Results for “materials characterization” — Page 16

NASA NTRS2020

Technology Advancements for Channel Wall Nozzle Manufacturing In Liquid Rocket Engines

A regeneratively-cooled or dump-cooled nozzle is a critical component for expansion of hot gases to enable high temperature and performance in liquid rocket engines systems. Regeneratively-cooled channel wall nozzles are a design solution used across the propulsion industry as a simplified method to fabricate the nozzle structure with internal coolant passages. The scale and complexity of the channel wall nozzle (CWN) design can be challenging to fabricate which results in extended lead times and higher costs. Some of these challenges include: 1) Unique and high temperature materials, 2) Tight tolerances on large parts during manufacturing and assembly to contain high pressure propellants, 3) Thin-walled features to maintain adequate wall temperatures, and 4) Unique manufacturing process operations and complex tooling. The United States (U.S.) National Aeronautics and Space Administration (NASA) and U.S. specialty manufacturing vendors are maturing modern fabrication techniques to reduce complexity and decrease costs associated with channel wall nozzle manufacturing technology. Additive Manufacturing (AM) is one of the key technology advancements under evaluation for channel wall nozzles. Much of additive manufacturing for propulsion components has focused on laser powder bed fusion (L-PBF), but the scale is not yet feasible for application to large scale nozzles. NASA is evolving directed energy deposition (DED) techniques for nozzles including arc-based deposition, blown powder deposition, and Laser Wire Direct Closeout (LWDC). There are different approaches being considered for fabrication of the nozzle, and each of these DED processes offer unique process steps for rapid fabrication. The arc-based and blown powder deposition techniques are used for the forming of the CWN liner. A variety of materials are being demonstrated including Inconel 625, Haynes 230, JBK-75, and NASA HR-1. The blown powder DED process is also being demonstrated for forming an integral channel nozzle in a single operation in similar materials. The LWDC process is a method for closing out the channels within the liner and forming the structural jacket using a localized laser wire deposition technique. Identical materials mentioned above have been used for this process in addition to bimetallic closeout (C-18150–SS347, and C-18150–Inconel 625). NASA has completed process development, material characterization, and hot-fire testing on a variety of these channel wall nozzle fabrication technique. This publication presents an overview of the various channel wall nozzle manufacturing processes and materials under evaluation including results from the hot-fire testing. Future development and technology focus areas is also discussed relative to channel wall nozzle manufacturing.

Additive manufacturing↗

NASA NTRSDate not supplied

Bond Line Thickness Estimation in Composite Structures Using Multiple Inspection Techniques

Imaging and other nondestructive evaluation techniques are commonly used for material characterization and defect recognition in safety critical aerospace applications, with data fusion providing the framework for uncertainty quantification in these contexts. Most commonly, forward physics-based modeling predicts the response conditioned on material properties and defect assumptions, and probabilistic methods are used to infer the hidden state of the subject of the inspection from a combination of prior information, likelihoods, and inspection data. In this paper Bayesian methods are used to estimate bond thickness in lap joints comprised of aluminum adherends using a combination of infrared thermography and ultrasound. The concept of the conflation of probability distributions is applied to combine the posterior distributions derived from thermography and ultrasound and the quality of the fused estimates are compared against the individual estimates against synthetic data that was created to mimic the inspection of a lap joint comprised of aluminum adherends.

thermal nondestructive evaluation↗

NASA NTRSDate not supplied

Bond Line Thickness Estimation in Composite Structures Using Multiple Inspection Techniques

Imaging and other nondestructive evaluation techniques are commonly used for material characterization and defect recognition in safety critical aerospace applications, with data fusion providing the framework for uncertainty quantification in these contexts. Most commonly, forward physics-based modeling predicts the response conditioned on material properties and defect assumptions, and probabilistic methods are used to infer the hidden state of subject of the inspection from a combination of prior information, likelihoods, and inspection data. In this paper Bayesian methods are used to estimate bond thickness in lap joints comprised of aluminum adherends using a combination of infrared thermography, digital radiography, and ultrasound. The accuracy of the fused estimates are validated against data generated from synthetic specimens, and by comparison against high resolution X-ray computed tomography inspections of built specimens.

thermal nondestructive evaluation↗

NASA NTRSDate not supplied

Recent Advances in LIBS for Real-Time Detection of Silicone Contaminants on CFRP Surfaces

Laser induced breakdown spectroscopy (LIBS) is a materials characterization technique that has been advanced and refined to provide in situ, real-time quality control of carbon fiber reinforced polymer (CFRP) surfaces. As part of an effort to improve process control for adhesive bonding, a LIBS system was designed and assembled at NASA Langley Research Center (LaRC) to enable detection of ultralow concentrations of silicone contamination on CFRP adherends. The LIBS instrument provides high sensitivity detection and quasi-nondestructive surface characterization of CFRP adherends prior to adhesive bonding. This review focuses on the advancements at LaRC using LIBS as a quality control tool for the detection of silicone contaminants for improved adhesive bonding reliability of CFRP materials. This work describes how the LIBS technique was advanced by analyzing the laser parameters, studying the laser-matter interactions, and performing time-resolved measurements to determine the optimal plasma conditions for elemental detection. In addition, LIBS results were compared to X-ray photoelectron spectroscopy data to enable quantitative analysis from analytical calibration curves. Examples are shown of how to perform surface mapping of silicone contamination. Finally, a brief discussion is also presented on LIBS instrumentation, recommendations for laser parameters and instrumentation components, and LIBS technique maturity.

Surface characterization, Carbon fiber reinforced ↗

NASA NTRSDate not supplied

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗

NASA NTRS2023

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗

NASA NTRS2016

Investigation of Desiccants and CO2 Sorbents for Advanced Exploration Systems 2015-2016

Advanced Environmental Control and Life Support System (ECLSS) design is critical for human space flight beyond Earth. Current systems enable extended missions in low-Earth orbit, but for deep-space missions, not only will astronauts be outside the reach of resupply operations from Earth but they will also need to handle malfunctions and compensate for the degradation of materials. These two daunting challenges must be overcome for long-term independent space flight. In order to solve the first, separation and reuse of onboard atmosphere components is required. Current systems utilize space vacuum to fully regenerate adsorbent beds, but this is not sustainable thus necessitating a closed-loop system. The second challenge stems from material and performance degradation due to operational cycling and on-board contaminants. This report will review the recent work by the ECLSS team at Marshall Space Flight Center towards overcoming these challenges by characterizing materials via novel methods for use in future systems.

Cmarik, Gregory E.↗

NASA NTRS2017

Investigation of Desiccants and CO2 Sorbents for Advanced Exploration Systems 2016-2017

Advanced Environmental Control and Life Support System (ECLSS) design is critical for manned space flight beyond Earth. Current systems enable extended missions in low-Earth orbit, but for deep-space missions, not only will astronauts be outside the reach of resupply operations from Earth but they will also need to handle malfunctions and compensate for the degradation of materials. These two daunting challenges must be overcome for long-term independent space flight. In order to solve the first, separation and recycling of onboard atmosphere is required. Current systems utilize space vacuum to fully regenerate CO2 sorbent beds, but this is not sustainable. The second challenge stems from material and performance degradation due to operational cycling and on-board contaminants. This report will review the recent work by the ECLSS team at Marshall Space Flight Center towards overcoming these challenges by characterizing materials via novel methods and by assessing new air revitalization systems.

Knox, Jim↗

NASA NTRS2017

Material Analysis and System Design for Exploration Life Support Systems 2017

Advanced Environmental Control and Life Support System (ECLSS) design is critical for manned space flight beyond Earth. Current systems enable extended missions in low-Earth orbit, but for deep-space missions, not only will astronauts be outside the reach of resupply operations from Earth but they will also need to handle malfunctions and compensate for the degradation of materials. These two daunting challenges must be overcome for long-term independent space flight. In order to solve the first, separation and recycling of onboard atmosphere is required. Current systems utilize space vacuum to fully regenerate CO2 sorbent beds, but this is not sustainable. The second challenge stems from material and performance degradation due to operational cycling and on-board contaminants. This report will review the recent work by the ECLSS team at Marshall Space Flight Center towards overcoming these challenges by characterizing materials via novel methods and by assessing new air revitalization systems.

Knox, Jim↗

NASA NTRS2019

Recent Advancements in Modeling and Simulation of Entry Systems at NASA

This paper describes recent development of modeling and simulation technologies for entry systems in support of NASA's exploration missions. Mission-tailored research and development in modeling of entry systems occurs across the Agency (e.g., within the Orion and Mars 2020 Programs), however the aim of this paper is to discuss the broad, cross-mission research conducted by NASA's Entry Systems Modeling (ESM) Project, which serves as the Agency's only concerted effort toward advancing entry systems across a range of technical disciplines. Technology development in ESM is organized and prioritized from a system-level perspective, resulting in four broad technical areas of investment: (1) Predictive material modeling, (2) Shock layer kinetics and radiation, (3) Computational and experimental aerosciences, and (4) Guidance, navigation, and control. Investments in thermal protection material modeling are geared toward high-fidelity, predictive models capable of handling complex structures, with an eye toward optimizing design performance and quantifying thermal protection system reliability. New computational tools have been developed to characterize material properties and behavior at the microstructural level, and experimental techniques (molecular beam scattering, micro-computed tomography, among others) have been developed to measure material kinetics, morphology, and other parameters needed to inform and validate detailed simulations. Advancements have also been made in macrostructural simulation capability to enable 3-D system-scale calculations of material response with complex topological features, including differential recession of tile gaps. Research and development in the area of shock layer kinetics has focused on air and CO2-based atmospheres. Capacity and capability of the NASA Ames Electric Arc Shock Tube (EAST) have been expanded in recent years and analysis of resulting data has led to several improvements in kinetic models, while simultaneously reducing uncertainties associated with radiative heat transfer predictions. First-principles calculations of fundamental kinetic, thermodynamic, and transport data, along with state-specific models for non-equilibrium flow regimes, have also yielded new insights and have the potential to vastly improve model fidelity. Aerosciences is a very broad area of interest in entry systems, yet a number of important challenges are being addressed: Coupled fluid-structure simulations of parachute inflation and dynamics; Experimental and computational studies of vehicle dynamics; Multi-phase flow with dust particles to simulate entry environments at Mars during dust storms; Studies of roughness-induced heating augmentation relevant to tiled and woven thermal protection systems; and Advanced numerical methods to optimize computational analyses for desired accuracy versus cost. Guidance and control in the context of entry systems has focused on development of methods for multi-axis control (i.e. pitch and yaw, rather than bank angle alone) of spacecraft during entry and descent. With precision landing requirements driven by Mars human exploration goals, recent efforts have yielded 6-DOF models of multi-axis control with propulsive descent of both inflatable and rigid ellipsled-like architectures.

Barnhardt, Michael D.↗

NASA NTRSDate not supplied

Recent Advances in LIBS for Real-Time Detection of Silicone Contaminants on CFRP Surfaces

Laser induced breakdown spectroscopy (LIBS) is a materials characterization technique that has been advanced and refined to provide in situ, real-time quality control of carbon fiber reinforced polymer (CFRP) surfaces. As part of an effort to improve process control for adhesive bonding, aLIBS system was designed and assembled at the NASA Langley Research Center (LaRC) to enable detection of ultra low concentrations of silicone contamination on CFRP adherends. The LIBS instrument provides high sensitivity detection and quasi-nondestructive surface characterization ofCFRP adherends prior to adhesive bonding. This review focuses on the advancements at LaRC using LIBS as a quality control tool for the detection of silicone contaminants for improved adhesive bonding reliability of CFRP materials. This work describes how the LIBS technique was advanced by analyzing the laser parameters, studying the laser-matter interactions, and performing time-resolved measurements to determine the optimal plasma conditions for elemental detection. In addition, LIBS results were compared to X-ray photoelectron spectroscopy data to enable quantitative analysis from analytical calibration curves. Examples are shown of how to perform surface mapping of silicone contamination. Finally, a brief discussion is presented on LIBS instrumentation, recommendations for laser parameters and instrumentation components, and LIBS technique maturity

Rodolfo I. Ledesma↗

NASA NTRS2021

Recent Advancements in Modeling and Simulation of Entry Systems at NASA

This paper describes recent development of modeling and simulation technologies for entry systems in support of NASA’s exploration missions. Mission-tailored research and development in modeling of entry systems occurs across the Agency (e.g., within the Orion and Mars 2020 Programs), however the aim of this paper is to discuss the broad, cross-mission research conducted by NASA’s Entry Systems Modeling (ESM) Project, which serves as the Agency’s only concerted effort toward advancing entry systems across a range of technical disciplines. Technology development in ESM is organized and prioritized from a system-level perspective, resulting in four broad technical areas of investment: (1) Predictive material modeling, (2) Shock layer kinetics and radiation, (3) Computational and experimental aerosciences, and (4) Guidance, navigation, and control. Investments in thermal protection material modeling are geared toward high-fidelity, predictive models capable of handling complex structures, with an eye toward optimizing design performance and quantifying thermal protection system reliability. New computational tools have been developed to characterize material properties and behavior at the microstructural level, and experimental techniques (molecular beam scattering, micro-computed tomography, among others) have been developed to measure material kinetics, morphology, and other parameters needed to inform and validate detailed simulations. Advancements have also been made in macrostructural simulation capability to enable 3-D system-scale calculations of material response with complex topological features, including differential recession of tile gaps. Research and development in the area of shock layer kinetics has focused on air and CO2-based atmospheres. Capacity and capability of the NASA Ames Electric Arc Shock Tube (EAST) have been expanded in recent years and analysis of resulting data has led to several improvements in kinetic models, while simultaneously reducing uncertainties associated with radiative heat transfer predictions. First-principles calculations of fundamental kinetic, thermodynamic, and transport data, along with state-specificmodels for non-equilibrium flow regimes, have also yielded new insights and have the potential to vastly improve model fidelity. Aerosciences is a very broad area of interest in entry systems, yet a number of important challenges are being addressed: Coupled fluid-structure simulations of parachute inflation and dynamics; Experimental and computational studies of vehicle dynamics; Multi-phase flow with dust particles to simulate entry environments at Mars during dust storms; Studies of roughness-induced heating augmentation relevant to tiled and woven thermal protection systems; and Advanced numerical methods to optimize computational analyses for desired accuracy versus cost. Guidance and control in the context of entry systems has focused on development of methods for multi-axis control (i.e. pitch and yaw, rather than bank angle alone) of spacecraft during entry and descent. With precision landing requirements driven by Mars human exploration goals, recent efforts have yielded 6-DOF models of multi-axis control with propulsive descent of both inflatable and rigid ellipsled-like architectures. Results for both configurations have demonstrated the ability to land within the 50-meter precision requirement demanded by Mars human exploration missions, while also reducing propellant requirements by enabling more efficient control through entry and descent. Ongoing research in GN&C is developing mechanical specifications for the systems and establishing engineering feasibility.

aerothermodynamics↗

NASA NTRSDate not supplied

Overview of Ablative TPS Modeling at NASA Ames

Over the past decade, NASA has invested in efforts to build predictive thermal protection system (TPS) material models from the micro-scale to the macro-scale. To complement the mission design cycle process and reduce the need for extensive testing, NASA is developing modeling and simulation tools that enable characterizing material properties and response to hot plasma experienced during atmospheric entry. Traditional material response and ablation modeling tools, such as the heritage code FIAT, and its multidimensional siblings, TITAN and 3dFIAT, are being complemented with newly developed software such as Icarus and PATO. Both of these programs are three-dimensional, finite-volume solvers that use unstructured meshes and 21st century programming paradigms to allow for efficient parallel simulations. FIAT and Icarus are also used for TPS sizing purposes. Today, these traditional tools are being supplemented with computational materials models at the atomistic level. The scales of interest range from computational chemistry (Density Functional Theory [DFT]), to atomistic simulations (Molecular Dynamics [MD]), to the microscale with the Porous Microstructure Analysis (PuMA) software that was recently awarded the 2022 NASA Software of the Year award. Finally, thermo-structural modeling is also of interest to the TPS Materials branch and done using commercial tools such as MSC MARC, MENTAT, NASTRAN and PATRAN. The present talk will also link the use of these computational tools to current NASA missions and projects associated with challenging and complex vehicles entries/reentries.

materials modeling↗

NASA NTRS1976

Basic principles of material selection in fracture-critical applications

A concept of material characterization which shows the relation between initial crack size, stress range, and life to failure is presented. This presentation is used to determine if a particular application is fracture-critical and to choose a suitable material. The influence of design stress inaccuracies on the material selection is assessed.

Elber, W.↗

NASA NTRS1990

Probabilistic high cycle fatigue failure analysis with application to liquid propellant rocket engines

A probabilistic high cycle fatigue (HCF) failure analysis of a welded duct in a rocket engine of the Space Shuttle main engine class is described. A state-of-the-art HCF failure prediction method was used in a Monte Carlo simulation to generate a distribution of failure lives. A stochastic stress/life model is used for material characterization, and a composite stress history is generated for accurately deriving the stress cycles for the fatigue-damage calculations. The HCF failure model expresses fatigue life as a function of stochastic parameters including environment, loads, material properties, geometry, and model specification errors. A series of HCF failure life analyses were performed to study the impact of a fixed parameter and to assess the importance of each stochastic input parameter through marginal analyses.

Sutharshana, S.↗

NASA NTRS1980

Simulation of transducer-couplant effects on broadband ultrasonic signals

The increasing use of broadband, pulse-echo ultrasonics in nondestructive evaluation of flaws and material properties has generated a need for improved understanding of the way signals are modified by coupled and bonded thin-layer interfaces associated with transducers. This understanding is most important when using frequency spectrum analyses for characterizing material properties. In this type of application, signals emanating from material specimens can be strongly influenced by couplant and bond-layers in the acoustic path. Computer synthesized waveforms were used to simulate a range of interface conditions encountered in ultrasonic transducer systems operating in the 20 to 80 MHz regime. The adverse effects of thin-layer multiple reflections associated with various acoustic impedance conditions are demonstrated. The information presented is relevant to ultrasonic transducer design, specimen preparation, and couplant selection.

Vary, A.↗

NASA NTRS1980

Simulation of transducer-couplant effects on broadband ultrasonic signals

The increasing use of broadband, pulse-echo ultrasonics in nondestructive evaluation of flaws and material properties has generated a need for improved understanding of the way signals are modified by coupled and bonded thin-layer interfaces associated with transducers. This understanding is most important when using frequency spectrum analyses for characterizing material properties. In this type of application, signals emanating from material specimens can be strongly influenced by couplant and bond-layers in the acoustic path. Computer synthesized waveforms were used to simulate a range of interface conditions encountered in ultrasonic transducer systems operating in the 20to 80-MHz regime. The adverse effects of thin-layer multiple reflections associated with various acoustic impedance conditions are demonstrated. The information presented is relevant to ultrasonic transducer design, specimen preparation, and couplant selection.

Vary, A.↗

NASA NTRSDate not supplied

Updates to the Predictive Materials Modeling Software Tools

Updates on NASA's efforts to build predictive material models from the micro-scale to the macro-scale are presented. To complement the mission design cycle process and reduce the need for extensive testing, NASA is developing modeling and simulation tools that enable characterizing material properties and response to hot plasma experienced during atmospheric entry. The PuMA (microstructure analysis), PATO (macroscale material response), SPARTA (direct simulation Monte Carlo) and ARCHeS (arc heater modeling) codes are described. A range of applications, encompassing calculating effective material properties, study of high shear boundary layer flow over woven materials, new model of silicone-based coatings, new model of mechanical erosion, as well as approach to loose multi-physics coupling, are presented.

material modeling↗