developed an ICME (Integrated Computational Materials Engineering) modeling framework, which supports the WAAM of the AUSC (Advanced Ultra-Supercritical) power plant components. The manufacturing design has been applied to Inconel 740H, steel P91, as well as the dissimilar alloy components between steel P91 and Inconel 740H. The ICME model framework is developed by considering two types of modeling. First, mechanistic modeling has been applied to control the printing quality and understand the sequence of the dissimilar printing of the wall structure. The following models have been included in the developed ICME framework: finite element thermal model, grain structure model, residual stress simulation, crystal plasticity model, CALPHAD-based precipitation kinetic model, phase stability prediction, thermal expansion predictive model, and heuristic creep model. Secondary, a physics-based machine learning model has also been developed based on the ICME model structure. The machine learning model development is based on the ICME model prediction with calibration of the experiments. In addition, the WAAM has been utilized as a high-throughput experimental tool rapidly generating a gradient of alloy composition to facilitate experimental database generation for process-structure-property relationships. Such a database directly supported the ICME-enhanced machine learning, which further assisted in intermediate composition block design between P91 and 740H. A high-throughput screening study of the oxidation resistance has been performed based on such high-throughput experimentation. Based on the computational design, several dissimilar alloy manufacturing with post-heat treatment have been performed with a comprehensive evaluation of mechanical performance, including hardness mapping, yield strength, creep resistance. In this project, the single component of P91 and 740H processed by WAAM after heat treatment designed by ICME has demonstrated higher performance in yield strength and creep resistance than the wrought materials. The P91 sample prepared by WAAM with ICME-designed heat treatment performs better than P92 in creep resistance. The designed graded alloy printing with intermediate block shows a promising performance that exceeds the traditional welding. Moreover, the current research indicates the high need for location-specific design analysis with uncertainty quantification, an important topic that deserves more dedicated research. The achievement of this project demonstrated the promising future of WAAM in structural alloy manufacturing for energy power plant development. Successful printing requires synergetic efforts made by manufacturing, mechanical, and materials sciences.
In this report we describe the testing of a novel scheme for state preparation of trapped ions in a quantum computing setup. This technique optimally would allow for similar precision and speed of state preparation while allowing for individual addressability of single ions in a chain using technology already available in a trapped ion experiment. As quantum computing experiments become more complicated, mid-experiment measurements will become necessary to achieve algorithms such as quantum error correction. Any mid-experiment measurement then requires the measured qubit to be re-prepared to a known quantum state. Currently this involves the protected qubits to be moved a sizeable distance away from the qubit being re-prepared which can be costly in terms of experiment length as well as introducing errors. Theoretical calculations predict that a three-photon process would allow for state preparation without qubit movement with similar efficiencies to current state preparation methods.
X-ray Computed Tomography (X-ray CT) is an analytical technique used in materials science to non–destructively characterize features in variety of materials like polymers, metals, composites, and explosives. It also has the capability of imaging additively manufactured, machined and assembled parts and processes. The non-destructive imaging allows for the analysis of features (voids and cracks), which give a fundamental understanding of material characteristics. Additionally, X-ray CT can obtain accurate measurements of dimensional and topographic variations as a result of different stimuli, and assess accuracy of material production. This study will focus on parts manufactured via metal additive manufacturing (AM). Although, AM produces parts faster and easier, the printing process can produce defects (pores and surface roughness) that undermine the part’s mechanical properties. The analysis of 3D printed objects has a strength in analysis in that the material has an STL file from which the item was printed, which is not available in many manufactured materials (i.e. foams) due to stochastic structures. For this study, the print accuracy of several 3D printed cylinders will be assessed via X-ray CT to determine optimal printing parameters for parts with less defects and stronger mechanical properties. This will be accomplished by having the original STL file serve as the baseline surface to compute difference in the CT rendering to calculate surface roughness for each cylinders.
This technical report documents the Computational and Autonomous Workflows (CAW) workshop held at ORNL (Oak Ridge National Laboratory) in July 2021. The theme of the workshop was "FAIR workflows". This document describes the workshop and takeaways for the Department of Energy, for ORNL, and for the Oak Ridge Leadership Computing Facility.
The goal of this project is to reconsider core market and reliability processes that can potentially yield to transformative advances in power grid security, reliability, and efficiency. Current electric power market designs are strongly a function of computing capabilities and limitations that were available in the mid-to-late 1990s, circa deregulation. This includes constructs such as: (1) a 2-tiered day-ahead/real-time market construct; and (2) linearized (“DC”) real power flow approximations in dispatch and pricing. At that time, state-of-the-art computational capabilities could at the limit address deterministic mixed-integer programming formulations of unit commitment (UC) and linear programming formulations of economic dispatch (ED) at limited fidelity and scale. Such constraints forced limited look-ahead time-horizons, crude approximations of AC power flow physics and operations, and artificial partitioning between day-ahead markets, hour(s)-ahead reliability processes, and real-time markets. Consequently, these limitations have resulted in limited security and reliability with increasing out-of-market payments, particularly as uncertainty associated with renewables and distributed energy resources grows.
This report identifies computational tools useful for addressing aspects of the dedicated carbon storage (Class VI) well permit application under the U. S. Environmental Protection Agency’s (EPA) Underground Injection Control (UIC) Program. The survey was conducted by researchers of the National Energy Technology Laboratory’s (NETL) Research and Innovation Center in collaboration with representatives of the U.S. EPA, Lawrence Berkeley National Laboratory (LBNL), Lawrence Livermore National Laboratory (LLNL), Los Alamos National Laboratory (LANL), Pacific Northwest National Laboratory (PNNL), and the four Regional Initiatives to Accelerate Carbon Capture, Utilization, and Storage: Carbon Utilization and Storage Partnership of the Western United States (CUSP), Plains CO 2 Reduction Partnership Initiative to Accelerate Carbon Capture, Utilization, and Storage Deployment (PCOR Partnership), Midwest Regional Carbon Initiative (MRCI), and the Southeast Regional Carbon Utilization and Storage Partnership (SECARB-USA). A total of 59 tools were identified through the elicitation for this report. It is intended to serve as a reference that can be used by geologic carbon storage stakeholders to identify computational tools that may be used to develop Class VI permit applications.
Heat Recovery Steam Generators (HRSGs) are widely used across United States in combined cycle power plants to recover waste heat from the gas turbine (GT). HRSGs are used either to generate electricity or to produce process steam for industrial applications. The primary components of HRSG consists of a duct and a heat exchanger (HX). It is known across the power industry that the high temperature oxidation and ensuing exfoliation problem is a major cause for the damage of HX materials of HRSG. Alloys and/or coatings that can prevent or mitigate oxidation are very expensive, therefore they must be used or applied on the select regions of the HX tubes where the tendency of oxide formation is the highest. The main goal of this project is to identify such regions through Computational Fluid Dynamics (CFD) simulations. Therefore, in this work, we developed a CFD framework using commercial code StarCCM+ for the prediction of the fluid flow and heat transfer in a HRSG and associated oxidation inside the tubes of the HX. The developed CFD framework was verified and validated with experimental data before deployment. We also developed an innovative method to model the effect of the fins on the heat transfer and the pressure drop using a porous media model (PMM) approach to keep the mesh size within reasonable limits. After validation, we performed high-fidelity CFD simulations of a real-scale HRSG using the PMM, with High Performance Computing (HPC) resources of ORNL. From the simulation results, we acquired oxide thickness maps for all the tubes of the select HX sections of HRSG prone to oxidation. These oxide maps can inform regions of the HX tubes that requires oxide-resistant coatings, thereby guiding engineers for cost-efficient manufacturing of the HX that can combat oxidation in HRSGs.
Making reliable predictions in the presence of uncertainty is critical to high-consequence modeling and simulation activities, such as those encountered at Sandia National Laboratories. Surrogate or reduced-order models are often used to mitigate the expense of performing quality uncertainty analyses with high-fidelity, physics-based codes. However, phenomenological surrogate models do not always adhere to important physics and system properties. This project develops surrogate models that integrate physical theory with experimental data through a maximally-informative framework that accounts for the many uncertainties present in computational modeling problems. Correlations between relevant outputs are preserved through the use of multi-output or co-predictive surrogate models; known physical properties (specifically monotoncity) are also preserved; and unknown physics and phenomena are detected using a causal analysis. By endowing surrogate models with key properties of the physical system being studied, their predictive power is arguably enhanced, allowing for reliable simulations and analyses at a reduced computational cost.
Many materials of interest to Sandia transition from fluid to solid or have regions of both phases coexisting simultaneously. Currently there are, unfortunately, no material models that can accurately predict this material response. This is relevant to applications that "birth stress" related to geoscience, nuclear safety, manufacturing, energy production and bioscience. Accurately capturing solidification and residual stress enables fully predictive simulations of the evolving front shape or final product. Accurately resolving flow of proppants or blood could reduce environmental impact or lead to better treatments for heart attacks, thrombosis, or aneurism. We will address a science question in this proposal: When does residual stress develop during the critical transition from liquid to solid and how does it affect material deformation? Our hypothesis is that these early phases of stress development are critical to predictive simulation of material performance, net shape, and aging. In this project, we use advanced constitutive models with yield stress to represent both fluid and solid behavior simultaneously. The report provides an abbreviated description of the results from our LDRD "Stress Birth and Death: Disruptive Computational Mechanics and Novel Diagnostics for Fluid-to-Solid Transitions," since we have written four papers that document the work in detail and which we reference. We give highlights of the work and describe the gravitationally driven flow visualization experiment on a model yield stress fluid, Carbopol, at various concentrations and flow rates. We were able to collapse the data on a single master curve by showing it was self-similar. We also describe the Carbopol rheology and the constitutive equations of interest including the Bingham-Carreau-Yasuda model, the Saramito model, and the HB-Saramito model including parameter estimation for the shear and oscillatory rheology. We present several computational models including the 3D moving mesh simulations of both the Saramito models and Bingham-Carreau-Yasuda (BCY) model. We also show results from the BCY model using a 3D level set method and two different ways of handling reduced order Hele-Shaw modeling for generalized Newtonian fluids. We present some first ever two-dimensional results for the modified Jeffries Kamani-Donley-Rogers constitutive equation developed during this project. We include some recent results with a successful Saramito-level set coupling that allows us to tackle problems with complex geometries like mold filling in a thin gap with an obstacle, without the need for remeshing or remapping. We report on some experiments for curing systems where fluorescent particles are used to track material flow. These experiments were carried out in an oven on Sylgard 184 as a model polymerizing system. We conclude the report with a summary of accomplishments and some thoughts on follow-on work.
Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. In principle, gamma radiation sources can be detected and identified by their unique spectral lines. However, detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. In recent prior work, we have developed a Hopfield Neural Network (HNN) in conjunction with an image processing algorithm to detect a weak signal anomaly hidden among the highly fluctuating background spectra. The objective of this work is to explore quantum computing methods to increase the speed of HNN. The approach is based on the Grover’s search algorithm in conjunction with a 3-SAT problem formalism. The Grover’s algorithm is implemented on a quantum computing simulator using Qiskit software. Performance of HNN algorithm is benchmarked using search data from an environmental screening campaign, where the anomaly is a subset of measurements containing a 137 Cs source. Results indicate that using Grover’s algorithm on a quantum simulator reduces runtime of HNN by two orders of magnitude.
Improvements in computing system security updates and purchasing policies could save the Army as much as 85 million kWh and $5 million per year when applied to the Army’s more than one million computers. This represents the largest and highest-priority plug load energy-and cost-saving measure identified by a recent study focused on Army buildings.
Space-based quantum links show great promise for connecting and communicating between quantum computers over ultra-long distances without the high loss incurred through fiber. A successful US quantum satellite would require large investment, national priority, and a diverse set of expertise. But, it would deliver US-owned quantum links that would allow for quantum-enhanced secure communications and the ability to connect quantum computers over long distances.
This discussion assesses the impact of sensitivity/uncertainty-based selection criteria on computational bias prediction. This presentation details the development of a tool to calculate the predicted computational bias for a given application as a function the c k threshold.
High Performance Computing (HPC) enables problem solving at the grandest scales, on the most complex challenges that face humanity. The problems often tackled by scientific computing are: Very complex; Cool; Using the fastest, most powerful supercomputers on the planet; Often important, impactful, effecting real lives.
Molecular- and nano- junctions, surfaces, interfaces, and interfacial processes have been the subjects of this DOE supported program for about 20 years. We have investigated electronic and magnetic structure, spin-dependent charge transport, and emerging phenomena at surfaces and interfaces in a number of physical systems ranging from nano-clusters, to nanowires, to two-dimensional (2D) crystals and their interactions, to bulk matter. Eighty-four refereed papers were published [1-84], with a majority effort from the group led by the PI, and over a hundred presentations were made at international conferences. We use first-principles methods in the framework of density functional theory in conjunction with nano-equilibrium Green function techniques, semi-empirical Boltzmann transport theory, tight-binding models, and the Hubbard model and many-body wavefunction approaches. In pursuing our scientific goals, we have also developed a number of computational algorithms and computational packages.