SEARCH · Engineering Papers

Results for “icme”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5

All sources NASA NTRS DOE OSTI

NASA NTRS2022

Constraints on Axions from Cosmic Distance Measurements

Axion couplings to photons could induce photon-axion conversion in the presence of magnetic fields in the Universe. This conversion could impact various cosmic distance measurements, such as luminosity distances to type Ia supernovae and angular distances to galaxy clusters, in different ways. In this paper we consider different combinations of the most up-to-date distance measurements to constrain the axion-photon coupling. Employing the conservative cell magnetic field model for the magnetic fields in the intergalactic medium (IGM) and ignoring the conversion in the intracluster medium (ICM), we find the upper bounds on axion-photon couplings to be around 5 × 10^(−12) (nG/B) √(Mpc/s) Ge/V for axion masses m(a) below 10^(−13) eV, where B is the strength of the IGM magnetic field, and s is the comoving size of the magnetic domains. When including the conversion in the ICM, the upper bound is lowered and could reach 5 × 10^(−13) Ge/V for m(a) < 5 × 10^(−12) eV. While this stronger bound depends on the ICM modeling, it is independent of the strength of the IGM magnetic field, for which there is no direct evidence yet. These constraints could be placed on firmer footing with an enhanced understanding and control of the astrophysical uncertainties associated with the IGM and ICM. All the bounds are determined by the shape of the Hubble rate as a function of redshift reconstructable from various distance measurements, and insensitive to today’s Hubble rate, of which there is a tension between early and late cosmological measurements. As an appendix, we discuss the model building challenges of the use of photon-axion conversion to make type Ia supernovae brighter to alleviate the Hubble problem/crisis.

Cosmology of Theories beyond the SM↗

NASA NTRS2023

Space Weather Investigation Frontier (SWIFT)

The Space Weather Investigation Frontier (SWIFT) mission will aim at making major discoveries on the three-dimensional structure and dynamics of heliospheric structures that drive space weather. The focus will be on Interplanetary Coronal Mass Ejections (ICMEs) that originate from massive expulsions of plasma and magnetic flux from the solar corona. They cause the largest geomagnetic storms and solar energetic particle events, threatening to endanger life and disrupt technology on Earth and in space. A big current problem, both regarding fundamental solar-terrestrial physics and space weather, is that we do not yet understand spatial characteristics and temporal evolution of ICMEs and that the existing remote-sensing and in-situ observatories are not suited for resolving multi-layered and evolutionary structures in these massive storm drivers. Here, we propose a groundbreaking mission concept study using solar sail technology that, for the first time, will make continuous, in-situ multi-point observations along the Sun-Earth line beyond the Lagrange point L1 (sub-L1). This unique position, in combination with L1 assets, will allow distinguishing between local and global processes, spatial characteristics, temporal evolution, and particle energization mechanisms related to ICMEs. In addition, measurements of the magnetic field in earthbound ICMEs and their sub-structures from the SWIFT location will double the current forecasting lead-times from L1. This concept also paves the way for missions with increasingly longer forecasting lead-times, addressing NASA and NOAA’s space weather goals, as set forth by the Decadal Survey. The objective of this communication is to inform the community of the ongoing effort, including plans to further develop the mission concept, supported by the Heliophysics Flight Opportunities Studies (HFOS) program under NASA’s Research Opportunities in Space and Earth Sciences (ROSES).

M. Akhavan-Tafti↗

NASA NTRS2024

XLSSC 122 Caught in the Act of Growing Up: Spatially Resolved SZ Observations of a z=1.98 Galaxy Cluster

Context. How protoclusters evolved from sparse galaxy overdensities to mature galaxy clusters is still not well understood. In this context, detecting and characterizing the hot intracluster medium (ICM) at high redshifts (z ∼ 2) is key to understanding how the continuous accretion from the filamentary large-scale structure and the mergers along it impact the first phases of cluster formation. Aims. We study the dynamical state and morphology of the z = 1:98 galaxy cluster XLSSC 122 with high-resolution observations (≈5″) of the ICM through the Sunyaev–Zeldovich (SZ) effect. XLSSC 122 is the highest redshift optically confirmed galaxy cluster found in an unbiased, widefield survey. Methods. Via Bayesian forward modeling, we mapped the ICM on scales from the virial radius down to the core of the cluster. To constrain such a broad range of spatial scales, we employed a new technique that jointly forward-models parametric descriptions of the pressure distribution to interferometric ACA and ALMA observations and multiband imaging data from ACT. Results. We detect the SZ effect with 11σ significance in the ALMA+ACA observations and find a flattened inner pressure profile that is consistent with a noncool core classification with a significance of ≥3σ. In contrast to the previous works, we find better agreement between the SZ effect signal and the X-ray emission as well as the cluster member distribution. Further, XLSSC 122 exhibits an excess of SZ flux in the south of the cluster where no X-ray emission is detected. By reconstructing the interferometric observations and modeling in the uv-plane, we obtain a tentative detection of an infalling group or filamentary-like structure in the southeast that is believed to boost and heat up the ICM while the density of the gas is still low. In addition to characterizing the dynamical state of the cluster, we provide an improved SZ mass estimate M500,c = 1.66−0.20+0.23 × 1014 M⊙ . Conclusions. Altogether, the observations indicate that we see XLSSC 122 in a dynamic phase of cluster formation while a large reservoir of gas is already thermalized.

large-scale structure of Universe↗

NASA NTRSDate not supplied

Materials Informatics at NASA GRC: Machine Learning Surrogate Modeling, Data Management, and Integrated Toolsets for Establishing/Maintaining the Digital Thread

Machine Learning↗

NASA NTRSDate not supplied

Py MILab: Capturing, Analyzing and Storing Test Data

Integrated Computational Materials Engineering (ICME) has recently received widespread attention due to its promises in reducing dependence on physical testing for engineering design by relying on simulation, reducing both time and cost to market for various applications. ICME however requires validated multiscale material models, which is heavily dependent on available test data with full material and test pedigree, including material processing, test and measurement equipment, raw data collection, and analysis methodology and results. Populating searchable information management systems with such rich data sets is often burdensome for data producers, resulting in a lack of findable data for modelers to validate and verify their models. To overcome these cultural barriers to ICME, NASA has developed of various database-integration toolsets that perform both data management activities within the organization’s best practices with additional functionality that relieves the effort of the data producer and promotes adoption of information management system. One such tool currently under development is Py MILab, an automatic framework for automatic capturing, analysis, maintenance, and storage of material test data. Py MILab uses a modular approach for capturing raw data, analyzing the data, and storing the data in a database, interfaced by neutral file structures, to promote plug-and-play capabilities for various analysis types. TMAnalysis is a Python-based tool that performs automatic data reduction and analysis of uniaxial thermomechanical test data. The TMAnalysis toolset can be implemented within the Analysis module of Py MILab, and thus requires a populated neutral file form the Raw Data Module of Py MILab and outputs a Analysis neutral file compatible with the Database Module of Py MILab. TMAnalysis is able to perform automatic segmentation of multistage tests and perform data analysis and reduction, including determination of point-wise properties in tension, compression, and shear, analysis of stress relaxation tests, creep analysis and zone identification, and combination of these stage types for tests with complex loading histories. The TMAnalysis code is accompanied with a graphical user interface (GUI) that allows users to easily analyze test data in bulk, verify the automatic, consistent analysis performed by the backend code, and edit stage segmentation if necessary before producing the output neutral files, ensuring data is properly analyzed and maintained with full traceability.

Data management↗

NASA NTRS1990

Origin and dynamics of emission line clouds in cooling flow environments

The author suggests that since clouds are born co-moving in a turbulent intra-cluster medium (ICM), the allowed parameter space can now be opened up to a more acceptable range. Large-scale motions can be driven in the central parts of cooling flows by a number of mechanisms including the motion of the central and other galaxies, and the dissipation of advected, focussed rotational and magnetic energy. In addition to the velocity width paradox, two other paradoxes (Heckman et al. 1989) can be solved if the ICM is turbulent. Firstly, the heating source for the emission line regions has always been puzzling - line luminosities are extremely high for a given (optical or radio) galaxy luminosity compared to those in non-cooling flow galaxies, therefore a mechanism peculiar to cooling flows must be at work. However most, if not all, previously suggested heating mechanisms either fail to provide enough ionization or give the wrong line ratios, or both. The kinetic energy in the turbulence provides a natural energy source if it can be efficiently converted to cloud heat. Researchers suggest that this can be done via magneto-hydrodynamic waves through plasma slip. Secondly, while the x ray observations indicate extended mass deposition, the optical line emission is more centrally concentrated. Since many of the turbulence-inducing mechanisms are strongest in the central regions of the ICM, so is the method of heating. In other words material is dropping out everywhere but only being lit up in the center.

Loewenstein, Michael↗

NASA NTRS1994

Detections of hard X-ray emissions from bright early-type galaxies with ASCA

Five bright elliptical galaxies in the Virgo Cluster, NGC 4365, NGC 4374 (M84), NGC 4406 (M86), NGC 4472 (M49), and NGC 4636, were observed with ASCA. In addition to the extended thermal X-ray emission of temperature kT approximately 1 keV, harder X-rays with color temperature kT greater than or equal to 2 keV were detected from all of them. The 2-10 keV luminosities of this hard component for the five galaxies, integrated within 5 min, are distributed within a relatively narrow range of (1-4) x 10(exp 40) ergs/s. The hard X-ray component is primarily attributed to the integrated emission from discrete X-ray sources. In NGC 4406 and NGC 4374 the data indicate that the hard component is contributed additionally by foreground/background emission from the hot intracluster medium (ICM) of the Virgo Cluster. The hard component of NGC 4472 seems also contributed by the Virgo ICM emission, but in this case there is evidence that the ICM brightness is locally enhanced within approximately 10 min of NGC 4472.

Matsushita, K.↗

NASA NTRS1995

Radio jet propagation and wide-angle tailed radio sources in merging galaxy cluster environments

The intracluster medium (ICM) within merging clusters of galaxies is likely to be in a violent or turbulent dynamical state which may have a significant effect on the evolution of cluster radio sources. We present results from a recent gas + N-body simulation of a cluster merger, suggesting that mergers can result in long-lived, supersonic bulk flows, as well as shocks, within a few hundred kiloparsecs of the core of the dominant cluster. These results have motivated our new two-dimensional and three-dimensional simulations of jet propagation in such environments. The first set of simulations models the ISM/ICM transition as a contact discontinuity with a strong velocity shear. A supersonic (M(sub j) = 6) jet crossing this discontinuity into an ICM with a transverse, supersonic wind bends continuously, becomes 'naked' on the upwind side, and forms a distended cocoon on the downwind side. In the case of a mildly supersonic jet (M(sub j) = 3), however, a shock is driven into the ISM and ISM material is pulled along with the jet into the ICM. Instabilities excited at the ISM/ICM interface result in the jet repeatedly pinching off and reestablishing itself in a series of 'disconnection events.' The second set of simulations deals with a jet encountering a shock in the merging cluster environment. A series of relatively high-resolution two-dimensional calculations is used to confirm earlier analysis predicting that the jet will not disrupt when the jet Mach number is greater than the shock Mach number. A jet which survives the encounter with the shock will decrease in radius and disrupt shortly thereafter as a result of the growth of Kelvin-Helmholtz instabilities. We also find, in disagreement with predictions, that the jet flaring angle decreases with increasing jet density. Finally, a three-dimensional simulation of a jet crossing an oblique shock gives rise to a morphology which resembles a wide-angle tailed radio source with the jet flaring at the shock and disrupting to form a long, turbulent tail which is dragged downstream by the preshock wind.

Loken, Chris↗

NASA NTRS2003

Importance of mitral valve repair associated with left ventricular reconstruction for patients with ischemic cardiomyopathy: a real-time three-dimensional echocardiographic study

BACKGROUND: Left ventricular (LV) reconstruction surgery leads to early improvement in LV function in ischemic cardiomyopathy (ICM) patients. This study was designed to evaluate the impact of mitral valve (MV) repair associated with LV reconstruction on LV function 1-year after surgery in ICM patients assessed by real-time 3-dimensional echocardiography (3DE). METHODS AND RESULTS: Sixty ICM patients who underwent the combination surgery (LV reconstruction in 60, MV repair in 30, and revascularization in 52 patients) were studied. Real-time 3DE was performed and LV volumes were obtained at baseline, discharge, 6-month and >or=12-month follow-up. Reduction in end-diastolic volumes (EDV) by 29% and in end-systolic volumes by 38% were demonstrated immediately after surgery and remained at subsequent follow-up (P<0.0001). The LV ejection fraction significantly increased by about 10% at discharge and was maintained >or=12-month (P<0.0001). Although the LV volumes were significantly larger in patients with MV repair before surgery (EDV, 235+/-87 mL versus 193+/-67 mL, P<0.05), they were similar to LV volumes of the patients without MV repair at subsequent follow-ups. However, the EDV increased from 139+/-24 mL to 227+/-79 mL (P<0.01) in 7 patients with recurrent mitral regurgitation (MR). Improvement in New York Heart Association functional class occurred in 81% patients during late follow-up. CONCLUSIONS: Real-time 3DE demonstrates that LV reconstruction provides significant reduction in LV volumes and improvement in LV function which is sustained throughout the 1-year follow-up with 84% cardiac event free survival. If successful, MV repair may prevent LV redilation, while recurrent MR is associated with increased LV volumes.

Clinical Trial↗

NASA NTRS2004

The Fraction of Interplanetary Coronal Mass Ejections That Are Magnetic Clouds: Evidence for a Solar Cycle Variation

"Magnetic clouds" (MCs) are a subset of interplanetary coronal mass ejections (ICMEs) characterized by enhanced magnetic fields with an organized rotation in direction, and low plasma beta. Though intensely studied, MCs only constitute a fraction of all the ICMEs that are detected in the solar wind. A comprehensive survey of ICMEs in the near- Earth solar wind during the ascending, maximum and early declining phases of solar cycle 23 in 1996 - 2003 shows that the MC fraction varies with the phase of the solar cycle, from approximately 100% (though with low statistics) at solar minimum to approximately 15% at solar maximum. A similar trend is evident in near-Earth observations during solar cycles 20 - 21, while Helios 1/2 spacecraft observations at 0.3 - 1.0 AU show a weaker trend and larger MC fraction.

Richardson, I. G.↗

NASA NTRS2004

In-Situ Solar Wind and Magnetic Field Signatures of Interplanetary Coronal Mass Ejections

The heliospheric counterparts of coronal mass ejections (CMEs) at the Sun, interplanetary coronal mass ejections (ICMEs), can be identified in-situ based on a number of magnetic field, plasma, compositional and energetic particle signatures, as well as combinations thereof. Although many of these signatures have been recognized since the early space era, recent observations from improved instrumentation on spacecraft such as Ulysses, Wind, and ACE, in conjunction with solar observations from SOHO, have advanced our understanding of the characteristics of ICMEs and their solar counterparts. We summarize these signatures and their implications for understanding the nature of these structures and the physical properties of coronal mass ejections. We conclude that our understanding of ICMEs is far from complete, and formulate several challenges that, if addressed, would substantially improve our knowledge of the relationship between CMEs at the Sun and in the heliosphere.

Zurbuchen, Thomas H.↗

NASA NTRS2007

CMEs and Low Density Events in the Solar Wind

From time to time the density of the Solar Wind falls below unity for a day or so. Despite studies of these events, their cause has not been established. In the solar wind there are frequent rarefactions but these are much shallower than the ones we are discussing here. In particular any role that CMEs might play in forming more extreme rarefactions is not clear . We discuss 9 cases of extreme events, leading to sub-alfvenic flow and well observed by Wind, which occurred between 1999 and 2002, and look for corresponding CME events in the LASCO cotalog. The catalog gives a fitted speed , good out to 20 Rs ,as well as a speed lower down in the corona, and this is used to distinguish between a CME and an ICME. We find one of the events ,which was simulated by Usmanov et al.,cannot be associated with an ICME. The other eight events can plausibly be associated with fast ICMEs, identified by the prescence of bi-streaming electrons also seen by Wind SWE, and by other phenomena The implications of these observations are discussed but do not seem to be conclusive as to the cause of the extreme rarefactions.

Ogilvie, K. W.↗

NASA NTRS2012

What We Have Learned About Clusters From a Decade of Arcsecond Resolution X-ray Observations

This talk will briefly review the main findings from Chandra high angular resolution observations of galaxy clusters, emphasizing results on cluster astrophysics. Chandra has discovered shock fronts in merging systems, providing information on the shock Mach number and velocity, and for best-observed shocks, constraining the microphysical properties of the intracluster medium (ICM). Cold fronts, a Chandra discovery, are ubiquitous both in merging clusters and in the cool ccres of relaxed systems. They reveal the structure and strength of the intracluster magnetic fields and constrain the ICM viscosity a combined with radio data, these observations also shed light on the production of ultra-relativistic particles that are known to coexist with thermal plasma. Finally, in nearly all cool cores, Chandra observes cavities in the ICM that are produced by the central AGN. All these phenomena will be extremely interesting for high-resolution SZ studies.

Markevitch, Maxim↗

NASA NTRS2012

Toward the Probabilistic Forecasting of High-latitude GPS Phase Scintillation

The phase scintillation index was obtained from L1 GPS data collected with the Canadian High Arctic Ionospheric Network (CHAIN) during years of extended solar minimum 2008-2010. Phase scintillation occurs predominantly on the dayside in the cusp and in the nightside auroral oval. We set forth a probabilistic forecast method of phase scintillation in the cusp based on the arrival time of either solar wind corotating interaction regions (CIRs) or interplanetary coronal mass ejections (ICMEs). CIRs on the leading edge of high-speed streams (HSS) from coronal holes are known to cause recurrent geomagnetic and ionospheric disturbances that can be forecast one or several solar rotations in advance. Superposed epoch analysis of phase scintillation occurrence showed a sharp increase in scintillation occurrence just after the arrival of high-speed solar wind and a peak associated with weak to moderate CMEs during the solar minimum. Cumulative probability distribution functions for the phase scintillation occurrence in the cusp are obtained from statistical data for days before and after CIR and ICME arrivals. The probability curves are also specified for low and high (below and above median) values of various solar wind plasma parameters. The initial results are used to demonstrate a forecasting technique on two example periods of CIRs and ICMEs.

scintillation↗

NASA NTRS2012

A New Method to Constrain Supernova Fractions Using X-ray Observations of Clusters of Galaxies

Supernova (SN) explosions enrich the intracluster medium (ICM) both by creating and dispersing metals. We introduce a method to measure the number of SNe and relative contribution of Type Ia supernovae (SNe Ia) and core-collapse supernovae (SNe cc) by directly fitting X-ray spectral observations. The method has been implemented as an XSPEC model called snapec. snapec utilizes a single-temperature thermal plasma code (apec) to model the spectral emission based on metal abundances calculated using the latest SN yields from SN Ia and SN cc explosion models. This approach provides a self-consistent single set of uncertainties on the total number of SN explosions and relative fraction of SN types in the ICM over the cluster lifetime by directly allowing these parameters to be determined by SN yields provided by simulations. We apply our approach to XMM-Newton European Photon Imaging Camera (EPIC), Reflection Grating Spectrometer (RGS), and 200 ks simulated Astro-H observations of a cooling flow cluster, A3112.We find that various sets of SN yields present in the literature produce an acceptable fit to the EPIC and RGS spectra of A3112. We infer that 30.3% plus or minus 5.4% to 37.1% plus or minus 7.1% of the total SN explosions are SNe Ia, and the total number of SN explosions required to create the observed metals is in the range of (1.06 plus or minus 0.34) x 10(exp 9), to (1.28 plus or minus 0.43) x 10(exp 9), fromsnapec fits to RGS spectra. These values may be compared to the enrichment expected based on well-established empirically measured SN rates per star formed. The proportions of SNe Ia and SNe cc inferred to have enriched the ICM in the inner 52 kiloparsecs of A3112 is consistent with these specific rates, if one applies a correction for the metals locked up in stars. At the same time, the inferred level of SN enrichment corresponds to a star-to-gas mass ratio that is several times greater than the 10% estimated globally for clusters in the A3112 mass range.

Observations↗

NASA NTRS2022

A Robust Machine Learning Schema for Developing, Maintaining, and Disseminating Machine Learning Models

Recent advances in the development of machine learning (ML) algorithms have enabled the creation of predictive models that can improve decision making, decrease computational cost, and improve efficiency in a variety of fields. As an organization begins to develop and implement such models, the data used in the training, validation, and testing of ML models, the model parameters, and the use cases or limitations of the models must be properly stored to ensure models are both fully traceable and used correctly. In the context of predicting material behavior, advances in computationally intense, physics-based modeling of material behavior at various length scales and the emergence of Integrated Computational Materials Engineering (ICME) have driven the need for developing data-driven surrogate models of the physics-based simulation tools using ML techniques. Surrogate model development allows for accurate material behavior prediction at a fraction of the cost of its physics-based counterpart, allowing for multiscale simulations of real-world applications, further enabling the ability to design fit-for-purpose materials for a reasonable computational investment. However, training such models requires extensive data, and thus, effective data management is necessary to reach the full potential that ML can offer to material design and ICME. This paper proposes a generalized, robust schema that allows organizations to store both real (experimental) and virtual (simulation) data used to train ML models and the defining model parameters and architectures within the Granta MI Platform. The developed schema allows for various types of data inputs and outputs, including single point values, time-series data, and images that can be used in the prediction of material behavior, while following outlined best practices for effective data management. An effective schema for ML data and models can help prevent the recreation of virtual/real training data and surrogate models, help reduce the time to create new models similar to existing ones by offering a starting point in the hyperparameter determination stages, minimize resources devoted to verification and validation (V&V) and certification of models, and ensure that data and surrogate models are not misused due to full traceability of both the data and ML model. It also allows organizations access to models that have already been developed, such that they can be used in the design of new materials, enabling the overall goals of ICME.

Brandon L. Hearley↗

NASA NTRSDate not supplied

Predicting Fiber Failure of Plain Weave Fabric with Recursive Multiscale Micromechanics

Recent advances in the development of machine learning (ML) algorithms have enabled the creation of predictive models that can improve decision making, decrease computational cost, and improve efficiency in a variety of fields. As an organization begins to develop and implement such models, the data used in the training, validation, and testing of machine learning models, the model parameters, and the use cases or limitations of the models must be properly stored to ensure models are both fully traceable and used correctly. In the context of predicting material behavior, advances in computationally intense, physics-based, modeling of material behavior at various length scales, and the emergence of Integrated Computational Materials Engineering (ICME) have driven the need for developing data-driven surrogate models of the physics-based simulation tools using machine learning (ML) techniques. Surrogate model development allows for accurate material behavior prediction at a fraction of the cost of its physics-based counterpart, allowing for multiscale simulations of real-world applications, further enabling the ability to design fit-for-purpose materials for a reasonable computational investment. However, training such models requires extensive data, and thus effective data management is necessary to reach the full potential that ML can offer to material design and ICME. This paper proposes a generalized, robust schema that allows organizations to store both real (experimental) and virtual (simulation) data used to train machine learning models and the defining model parameters and architectures. The developed schema allows for various types of data inputs and outputs, including single point values, time-series data, and images that can be used in for various types of machine learning models while following outlined best practices for effective data management. An effective schema for machine learning data and models can help prevent the recreation of virtual/real training data and surrogate models, can help reduce the time to create new models similar to existing ones by offering a starting point in the hyperparameter determination stages, minimize resources devoted to verification and validation (V&V) and certification of models, and ensure that data and surrogate models are not misused due to full traceability of both the data and ML model. It also allows organizations access to models that have already been developed, such that they can be used in the design of new materials, enabling the overall goals of ICME.

Failure↗

NASA NTRSDate not supplied

Towards Integrated Computational Materials Engineering for Quantifying Performance Impacts of Microstructure and Defect Interactions in Powder Bed Fusion Parts

Powder bed fusion (PBF) additive manufacturing (AM) enables the creation of parts with complexity and functionality levels that were previously impossible with traditional manufacturing methods. By modifying the laser power, hatch spacing, or the numerous other processing parameters, the PBF process supports the production of a wide set of materials and geometries. However, that same process parameter design flexibility causes the process-design space of PBF to be massive and expensive to explore experimentally. Another challenge is quality variation across a build. As a part is being built, geometric variance between locations, such as at a thin-wall section vs. the bulk material, may cause the specified processing parameters to no longer be acceptable for producing defect-free printing. Furthermore, if the processing parameters deviate during the print process, it is difficult to assess if the part will still perform satisfactorily. Integrated Computational Materials Engineering (ICME) provides a way to understand and address these various challenges. This talk will present advancements in process-structure simulations of PBF at NASA Langley Research Center. The ability to simulate grain-scale PBF microstructures using the Physically Based Monte Carlo method will be demonstrated and compared to experimental measurements. Techniques for simulating three-dimensional lack-of-fusion and keyhole porosity defects based on the specific processing conditions and approaches for integrating the two porosity prediction techniques alongside the computational microstructure evolution models will be shown. Finally, the integration of simulated PBF microstructures, embedded process defects, and crystal plasticity finite element models to elucidate the interaction of porosity and microstructure on micromechanical fields will be demonstrated. These integrated techniques demonstrate an example of using ICME to relate processing to performance for PBF AM materials. With continued maturity, it is hoped that such ICME approaches will lead to next-generation computational-materials supported qualification and certification of AM parts.

Additive manufacturing↗