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NASA NTRSDate not supplied

An Integrated Design Tool for Tow-Steered Laminates of Composites in Abaqus and MSC.Patran/Nastran

Tow-steered composites can be tailored for optimal mechanical performance of lightweight structures. However, there are no commercial-grade design tools for tow-steered composite structures, which hinders the design innovation of tow-steered composites in realistic structures. The novelty of this paper is to develop an integrated design framework along with the development of graphical user interface (GUI) plug-ins in commercial finite element (FE) software Abaqus and MSC.Patran/Nastran. The GUI plug-ins take all the design setups and communicate with external codes for the material modeling and optimization, and hence provide a unified design environment within the FE codes. The mechanics of structure genome (MSG) plate model computes shell element properties based on user-defined fiber paths and layup, which are defined via the GUI plug-ins. The optimization is performed by an open-source code, Dakota, from Sandia National Laboratories (Sandia), which also coordinates the structural analysis, material modeling, and optimization in design iterations. Two examples are presented to demonstrate the user-friendliness and versatility of the developed GUI plug-ins. The developed tools will ease the design process and facilitate the application of tow-steered composites in realistic aerospace structures.

Xin Liu↗

NASA NTRSDate not supplied

Recent Advancements in the PATO Material Response Code

Introduction: Predicting the complicated multiphysics phenomena during atmospheric entry requires high-fidelity modeling tools to refine estimates of mission risks during entry. To this end, new capabilities are being added to the Porous-material Analysis Toolbox based on OpenFOAM (PATO). PATO is an open-source software for Computational Material Response (CMR) of reactive porous materials submitted to high-temperature environments. The objective of this work is to highlight current efforts to add to and improve upon the modeling capabilities of PATO. These include efforts to loosely couple PATO with other discipline specialized codes including hypersonic Computational Fluid Dynamics (CFD), to assess the interaction effects between pyrolysis gas blowing and the boundary layer, and Computational Solid Mechanics (CSM), to address modeling of mechanical erosion. Other refinements include surface phenomena modeling capabilities to address the effects of silicone-based coatings applied to the TPS during flight preparation, and a unified multiphase solver for a mixed porous-material and plain-fluid domain. Coupling CMR with CFD (CMR/CFD): A loose coupling between PATO and the Data Parallel Line Relaxation (DPLR) CFD code has been achieved by making use of a blowing boundary condition at the heatshield surface available in DPLR. Starting with heat flux estimates with no pyrolysis gas blowing at the surface, blowing gases are computed by the CMR and passed to the CFD such that aerothermal properties of the environment can be recomputed for a new CMR computation. This leads to an iterative process which is supplemented with an estimate of the radiative heat flux using the Nonequilibrium air radiation (NEQAIR) program. The entire iterative process is illustrated in Figure 1. This coupling strategy has been utilized in computing the MSL material response. The goal is to compare the coupled CMR/CFD results with material response results obtained using traditional blowing corrections. Coupling CMS with CMR: A mechanical erosion model is currently being implemented in PATO to account for the additional mass removal induced by high shear conditions. The modeling process at each timestep consists of updating the mechanical properties as a function of temperature and computing the stress tensor and displacement fields of the material. Then, a failure criteria model determines the regions in which the stress exceeds the ultimate strength values resulting in mesh movement to account for mass removal. This model allows the material response simulation to compute the recession due to both oxidation and shear-induced erosion. The model is demonstrated by computing material response of sphere-cone arc jet samples. Surface Modeling Capabilities: NuSil, a silicone-based coating, was sprayed onto the MSL and Mars 2020 heatshields to mitigate shedding of phenolic dust. To better understand the effects of the NuSil coating on the material response, a novel model has been implemented in PATO. In this model, the equilibrium of the charred NuSil surface is modeled as pure silica, and a constant offset, inspired by the classical spallation model, is added to the the char blowing rate and wall enthalpy to reproduce HyMETS experimental results. The model has also been used to estimate the 3D material response of the MSL heatshield. Unified Solver: In addition to the iterative loose coupling approach mentioned above, a multiphase unified solver is being developed to couple the environment (plain-fluid phase) and the porous-material phase. The solver is based on the volume averaged conservation of mass, momentum, and energy for the macroscale with closure models which include microscale effects through effective physicochemical properties. The unified solver has been used to compute flow through a porous plug and solve the Beavers and Joseph problem. Since the strong coupling between phases is inherent to this solver, modeling assumptions present in other coupling methods of material response are mitigated. This strategy also makes it feasible to capture the competition between surface and volume ablation in the same computational domain, which is usually not possible with other coupling approaches.

Material Response↗

NASA NTRS2014

Prediction of the Aero-Acoustic Performance of Open Rotors

The rising cost of jet fuel has renewed interest in contrarotating open rotor propulsion systems. Contemporary design methods offer the potential to maintain the inherently high aerodynamic efficiency of open rotors while greatly reducing their noise output, something that was not feasible in the 1980's designs. The primary source mechanisms of open rotor noise generation are thought to be the front rotor wake and tip vortex interacting with the aft rotor. In this paper, advanced measurement techniques and high-fidelity prediction tools are used to gain insight into the relative importance of the contributions to the open rotor noise signature of the front rotor wake and rotor tip vortex. The measurements include three-dimensional particle image velocimetry of the intra-rotor flowfield and the acoustic field of a model-scale open rotor. The predictions provide the unsteady flowfield and the associated acoustic field. The results suggest that while the front rotor tip vortex can have a significant influence on the blade passing tone noise produced by the aft rotor, the front rotor wake plays the decisive role in the generation of the interaction noise produced as a result of the unsteady aerodynamic interaction of the two rotors. At operating conditions typical of takeoff and landing operations, the interaction noise level is easily on par with that generated by the individual rotors, and in some cases is even higher. This suggests that a comprehensive approach to reducing open rotor noise should include techniques for mitigating the wake of the front rotor as well as eliminating the interaction of the front rotor tip vortex with the aft rotor blade tip.

VanZante, Dale↗

NASA NTRS2014

Prediction of the Aero-Acoustic Performance of Open Rotors

Van Zante, Dale E.↗

NASA NTRS2022

VESIcal: An Open-source Thermodynamic Model Engine for Mixed Volatile (H2O-CO2) Solubility in Silicate Melts

Modeling the solubility of volatiles in silicate melts is fundamental to the interpretation of volcanic systems and has implications for magma dynamics, eruption style, and material transport between the mantle, crust, and atmosphere. Recent advancements in computational capabilities and access to computing tools has outpaced the functionality and extensibility of previously available modeling platforms. Here we present VESIcal (Volatile Equilibria and Saturation Index calculator), the first comprehensive modeling tool for H2O, CO2, and mixed (H2O-CO2) solubility in silicate melts that: a) allows users access to seven popular models, with easy inter-comparison between models; b) provides universal functionality for all models (e.g., functions for calculating saturation pressures, degassing paths, etc.); c) can process large datasets (1,000’s of samples) automatically; d) can output computed data into an Excel spreadsheet or CSV file for post-modeling analysis; e) integrates plotting capabilities directly within the tool; and f) provides all of this within the framework of a python library, making the tool extensible by the user and allowing any of the model functions to be incorporated into any other code capable of calling python.Here we will provide a demonstration of VESIcal and its capabilities with applications to various volcanic processes affected by volatiles. VESIcal represents the first tool capable of directly comparing multiple solubility models and equations of state. We find that commonly used models predict surprisingly different volatile solubilities, particularly for pure CO2 or mixed CO2-H2O fluids. Even for melt compositions that are well represented in the calibration datasets of multiple models (e.g., MORBs), calculated solubilities for pure CO2 and pure H2O can deviate from one another by factors of >2 leading to 2x deviations in calculated saturation pressures (e.g., 5 to 10 kbar). The solubility of CO2 predicted by different rhyolitic models also differs substantially, overwhelming other sources of uncertainty such as analytical errors on measurements of volatile contents or uncertainties in crustal density profiles. This highlights the importance of model choice when drawing geological conclusions based on volatiles in magmas.

Kayla Iacovino↗

NASA NTRS2004

Using the GeoFEST Faulted Region Simulation System

GeoFEST (the Geophysical Finite Element Simulation Tool) simulates stress evolution, fault slip and plastic/elastic processes in realistic materials, and so is suitable for earthquake cycle studies in regions such as Southern California. Many new capabilities and means of access for GeoFEST are now supported. New abilities include MPI-based cluster parallel computing using automatic PYRAMID/Parmetis-based mesh partitioning, automatic mesh generation for layered media with rectangular faults, and results visualization that is integrated with remote sensing data. The parallel GeoFEST application has been successfully run on over a half-dozen computers, including Intel Xeon clusters, Itanium II and Altix machines, and the Apple G5 cluster. It is not separately optimized for different machines, but relies on good domain partitioning for load-balance and low communication, and careful writing of the parallel diagonally preconditioned conjugate gradient solver to keep communication overhead low. Demonstrated thousand-step solutions for over a million finite elements on 64 processors require under three hours, and scaling tests show high efficiency when using more than (order of) 4000 elements per processor. The source code and documentation for GeoFEST is available at no cost from Open Channel Foundation. In addition GeoFEST may be used through a browser-based portal environment available to approved users. That environment includes semi-automated geometry creation and mesh generation tools, GeoFEST, and RIVA-based visualization tools that include the ability to generate a flyover animation showing deformations and topography. Work is in progress to support simulation of a region with several faults using 16 million elements, using a strain energy metric to adapt the mesh to faithfully represent the solution in a region of widely varying strain.

Geophyical Finite Element Simulation Tool (GeoFEST↗

NASA NTRSDate not supplied

Recent Advancements in the PATO Material Response Code

Introduction: Predicting the complicated multiphysics phenomena during atmospheric entry requires high-fidelity modeling tools to refine estimates of mission risks during entry. To this end, new capabilities are being added to the Porous-material Analysis Toolbox based on OpenFOAM (PATO) [1,2,3]. PATO is an open-source software for Computational Material Response (CMR) of reactive porous materials submitted to high-temperature environments. The objective of this work is to highlight current efforts to add to and improve upon the modeling capabilities of PATO. These include efforts to loosely couple PATO with other discipline specialized codes including hypersonic Computational Fluid Dynamics (CFD), to assess the interaction effects between pyrolysis gas blowing and the boundary layer, and Computational Solid Mechanics (CSM), to address modeling of mechanical erosion. Other refinements include surface phenomena modeling capabilities to address the effects of silicone-based coatings applied to the TPS during flight preparation, and a unified multiphase solver for a mixed porous-material and plain-fluid domain. Coupling CMR with CFD (CMR/CFD): A loose coupling between PATO and the Data Parallel Line Relaxation (DPLR) [4] CFD code has been achieved by making use of a blowing boundary condition at the heatshield surface available in DPLR. Starting with heat flux estimates with no pyrolysis gas blowing at the surface, blowing gases are computed by the CMR and passed to the CFD such that aerothermal properties of the environment can be recomputed for a new CMR computation. This leads to an iterative process which is supplemented with an estimate of the radiative heat flux using the Nonequilibrium air radiation (NEQAIR) [5] program. The entire iterative process is illustrated in Figure 1. This coupling strategy has been utilized in computing the MSL material response. The goal is to compare the coupled CMR/CFD results with material response results obtained using traditional blowing corrections. Coupling CMS with CMR: A mechanical erosion model is currently being implemented in PATO to account for the additional mass removal induced by high shear conditions. The modeling process at each timestep consists of updating the mechanical properties as a function of temperature and computing the stress tensor and displacement fields of the material. Then, a failure criteria model determines the regions in which the stress exceeds the ultimate strength values resulting in mesh movement to account for mass removal. This model allows the material response simulation to compute the recession due to both oxidation and shear-induced erosion. The model is demonstrated by computing material response of sphere-cone arc jet samples. Surface Modeling Capabilities: NuSil, a silicone-based coating, was sprayed onto the MSL and Mars 2020 heatshields to mitigate shedding of phenolic dust. To better understand the effects of the NuSil coating on the material response, a novel model has been implemented in PATO. In this model, the equilibrium of the charred NuSil surface is modeled as pure silica, and a constant offset, inspired by the classical spallation model, is added to the the char blowing rate and wall enthalpy to reproduce HyMETS experimental results. The model has also been used to estimate the 3D material response of the MSL heatshield [6]. Unified Solver: In addition to the iterative loose coupling approach mentioned above, a multiphase unified solver is being developed to couple the environment (plain-fluid phase) and the porous-material phase. The solver is based on the volume averaged conservation of mass, momentum, and energy for the macroscale with closure models which include microscale effects through effective physicochemical properties. The unified solver has been used to compute flow through a porous plug and solve the Beavers and Joseph problem [7]. Since the strong coupling between phases is inherent to this solver, modeling assumptions present in other coupling methods of material response are mitigated. This strategy also makes it feasible to capture the competition between surface and volume ablation in the same computational domain, which is usually not possible with other coupling approaches.

Thermal Protection Systems↗

NASA NTRS2022

Maldives Climate: Monitoring Shoreline Changes and Island Loss in Response to Climate Change

Global sea level rise as a result of climate change continues to pose a critical threat to coastal ecosystems and populations. The archipelagic country of the Maldives is of critical concern due to being one of the lowest lying areas in the world. The development of reclaimed land in the Maldives by sand dredging has been a frequent response to both increasing sea levels and population increase. Such disturbance can lead to increased sedimentation off the coast and negatively impact coastal environments. Remote sensing tools such as satellite imagery have proved to be an effective tool in observing coastal changes in response to climate change and development. NASA DEVELOP created a methodology to analyze both water quality and shoreline erosion in the Maldives utilizing satellite imagery. Methods relied on open-source software such as QGIS and Google Earth Engine (GEE) and Satellite Imagery from PlanetScope, Landsat 8 Operational Land Instrument (OLI), Sentinel-2 Multi Spectral Instrument (MSI), and Aqua & Terra Moderate Resolution Imaging Radiospectrometer (MODIS) to analyze the changes in shorelines and assess water quality of select atolls within the Maldives. Findings show less shoreline change in developed parts of the island and more shoreline change in natural parts of the island. Additionally, water quality varies throughout the year and our data did not indicate seasonal trends. The methodology will be replicated to continue to monitor island erosion and water quality with the Maldives and will be applicable to other island and coastal systems.

remote sensing↗

NASA NTRSDate not supplied

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.

machine learning↗

NASA NTRS2023

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be easily expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.

machine learning↗

NASA NTRS2010

The General Mission Analysis Tool (GMAT): Current Features And Adding Custom Functionality

The General Mission Analysis Tool (GMAT) is a software system for trajectory optimization, mission analysis, trajectory estimation, and prediction developed by NASA, the Air Force Research Lab, and private industry. GMAT's design and implementation are based on four basic principles: open source visibility for both the source code and design documentation; platform independence; modular design; and user extensibility. The system, released under the NASA Open Source Agreement, runs on Windows, Mac and Linux. User extensions, loaded at run time, have been built for optimization, trajectory visualization, force model extension, and estimation, by parties outside of GMAT's development group. The system has been used to optimize maneuvers for the Lunar Crater Observation and Sensing Satellite (LCROSS) and ARTEMIS missions and is being used for formation design and analysis for the Magnetospheric Multiscale Mission (MMS).

Conway, Darrel J.↗

NASA NTRSDate not supplied

Open Science for Climate at NASA

The US National Aeronautics and Space Administration (NASA) currently provides more than 75 Petabytes of open access data through NASA’s Earth Science Data and Information System (ESDIS). Many of these datasets are commonly used in WCRP research, including the Global Precipitation Measurement (GPM) mission dataset, the Soil Moisture Active Passive (SMAP) mission dataset, the IceBridge dataset, and the Atmospheric Infrared Sounder (AIRS) dataset. These datasets are essential tools for understanding patterns in precipitation, soil moisture, ice mass changes, atmospheric circulation, and their impacts on climate. NASA is also a leader in the White House OSTP’s “Federal Year of Open Science” in 2023, promoting open access and open source solutions.

Climate Data↗

NASA NTRSDate not supplied

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of trained machine-learning models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a Windows app that has been created to deploy trained machine-learning models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of machine-learning application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). Current version of the app focuses on the performance prediction of conventional turbofans. The app gets user input for a turbofan design, preprocesses the input data, and deploys trained machine-learning models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The machine-learning predictive models were built by employing supervised deep-learning algorithm to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these machine-learning models using the app shows that Aero-Engines AI is an easy-to-use and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage.

machine learning↗

NASA NTRS2013

Detecting and Characterizing Semantic Inconsistencies in Ported Code

Adding similar features and bug fixes often requires porting program patches from reference implementations and adapting them to target implementations. Porting errors may result from faulty adaptations or inconsistent updates. This paper investigates (1) the types of porting errors found in practice, and (2) how to detect and characterize potential porting errors. Analyzing version histories, we define five categories of porting errors, including incorrect control- and data-flow, code redundancy, inconsistent identifier renamings, etc. Leveraging this categorization, we design a static control- and data-dependence analysis technique, SPA, to detect and characterize porting inconsistencies. Our evaluation on code from four open-source projects shows that SPA can detect porting inconsistencies with 65% to 73% precision and 90% recall, and identify inconsistency types with 58% to 63% precision and 92% to 100% recall. In a comparison with two existing error detection tools, SPA improves precision by 14 to 17 percentage points.

Semantic Errors↗

NASA NTRSDate not supplied

Configuration Management of Distributed cFS Repositories for Multiple Projects

The presentation describes how we use Git and Jira tools to do configuration management (CM) of a multitude of Git repositories for various cFS products being used by multiple cFS projects. Our CM setup takes in considerations the followings: 1. A Git repository can come from an open-source site or an internal site; AND 2. A Git repository can be owned and maintained by an organization, where release milestones and planned changes are controlled internally; AND 3. A Git repository can be shared among projects, where each project uses a specific branch of the code base; AND 4. Tracking and tracing tasks/issues that affect multiple Git repositories. This presentation complements the talk on cFS latest updates, to be presented by Daniel Knutsen from Goddard Space Flight Center.

Tam M Ngo↗

NASA NTRS2015

Development and Preliminary Tests of an Open-Path Airborne Diode Laser Absorption Instrument for Carbon Dioxide

Carbon dioxide (CO2) is well known for its importance as an atmospheric greenhouse gas, with many sources and sinks around the globe. Understanding the fluxes of carbon into and out of the atmosphere is a complex and daunting challenge. One tool applied by scientists to measure the vertical flux of CO2 near the surface uses the eddy covariance technique, most often from towers but also from aircraft flying specific patterns over the study area. In this technique, variations of constituents of interest are correlated with fluctuations in the local vertical wind velocity. Measurement requirements are stringent, particularly with regard to precision, sensitivity to small changes, and temporal sampling rate. In addition, many aircraft have limited payload capability, so instrument size, weight, and power consumption are also important considerations. We report on the development and preliminary application of an airborne sensor for the measurement of atmospheric CO2. The instrument, modeled on the successful DLH (Diode Laser Hygrometer) series of instruments, has been tested in the laboratory and on the NASA DC-8 aircraft. Performance parameters such as accuracy, precision, sensitivity, specificity, and temporal response are discussed in the context of typical atmospheric variability and suitability for flux measurement applications. On-aircraft, in-flight data have been obtained and are discussed as well. Performance of the instrument has been promising, and continued flight testing is planned during 2016.

Diskin, Glenn S.↗

NASA NTRS2012

Deterministic Design Optimization of Structures in OpenMDAO Framework

Nonlinear programming algorithms play an important role in structural design optimization. Several such algorithms have been implemented in OpenMDAO framework developed at NASA Glenn Research Center (GRC). OpenMDAO is an open source engineering analysis framework, written in Python, for analyzing and solving Multi-Disciplinary Analysis and Optimization (MDAO) problems. It provides a number of solvers and optimizers, referred to as components and drivers, which users can leverage to build new tools and processes quickly and efficiently. Users may download, use, modify, and distribute the OpenMDAO software at no cost. This paper summarizes the process involved in analyzing and optimizing structural components by utilizing the framework s structural solvers and several gradient based optimizers along with a multi-objective genetic algorithm. For comparison purposes, the same structural components were analyzed and optimized using CometBoards, a NASA GRC developed code. The reliability and efficiency of the OpenMDAO framework was compared and reported in this report.

Coroneos, Rula M.↗

NASA NTRS2012

Deterministic Design Optimization of Structures in OpenMDAO Framework

Coroneos, Rula M.↗