Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “martensite”

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.

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

At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS1980

Stress corrosion cracking evaluation of martensitic precipitation hardening stainless steels

The resistance of the martensitic precipitation hardening stainless steels PH13-8Mo, 15-5PH, and 17-4PH to stress corrosion cracking was investigated. Round tensile and c-ring type specimens taken from several heats of the three alloys were stressed up to 100 percent of their yield strengths and exposed to alternate immersion in salt water, to salt spray, and to a seacoast environment. The results indicate that 15-5PH is highly resistant to stress corrosion cracking in conditions H1000 and H1050 and is moderately resistant in condition H900. The stress corrosion cracking resistance of PH13-8Mo and 17-4PH stainless steels in conditions H1000 and H1050 was sensitive to mill heats and ranged from low to high among the several heats included in the tests. Based on a comparison with data from seacoast environmental tests, it is apparent that alternate immersion in 3.5 percent salt water is not a suitable medium for accelerated stress corrosion testing of these pH stainless steels.

Humphries, T. S.↗
NASA NTRS1952

Influence of nonmartensitic transformation products on mechanical properties of tempered martensite

The influence of nonmartensitic transformations products on the mechanical properties of tempered martensite is presented for samples of a SAE 4340 steel, partially isothermally transformed to specific high-temperature transformation products and quenched and tempered to hardness values of from 25 to 40 Rockwell c. The effects of upper bainite in amounts of 1,5, 10, 20 and 50 percent, of 5 percent ferrite, and of 5 percent pearlite on the tensile, impact, and fatigue properties are evaluated. (author)

Hodge, J M↗
NASA NTRS2006

Dependence of Microelastic-plastic Nonlinearity of Martensitic Stainless Steel on Fatigue Damage Accumulation

Self-organized substructural arrangements of dislocations formed in wavy slip metals during cyclic stress-induced fatigue produce substantial changes in the material microelastic-plastic nonlinearity, a quantitative measure of which is the nonlinearity parameter Beta extracted from acoustic harmonic generation measurements. The contributions to Beta from the substructural evolution of dislocations and crack growth for fatigued martensitic 410Cb stainless steel are calculated from the Cantrell model as a function of percent full fatigue life to fracture. A wave interaction factor f(sub WI) is introduced into the model to account experimentally for the relative volume of material fatigue damage included in the volume of material swept out by an interrogating acoustic wave. For cyclic stress-controlled loading at 551 MPa and f(sub WI) = 0.013 the model predicts a monotonic increase in Beta from dislocation substructures of almost 100 percent from the virgin state to roughly 95 percent full life. Negligible contributions from cracks are predicted in this range of fatigue life. However, over the last five percent of fatigue life the model predicts a rapid monotonic increase of Beta by several thousand percent that is dominated by crack growth. The theoretical predictions are in good agreement with experimental measurements of 410Cb stainless steel samples fatigued in uniaxial, stress-controlled cyclic loading at 551 MPa from zero to full tensile load with a measured f(sub WI) of 0.013.

Cantrell, John H.↗
NASA NTRSDate not supplied

A Machine Learning Approach to Predict Martensitic Transition Temperatures for Shape Memory Alloys

Shape memory alloys (SMAs) are a unique class of materials with several remarkable properties including shape recovery, superelasticity, etc. Especially important for many NASA applications is the ability to tune the martensitic phase transition temperature by varying the alloy composition. Nickel-titanium (NiTi) based alloys are the most widely studied of this class, with compositions involving ternary, quaternary, or higher additions being considered. Over the past several years, a significant database of SMA properties has been assembled by NASA researchers. Such a database is ideal for data science-based approaches including machine learning. We present results from a developed machine learning model capable of accurately predicting the transition temperature of SMAs across a wide range of compositions. Our model has the added benefit of interpretability and even provides confidence intervals for our predictions. This model will make rapid screening and design of new SMA materials possible. Predictions from the machine learning model can be validated by empirical and/or atomistic scale modeling.

Shreyas Honrao↗
NASA NTRSDate not supplied

Ab Initio Simulations of Martensitic Phase Transformations in NiTi-based High Temperature Ternary Shape Memory Alloys: NiTiHf and NiTiZr

Ab initio simulations of phase stability and martensitic phase transitions are performed for NiTi-based ternary shape memory alloys (SMAs). Specifically, we considered NiTiHf and NiTiZr, which are highly studied for high temperature SMA applications. Previously, we performed investigations of ordered NiTi and related binaries [1,2]. However, similar approaches for chemically disordered compounds present additional difficulties. In this work, special quasi-random structures (SQS) were generated for various compositions, x∈[0,0.5], of Ni0.5Ti(0.5-x)Hfx and Ni0.5Ti(0.5-x)Zrx to capture chemical disorder of off-stoichiometric compounds. Phase stability was evaluated through analysis of finite temperature phonon spectra using temperature dependent effective potential (TDEP) method. Free energies for the cubic B2 phase of NiTiHf and NiTiZr were computed using ab initio thermodynamic integration (AITI) developed previously [1,2]. Free energies for monoclinic B19’ and orthorhombic B33 phases were evaluated via quasi harmonic approximations (QHA). Our results show a critical composition (xc) where the three phases of B2, B19’ and B33 meet, i.e. there is a tri-critical point. For x xc, the transition is between B33 and B2, i.e. it is not a shape memory transition. The approach presented here opens the door to ab initio based predictions of MTT for arbitrary ternary SMAs.

Hessam Malmir↗
DOE OSTI2024

Development of creep resistant ferritic-martensitic steels for operation at 650°C

Creep resistant ferritic-martensitic steels capable of operating at 650°C have long been under development for electricity generation. This presentation will showcase the design and development of alloys CPJ-7 and JMP with approximately 420,000 h of in-house creep testing, the equivalent of ~50 years of cumulative creep tests. Approximately fifty different alloy compositions were manufactured as part of an ICME approach. CPJ-7 present superior creep properties compared to COST alloys for steam turbine and P91/92 for boiler applications. The prolonged creep life was attributed to slowing down the destabilization of fine precipitates at 650°C. The JMP steels were designed with higher Co for solid solution strengthening and increased W for matrix strength and stability. The creep life compared to CPJ-7 increased between 118 and 150% at 650°C depending on the stress. On a Larson-Miller plot, the performance of the JMP steels surpasses that of MARBN steel. Composition-microstructure-properties relationships will be discussed.

Detrois, Martin↗
DOE OSTI2022

Uncertainty Quantification Framework for Predicting Material Response with Large Number of Parameters: Application to Creep Prediction in Ferritic-Martensitic Steels Using Combined Crystal Plasticity and Grain Boundary Models

This paper presents an uncertainty quantification (UQ) framework for the physics-based model prediction of material response with a large number of parameters. The application problem presented in this work is that of predicting creep in Grade 91 steel at 600°C. The material response is defined with a physically based microstructural model with constitutive equations emulating several observed phenomena in Grade 91 and embodied into an explicit geometry mesoscale finite element model for prior austenite grains and grain boundaries. Creep within the grains and in grain boundaries are represented by crystal plasticity for dislocation motion and a physics-based model for cavity growth and nucleation, respectively. The creep behavior of this material is influenced by several parameters, some of which have a wide range of variation based on experimental data. UQ combined with microstructural modeling can discover the core microstructural causes of experimental variability, leading to improved materials with lower variability in critical long-term material properties. In this study, we investigate the model's uncertainty to identify material properties that may be modified during production to increase creep life and analyze different components of the crystal plasticity model for improvements. For this purpose, a quantity of interest is defined as time to minimum creep rate, which correlates well to the creep failure of the material. A deep neural network model was trained and validated to be used as a surrogate for the finite element model. Then, a variance-based sensitivity analysis is performed on the surrogate model to find the Sobol indices of the input parameters in respect to the output quantity of interest. The Sobol indices are used to reduce the dimensionality of the model. Generalized polynomial chaos expansion is used on the reduced basis models to propagate the uncertainty from the input parameters to the quantity of interest using the deep neural network surrogate model. These results are benchmarked against uncertainty propagation using Monte Carlo simulations. In conclusion, the UQ performed through the reduced basis model captures almost all the uncertainty in the model with significantly fewer simulations, making it possible to perform the UQ directly via simulations with the finite element model rather than surrogate machine-learned models.

36 MATERIALS SCIENCE↗