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Computational simulation of coupled material degradation processes for probabilistic lifetime strength of aerospace materials

The research included ongoing development of methodology that provides probabilistic lifetime strength of aerospace materials via computational simulation. A probabilistic material strength degradation model, in the form of a randomized multifactor interaction equation, is postulated for strength degradation of structural components of aerospace propulsion systems subjected to a number of effects or primative variables. These primative variable may include high temperature, fatigue or creep. In most cases, strength is reduced as a result of the action of a variable. This multifactor interaction strength degradation equation has been randomized and is included in the computer program, PROMISS. Also included in the research is the development of methodology to calibrate the above described constitutive equation using actual experimental materials data together with linear regression of that data, thereby predicting values for the empirical material constraints for each effect or primative variable. This regression methodology is included in the computer program, PROMISC. Actual experimental materials data were obtained from the open literature for materials typically of interest to those studying aerospace propulsion system components. Material data for Inconel 718 was analyzed using the developed methodology.

Boyce, Lola↗

Computational technology for high-temperature aerospace structures

The status and some recent developments of computational technology for high-temperature aerospace structures are summarized. Discussion focuses on a number of aspects including: goals of computational technology for high-temperature structures; computational material modeling; life prediction methodology; computational modeling of high-temperature composites; error estimation and adaptive improvement strategies; strategies for solution of fluid flow/thermal/structural problems; and probabilistic methods and stochastic modeling approaches, integrated analysis and design. Recent trends in high-performance computing environment are described and the research areas which have high potential for meeting future technological needs are identified.

Noor, A. K.↗

Robust Informatics Infrastructure Required For ICME: Combining Virtual and Experimental Data

With the increased emphasis on reducing the cost and time to market of new materials, the need for robust automated materials information management system(s) enabling sophisticated data mining tools is increasing, as evidenced by the emphasis on Integrated Computational Materials Engineering (ICME) and the recent establishment of the Materials Genome Initiative (MGI). This need is also fueled by the demands for higher efficiency in material testing; consistency, quality and traceability of data; product design; engineering analysis; as well as control of access to proprietary or sensitive information. Further, the use of increasingly sophisticated nonlinear, anisotropic and or multi-scale models requires both the processing of large volumes of test data and complex materials data necessary to establish processing-microstructure-property-performance relationships. Fortunately, material information management systems have kept pace with the growing user demands and evolved to enable: (i) the capture of both point wise data and full spectra of raw data curves, (ii) data management functions such as access, version, and quality controls;(iii) a wide range of data import, export and analysis capabilities; (iv) data pedigree traceability mechanisms; (v) data searching, reporting and viewing tools; and (vi) access to the information via a wide range of interfaces. This paper discusses key principles for the development of a robust materials information management system to enable the connections at various length scales to be made between experimental data and corresponding multiscale modeling toolsets to enable ICME. In particular, NASA Glenn's efforts towards establishing such a database for capturing constitutive modeling behavior for both monolithic and composites materials

Mutli-scale models↗

Establishing and Maintaining the Digital Thread of Additively Manufactured Materials and Applications

Additive Manufacturing (AM) and Integrated Computational Materials Engineering (ICME) are complementary enabling technologies for design and manufacturing of “fit-for-purpose” materials. Both technologies will impact rapid material design, reduction in cost- and time-to-market for new applications, and discovery and implementation of new materials. An ICME approach to design, however, requires experimentally validated material models at multiple length and time scales, an integrated framework that can connect analysis tools with one another to ensure the digital thread of an application is maintained, and the manufacturing (e.g., AM) capability to leverage processing-structure-property-performance (PSPP) relationships to achieve spatially varying material properties where desired. AM enables the implementation of the design of an optimized, spatially varying microstructure through careful selection of the processing parameters used during an additively manufactured build. In order to establish these PSPP relations, a large amount of data is necessary, and that data must be properly captured, analyzed and maintained in an information management system that can establish the required traceability between various aspects of the design process to ensure an application’s digital thread is maintained (from design to end of life). Such an information management system must be able to capture feedstock material pedigree, resulting microstructure from various build parameters, subsequent mechanical properties derived from testing, developed material models, and enable spatial variations in material assignment in an engineering application. Furthermore, the information management system should be easily integrated with traditionally engineered materials in a single, centralized platform to enable an ICME optimization tool to explore both types of manufacturing processes. At NASA GRC, a robust, 21st century materials information management system has been previously developed with a focus towards enabling ICME. In this work, GRC’s ICME schema is extended to accommodate additively manufactured materials, enabling storage of both traditionally and additively manufactured materials in the same construct. The methodology for properly capturing additively manufactured materials across the entire material lifecycle is presented, following the previously established database best practices, as a potential framework for establishing PSPP relationships for additively manufactured materials and applying them to engineering applications.

Data management↗

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↗

Database Design Strategies for Coordinated Simulation and Testing in Additive Manufacturing

The qualification and certification (Q&C) process presents a significant challenge for widespread adoption of additive manufacturing (AM) materials and processes for aerospace applications. A relational database framework will be presented as a tool for data curation of coordinated experimental and computational materials modeling research activities. A comparison of relational and hierarchical data structures in this domain will be emphasized through the evolution of a database design strategy. This framework’s mission is to support the advancement of computational materials-informed Q&C by providing the necessary data infrastructure to trace reliability and reproducibility measures through unified AM materials simulation and experimental testing. FAIR (findable, accessible, interoperable, and reusable) data will be highlighted as a necessary precursor for automation of specific actions, which ultimately reduces the time and expense burden for Q&C. The discussion will be mostly limited to back-end design elements, though a few front-end user experience examples will also be shared.

Qualification↗

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↗

Advances and trends in computational structures technology

The major goals of computational structures technology (CST) are outlined, and recent advances in CST are examined. These include computational material modeling, stochastic-based modeling, computational methods for articulated structural dynamics, strategies and numerical algorithms for new computing systems, multidisciplinary analysis and optimization. The role of CST in the future development of structures technology and the multidisciplinary design of future flight vehicles is addressed, and the future directions of CST research in the prediction of failures of structural components, the solution of large-scale structural problems, and quality assessment and control of numerical simulations are discussed.

Noor, A. K.↗

Innovative Materials for Aircraft Morphing

Reported herein is an overview of the research being conducted within the Materials Division at NASA Langley Research Center on the development of smart material technologies for advanced airframe systems. The research is a part of the Aircraft Morphing Program which is a new six-year research program to develop smart components for self-adaptive airframe systems. The fundamental areas of materials research within the program are computational materials; advanced piezoelectric materials; advanced fiber optic sensing techniques; and fabrication of integrated composite structures. This paper presents a portion of the ongoing research in each of these areas of materials research.

Simpson, J. O.↗

Recent advances in engineering science; Proceedings of the A. Cemal Eringen Symposium, University of California, Berkeley, June 20-22, 1988

Various papers on recent advances in engineering science are presented. Some individual topics addressed include: advances in adaptive methods in computational fluid mechanics, mixtures of two medicomorphic materials, computer tests of rubber elasticity, shear bands in isotropic micropolar elastic materials, nonlinear surface wave and resonator effects in magnetostrictive crystals, simulation of electrically enhanced fibrous filtration, plasticity theory of granular materials, dynamics of viscoelastic media with internal oscillators, postcritical behavior of a cantilever bar, boundary value problems in nonlocal elasticity, stability of flexible structures with random parameters, electromagnetic tornadoes in earth's ionosphere and magnetosphere, helicity fluctuations and the energy cascade in turbulence, mechanics of interfacial zones in bonded materials, propagation of a normal shock in a varying area duct, analytical mechanics of fracture and fatigue.

Koh, Severino L.↗

Alloy Design Workbench-Surface Modeling Package Developed

NASA Glenn Research Center's Computational Materials Group has integrated a graphical user interface with in-house-developed surface modeling capabilities, with the goal of using computationally efficient atomistic simulations to aid the development of advanced aerospace materials, through the modeling of alloy surfaces, surface alloys, and segregation. The software is also ideal for modeling nanomaterials, since surface and interfacial effects can dominate material behavior and properties at this level. Through the combination of an accurate atomistic surface modeling methodology and an efficient computational engine, it is now possible to directly model these types of surface phenomenon and metallic nanostructures without a supercomputer. Fulfilling a High Operating Temperature Propulsion Components (HOTPC) project level-I milestone, a graphical user interface was created for a suite of quantum approximate atomistic materials modeling Fortran programs developed at Glenn. The resulting "Alloy Design Workbench-Surface Modeling Package" (ADW-SMP) is the combination of proven quantum approximate Bozzolo-Ferrante-Smith (BFS) algorithms (refs. 1 and 2) with a productivity-enhancing graphical front end. Written in the portable, platform independent Java programming language, the graphical user interface calls on extensively tested Fortran programs running in the background for the detailed computational tasks. Designed to run on desktop computers, the package has been deployed on PC, Mac, and SGI computer systems. The graphical user interface integrates two modes of computational materials exploration. One mode uses Monte Carlo simulations to determine lowest energy equilibrium configurations. The second approach is an interactive "what if" comparison of atomic configuration energies, designed to provide real-time insight into the underlying drivers of alloying processes.

Abel, Phillip B.↗

Convolution-Based Numerical Solutions of Transient Temperature Fields during Powder Bed Fusion Additive Manufacturing: Theory, Accuracy, and Computational Cost

Powder bed fusion (PBF) additive manufacturing has found numerous applications in the aerospace domain. However, components fabricated via PBF have a complex time-temperature history that significantly impacts subsequent mechanical performance. This study examines convolution-based numerical solutions of transient temperature fields that support simulations involving arbitrary beam shapes and paths during PBF. The convolutional approach is verified through comparisons with analytical solutions of the temperature field. The computational speed and accuracy of the method are assessed through comparisons with other explicit and implicit numerical techniques. In addition, the straightforward translation of the approach from a CPU to a GPU implementation and the resultant performance improvement are presented. The role of the technique in predicting microstructure evolution during PBF (for a greater process-structure-property-performance framework) is also demonstrated. This work supports the development of computational materials methods for understanding and controlling the time-temperature history during PBF.

powder bed fusion↗

SA-GAT-SR: self-adaptable graph attention networks with symbolic regression for high-fidelity material property prediction

Recent advances in machine learning have demonstrated an enormous utility of deep learning approaches, particularly Graph Neural Networks (GNNs) for materials science. These methods have emerged as powerful tools for high-throughput prediction of material properties, offering a compelling enhancement and alternative to traditional first-principles calculations. While the community has predominantly focused on developing increasingly complex and universal models to enhance predictive accuracy, such approaches often lack physical interpretability and insights into materials behavior. Here, we introduce a novel computational paradigm—Self-Adaptable Graph Attention Networks integrated with Symbolic Regression (SA-GAT-SR)—that synergistically combines the predictive capability of GNNs with the interpretative power of symbolic regression. Our framework employs a self-adaptable encoding algorithm that automatically identifies and adjust attention weights so as to screen critical features from an expansive 180-dimensional feature space while maintaining O(n) computational scaling. The integrated SR module subsequently distills these features into compact analytical expressions that explicitly reveal quantum-mechanically meaningful relationships, achieving 23 × acceleration compared to conventional SR implementations that heavily rely on first-principle calculations-derived features as input. This work suggests a new framework in computational materials science, bridging the gap between predictive accuracy and physical interpretability, offering valuable physical insights into material behavior.

36 MATERIALS SCIENCE↗

Control of Permanent Porosity in Type 3 Porous Liquids via Solvent Clustering

Porous liquids (PLs) are an exciting new class of materials for carbon capture due to their high gas adsorption capacity and ease of industrial implementation. They are composed of sorbent particles suspended in a nonadsorbed solvent, forming a liquid with permanent porosity. While PLs have a vast number of potential compositions based on the number of solvents and sorbent materials available, most of the research has been focused on the selection of the sorbent rather than the solvent. Therefore, PL design criteria on the supramolecular structures of the solvent are explored to create a fundamental understanding of how the solvent enables PL formation for rapid discovery of new PL compositions. Atomistic molecular dynamics simulation of eight solvents with a range of molecular sizes, shapes, and intramolecular bonding was performed, identifying that the shape and size of molecular clusters formed in the solvent are the driving predictor of PL formation rather than the size of the individual solvent molecule. The results demonstrate a significant departure from common approaches to PL formation based on the steric exclusion of solvent molecules from the sorbent via the size of the pore aperture. A modeling and experimental validation study further supports these findings. In conclusion, through this computational material design study, a previously unexplored mechanism in PL formation, solvent–solvent clustering, is identified as a critical factor for the accelerated discovery of liquid phase carbon capture materials.

Carbon capture↗

Anomaly Detection in Materials Digital Twins with Multiscale ICME for Additive Manufacturing

Detecting anomaly in fatigue and fracture experimental materials science is an interesting yet challenging topic. The reasons are threefold. First, the anomalous microstructure feature that gives rise to structural failure is small, sometimes in the order of 10 -7 of the interrogated volume. This, in turn, results in a highly imbalanced classification problem in machine learning (ML). Second, the consequence is high, in the sense that the test specimen is destructed in such case. Third, the convolution between microstructure stochasticity and the small probability of void nucleation, growth, and coalescence makes failure and fracture a hard-to-predict and challenging problem in materials science due to its irreproducibility, even experimentally. In this paper, we developed a materials digital twin and applied anomaly detection methods to detect voids and anomaly in additive manufacturing (AM). The materials digital twin is driven by two integrated computational materials engineering (ICME) models, which are kinetic Monte Carlo (kMC) and crystal plasticity finite element method (CPFEM). In conclusion, we demonstrated that by using anomaly detection, it is possible to detect voids and other defects in materials digital twin, which paves way for future research in integrating materials digital twin with its physical counterpart.

ICME↗

Materials Development: Pitfalls, Successes, and Lessons

The incorporation of new or improved materials in aerospace systems, or indeed any systems, can yield tremendous payoffs in the system performance or cost, and in many cases can be enabling for a mission or concept. However, the availability of new materials requires advance development, and too often this is neglected or postponed, leaving a project or mission with little choice. In too many cases, the immediate reaction is to use what was used before; this usually turns out not to be possible and results in large sums of money, and amounts of time, being expended on reinvention rather than development of a material with extended capabilities. Material innovation and development is time consuming, with some common wisdom claiming that the timeline is at least 20 years. This time expands considerably when development is stopped and restarted, or knowledge is lost. Down selection of materials is necessary, especially as the Technical Readiness Level (TRL) increases. However, the costs must be considered and approaches should be taken to retain knowledge and allow for restarting the development process. Regardless of the exact time required, it is clear that it is necessary to have materials, at all stages of development, in a research and development pipeline and available for maturation as required. This talk will discuss some of theses issues, including some of the elements for a development path for materials. Some history of materials developments will be included. The usefulness of computational materials science, as a route to decreasing material development time, will be an important element of this discussion. Collaboration with outside institutions and nations is also critical for innovation, but raises the issues of intellectual property and protections, and national security (ITAR rules, for example).

Johnson, Sylvia M.↗

Material Response Modeling of Ablative Thermal Protection Systems using PATO

Developing thermal protection systems (TPS) for future space vehicles involves an extensive design and test cycle. A key component of the cycle is determining margins for a safe design that rely on confidence in the performance of the TPS. Predicting the complicated multiphysics phenomena that occur during atmospheric entry requires high-fidelity modeling tools. To this end, the Porous-material Analysis Toolbox based on OpenFOAM (PATO) has been developed. PATO is an open-source software for computing material response of reactive porous materials submitted to high-temperature environments. Recent applications of PATO include computation of the full heatshield 3D material response from the Mars Science Laboratory atmospheric entry and ablation during arc jet testing. Current efforts are underway to loosely couple PATO with other discipline specialized codes including hypersonic computational fluid dynamics (CFD) to assess the effects of pyrolysis-gas blowing into the boundary layer and computational solid mechanics to address modeling of mechanical erosion. Surface phenomena modeling capabilities to address the effects of silicone-based coatings applied to the TPS during flight preparation are also being added.

Thermal Protection Systems↗

Material Response Modeling of Ablative Thermal Protection Systems using PATO

Developing thermal protection systems (TPS) for future space vehicles involves an extensive design and test cycle. A key component of the cycle is determining margins for a safe design that rely on confidence in the performance of the TPS. Predicting the complicated multiphysics phenomena that occur during atmospheric entry requires high-fidelity modeling tools. To this end, the Porous-material Analysis Toolbox based on OpenFOAM (PATO) has been developed. PATO is an open-source software for computing material response of reactive porous materials submitted to high-temperature environments. Recent applications of PATO include computation of the full heatshield 3D material response from the Mars Science Laboratory atmospheric entry and ablation during arc jet testing. Current efforts are underway to loosely couple PATO with other discipline specialized codes including hypersonic computational fluid dynamics (CFD) to assess the effects of pyrolysis-gas blowing into the boundary layer and computational solid mechanics to address modeling of mechanical erosion. Surface phenomena modeling capabilities to address the effects of silicone-based coatings applied to the TPS during flight preparation are also being added.

Thermal Protection Systems↗