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At least 199 records · Page 11

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NASA NTRS1997

Error Estimation and h-Adaptivity for Optimal Finite Element Analysis

The objective of adaptive meshing and automatic error control in finite element analysis is to eliminate the need for the application engineer from re-meshing and re-running design simulations to verify numerical accuracy. The user should only need to enter the component geometry and a coarse finite element mesh. The software will then autonomously and adaptively refine this mesh where needed, reducing the error in the fields to a user prescribed value. The ideal end result of the simulation is a measurable quantity (e.g. scattered field, input impedance), calculated to a prescribed error, in less time and less machine memory than if the user applied typical uniform mesh refinement by hand. It would also allow for the simulation of larger objects since an optimal mesh is created.

Cwik, Tom↗

NASA NTRS2010

Reconnection Onset in the Breakout Model for CME Initiation

Fast coronal mass ejections (CMEs) are the most massive explosions in the heliosphere, and the primary drivers of geoeffective space weather. Although it is generally agreed that magnetic reconnection is the key to fast CME initiation, different models incorporate reconnection in different ways. One promising model --- the breakout scenario --- involves reconnection in two distinct yet interconnected locations: breakout reconnection ahead of the CME, and flare reconnection behind it. We will discuss what we have learned about the early evolution of breakout and flare reconnection from recent high-resolution 2.5D adaptively refined MHD simulations of CME initiation, including the evolving properties of the breakout and flare current sheets, the conditions that trigger reconnection onset in each sheet, the ensuing positive feedback between breakout and flare reconnections, and implications for electron acceleration in flares.

Karpen, Judy T.↗

NASA NTRS2012

Plume-Free Stream Interaction Heating Effects During Orion Crew Module Reentry

During reentry of the Orion Crew Module (CM), vehicle attitude control will be performed by firing reaction control system (RCS) thrusters. Simulation of RCS plumes and their interaction with the oncoming flow has been difficult for the analysis community due to the large scarf angles of the RCS thrusters and the unsteady nature of the Orion capsule backshell environments. The model for the aerothermal database has thus relied on wind tunnel test data to capture the heating effects of thruster plume interactions with the freestream. These data are only valid for the continuum flow regime of the reentry trajectory. A Direct Simulation Monte Carlo (DSMC) analysis was performed to study the vehicle heating effects that result from the RCS thruster plume interaction with the oncoming freestream flow at high altitudes during Orion CM reentry. The study was performed with the DSMC Analysis Code (DAC). The inflow boundary conditions for the jets were obtained from Data Parallel Line Relaxation (DPLR) computational fluid dynamics (CFD) solutions. Simulations were performed for the roll, yaw, pitch-up and pitch-down jets at altitudes of 105 km, 125 km and 160 km as well as vacuum conditions. For comparison purposes (see Figure 1), the freestream conditions were based on previous DAC simulations performed without active RCS to populate the aerodynamic database for the Orion CM. Other inputs to the analysis included a constant Orbital reentry velocity of 7.5 km/s and angle of attack of 160 degrees. The results of the study showed that the interaction effects decrease quickly with increasing altitude. Also, jets with highly scarfed nozzles cause more severe heating compared to the nozzles with lower scarf angles. The difficulty of performing these simulations was based on the maximum number density and the ratio of number densities between the freestream and the plume for each simulation. The lowest altitude solutions required a substantial amount of computational resources (up to 1800 processors) to simulate approximately 2 billion molecules for the refined (adapted) solutions.

Marichalar, J.↗

NASA NTRS2012

The Effect of Magnetic Topology on the Escape of Flare Particles

Magnetic reconnection in the solar atmosphere is believed to be the driver of most solar explosive phenomena. Therefore, the topology of the coronal magnetic field is central to understanding the solar drivers of space weather. Of particular importance to space weather are the impulsive Solar Energetic particles that are associated with some CME/eruptive flare events. Observationally, the magnetic configuration of active regions where solar eruptions originate appears to agree with the standard eruptive flare model. According to this model, particles accelerated at the flare reconnection site should remain trapped in the corona and the ejected plasmoid. However, flare-accelerated particles frequently reach the Earth long before the CME does. We present a model that may account for the injection of energetic particles onto open magnetic flux tubes connecting to the Earth. Our model is based on the well-known 2.5D breakout topology, which has a coronal null point (null line) and a four-flux system. A key new addition, however, is that we include an isothermal solar wind with open-flux regions. Depending on the location of the open flux with respect to the null point, we find that the flare reconnection can consist of two distinct phases. At first, the flare reconnection involves only closed field, but if the eruption occurs close to the open field, we find a second phase involving interchange reconnection between open and closed. We argue that this second reconnection episode is responsible for the injection of flare-accelerated particles into the interplanetary medium. We will report on our recent work toward understanding how flare particles escape to the heliosphere. This work uses high-resolution 2.5D MHD numerical simulations performed with the Adaptively Refined MHD Solver (ARMS).

Antiochos, S. K.↗

NASA NTRS2012

A Model for SEP Escape

Antiochos, S. K.↗

NASA NTRS2012

Lightning Jump Algorithm and Relation to Thunderstorm Cell Tracking, GLM Proxy and other Meteorological Measurements

Objective of project is to refine, adapt and demonstrate the Lightning Jump Algorithm (LJA) for transition to GOES -R GLM (Geostationary Lightning Mapper) readiness and to establish a path to operations Ongoing work . reducing risk in GLM lightning proxy, cell tracking, LJA algorithm automation, and data fusion (e.g., radar + lightning).

Schultz, Christopher J.↗

NASA NTRS2021

Effects of Pseudostreamer Boundary Dynamics on Heliospheric Field and Wind

Interchange reconnection has been proposed as a mechanism for the generation of the slow solar wind, and a key contributor to determining its characteristic qualities. In this paper we study the implications of interchange reconnection for the structure of the plasma and field in the heliosphere. We use the Adaptively Refined Magnetohydrodynamic Solver (ARMS) to simulate the coronal magnetic evolution in a coronal topology containing both a pseudostreamer and helmet streamer. We begin with a geometry containing a low-latitude coronal hole that is separated from the main polar coronal hole by a pseudostreamer. We drive the system by imposing rotating flows at the Solar surface within and around the low-latitude coronal hole, which leads to a corrugation (at low altitudes) of the separatrix surfaces that separate open from closed magnetic flux. Interchange reconnection is induced both at the null points and separators of the pseudostreamer, and at the global helmet streamer. Wedemonstrate that a preferential occurrence of interchange reconnection in the “lanes” between our driving cells leads to a filamentary pattern of newly-opened flux in the heliosphere. These flux bundles connect to but extend far from the separatrix-web (S-Web) arcs at the source surface. We propose that the pattern of granular and supergranular flows on the photosphere should leave an observable imprint in the heliosphere.

V Aslanyan↗

NASA NTRSDate not supplied

Goal-Oriented Discretization Error Control in Coupled Nearfield-Farfield Low-Boom Simulations

The method of adjoint weighted residuals is used to determine the level of discretization error in loudness predictions of sonic booms on the ground. We analyze the standard nearfied-farfield domain decomposition approach. In the nearfield domain, the three-dimensional Euler equations are solved to obtain a pressure signature generated by the aircraft. In the farfield, this waveform is propagated through the atmosphere to the ground by solving the augmented Burgers’ equation. Loudness is characterized using weighted sound-exposure-level metrics. We formulate discretization error estimates for the ground signature and loudness metrics for this one-way coupled system. Although the nearfield solution is independent of the farfield, the adjoint formulation for the coupled system provides feedback from the farfield to identify high-error regions in the nearfield. The results demonstrate that the discrete adjoint implementation is asymptotically consistent and provides reliable error estimates. Furthermore, we show how the error can be controlled through adaptive refinement of the nearfield mesh. The approach is evaluated on two- and three-dimensional problems, including the X-59 flight demonstration aircraft.

CST↗

NASA NTRS2023

Interchange Reconnection Dynamics in A Solar Coronal Pseudo-Streamer

Context. The generation of the slow solar wind remains an open problem in heliophysics. One of the current theories among those aimed at explaining the injection of coronal plasma in the interplanetary medium is based on interchange reconnection. It assumes that the exchange of magnetic connectivity between closed and open fields allows the injection of coronal plasma in the interplanetary medium to travel along the newly reconnected open field. However, the exact mechanism underlying this effect is still poorly understood. Aims. Our objective is to study this scenario in a particular magnetic structure of the solar corona: a pseudo-streamer. This topological structure lies at the interface between open and closed magnetic field and is thought to be involved in the generation of the slow solar wind. Methods. We performed innovative 3D magnetohydrodynamic (MHD) simulations of the solar corona with a pseudo-streamer, using the Adaptively Refined MHD Solver (ARMS). By perturbing the quasi-steady ambient state with a simple photospheric, large-scale velocity flow, we were able to generate a complex dynamics of the open-and-closed boundary of the pseudo-streamer. We studied the evolution of the connectivity of numerous field lines to understand its precise dynamics. Results. We witnessed different scenarios of opening of the magnetic field initially closed under the pseudo-streamer: one-step interchange reconnection dynamics, along with more complex scenarios, including a coupling between pseudo-streamer and helmet streamer, as well as back-and-forth reconnections between open and closed connectivity domains. Finally, our analysis revealed large-scale motions of a newly opened magnetic field high in the corona that may be explained by slipping reconnection. Conclusions. By introducing a new analysis method for the magnetic connectivity evolution based on distinct closed-field domains, this study provides an understanding of the precise dynamics underway during the opening of a closed field, which enables the injection of closed-field, coronal plasma in the interplanetary medium. Further studies shall provide synthetic observations for these diverse outgoing flows, which could be measured by Parker Solar Probe and Solar Orbiter.

magnetic fields↗

NASA NTRSDate not supplied

CFD Modeling & Validation of Single Bubble Growth During Cavitation in Zero Gravity

Cryogenic Fluid Management (CFM) in zero gravity represents a vital area of research as NASA pursues future long-duration space missions. Success of these missions relies heavily on the ability to store and transfer the cryogenic liquid fuels efficiently and safely. In the Zero Boil-Off Tank (ZBOT-1) Experiment in 2018, unexpected and intense phase change occurred at the screened Liquid Acquisition Device (LAD) during tank pressure control that was later attributed to cavitation. The cavitation ultimately resulted in pump failure. Since cryogen cavitation during storage and transfer of propellants in microgravity has been a source of serious concern for the aerospace community, a series of drop tower experiments with liquid methane were also performed at the University of Bremen to carefully study cavitation from a single bubble nucleus in an engineered micron-sized wall cavity. As the test cell is depressurized during the 10-second drop, cavitation causes bubble growth from the nucleus. Here, in a collaboration between the Case Western Reserve University and University of Bremen, we present a two-phase Computational Fluid Dynamics (CFD) model of bubble growth from a single wall cavity during depressurization in microgravity. Both 2D axisymmetric and 3D parametric simulation case studies are performed to show the evolution of the bubble growth. The model uses a Volume of Fluid (VOF) approach with and without adaptive mesh refinement and adaptive time stepping to capture the growth dynamics using an explicit formulation with algebraic reconstruction of the interface. CFD results show good agreement with corresponding experiment. Based on the velocity, temperature, and volume fraction fields generated by the validation case studies, detailed discussion and analysis of fluid flow and heat transfer during bubble growth process are presented to delineate the intricacies of the cavitation process. Strengths and shortcomings of the present numerical approach in capturing this phenomenon are also highlighted.

Two Phase Flow↗

DOE OSTI2024

Multiphysics Degradation Modeling of Energy Storage Materials via RKPM with a Neural Network-Enhancement

In energy storage materials, strong electrochemical-mechanical coupling and highly anisotropic material properties contribute to the formation and propagation of micro-cracking during charge/discharge cycling, resulting in reduced performance and service life. A coupled electro-chemo-mechanical reproducing kernel particle method (RKPM) formulation is developed, and a patch-test is formulated to certify optimal convergence of the proposed RKPM method for the coupled physics system. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), pixel-based model construction by RKPM is then used to represent the complex material microstructures for modeling the coupled physics of these systems. Further, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1, 2] is introduced to effectively model damage and crack propagation in the material microstructures; the location, orientation, and solution transition near a localization are automatically captured by superimposed block-level NN optimizations. This NN enrichment approach allows for effective modeling of localizations via a fixed background discretization, relieving tedious efforts for adaptive refinement in traditional mesh-based methods. Applications to the heterogeneous microstructures of Li-ion battery cathodes will be presented to demonstrate the effectiveness of the proposed methods. Reference: [1] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, pp 4422-4454, https://doi.org/10.1002/nme.7040, 2022. [2] Baek, J., Chen, J. S., "A Neural Network-Based Enrichment of Reproducing Kernel Approximation for Modeling Brittle Fracture", Computer Methods in Applied Mechanics and Engineering Vol. 410, 116590, 2024.

electro-chemo-mechanical coupling↗

DOE OSTI2024

Leveraging a Neural Network-Enhanced Reproducing Kernel Particle Method for Multiphysics Degradation Modeling of Energy Storage Materials

Energy storage materials exhibit strong electro-chemo-mechanical coupling and highly anisotropic material properties, contributing to the formation and propagation of micro-cracking during charge/discharge cycling and resulting in reduced performance and service life. A coupled electro-chemo-mechanical reproducing kernel particle method (RKPM) formulation has been developed to analyze this system. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), pixel-based model construction by RKPM is used to represent the complex material microstructures that dictate the coupled physics of these systems. Traditional electro-chemo-mechanical models rely on mesh-based finite element methods, which can lead to difficulties in meshing such complex geometries and capturing crack propagation due to mesh dependency. Here, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1, 2] is introduced to effectively model damage and crack propagation in the material microstructures; the location, orientation, and solution transition near a localization are automatically captured by superimposed block-level NN optimizations. This NN enrichment approach allows for effective modeling of localizations via a fixed background discretization, relieving tedious efforts for adaptive refinement in traditional mesh-based methods. Applications to the heterogeneous microstructures of Li-ion battery cathodes will be presented to demonstrate the effectiveness of the proposed methods. NN-RKPM is additionally used to inform how crack opening and closure in turn affect the coupled chemical equations and material microstructure. Reference: [1] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, pp 4422-4454, https://doi.org/10.1002/nme.7040, 2022. [2] Baek, J., Chen, J. S., "A Neural Network-Based Enrichment of Reproducing Kernel Approximation for Modeling Brittle Fracture", Computer Methods in Applied Mechanics and Engineering Vol. 410, 116590, 2024.

degradation↗

DOE OSTI2024

The Pele Simulation Suite for Reacting Flows at Exascale

In this work, we present the Pele suite of software tools for compressible and incompressible reacting flows. The Pele suite leverages several different libraries, notably AMReX and SUNDIALS, to achieve performance portability on heterogeneous computing architectures across the supercomputing landscape. The Pele suite is comprised of PeleC, a compressible reacting flow block-structured adaptive mesh refinement solver, PeleLMeX, a low-Mach number reacting flow block-structured adaptive mesh refinement solver, Pele-Physics, a library for transport, thermodynamics, finite rate chemistry, soot, spray and radiation physics. The objective of this paper is (i) to present the code development efforts necessary to achieve highly effective and scalable applications for exascale machines and (ii) to detail the performance results of the Combustion-Pele project applications on Oak Ridge National Laboratory's Frontier. We show good weak and strong scaling results for both PeleC and PeleLMeX up to more than 50 billion cells on more than 4096 Frontier graphics processing unit nodes. We also present a capability demonstration simulation of a dual-fuel pulse compression ignition engine (six adaptive mesh refinement levels, and 60 billion cells or 2.1 trillion degrees of freedom) on Frontier, to date one of the largest simulations performed on the first exascale-class supercomputer.

adaptive mesh refinement↗

NASA NTRS1998

Progressive content-based retrieval of image and video with adaptive and iterative refinement

A method and apparatus for minimizing the time required to obtain results for a content based query in a data base. More specifically, with this invention, the data base is partitioned into a plurality of groups. Then, a schedule or sequence of groups is assigned to each of the operations of the query, where the schedule represents the order in which an operation of the query will be applied to the groups in the schedule. Each schedule is arranged so that each application of the operation operates on the group which will yield intermediate results that are closest to final results.

Li, Chung-Sheng↗

NASA NTRS1990

Solution adaptive local rectangular grid refinement for transonic aerodynamic flow problems

This paper describes the use of solution-adaptive local grid refinement in a numerical method for solving transonic flow problems about complex three-dimensional aircraft configurations. The method is implemented in the TRANAIR code, which has been applied to help solve many practical engineering problems. Attention is focused here on the principal components of the solution-adaptive grid algorithms currently being developed and on two applications that demonstrate the capabilities of the algorithms.

Bieterman, Michael B.↗

NASA NTRS1990

Adaptive quadrilateral and triangular finite-element scheme for compressible flows

The development of an adaptive mesh refinement procedure for analyzing high-speed compressible flows using the finite-element method is described. This new adaptation procedure, which uses both quadrilateral and triangular elements, was implemented with two explicit finite-element algorithms - the two-step Taylor-Galerkin and the multistep Galerkin-Runge-Kutta schemes. A von Neumann stability analysis and a rotating 'cosine hill'problem demonstrate the instability of the Taylor-Galerkin scheme when coupled with the adaptation procedure. For the same adaptive refinement scheme, the Galerkin-Runge-Kutta procedure yields stable solutions within its explicit stability limit. The utility of this new adaptation procedure for the prediction of compressible flow features is illustrated for inviscid problems involving strong shock interactions at hypersonic speeds.

Ramakrishnan, R.↗

DOE OSTI2026

A GPU-based compressible combustion solver for applications exhibiting disparate space and time scales

High-speed chemically active flows pose significant computational challenges due to their disparate space and time scales, with stiff chemistry often dominating simulation time. While modern scientific computing programs achieve exascale performance by leveraging graphics processing units (GPUs), existing GPU-based compressible combustion solvers face critical limitations in memory management, load balancing, and handling the highly localized nature of chemical reactions. To this end, we present a high-performance compressible reacting flow solver built on the AMReX framework and optimized for multi-GPU settings. Here, our approach addresses three GPU performance bottlenecks: memory access patterns through column-major storage optimization, computational workload variability via a bulk-sparse integration strategy for chemical kinetics, and multi-GPU load distribution for adaptive mesh refinement applications. The solver adapts existing matrix-based chemical kinetics formulations to multi-grid contexts. Using representative combustion applications, including 2D and 3D detonations and a 3D jet-in-crossflow configuration, we demonstrate 1.4–5× performance improvements over initial implementations on an in-house cluster of NVIDIA H100 GPUs, and near-ideal weak scaling on the Frontier supercomputer (Oak Ridge Leadership Computing Facility) with up to 1024 AMD Instinct MI250X GPUs. Roofline analysis reveals substantial improvements in arithmetic intensity for both convection (∼ 10 ×) and chemistry (∼ 4 ×) routines, confirming efficient utilization of GPU memory bandwidth and computational resources.

42 ENGINEERING↗

NASA NTRS2017

Reconfigurable Model Execution in the OpenMDAO Framework

NASA's OpenMDAO framework facilitates constructing complex models and computing their derivatives for multidisciplinary design optimization. Decomposing a model into components that follow a prescribed interface enables OpenMDAO to assemble multidisciplinary derivatives from the component derivatives using what amounts to the adjoint method, direct method, chain rule, global sensitivity equations, or any combination thereof, using the MAUD architecture. OpenMDAO also handles the distribution of processors among the disciplines by hierarchically grouping the components, and it automates the data transfer between components that are on different processors. These features have made OpenMDAO useful for applications in aircraft design, satellite design, wind turbine design, and aircraft engine design, among others. This paper presents new algorithms for OpenMDAO that enable reconfigurable model execution. This concept refers to dynamically changing, during execution, one or more of: the variable sizes, solution algorithm, parallel load balancing, or set of variables-i.e., adding and removing components, perhaps to switch to a higher-fidelity sub-model. Any component can reconfigure at any point, even when running in parallel with other components, and the reconfiguration algorithm presented here performs the synchronized updates to all other components that are affected. A reconfigurable software framework for multidisciplinary design optimization enables new adaptive solvers, adaptive parallelization, and new applications such as gradient-based optimization with overset flow solvers and adaptive mesh refinement. Benchmarking results demonstrate the time savings for reconfiguration compared to setting up the model again from scratch, which can be significant in large-scale problems. Additionally, the new reconfigurability feature is applied to a mission profile optimization problem for commercial aircraft where both the parametrization of the mission profile and the time discretization are adaptively refined, resulting in computational savings of roughly 10% and the elimination of oscillations in the optimized altitude profile.

Hwang, John T.↗