Database Of Material Properties Research Articles

Small dataset machine-learning approach for efficient design space exploration: engineering ZnTe-based high-entropy alloys for water splitting

Aiming toward a sustainable energy era, the design of efficient photocatalysts for water splitting by engineering their band properties has been actively studied. One promising avenue for the band engineering of active photocatalysts is the use of solid-solution alloying. However, the enormous possible configurations of multicomponent alloys hinders the experimental screening of this multidimensional material space, providing an opportunity for machine learning (ML) approaches to help accelerate the discovery of new multicomponent alloy materials. A conventional prerequisite for ML approaches is a large database of accurate material properties, which may require exhaustive computational and/or experimental resources. This study demonstrates that the screening of solid-solution alloys (up to hexanary systems) can be performed using a small database to minimize (and optimize) the number of high-level computational calculations. Specifically, we use ZnTe-based alloys as a prototypical example and employ a secure independent screening and sparsifing operator with the recently developed agreement method (α-method). Furthermore, we discuss and propose design routes to determine the optimal solid-solution ZnTe-based alloys for photoassisted water-splitting reactions.

Review of testing methods to inform materials selection in high‐temperature structural applications

AbstractCareful material selection is paramount to meet the significant challenges posed by harsh environments in advanced applications. Ceramic matrix composites (CMCs) have come to the forefront of consideration for many of these applications where environmental resistance needs to be combined with structural stability at high temperatures (1200°C+). Many gaps exist in understanding how material variations pose unique material and design challenges that affect the final performance in a particular application. Thorough materials testing at relevant temperatures is required for various candidate materials to realize an analytical approach to materials selection. This review will discuss mechanical and environmental tests and their use at high temperatures including tensile tests, flexure tests, lifetime testing methods, interlaminar tests, and environmentally relevant tests. Challenges for performing these tests at high temperatures and on CMCs will be discussed. A literature review will provide examples of state‐of‐the‐art testing, and the test results from historical work and improvement opportunities will be addressed. This review aims to provide an overview of the current capabilities and practices for high‐temperature testing and recommend best practices for performing high‐temperature tests and interpreting and sharing the results and metadata with the larger community to expand the CMC material property database.

Path Forward: Materials Data Modernization for ASME Codes and Standards in the Artificial Intelligence Era

Abstract Development of the ASME Materials Properties Database was initiated in the early 2010s to support the ASME Codes and Standards. As information technologies advance at an accelerated pace with the artificial intelligence era on the horizon, the ASME Materials Properties Database must be further modernized from a database to a knowledgebase to ride the wave of digital information revolution and effectively support the ASME Codes and Standards in the new era. This paper is intended to provide an overview of the ASME Materials Properties Database and discuss a roadmap for its future development to facilitate understanding of and participation from different sectors of the Codes and Standards community. It first reviews the basic concepts of data, information, knowledge, database, and database system as well as the pros and cons in different types of data management and then discusses the path forward for a desired evolution of the database into a self-explanatory and machine-readable knowledgebase that is consistent with human cognitive processes for the Codes and Standards development and, furthermore, provides resources for data processing and analysis to reach an eventual goal of streamlining the Codes and Standards development from the initial inquiry, throughout data submission, analysis, …, to Codes and Standards rule establishment for final publication.

Tritium transport modelling in Europe: Current status, multi-physics integration and proposal of material property data

The capability to predict tritium retention and permeation by accurate and reliable tritium transport models for the breeding blanket design development is one of the most relevant objectives of the European Test Blanket Module (TBM) Programme. This also represents a key aspect for the development and licensing of next-future fusion systems and power plants, including the European DEMO. Tritium analyses can be performed using either system-level models to calculate global blanket and the related ancillary system performances or detailed 3D models for spatially limited areas and critical regions: the choice between these two modelling approaches depend on the objective of each analyse. When looking at the accuracy of tritium transport simulations, two main aspects have to be considered: the first one is the completeness of the description of the physical phenomena participating to the tritium transport dynamics; the second one is the reliability of the tritium transport material property values used in the simulation activities.In this context, this paper has the objective to carry out a critical analysis on the status of the integration of the most relevant multi-physics elements, providing recommendations on the approach to follow, with a particular focus on the system-level codes. Moreover, a set of data and correlations on tritium transport materials properties is proposed as a first step towards a material property database to be shared within the breeding blanket community.

CFD-Based Study on the Flow and Kinetic Energy Characteristics of a Supercritical Suspended Abrasive Water Jet in the Deep-Sea Environment

A supercritical suspended abrasive water jet with dual inputs of pressure and heat is proposed to improve the cutting performance of the conventional suspended abrasive water jet in deep-sea environments. The paper studies the flow and kinetic characteristics of the supercritical suspended abrasive water jet. The CFD simulation method is proposed to investigate these characteristics by integrating a programmed database of supercritical water material properties with Ansys Fluent. The simulation and comparison show that abrasive particle density, abrasive particle size, inlet pressure, and water temperature affect the acceleration process of the abrasive particles. At the nozzle outlet, the velocity of the abrasive particles reaches over 95% of the supercritical water velocity. With the proposed supercritical abrasive water jet, the jet velocity is increased by 192.2% to 402.40 m/s compared to the conventional suspended abrasive water jet, reducing the amount of water used by 67.7% at a specified temperature of 773.15 K. Correspondingly, the medium kinetic energy is increased by 177.7% and the medium kinetic energy ratio is 2.78. The particle kinetic energy is increased by 723.2% and the particle kinetic energy ratio is 8.23.

Design of modelling and online simulation for energy systems in papermaking mill

This paper presents a general framework of modelling and online simulation for papermaking mill’s energy systems. The energy flow models of thermal power plant, DIP and paper machine for a papermaking mill was designed and implemented an online simulation. The traditional offline, closed and scattered modelling and simulation methods were changed into online, open and integrated forms. Through the analysis of energy systems of papermaking mills, the framework has implemented the followings. The unit (equipment) models were established, it can analyse the energy of energy flow material. The corresponding material flow models library was developed. The craftwork models were designed and the energy flow models library was developed of thermal power plant, DIP and paper machine. The mathematical models of unit (equipment), energy flow, exergy flow, thermal power plant, DIP and paper machine were built. The material properties database was developed. General simulation software architecture and online simulation software architecture were designed. The framework implemented online simulation, analysis, diagnosis and optimization for energy systems of whole papermaking mill. The practical application results are fine when the framework was applied in Guangzhou Paper Group Co., Ltd., and Gold East Paper (Jiangsu) Co., Ltd. The practical application results indicate that the emulation, simulation, analysis, diagnosis and optimization of material flow and energy flow for papermaking mill whole production process can be implemented by online simulation, and the purposes of maximizing the utility efficiency of material and energy resource, reducing resource, reusing and recycling can be achieved.

Irradiated mechanical properties predicted by a machine learning method with the Fourier-transform-based feature extension

ABSTRACT High-dimensional nonlinear relationships between the irradiated yield strength and its influencing factors, including doses, temperatures, and crystal structures, are difficult to explicitly characterize in the absence of a comprehensive database. In this study, we developed a machine learning method with the Fourier-transform-based feature extension, successfully constructing the prediction model of irradiated yield strength by a relatively small and sparse database of irradiated material properties. The analysis suggests that the proposed feature extension method improves the training performances of machine learning with small dataset. And the present model is accurate and feasible for predicting the irradiated yielding behaviors. Furthermore, we attempt the inverse machine learning model to determine material properties and irradiation conditions according to the desired yield strength. Since the parameter combinations commensurate with a fixed strength are diverse, the optimal model is helpful in reversely calculating and optimizing material performances. The data-driven machine learning method, which can detect the implicit correlations among numerous data, exhibits great prospects in investigating irradiated mechanical properties and exploring multiscale links in the nuclear material field. This work holds the promise for optimizing the design of in-pile structural components and can be further extended to other machine learning problems with the small dataset.

Designing Nuclear Fuels with a Multi-Principal Element Alloying Approach

Previous research has shown that multi-principal element alloys (MPEAs) using chromium, molybdenum, niobium, tantalum, titanium, vanadium, and zirconium can form stable body-centered-cubic (BCC) structures across a large temperature region (25°C to 1000°C). This is the same crystal structure as γ-uranium (U), which has shown desirable thermal and irradiation behavior in previous alloy fuel research. It is hypothesized then that the MPEA alloying approach can be used to produce a stable BCC uranium-bearing alloy and to retain its stability throughout anticipated operating regimes of power-producing reactors. Candidate elements were assessed using Monte Carlo N-Particle (MCNP) analysis to determine uranium densities necessary to make the alloy an economically viable fuel compared to conventional fuel forms. Following neutronic considerations, materials property databases and empirical predictors were used to determine the compositions with a high potential to form a BCC solid solution alloy. The final four alloys were MoNbTaU2, MoNbTiU2, NbTaTiU2, and NbTaVU2, which were cast using arc melting of raw elemental foils and chunks. Characterization of the fabricated alloys included scanning electron microscopy, X-ray diffraction, energy dispersive X-ray spectroscopy, and transmission electron microscopy. The results showed a two-phase system with a U-rich matrix phase surrounding the refractory precipitates. The U phase was found to contain varying concentrations of the alloying elements and was a BCC γ-U phase. These results warrant further research to identify ideal compositions for use as an advanced alloy fuel.

MDLab: AI frameworks for carbon capture and battery materials

There is a growing urgency to discover better materials that capture CO2 from air and improve battery performance. An important step is to search large databases of materials properties to find examples that resemble known carbon capture agents or electrolytes and then test them for effectiveness. This paper describes novel computational tools for accelerated discovery of solvents, nano-porous materials, and electrolytes. These tools have produced interesting results so far, such as the identification of a relatively isolated location in amine configuration space for the solvents with known carbon capture use, and the demonstration of an end-to-end simulation and process model for carbon capture in MOFs.

Application of the MELCOR for Fusion Code to the Transient Accident Analysis of the IFMIF-DONES Test Cell

According to the European Fusion Roadmap, to demonstrate the feasibility of fusion energy, it is necessary to build a comprehensive database of materials properties to be used in future fusion power plants. This is the objective of the IFMIF-DONES facility, which will drive a deuteron beam on a liquid lithium target to produce high energy neutron fluxes for irradiating candidate fusion materials. Among the several ongoing activities in the frame of the EUROfusion Early Neutron Source Work Package (WPENS) project, deterministic accident analyses play an important role, since they help identifying a set of reference accident scenarios and related safety class components. Some of these scenarios are being studied with the MELCOR-fusion code, an integrated engineering code which is able to perform thermal-hydraulic transient calculations. In this work, the MELCOR-fusion code has been applied to two potential accident scenarios involving the degradation of the primary lithium loop of IFMIF-DONES. A rupture in the Quench Tank and a break in the inlet nozzle to the Target Vacuum Chamber were postulated as the two initiating events followed by a lithium spill into the Test Cell room. The purpose of this study was to obtain different key metrics, such as the maximum pressure and temperature loads reached in the TC room, the amount of the leaked lithium mass, and the time for lithium solidification. The computed results will help identify the safety requirements to be applied to the final design of the TC room.

A Perspective on Materials, Technology and Property Data Base Development for Aero Gas Turbine Discs

Modern aero engine concepts set very high technology standards for the component design, as they operate in an increasingly hostile thermo-mechanical environment. With rapid advancement in material and manufacturing processes there is a constant demand to upgrade mechanical technologies and design analysis methods. The design complexity of aero engines can be best appreciated by its demands to confirm to stringent design and operational specifications with minimum weight. The complexity of loads and constraints on weight and space has led to search of new materials and mechanical technologies which will play a key role in design of discs in future. The paper presents a perspective on integrated approach to material selection, current material & structural technologies and challenges of material property data base for application to gas turbine engines, with specific focus on rotating discs

The Industrialisation of Sustainable Construction: A Transdisciplinary Approach to the Large-Scale Introduction of Compacted Mineral Mixtures (CMMs) into Building Construction

Increasing demand for sustainable, resilient, and low-carbon construction materials has highlighted the potential of Compacted Mineral Mixtures (CMMs), which are formulated from various soil types (sand, silt, clay) and recycled mineral waste. This paper presents a comprehensive inter- and transdisciplinary research concept that aims to industrialise and scale up the adoption of CMM-based construction materials and methods, thereby accelerating the construction industry’s systemic transition towards carbon neutrality. By drawing upon the latest advances in soil mechanics, rheology, and automation, we propose the development of a robust material properties database to inform the design and application of CMM-based materials, taking into account their complex, time-dependent behaviour. Advanced soil mechanical tests would be utilised to ensure optimal performance under various loading and ageing conditions. This research has also recognised the importance of context-specific strategies for CMM adoption. We have explored the implications and limitations of implementing the proposed framework in developing countries, particularly where resources may be constrained. We aim to shed light on socio-economic and regulatory aspects that could influence the adoption of these sustainable construction methods. The proposed concept explores how the automated production of CMM-based wall elements can become a fast, competitive, emission-free, and recyclable alternative to traditional masonry and concrete construction techniques. We advocate for the integration of open-source digital platform technologies to enhance data accessibility, processing, and knowledge acquisition; to boost confidence in CMM-based technologies; and to catalyse their widespread adoption. We believe that the transformative potential of this research necessitates a blend of basic and applied investigation using a comprehensive, holistic, and transfer-oriented methodology. Thus, this paper serves to highlight the viability and multiple benefits of CMMs in construction, emphasising their pivotal role in advancing sustainable development and resilience in the built environment.

Seismic vulnerability assessment of Portuguese masonry buildings

According to the 2011 National Housing Census, masonry buildings comprise more than 50% of the Portuguese building stock. This type of construction has demonstrated a poor performance during the destructive events that struck Portugal in the last century (e.g., 1909 M6.3 Benavente, 1969 M7.8 Algarve, 1980 M6.9 Azores). While some efforts have been made towards the vulnerability assessment of individual masonry buildings or the residential building stock at the national scale, this building class has been the target of limited research. In this study, a comprehensive database of material and geometric properties of granite and limestone buildings was used to generate complex 3D numerical models. These models were subjected to nonlinear time history analysis to derive fragility curves considering the record-to-record and building-to-building variability. The structural damage was defined using two novel engineering demand parameters, and the resulting fragility functions were compared against other existing models. These results can be used directly to generate earthquake scenarios at the country scale or in probabilistic seismic risk analysis to identify regions where risk reduction measures should be prioritized.

A Data-Driven Approach to Predicting Tablet Properties after Accelerated Test Using Raw Material Property Database and Machine Learning.

The purpose of this study was to develop a model for predicting tablet properties after an accelerated test and to determine whether molecular descriptors affect tablet properties. Tablets were prepared using 81 types of active pharmaceutical ingredients, with the same formulation and three different levels of compression pressure. The tablet properties measured were the tensile strength and disintegration time of tablets after two weeks of accelerated test. The material properties measured were the change in tablet thickness before and after the accelerated test, maximum swelling force, swelling time, and swelling rate. The acquired data were added to our previously constructed database containing a total of 20 material properties and 3381 molecular descriptors. The feature importance values of molecular descriptors, material properties and the compression pressure for each tablet property were calculated by random forest, which is one type of machine learning (ML) that uses ensemble learning and decision trees. The results showed that more than half of the top 25 most important features were molecular descriptors for both tablet properties, indicating that molecular descriptors are strongly related to tablet properties. A prediction model of tablet properties was constructed by eight ML types using 25 of the most important features. The results showed that the boosted neural network exhibited the best prediction accuracy and was able to predict tablet properties with high accuracy. A data-driven approach is useful for discovering intricate relationships hidden within complex and large data sets and predicting tablet properties after an accelerated test.

Dynamic characterization of off the shelf polymer composites printed with fused deposition modeling

Fused deposition modeling (FDM) is the most common and cost effective form of additive manufacturing (AM). Due to the ubiquity of the approach, a range of off the shelf composite materials have been developed by companies, often with the goal of improving the printed part's physical appearance or increasing or decreasing the weight of the part. These composites typically have a base material of a standard FDM printed polymer, with additives such as metal particulates or lightweight filler added. This study will examine the dynamic properties of a set of off the shelf materials to characterize acoustic sound speed, complex elastic moduli, and loss. All of the materials in this study have a base material of polylactic acid (PLA) making it possible to easily print them into multi-material structures. The filler materials showed minimal impact on some viscoelastic properties but resulted in significant changes in acoustic sound speed. Characterization of this type is a critical component in development of an expanded database of material properties for use in design.

The Effect of Radial-Shear Rolling Deformation Processing on the Structure and Properties of Zr-2.5Nb Alloy.

The rheological properties of the Zr-2.5Nb alloy by the strain rate range of 0.5-15 s-1 and by the temperature range of 20-770 °C was studied. The dilatometric method for phase states temperature ranges was experimentally determined. A material properties database for computer FEM simulation regards the indicated temperature-velocity ranges were created. Using this database and DEFORM-3D FEM-softpack, the radial shear rolling complex process numerical simulation was carried out. The contributed conditions for the ultrafine-grained state alloy structure refinement were determined. Based on the simulation results, a full-scale experiment of Zr-2.5Nb rod rolling a on a radial-shear rolling mill RSP-14/40 was carried out. It takes in seven passes from a diameter of 37-20 mm with a total diameter reduction ε = 85%. According to this case simulation data, the total equivalent strain in the most processed peripheral zone 27.5 mm/mm was reached. Due to the complex vortex metal flow, the equivalent strain over the section distribution was uneven with a gradient reducing towards the axial zone. This fact should have a deep effect on the structure change. Changes and structure gradient by sample section EBSD mapping with 2 mm resolution were studied. The microhardness section gradient by the HV 0.5 method was also studied. The axial and central zones of the sample by the TEM method were studied. The rod section structure has an expressed gradient from the formed equiaxed ultrafine-grained (UFG) structure on a few outer millimeters of the peripheral section to the elongated rolling texture in the center of the bar. The work shows the possibility of processing with the gradient structure obtaining and enhanced properties for the Zr-2.5Nb alloy, and a database for this alloy FEM numerical simulations are also presents.

Teaching ceramic materials in mechanical engineering: An active learning experience

Teaching in higher education is a very demanding task, as students have easy access to information through a computer or mobile device, and nothing seems to surprise them. One of the main concerns of Mechanical Engineering professors is the transmission of the best general competencies and scientific knowledge, and the development of skills that will be very useful in their future professional life in a global employment market. In this way, teachers must continually innovate to motivate students and catch their attention, adopting new teaching methodologies, such as active learning. The main objectives of this teaching methodology are: to provide advanced knowledge in engineering; resolve of engineering problems; experimentation and knowledge discovering and, improvement of soft skills. This paper describes a project entitled “Production of a ceramic component,” included in the Curricular Unit of Non-Metallic Materials, proposed to the students of the third year, the first semester of a Mechanical Engineering course. Each group is assigned a ceramic component and is asked to research and prepare an oral presentation and a poster where they describe all the stages of the manufacturing process. They must consider component requirements, and the scientific and technical details of its production from materials selection to its sale. To carry out this assignment, students must review their basic knowledge of materials science, technical drawing, and materials and manufacture of ceramic components (subjects previously taught in different courses), and search in databases of material properties. Although this assignment is very challenging and time-consuming, students report in informal conversations promoted by teachers that they enjoy it and feel very motivated. They consider that this type of work better prepares them for their future professional life, by improving their soft skills.

Temperature-Dependent Material Property Databases for Marine Steels—Part 3: HSLA-80

Integrated Computational Materials Engineering (ICME)-based tools and techniques have been identified as the best path forward for distortion mitigation in thin-plate steel construction at shipyards. ICME tools require temperature-dependent material properties—including specific heat, thermal conductivity, coefficient of thermal expansion, elastic modulus, yield strength, flow stress, and microstructural evolution—to achieve accurate computational results for distortion and residual stress. However, the required temperature-dependent material property databases of US Navy-relevant steels are not available in the literature. Therefore, a comprehensive testing plan for some of the most common marine steels used in the construction of US Naval vessels was completed. This testing plan included DH36, HSLA-65, HSLA-80, HSLA-100, HY-80, and HY-100 steel with a nominal thickness of 4.76 mm (3/16-in.). This report is the second part of a seven-part series detailing the pedigreed steel data. The first six reports will report the material properties for each of the individual steel grades, whereas the final report will compare and contrast the measured steel properties across all six steels. This report will focus specifically on the data associated with HSLA-80 steel.

RadonPy: automated physical property calculation using all-atom classical molecular dynamics simulations for polymer informatics

The spread of data-driven materials research has increased the need for systematically designed materials property databases. However, the development of polymer databases has lagged far behind other material systems. We present RadonPy, an open-source library that can automate the complete process of all-atom classical molecular dynamics (MD) simulations applicable to a wide variety of polymeric materials. Herein, 15 different properties were calculated for more than 1000 amorphous polymers. The MD-calculated properties were systematically compared with experimental data to validate the calculation conditions; the bias and variance in the MD-calculated properties were successfully calibrated by a machine learning technique. During the high-throughput data production, we identified eight amorphous polymers with extremely high thermal conductivity (>0.4 W ∙ m–1 ∙ K–1) and their underlying mechanisms. Similar to the advancement of materials informatics since the advent of computational property databases for inorganic crystals, database construction using RadonPy will promote the development of polymer informatics.

Development of reduced activation ferritic/martensitic steels in China

Reduced-activation ferritic-martensitic (RAFM) steel is considered as the primary candidate structural material for blankets of ITER, CFETR and DEMO. Several RAFMs have been developed in China (CN-RAFMs) such as CLAM, CLF-1, modified RAFMs and oxide dispersion strengthened RAFMs (ODS-RAFMs). The mechanical properties, irradiation behaviors, additive manufacturing and joining technologies of structural materials have been comprehensively studied. Qualifications of CN-RAFMs are on-going for ITER-TBM fabrication, with emphasis on material property databases and standardization. A new neutron irradiation program is being carried out for a series of fusion materials e.g. RAFMs and ODS-RAFMs with doses up to 40 dpa. Joining technologies (e.g. electron beam welding, tungsten inert gas welding, laser beam welding and diffusion welding, etc.) and advanced additive manufacturing are performed to overcome the big challenges of the blanket fabrication before ITER, CFETR and DEMO. The databases and standards for CLAM and CLF-1 were established respectively, and the specification and qualification of them are underway for ITER-TBM.In this paper, the latest development and strategy on fusion energy in China are briefly introduced, and the progresses of R&D activities on CN-RAFMs aiming for the engineering applications are reviewed.