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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
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 OSTI2024

A large deformation multiphase continuum mechanics model for shock loading of soft porous materials

A large deformation, coupled finite-element (FE) model is developed to simulate the multiphase response of soft porous materials subjected to high strain-rate loading. The approach is based on the theory of porous media (TPM) at large deformations. Simplifications to the one-dimensional regime studied in the numerical simulations follow. An overview of several different time integration schemes is presented for the purpose of solving the nonlinear dynamic coupled balance of momenta (mixture and fluid) and balance of mass of the mixture equations. Numerical examples are presented for (i) verification against closed-form analytical solutions assuming small loads, (ii) demonstrating large deformation effects at high strain-rate, and (iii) showing differences in deformations between a single-phase elastodynamics model with occluded compressible pore fluid and a multiphase poroelastodynamics model at high strain-rate. The multiphase model shows that the relative motion of the pore fluid significantly dampens the deformation response of the solid skeleton as compared to the single-phase model, and makes it possible to extract quantitative values for the stresses of the different constituents, thereby allowing one to form preliminary conclusions about the onset of damage in the solid skeleton. The novelty of the current work is developing a multiphase, large deformation, mixture theory numerical model for high strain-rate loading of soft porous materials. It was discovered that explicit, adaptive time-stepping Runge–Kutta schemes offer high accuracy at relatively low cost when compared to traditional implicit or explicit central difference time-stepping schemes for shock-like loadings. Here, shock viscosity is added to the mixture momentum balance equation to regularize the shock front, and a stabilization term is added to the mixture mass balance equation to stabilize equal order interpolation finite elements for the coupled finite element solution of multiphase materials.

Engineering↗
DOE OSTI2021

Coupling Finite Element and Finite Volume Simulation Within MOOSE

This work focuses on adding capability to couple finite element (FE) variables into finite volume (FV) physics within INL’s Multiphysics Object-Oriented Simulation Environment (MOOSE). This coupling can allow for improvement of multiphysics simulations where one set of physics is best suited for an FE discretization, while another set is best suited for an FV discretization. Electrohydrodynamics, which govern liquid metal reactor concepts and plasma dynamics, is a fitting example where the electromagnetic field equations are solved with FE and the fluid flow is solved with FV. The new FE to FV coupling method can be summarized as taking the element or face average of the FE variable value or gradient and applying that quantity directly in the FV equation objects.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Micro-cantilever beam experiments and modeling in porous polycrystalline UO 2

Understanding the impact of microstructure on the thermo-mechanical behavior of oxide nuclear fuels is vital to predicting their performance through multiscale models. Evaluating the mechanical properties at the sub-grain length scale is key to developing these multiscale models. In this work, 3D finite element (FE) models were constructed to simulate the micrometer-scale bending of micro-cantilever beams fabricated using porous polycrystalline uranium dioxide (UO 2 ) and tested at room temperature. Here, the results showed that the porosity and elastic anisotropy of individual grains can play a significant role in determining the effective mechanical properties of the material deduced from the tests. Specifically, the porosity had a non-negligible effect, given that the pore size was of the same order of magnitude as the dimensions of the micro-beams. Correlations between load-deflection data, pore location, and elastic properties (effective Young's modulus) were investigated using UO 2 micro-beam FE models, where pore clusters were included and placed at different locations along the length of the beam. Results indicated that the presence of pore clusters near the substrate, i.e., the clamp of the micro-cantilever beam, has the strongest effect on the load-deflection behavior, with the porosity leading to a reduction of stiffness that is the largest for any location of the pore clusters. Furthermore, it was also found that pore clusters located towards the middle of the span and close to the end of the beam have a comparatively small effect on the load-deflection behavior. Therefore, it is concluded that accurate estimates of Young's modulus can be obtained from micro-cantilever experiments after accounting for porosity on the one third of the beam length close to the clamp. This, in turn, provides an avenue to improve microscale experiments and their analysis in porous, anisotropic elastic materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

MURR LEU structural and thermal hydraulics analyses: Part I – Preliminary irradiation thermo-mechanical behavior

The University of Missouri Research Reactor (MURR) is expected to be converted from highly enriched uranium (HEU, ≥ 20 wt% U-235) U-Al x dispersion fuel to low-enriched uranium (LEU, < 20 wt% U-235) with U-10Mo monolithic fuel. This work introduces high-fidelity irradiation thermo–mechanical (T-M) analysis of the MURR LEU focusing on changes in coolant channel gap thickness. Three-dimensional (3D) finite element (FE) models were developed to simulate the irradiation T-M behavior of the MURR LEU element with all 23 curved fuel plates, the two side plates, and the combs. It was shown that channel gap thickness changes were influenced not only by plate thickness variations due to fuel swelling and creep but also by the radial displacement of consecutive MURR LEU plates. Modeling the fuel element assembly captured side plate displacements, which were shown to reduce radial fuel plate displacements towards the convex side. The maximum local radial displacement in the element was predicted at the end of life (EOL) as 23.7 mil (602.0 µm) on the lateral centerline of plate 23 towards the convex side. The maximum stripe-averaged reduction in channel gap thickness, particularly relevant for thermal hydraulics (TH) safety analysis, was calculated as 15.9 mil (403.9 µm) in single-side heated channel 24 (the outermost channel). These results account for the thermal resistance from the oxide build-up on cladding surfaces which was shown to be up to 0.82 mil (20.8 µm) thick. It was demonstrated that accounting for oxide layer thermal resistance led to a 10 °C higher peak fuel temperature and a 4.4 mil (111.8 µm) greater maximum local radial displacement. The impact of the calculated channel gap thickness changes on the MURR LEU TH safety analysis is evaluated in Part II.

Irradiation thermo-mechanical analysis↗
DOE OSTI2024

Tardigrade-examples V0.1.0

Tardigrade-examples is a repository of computational workflows that exercise the Tardigrade software package. The Tardigrade software package is an implementation of Eringen’s micromorphic continuum theory with capabilities to support multiscale material modeling. These capabilities include homogenization through the Micromorphic Filter, calibration of micromorphic material models, and macroscale simulation in Tardigrade-MOOSE. This repository investigates continuum upscaling of various direct numerical simulations (DNS) conducted in Abaqus finite element (FE), Ratel FE, and GEOS material point method (MPM) software. Verification of the upscaling workflow is first investigated by considering DNS of trivial stress states for homogeneous materials, results of which indicate that classical continuum behavior is recovered as expected. DNS of heterogeneous materials are then considered.

Allard, Thomas↗
DOE OSTI2021

A REDUCED ORDER MODELING APPROACH TO PROBABILISTIC CREEP-DAMAGE PREDICTIONS IN FINITE ELEMENT ANALYSIS

This paper introduces a computationally efficient Reduced Order Modeling (ROM) approach for the probabilistic prediction of creep-damage failure. Component-level probabilistic simulations are needed to assess the reliability and safety of high-temperature components. Full-scale probabilistic creep-damage modeling in finite element (FE) approach is computationally expensive requiring many hundreds of simulations to replicate the uncertainty of component failure. To that end, ROM is proposed to minimize the elevated computational cost while controlling the loss of accuracy. It is proposed that full-scale probabilistic simulations can be completed in 1D at a reduced cost, the extremum conditions extracted, and those conditions applied for lower-cost 2D/3D probabilistic simulations of components that capture the mean and uncertainty of failure. The probabilistic Sine-hyperbolic (Sinh) model is selected which in previous work was calibrated to alloy 304 stainless steel. The Sinh model includes probability density functions (pdfs) for test condition (stress and temperature), initial damage (i.e. microstructure), and material properties uncertainty. The Sinh model is programmed into ANSYS finite element software using the USERCREEP.F material subroutine. First, the Sinh model and FE code are subject to verification and validation to ensure the accuracy of the simulations. Numerous Monte Carlo simulations are executed in a 1D model to generate probabilistic creep deformation, damage, and rupture data. This data is analyzed and the probabilistic parameters corresponding to extreme creep response are extracted. The ROM concept is applied where only the extreme conditions are applied in the 2D probabilistic prediction of a component. The probabilistic predictions between the 1D and 2D geometry is compared to assess ROM for creep. The accuracy of the probabilistic prediction employing the ROM approach will potentially reduce the time and cost of simulating complex engineering systems. Future studies will introduce multi-stage Sinh, stochasticity, and spatial uncertainty for improved prediction.

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

Integration of Electromagnetic Geophysics Forward Simulation in Coupled Flow and Geomechanics for Monitoring a Gas Hydrate Deposit Located in the Ulleung Basin, East Sea, Korea

We investigate the feasibility of electromagnetic (EM) geophysics methods to detect the dissociation of gas hydrate specifically from a gas hydrate deposit located in the Ulleung Basin, East Sea, Korea via an integrated flow-geomechanics-EM geophysics simulation. To this end, coupled flow and geomechanics simulation is first performed with the multiple porosity model employed, where a mixed formulation with the finite volume (FV) and finite element (FE) methods are taken for the flow and geomechanics, respectively. From the saturation and porosity fields obtained from the coupled flow and geomechanics, the electrical conductivity model is established for the EM simulation. Solving the partial differential equation of electrical diffusion which is linearized using the 3D finite element method (FEM), the EM fields are then computed. For numerical experiments, particularly two approaches in the configuration for the EM methods are compared in this contribution: the surface-to-surface and the surface-to-borehole methods. When the surface-to-surface EM method is employed, the EM is found to be less sensitive, implying low detectability. Especially for the short term of production, the low detectability is attributed to the similarity of electrical resistivity between the dissociated gas (CH4) and hydrate as well as the specific dissociation pattern within the intercalated composites of the field. On the other hand, when the surface-to-borehole EM method is employed, its sensitivity to capture the produced gas flow is improved, confirming its detectability in monitoring gas flow. Hence, the EM geophysics simulation integrated with coupled flow and geomechanics can be a potential tool for monitoring gas hydrate deposits.

depressurization↗
DOE OSTI2025

Finite Element Analysis of the TRUST Nonlinear Dynamics Testbed

This paper builds on prior work conducted within the Los Alamos National Laboratory (LANL) Testbeds to Reduce Uncertainty in Simulations and Tests (TRUST) program. Specifically, it builds on finite element (FE) modeling efforts for the TRUST program’s Nonlinear Dynamics (ND) testbed. Historically, the FE model for the ND testbed has exclusively utilized a Lanczos eigensolver that linearly extracts the system’s natural frequencies. This paper investigates the Abaqus 2024’s explicit dynamic solver, which implements a central difference explicit solver. The central difference method used in Abaqus can capture nonlinear material responses in structural dynamic simulations making it suitable for the ND FE model.

42 ENGINEERING↗
DOE OSTI2021

Validation of a Pressure Dependent PBX 9501 Cookoff Model

A pressure dependent cookoff model for PBX 9501 was developed. This cookoff model was implemented in the finite element (FE) heat transfer code Aria and was used to simulate a set of cookoff experiments. This set is called the Large Scale Annular Cookoff (LSAC) experiments. Three dimensional and axisymmetric heat conduction FE models were implemented to simulate these experiments. The predictive ability of the PBX 9501 pressure dependent cookoff model was evaluated using Latin Hypercube sampling (LHS). Predictions of the thermal times to ignition and temperatures are compared with the experiments.

42 ENGINEERING↗
DOE OSTI2020

Effect of Computational Schemes on Coupled Flow and Geo-Mechanical Modeling of CO 2 Leakage through a Compromised Well

Carbon capture, utilization, and storage (CCUS) describes a set of technically viable processes to separate carbon dioxide (CO 2 ) from industrial byproduct streams and inject it into deep geologic formations for long-term storage. Legacy wells located within the spatial domain of new injection and production activities represent potential pathways for fluids (i.e., CO 2 and aqueous phase) to leak through compromised components (e.g., through fractures or micro-annulus pathways). The finite element (FE) method is a well-established numerical approach to simulate the coupling between multi-phase fluid flow and solid phase deformation interactions that occur in a compromised well system. We assumed the spatial domain consists of a three-phases system: a solid, liquid, and gas phase. For flow in the two fluids phases, we considered two sets of primary variables: the first considering capillary pressure and gas pressure (PP) scheme, and the second considering liquid pressure and gas saturation (PS) scheme. Fluid phases were coupled with the solid phase using the full coupling (i.e., monolithic coupling) and iterative coupling (i.e., sequential coupling) approaches. The challenge of achieving numerical stability in the coupled formulation in heterogeneous media was addressed using the mass lumping and the upwinding techniques. Numerical results were compared with three benchmark problems to assess the performance of coupled FE solutions: 1D Terzaghi’s consolidation, Liakopoulos experiments, and the Kueper and Frind experiments. We found good agreement between our results and the three benchmark problems. For the Kueper and Frind test, the PP scheme successfully captured the observed experimental response of the non-aqueous phase infiltration, in contrast to the PS scheme. These exercises demonstrate the importance of fluid phase primary variable selection for heterogeneous porous media. We then applied the developed model to the hypothetical case of leakage along a compromised well representing a heterogeneous media. Considering the mass lumping and the upwinding techniques, both the monotonic and the sequential coupling provided identical results, but mass lumping was needed to avoid numerical instabilities in the sequential coupling. Additionally, in the monolithic coupling, the magnitude of primary variables in the coupled solution without mass lumping and the upwinding is higher, which is essential for the risk-based analyses.

deformation flow↗
DOE OSTI2024

Chapter 4: Physically informed deep learning networks for simulating microstructure evolution of 3D polycrystals

As discussed in the previous chapter, high energy diffraction microscopy (HEDM) is used to study the micromechanical evolution of a material during in situ loading. HEDM experiments have been used to verify crystal plasticity (CP) simulations [119, 91, 90, 120], for experimental planning, material design, and to further analyze experimental results. However, Fast Fourier transform-based CP (CP-FFT) or finite element-based CP (CP-FE) methods are often too slow to be used in real-time during an experiment. CP-FFT is faster than CP-FE simulations due to the absence of meshing, but can still take hours to simulate the response of a single volume depending on the size and number of strain steps [127]. Reducing computation time would create a larger exploration space in planning and design, and enable faster analysis of experimental results and real-time feedback during an experiment. This research expands upon previous works to develop a workflow for predicting the full-field evolution of a 3D polycrystal. The workflow is simplified from previous works to predict only orientation and elastic strain tensors (from which stress tensors are calculated). The network is physically informed through loss functions and network architecture for a more robust model. The orientation predictions are informed about the cubic crystal symmetry of the material by incorporating disorientation and misorientation information into the network architecture and loss. The Von Mises stress is used to enforce the correct stress-strain trends in the strain tensor predictions. Additional total strain steps from the elastic and elastoplastic region are included to better capture the stress-strain evolution at smaller total strain steps. Material and hardening parameters are additional inputs into the networks to further inform the network and to study the network’s ability to predict different materials other than those used for training.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Efficient quadrature rules for finite element discretizations of nonlocal equations

Here, in this paper, we design efficient quadrature rules for finite element (FE) discretizations of nonlocal diffusion problems with compactly supported kernel functions. Two of the main challenges in nonlocal modeling and simulations are the prohibitive computational cost and the nontrivial implementation of discretization schemes, especially in three-dimensional settings. In this work, we circumvent both challenges by introducing a parametrized mollifying function that improves the regularity of the integrand, utilizing an adaptive integration technique, and exploiting parallelization. We first show that the “mollified” solution converges to the exact one as the mollifying parameter vanishes, then we illustrate the consistency and accuracy of the proposed method on several two- and three-dimensional test cases. Furthermore, we demonstrate the good scaling properties of the parallel implementation of the adaptive algorithm and we compare the proposed method with recently developed techniques for efficient FE assembly.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A Realistic Full-Scale 3D Modeling of Turning Using Coupled Smoothed Particle Hydrodynamics and Finite Element Method for Predicting Cutting Forces

Computational modelling is an effective technique for understanding the complex physics of machining. Large deformations, material separation, and high computational requirements are the key challenges faced while simulating machining. This work introduces a full-scale three-dimensional model of turning operations using a combined approach based on the Smoothed Particle Hydrodynamics (SPH) and Finite Element (FE) methods. By exploiting the advantages of each method, this approach leads to high-fidelity coupled SPH-FE machining models. Cutting forces and chip morphology are the primary results of interest. The machining models are validated with the results of turning experiments. Two-dimensional machining model underpredicts the cutting force and feed force by approximately 49% and 70%, respectively. Moreover, passive force cannot be predicted using the two-dimensional model. On the other hand, with the three-dimensional models developed in this manuscript, the difference between the total simulated force and experimentally measured force is ∼17%. The chip morphologies correlate with experiments in terms of the direction of the chip movement and the “long” continuous chips observed while turning Al 6061. This work expands the realm of machining simulations from two-dimensional orthogonal machining or sectional three-dimensional model to a full-scale realistic simulation. The encouraging simulation results show the potential to study more complex phenomena, such as machining stability and tool path modulation.

42 ENGINEERING↗
DOE OSTI2023

Temporally continuous thermofluidic–thermomechanical modeling framework for metal additive manufacturing

Additive manufacturing (AM) is known to generate large magnitudes of residual stresses (RS) within builds due to steep and localized thermal gradients. In the current state of commercial AM technology, manufacturers generally perform heat treatments in effort to reduce the generated RS and its detrimental effects on part distortion and in-service failure. Computational models that effectively simulate the deposition process can provide valuable insights to improve RS distributions. Accordingly, it is common to employ Computational fluid dynamics (CFD) models or finite element (FE) models. While CFD can predict geometric and thermal-fluid behavior, it cannot predict the structural response (e.g., stress–strain) behavior. On the other hand, an FE model can predict mechanical behavior, but it lacks the ability to predict geometric and fluid behavior. Thus, an effectively integrated thermofluidic–thermomechanical modeling framework that exploits the benefits of both techniques while avoiding their respective limitations can offer valuable predictive capability for AM processes. In contrast to previously published efforts, the work herein describes a one-way coupled CFD-FEA framework that abandons major simplifying assumptions, such as geometric steady-state conditions, the absence of material plasticity, and the lack of detailed RS evolution/accumulation during deposition, as well as insufficient validation of results. Here, the presented framework is demonstrated for a directed energy deposition (DED) process, and experiments are performed to validate the predicted geometry and RS profile. Both single- and double-layer stainless steel 316L builds are considered. Geometric data is acquired via 3D optical surface scans and X-ray micro-computed tomography, and residual stress is measured using neutron diffraction (ND). Comparisons between the simulations and measurements reveal that the described CFD-FEA framework is effective in capturing the coupled thermomechanical and thermofluidic behaviors of the DED process. The methodology presented is extensible to other metal AM processes, including power bed fusion and wire-feed-based AM.

42 ENGINEERING↗
DOE OSTI2020

Control of Weld Residual Stress in a Thin Steel Plate through Low Transformation Temperature Welding Consumables

Low transformation temperature welding (LTTW) consumables are characterized by a low martensite start temperature and a large fraction of martensite forming in the weld. It can efficiently reduce the tensile residual stress because the volume expansion associated with the martensitic transformation compensates for the thermal contraction during cooling. In this work, a LTTW wire, designated as EH200B, was created for the arc welding of advanced high-strength steel thin plates. In comparison to conventional ER70S-3 wires, this LTTW wire generated an opposite distortion pattern. Neutron diffraction measurements along the center thickness of the welded plates showed the maximum residual stress along the longitudinal direction (LD) in the weld region, and the heat-affected zone (HAZ) immediately adjacent to the weld region was reduced from ~330 MPa to below 240 MPa by using the LTTW wire. A finite element (FE) model was developed to predict the residual stress distributions of the plates welded under these two wires. The simulation results showed reasonable agreement with the volume-average neutron diffraction data. Compressive residual stress in the weld region using the LTTW wire was predicted by the FE method. Electron backscattered diffraction and x-ray diffraction measurements confirmed ~90% martensite was present in the LTTW weld. The fatigue life of DP980 steel lap joint panels using EH200B wire nearly doubled that of ER70S-3 wire. This improvement was attributed to the high strength and low LD residual stress in the weld and HAZ immediately adjacent to the weld.

36 MATERIALS SCIENCE↗
DOE OSTI2022

High Throughput Computational Framework of Materials Properties for Extreme Environments

This project aims to establish a framework capable of efficiently predicting the properties of structural materials for service in harsh environments over a wide range of temperatures and over long periods of time. The approach is to develop and integrate high throughput first-principles calculations in combination with machine learning (ML) methods, perform high throughput CALPHAD (calculations of phase diagrams) modeling, and carry out finite element method (FEM) simulations. Relevant to high temperature service in fossil power system, nickel-based superalloys such as Inconel 740 and Haynes 282 as well as the associated (Ni-Cr-Co)-Al-C-Fe-Mn-Mo-Nb-Si-Ti system, were investigated. The present framework was built on the concept of phase-based property data, in which properties of individual phases are modeled as a function of internal and external independent variables. This project established an open-source infrastructure with the following capabilities: (1) High throughput implementation of first-principles calculations at finite temperatures and variable compositions using both accurate phonon calculations and the efficient Debye model for thermodynamic properties, elastic constants, diffusion coefficients, vacancy formation, stacking and twin faults, and dislocation mobility; i.e., using the developed code DFTTK; (2) Machine learning capabilities to predict the above properties so that the number of first-principles calculations can be significantly reduced; e.g., using the developed code SIPFENN; (3) High throughput CALPHAD modeling of the above properties as a function of temperature and composition using our unique capability based on ESPEI and PyCalphad; (4) New capabilities to predict the stress-strain behavior of individual phases; and (5) New models for tensile strength prediction in common FEM software with the crystal plasticity finite element simulations (CPFEM).

, Ni-based superalloys↗