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NASA NTRS2004

Annual Research Briefs, 2004: Center for Turbulence Research

This report contains the 2004 annual progress reports of the Research Fellows and students of the Center for Turbulence Research in its eighteenth year of operation. Since its inception in 1987, the objective of the CTR has been to advance the physical understanding of turbulent flows and development of physics based predictive tools for engineering analysis and turbulence control. Turbulence is ubiquitous in nature and in engineering devices. The studies at CTR have been motivated by applications where turbulence effects are significant; these include a broad range of technical areas such as planetary boundary layers, formation of planets, solar convection, magnetohydrodynamics, environmental and eco systems, aerodynamic noise, propulsion systems and high speed transportation. Numerical simulation has been the predominant research tool at CTR which has required a critical mass of researchers in numerical analysis and computer science in addition to core disciplines such as applied mathematics, chemical kinetics and fluid mechanics. Maintaining and promoting this interdisciplinary culture has been a hallmark of CTR and has been responsible for the realization of the results of its basic research in applications. The first group of reports in this volume are directed towards development, analysis and application of novel numerical methods for ow simulations. Development of methods for large eddy simulation of complex flows has been a central theme in this group. The second group is concerned with turbulent combustion, scalar transport and multi-phase ows. The nal group is devoted to geophysical turbulence where the problem of solar convection has been a new focus of considerable attention recently at CTR.

Moin, Parviz↗
NASA NTRS2006

Applying Parallel Adaptive Methods with GeoFEST/PYRAMID to Simulate Earth Surface Crustal Dynamics

This viewgraph presentation reviews the use Adaptive Mesh Refinement (AMR) in simulating the Crustal Dynamics of Earth's Surface. AMR simultaneously improves solution quality, time to solution, and computer memory requirements when compared to generating/running on a globally fine mesh. The use of AMR in simulating the dynamics of the Earth's Surface is spurred by future proposed NASA missions, such as InSAR for Earth surface deformation and other measurements. These missions will require support for large-scale adaptive numerical methods using AMR to model observations. AMR was chosen because it has been successful in computation fluid dynamics for predictive simulation of complex flows around complex structures.

GeoFEST↗
NASA NTRS2008

'Towers in the Tempest' Computer Animation Submission

The following describes a computer animation that has been submitted to the ACM/SIGGRAPH 2008 computer graphics conference: 'Towers in the Tempest' clearly communicates recent scientific research into how hurricanes intensify. This intensification can be caused by a phenomenon called a 'hot tower.' For the first time, research meteorologists have run complex atmospheric simulations at a very fine temporal resolution of 3 minutes. Combining this simulation data with satellite observations enables detailed study of 'hot towers.' The science of 'hot towers' is described using: satellite observation data, conceptual illustrations, and a volumetric atmospheric simulation data. The movie starts by showing a 'hot tower' observed by NASA's Tropical Rainfall Measuring Mission (TRMM) spacecraft's three dimensional precipitation radar data of Hurricane Bonnie. Next, the dynamics of a hurricane and the formation of 'hot towers' are briefly explained using conceptual illustrations. Finally, volumetric cloud, wind, and vorticity data from a supercomputer simulation of Hurricane Bonnie are shown using volume techniques such as ray marching.

Shirah, Greg↗
NASA NTRS2008

A Flexible Evolvable Architecture for Constellation Mission Systems User Applications

While simulating a complex set of repair tasks to be performed by EVA crewmembers on an upcoming mission, flight controllers and astronauts determine that the repair will take much longer than originally anticipated. All equipment in the vicinity of the worksite must be powered off to maintain a safe environment for the astronauts. Because heater power will be unavailable, several critical components will now be at risk of freezing and permanent damage. If an impending thermal violation is detected, Mission Control will have very limited time to react. Therefore, flight controllers must not only modify their procedures to account for these risks, they must also incorporate into their displays outputs from thermal models, alternate temperature measurements, new alarm limits, and emergency power-on commands to enable the detection and response to freezing conditions. Current software for mission control systems makes scenarios like this difficult to address. Given the time frame for modifying software, operations teams are left with labor-intensive operational workarounds as their only options. NASA Ames Research Center (ARC) and NASA Johnson Space Center (JSC) are collaborating on the development of a flexible software system for mission operations that will enable greater user flexibility than has been available to date. Using composable software, end users in the scenario described above could recompose procedures and command and control displays to allow flight controllers to monitor temperature measurements, identify time-critical conditions, and execute the procedures required to respond to these conditions before flight hardware is permanently damaged.

Trimble, Jay P.↗
NASA NTRS2012

LEGION: Lightweight Expandable Group of Independently Operating Nodes

LEGION is a lightweight C-language software library that enables distributed asynchronous data processing with a loosely coupled set of compute nodes. Loosely coupled means that a node can offer itself in service to a larger task at any time and can withdraw itself from service at any time, provided it is not actively engaged in an assignment. The main program, i.e., the one attempting to solve the larger task, does not need to know up front which nodes will be available, how many nodes will be available, or at what times the nodes will be available, which is normally the case in a "volunteer computing" framework. The LEGION software accomplishes its goals by providing message-based, inter-process communication similar to MPI (message passing interface), but without the tight coupling requirements. The software is lightweight and easy to install as it is written in standard C with no exotic library dependencies. LEGION has been demonstrated in a challenging planetary science application in which a machine learning system is used in closed-loop fashion to efficiently explore the input parameter space of a complex numerical simulation. The machine learning system decides which jobs to run through the simulator; then, through LEGION calls, the system farms those jobs out to a collection of compute nodes, retrieves the job results as they become available, and updates a predictive model of how the simulator maps inputs to outputs. The machine learning system decides which new set of jobs would be most informative to run given the results so far; this basic loop is repeated until sufficient insight into the physical system modeled by the simulator is obtained.

Burl, Michael C.↗
NASA NTRS2011

Simulations of Cyclone Sidr in the Bay of Bengal with a High-Resolution Model: Sensitivity to Large-Scale Boundary Forcing

The predictability of Cyclone Sidr in the Bay of Bengal was explored in terms of track and intensity using the Advanced Research Hurricane Weather Research Forecast (AHW) model. This constitutes the first application of the AHW over an area that lies outside the region of the North Atlantic for which this model was developed and tested. Several experiments were conducted to understand the possible contributing factors that affected Sidr s intensity and track simulation by varying the initial start time and domain size. Results show that Sidr s track was strongly controlled by the synoptic flow at the 500-hPa level, seen especially due to the strong mid-latitude westerly over north-central India. A 96-h forecast produced westerly winds over north-central India at the 500-hPa level that were notably weaker; this likely caused the modeled cyclone track to drift from the observed actual track. Reducing the model domain size reduced model error in the synoptic-scale winds at 500 hPa and produced an improved cyclone track. Specifically, the cyclone track appeared to be sensitive to the upstream synoptic flow, and was, therefore, sensitive to the location of the western boundary of the domain. However, cyclone intensity remained largely unaffected by this synoptic wind error at the 500-hPa level. Comparison of the high resolution, moving nested domain with a single coarser resolution domain showed little difference in tracks, but resulted in significantly different intensities. Experiments on the domain size with regard to the total precipitation simulated by the model showed that precipitation patterns and 10-m surface winds were also different. This was mainly due to the mid-latitude westerly flow across the west side of the model domain. The analysis also suggested that the total precipitation pattern and track was unchanged when the domain was extended toward the east, north, and south. Furthermore, this highlights our conclusion that Sidr was influenced from the west side of the domain. The displacement error was significantly reduced after the domain size from the western model boundary was decreased. Study results demonstrate the capability and need of a high-resolution mesoscale modeling framework for simulating the complex interactions that contribute to the formation of tropical cyclones over the Bay of Bengal region

Kumar, Anil↗
NASA NTRS2013

Recent Advances in Agglomerated Multigrid

We report recent advancements of the agglomerated multigrid methodology for complex flow simulations on fully unstructured grids. An agglomerated multigrid solver is applied to a wide range of test problems from simple two-dimensional geometries to realistic three- dimensional configurations. The solver is evaluated against a single-grid solver and, in some cases, against a structured-grid multigrid solver. Grid and solver issues are identified and overcome, leading to significant improvements over single-grid solvers.

Nishikawa, Hiroaki↗
NASA NTRS2014

Computer-Aided Design (CAD) Tools to Support the Human Factors Design Teams

The scope of this assessment was to develop a library of basic 1-Gravity (G) human posture and motion elements used to construct complex virtual simulations of ground processing and maintenance tasks for spaceflight vehicles, including launch vehicles, crewed spacecraft, robotic spacecraft, satellites, and other payloads. The report herein describes the task, its purpose, performance, findings, NASA Engineering and Safety Center (NESC) recommendations, and conclusions in the definition and assemblage of the postures and motions database (PMD).

Null, Cynthia H.↗
NASA NTRS2017

A Method for Obtaining High Frequency, Global, IR-Based Convective Cloud Tops for Studies of the TTL

Models of varying complexity that simulate water vapor and clouds in the Tropical Tropopause Layer (TTL) show that including convection directly is essential to properly simulating the water vapor and cloud distribution. In boreal winter, for example, simulations without convection yield a water vapor distribution that is too uniform with longitude, as well as minimal cloud distributions. Two things are important for convective simulations. First, it is important to get the convective cloud top potential temperature correctly, since unrealistically high values (reaching above the cold point tropopause too frequently) will cause excessive hydration of the stratosphere. Second, one must capture the time variation as well, since hydration by convection depends on the local relative humidity (temperature), which has substantial variation on synoptic time scales in the TTL. This paper describes a method for obtaining high frequency (3-hourly) global convective cloud top distributions which can be used in trajectory models. The method uses rainfall thresholds, standard IR brightness temperatures, meteorological temperature analyses, and physically realistic and documented corrections IR brightness temperature corrections to derive cloud top altitudes and potential temperatures. The cloud top altitudes compare well with combined CLOUDSAT and CALIPSO data, both in time-averaged overall vertical and horizontal distributions and in individual cases (correlations of .65-.7). An important finding is that there is significant uncertainty (nearly .5 km) in evaluating the statistical distribution of convective cloud tops even using lidar. Deep convection whose tops are in regions of high relative humidity (such as much of the TTL), will cause clouds to form above the actual convection. It is often difficult to distinguish these clouds from the actual convective cloud due to the uncertainties of evaluating ice water content from lidar measurements. Comparison with models show that calculated cloud top altitudes are generally higher than those calculated by global analyses (e.g., MERRA). Interannual variability in the distribution of convective cloud top altitudes is also investigated.

hydration↗
NASA NTRS2016

A Method for Obtaining High Frequency, Global, IR-Based Convective Cloud Tops for Studies of the Tropical Tropopause Layer

Models of varying complexity that simulate water vapor and clouds in the Tropical Tropopause Layer (TTL) show that including convection directly is essential to properly simulating the water vapor and cloud distribution. In boreal winter, for example, simulations without convection yield a water vapor distribution that is too uniform with longitude, as well as cloud incidences that are too low. Two things are important for convective simulations. First, it is important to get the convective cloud top potential temperature correctly, since unrealistically high values (reaching above the cold point tropopause too frequently) will cause excessive hydration of the stratosphere. Second, one must capture the time variation as well, since hydration by convection depends on the local relative humidity (temperature), which has substantial variation on synoptic time scales in the TTL. This paper describes a method for obtaining high frequency (3-hourly) global convective cloud top distributions which can be used in trajectory models. The method uses rainfall thresholds, standard IR (infra-red) brightness temperatures, meteorological temperature analyses, and physically realistic and documented corrections to IR brightness temperatures to derive cloud top altitudes and potential temperatures. The cloud top altitudes compare well with combined CLOUDSAT and CALIPSO data, both in time-averaged overall vertical and horizontal distributions and in individual cases (correlations of .65-.7). Results from the method are compared to convective distributions currently used by global models. In general, the method shows that models underestimate convective cloud top altitudes.

tropopause↗
NASA NTRS2016

A Touchpad-Based Method for Inducing Attentional Tunneling

Attentional tunneling is a recognized problem for aviation safety in the flight deck. A prototype system (touchpad and associated application and experimental software) was developed and evaluated for its success in inducing attentional tunneling in a reliable and predictable manner in training and experimental contexts. Two experiments involving a total of sixteen participants examined baseline performance for visual memory of a color or number sequence, simultaneous with performing a competing auditory detection task. Spatial auditory separation of the auditory stimuli was also evaluated. Data are provided for various aspects of touchpad entry (accuracy, speed) as well as hit and false alarm rates for the auditory task. The results will help determine means of inducing attentional tunneling in more complex flight simulator experiments, and for developing an inexpensive prototype for pilots to measure cognitive fixation and develop mitigation strategies.

aviation safety↗
NASA NTRS2019

Fully-Coupled Fluid-Structure Interaction Simulations of a Supersonic Parachute

A validated computational fluid-structure interaction method for simulating the complex interaction between the large deformation of very thin, highly deformable structures and compressible flows is extended to consider large-scale problems in supersonic flows using parallel computing. The coupled fluid-structure interaction system is solved in a partitioned, or weakly-coupled, manner. The foundations of the applied fluid-structure interaction method are a higher-order, block-structured Cartesian, sharp immersed boundary method for the compressible Navier-Stokes equations and a computational structural dynamics solver employing a geometrically nonlinear 3-node shell element based on the mixed interpolation of tensorial components formulation. The method is applied to large deformation fluid-structure interaction validation cases before being applied to the inflation of a supersonic parachute in the upper Martian atmosphere where the goal is to demonstrate the capabilities of the solver when considering large-scale problems in supersonic flows.

Boustani, Jonathan↗
NASA NTRSDate not supplied

ICE-RASSOR: Intelligent Capabilities Enhanced Regolith Advanced Surface Systems Operations Robot

NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for In-Situ Resource Utilization (ISRU)processing. RASSOR’s design enables it to efficiently collect and deposit regolith, return collected material for processing, and myriad related ISRU activities. To reliably perform these operations on the lunar surface, RASSOR software and sensory systems need to be robust and maximize the information extracted from a reduced sensor payload. Herein, we present preliminary findings from the Intelligent Capabilities Enhanced RASSOR project. We apply supervised learning using real data to estimate the soil mass collected without the need for mass flow rate monitors or other explicate sensing techniques. We also create a reduced-order simulation environment to develop autonomous trenching controllers via reinforcement learning and prototype state estimation architectures. Our initial results suggest that excavated regolith mass can be inferred within 2.9% RMS error of full scale, and reinforcement learning for autonomous operations has learned viable trenching strategies and helped identify desirable sensing capabilities, arrangements, and considerations. Future work includes regolith mass estimation during dynamic operation, expanding our simulation to more complex environments, and transfer learning from simulation to hardware.

machine learning↗
NASA NTRSDate not supplied

ICE-RASSOR: Intelligent Capabilities Enhanced

NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for In-Situ Resource Utilization (ISRU) processing. RAS-SOR’s design enables it to efficiently collect and deposit regolith, return collected material for processing, and myriad related ISRU activities. To reliably perform these operations on the lunar sur-face, RASSOR software and sensory systems need to be robust and maximize the information extracted from on-board sensing. Herein, we present preliminary findings from the Intelligent Capabilities Enhanced RASSOR project. We apply supervised learning using real data to estimate the soil mass collected without the need for mass flow rate monitors or other explicate sensing techniques. We also create a reduced-order simulation environment to develop autonomous trenching controllers via reinforcement learning and proto-type state estimation architectures. Our initial results suggest that excavated regolith mass can be inferred within 2.9% RMS error of full scale, and reinforcement learning for autonomous operations has learned viable trenching strategies and helped identify desirable sensing capabilities, arrangements, and considerations. Future work includes regolith mass estimation during dynamic operation, expanding our simulation to more complex environments, and transfer learning from simulation to hardware.

machine learning↗
NASA NTRSDate not supplied

ICE-RASSOR: Intelligent Capabilities Enhanced Regolith Advanced Surface Systems Operations Robot

NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for In-Situ Resource Utilization (ISRU) processing. RASSOR’s design enables it to efficiently collect and deposit regolith, return collected material for processing, and myriad related ISRU activities. To reliably perform these operations on the lunar surface, RASSOR software and sensory systems need to be robust and maximize the information extracted from on-board sensory. Herein, we present preliminary findings from the Intelligent Capabilities Enhanced RASSOR project. We apply supervised learning using real data to estimate the soil mass collected without the need for mass flow rate monitors or other explicate sensing techniques. We also create a reduced-order simulation environment to develop autonomous trenching controllers via reinforcement learning and proto-type state estimation architectures. Our initial results suggest that excavated regolith mass can be inferred within 2.9% RMS error of full scale, and reinforcement learning for autonomous operations has learned viable trenching strategies and helped identify desirable sensing capabilities, arrangements, and considerations. Future work includes regolith mass estimation during dynamic operation, expanding our simulation to more complex environments, and transfer learning from simulation to hardware.

machine learning↗
NASA NTRSDate not supplied

Recent Developments to the Porous Microstructure Analysis (PuMA) Software

Introduction The Porous Microstructure Analysis (PuMA) software is an open source framework for image-based simulation, primarily used to determine effective properties based on material microstructure. PuMA was originally developed for the study of NASA thermal protection materials; however, many of the solvers in PuMA have applicability to a broad range of materials science applications. PuMA version 3.2 computes material surface area, pore diameters, effective thermal conductivity, continuum and rarefied tortuosity, and permeability. For anisotropic materials, PuMA can estimate material orientation and compute anisotropic thermal conductivity and elasticity. In this talk, a brief overview of the PuMA software and underlying methods will be presented, as well as some recent and ongoing developments, including the use of immersed boundary methods for image-based simulation and the development of a new weave segmentation tool, called TomoSAM. Cut-Cell method for heat and mass transfer For simulations on complex microstructures, traditional unstructured meshing techniques often prove to be difficult and time-intensive. Voxel-based solvers, which represent the surface as a staircase structure, are relatively simple to implement but can lose accuracy when feature resolution is poor. In this work, we present a novel 3D cut-cell method for solving the variable coefficient Poisson equation on complex microstructures, suitable for the determination of effective thermal conductivity or tortuosity of a material. The method uses a Marching Cubes/Marching Squares surface reconstruction to create cut-cells and determine geometric quantities. A flux-correction method is extended to 3D, with least squares gradient reconstruction, to solve for the boundary fluxes in the cut-cells. Verification cases show the solver achieves globally 2nd order accuracy on complex microstructures. TomoSAM TomoSAM, a module of the PuMA software, has been developed as a plugin for 3D Slicer, a software platform used for 3D image processing and visualization. It utilizes the Segment Anything Model (SAM), a deep learning model capable of identifying objects and generating image masks based on minimal user input. This feature enables efficient segmentation of complex 3D datasets, particularly of woven materials, from tomography or similar imaging methods, reducing the need for manual segmentation.

Tomography↗
NASA NTRSDate not supplied

Recent Developments to the Porous Microstructure Analysis (PuMA) Software

The Porous Microstructure Analysis (PuMA) software is an open source framework for image-based simulation, primarily used to determine effective properties based on material microstructure. PuMA was originally developed for the study of NASA thermal protection materials; however, many of the solvers in PuMA have applicability to a broad range of materials science applications. PuMA version 3.2 computes material surface area, pore diameters, effective thermal conductivity, continuum and rarefied tortuosity, and permeability. For anisotropic materials, PuMA can estimate material orientation and compute anisotropic thermal conductivity and elasticity. In this talk, a brief overview of the PuMA software and underlying methods will be presented, as well as some recent and ongoing developments, including the use of immersed boundary methods for image-based simulation and the development of a new weave segmentation tool, called TomoSAM. Cut-Cell method for heat and mass transfer For simulations on complex microstructures, traditional unstructured meshing techniques often prove to be difficult and time-intensive. Voxel-based solvers, which represent the surface as a staircase structure, are relatively simple to implement but can lose accuracy when feature resolution is poor. In this work, we present a novel 3D cut-cell method for solving the variable coefficient Poisson equation on complex microstructures, suitable for the determination of effective thermal conductivity or tortuosity of a material. The method uses a Marching Cubes/Marching Squares surface reconstruction to create cut-cells and determine geometric quantities. A flux-correction method is extended to 3D, with least squares gradient reconstruction, to solve for the boundary fluxes in the cut-cells. Verification cases show the solver achieves globally 2nd order accuracy on complex microstructures. TomoSAM TomoSAM, a module of the PuMA software, has been developed as a plugin for 3D Slicer, a software platform used for 3D image processing and visualization. It utilizes the Segment Anything Model (SAM), a deep learning model capable of identifying objects and generating image masks based on minimal user input. This feature enables efficient segmentation of complex 3D datasets, particularly of woven materials, from tomography or similar imaging methods, reducing the need for manual segmentation.

Tomography↗
NASA NTRSDate not supplied

On the Interaction Between a Flow Instability and Inertial Particles in Supersonic Jet Plume-Surface Interaction

Extraterrestrial landings often require spacecraft to deploy supersonic jets directed at dusty planetary surfaces to ensure controlled descents. This interaction, termed plume-surface interaction (PSI), critically influences crater formation and the dynamics of ejected particles, which can obscure vision, damage onboard sensors, and impact nearby infrastructure with significant particle velocities. The physics of PSI remain poorly understood due to the complexities in simulating the involved multiphysics and experimentally replicating reduced atmospheric pressure conditions. This article discusses a collaborative effort to investigate PSI in reduced atmospheric pressure environments using a Mach 5.3 jet impinging on a granular bed. The results highlight observations of an azimuthal pattern in the ejecta distribution, characterized by alternating ejecta streaks of varying particle concentrations. This phenomenon suggests the presence of a low Reynolds number instability, a notable discovery given the supersonic nature of the jet.

PSI↗