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At least 991 records · Page 55

Modeling a Million-Node Slim Fly Network Using Parallel Discrete-Event Simulation

As supercomputers close in on exascale performance, the increased number of processors and processing power translates to an increased demand on the underlying network interconnect. The Slim Fly network topology, a new lowdiameter and low-latency interconnection network, is gaining interest as one possible solution for next-generation supercomputing interconnect systems. In this paper, we present a high-fidelity Slim Fly it-level model leveraging the Rensselaer Optimistic Simulation System (ROSS) and Co-Design of Exascale Storage (CODES) frameworks. We validate our Slim Fly model with the Kathareios et al. Slim Fly model results provided at moderately sized network scales. We further scale the model size up to n unprecedented 1 million compute nodes; and through visualization of network simulation metrics such as link bandwidth, packet latency, and port occupancy, we get an insight into the network behavior at the million-node scale. We also show linear strong scaling of the Slim Fly model on an Intel cluster achieving a peak event rate of 36 million events per second using 128 MPI tasks to process 7 billion events. Detailed analysis of the underlying discrete-event simulation performance shows that a million-node Slim Fly model simulation can execute in 198 seconds on the Intel cluster.

interconnection networks↗

Analytical methodology for determination of helicopter IFR precision approach requirements

A systematic analytical approach to the determination of helicopter IFR precision approach requirements is formulated. The approach is based upon the hypothesis that pilot acceptance level or opinion rating of a given system is inversely related to the degree of pilot involvement in the control task. A nonlinear simulation of the helicopter approach to landing task incorporating appropriate models for UH-1H aircraft, the environmental disturbances and the human pilot was developed as a tool for evaluating the pilot acceptance hypothesis. The simulated pilot model is generic in nature and includes analytical representation of the human information acquisition, processing, and control strategies. Simulation analyses in the flight director mode indicate that the pilot model used is reasonable. Results of the simulation are used to identify candidate pilot workload metrics and to test the well known performance-work-load relationship. A pilot acceptance analytical methodology is formulated as a basis for further investigation, development and validation.

Phatak, A. V.↗

Development of the GPM Observatory Thermal Vacuum Test Model

A software-based thermal modeling process was documented for generating the thermal panel settings necessary to simulate worst-case on-orbit flight environments in an observatory-level thermal vacuum test setup. The method for creating such a thermal model involved four major steps: (1) determining the major thermal zones for test as indicated by the major dissipating components on the spacecraft, then mapping the major heat flows between these components; (2) finding the flight equivalent sink temperatures for these test thermal zones; (3) determining the thermal test ground support equipment (GSE) design and initial thermal panel settings based on the equivalent sink temperatures; and (4) adjusting the panel settings in the test model to match heat flows and temperatures with the flight model. The observatory test thermal model developed from this process allows quick predictions of the performance of the thermal vacuum test design. In this work, the method described above was applied to the Global Precipitation Measurement (GPM) core observatory spacecraft, a joint project between NASA and the Japanese Aerospace Exploration Agency (JAXA) which is currently being integrated at NASA Goddard Space Flight Center for launch in Early 2014. From preliminary results, the thermal test model generated from this process shows that the heat flows and temperatures match fairly well with the flight thermal model, indicating that the test model can simulate fairly accurately the conditions on-orbit. However, further analysis is needed to determine the best test configuration possible to validate the GPM thermal design before the start of environmental testing later this year. Also, while this analysis method has been applied solely to GPM, it should be emphasized that the same process can be applied to any mission to develop an effective test setup and panel settings which accurately simulate on-orbit thermal environments.

Yang, Kan↗

A model of nitrous oxide evolution from soil driven by rainfall events. I - Model structure and sensitivity. II - Model applications

Simulations of N2O and CO2 emissions from soils were conducted with a rain-event driven, process-oriented model (DNDC) of nitrogen and carbon cycling processes in soils. The magnitude and trends of simulated N2O (or N2O + N2) and CO2 emissions were consistent with the results obtained in field experiments. The successful simulation of these emissions from the range of soil types examined demonstrates that the DNDC will be a useful tool for the study of linkages among climate, soil-atmosphere interactions, land use, and trace gas fluxes.

Changsheng, LI↗

System Model for MEMS based Laser Ultrasonic Receiver

A need has been identified for more advanced nondestructive Evaluation technologies for assuring the integrity of airframe structures, wiring, etc. Laser ultrasonic inspection instruments have been shown to detect flaws in structures. However, these instruments are generally too bulky to be used in the confined spaces that are typical of aerospace vehicles. Microsystems technology is one key to reducing the size of current instruments and enabling increased inspection coverage in areas that were previously inaccessible due to instrument size and weight. This paper investigates the system modeling of a Micro OptoElectroMechanical System (MOEMS) based laser ultrasonic receiver. The system model is constructed in software using MATLAB s dynamical simulator, Simulink. The optical components are modeled using geometrical matrix methods and include some image processing. The system model includes a test bench which simulates input stimuli and models the behavior of the material under test.

Wilson, William C.↗

Preliminary Analysis of Facility Design for Pyro-processing Safeguardability

The deployment of advanced nuclear energy systems can assure sustainable energy use worldwide. For some of these systems, nuclear fuel will be fabricated in remotely-handled facilities in which batch-type processing will occur in heavily-shielded hot cells. Because special nuclear material is in both different physical and chemical form than that of the contemporary fuel cycle, new challenges have arisen in terms of safeguards, safety, and security, integrated with facility design; i.e., safeguardability. There is a need to develop models that can quantitatively assess safeguardability from a design-driven perspective. Previously, the first build of a material throughput model was established using discrete event simulation (DES) for the fuel fabrication process. Results showed that the DES model potentially can detect diversion attempts. This paper continues the study by further developing the the model. We test the use of the probability of the false alarm; i.e., the Type I error, as a metric for safeguardability assessment. Initiating events leading to a false alarm arise from aspects of safeguards, safety, and security. Different conceptual facility designs could exhibit different frequencies for the Type I error, and this could provide a measure of the safeguardability of each design. A quantitative assessment for safeguardable facility designs has not been developed in this manner. Much of the research into safeguardability has been qualitative. Engineering design approaches for a safeguardable facility are now needed. As there are not accepted metrics for safeguardability assessment, our motivation is to develop this design facet of safeguardability further.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Leveraging prior mean models for faster Bayesian optimization of particle accelerators

Tuning particle accelerators is a challenging and time-consuming task that can be automated and carried out efficiently using suitable optimization algorithms, such as model-based Bayesian optimization techniques. One of the major advantages of Bayesian algorithms is the ability to incorporate prior information about beam physics and historical behavior into the model used to make control decisions. In this work, we examine incorporating prior accelerator physics information into Bayesian optimization algorithms by utilizing fast executing, neural network models trained on simulated or historical datasets as prior mean functions in Gaussian process models. We show that in ideal cases, this technique substantially increases convergence speed to optimal solutions in high-dimensional tuning parameter spaces. Additionally, we demonstrate that even in non-ideal cases, where prior models of beam dynamics do not exactly match experimental conditions, the use of this technique can still enhance convergence speed. Finally, we demonstrate how these methods can be used to improve optimization in practical applications, such as transferring information gained from beam dynamics simulations to online control of the LCLS injector, and transferring knowledge gained from experimental measurements across different operating modes, such as accelerating different ion species at the ATLAS heavy ion accelerator.

43 PARTICLE ACCELERATORS↗

Nonlinear electromagnetic formulation for particle-in-cell simulation of lower hybrid waves in toroidal geometry

An electromagnetic particle simulation model has been formulated and verified for nonlinear processes of lower hybrid (LH) waves in fusion plasmas. Electron dynamics are described by the drift kinetic equation using either kinetic momentum or canonical momentum. Ion dynamics are treated as the fluid system or by the Vlasov equation. Compressible magnetic perturbation is retained to simulate both the fast and slow LH waves. Numerical properties are greatly improved by using the electron continuity equation to enforce the consistency between electrostatic potential and vector potential, and by using the importance sampling scheme. The simulation model has been implemented in the gyrokinetic toroidal code (GTC), and verified for the dispersion relation and nonlinear particle trapping of the electromagnetic LH waves.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Forest regeneration within Earth system models: current process representations and ways forward

Earth system models must predict forest responses to global change in order to simulate future global climate, hydrology, and ecosystem dynamics. These models are increasingly adopting vegetation demographic approaches that explicitly represent tree growth, mortality, and recruitment, enabling advances in the projection of forest vulnerability and resilience, as well as evaluation with field data. To date, simulation of regeneration processes has received far less attention than simulation of processes that affect growth and mortality, in spite of their critical role maintaining forest structure, facilitating turnover in forest composition over space and time, enabling recovery from disturbance, and regulating climate-driven range shifts. Here, our critical review of regeneration process representations within current Earth system vegetation demographic models reveals the need to improve parameter values and algorithms for reproductive allocation, dispersal, seed survival and germination, environmental filtering in the seedling layer, and tree regeneration strategies adapted to wind, fire, and anthropogenic disturbance regimes. These improvements require synthesis of existing data, specific field data-collection protocols, and novel model algorithms compatible with global-scale simulations. Vegetation demographic models offer the opportunity to more fully integrate ecological understanding into Earth system prediction; regeneration processes need to be a critical part of the effort.

54 ENVIRONMENTAL SCIENCES↗

Development of a deep potential model for F and CF 2 etching of Si and SiO 2

An understanding of plasma-surface interactions at increasingly smaller scales is invaluable for the development of novel technologies and processing techniques. Molecular dynamics (MD) simulations can provide insights into atomic-scale interactions, though they are restricted by the availability of interatomic potentials. Machine learning methods, such as Deep Potential Molecular Dynamics (DeepMD), provide a systematic framework for the development of accurate and flexible ab initio-based models. In this work, we develop DeepMD models for the ion-enhanced etching of Si and SiO 2 by F and CF 2 radicals. We employ an active learning process to expand the data set on which the model is trained and demonstrate its effect on the model accuracy. The DeepMD results are compared to data from classical MD simulations and experiments. Physical sputtering yields of SiO 2 by Ar + ions show good agreement with previous simulation results using conventional interatomic potentials, though the predicted depth profiles are different. Etching yields are calculated as a function of ion energy and neutral to ion flux ratio for the Ar + ion-enhanced etching of SiO 2 and Si by F atoms, as well as for etching of SiO 2 by CF 2 radicals, showing reasonable agreement with experimental data. Finally, an ion-enhanced surface kinetic model is fitted to the DeepMD etch yields, and the fitted parameters are compared to quantities computed directly from DeepMD simulations. This study illustrates how molecular dynamics simulations using machine learning potentials can provide an accurate model of etching processes relevant to device manufacturing.

Kounis-Melas, Andreas [Princeton Univ., NJ (United↗

An Open-Source Framework for the Computational Analysis and Design of Autothermal Chemical Processes

The main project object was to develop software tools for simulating non-equilibrium autothermal processes, improving the prospects for identifying and designing such systems. The project demonstrates the use of these tools to simulate autothermal pyrolysis, a process recently developed at the pilot-plant scale at Iowa State University. In such process, instead of externally heating a reactor to pro-vide the enthalpy of pyrolysis, sufficient oxygen in the form of air is introduced into the reactor to support partial oxidation of reactants and products with the exothermic energy released supporting endothermic pyrolysis reactions. A fluidized bed is used to assure good mixing of biomass and oxidant and provide an isothermal reaction environment. The amount of oxygen required depends upon the kind of biomass being pyrolyzed and parasitic heat losses from the reactor. For example, for woody biomass pyrolyzed under conditions that simulate adiabatic operation, equivalence ratios can be as low as 0.06, compared to 0.20 or higher for autothermal gasifiers. By removing the heat transfer bottleneck of conventional pyrolysis, operation in autothermal mode allowed a significant increase in reactor throughput process, approaching five times the throughput of the conventionally operated pyrolyzer. Different simulation strategies were considered and developed: a zero-dimensional chemistry model was used to verify the applicability of kinetic schemes to predict biomass fast pyrolysis in autothermal conditions. Conventional chemical reactor models such as the plug flow reactor and the partially stirred reactor were used to investigate the role of mixing in the fluidized bed pyrolyzer and to establish the impact of mixing time on the gas-phase reactions. A comprehensive multiphase computational fluid dynamics (mCFD) framework, including polydisperse granular phase modeling and detailed chemical kinetics was formulated and used to model the experimental setup for autothermal biomass fast pyrolysis at ISU. Multiphase CFD was also used to investigate the role of biomass feed positioning on the mixing of biomass in the pyrolizer. Finally, a reduced order model (ROM), suitable to be implemented in process simulators was obtained. Both the mCFD and the ROM were validated against experiments.

09 BIOMASS FUELS↗

Control and Real-Time Simulation of Microgrids with EV Fast-Charging and Grid-Forming Resources

Fast charging stations (FCSs) for electric vehicles (EVs) can behave as constant power loads, which is problematic for the operation of grid-forming (GFM) inverter-based resources (IBRs). To tackle this problem, this paper proposes a control suite that makes FCSs responsive to ac voltage and frequency disturbances. To test the performance of the FCS controls, the paper also sets forth theory to compensate for time-delays appearing in the real-time simulation of microgrid models divided into several central processing units (CPUs). Furthermore, these contributions are showcased via multi-CPU real-time simulations of a faulted microgrid having EV FCSs and GFM IBRs powered by photovoltaic solar arrays as well as power hardware-in-the-loop experiments. These contributions are significant to address NERC recommendations and IEEE standards.

14 SOLAR ENERGY↗

A numerical study of the meso-structure variability in the compaction process of prepreg platelet molded composites

Compaction deformations in a thermoplastic composite system molded from chopped prepreg platelets were analyzed to obtain statistical distributions of local distortions that define the final consolidated meso-structure. This study provides an insight into and allows quantification of the intrinsic geometric variability of the composite meso-structure that controls the variability of the effective composite mechanical properties. A two-phase viscoelastic finite element model was utilized to treat the behavior of molten prepreg platelets, which were assumed to be incompressible and inextensible in the fiber direction during processing. A two-step simulation procedure with discretely modeled platelets and platelet-to-platelet contacts is proposed. The analysis begins with a drop simulation of the uncontrolled material deposition followed by a 3D finite element analysis of the preform compaction and bulk material flow. The proposed model provides a method for generating a composite meso-structure ab initio starting from a statistical sampling of the initial conditions for platelet deposition. The predicted platelet thickness distribution, intra-platelet fiber waviness and out-of-plane platelet undulations were found to be in good agreement with experimental measurements. An estimate of the volume of resin pockets was inferred from the compaction simulation.

36 MATERIALS SCIENCE↗

TOWARD THE FORMATION OF CARBONACEOUS REFRACTORY MATTER IN HIGH TEMPERATURE HYDROCARBON-RICH ATMOSPHERES OF EXOPLANETS UPON MICROMETEOROID IMPACT

We report on laboratory simulation experiments mimicking the chemical processing of model atmospheres of exoplanets containing C3 and C4 hydrocarbons at moderate temperatures of 400 K upon interaction of catalytic surfaces of micrometeoroids. By utilizing an ultrasonic levitator device and heating singly levitated particles under simulated microgravity conditions, Raman spectroscopy is utilized as a non-invasive tool to probe on line and in situ the conversion of C3 and C4 hydrocarbons to refractory carbonaceous matter on the surfaces of levitated particles. Secondary Ion Mass Spectrometry and electron microscopic imaging were also conducted to gain further insight into the elementary composition and structures of the refractories formed. Our results provide compelling evidence that in the presence of a catalytic surface, which can be supplied in the form of micrometeoroids and atmospheric dust particles, hydrocarbon gases present in the atmospheres of exoplanets can be converted to refractory, carbon-rich carbonaceous matter of mainly graphitic structure with a carbon content of at least 90% at elevated temperatures. This finding might explain the low methane to carbon monoxide (CH{sub 4}–CO) ratio in the hot Neptune GJ 436b, where the abundant methane photochemically converts to higher order hydrocarbons and ultimately to refractory graphite-like carbon in the presence of a silicon surface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Improved representations of coupled soil–canopy processes in the CABLE land surface model (Subversion revision 3432)

CABLE is a global land surface model, which has been used extensively in offline and coupled simulations. While CABLE performs well in comparison with other land surface models, results are impacted by decoupling of transpiration and photosynthesis fluxes under drying soil conditions, often leading to implausibly high water use efficiencies. Here, we present a solution to this problem, ensuring that modelled transpiration is always consistent with modelled photosynthesis, while introducing a parsimonious single-parameter drought response function which is coupled to root water uptake. We further improve CABLE's simulation of coupled soil–canopy processes by introducing an alternative hydrology model with a physically accurate representation of coupled energy and water fluxes at the soil–air interface, including a more realistic formulation of transfer under atmospherically stable conditions within the canopy and in the presence of leaf litter. The effects of these model developments are assessed using data from 18 stations from the global eddy covariance FLUXNET database, selected to span a large climatic range. Here, marked improvements are demonstrated, with root mean squared errors for monthly latent heat fluxes and water use efficiencies being reduced by 40 %. Results highlight the important roles of deep soil moisture in mediating drought response and litter in dampening soil evaporation.

58 GEOSCIENCES↗

Using Co-Optimized Machine Learned Manifolds for Modeling Chemically Reacting Flows

Chemically reacting flows play a key role in a wide range of engineered systems, from chemical and polymer processing to combustion-based energy conversion technologies. Simulations of these flows involve solving a coupled set of partial differential equations for mass, momentum, energy, and all relevant chemical species in the system. Chemical reaction pathways may be extremely complex and involve hundreds or more intermediate species, with reactions that occur over timescales varying by several orders of magnitude - presenting a significant numerical stiffness challenge. The combination of these factors makes simulation of chemically reacting flows vastly more expensive than nonreactive simulations, and often makes direct solution of the governing equations intractable. It is necessary to apply lower-fidelity models in place of the detailed governing equations in order to reduce computational cost to enable reacting flow simulation tools to be used in the engineering design process. Many of the models employed for this purpose are based on reducing the dimension of the thermochemical state, motivated by the observation that the observed thermochemical states in a system lie on a low-dimensional manifold in thermochemical state space. This behavior occurs due to the fast equilibration of certain reactive and transport processes, and physics-based manifold models rely on idealized assumptions about the balance of timescales and the way in which chemistry and transport are coupled. In this work, we apply a novel method for data-driven manifold-based modeling that can leverage data from high-fidelity reacting flow simulations to improve model accuracy in cases where the physics-based modeling assumptions break down. The approach is designed to be broadly applicable across chemically reacting flow systems but is applied here to turbulent combustion modeling.

machine learning↗

Science-return modeling and simulation

In this paper, an on-going effort to integrate science-return modeling and simulation with JPL's institutional mission system design process will be presented.

modeling simulation↗

A Multichannel Silicon Package for Large-Scale Skipper-CCD Experiments

The next generation of experiments for light-dark-matter and neutrino searches based on skipper charge-coupled devices (skipper-CCDs) introduces new challenges for the sensor packaging and readout architecture. Scaling the active mass while simultaneously reducing the experimental backgrounds in orders of magnitude requires a novel high-density silicon-based package that must be massively produced and tested. In this work, we present a silicon multichip module design capable of hosting up to 16 skipper-CCDs, along with the fabrication process for the first prototypes. We thoroughly tested and characterized the assembled prototypes to build an empirical model for the video output signal of skipper-CCDs integrated into the silicon package. We then used this model in simulations to optimize the fabrication process and achieve the robust performance required for the full-scale array, which we validated through a new round of prototype fabrication. We outline the specifications selected for the ongoing production of 1500 silicon wafers that will ultimately add up to a 10-kg skipper-CCD array with 24000 readout channels.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗