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At least 847 records · Page 47

Tailoring Surface Roughness Using Additive Manufacturing to Improve Internal Cooling

Abstract Surface roughness present on internal cooling channels produced with additive manufacturing has been previously shown to augment heat transfer and pressure loss to levels similar to traditionally cast turbulators. Given the ability of the surface roughness to improve the cooling performance of small cooling channels, the question arises on whether there is an optimal combination of random roughness features to maximize internal cooling performance. To investigate this question, test coupons with different surface roughness morphologies and magnitudes were manufactured by manipulating the parameters in the laser powder bed fusion additive manufacturing process. The coupons were tested to characterize the friction factor and Nusselt number of the cooling channels over a range of Reynolds numbers. Results showed that certain roughness combinations outperformed others, increasing the internal cooling performance of the channels. Additionally, manipulation of the performance using the process parameters allowed for reductions in build time, which could be useful for controlling component cost.

Engineering↗

Impacts of the Additive Manufacturing Process on the Roughness of Engine Scale Vanes and Cooling Channels

Abstract By leveraging the additive manufacturing (AM) platform, development time and costs for turbine component testing can be reduced relative to traditional investment casting. Surface roughness is a key characteristic of the additive manufacturing process that can impact flow, heat transfer, and mechanical integrity of printed components. There are multiple design and build considerations that result in variability in surface roughness, especially when additively fabricating complicated three-dimensional vanes and internal cooling passages. This study characterizes the surface roughness of internal cooling passages, vanes, and flat external surface samples made using additive manufacturing, specifically the direct metal laser sintering process. The samples were manufactured with various wall thicknesses, layer thicknesses, build locations, build directions, and on different AM machines. A combination of computed tomography scanning and optical profilometry was used to evaluate surface roughness levels. The data indicate that the dominant factors in roughness for a given layer thickness are a function of wall thickness, build location, and build direction.

Engineering↗

A High-Ceramic-Loading LATP–PVDF–Al 2 O 3 Composite Film for Lithium-Ion Batteries With Favorable Porous Microstructure and Enhanced Thermal Stability

A separator plays a crucial role in a Li-ion battery to carry liquid electrolytes while preventing short-circuiting between electrodes. Nevertheless, conventional commercial separators often exhibit poor wettability and are prone to shrink at elevated temperatures due to their limited thermal stability. Herein, we report a heat-resistant LATP–PVDF–Al 2 O 3 composite film with outstanding wetting performance. The thin film was prepared using ball mill mixing and tape-casting processes. Two solvents, NMP and glycerol, were applied to prepare the slurry, and a favorable microstructure in the film was created after drying. Here, the ionic conductivity of the film was tested at 1.39 mS cm –1 when paired with liquid electrolyte, almost double that of the commercial counterpart. The high ceramic loading of 70% improved both the thermal shrinkage resistance and dendrite inhibition of the membrane. When assembled in an NMC half-cell, the cycling capacity retentions of 92.8% and 92.1% are achieved after 50 cycles at 0.5 C and 1 C, demonstrating its capability to be used in Li-ion batteries.

25 ENERGY STORAGE↗

Detailed Design and Cost Estimation of a 300 MWe Oxy-Fuel sCO2 Turbine

The detailed design of a 300 MWe, utility scale oxy-fuel turbine has been completed for purposed operation in the sCO2 direct fired Allam-Fetvedt cycle, targeting near-zero emissions and a 50% LHV system efficiency. The turbine and its supporting plant aim to offer a lower levelized cost of energy than a natural gas combined cycle plant employing carbon capture. The oxy-fuel turbine conditions include an inlet temperature of 1150°C and inlet pressure of 305 bar, representing temperatures near that of a gas turbine simultaneously with pressures near an ultra-supercritical steam turbine. The combustor housing and turbine designs were completed according to the ASME BPVC; the turbine case specifically incorporates a multi-body design with inner high-pressure barrel case and low-pressure (30 bar) horizontally split outer case of low-chromium steel material. Lateral rotordynamic evaluation demonstrated acceptable vibration response for a range of imbalance conditions per API standards. The cooling flow required in the six-stage turbine flowpath for 30,000 hr. blade and stator lifetime is predicted through thermal and structural modeling of the first stage. The provided cost estimate of the turbine is formed through a combination of scaled up-costs from procured 10 MWe scale sCO2 turbomachinery hardware, and vendor provided budgetary quotes of larger components including the turbine case requiring casting, welding, and final machining processes. The performance and cost estimation of the oxy-fuel turbine predicted for the completed detailed design provides important information towards future development needs for market penetration of utility scale direct fired sCO2 power cycles.

Marshall, Michael [Southwest Research Institute, S↗

Micromechanical Surrogate Machine Learning Model for Creep Deformation Modeling

Process variability during the manufacture of gas turbine engine hot section components can significantly affect the material’s resulting microstructure. In casting, for instance, geometric variation within a component (thin sections versus thick sections, radial location) influences cooling rates and the resulting grain size. The high temperature creep response is known to be sensitive to grain size owing to a diffusional creep mechanism which occurs more readily along grain boundaries. Microstructural variation correspondingly drives mechanical behavior which propagates into component scale performance uncertainty. These factors are essential when planning inspection, maintenance, and repair strategies within a reliability framework. These benefits provide opportunities to increase overall energy efficiency through refined margins. Critically, there is an opportunity to bolster existing data-driven reliability models using physics-driven process-structure-property relations. Here we present recent work establishing a framework for evaluating the probabilistic creep performance of high-temperature materials. A novel microstructure-sensitive crystal plasticity finite element model is established that captures both grain boundary and crystallographic deformation effects. The computationally expensive physics model is calibrated using a statistical approach and this high-fidelity model is subsequently used to train a computationally efficient machine learning surrogate model. The surrogate model is essential for sampling a large ensemble of simulated structure-property pair results. The ensemble data are then mined to extract salient trends to be incorporated into a microstructure-sensitive reliability model. The proposed approach represents a novel way to capture microstructure-sensitive trends from physics-based models within a modern reliability framework.

Fernandez-Zelaia, Patxi [ORNL]↗

Computational Modeling of Multi-Pass Rolling Parameters Effect on Resulting Fuel Foil Shape

A focus of the U.S. Department of Energy is to improve production yield and reduce the cost of Low Enriched Uranium (LEU)-molybdenum alloy (U-10Mo) monolithic fuel plates that will be replacing High Enriched Uranium (HEU) oxide dispersion fuels used currently in the United States High Performance Research Reactors (USHPRR). One area of improvement is lowering the high transverse waviness and longitudinal waviness currently present within rolled foils prior to cladding to produce fuel plates. Traditional rolling manufacturing techniques for other metal foils use winders to pull and straighten the foil as it is rolled back and forth to the final thickness. This approach cannot be used to roll thin U-10Mo foils (0.008-0.025” thick) because only small castings can be rolled due to nuclear criticality safety concerns. As a result, the fuel foils are too short (1 m in length) to use traditional winders. Therefore, it is crucial to identify other rolling parameters (i.e., roller friction, axial tension load, roller diameter, and roll pass reduction percent) that might reduce transverse waviness and longitudinal waviness in the rolled fuel foil and develop a high-yield, low-cost multi-pass rolling manufacturing process. This report documents a systematic finite element modeling study to investigate the effects of numerous rolling parameters to reduce resulting transverse waviness and longitudinal waviness in the fuel foil during multi-pass rolling of U-10Mo foils. The rolling of a U-10Mo plate with initial dimensions of 1”x1”x 0.048” is modeled using Abaqus CAE. This rolling is modeled to undergo eight 20% reduction roll passes to a final fuel foil thickness of 0.01”. The elastic-plastic constitutive model of the U-10Mo alloy was input to the fuel foil rolling model. The rollers were modeled as rigid bodies. A comparison of rolling friction coefficients of 0.3 and 0.7 over a wide range of applied axial tension loads were investigated in order to evaluate the effect of using a lubricant during rolling. The effect of roller diameter on the resulting transverse waviness and longitudinal waviness of the fuel foil over a wide range of axial tension loads were also investigated by modeling rollers 7/8” and 3.75” in diameter. The results of this systematic finite element method study will aid manufacturers in producing low transverse waviness and reduced longitudinal waviness in U-10Mo fuel foils.

U-10Mo, FEA, Rolling, Residual Stress, Fuel Foil↗

Aluminum hydroxide, bayerite, boehmite, and gibbsite ToF-SIMS spectra in the negative ion mode. I

We report time-of-flight secondary ion mass spectrometry (ToF-SIMS) was performed for boehmite (AOH-60) and its potential products of oxidation including pseudo-boehmite (AOH-180), α- and γ-Al 2 O 3 , and α- and γ-Al(OH) 3 . Since boehmite is often incorporated on cladding materials to prevent corrosion, surface analysis techniques are performed to determine the amount of oxidation present. This ToF-SIMS spectral library is of significance because it includes boehmite and its potential oxidation products (i.e., aluminum oxide and hydroxide), which can be used to compare to spectra obtained for real-world samples containing boehmite. Furthermore, ToF-SIMS is often used as a complementary technique to x-ray photoelectron spectroscopy due to its surface sensitivity and ability to compare spectra via a multivariate analysis, therefore establishing that the molecular signatures of boehmite and relevant compounds are essential for peak identification. The SIMS spectra shown are acquired from commercially available powders, which were deposited onto a silicon wafer substrate via liquid slurry drop casting. This library of SIMS mass spectra will serve as a comparison of boehmite [γ-AlO(OH)], pseudo-boehmite [AlOOH∙nH 2 O], α- and γ-Al 2 O 3 aluminum oxide, and α- and γ-Al 2 O 3 aluminum hydroxide in the negative ion mode, which compliments those reported in the positive ion mode.

36 MATERIALS SCIENCE↗

Aluminum hydroxide, bayerite, boehmite, and gibbsite ToF-SIMS spectra in the positive ion mode. II

We report time-of-flight secondary ion mass spectrometry (ToF-SIMS) was performed for boehmite (AOH-60) and its potential products of oxidation including pseudo-boehmite (AOH-180), α- and γ-Al 2 O 3 , and α- and γ-Al(OH) 3 . Since boehmite often forms on the cladding materials to prevent corrosion, surface analysis techniques are performed to determine the amount of oxidation present. This ToF-SIMS spectral library is of significance because it includes boehmite and its potential oxidation products (i.e., aluminum oxide and hydroxide), which can be used to compare to spectra obtained for real-world samples containing boehmite. Furthermore, ToF-SIMS is often used as a complementary technique to x-ray photoelectron spectroscopy (XPS) due to its surface sensitivity and ability to compare spectra via multivariate analysis, therefore establishing the molecular signatures of boehmite and relevant compounds are essential for peak identification. The SIMS spectra shown are acquired from commercially available powders, which were deposited onto a Si wafer substrate via liquid slurry drop casting. This library of SIMS mass spectra will serve as a comparison of boehmite [γ-AlO(OH)], pseudo-boehmite [AlOOH∙nH 2 O], α- and γ-Al 2 O 3 aluminum oxide, and α- and γ-Al 2 O 3 aluminum hydroxide in the positive ion mode, which compliments those reported in the negative ion mode (Part 1).

36 MATERIALS SCIENCE↗

Preshaping clear glass at low temperatures

Advances in available glass chemistries and glass processing methods have accompanied and enabled some of the biggest technology revolutions, from the development and mass production of light bulbs to low-loss fiber optics and durable smartphone touchscreens. An emerging generation of low-temperature processing technologies aims to continue this important trend and make a broader array of glass components mass producible. In the issue, Mader et al. (1) describe one such innovation in glass processing—the use of low-temperature injection molding to preshape silica particle–filled composites that can later be transformed into transparent fused silica glass objects. Traditionally, transparent glass objects are manufactured in high volume from molten or softened glass, which is floated, drawn, blown, cast, or blow-molded to a desired shape (see the figure, top). The glass composition and processing technique dictate the working temperature, which is usually quite high (near 1000°C) and often restricts the choice of compatible equipment or limits the choice of glass composition. Because geometry-specific capital investment is required for production, drastic or frequent component design changes or small batches may be cost prohibitive. Alternatively, transparent glass components can also be shaped at ambient temperature from solid glass by a series of subtractive processes, including cutting or multiple stages of grinding, followed by slower processing steps, such as polishing or etching. This approach is somewhat less amenable to mass production, and certain geometries containing tool-inaccessible regions cannot be fabricated in this way. Several emerging glass-shaping technologies aim to reduce the required manufacturing temperatures and still provide access to a broader range of glass compositions and component geometries (see the figure). These approaches use a three-step process. First, a desired shape is preformed at low temperature from a glass-forming, organic-inorganic composite. Next, the preform is dried, and organic materials used to bind particles are removed. Finally, the preform is heated (sintered) well below the glass-melting temperature to densify to transparent glass. Although the second and third steps do occur at increased temperatures, only standard, geometry-agnostic driers and furnaces are required. This strategy builds on the well-studied sol-gel approach to forming monolithic glass, where silica network–forming chemical solutions are poured into molds, slowly dried, and condensed into dense glass without melting (2). In a departure from the sol-gel process, these new technologies use solvents, cross-linkers, and polymers to formulate organic-inorganic composites tuned for compatibility with a particular shaping process, with formats ranging from photocurable liquids to shear-thickening pastes to solids. The composite inorganic loadings are also typically higher than those in the pure sol-gel approach, which drastically reduces shrinkage in comparison.

36 MATERIALS SCIENCE↗

Layer-Parallel Training of Deep Residual Neural Networks

Residual neural networks (ResNets) are a promising class of deep neural networks that have shown excellent performance for a number of learning tasks, e.g., image classification and recognition. Mathematically, ResNet architectures can be interpreted as forward Euler discretizations of a nonlinear initial value problem whose time-dependent control variables represent the weights of the neural network. Hence, training a ResNet can be cast as an optimal control problem of the associated dynamical system. For similar time-dependent optimal control problems arising in engineering applications, parallel-in-time methods have shown notable improvements in scalability. This paper demonstrates the use of those techniques for efficient and effective training of ResNets. The proposed algorithms replace the classical (sequential) forward and backward propagation through the network layers with a parallel nonlinear multigrid iteration applied to the layer domain. This adds a new dimension of parallelism across layers that is attractive when training very deep networks. From this basic idea, we derive multiple layer-parallel methods. The most efficient version employs a simultaneous optimization approach where updates to the network parameters are based on inexact gradient information in order to speed up the training process. Finally, using numerical examples from supervised classification, we demonstrate that the new approach achieves a training performance similar to that of traditional methods, but enables layer-parallelism and thus provides speedup over layer-serial methods through greater concurrency.

97 MATHEMATICS AND COMPUTING↗

Stochastic Learning Approach for Binary Optimization: Application to Bayesian Optimal Design of Experiments

Here, we present a novel stochastic approach to binary optimization suited for optimal experimental design (OED) for Bayesian inverse problems governed by mathematical models such as partial differential equations. The OED utility function, namely, the regularized optimality criterion, is cast into a stochastic objective function in the form of an expectation over a multivariate Bernoulli distribution. The probabilistic objective is then solved by using a stochastic optimization routine to find an optimal observational policy. This formulation (a) is generally applicable to binary optimization problems with soft constraints and is ideal for OED and sensor placement problems; (b) does not require differentiability of the original objective function (e.g., a utility function in OED applications) with respect to the design variable, and thus it enables direct employment of sparsity-enforcing penalty functions such as $\ell_0$, without needing to utilize a continuation procedure or apply a rounding technique; (c) exhibits much lower computational cost than traditional gradient-based relaxation approaches; and (d) can be applied to both linear and nonlinear OED problems with proper choice of the utility function. The proposed approach is analyzed from an optimization perspective with detailed convergence analysis of the optimization approach and is also analyzed from a machine learning perspective with correspondence to policy gradient reinforcement learning. The approach is demonstrated numerically by using an idealized two-dimensional Bayesian linear inverse problem and validated by extensive numerical experiments carried out for sensor placement in a parameter identification setup.

97 MATHEMATICS AND COMPUTING↗

PASSAT: particle accelerator helioScopes for Slim Axion-like-particle deTection

We propose a novel method to search for axion-like particles (ALPs) at particle accelerator experiments. ALPs produced at the target via the Primakoff effect subsequently enter a region with a magnetic field, where they are converted to photons that are then detected. Dubbed Particle Accelerator helioScopes for Slim Axion-like-particle deTection (PASSAT), our proposal uses the principle of the axion helioscope but replaces ALPs produced in the Sun with those produced in a target material. Since we rely on ALP-photon conversions , our proposal probes light (slim) ALPs that are otherwise inaccessible to laboratory-based experiments which rely on ALP decay, and complements astrophysical probes that are more model-dependent. As a first application, we reinterpret existing data from the NOMAD experiment in light of PASSAT, and constrain the parameter space for ALPs lighter than $$\sim 100~\mathrm{eV}$$ ~ 100 eV and ALP-photon coupling larger than $$\sim 10^{-4}~\mathrm{GeV}^{-1}$$ ~ 10 - 4 GeV - 1 . As benchmarks of feasible low-cost experiments improving over the NOMAD limits, we study the possibility of re-using the magnets of the CAST and the proposed BabyIAXO experiments and placing them at the proposed BDF facility at CERN, together with some new detectors. We find that these realizations of PASSAT allow for a direct probe of the parameter space for ALPs lighter than $$\sim 100~\mathrm{eV}$$ ~ 100 eV and ALP-photon coupling larger than $$\sim 4\times 10^{-6}~\mathrm{GeV}^{-1}$$ ~ 4 × 10 - 6 GeV - 1 , which are regions that have not been probed yet by experiments with laboratory-produced ALPs. In contrast to other proposals aiming at detecting single or two-photon only events in hadronic beam dump environments, that rely heavily on Monte Carlo simulations, the background in our proposal can be directly measured in-situ , its suppression optimized, and the irreducible background statistically subtracted. Sensitivity evaluations with other beams will be the subject of a future paper. The measurements suggested in this paper represent an additional physics case for the BDF at CERN beyond those already proposed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Deffe: A Data-efficient Framework for Performance Characterization in Domain-Specific Computing

Predicting workload performance is a crucial task for many architecture and system research. In this paper we present De￿e, a framework to estimate the workload performances under varying architectural configurations. The infrastructure component of Deffe is based on scalable and easy-to-use open-source software components. By casting the performance modeling as transfer learning tasks, the modeling component of Deffe can leverage the learned knowledge on one workload, and “transfer” it to a new workload. Extensive experimental results show that the method can achieve superior testing accuracy with an effective reduction of 32-80x in terms of the amount of required training data.

Liu, Frank↗

Mixed-Precision S/DGEMM Using the TF32 and TF64 Frameworks on Low-Precision AI Tensor Cores

Using NVIDIA graphics processing units (GPUs) equipped with Tensor Cores has enabled the significant acceleration of general matrix multiplication (GEMM) for applications in machine learning (ML) and artificial intelligence (AI) and in high-performance computing (HPC) generally. The use of such power-efficient, specialized accelerators can provide a performance increase between 8 × and 20 ×, albeit with a loss in precision. However, a high level of precision is required in many large scientific and HPC applications, and computing in single or double precision is still necessary for many of these applications to maintain accuracy. Fortunately, mixed-precision methods can be employed to maintain a higher level of numerical precision while also taking advantage of the performance increases from computing with lower-precision AI cores. With this in mind, we extend the state of the art by using NVIDIA’s new TF32 framework. This new framework not only burdens some constraints of the previous frameworks, such as costly 32 16-bit castings but also provides an equivalent precision and performance by using a much simpler approach. We also propose a new framework called TF64 that attempts double-precision arithmetic with low-precision Tensor Cores. Although this framework does not exist yet, we validated the correctness of this idea and achieved an equivalent of 64-bit precision on 32-bit hardware.

Valero Lara, Pedro↗

A Simple, Scalable Large Deformation Solid Mechanics Implementation in the MOOSE Framework

This article describes a large deformation solid mechanics solver implemented as part of the freely available and open source MOOSE finite element simulation framework. The article documents the choices made in developing the solid mechanics framework and describes novel formulations for the gradient operator and constitutive modeling framework made to simplify implementations of different coordinate systems, stabilized gradient operators, and different constitutive model inputs and outputs. In the process, the article describes a new formulation that casts objective integration of the Cauchy stress as a linear transformation of the small stress rate. Finally, the article presents key implementation details and examines the parallel efficiency of the solid mechanics solver implemented in MOOSE. The implementation retains a good weak scaling efficiency beyond 1,000 parallel processes. The article includes a discussion of the factors limiting the parallel efficiency of implicit, large deformation solid mechanics codes on current high-performance computers, with the main current limitation being the scalability of the algebraic multigrid methods used to solve the linearized equilibrium equations.

Applied computing → Computer-aided design↗

Editors’ Choice—Ionomer Side Chain Length and Equivalent Weight Impact on High Current Density Transport Resistances in PEMFC Cathodes

Cell voltage at high current densities (HCD) of an operating proton-exchange membrane fuel cell (PEMFC) suffers from losses due to the local-O 2 and bulk-H + transport resistances in the cathode catalyst layer (CCL). Particularly, the interaction of perfluorosulfonic acid (PFSA) ionomer with the carbon supported platinum catalyst plays a critical role in controlling reactant transport to the active site. In this study, we perform a systematic analysis of the side chain length and equivalent weight (EW) of PFSA ionomers on the CCL transport resistances. Ex situ measurements were carried out to quantify the ionomer characteristics such as the molecular weight, proton conductivity and water uptake. Nanomorphology of ionomers cast as 60–120 nm thin-films is characterized using grazing-incidence X-ray scattering. In situ fuel cell electrochemical diagnostic measurements were carried out to quantify the reactant (H + /O 2 ) transport properties of the CCL. Ionomer EW was found to play a major role with decreasing EW yielding higher proton conductivity and water uptake that led to lower bulk-H + and local-O 2 transport resistances in the CCL. Finally, a 1D-semi-empirical performance model has been developed to quantify the impact of ionomer EW on cell voltage loss factors.

08 HYDROGEN↗

An In-Depth Analysis of the Transformation of Tin Foil Anodes during Electrochemical Cycling in Lithium-Ion Batteries

Tin foils have an impressive lithium-storage capacity more than triple that of graphite anodes, and their adoption could facilitate a drastic improvement in battery energy density. However, implementation of a dense foil electrode architecture represents a significant departure from the standard blade-cast geometry with a distinct electrochemical environment, and this has led to confusion with regards to the first cycle efficiency of the system. In this work, we investigate the unique behavior of a tin active material in a foil architecture to understand its performance as an anode. We find shallow cycling of the foil results in an irreversible formation (< 40%) due to diffusional trapping, but intermediate and complete utilization allows for a remarkably reversible formation reaction (> 90%). This striking nonlinearity stems from an in situ transformation from bulk metal to porous electrode that occurs during formation cycles and defines electrode-level lithium-transport on subsequent cycles. As a result, an alternative cycling procedure for assessing the stability of foils is proposed to account for this chemomechanical effect.

25 ENERGY STORAGE↗

Design of a Single-Ion Conducting Polymer Electrolyte for Sodium-Ion Batteries

Here, a sodium bis(fluoroallyl)malonato borate salt (NaBFMB) is synthesized. Using a Click thiol-ene reaction, NaBFMB can be photo-crosslinked with a tri-thiol (trimethylolpropane tris(3-mercapto propionate), TMPT) to create a single-ion conducting electrolyte (NaSIE), with all negative charges residing on the borate moieties and anions immobilized through the 3-D crosslinked network. The NaSIE can be prepared either as a free-standing film or through a drop-cast method followed by a photo crosslinking method for an in-situ formation on top of the electrodes. The free-standing film of NaSIE has a high ionic conductivity of 2 × 10 –3 S cm –1 at 30 °C, and a high transference number (t Na + ) of 0.91 as measured through the Bruce-Vincent method. The electrochemical stability of NaSIE polymer electrolyte is demonstrated via cyclic voltammetry (CV) to be stable up to 5 V vs Na/Na + . When tested inside a symmetrical Na//Na cell, the NaSIE shows a critical current density (CCD) of 0.4 mA cm –2 . The stability of NaSIE is further demonstrated via a long cycling of the stripping/plating test with a current density of 0.1 mA cm –2 at five-minute intervals for over 10,000 min. Using the in-situ method, NaSIE is used as the electrolyte for a sodium metal battery using P2 (Na resides at prismatic sites with with ABBAAB stacking)-cathode of Na 0.67 Ni 0.33 Mn 0.67 O 2 (NNMO) and is cycled between the cut-off voltages of 2.0–4.0 V. A high initial specific capacity (85.7 mAh g –1 ) with a capacity retention of 86.79% after 150 cycles is obtained.

25 ENERGY STORAGE↗