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تسجيل مستخدم جديدLow activation, refractory, high entropy alloys for nuclear applications
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Two new, low activation high entropy alloys (HEAs) TiVZrTa and TiVCrTa are studied for use as in-core, structural nuclear materials for in-core nuclear applications. Low-activation is a desirable property for nuclear reactors, in an attempt to reduce the amount of high level radioactive waste upon decommissioning, and for consideration in fusion applications.The alloy TiVNbTa is used as a starting composition to develop two new HEAs; TiVZrTa and TiVCrTa. The new alloys exhibit comparable indentation hardness and modulus, to the TiVNbTa alloy in the as-cast state. After heavy ion implantation the new alloys show an increased irradiation resistance.
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Whereas exceptional mechanical and radiation performances have been found in the emergent medium- and high-entropy alloys (MEAs and HEAs), the importance of their complex atomic environment, reflecting diversity in atomic size and chemistry, to defec
t transport has been largely unexplored at the atomic level. Here we adopt a local structure approach based on the atomic pair distribution function measurements in combination with density functional theory calculations to investigate a series of body-centered cubic (BCC) MEAs and HEAs. Our results demonstrate that all alloys exhibit local lattice distortions (LLD) to some extent, but an anomalous LLD, merging of the first and second atomic shells, occurs only in the Zr- and/or Hf-containing MEAs and HEAs. In addition, through the ab-initio simulations we show that charge transfer among the elements profoundly reduce the size mismatch effect. The observed competitive coexistence between LLD and charge transfer not only demonstrates the importance of the electronic effects on the local environments in MEAs and HEAs, but also provides new perspectives to in-depth understanding of the complicated defect transport in these alloys.
Understanding the strengthening and deformation mechanisms in refractory high-entropy alloys (HEAs), proposed as new high-temperature material, is required for improving their typically insufficient room-temperature ductility. Here, density-functiona
l theory simulations and a continuum mechanics analysis were conducted to systematically investigate the competition between cleavage decohesion and dislocation emission from a crack tip in the body-centered cubic refractory HEAs HfNbTiZr, MoNbTaVW, MoNbTaW, MoNbTiV, and NbTiVZr. This crack-tip competition is evaluated for tensile loading and a totality of 15 crack configurations and slip systems. Our results predict that dislocation plasticity at the crack tip is generally unfavorable -- although the competition is close for some crack orientations, suggesting intrinsic brittleness and low crack-tip fracture toughness in these five HEAs at zero temperature. Fluctuations in local alloy composition, investigated for HfNbTiZr, can locally reduce the resistance to dislocation emission for a slip system relative to the configuration average of that slip system, but do not change the dominant crack-tip response. In the case of single-crystal MoNbTaW, where an experimental, room-temperature fracture-toughness value is available for a crack on a {100} plane, theoretical and experimental results agree favorably. Factors that may limit the agreement are discussed. We survey the effect of material anisotropy on preferred crack tip orientations, which are found to be alloy specific. Mixed-mode loadings are found to shift the competition in favor of cleavage or dislocation nucleation, depending on crack configuration and amplified by the effect of material anisotropy on crack tip stresses.
Generative deep learning is powering a wave of new innovations in materials design. In this article, we discuss the basic operating principles of these methods and their advantages over rational design through the lens of a case study on refractory h
igh-entropy alloys for ultra-high-temperature applications. We present our computational infrastructure and workflow for the inverse design of new alloys powered by these methods. Our preliminary results show that generative models can learn complex relationships in order to generate novelty on demand, making them a valuable tool for materials informatics.
Multi-principal-element metallic alloys have created a growing interest that is unprecedented in metallurgical history, in exploring the property limits of metals and the governing physical mechanisms. Refractory high-entropy alloys (RHEAs) have draw
n particular attention due to their (i) high melting points and excellent softening-resistance, which are the two key requirements for high-temperature applications; and (ii) compositional space, which is immense even after considering cost and recyclability restrictions. However, RHEAs also exhibit intrinsic brittleness and oxidation-susceptibility, which remain as significant challenges for their processing and application. Here, utilizing natural-mixing characteristics amongst refractory elements, we designed a Ti38V15Nb23Hf24 RHEA that exhibits >20% tensile ductility already at the as-cast state, and physicochemical stability at high-temperatures. Exploring the underlying deformation mechanisms across multiple length-scales, we observe that a rare beta prime precipitation strengthening mechanism governs its intriguing mechanical response. These results also reveal the effectiveness of natural-mixing tendencies in expediting HEA discovery.
High entropy alloys (HEAs) are a series of novel materials that demonstrate many exceptional mechanical properties. To understand the origin of these attractive properties, it is important to investigate the thermodynamics and elucidate the evolution
of various chemical phases. In this work, we introduce a data-driven approach to construct the effective Hamiltonian and study the thermodynamics of HEAs through canonical Monte Carlo simulation. The main characteristic of our method is to use pairwise interactions between atoms as features and systematically improve the representativeness of the dataset using samples from Monte Carlo simulation. We find this method produces highly robust and accurate effective Hamiltonians that give less than 0.1 mRy test error for all the three refractory HEAs: MoNbTaW, MoNbTaVW, and MoNbTaTiW. Using replica exchange to speed up the MC simulation, we calculated the specific heats and short-range order parameters in a wide range of temperatures. For all the studied materials, we find there are two major order-disorder transitions occurring respectively at $T_1$ and $T_2$, where $T_1$ is near room temperature but $T_2$ is much higher. We further demonstrate that the transition at $T_1$ is caused by W and Nb while the one at $T_2$ is caused by the other elements. By comparing with experiments, {color{black} the results provide insight into the role of chemical ordering in the strength and ductility of HEAs.
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