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Tribol is a modular interface physics library featuring Lagrangian contact algorithms for explicit and implicit solid mechanics applications. More broadly, Tribol supports the research, development, and implementation of numerical methods for interface physics.
The term 'ring' is generally used in the case of a subdivision of ocean eddies. in the present investigation, it denotes mesoscale features which are spawned by the Gulf Stream. This investigation is concerned with the mechanism involved in the regulation of the growth of phytoplankton by the physical oceanographic features of rings. Gulf Stream rings were first observed by Parker (1971) and Fuglister (1972) as a result of extensive temperature measurements from ships in the Gulf Stream. Attention is given to changes in density boundaries associated with the rotation of rings, a synthetic model of a newly formed warm core ring, convection-stabilization, the role of light, the influence of convective overturn in adding nutrients to surface waters of warm core rings, and two major areas which require study.
The performance of nuclear reactors and other nuclear systems depends on a precise understanding of the neutron interaction cross sections for materials used in these systems. These cross sections exhibit resonance structure whose shape is determined in part by the angular momentum quantum numbers of the resonances. The correct assignment of the quantum numbers of neutron resonances is therefore of paramount importance. In this project, we apply a machine learning technique, namely decision trees, to automate the quantum number assignments. The tree is trained from simulated data generated to mimic the errors found in real data. We explore the use of several physics-motivated features for training our trees, including the nearest neighbor spacing distribution, cumulative level distribution, and channel width distributions. Initial results using random matrix theory motivated fits which demonstrated that we can determine resonance spin groups somewhat reliably. If we use these fits as features in our trees, we can train them to spot outliers corresponding to misassigned resonances. We found that with the large number of features used in this project that the decision tree tended to over t training data resulting in poor performance with respect to the test data. By reducing the number of features, we can achieve nearly perfect assignment of quantum numbers with our training data.
This document represents the status of Proliferation Resistance and Physical Protection (PR&PP) characteristics for the Molten Salt Reactor designs selected by the Generation IV International Forum (GIF) Molten Salt Reactor (MSR) provisional System Steering Committee (pSSC) as representatives of three broad classes of MSRs. The three classes of MSRs are: (1) Liquid-fueled with integrated salt processing, (2) Liquid-fueled without integrated salt processing, (3) Solid-fueled with salt coolant. The intent is to generate preliminary information about the PR&PP merits of the MSR Reactor Technology and to provide insights for optimizing their PR&PP performance for the benefit of MSR system designers. It updates the MSR analysis published in the 2011 report “Proliferation Resistance and Physical Protection of the Six Generation IV Nuclear Energy Systems”, prepared Jointly by the Proliferation Resistance and Physical Protection Working Group (PRPPWG) and the System Steering Committees of the Generation IV International Forum, taking into account the evolution of both the systems and the GIF R&D activities since its publication. The document, prepared jointly by the GIF PRPPWG and the GIF MSR pSSC, follows the high-level paradigm of the GIF Proliferation Resistance and Physical Protection Evaluation Methodology to investigate the Proliferation Resistance and Physical Protection features of the GIF MSR representative designs. For PR, the document analyses and discusses the proliferation resistance aspects in terms of robustness against State-based threats associated with diversion of materials, misuse of facilities, breakout scenarios, and production in clandestine facilities. Similarly, for physical protection, the document discusses the robustness against theft of material and sabotage by non-State actors.
Meshfree methods have undergone substantial development and have received much attention in the last two decades. This new family of numerical methods is designed to inherit the main advantages of the finite element method such as compact supports of shape functions and good approximation properties while, at the same time, overcome the main disadvantages of the finite element method caused by the mesh dependence. The meshfree methods share a common feature that no mesh is needed and shape functions are constructed from sets of points, thus eliminating the need for time consuming mesh generation. The most significant advantage of meshfree methods is the flexibility in customizing approximation functions for desired regularity and for capturing essential physics and features of the particular problems of interest. Adaptivity formulation and multiple-scale solution strategies also can be implemented with relative ease. It has become clear that the meshfree methods provide considerable advantages over the conventional finite element methods in solving problems involving moving discontinuities, evolving material interfaces, multiple-scale phenomena, large material distortion and structural deformation, and fracture and damage processes. This Chapter gives an overview of many classes of meshfree methods, with more detailed discussions on Smoothed Particle Hydrodynamics (SPH), the Reproducing Kernel Particle Method (RKPM), Peridynamics (PD), the Material Point Method (MPM), as well as their applications in various challenging engineering problems.2
Various 3D field physics challenges of magnetically confined plasmas arise when the driving source comes from either externally applied non-axisymmetric 3D magnetic perturbations or plasma instabilities inside the plasma. Recently, several key outstanding topics of 3D field physics have been extensively studied in the Korean Superconducting Tokamak Advanced Research (KSTAR), such as edge-localized-mode (ELM) control by resonant magnetic perturbation (RMP), error field (EF) control, 3D field effects on rotation and transport, and RMP-induced alteration of divertor heat flux and detachment. KSTAR has a few physically unique features (i.e., high rotation and long-pulse plasmas with a low intrinsic EF) and machine/diagnostic capabilities (i.e., 3-row in-vessel control coil and state-of-the-art 2D/3D imaging diagnostics), which have been taken advantage of until now to address critical 3D field physics issues relevant to ITER and K-DEMO. Among many remarkable achievements are the robust access to and control of n = 1 RMP ELM suppression, along with a development of its physics basis tools, parameter expansion, optimization, and long-pulse control techniques. Nonetheless, a series of unresolved 3D physics themes, as well as limited coverage of 3D field operating regimes, have also been identified as future works for the 3D field research in KSTAR. In this paper, we provide an overview about the recent progress of KSTAR 3D field physics and present future plans of KSTAR 3D research toward a future fusion reactor.
The complex Ginzburg-Landau equation with additive noise is a stochastic partial differential equation that describes a remarkably wide range of physical systems which include coupled non-linear oscillators subject to external noise near a Hopf bifurcation instability and spontaneous structure formation in non-equilibrium systems, e.g., in cyclically competing populations or oscillatory chemical reactions. Here, we employ a finite-difference method to numerically solve the noisy complex Ginzburg-Landau equation on a two-dimensional domain with the goal to investigate its non-equilibrium dynamics when the system is quenched into the “defocusing spiral quadrant”. We observe slow coarsening dynamics as oppositely charged topological defects annihilate each other, and characterize the ensuing aging scaling behavior. We conclude that the physical aging features in this system are governed by non-universal aging scaling exponents. We also investigate systems with control parameters residing in the “focusing quadrant”, and identify slow aging kinetics in that regime as well. Finally, we provide heuristic criteria for the existence of slow coarsening dynamics and physical aging behavior in the complex Ginzburg-Landau equation.
Geologic framework models (GFMs) provide a methodology for integrating geology into, and thus geologically informing, other modeling and simulation activities. GFMs provide a threedimensional (3-D), geology-based digital framework for developing and parametrizing meshes and evaluating model and simulation results. This report describes a 3-D GFM constructed for the Source Physics Experiment Phase II Dry Alluvium Geology test series located in Yucca Flat at the Nevada National Security Site. The geology in the Yucca Flat region is complex and diverse, which creates challenges to modeling seismic wave propagation from SPE tests. The Yucca Flat GFM helps address these challenges by providing the 3-D distribution of relevant geologic features and physical properties necessary to more effectively model seismic wave propagation. The GFM includes 7 model layers and 48 faults that cut and offset the layers. An appendix is included that provides quantitative data on physical properties for each model layer.
Geologic framework models (GFMs) provide a methodology for integrating geology into, and thus geologically informing, other modeling and simulation activities. GFMs provide a three-dimensional (3-D), geology-based digital framework for developing and parametrizing meshes and evaluating model and simulation results. This report describes a 3-D GFM constructed for the Source Physics Experiment Phase II Dry Alluvium Geology test series located in Yucca Flat at the Nevada National Security Site. The geology in the Yucca Flat region is complex and diverse, which creates challenges to modeling seismic wave propagation from SPE tests. The Yucca Flat GFM helps address these challenges by providing the 3-D distribution of relevant geologic features and physical properties necessary to more effectively model seismic wave propagation. The GFM includes 7 model layers and 48 faults that cut and offset the layers. An appendix is included that provides quantitative data on physical properties for each model layer.
We seek to improve our ability to predict and understand the Richtmyer-Meshkov (RM) unstable dynamics of explosively shocked isolated features interfaced with vacuum or gases, and an HE products cavity. Explosively shocked features form as 2D or 3D RM phenomena. Liquid cavitation cause cavitation bubbles when shockwaves collide, and cavitation waves from wave interactions with isolated features form that source mass into the RM unstable sheets or spikes, and the cavitation waves may slice through bulk material into the HE products cavity.
Prediction of climate relies on models, and better model prediction depends on good model physics. Improving model physics requires the maximal utilization of climate data of the past, present and future. CEOP provides the first example of a comprehensive, integrated global and regional data set, consisting of globally gridded data, reference site in-situ observations, model location time series (MOLTS), and integrated satellite data for a two-year period covering two complete annual cycles of 2003-2004. The monsoon regions are the most important socio-economically in terms of devastation by floods and droughts, and potential impacts from climate change md fluctuatinns nf the hydrologic cyc!e. Scientifically, it is most challenging, because of complex interactions of atmosphere, land and oceans, local vs. remote forcings in contributing to climate variability and change in the region. Given that many common features, and physical teleconnection exist among different monsoon regions, an international research focus on monsoon must be coordinated and sustained. Current models of the monsoon are grossly inadequate for regional predictions. For improvement, models must be confronted with relevant observations, and model physic developers must be made to be aware of the wealth of information from existing climate data, field measurements, and satellite data that can be used to improve models. Model transferability studles must be conducted. CIMS is a major initiative under CEOP to engage the modeling and the observational communities to join in a coordinated effort to study the monsoons. The objectives of CIMS are (a) To provide a better understanding of fundamental physical processes (diurnal cycle, annual cycle, and intraseasonal oscillations) in monsoon regions around the world and (b) To demonstrate the synergy and utility of CEOP data in providing a pathway for model physics evaluation and improvement. In this talk, I will present the basic concepts of CIMS and the key scientific problems facing monsoon climates and provide examples of common monsoon features, and possible monsoon induced teleconnections linking different parts of the world.
Trigger (interesting events) detection is crucial to high-energy and nuclear physics experiments because it improves data acquisition efficiency. It also plays a vital role in facilitating the downstream offline data analysis process. The sPHENIX detector, located at the Relativistic Heavy Ion Collider in Brookhaven National Laboratory, is one of the largest nuclear physics experiments on a world scale and is optimized to detect physics processes involving charm and beauty quarks. Furthermore, these particles are produced in collisions involving two proton beams, two gold nuclei beams, or a combination of the two and give critical insights into the formation of the early universe. This paper presents a model architecture for trigger detection with geometric information from two fast silicon detectors. Transverse momentum is introduced as an intermediate feature from physics heuristics. We also prove its importance through our training experiments. Each event consists of tracks and can be viewed as a graph. A bipartite graph neural network is integrated with the attention mechanism to design a binary classification model. Compared with the state-of-the-art algorithm for trigger detection, our model is parsimonious and increases the accuracy and the AUC score by more than 15%.
A collaborative team of researchers from fields of Computational Fluid Dynamics (CFD), fluid physics, computer architectures, and computer science and knowledge engineering have begun work on a prototype system that addresses several of industry's concerns in using NASA-developed CFD codes as part of the design cycle. A major problem exists in the application of CFD technologies within the aeronautics design cycle due primarily to misunderstandings in the ranges of applicability of the various solver codes or turbulence models. Features that arise during the CFD solution process need to be discriminated and recognized as actual flow features with physical support in the geometry and flow conditions of the problem being solved, or as numerical or non-physical errors arising from mis-application of solver code and its parameters, gridding strategies, or discretization. interpolations. The fundamental concept is to develop an intelligent computational system that can accept the engineer's definition of the problem and construct an optimal CFD solution. To do this requires capturing both the knowledge of how to apply the various CFD tools and how to adapt the application of those tools to flow structures as they evolve during the flow simulation. Embedded within this adaptive system approach is the additional desire to automatically identify and quantify the quality of resolution of the pertinent flow structures, be they genuine or error-induced, and then to adjust the solution strategy accordingly. This paper discusses the status of that prototyping effort.
We examine techniques for achieving zero intrinsic stress in thin films of iridium, chromium, and nickel deposited by magnetron sputter deposition. The intrinsic stress is further correlated to the microstructural features and physical properties such as surface roughness and optical density at a scale appropriate to soft X-ray wavelengths. The examination of the stress in these materials is motivated by efforts to advance the optical performance of light-weight X-ray space telescopes into the regime of sub-arcsecond resolution through various deposition techniques that rely on control of the film stress to values within 10-100 MPa. A characteristic feature of the intrinsic stress behavior in chromium and nickel is their sensitivity to the magnitude and sign of the intrinsic stress with argon gas pressure and deposition rate, including the existence of a critical argon process pressure that results in zero film stress which scales linearly with the atomic mass of the sputtered species. While the effect of stress reversal with argon pressure has been previously reported by Hoffman and others for nickel and chromium, we report this effect for iridium. In addition to stress reversal, we identify zero stress in the optical functioning iridium layer shortly after island coalescence for low process pressures at a film thickness of approximately 35nm. The measurement of the low values of stress during deposition was achieved with the aid of a sensitive in-situ instrument capable of a minimum detectable level of stress, assuming a 35nm thick film, in the range of 0.40-6.0 MPa for <111> oriented crystalline silicon substrate thicknesses of 70-280 microns, respectively.
This issue of Physics Flash features the recent success of our newest APS Fellow, Takeyasu Ito, and some of the new LDRD projects that are beginning this year. These projects will aid in setting the division’s future direction and I look forward to learning more as these projects progress. At the same time, new and exciting projects are being proposed for start in fiscal year 2023. The innovations that are captured in these proposals are important and will power the future of Physics Division.
Thermoset and thermoplastic polyimides have complementary physical and mechanical properties. Whereas thermoset polyimides are brittle and generally easier to process, thermoplastic polyimides are tough but harder to process. A combination of these two types of polyimides may help produce polymers more suitable for aerospace applications. Semi-Interpenetrating Polymer Networks (S-IPN) of thermoset LaRC(TM)-RP46 and thermoplastic LaRC(TM)-IA polyimides were prepared in weight percent ratios ranging from 100:0 to 0:100. Positron lifetime measurements were made in these samples to correlate their free volume features with physical and mechanical properties. As expected, positronium atoms are not formed in these samples. The second lifetime component has been used to infer the positron trap dimensions. The 'free volume' goes through a minimum at a ratio of about 50:50, and this suggests that S-IPN samples are not merely solid solutions of the two polymers. These data and related structural properties of the S-IPN samples are discussed.
The exponential computational cost of describing strongly correlated electrons can be mitigated by adopting a reduced-density-matrix (RDM)-based description of the electronic structure. While variational two-electron RDM (v2RDM) methods can enable large-scale calculations on such systems, the quality of the solution is limited by the fact that only a subset of known necessary N-representability constraints can be applied to the 2RDM in practical calculations. Here, we demonstrate that violations of partial three-particle (T1 and T2) N-representability conditions, which can be evaluated with knowledge of only the 2RDM, can serve as physics-based features in a machine-learning (ML) protocol for improving energies from v2RDM calculations that consider only two-particle (PQG) conditions. Proof-of-principle calculations demonstrate that the model yields substantially improved energies, relative to reference values from configuration-interaction-based calculations.
Physics-based reservoir simulation for fluid flow in porous media is a numerical simulation method to predict the temporal-spatial patterns of state variables (e.g. pressure p) in porous media, and usually requires prohibitively high computational expense due to its non-linearity and the large number of degrees of freedom (DoF). This work describes a deep learning (DL) workflow to predict the pressure evolution as fluid flows in large-scale 3-dimensional(3D) heterogeneous porous media. In particular, we develop an efficient feature coarsening technique to extract the most representative information and perform the training and prediction of DL at the coarse scale, and further recover the resolution at the fine scale by spatial interpolation. We validate the DL approach to predict pressure field against physics-based simulation data for a field-scale 3D geologic CO 2 sequestration reservoir model. We evaluate the impact of feature coarsening on DL performance, and observe that the feature coarsening not only decreases the training time by >74% and reduces the memory consumption by >75%, but also maintains temporal error 0.63% on average. Besides, the DL workflow provides predictive efficiency with 1406 times speedup compared to physics-based numerical simulation. The key findings from this research significantly improve the training and prediction efficiency of deep learning model to deal with large-scale heterogeneous reservoir models, and thus it can also be further applied to accelerate workflows of history matching and reservoir optimization for close-loop reservoir management.