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Manufacturability and performance response of developed Ni-based alloys for reactor environments

Under the Advanced Materials and Manufacturing Technologies program, two Ni-based alloys fabricated by laser powder bed fusion (LPBF) at ORNL and laser powder directed energy deposition (LP-DED) at INL have been evaluated: γ′-strengthened Haynes 282 and solution-strengthened Inconel 625. Printing defects were observed in the LPBF 282 alloy fabricated using a Renishaw AM250 machine, likely due to particle spattering during printing. For both the LPBF and LP-DED 282 alloys, annealing at 1,180°C for 1 h resulted in partial recrystallization and a bimodal grain distribution, while subsequent aging for 4 h at 800°C led to the formation of a high density of nano size γ′-strengthening precipitates. Creep testing performed at 750°C revealed lower creep life and ductility for the LPBF and LP-DED 282 compared with wrought 282. X-ray computed tomography combined with optical and scanning electron microscopy microstructural characterization revealed crack formation in the LPBF 282 alloy during creep testing, initiated either from printing defects or from creep cavitation at grain boundaries. Creep cavitation and cracking at grain boundaries were also observed for the LP-DED 282 alloy, and optimization of the printing strategies and post heat treatments will be needed to fabricate high performance 282 components for high temperature nuclear applications.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Environmental effects on aerosol–cloud interaction in non-precipitating marine boundary layer (MBL) clouds over the eastern North Atlantic

Abstract. Over the eastern North Atlantic (ENA) ocean, a total of 20 non-precipitating single-layer marine boundary layer (MBL) stratus and stratocumulus cloud cases are selected to investigate the impacts of the environmental variables on the aerosol–cloud interaction (ACIr) using the ground-based measurements from the Department of Energy Atmospheric Radiation Measurement (ARM) facility at the ENA site during 2016–2018. The ACIr represents the relative change in cloud droplet effective radius re with respect to the relative change in cloud condensation nuclei (CCN) number concentration at 0.2 % supersaturation (NCCN,0.2 %) in the stratified water vapor environment. The ACIr values vary from −0.01 to 0.22 with increasing sub-cloud boundary layer precipitable water vapor (PWVBL) conditions, indicating that re is more sensitive to the CCN loading under sufficient water vapor supply, owing to the combined effect of enhanced condensational growth and coalescence processes associated with higher Nc and PWVBL. The principal component analysis shows that the most pronounced pattern during the selected cases is the co-variations in the MBL conditions characterized by the vertical component of turbulence kinetic energy (TKEw), the decoupling index (Di), and PWVBL. The environmental effects on ACIr emerge after the data are stratified into different TKEw regimes. The ACIr values, under both lower and higher PWVBL conditions, more than double from the low-TKEw to high-TKEw regime. This can be explained by the fact that stronger boundary layer turbulence maintains a well-mixed MBL, strengthening the connection between cloud microphysical properties and the below-cloud CCN and moisture sources. With sufficient water vapor and low CCN loading, the active coalescence process broadens the cloud droplet size spectra and consequently results in an enlargement of re. The enhanced activation of CCN and the cloud droplet condensational growth induced by the higher below-cloud CCN loading can effectively decrease re, which jointly presents as the increased ACIr. This study examines the importance of environmental effects on the ACIr assessments and provides observational constraints to future model evaluations of aerosol–cloud interactions.

54 ENVIRONMENTAL SCIENCES↗

A physics-informed and hierarchically regularized data-driven model for predicting fluid flow through porous media

This paper presents a new deep learning data-driven model for predicting structure dependent pore-fluid velocity fields in rock. The model is based on a Convolutional Auto-Encoder (CAE) artificial neural network capable of learning from image data generated by direct numerical simulations of fluid flow through pore-structures, such as by Lattice Boltzmann or molecular dynamics methods. The main novelty of the model in comparison to previous CAE-based data-driven approaches consists of three parts. The first is a methodology for decomposing the full-domain of the porous media into sub-regions, or “sub-domains”, in order to reduce the overall size of the CAE, batch process the sub-domains in parallel, and enable the CAE to learn local and generalizable nonlinear mappings of pore-fluid velocities. The second consists of embedding the finite difference solutions of the incompressible Navier-Stokes and continuity equations into convolutional layers prior to the CAE in order to provide the CAE with knowledge of fluid dynamics physics (PhyFlow). The third main novelty is that the training of the CAE is regularized with a hierarchical loss function that encourages the learning of fluid flow patterns (in a way similar to ranked modes in principal component analysis), ranking from most to least important. This is shown to increase the stability in learning, reduce over-fitting, and promote interpretability of the CAE neural network layers (HierCAE). The comprehensive new data-driven model, which we call the PhyFlow-HierCAE model, is shown to exhibit improved accuracy and generalizability of flow field predictions over conventional CAE models, attributable to the embedded physical knowledge and the hierarchical regularization, as well as realize orders of magnitude speed-ups in computation times as a surrogate for the direct numerical simulations. Examples of training and forward predictions on unseen pore-structures are provided and evaluated for data from Lattice Boltzmann and molecular dynamics simulations of pore-fluid flow. The model is shown to be a fast and accurate emulator (or “surrogate”) for predicting effective permeability of unseen pore-structures based on learning from relatively small direct numerical simulation datasets.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Coastal Acoustic Buoy for Offshore Wind: Project Synthesis

The population of North Atlantic right whales is critically endangered and their habitat overlaps with offshore windfarm leases. It is therefore imperative that effective mitigation strategies be used to avoid impacts on right whales during the construction of offshore windfarms. The Department of Energy issued FOA Number DE-FOA-0001924 to encourage the development of technology that could monitor large exclusion zones for right whales in order to mitigate potential impact of construction noise on right whales. This report summarizes the past two years of the development and evaluation of the Coastal Acoustic Buoy for Offshore Wind (CABOW) project which aimed to develop technology to monitor large exclusion zones for North Atlantic right whales. Over the course of the project SMRU Consulting have implemented a rigorous design process including comparison of different approaches (e.g., single sensor vs multiple sensors), as well as consideration of placement and timing of acoustic monitoring. We have evaluated critical components of the CABOW system including, reliability, detection range, and bearing accuracy in areas adjacent to offshore windfarm leases in Maryland by conducting 3,536 playbacks of simulated right whale upcalls. The maximum call detection range was 7.5 km when noise was 99 dB re 1µPa rms (50-225 Hz), but this reduced to < 1 km when ambient noise levels were high. Our detection probability in the field was measured as a function of range as well as the source-to-noise level ratio allowing us to build a model to predict the probability of detection under various scenarios (sample size: 3,536 calls x 5 buoys = 17,680). The median bearing error was -0.25° but this is likely an underestimate of error due to experimental design. Using the published recall and precision of the two detectors we implemented in the CABOW system, we estimate that at a recall of 80%, our precision was > 80%, within the range of what we were aiming for in this project. To estimate our exclusion zone false negative and false positive rates, we built a simulation model using the empirical data from our field trial. We modelled three to nine CABOW units placed on the 10 km exclusion zone and estimated our false negative rate to be 1% or less (which was our project goal) and our false positive rate to be between seven and nine percent, slightly above our goal of 5%. However, we also modelled an equivalent PAM system that does not have bearing capabilities and found the false positive rates for that system to be six to eight times higher than the CABOW rate. This higher false positive rate of PAM systems with low spatial information could have significant cost repercussions for offshore wind developers by adding work shutdowns or delays without providing additional protection for right whales. The model we built allows us to explore the placement of PAM systems under various scenarios and will thus help facilitate planning of PAM mitigation systems to meet NOAA Incidental Harassment Authorizations for specific windfarms. We achieved an average system uptime of 98.3%, just below our goal of 99%. The issues that caused these short losses of data have been identified and fixed. Right whale detections and audio clips were typically transferred via radio from the buoys to the base station in two to four seconds. We therefore believe we have developed a highly robust real-time PAM system. Based on the above, we feel we have achieved the stated funding goal of developing a cost-effective and robust real-time PAM system that enables the monitoring of large exclusion zones for right whales during the constructions of offshore windfarms. This should lead to decreased costs and risks for the offshore wind sector while providing robust mitigation for right whales. It is important to state that PAM mitigation will need to be implemented with other mitigation strategies (e.g., visual observers) to provide a complete mitigation strategy to ensure that any effects on right whales from offshore windfarm construction is minimized.

17 WIND ENERGY↗

Growth of Well-Defined Model Catalysts for Electrochemistry: From Surface Science Studies to Electrocatalytic CO2 Conversion

We combined ultrahigh vacuum (UHV) surface science techniques, electrochemical measurements, and computational modeling to investigate electrocatalytic systems that are crucial components of the carbon management effort, including oxygen evolution reactions (OER) and CO2 reduction reactions (CO2RR). Well-defined Model catalysts were grown on substrates in the UHV chamber and characterized with X-ray photoelectron spectroscopy (XPS) and scanning tunneling microscopy (STM). Selected model electrocatalysts were then tested in electrochemical cells to establish the structure-property relationships in OER and CO2RR. Our results showed that the edge sites of Fe2O3 grown on Au(111) were the most active toward OER and incorporation of Ni at the edge sites (NiFeOx) further boosted their OER activity. We also resolved the size-dependent electrocatalytic CO2-to-CO conversion of the Ag nanoparticle electrocatalysts with average particle diameter between 2 to 6 nm: smaller diameter (< 3 nm) particles favored H2 evolution reaction (HER) due to a high population of Ag edge sites, whereas larger diameter particles favored CO2RR as the population of Ag(100) surface sites grew. We further discovered that electronic interactions between small diameter Ag particles and highly defective carbon supports could break the size-dependent CO2RR selectivity, resulting in highly selective (CO Faradaic Efficiency > 90%) and active Ag nanoparticle electrocatalysts with sizes < 2 nm diameter.

Deng, Xingyi↗

Microanalytical Approaches to Characterizing REE in Appalachian Basin Underclays

The search for a reliable U.S. domestic source of rare earth elements (REE) is necessary to support the demand of advanced energy applications (e.g., catalysts, electronics, magnets). Sedimentary deposits may be sources for selectively recovering REE and critical metals—specifically the interbedded seat rock, or underclay, that underlies or forms the floor of a coal seam. This material is often a major component of coal waste fines and refuse and thus readily available. This study examines several Appalachian Basin underclays associated with actively mined coal seams as potential feedstocks for the REE. Multimodal microanalytical electron microscopy (SEM, FIB-SEM, EMPA) synchrotron-based µXRF, and image processing techniques are coupled with detailed elemental and mineral data to classify the 2D and 3D petrophysical properties of the materials. The REE contents of Appalachian Basin underclays were measured from 235–399 ppm and predominantly observed as discrete REE-bearing minerals such as monazite and xenotime on the order of 10–100 µm in size. These REE-bearing minerals typically accounted for less than 1% of the scanned areas and volumes under SEM and FIB-SEM analysis, with the exception of regions enriched in crandallite. Synchrotron-based µXRF elemental maps further identified several REE deposition environments in different underclays, including micro-scale (10–100 µm) light REEs co-localizing with Ca and P, micro-scale heavy REEs with Fe, and large-scale light REEs (>200 µm) co-localizing with Sr, Ba, Ca and P.

36 MATERIALS SCIENCE↗

Pulsed laser synthesis of mesoporous metal chalcogenide thin films

Mesoporous films of the metal chalcogenide B-FeSe were grown on MgO substrates by KrF pulsed laser deposition (PLD) in an argon background. At 100 mTorr, gated intensified charge-coupled device imaging and ion probe measurements showed that the plasma plume responsible for crystal growth initially comprised three components, with distinct expansion velocities. Plume interactions with the substrate heater and ablation target gave rise to complex dynamics, including collisions between the charged leading edge—rebounding between the substrate and the target—and slower-moving species in the plume interior. Film growth was dominated by species with kinetic energies ≤0.5 eV/atom. X-ray reflectivity revealed that films grown in this environment—with a substrate temperature of 350 ° C, a laser fluence of 1.0 J cm −2 , and a 7.5 mm 2 spot area—formed a porous framework with 15% porosity. Atomic force microscopy showed surface features that suggest pore sizes below 100 nm. X-ray diffraction indicated that the porous films were epitaxial with respect to the substrate and likely grew by oriented-attachment of gas-phase molecular clusters or very small nanoparticles, in contrast to the conventional epitaxy of vacuum films from atomic constituents. The in-plane orientation of the mesoporous films was B-FeSe [100]||[110] MgO, attributed to the soft landing of pre-formed crystallites on the MgO substrates, where protruding Se rows of B-FeSe aligned with corrugations of the MgO surface. In conclusion, this work implies that growth of candidate electrocatalyst materials by PLD in inert gas background may allow mesoporous frameworks with a single crystallographic orientation that expose specific crystal facets for electrochemical reactions and active site engineering.

cluster epitaxy↗

Probabilistic-learning-based stochastic surrogate model from small incomplete datasets for nonlinear dynamical systems

We consider a high-dimensional nonlinear computational model of a dynamical system, parameterized by a vector-valued control parameter, in the presence of uncertainties represented by an uncontrolled parameter modeled by a vector-valued random variable, and possibly with stochastic excitation. The objective is to construct a statistical surrogate model where the input is any deterministic value of the control parameter, and the output is a vector-valued observation of the computational model, which is a random vector whose probability measure is updated using a target dataset. To construct this statistical surrogate model, the stochastic response of the computational model must be built, which is a vector-valued time-discretized stochastic process in high dimension, depending on the control parameter. It is assumed that the computational cost of a single evaluation of the deterministic model is high. For the probabilistic updating, we consider a subset of the components of the observation of the computational model, defined as the “identification observation” of the computational model, for which a small target dataset is available. Therefore, the target dataset is associated with partial observability, corresponding to an incomplete data case. Given a prior probability model of the random control and uncontrolled parameters, a training dataset is constructed, consisting of realizations of the random triplet composed of the stochastic response, the random identification observation, and the random control parameter. Since the computational cost of a single evaluation of the deterministic model is assumed to be large, the training dataset is also of small size. The main challenges in this problem are the high dimensionality, partial observability leading to incomplete data in the target dataset for the identification observation of the computational model (which is not sufficient to identify the computational stochastic responses), and the availability of a small training dataset. To address these challenges, we propose a methodology based on statistical methods for constructing necessary reduced representations, direct probabilistic learning under constraints using probabilistic learning on manifolds (PLoM) constrained by the target dataset, and the use of a weak formulation of the Fourier transform of probability measures. Statistical conditioning is also employed to explore the learned dataset. The constructed predictive statistical surrogate model can be implemented in the context of online computation. Here, we apply this approach to a problem of nonlinear stochastic dynamics in high dimensions within the framework of deformable solids mechanics.

Engineering↗

Ensuring statistical reproducibility of ocean model simulations in the age of hybrid computing

Novel high performance computing systems that feature hybrid architectures require large scale code refactoring to unravel underlying exploitable parallelism. Such redesign can often be accompanied with machine-precision changes as the order of computation cannot always be maintained. For chaotic systems like climate models, these round-off level differences can grow rapidly. Systematic errors may also manifest initially as machine-precision differences. Isolating genuine round off level differences from such errors remains a challenge. Here, we apply two-sample equality of distribution tests to evaluate statistical reproducibility of the ocean model component of US Department of Energy's Energy Exascale Earth System Model (E3SM). A 2-year control simulation ensemble is compared to a modified ensemble as a test case - after a known non-bit-for-bit change in a model component is introduced - to evaluate the null hypothesis that the two ensembles are statistically indistinguishable. To quantify the false negative rates of these tests, we conduct a formal power analysis using a targeted suite of short simulation ensembles. The ensemble suite contains several perturbed ensembles, each with a progressively different climate than the baseline ensemble - obtained by perturbing the magnitude of a single model tuning parameter, the Gent and McWilliams κ, in a controlled manner. The null hypothesis is evaluated for each of perturbed ensembles using these tests. The power analysis informs on the detection limits of the tests for given ensemble size allowing model developers to evaluate the impact of an introduced non-bit-for-bit change to the model.

Mahajan, Salil↗

Characterization and repair of core gap manufacturing defects for wind turbine blades

Various wind turbine blade components, such as shear webs and skins, commonly use fiber-reinforced composite sandwich structures with a core material like balsa or foam. During manufacturing, core gap defects may result from the misalignment of adjacent foam or balsa core sheets in the blade mold. It is important to understand the influence that core gaps have on the structural integrity of wind turbine blades and how to mitigate their influence. This research characterized the effects of core gap defects at the manufacturing and mechanical levels for both epoxy and next-generation thermoplastic composites. Common repair methods were assessed and compared. Multiple defect sizes were compared using temperature data gathered with thermocouples embedded during manufacturing to core gap defect characteristics obtained using image-mapping techniques, optical microscopy, and mechanical characterization by long beam flexure. Results showed that peak exothermic temperatures during curing were closely related to core gap size. The long beam flexure tests determined that transverse core gaps under pure bending loads can have a substantial effect on the ultimate facesheet strength of both epoxy and thermoplastic composite sandwich structures (up to 25% strength reduction), although the size of the defect itself had less of an influence on the magnitude of the strength reduction. The supporting image-mapping techniques indicated that the distortion of the composite facesheets by the core gaps contributed to the premature failures. The repair methods used in this study did very little to improve the ultimate strength of the sandwich panels that previously had core gap defects. The repair of the thermoplastic panel resulted in a further loss in ultimate facesheet strength. This research demonstrated that there is a vital need for the development of a compatible thermoplastic polymer repair resin system and appropriate resin specific repair procedures for the next generation of recyclable thermoplastic wind blades.

17 WIND ENERGY↗

Mathematical model of a personalized neoantigen cancer vaccine and the human immune system

Cancer vaccines are an important component of the cancer immunotherapy toolkit enhancing immune response to malignant cells by activating CD4 + and CD8 + T cells. Multiple successful clinical applications of cancer vaccines have shown good safety and efficacy. Despite the notable progress, significant challenges remain in obtaining consistent immune responses across heterogeneous patient populations, as well as various cancers. We present a mechanistic mathematical model describing key interactions of a personalized neoantigen cancer vaccine with an individual patient’s immune system. Specifically, the model considers the vaccine concentration of tumor-specific antigen peptides and adjuvant, the patient’s major histocompatibility complexes I and II copy numbers, tumor size, T cells, and antigen presenting cells. We parametrized the model using patient-specific data from a clinical study in which individualized cancer vaccines were used to treat six melanoma patients. Model simulations predicted both immune responses, represented by T cell counts, to the vaccine as well as clinical outcome (determined as change of tumor size). This model, although complex, can be used to describe, simulate, and predict the behavior of the human immune system to a personalized cancer vaccine.

60 APPLIED LIFE SCIENCES↗

Implement and Test 3D Mortar Contact in BISON

We leverage the extension of the generation of mortar segment meshes to three dimensions in MOOSE’s framework to extend thermomechanical modeling capabilities to problems with three dimensions. A modular approach to gap heat transfer physics using the mortar finite element method was created and documented, mechanical contact was extended to three dimensions—including frictional behavior, performance and ease of use were improved, and steps towards scalability of solid mechanics problems involving contact were taken. Many of these new developments are demonstrated in the simulation of 3D light-water reactor (LWR) problems, where the thermomechanical interface problem is solved using the mortar finite element method. Usage of the mortar framework has improved convergence in 2D problems and has enabled employing friction in 3D problems, of which we show results of a short, local stack of 3D pellets. Consequently, the benefits of mortar in terms of solution convergence and quality are extended to three dimensions. Section 2 discusses fundamental developments that enabled the simulation of practical mortar problems in three dimensions and other general improvements, including the reduction of the derivative container size, the modification of dual basis computations when edge dropping (lack of secondary element projection) takes place, the improvement of conditioning when employing the VCP in-edge dropping conditions, and code usability and quality improvements. These latter code enhancements include the migration of tests using “old” mortar contact constraints to using dual mortar with a semi-smooth Newton solution strategy and the reuse of lower dimensional domains for straightforwardly setting up a mortar thermomechanical LWR problem, i.e. the MOOSE action is employed for mechanical contact and the thermal LWR action is employed to capture the gas conductance, contact, and radiation components of gap heat transfer physics. Independently of the mortar LWR thermal action, we developed a modular approach to gap heat transfer that resides in MOOSE and can be leveraged, e.g., in metallic fuel problems. This approach, whose code design based on MOOSE’s user objects to model specific physics was proposed by the maintenance activity, is detailed in Section 3. Based on the dual mortar finite element method, the frictional contact constraints were extended to three dimensions. A block sheared in two directions in and out of contact with a rigid plane is employed in Section 4 to show the way the approach handles changes in frictional states (e.g. stick to slip) within a competitive number of Newton iterations. Equations and numerical results on the use of the VCP with Cartesian Lagrange multipliers, whose combination enables their direct condensation, are described in Section 5.3. Two-dimensional and three-dimensional BISON LWR simulations are discussed in Section 6. Particularly, a stack of five eccentric pellets with a surface defect is simulated and the effect of pellet-cladding friction is assessed. Finally, conclusions are outlined in Section 7.

42 ENGINEERING↗

Grand challenges in the design, manufacture, and operation of future wind turbine systems

Abstract. Wind energy is foundational for achieving 100 % renewable electricity production, and significant innovation is required as the grid expands and accommodates hybrid plant systems, energy-intensive products such as fuels, and a transitioning transportation sector. The sizable investments required for wind power plant development and integration make the financial and operational risks of change very high in all applications but especially offshore. Dependence on a high level of modeling and simulation accuracy to mitigate risk and ensure operational performance is essential. Therefore, the modeling chain from the large-scale inflow down to the material microstructure, and all the steps in between, needs to predict how the wind turbine system will respond and perform to allow innovative solutions to enter commercial application. Critical unknowns in the design, manufacturing, and operability of future turbine and plant systems are articulated, and recommendations for research action are laid out. This article focuses on the many unknowns that affect the ability to push the frontiers in the design of turbine and plant systems. Modern turbine rotors operate through the entire atmospheric boundary layer, outside the bounds of historic design assumptions, which requires reassessing design processes and approaches. Traditional aerodynamics and aeroelastic modeling approaches are pressing against the limits of applicability for the size and flexibility of future architectures and flow physics fundamentals. Offshore wind turbines have additional motion and hydrodynamic load drivers that are formidable modeling challenges. Uncertainty in turbine wakes complicates structural loading and energy production estimates, both around a single plant and for downstream plants, which requires innovation in plant operations and flow control to achieve full energy capture and load alleviation potential. Opportunities in co-design can bring controls upstream into design optimization if captured in design-level models of the physical phenomena. It is a research challenge to integrate improved materials into the manufacture of ever-larger components while maintaining quality and reducing cost. High-performance computing used in high-fidelity, physics-resolving simulations offer opportunities to improve design tools through artificial intelligence and machine learning, but even the high-fidelity tools are yet to be fully validated. Finally, key actions needed to continue the progress of wind energy technology toward even lower cost and greater functionality are recommended.

17 WIND ENERGY↗

Structural and Dynamical Insights into the Formation Process of a Cross-Linked Polymer Network in Acrylic Adhesives During Thermal Curing

Many modern adhesives, sealants, and coatings rely on the controlled transition from a liquid to a solid state by forming a three-dimensional cross-linked polymer network, often referred to as curing. The curing process, which is initiated by the mixing of reactive components or an external trigger, defines the structure of a network and further controls the final mechanical properties of cured materials. However, the curing mechanism is not fully understood yet due to the lack of experimental tools capable of directly probing the structure and dynamics of a network over relevant time- and length scales. Here, in this paper, we report the curing process of a commercial two-component methyl methacrylate (MMA) adhesive using in operando X-ray photon correlation spectroscopy (XPCS), a method that closely simulates the target manufacturing environment of the adhesive. The results are then integrated with those obtained by rheology, differential scanning calorimetry (DSC), and transmission electron microscopy to establish the structure–dynamics–process–property relationship. The XPCS results identify four distinct stages in the curing process after the mixing, extrusion, and deposition of the acrylic adhesive: (i) At a cure time (or “aging time”, t age ) of less than 1 min, nanodomains of polymerized MMA are formed within a liquid monomeric MMA matrix. The average size is several nm and remains constant over t age , while the dynamics of the nanodomains are slowed down with t age due to an increase in the viscosity of the MMA matrix. (ii) After t age > 1 min, the size of the nanodomains increases with t age until the gel point (= 6.3 min after mixing as determined by rheology). The dynamics of the nanodomains also increase due to the heat generated by the exothermic reaction. (iii) At the gel point, the nanodomains begin to interconnect each other, resulting in a network structure with a characteristic length of about 100 nm. This characteristic network size does not change for the rest of the curing process up to t age = 500 min. The dynamics of the network structure, however, show a rapid slowing down with t age up to t age ≈ 12 min, corresponding to the onset of vitrification (as determined by rheology). (iv) At t age > 12 min, when the DSC and rheology data can no longer provide meaningful information, the XPCS data show a further slowing down of the network dynamics associated with vitrification. Our results provide rich and complex insights into the physics and material design of thermosets in commercially relevant processes, which are essential for future industrial applications.

36 MATERIALS SCIENCE↗

Design and Demonstration of an 850 V dc to 13.8 kV ac 100 kW Three-phase Four-wire Power Conditioning System Converter Using 10 kV SiC MOSFETs

In this paper, an 850 V dc to 13.8 kV ac 100 kW modular multilevel three-phase four-wire dc/ac converter based on 10 kV SiC MOSFETs is designed and demonstrated. The design considerations of key components, including the dc-link, device cooling, gate driver, isolated gate driver power supply (GDPS), medium voltage (MV) ac filter inductor, MV and medium frequency transformer, and the mechanical design are discussed. Two converters are designed, and two prototypes are developed, to study the converter paralleling operation and scalability. Both converters are fully tested up to their voltage and power ratings. However, the two converters are not identical. Based on the design and test results of the first converter, the MV power stage, transformer design, GDPS, as well as the low voltage power stage in the second converter are improved for smaller size and/or higher efficiency. Compared to the version 1 converter, the version 2 converter achieves 49% volume reduction and 2 percentage point efficiency improvement, with a peak efficiency of 98.4% at the rated power.

Li, Haiguo↗

Contrasting responses to climate change at Himalayan treelines revealed by population demographics of two dominant species

Abstract Alpine treelines are expected to shift upward due to recent climate change. However, interpretation of changes in montane systems has been problematic because effects of climate change are frequently confounded with those of land use changes. The eastern Himalaya, particularly Langtang National Park, Central Nepal, has been relatively undisturbed for centuries and thus presents an opportunity for studying climate change impacts on alpine treeline uncontaminated by potential confounding factors. We studied two dominant species, Abies spectabilis (AS) and Rhododendron campanulatum (RC) , above and below the treeline on two mountains. We constructed 13 transects, each spanning up to 400 m in elevation, in which we recorded height and state (dead or alive) of all trees, as well as slope, aspect, canopy density, and measures of anthropogenic and animal disturbance. All size classes of RC plants had lower mortality above treeline than below it, and young RC plants (<2 m tall) were at higher density above treeline than below. AS shows little evidence of a position change from the historic treeline, with a sudden extreme drop in density above treeline compared to below. Recruitment, as measured by size–class distribution, was greater above treeline than below for both species but AS is confined to ~25 m above treeline whereas RC is luxuriantly growing up to 200 m above treeline. Synthesis . Evidence suggests that the elevational limits of RC have shifted upward both because (a) young plants above treeline benefited from facilitation of recruitment by surrounding vegetation, allowing upward expansion of recruitment, and (b) temperature amelioration to mature plants increased adult survival. We predict that the current pure stand of RC growing above treeline will be colonized by AS that will, in turn, outshade and eventually relegate RC to be a minor component of the community, as is the current situation below the treeline.

54 ENVIRONMENTAL SCIENCES↗

High-Throughput Statistical Interrogation of Mechanical Properties with Build Plate Location and Powder Reuse in AlSi10Mg

Abstract Additive manufacturing (AM) allows agile, rapid manufacturing of geometrically complex components that would otherwise be impossible through traditional manufacturing methods. With this maturing manufacturing technology comes the need to adopt testing methods that are commensurate with the speed of additive manufacturing and take advantage of its geometric flexibility. High-throughput tensile testing (HTT) is a technique that allows a large number of tensile bars to be tested in a short amount of time. In the present study, HTT is used to evaluate AM AlSi10Mg produced using powder bed fusion with a Renishaw AM250 machine. Three parameters were varied in this study: (1) powder reuse history, (2) location on the build plate, and (3) size of the tensile specimen. For all parameter combinations, at least 22 specimens were tested; in several cases, over 40 were tested. This large dataset, consisting of over 500 tensile tests, permits Weibull statistical analysis and provides sufficient fidelity to isolate subtle trends that would have likely been missed in smaller, traditional datasets. The observed trends are rationalized in terms of the role of porosity and surface crust on mechanical response.

Carroll, Jay D. (ORCID:0000000258184709)↗