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It would be hard to argue against the fact that Integrated Computational Materials Engineering (ICME) is a fast growing discipline within material science and engineering. A quick scan of the proceedings from conferences such as Aeromat, Material Science and Technology, and the TMS Annual Meeting clearly shows it. What began a few years ago as one symposium has grown into multiple ICME related symposia at each of these conferences. As encouraging as the number of symposia being offered is the attendance at the symposia. For example, one of the ICME symposia at MS&T 10, the symposium in which this book is based, had five sessions which culminated in a panel discussion that was standing room only. In addition to the large, annual materials science and engineering conferences, smaller 1 to 2-day conferences/workshops sponsored by government agencies (e.g. AFRL and NIST) on specific aspects of ICME and by universities promoting their ICME work are regularly offered. And arguably the most significant news with regards to ICME and conferences is the July 2011 First World Congress on ICME. This five day TMS sponsored conference, specifically focused on ICME with an international advisory board of ICME leaders, shows how far ICME has spread across the globe. Evidence for the growth of ICME can also be found in Academia. The University Materials Council (UMC) is composed of department heads for material science and engineering from major U.S. and Canadian universities. Meeting twice a year to share best practices in order to strengthen both the engineering content [1] and the educational process, the UMC s agenda for their Spring 2010 meeting was dedicated to ICME [2]. This meeting was held in response to the growing awareness that the universities play a major role in the success of ICME and therefore need to develop ICME curriculum in order to meet that need. To aid educators in the development of ICME courses, NSF is funding a "Summer School" on ICME to be held at the University of Michigan in 2011 [3]. Northwestern University recently announced a MS Certificate Program in ICME [4]. Course work for this certificate begins in the Fall of 2011. Other signs that ICME is growing comes from the formation of ICME initiatives from work that did not start off with ICME in mind. One of the committees in ASM International is the Materials Properties Database Committee (MPDC). In the 2010 meeting of the MPDC, based on a study by ASM, the committee decided that it would create an ICME sub-committee in order to determine how ASM can meet the growing needs of the ICME community [5]. In 1999, the Air Force Research Laboratory (AFRL) created a consortium, the Metals Affordability Initiative (MAI), with members from both industry and government with a goal of reducing the cost and time to market of producing metal parts for aerospace applications [6].
We present NASA ESTO FluidCam 1 & 2, Visible and NIR Fluid-Lensing-enabled imaging payloads for Unmanned Aerial Vehicles (UAVs). Developed as part of a focused 2014 earth science technology grant, FluidCam 1&2 are Fluid-Lensing-based computational optical imagers designed for automated 3D mapping and remote sensing of underwater coastal targets from airborne platforms. Fluid Lensing has been used to map underwater reefs in 3D in American Samoa and Hamelin Pool, Australia from UAV platforms at sub-cm scale, which has proven a valuable tool in modern marine research for marine biosphere assessment and conservation. We share FluidCam 1&2 instrument validation and testing results as well as preliminary processed data from field campaigns. Petabyte-scale aerial survey efforts using Fluid Lensing to image at-risk reefs demonstrate broad applicability to large-scale automated species identification, morphology studies and reef ecosystem characterization for shallow marine environments and terrestrial biospheres, of crucial importance to improving bathymetry data for physical oceanographic models and understanding climate change’s impact on coastal zones, global oxygen production, carbon sequestration.
PLANT (Polarimetric-interferometric Lab and Analysis Tools) is a new collection of software tools developed at the Jet Propulsion Laboratory to support processing and analysis of Synthetic Aperture Radar (SAR) data for ecosystem and land-cover/land-use change science and applications. PLANT inherits code components from the Interferometric Scientific Computing Environment (ISCE) to generate highresolution, coregistered polarimetric-interferometric SLC stacks from Level-0/1 data for a variety of airborne and spaceborne sensors. The goal is to provide the ecosystem and land-cover/land-use change communities with rigorous and efficient tools to perform multi-temporal, polarimetric and tomographic analyses in order to generate calibrated, geocoded and mosaicked Level-2 and Level-3 products (e.g.,maps of above-ground biomass and forest disturbance). In this paper we introduce the capabilities of PLANT and report first results obtained with the tools developed up to date.
Exploration of our Solar System is a foundational element of NASA’s identity. Delivering a scientific payload through an atmosphere to the surface of a planetary body requires safely navigating the extreme temperatures and stresses generated by flying many times the speed of sound. An entry system is the outer shell of a vehicle designed to protect the payload and, as a single point of failure for a mission, reliability is paramount. Unfortunately, it is not possible to fully replicate the flight environment in ground test facilities – so how do we confidently design a vehicle that needs to work the first time, every time? Modeling and simulation are critical tools for filling gaps in ground test capability and providing traceability from ground to flight. Modeling an entry system is a truly interdisciplinary effort, requiring knowledge of fluid dynamics, high-temperature chemistry, radiation, materials science, structural dynamics, guidance and control – and, finally, the mathematical and computing capability to pull it all together. In this talk, Dr. Michael Barnhardt will discuss the latest research in modeling entry systems and how it is being used in NASA’s exploration missions.
The Global Learning and Observation to Benefit the Environment (GLOBE) Data and Information System supports an international science and education program with capabilities to accept local environment observations, archive, display and visualize them along with global satellite observations. Since its inception twenty years ago, the Web and database system has been upgraded periodically to accommodate the changes in technology and the steady growth of GLOBEs education community and collection of observations. Recently, near the end-of-life of the system hardware, new commercial computer platform options were explored and a decision made to utilize Cloud services. Now the GLOBE DIS has been fully deployed and maintained using Amazon Cloud services for over two years now. This paper reviews the early risks, actual challenges, and some unexpected findings as a result of the GLOBE DIS migration. We describe the plans, cost drivers and estimates, highlight adjustments that were made and suggest improvements. We present the trade studies for provisioning, for load balancing, networks, processing, storage, as well as production, staging and backup systems. We outline the migration teams skills and required level of effort for transition, and resulting changes in the overall maintenance and operations activities. Examples include incremental adjustments to processing capacity and frequency of backups, and efforts previously expended on hardware maintenance that were refocused onto application-specific enhancements.
The Global Learning and Observation to Benefit the Environment (GLOBE) Data and Information System supports an international science and education program with capabilities to accept local environment observations, archive, display and visualize them along with global satellite observations. Since its inception twenty years ago, the Web and database system has been upgraded periodically to accommodate the changes in technology and the steady growth of GLOBEs education community and collection of observations. Recently, near the end-of-life of the system hardware, new commercial computer platform options were explored and a decision made to utilize Cloud services. Now the GLOBE DIS has been fully deployed and maintained using Amazon Cloud services for over two years now. This paper reviews the early risks, actual challenges, and some unexpected findings as a result of the GLOBE DIS migration. We describe the plans, cost drivers and estimates, highlight adjustments that were made and suggest improvements. We present the trade studies for provisioning, for load balancing, networks, processing, storage, as well as production, staging and backup systems. We outline the migration teams skills and required level of effort for transition, and resulting changes in the overall maintenance and operations activities. Examples include incremental adjustments to processing capacity and frequency of backups, and efforts previously expended on hardware maintenance that were refocused onto application-specific enhancements.
In February of 1994, an effort from the Fluid Dynamics and Information Sciences Divisions at NASA Ames Research Center with McDonnel Douglas Aerospace Company and Stanford University was initiated to develop, demonstrate, validate and disseminate automated software for numerical aerodynamic simulation. The goal of the initiative was to develop a tri-discipline approach encompassing CFD, Intelligent Systems, and Automated Flow Feature Recognition to improve the utility of CFD in the design cycle. This approach would then be represented through an intelligent computational system which could accept an engineer's definition of a problem and construct an optimal and reliable CFD solution. Stanford University's role focused on developing technologies that advance visualization capabilities for analysis of CFD data, extract specific flow features useful for the design process, and compare CFD data with experimental data. During the years 1995-1997, Stanford University focused on developing techniques in the area of tensor visualization and flow feature extraction. Software libraries were created enabling feature extraction and exploration of tensor fields. As a proof of concept, a prototype system called the Integrated Computational System (ICS) was developed to demonstrate CFD design cycle. The current research effort focuses on finding a quantitative comparison of general vector fields based on topological features. Since the method relies on topological information, grid matching and vector alignment is not needed in the comparison. This is often a problem with many data comparison techniques. In addition, since only topology based information is stored and compared for each field, there is a significant compression of information that enables large databases to be quickly searched. This report will (1) briefly review the technologies developed during 1995-1997 (2) describe current technologies in the area of comparison techniques, (4) describe the theory of our new method researched during the grant year (5) summarize a few of the results and finally (6) discuss work within the last 6 months that are direct extensions from the grant.
The surface modeling and grid generation requirements, motivations, and methods used to develop Computational Fluid Dynamic volume grids for the X34-Phase 1 are presented. The requirements set forth by the Aerothermodynamics Branch at the NASA Langley Research Center serve as the basis for the final techniques used in the construction of all volume grids, including grids for parametric studies of the X34. The Integrated Computer Engineering and Manufacturing code for Computational Fluid Dynamics (ICEM/CFD), the Grid Generation code (GRIDGEN), the Three-Dimensional Multi-block Advanced Grid Generation System (3DMAGGS) code, and Volume Grid Manipulator (VGM) code are used to enable the necessary surface modeling, surface grid generation, volume grid generation, and grid alterations, respectively. All volume grids generated for the X34, as outlined in this paper, were used for CFD simulations within the Aerothermodynamics Branch.
Efforts to build a Predictive Material Modeling (PMM) framework from the micro-scale to the macro-scale are presented in this abstract. To reduce the need for extensive testing, accelerate the design cycle process, and reduce uncertainty margins applied to final designs, NASA is developing simulation and modeling tools that enable characterization of material properties and response to high-enthalpy environments. The Porous Microstructure Analysis (PuMA) code has been developed for computing macroscale (volume averaged) properties of porous materials using microscale images from micro-computed tomography (micro-CT). Microscale modeling requires a realistic representation of a material microstructure; these are obtained either synthetically during the design of the material or through X-ray micro-CT. Volume averaged properties are then used to inform macroscale material response models, such as those implemented in the Porous-material Analysis Toolbox based on OpenFOAM (PATO) software, also actively developed by NASA. The computational model in PATO is a generic heat and mass transfer model for porous reactive materials containing several solid phases and a single gas phase. The detailed chemical interactions occurring between the solid phases and the gas phase are modeled at the pore scale assuming local thermal equilibrium. These tools were developed to efficiently interface with other pre-existing codes such as SPARTA (direct simulation Monte Carlo), DPLR (hypersonic CFD), NEQAIR (radiative transport) and DAKOTA (uncertainty quantification and optimization). Detailed flight data (Mars Science Laboratory [MSL] Entry Descent and Landing Instrument [MEDLI]) is critical for validating these computational tools for NASA applications. Examples of modeling ablative material response using these codes will be presented including 3D simulations of the full-scale heatshield of the MSL capsule. The simulations demonstrate the ability of the modern material response code, PATO, to handle the material response of geometrically complex and large domains, through the use of massively parallel computations.
The Automaton Rover for Extreme Environments (AREE) is a NASA Innovative Advanced Concept (NIAC) funded study focused on enabling long duration science on Venus by replacing vulnerable electronics with an entirely mechanical design. By utilizing high temperature alloys, the rover would survive on the surface of Venus for weeks if not months. The rover concept harvests wind energy using a turbine and stores it in a constant force spring. The mobility system would be guided by a mechanical computer and logic system, programed to carry out the mission. It would collect basic science data such as wind speed, temperature, and seismic events. Communicating the data back to Earth is the most challenging aspect of the system design with multiple options being explored in a trade: a simple electronic high temperature transponder, a retroreflector target or inscribing phonograph style records to be launched via a balloon to a high altitude drone capable of transmitting the data back to Earth. AREE is not only a new exciting in-situ rover concept, but also a paradigm shift to conducting in-situ science in extreme environments. Traditional extreme environment vehicles collect as many diverse data points as possible in the short period of time before system failure. AREE breaks that trend by exploring what can be done with only a few basic scientific measurements, but recorded over long periods of time. In addition to Venus, the concept can be useful in other extreme environments in the solar system including Mercury, Jupiter's radiation belts, the interiors of gas giants, the mantle of the Earth and volcanoes throughout the solar system.
During Mars atmospheric entry, the Mars Science Laboratory (MSL) was protected by a 4.5 meters diameter ablative heatshield assembled in 113 tiles. The heatshield was made of NASA's flagship ablative material, the Phenolic Impregnated Carbon Ablator (PICA). Prior work compared the traditional one-dimensional and three-dimensional material response models at different locations in the heatshield. It was observed that the flow was basically one-dimensional in the nose and flank regions, but three-dimensional flow effects were observed in the outer flank. The objective of this work is to study the effects of the aerothermal environment on the material response. We extend prior work by computing aerothermal environments using the direct simulation Monte Carlo (DSMC) code SPARTA and the CFD code Data Parallel Line Relaxation (DPLR). SPARTA is used to compute environment in the rarefied regime prior to 48.4s of entry where the Knudsen number is such that the Navier-Stokes equations can be inaccurate. Similarly to previous work, the DPLR software is used to compute the hypersonic environment for laminar then turbulent boundary layer assumptions from 48.4 s up to 100 s after Entry Interface (EI) along the MSL 08-TPS-02/01a trajectory. We observe that extending the aerothermal environments to times prior to 48.4 s modifies the thermal response of the heat shield at the surface and in-depth; however the effects on the recession are minimal. Additionally, using the assumption of a turbulent boundary layer versus a laminar one leads to higher surface and in-depth temperatures, larger recession, and a displacement of the peak heating and peak recession location.
The NASA Science Internet, (NSI) was established in 1987 to provide NASA's Offices of Space Science and Applications (OSSA) missions with transparent wide-area data connectivity to NASA's researchers, computational resources, and databases. The NSI Office at NASA/Ames Research Center has the lead responsibility for implementing a total, open networking program to serve the OSSA community. NSI is a full-service communications provider whose services include science network planning, network engineering, applications development, network operations, and network information center/user support services. NSI's mission is to provide reliable high-speed communications to the NASA science community. To this end, the NSI Office manages and operates the NASA Science Internet, a multiprotocol network currently supporting both DECnet and TCP/IP protocols. NSI utilizes state-of-the-art network technology to meet its customers' requirements. THe NASA Science Internet interconnects with other national networks including the National Science Foundation's NSFNET, the Department of Energy's ESnet, and the Department of Defense's MILNET. NSI also has international connections to Japan, Australia, New Zealand, Chile, and several European countries. NSI cooperates with other government agencies as well as academic and commercial organizations to implement networking technologies which foster interoperability, improve reliability and performance, increase security and control, and expedite migration to the OSI protocols.
From its bold start nearly 30 years ago and continuing today, the NASA Advanced Supercomputing (NAS) facility at Ames Research Center has enabled remarkable breakthroughs in the space agency s science and engineering missions. Throughout this time, NAS experts have influenced the state-of-the-art in high-performance computing (HPC) and related technologies such as scientific visualization, system benchmarking, batch scheduling, and grid environments. We highlight the pioneering achievements and innovations originating from and made possible by NAS resources and know-how, from early supercomputing environment design and software development, to long-term simulation and analyses critical to design safe Space Shuttle operations and associated spinoff technologies, to the highly successful Kepler Mission s discovery of new planets now capturing the world s imagination.
For long-duration in-space storage of cryogenic propellants, an axial jet mixer is one concept for controlling tank pressure and reducing thermal stratification. Extensive ground-test data from the 1960s to the present exist for tank diameters of 10 ft or less. The design of axial jet mixers for tanks on the order of 30 ft diameter, such as those planned for the Ares V Earth Departure Stage (EDS) LH2 tank, will require scaling of available experimental data from much smaller tanks, as well designing for microgravity effects. This study will assess the ability for Computational Fluid Dynamics (CFD) to handle a change of scale of this magnitude by performing simulations of existing ground-based axial jet mixing experiments at two tank sizes differing by a factor of ten. Simulations of several axial jet configurations for an Ares V scale EDS LH2 tank during low Earth orbit (LEO) coast are evaluated and selected results are also presented. Data from jet mixing experiments performed in the 1960s by General Dynamics with water at two tank sizes (1 and 10 ft diameter) are used to evaluate CFD accuracy. Jet nozzle diameters ranged from 0.032 to 0.25 in. for the 1 ft diameter tank experiments and from 0.625 to 0.875 in. for the 10 ft diameter tank experiments. Thermally stratified layers were created in both tanks prior to turning on the jet mixer. Jet mixer efficiency was determined by monitoring the temperatures on thermocouple rakes in the tanks to time when the stratified layer was mixed out. Dye was frequently injected into the stratified tank and its penetration recorded. There were no velocities or turbulence quantities available in the experimental data. A commercially available, time accurate, multi-dimensional CFD code with free surface tracking (FLOW-3D from Flow Science, Inc.) is used for the simulations presented. Comparisons are made between computed temperatures at various axial locations in the tank at different times and those observed experimentally. The affect of various modeling parameters on the agreement obtained are assessed.
The acceleration of technological progress has been the central feature of this century. I argue in this paper that we are on the edge of change comparable to the rise of human life on Earth. The precise cause of this change is the imminent creation by technology of entities with greater than human intelligence. There are several means by which science may achieve this breakthrough (and this is another reason for having confidence that the event will occur): (1) the development of computers that are 'awake' and superhumanly intelligent (to date, most controversy in the area of AI relates to whether we can create human equivalence in a machine. But if the answer is 'yes, we can', then there is little doubt that beings more intelligent can be constructed shortly thereafter); (2) large computer networks (and their associated users) may 'wake up' as a superhumanly intelligent entity; (3) computer/human interfaces may become so intimate that users may reasonably be considered superhumanly intelligent; and (4) biological science may find ways to improve upon the natural human intellect. The first three possibilities depend in large part on improvements in computer hardware. Progress in computer hardware has followed an amazingly steady curve in the last few decades. Based largely on this trend, I believe that the creation of greater than human intelligence will occur during the next thirty years.
An investigation has been conducted in order to assess the impact of magnetic fields generated by the propulsion module upon the scientific instrumentation of the spacecraft as proposed for the mission to comet Encke. It was found that dc magnetic fields could potentially be a threat to the magnetometer experiment, whereas ac fields could affect the plasma wave experiment instrumentation. Studies included a set of measurements, computer simulation, and mathematical analyses. Ways for achieving the compatibility of the propulsion module with science are studied and recommendations advanced.
In 1999, NASA's New Millennium Program called for proposals to validate new technology in high-earth orbit for the Earth Observing-3 (NMP EO3) mission to fly in 2003. In response, we proposed to test a large aperture, active optics telescope in geosynchronous orbit. This would flight-qualify new technologies for both Earth and Space science: 1) a future instrument with LANDSAT image resolution and radiometric quality watching continuously from geosynchronous station, and 2) the Next Generation Space Telescope (NGST) for deep space imaging. Six enabling technologies were to be flight-qualified: 1) a 3-meter, lightweight segmented primary mirror, 2) mirror actuators and mechanisms, 3) a deformable mirror, 4) coarse phasing techniques, 5) phase retrieval for wavefront control during stellar viewing, and 6) phase diversity for wavefront control during Earth viewing. Three enhancing technologies were to be flight- validated: 1) mirror deployment and latching mechanisms, 2) an advanced microcontroller, and 3) GPS at GEO. In particular, two wavefront sensing algorithms, phase retrieval by JPL and phase diversity by ERIM International, were to sense optical system alignment and focus errors, and to correct them using high-precision mirror mechanisms. Active corrections based on Earth scenes are challenging because phase diversity images must be collected from extended, dynamically changing scenes. In addition, an Earth-facing telescope in GEO orbit is subject to a powerful diurnal thermal and radiometric cycle not experienced by deep-space astronomy. The Horizon proposal was a bare-bones design for a lightweight large-aperture, active optical system that is a practical blend of science requirements, emerging technologies, budget constraints, launch vehicle considerations, orbital mechanics, optical hardware, phase-determination algorithms, communication strategy, computational burdens, and first-rate cooperation among earth and space scientists, engineers and managers. This manuscript presents excerpts from the Horizon proposal's sections that describe the Earth science requirements, the structural -thermal-optical design, the wavefront sensing and control, and the on-orbit validation.