A Constraint Satisfaction Problem Approach to High-Entropy Alloy Design

High-entropy alloys (HEAs) are multi-principal element alloys at near-equiatomic concentrations that can have superior properties such as high irradiation resistance, high fatigue resistance, and high temperature usage, compared to conventional alloys. Because of their properties, HEAs may find applications in industries such as nuclear, aerospace, medical, and electronic. However, the design and discovery of HEAs has been largely limited to trial and error, Edisonian, methods, and only a fraction of the possibilities have been produced. A computational alloy design methodology using the constraint satisfaction problem (CSP) approach is proposed to accelerate the design and discovery of HEAs. This approach consists of three major steps: mapping design requirements into mathematical constraints and using computational thermodynamic calculations to implement them, sampling the HEA space of composition and temperature within the constraints to search for solutions, and describing the final solution space using machine learning methods. Ultimately, the CSP approach enables the identification of potentially all regions in composition space that satisfy material design requirements. A Thermo-Calc database used to encode the thermodynamic information of all phases in a given alloy system was verified against experimental data to be implemented for phase stability calculations. With kinetic considerations, 70.8% of the 216 evaluated alloys showed good agreement between experiments and calculations using the database. This database was used to map out single-phase solid solution regions for the known CoCrFeMnNi HEA and all its subsequent near-equiatomic quaternary and ternary systems. Afterwards, regions of possible precipitation hardening potential were determined in the AlCoCrFeNi system. The results demonstrate the CSP algorithm’s capability to search HEA thermodynamic space and to accelerate HEA design and discovery

A Constraint Satisfaction Problem Approach to High-Entropy Alloy Design

High-entropy alloys (HEAs) are multi-principal element alloys at near-equiatomic concentrations that can have superior properties such as high irradiation resistance, high fatigue resistance, and high temperature usage, compared to conventional alloys. Because of their properties, HEAs may find applications in industries such as nuclear, aerospace, medical, and electronic. However, the design and discovery of HEAs has been largely limited to trial and error, Edisonian, methods, and only a fraction of the possibilities have been produced. A computational alloy design methodology using the constraint satisfaction problem (CSP) approach is proposed to accelerate the design and discovery of HEAs. This approach consists of three major steps: mapping design requirements into mathematical constraints and using computational thermodynamic calculations to implement them, sampling the HEA space of composition and temperature within the constraints to search for solutions, and describing the final solution space using machine learning methods. Ultimately, the CSP approach enables the identification of potentially all regions in composition space that satisfy material design requirements. A Thermo-Calc database used to encode the thermodynamic information of all phases in a given alloy system was verified against experimental data to be implemented for phase stability calculations. With kinetic considerations, 70.8% of the 216 evaluated alloys showed good agreement between experiments and calculations using the database. This database was used to map out single-phase solid solution regions for the known CoCrFeMnNi HEA and all its subsequent near-equiatomic quaternary and ternary systems. Afterwards, regions of possible precipitation hardening potential were determined in the AlCoCrFeNi system. The results demonstrate the CSP algorithm’s capability to search HEA thermodynamic space and to accelerate HEA design and discovery

Exploration of the High Entropy Alloy Space as a Constraint Satisfaction Problem

High Entropy Alloys (HEAs), Multi-principal Component Alloys (MCA), or Compositionally Complex Alloys (CCAs) are alloys that contain multiple principal alloying elements. While many HEAs have been shown to have unique properties, their discovery has been largely done through costly and time-consuming trial-and-error approaches, with only an infinitesimally small fraction of the entire possible composition space having been explored. In this work, the exploration of the HEA composition space is framed as a Continuous Constraint Satisfaction Problem (CCSP) and solved using a novel Constraint Satisfaction Algorithm (CSA) for the rapid and robust exploration of alloy thermodynamic spaces. The algorithm is used to discover regions in the HEA Composition-Temperature space that satisfy desired phase constitution requirements. The algorithm is demonstrated against a new (TCHEA1) CALPHAD HEA thermodynamic database. The database is first validated by comparing phase stability predictions against experiments and then the CSA is deployed and tested against design tasks consisting of identifying not only single phase solid solution regions in ternary, quaternary and quinary composition spaces but also the identification of regions that are likely to yield precipitation-strengthened HEAs.Comment: 14 pages, 13 figure

Atomistic Simulations of Dislocation Core Reconfiguration in FCC Metals and Alloys

Dislocations are line defects present in crystalline materials that govern the plastic deformationof metals and metallic alloys. These defects are described by continuum approaches in the far field, but the center of a dislocation core requires atomistic detail. The computational efficiency of continuum approaches allows for the use of mesoscale models probing length and time scales beyond those available in atomistic simulations. However, many of the assumptions involved which sacrifice finer details associated with dislocation core reconfiguration have been left unchecked. This dissertation presents the methodology and results of atomistic simulations applied to three main dislocation processes relevant for mesoscale modeling of FCC metals and alloys. The first is the process of dislocation climb, where dislocations migrate through the absorption/emission of point defects. Mesoscale models typically assume a dislocation to be an ideal cylindrical sink. This assumption is only valid when there is a sufficient density of jogs along the dislocation line, and through the combination of atomistic calculations of jog free energies and analytical theory, it is shown that obtaining such a sufficient density of jogs requires high homologous temperatures and/or a high supersaturation of defects. The second application is on the process of dislocation cross-slip in the presence of short-range ordering (SRO). Mesoscale models typically do not include the effect of solutes on cross-slip, and if they do, assume them to be distributed in a random fashion instead of having SRO. Through the atomistic calculation of many dislocation cross-slip energy barriers with and without the presence of SRO, it is shown that the effect of SRO on planar defect energies can significantly increase the cross-slip energy barrier, potentially reducing cross-slip rates by orders of magnitude and altering work-hardening processes during deformation. The third and final application is on the process of core restructuring of Lomer/Lomer-Cottrell dislocations as a function of stress and alloy composition. Mesoscale models assume that these dislocations, which are central to the work-hardening of FCC metals and alloys, evolve under stress through only one mechanism. It is shown through atomistic simulations that a variety of evolution mechanisms, including twin nucleation, can occur through the core restructuring of these dislocations depending on the local stress and alloy composition

Performance Analysis of Sink Mobility Models for Wireless Sensor Networks: A Comparative Study

Wireless sensor networks (WSNs), deployed in the area of interest to gather data unattended, comprise numerous tiny, ponderous, and battery-operated sensor nodes (SNs). Numerous research publications presented strategies for extending the lifespan and performance of wireless sensor networks because SNs lifetime depends on limited battery life. One strategy for enhancing the performance of wireless sensor networks is to deploy an energy-rich sink capable of mobility to gather data sensed by stationary SNs. Therefore, several mobility models (MMs) were suggested. The primary objective of this investigation is to compare the effectiveness of wireless sensor networks using two MMs for mobile sinks (MSs): Kohonen’s self-organizing map-based model and the genetic algorithm-based model, in order to find the most suitable conditions under which each one of them can be used. As a result, network performance is investigated using the NS-2 simulator under various scenarios and MS speeds. Additionally, throughput, packet delivery ratio (PDR), and end-to-end (E2E) delay are the metrics used to analyze performance. Finally, messages are forwarded from their sources to the MS using the AODV routing protocol. The results show that the Kohonen-based model is suitable for small networks with moderate speeds of the mobile sink. On the other hand, the genetic algorithm-based model is suitable to be used with medium-sized networks with low speeds of the mobile sink

Reconsidering short-range order in complex concentrated alloys

Abstract: The seemingly contradictory state of research on short-range order in many-component alloys is addressed through a critical review of the characterization of face-centered-cubic 3d systems. Despite the paucity of direct observations, the ordering of many widely studied alloys is argued to be primarily interesting for its potential ubiquity. To clarify this situation, future research directions are proposed with reference to historical results, including a review of the fundamental principles of ordering and clustering. Graphical abstract: [Figure not available: see fulltext.

Structure and glide of Lomer and Lomer-Cottrell dislocations: Atomistic simulations for model concentrated alloy solid solutions

Lomer (L) and Lomer-Cottrell (LC) dislocations have long been considered to be central to work hardening in face-centered cubic (FCC) metals and alloys. These dislocations act as barriers of motion for other dislocations, and can serve as sites for twin nucleation. Recent focus on multicomponent concentrated FCC solid solution alloys has resulted in many reported observations of LC dislocations. While these and L dislocations are expected to have a role in the mechanical behavior of these alloys, little is understood about how variations in composition and associated fault energies change the response of these dislocations under stress. We present atomistic simulations of L and LC dislocations in a model Cu-Ni system and find that changes in composition and applied stress conditions result in a wide variety of responses, including changes in core configuration and (100) glide. The results are compared to and extend previous literature related to the nature of L/LC core structures and how they vary with respect to intrinsic materials properties and stress states. This study also provides insights into mechanisms such as twin nucleation that could have important implications for work hardening in FCC solid-solution alloys

Barrier-free predictions of short-range ordering/clustering kinetics in binary FCC solid solutions

We present comparisons of kinetic Monte Carlo (kMC) simulations of isothermal short-range ordering (SRO) and clustering (SRC) kinetics in binary FCC alloys with a mean-field concentration wave (CW) model. We find that the CW model is able to give order-of-magnitude agreement with kMC simulations for ordering/clustering relaxation times over a wide range of temperatures and compositions. The advantage of the CW model is that it does not require parameterization of vacancy hopping energy barriers, which, for a concentrated alloy, becomes prohibitive. We assess limits in the accuracy of the model, and discuss the effect of cooling rates as well as the extension to multi-component systems. Ultimately, the simplicity and performance of the CW model compared to kMC simulations suggests that it is a useful tool to connect with models of properties dependent on SRO/SRC as well as for designing thermal treatments to control formation of SRO/SRC

The Influence of Geology on Landscape Typology in Jordan: Theoretical Understanding and Planning Implications

Landscape Character Assessment (LCA) has been introduced into Jordan through the MEDSCAPES project. The purpose of this project was to streamline landscape studies and integrate them into the land use planning practices in Jordan. Two areas within the Mediterranean and arid climatic zones of the country were chosen as test areas for the methodology. These were the Yarmouk River drainage basin in the northwest of the country and the Mujib River area in the west of Jordan within the Dead Sea basin. Landscape Character Mapping resulted in 22 and 64 Land Description Units (LDUs) for the Yarmouk and Mujib areas, respectively, which were then classified into 14 landscape types. The factors which control the spatial distributions of these units are geology, land cover, landform, and settlements. However, the study suggests that the underlying geology, which influences topography, impacts indirectly on soil types, climate zones, and human activities, and hence has a predominant influence on the character of these units. Specifically, the transition between the Dead Sea Rift Valley and the adjacent highlands create variations in the topographical relief, climate, water availability, and human settlements. Implementation of LCA in Jordan has done much to highlight geological hazards, such as sinkholes, as constraints to development in certain areas. Here, we described how the LCA process could be implemented in Jordan and how this can help in improving land use management practices in the country

Atomistic simulations of dislocation mobility in refractory high-entropy alloys and the effect of chemical short-range order.

Refractory high-entropy alloys (RHEAs) are designed for high elevated-temperature strength, with both edge and screw dislocations playing an important role for plastic deformation. However, they can also display a significant energetic driving force for chemical short-range ordering (SRO). Here, we investigate mechanisms underlying the mobilities of screw and edge dislocations in the body-centered cubic MoNbTaW RHEA over a wide temperature range using extensive molecular dynamics simulations based on a highly-accurate machine-learning interatomic potential. Further, we specifically evaluate how these mechanisms are affected by the presence of SRO. The mobility of edge dislocations is found to be enhanced by the presence of SRO, whereas the rate of double-kink nucleation in the motion of screw dislocations is reduced, although this influence of SRO appears to be attenuated at increasing temperature. Independent of the presence of SRO, a cross-slip locking mechanism is observed for the motion of screws, which provides for extra strengthening for refractory high-entropy alloy system