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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.

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16 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Corrosion of Al-Fe self-pierce riveting joints with multiphysics-based modeling and experiments

Self-piercing riveting (SPR) is an extensively used joining technique to assemble dissimilar materials. However, this joining of the dissimilar materials can generate galvanic/crevice corrosion, which can drastically impact the mechanical properties and the service life of the joint. In this study, a multiphysics-based hybrid modeling approach is developed for the galvanic corrosion of the Al-Fe SPR joints, which can consider the corrosion initiation and corrosion evaluation jointly. Experimental studies are first performed to extract information regarding the corrosion initiation sites, corrosion evolution and overall corrosion induced material loss in SPR joints. The initiation information is then passed onto the multiphysics FE model thus making it a hybrid model. This model can help understand the influences of metal microstructure on the corrosion propagation while the morphology changes can also be analyzed. Using the developed hybrid modeling approach, thorough parametric studies can be performed to explore the coupled impacts of multiple corrosion factors on the corrosion behavior of the joints. The developed hybrid model is validated on the prediction of galvanic corrosion for the SPR joints by comparing with experimental observations.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A modified scheil approach for nucleation-dependent solidification pathways

As-solidified microstructures of near-eutectic alloys often contain multiple primary phases that are not expected from equilibrium phase diagrams. Such microstructures are caused by cooling-rate-dependent solidification pathways, a factor not captured by the Scheil–Gulliver model or variations thereof. Here, we present a model and algorithm that incorporate the critical nucleation undercooling for each solid phase into the Scheil–Gulliver model. We hypothesize that the non-equilibrium microstructure formation is primarily governed by a nucleation-competition mechanism. This mechanism accounts for both stable/metastable phase selection and primary-phase formation within eutectic regions driven by asymmetric nucleation barriers. The model is validated against a hypereutectic Al-Fe alloy, where it successfully reproduces the observed microstructural constituents, revealing the key dependencies of solidification microstructure on nucleation kinetics. Applicability to multicomponent systems is demonstrated through a hypereutectic Al–Fe–Si ternary alloy, where the model successfully predicts divorced eutectic microstructures and the associated oscillatory solidification pathways along univariant lines. As a result, the proposed framework establishes a nucleation-dependent computational approach for interpreting and predicting solidification microstructures.

Alloy design↗
DOE OSTI2024

Analytically differentiable metrics for phase stability

Here, in this work, a long-established but sparsely documented method of obtaining semi-analytic derivatives of thermodynamic properties with respect to equilibrium conditions is briefly reviewed and rigorously derived. This procedure is then leveraged to construct general forms of derivatives of the residual driving force, a metric for measuring phase stability used in CALPHAD model optimization, with respect to overall system and individual phase compositions. Applied examples – calculating heat capacity in the Al-Fe system, thermodynamic factors in the Nb-V-W system, and residual driving force derivatives in the Ni-Ti system – demonstrate the versatility, accuracy, and extensibility of this method. Using the developed method, residual driving force gradients can be applied directly in CALPHAD model optimizers, as well as in materials design frameworks, to identify regions of phase stability with an efficient, gradient-based approach.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Physics-informed machine learning assisted uncertainty quantification for the corrosion of dissimilar material joints

Jointing techniques like the Self-Piercing Riveting (SPR), Resistance Spot Welding (RSW) and Rivet-Weld (RW) joints are used for mass production of dissimilar material joints due to their high performance, short cycle time, and adaptability. However, the service life and safety usage of these joints can be largely impacted by the galvanic corrosion due to the difference in equilibrium potentials between the metals with the presence of electrolyte. Here, in this paper, we focus on Al-Fe galvanic corrosion and develop physics-informed machine learning based surrogate model for statistical corrosion analysis, which enables the reliability analysis of dissimilar material joints under corrosion environment. In this study, a physics-based finite element (FE) corrosion model has been developed to simulate the galvanic corrosion between a Fe cathode and an Al anode. Geometric and environmental factors including crevice gap, roughness of anode, conductivity, and the temperature of the electrolyte are investigated. Further, a thorough Uncertainty Quantification (UQ) analysis is conducted for the overall corrosion behavior of the Fe-Al joints. It is found that the electrolyte conductivity has the largest effects on the material loss and needs to be managed closely for better corrosion control. This will help in designing and manufacturing joints with improved corrosion performance.

42 ENGINEERING↗
DOE OSTI2022

Uncertainty Quantification for Dissimilar Material Joints Under Corrosion Environment

Abstract Self-Piercing Riveting (SPR) is one of the most commonly used methods for joining dissimilar materials in the automotive industry. These joints are popular due to their adaptability, high performance and short cycle time. However, since these joints involve two dissimilar materials, they are susceptible to galvanic corrosion in the presence of an electrolyte which is driven by the difference in the equilibrium potential of the metals. This can affect the safety and resilience of these joints. In this paper, we focus on galvanic corrosion in Al-Fe SPR joints. A Machine learning (ML) based surrogate model, which is based off of FE simulations, for statistical corrosion analysis is developed. This model enables the resilience and reliability analysis of SPR joints under corrosion environment. In this study, first a physics-based finite element (FE) corrosion model has been developed to simulate the galvanic corrosion between a Fe cathode and an Al anode of a SPR joint. This model takes into account the effect of the crystal microstructure of the Al anode and the precipitation of the corrosion product. Several geometric and environmental factors including crevice gap, roughness of anode, conductivity, pH and the temperature of the electrolyte that effect corrosion are investigated. A thorough Uncertainty Quantification (UQ) analysis is conducted for the overall corrosion behavior of the Fe-Al SPR joints using a novelistic Probabilistic Confidence-Based Adaptive Sampling (PCAS) technique. PCAS is used to train the surrogate model by identifying the critical sampling points and thus reducing the overall computational costs. It is found that the electrolyte temperature has the largest effects on the material loss and needs to be managed closely for better corrosion control. By understanding the corrosion performance and resultant uncertainty impact on SPR joints, the reliability and resilience of these joints can be improved.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Enhanced Phase Stability of Sm 2 (Fe, Al) 17 C x

Aluminum doping can improve the phase stability of metastable compound Sm 2 Fe 17 C x with a high carbon content (x > 1.5). We investigated the preferential site substitution of Al, chemical bonding, and structural stability in Sm 2 (Fe,Al) 17 C 3 using first-principle calculations. Our results reveal a strong correlation between the preferential substitution of Fe by Al and the atomic site chemical environment, which affects the overall phase stability. Specifically, Al preferentially occupies the 9d site in Sm 2 (Fe,Al) 17 C 3 . At the same time, Al prefers the site 6c in its parent phase Sm 2 (Fe,Al) 17 . Partial replacement of Fe with Al leads to a more negative formation energy, indicating enhanced thermodynamic stability. Crystal Orbital Hamilton Population (COHP) and Crystal Orbital Bond Index (COBI) analysis suggest that insertion of carbon weakens the bonding strength of Sm-Fe (18f) and Sm-Fe (18h), resulting in metastability of Sm 2 Fe 17 C x . Doping Al strengthens Al-Fe, Al-Sm, Sm-Fe (18f, 18h) and Fe–C bonding in Sm 2 (Fe,Al) 17 C 3 , as revealed by calculated COHP and COBI. These effects contribute to improved phase stability in the Al-doped 2:17 interstitial compound.

chemical bonding↗
DOE OSTI2022

A New Recycled Al–Si–Mg Alloy for Sustainable Structural Die Casting Applications

The use of secondary aluminum for structural components in the automotive industry is limited by the high Fe contents in recycled alloys which often result in the formation of brittle β-Al 5 FeSi phase which reduces the ductility of aluminum castings. In this study, a new secondary Al-Si-Mg alloy with high Fe content (about 0.44 wt.%) was developed for die casting applications. Based on thermodynamic modeling, manganese was added to obtain a designed Fe-to-Mn ratio of 2 which successfully suppressed the formation of β-Al 5 FeSi phase, by forming α-Al 15 (Fe,Mn) 3 Si 2 phase with rounded or hexagonal morphology. Additionally, a fine needle-like π-Al 8 FeMg 3 Si 2 phase was also formed within the eutectic regions. The new recycled alloy showed comparable mechanical properties in as-cast and heat treated (T5 and T6) conditions to three major primary die cast alloys (≤ 0.2 wt.% Fe) with similar composition. Ductility up to 7.4% from tensile elongation was achieved in as-cast recycled alloy due to the modification and refinement of α-Al 15 (Fe,Mn) 3 Si 2 by Mn and Sr additions. Tensile elongation was further improved to 9.1% after T6 treatment as a result of dissolution of π-Al 8 FeMg 3 Si 2 phase, defragmentation of α-Al 15 (Fe,Mn) 3 Si 2 and the spheroidization of Si phase. This new alloy provides a promising path for increasing usage of recycled aluminum with high Fe content in structural die castings for automotive and other applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on AlFe3 by Materials Project

Fe3Al is alpha bismuth trifluoride structured and crystallizes in the cubic Fm-3m space group. The structure is three-dimensional. there are two inequivalent Fe sites. In the first Fe site, Fe is bonded in a 8-coordinate geometry to eight equivalent Fe and six equivalent Al atoms. All Fe–Fe bond lengths are 2.49 Å. All Fe–Al bond lengths are 2.87 Å. In the second Fe site, Fe is bonded in a distorted body-centered cubic geometry to four equivalent Fe and four equivalent Al atoms. All Fe–Al bond lengths are 2.49 Å. Al is bonded in a distorted body-centered cubic geometry to fourteen Fe atoms.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on AlFe by Materials Project

FeAl is Tetraauricupride structured and crystallizes in the cubic Pm-3m space group. The structure is three-dimensional. Fe is bonded in a body-centered cubic geometry to eight equivalent Al atoms. All Fe–Al bond lengths are 2.49 Å. Al is bonded in a body-centered cubic geometry to eight equivalent Fe atoms.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on AlFe2 by Materials Project

Fe2Al is Cubic Laves structured and crystallizes in the cubic Fd-3m space group. The structure is three-dimensional. Fe is bonded to six equivalent Fe and six equivalent Al atoms to form a mixture of edge, corner, and face-sharing FeAl6Fe6 cuboctahedra. All Fe–Fe bond lengths are 2.37 Å. All Fe–Al bond lengths are 2.78 Å. Al is bonded in a 12-coordinate geometry to twelve equivalent Fe and four equivalent Al atoms. All Al–Al bond lengths are 2.90 Å.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on Al6Fe by Materials Project

Al6Fe crystallizes in the orthorhombic Cmc2_1 space group. The structure is three-dimensional. Fe is bonded in a distorted q6 geometry to ten Al atoms. There are a spread of Fe–Al bond distances ranging from 2.43–2.61 Å. There are four inequivalent Al sites. In the first Al site, Al is bonded in a 2-coordinate geometry to two equivalent Fe and nine Al atoms. There are a spread of Al–Al bond distances ranging from 2.64–2.90 Å. In the second Al site, Al is bonded in a 1-coordinate geometry to one Fe and four equivalent Al atoms. In the third Al site, Al is bonded in a 1-coordinate geometry to one Fe and four equivalent Al atoms. In the fourth Al site, Al is bonded in a 2-coordinate geometry to two equivalent Fe and four equivalent Al atoms.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on Al3Fe by Materials Project

FeAl3 is beta Cu3Ti-like structured and crystallizes in the hexagonal P6_3/mmc space group. The structure is three-dimensional. Fe is bonded to twelve equivalent Al atoms to form FeAl12 cuboctahedra that share corners with six equivalent FeAl12 cuboctahedra, corners with twelve equivalent AlAl8Fe4 cuboctahedra, edges with eighteen equivalent AlAl8Fe4 cuboctahedra, faces with eight equivalent FeAl12 cuboctahedra, and faces with twelve equivalent AlAl8Fe4 cuboctahedra. There are six shorter (2.66 Å) and six longer (2.69 Å) Fe–Al bond lengths. Al is bonded to four equivalent Fe and eight equivalent Al atoms to form AlAl8Fe4 cuboctahedra that share corners with four equivalent FeAl12 cuboctahedra, corners with fourteen equivalent AlAl8Fe4 cuboctahedra, edges with six equivalent FeAl12 cuboctahedra, edges with twelve equivalent AlAl8Fe4 cuboctahedra, faces with four equivalent FeAl12 cuboctahedra, and faces with sixteen equivalent AlAl8Fe4 cuboctahedra. There are a spread of Al–Al bond distances ranging from 2.61–2.76 Å.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on AlFe by Materials Project

FeAl crystallizes in the hexagonal P6/mmm space group. The structure is one-dimensional and consists of one FeAl ribbon oriented in the (0, 0, 1) direction. Fe is bonded in a distorted linear geometry to two equivalent Al atoms. Both Fe–Al bond lengths are 2.45 Å. Al is bonded in a distorted linear geometry to two equivalent Fe atoms.

36 MATERIALS SCIENCE↗