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At least 1,153 records · Page 64

3D printing of metal-based materials for renewable energy applications

Large-scale renewable energy must overcome conversion and storage challenges before it can replace fossil fuels due to its intermittent nature. However, current sustainable energy devices still suffer from high cost, low efficiency, and poor service life problems. Recently, porous metal-based materials have been widely used as desirable cross-functional platforms for electrochemical and photochemical energy systems for their unique electrical conductivity, catalytic activity, and chemical stability. To tailor the porosity length scale, ordering, and compositions, 3D printing has been applied as a disruptive manufacturing revolution to create complex architected components by directly joining sequential layers into designed structures. In conclusion, this article intends to summarize cutting-edge advances of metal-based materials for renewable energy devices (e.g., fuel cells, solar cells, supercapacitors, and batteries) over the past decade.

36 MATERIALS SCIENCE↗

Transfer learning for probabilistic localization of hidden cracks in concrete structures

Abstract The utility of discriminative supervised learning models built using multiple training-data sources is investigated for hidden crack localization in concrete. Feed-forward neural network (FFNN) is chosen as the model architecture, and transfer learning is used to assimilate the information obtained from different sources (computational physics simulations and laboratory experiments). The labeled training data consists of values of a damage index and the known locations of hidden cracks. The classification models need to learn how the presence of damage (hidden cracks) affects the damage index at different sensors for different test conditions. To this end, diagnostic FFNN models are built by sequentially adding and training new hidden layers to assimilate labeled information from computer models (different model geometries, test conditions, crack lengths, crack locations) and laboratory experiments on a plain cement slab. These transfer learning-based models are then used to localize damage in concrete specimens that reflect real-world conditions (i.e., specimens with steel reinforcement and randomly distributed aggregate). The actual damage state in these specimens is determined by extracting cores and performing petrographic studies on the extracted cores. The damage probability estimated by transfer learning-based models is compared with the petrographic damage rating index (DRI) to identify the most suitable approach to train the diagnostic models. The transfer learning-based diagnostic methodology shows promise and could be used in various structural health monitoring applications, where sufficient labeled data are typically not available from a single data source.

Miele, S.↗

Direct Fabrication of Vertically Stacked Double Barrier Tunnel Junctions Based on Graphene and h-BN

Direct manufacturing of two-dimensional material-based double barrier (DB) tunnel junctions, based on a lithography-free approach was developed. Graphene/h-BN/Graphene/h-BN/Graphene devices were deposited on Si/SiO2 substrates by employing a plasma enhanced chemical vapor deposition technique in a sequential manner. DB tunneling junctions with varying barrier widths (by varying the thickness of the second graphene layer) were studied. Samples were characterized using Raman, Atomic Force Microscopy and X-ray photoemission spectroscopy. The I–V characteristics of tunneling current showed resonant tunneling behavior at room temperature with a negative differential conductance. The behavior could be explained with quantum mechanical double barrier tunneling model in which analytic solutions to Schrödinger’s equation were obtained in each region of the system. Resonances in transmission probability coefficient for varying barrier widths were evaluated and compared with the experimental results.

36 MATERIALS SCIENCE↗

A New Method for Polishing Laminated Dissimilar Metals

Here, this paper presents a novel method for preparing metallographic samples of laminated dissimilar metals, specifically a layered structure of zirconium (Zr), vanadium (V), and tungsten (W), which pose a challenge due to their differing crystal structures and hardness levels. The work was motivated by the need to develop a cladding for tungsten targets in spallation neutron sources, where a zirconium face is applied over an interlayer of either vanadium or titanium to prevent the formation of brittle intermetallic compounds such as ZrW 2 . Traditional polishing techniques prove inadequate because each constituent metal requires a different polishing approach based on its unique material properties. The methodology outlined includes a sequential process involving conductive epoxy mounting, successive grinding and polishing with specific abrasives, and a final chemical-mechanical polish using a colloidal silica-based solution. Optical microscopy confirmed that this multi-step process successfully produced scratch-free surfaces and minimized surface relief across the different metal interfaces. Although developed for refractory metals, this approach shows promise for other layered structures comprised of transition metals, offering a meticulous yet effective technique for high-quality metallographic sample preparation.

Newberry, Daniel [Oak Ridge National Laboratory (O↗

Statistical Estimation of Strain Using Spatial Correlation Functions

Ex-situ estimation of strains from deformed micrographs is not possible as there are no persistent features which can be tracked. Two point spatial statistics enable the rigorous quantification of spatial patterns in heterogeneous media. In this paper, we propose a novel method for estimating strains directly from dissimilar micrographs using a continuum mechanics approach. Rather than operating directly on images from sequential frames, as is done in digital image correlation, we operate on different microstructure realizations. This is made possible by comparing the spatial autocorrelation maps of deformed and undeformed micrographs rather than direct comparison of images. Additionally, a Bayesian framework is proposed for quantifying uncertainty. We first illustrate the efficacy of this method on speckle pattern images from digital image correlation experiments. Then, we demonstrate that the method is capable of operating on dissimilar micrographs using deformed synthetic binary microstructures. Finally, we present a case study on polycrystalline additively manufactured 316L deformed via tension. The proposed method works well and we discuss implications and limitations of the presented work.

36 MATERIALS SCIENCE↗

Laser Speckle Pattern Stability in Hypersonic Regimes for Experimental Mechanics and Metrological Studies

Here, an empirical study assessing the suitability of laser speckle patterns for use in experimental mechanics research conducted at hypersonic conditions is presented. Speckle patterns generated via coherent optical scattering from a gold-coated graphite surface inserted into a Mach 10 air stream were collected and analyzed using Computer-Aided Speckle Interferometry using two methodologies: a typical spanwise approach involving increasing temporal span between image pairs, and a sequential approach that utilizes a combination of temporal re-referencing and spatial resampling. Results were characterized for spatial-temporal stability and variance. Our observations demonstrate that optical methods relying on stable and properly formed speckle patterns can be used in a hypersonic regime at various Reynolds numbers and common camera frame rates, but the technique is sensitive to the conditions of the airflow and the processing methodology chosen.

42 ENGINEERING↗

Physical Regime Sensitivity

Abstract This work presents a novel sensitivity approach that quantifies sensitivity to regimes of a model’s state variables rather than constitutive model parameters. This Physical Regime Sensitivity (PRS) determines which regimes of a model’s independent variables have the biggest influence on an experiment or application. PRS analysis is demonstrated on a strength model used in the simulation of a copper Taylor cylinder. In a series of simulations, the strength model was perturbed sequentially in local regimes of plastic strain, plastic strain rate, temperature and pressure, and then the prediction of cylinder shape was compared to unperturbed calculations. Results show, for example, that the deformed length of the cylinder was most sensitive to strength at a strain rate of 1.0 × 10 4 /sec., but the deformed footprint radius was most sensitive to strength at a strain rate of about 4.0 × 10 4 /sec. Compared to current histogram approaches, PRS can be used to design or interpret integrated experiments by identifying not just which regimes are accessed somewhere in the experiment but the causality question of which regimes actually affect the measured data. PRS should allow one to focus experimental and modeling efforts where they are most needed and to better interpret experiments.

36 MATERIALS SCIENCE↗

Splitting tensile strength of shale cores: intact versus fractured and sealed with ureolysis-induced calcium carbonate precipitation (UICP)

Ureolysis-induced calcium carbonate precipitation (UICP) is a biomineral solution where the urease enzyme converts urea and calcium into calcium carbonate. The resulting biomineral can bridge gaps in fractured shale, reduce undesired fluid flow, limit fracture propagation, better store carbon dioxide, and potentially enhance well efficiency. The mechanical properties of shale cores were investigated using a modified Brazilian indirect tensile strength test. An investigation of intact shale using Eagle Ford and Wolfcamp cores was conducted at varying temperatures. Results show no significant difference between shale types (average tensile strength = 6.19 MPa). Eagle Ford displayed higher strength at elevated temperature, but temperature did not influence Wolfcamp. Comparatively, cores with a single, lengthwise heterogeneous fracture were sealed with UICP and further tested for tensile strength. UICP was delivered via a flow-through method which injected 20–30 sequential patterns of ureolytic microorganisms and UICP-promoting fluids into the fracture until permeability reduced by three orders of magnitude or with an immersion method which placed cores treated with guar gum and UICP-promoting fluids into a batch reactor, demonstrating that guar gum is a suitable inclusion and may reduce the number of flow-through injections required. Tensile results for both delivery methods were variable (0.15–8 MPa), and in some cores the biomineralized fracture split apart, possibly due to insufficient sealing and/or heterogeneity in the composite UICP-shale cores. Notably in other cores the biomineralized fracture remained intact, demonstrating more cohesion than the surrounding shale, indicating that UICP may produce a strong seal for subsurface application.

58 GEOSCIENCES↗

Chapter Nine - Automated Optimal Control in Energy Systems: The Reinforcement Learning Approach

With the development of smart grid technologies an increasing number of new devices and participants have joined modern energy systems and are inevitably making them more complicated and interdependent than ever. Optimally controlling such a complex energy system and maintaining its operation in a high-efficient, secure, and resilient manner are challenging tasks to the system operators. Fortunately, the revolution in deep learning and artificial intelligence (AI), both from hardware and algorithms perspectives, has provided new ideas and solutions to many previously intractable problems. As a result, this advance in computer science also sparked great research interests in utilizing AI in solving engineering problems related to the modern energy systems. Among many AI techniques, deep reinforcement learning (DRL) has demonstrated great potential for solving sequential optimization problems, which are very common in the engineering domains. Its ability to handle nonlinearity and stochasticity in controlled systems has out-competed many traditional optimal control algorithms. Therefore in this chapter, we focus on the state-of-the-art of DRL concepts and related algorithms, compare their pros and cons with traditional optimal control approaches and discuss the typical workflow for leveraging RL in solving complex problems in modern energy systems.

artificial intelligence↗

Payload concepts for investigations of electrostatic dust motion on the lunar surface

Significant experimental and computational investigations have explored the feasibility of electrostatically-motivated dust motion on the lunar surface. The motion of lunar dust influences our understanding of the evolution of the surface and may also present a hazard to future exploration vehicles and astronauts. The possibility of a sustained exploration presence on the lunar surface opens the door to long-term experiments on the lunar surface, akin to the science facilities on the International Space Station. We have identified four measurements/observations that would significantly advance our understanding of dust-plasma interactions on the lunar surface. In this context, we provide conceptual designs for payloads to obtain these observations: a Langmuir probe, dust deposit witness plate, regolith charge measurement instrument, and cameras to look for evidence of horizon glow. These payloads could deploy independently and sequentially, or together as a suite. The proposed payloads would provide key observations that would inform future modeling efforts and direct future in situ experiments to understand the dust-plasma environment, both for planetary science and spacecraft design applications.

42 ENGINEERING↗

High entropy alloys: A focused review of mechanical properties and deformation mechanisms

The high-entropy alloy (HEA) concept was based on the idea that high mixing entropy can promote formation of stable single-phase microstructures. During the past 15 years, various alloy systems have been explored to identify HEA systems with improved property combinations, leading to an extraordinary growth of this field. In the large pool of alloys with varying characteristics, the first single-phase HEA with good tensile properties, the equiatomic CrMnFeCoNi alloy has become the benchmark material, and it forms the basis of much of our current fundamental understanding of HEA mechanical behavior. As the field is evolving to the more broadly defined complex concentrated alloys (CCAs) and the available data in the literature increase exponentially, a fundamental question remains unchanged: how special are these new materials? In the first part of this review, select mechanical properties of HEAs and CCAs are compared with those of conventional engineering alloys. This task is difficult because of the limited tensile data available for HEAs and CCAs. Additionally, the wider suite of mechanical properties needed to assess structural materials is woefully lacking. Nonetheless, our evaluations have not revealed many HEAs or CCAs with properties far exceeding those of conventional engineering alloys, although specific alloys can show notable enhancements in specific properties. Consequently, it is reasonable to first approach the understanding of HEAs and CCAs through the assessment of how the well-established deformation mechanisms in conventional alloys operate or are modified in the presence of the high local complexity of the HEAs and CCAs. The second part of the paper provides a detailed review of the deformation mechanisms of HEAs with the FCC and BCC structures. For the former, we chose the CrMnFeCoNi (Cantor) alloy because it is the alloy on which the most rigorous and thorough investigations have been performed and, for the latter, we chose the TiZrHfNbTa (Senkov) alloy because this is one of the few refractory HEAs that exhibits any tensile ductility at room temperature. As expected, our review shows that the fundamental deformation mechanisms in these systems, and their dependence on basic physical properties, are broadly similar to those of conventional FCC and BCC metals. The third part of this review examines the theoretical and modeling efforts to date that seek to provide either qualitative or quantitative understanding of the mechanical performance of FCC and BCC HEAs. Since experiments reveal no fundamentally new mechanisms of deformation, this section starts with an overview of modeling perspectives and fundamental considerations. Next, the review turns to the evolution of modeling and predictions as compared to recent experiments, highlighting both successes and limitations. Finally, in spite of some significant successes, important directions for further theory development are discussed. Overall, while the individual deformation mechanisms or properties of the HEAs and CCAs are not, by and large, “special” relative to conventional alloys, the present HEA rush remains valuable because the compositional freedom that comes from the multi-element space will allow exploration of whether multiple mechanisms can operate sequentially or simultaneously, which may yet lead to the creation of new alloys with a spectrum of mechanical properties that are significantly superior to those of current engineering alloys.

36 MATERIALS SCIENCE↗

Localized corrosion at nm-scale hardening precipitates in Al-Cu-Li alloys

The localized corrosion of Li-containing nm hardening precipitates in the 3rd generation of Al-Cu-Li alloys was investigated based on a quasi in situ approach by sequentially exposing the material to NaCl solution and characterizing the structural, chemical, and electrochemical evolution at atomic scale using electron microscopy, spectroscopy, 3D tomography, electrochemical measurements, and DFT calculations. Localized corrosion of Al 7.5 Cu 4 Li (TB phase) initiated along {001} family of planes through the dealloying of Al and Li due to a low surface work function. Cu was enriched along the Cu (110) // T B (011) // Al (100) orientations on and around corroded T B precipitates. No strong galvanic interactions were observed at the T B and Al matrix interface due to the formation of a Li-C-O rich passivation layer during electrolyte exposure. Similarities and differences between T B and other common Al-Cu-Li precipitates (Al 2 CuLi, Al 6 CuLi 3 , and Al 3 Li) with respect to corrosion are discussed. Overall, the reported corrosion mechanism can assist in the assessment of the localized corrosion susceptibility of precipitation-hardened Al alloys and assist in the design of new alloys.

36 MATERIALS SCIENCE↗

Mapping the kinetic evolution of metastable grain boundaries under non-equilibrium processing

The kinetic evolution of a multiplicity of metastable grain boundaries (GBs) under fast driving conditions are studied by atomistic modeling. Assisted with an enhanced statistical analysis, the energetic evolution of GBs over a broad metastability-temperature space is mapped out, wherein two distinct regimes—an ageing regime and a rejuvenating regime—are retrieved with high fidelity. By comparing the results under various conditions (e.g. random perturbations, isothermal annealing, and fast heating/cooling), it is shown that such ageing/rejuvenating mechanism map is universal, irrespective of the actual stimuli used to elicit the metastable GBs. Here, the ageing/rejuvenating phenomena are demonstrated to stem from the energy imbalance of uphill climbing and downhill dropping during sequential transitions in the system's potential energy landscape. Without the necessity of introducing free parameters, such model can reconcile experimentally measured hardness variation of nanocrystalline metals subjected to femto-second laser irradiation, and it therefore provides a novel perspective on achieving a plurality of interfacial states and facilitating previously inaccessible property regimes.

36 MATERIALS SCIENCE↗

Investigation of twin growth mechanisms in precipitate hardened AZ91

Here, in this work, an elasto-viscoplastic fast-Fourier-transform (EVP-FFT) model with a dislocation-density (DD) based hardening law is employed to study the growth of a {10$\bar{1}$2} tensile twin that is blocked by basal-precipitates in precipitate-hardened AZ91 magnesium alloy. It is frequently reported that twin growth is hindered in precipitate-hardened Mg alloys; however, thick twin domains are often observed experimentally in these material systems. Detailed numerical investigation of deformation twinning starting from an early propagation stage, before twin growth, reveals that the stress fields that result from two sequentially propagated twins co-impinging on a precipitate relaxes the twin back stress locally and promotes twin growth at the twin-precipitate junction. Based on these findings, a two-step growth mechanism is proposed for twins arrested by precipitates. In the first step, the interaction of a twin tip with a precipitate develops a stress concentration on the other side of the precipitate, prompting the formation of a second twin. Subsequently, the back stresses associated with the first twin are relaxed by the formation of the second twin, allowing the first twin to grow at the twin-precipitate junction and eventually engulf the precipitate. This mechanism suggests that twin growth can be achieved locally with minimal additional external forces, explaining how relatively large twin domains can develop even in the presence of arrays of precipitates.

36 MATERIALS SCIENCE↗

In-situ TEM investigation of void swelling in nickel under irradiation with analysis aided by computer vision

Understanding the stability of irradiation-induced voids in materials is important for engineering material's swelling behavior under irradiation. In-situ TEM offers a spatial and temporal resolution that is suitable for investigating the evolution of voids under irradiation. However, the in-situ videos have often been too large to be analyzed manually, leaving the valuable data underutilized. For this work, we developed a deep learning-based semantic segmentation model to consistently study the growth and shrinkage of voids in nickel under 1 MeV krypton ion irradiation at various temperatures from 525 °C to 650 °C. With a foil thickness near 100 nm and ion flux of 6.3 × 10 11 ions∙ cm –2 ∙s –1 , the pre-existing voids, which were created beforehand by irradiation at 600 °C to 0.5 dpa, shrank at low temperatures and grew at high temperatures under irradiation, where the transition occurred at 575 °C (~0.5 T M ). The observed stability transition provided new insight for the shrinkage mechanism of voids under irradiation. In addition, an annealing experiment on nickel, previously irradiated at 600 °C to 3 dpa, was performed sequentially at 650 °C to 720 °C to reveal the shrinkage rate of void as a function of temperature and void size. The advantage of combining computer vision and in-situ TEM to obtain comprehensive void evolution was demonstrated.

36 MATERIALS SCIENCE↗

Characterization of boundary precipitation in a heavy ion irradiated tungsten heavy alloy under the simulated fusion environment

In the concerted effort to identify materials capable of surviving the adverse environment of a fusion reactor interior, tungsten heavy alloys have been put forth as leading candidates. Experimental trials and behavioral studies have yielded positive results for their adoption by taking advantage of the alloy’s unique balance of inherently high fracture toughness and low sputter yields; yet due to their relative novelty in the fusion community, there remains a lack of understanding on the response of these materials to the extended high temperature irradiation environment of the reactor interior. Further, to alleviate this issue and provide the necessary data on the behavior of tungsten heavy alloys to the simulated fusion environment, a 90W-7Ni-3Fe alloy has been subjected to elevated temperature sequential Ni + and He + ion irradiations to mimic the expected displacement damage and gas production expected after five years of service as a plasma facing material component. Atomic-scale structural analyses and nanoscale chemical mapping have identified the formation of two distinct precipitation structures, a surface localized η-carbide and an a-W 2 C type tungsten carbide, both of which appear to originate at the bi-phase interface between W and the ductile phase. This irradiation assisted and induced precipitate formation respectively is anticipated to adversely affect these materials by selective embrittlement at bi-phase interfaces leading to a reduction in the material’s overall fracture toughness during prolonged high temperature irradiation. It is asserted that any potential exposure to C during fusion reactor operational service should be minimized as to prevent the formation of these phases.

36 MATERIALS SCIENCE↗

Kinetics of amorphous defect phases measured through ultrafast nanocalorimetry

Recognition of the role of extended defects on local phase transitions has led to the conceptualization of the defect phase, localized thermodynamically stable interfacial states that have since been applied in a myriad of material systems to realize significant enhancements in material properties. Here, we explore the kinetics of grain boundary confined amorphous defect phases, utilizing the high temperature and scanning rates afforded by ultrafast differential scanning calorimetry to apply targeted annealing/quenching treatments at high rates capable of capturing the kinetic behavior. Four Al-based nanocrystalline alloys, including two binary systems, Al–Ni and Al–Y, and two ternary systems, Al–Mg–Y and Al–Ni–Y, are selected to probe the materials design space (enthalpy of mixing, enthalpy of segregation, chemical complexity) for amorphous defect phase formation and stability, with correlative transmission electron microscopy applied to link phase evolution and grain stability to nanocalorimetry signatures. A series of targeted isothermal annealing heat treatments is utilized to construct a Time–Temperature-Transformation curve for the Al–Ni system, from which a critical cooling rate of 2400 °C/s was determined for the grain boundary confined disordered-to-ordered transition. Finally, a thermal profile consisting of 1000 repeated annealing sequences was created to quantify the recovery of the amorphous defect phase following sequential annealing treatments, with results indicating remarkable microstructural stability after annealing at temperatures above 90% of the melting temperature. This work contributes to a deeper understanding of grain boundary localized thermodynamics and kinetics, with potential implications for the design and optimization of advanced materials with enhanced stability and performance.

Amorphous defect phases↗

Localized keyhole pore prediction during laser powder bed fusion via multimodal process monitoring and X-ray radiography

Systematic fault detection and control during laser powder bed fusion (L-PBF) has been a long-standing objective for system manufacturers and researchers in the additive manufacturing (AM) industry. This manuscript investigates a data fusion approach for detection of keyhole porosity formation during laser irradiation of Ti-6Al-4V substrates by concurrent recording of thermally induced optical emission measured using both off-axis and coaxial photodiode sensors, and acoustic emission. Subsurface defect formation was monitored via high-speed synchrotron X-ray imaging at 20,000 frames per second, enabling temporal registration of keyhole pore formation events to the monitoring signals at a resolution of 50 µs. We developed data fusion machine learning (ML) models for localized prediction of keyhole pore formation at various time scales ranging from 0.5 ms to 2 ms. The signal segments were featurized using two independent approaches: (1) power spectral density (PSD) and (2) highly comparative time series analysis (HCTSA) framework. The extracted features from different sensor modalities were fused together to construct a multimodal feature space and sequential feature selection was used to determine the most informative features for training the ML models. The predictive performance was evaluated for three classifying algorithms: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Gaussian Naive Bayes (GNB). As a result, pore formation events were predicted with up to 0.95 F1-score, 1.0 recall and 0.94 accuracy. The most heavily weighted features indicate that model performance is chiefly governed by the acoustic monitoring signal, with a secondary contribution from the optical emission sensors.

36 MATERIALS SCIENCE↗