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The contact of the chip with the tool’s front surface in cutting is analyzed by numerical modeling. The high-pressure zones and buildup at the tool’s contact surface are studied. The influence of plastic instability of the chip surface and vortex deformation modes on tool wear is considered.
We report superconducting thin films of niobium have been extensively employed in transmon qubit architectures. Although these architectures have demonstrated remarkable improvements in recent years, further improvements in performance through materials engineering will aid in large-scale deployment. Here, we use information retrieved from secondary ion mass spectrometry and electron microscopy to conduct a detailed assessment of the surface oxide that forms in ambient conditions for transmon test qubit devices patterned from a niobium film. We observe that this oxide exhibits a varying stoichiometry with NbO and NbO 2 found closer to the niobium film and Nb 2 O 5 found closer to the surface. In terms of structural analysis, we find that the Nb 2 O 5 region is semicrystalline in nature and exhibits randomly oriented grains on the order of 1-2 nm corresponding to monoclinic N-Nb 2 O 5 that are dispersed throughout an amorphous matrix. Using fluctuation electron microscopy, we are able to map the relative crystallinity in the Nb 2 O 5 region with nanometer spatial resolution. Through this correlative method, we observe that amorphous regions are more likely to contain oxygen vacancies and exhibit weaker bonds between the niobium and oxygen atoms. Based on these findings, we expect that oxygen vacancies likely serve as a decoherence mechanism in quantum systems.
Quantification of the predictive uncertainty of subsurface models has long been investigated. The traditional workflow is to calibrate prior models to match observed data, and then use the posterior models to simulate future system performance. Not only are these procedures computationally expensive, but they also have issues in maintaining geological model constraints during the calibration step. Data space inversion (DSI) was introduced recently to predict future system performance without the iterative history matching or model calibration step. In general, DSI approaches seek to establish a statistical relationship between the observed and forecast variables, as well as to quantify the predictive uncertainty of the forecast variables, by using an ensemble of uncalibrated prior models. Existing DSI approaches all require a number of complex transformation and mapping operations, which may deter their widespread use. Here in this study, we introduce a new and simpler DSI approach, the learning-based, data-driven forecast approach (LDFA), by combining dimension reduction and machine learning techniques to quickly provide accurate forecast results and reliably quantify corresponding uncertainty in the results. Our LDFA framework is demonstrated using two supervised learning algorithms, artificial neural network (ANN) and support vector regression (SVR), on two representative examples from reservoir engineering and geological carbon storage. Results suggest that our approach provides accurate forecast results (e.g., future oil production rate or cumulative injected CO 2 ) and reasonable predictive uncertainty intervals. Our framework is generic and may be applied to other surface and subsurface problems.
Engine knock and misfire are barriers to pathways leading to higher efficiency natural gas (NG) engines, where the general tendency to knock is highly dependent on engine operating conditions and the fuel reactivity. The problem is further complicated by low emissions limits and the wide range of chemical reactivity in pipeline quality NG, making the understanding of fuel-engine interactions essential to achieving high efficiencies in spark-ignited NG engines. The present work highlights a three-dimensional computational fluid dynamics (CFD) model of the Cooperative Fuel Research (CFR) engine to investigate knock behavior when running on NG. Using a Converge CFD model of the CFR engine fuel-engine interactions that lead to knock with NG, including effects of fuel reactivity, engine operating parameters, and exhaust gas recirculation, are being investigated. A GT-Power model is used to estimate appropriate initial conditions in the Converge CFD model, including cylinder wall, piston, valves, and port surface temperatures as well as residual gas fraction. In order to validate the model, in-cylinder pressure data taken with a CFR engine is compared to model predictions while varying compression ratio, CA50, and BMEP.
Abstract: Engine knock and misfire are barriers to pathways leading to higher efficiency natural gas (NG) engines, where the general tendency to knock is highly dependent on engine operating conditions and the fuel reactivity. The problem is further complicated by low emissions limits and the wide range of chemical reactivity in pipeline quality NG, making the understanding of fuel- engine interactions essential to achieving high efficiencies in spark-ignited NG engines. The present work highlights a three-dimensional computational fluid dynamics (CFD) model of the Cooperative Fuel Research (CFR) engine to investigate knock behavior when running on NG. Using a Converge CFD model of the CFR engine fuel-engine interactions that lead to knock with NG, including effects of fuel reactivity, engine operating parameters, and exhaust gas recirculation, are being investigated. A GT-Power model is used to estimate appropriate initial conditions in the Converge CFD model, including cylinder wall, piston, valves, and port surface temperatures as well as residual gas fraction. In order to validate the model, in-cylinder pressure data taken with a CFR engine is compared to model predictions while varying compression ratio, CA50, and BMEP.
The performance of electrocatalysts is critical for renewable energy technologies. While the electrocatalytic activity can be modulated through structural and compositional engineering following the Sabatier principle, the insufficiently explored catalyst-electrolyte interface is promising to promote microkinetic processes such as physisorption and desorption. By combining experimental designs and molecular dynamics simulations with explicit solvent in high accuracy, we demonstrated that dimethylformamide can work as an effective surface molecular pump to facilitate the entrapment of oxygen and outflux of water. Dimethylformamide disrupts the interfacial network of hydrogen bonds, leading to enhanced activity of the oxygen reduction reaction by a factor of 2 to 3. This strategy works generally for platinum-alloy catalysts, and we introduce an optimal model PtCuNi catalyst with an unprecedented specific activity of 21.8 ± 2.1 mA/cm 2 at 0.9 V versus the reversible hydrogen electrode, nearly double the previous record, and an ultrahigh mass activity of 10.7 ± 1.1 A/mg Pt .
Thermal barrier coatings (TBCs) have been investigated both experimentally and through simulation for mixing controlled combustion (MCC) concepts as a method for reducing heat transfer losses and increasing cycle efficiency, but it is still a very active research area. Early studies were inconclusive, with different groups discovering obstacles to realizing the theoretical potential. Nuanced papers have shown that coating material properties, thickness, microstructure, and surface morphology/roughness all can impact the efficacy of the thermal barrier coating and must be accounted for. Adding to the complexities, a strong spatial and temporal heat flux inhomogeneity exists for mixing controlled combustion (diesel) imposed onto the surfaces from the impinging flame jets. In support of the United States Department of Energy SuperTruck II program goal to achieve 55% brake thermal efficiency on a heavy-duty diesel engines, this study sought to develop a deeper insight into the inhomogeneous heat flux from mixing controlled combustion on thermal barrier coatings and to infer concrete guidance for designing coatings. To that end, a co-simulation approach was developed that couples high-fidelity computational fluid dynamics (CFD) modeling of in-cylinder processes and combustion, and finite element analysis (FEA) modeling of the thermal barrier-coated and metal engine components to resolve spatial and temporal thermal boundary conditions. The models interface at the surface of the combustion chamber; FEA modeling predicts the spatially resolved surface temperature profile, while CFD develops insights into the effect of the thermal barrier coating on the combustion process and the boundary conditions on the gas side. The paper demonstrates the capability of the framework to estimate cycle impacts of the temperature swing at the surface, as well as identify critical locations on the piston/thermal barrier coating that exhibit the highest charge temperature and highest heat fluxes. In addition, the FEA results include predictions of thermal stresses, thus enabling insight into factors affecting coating durability. An example of the capability of the framework is provided to illustrate its use for investigating novel coatings and provide deeper insights to guide future coating design.
Superconducting electron linear accelerators open up new opportunities for advanced materials processing. High energy electrons penetrate deeper within materials, increase the processing depth and rate and enables production of far-from-equilibrium materials and selective processing within a material surface and subsurface. With support from the Office of Naval Research, NASA, and DoE, Mainstream Engineering commissioned a superconducting linear electron accelerator to investigate advanced materials processing and additive manufacturing. The goal of Mainstream's Electron Beam Enabled Advanced Manufacturing (EBEAM) center is to develop unique, far-from-equilibrium materials and alleviate significant technological deficiencies. We will present results of irradiating various metals and their unique material properties. Monte Carlo simulations electron beam and sample interaction were used to optimize the processing parameters and study far-from-equilibrium processing conditions. Applications include stainless steel additive manufacturing for small modular nuclear reactors and dissimilar metal bonding of corrosion-resistant cladding for next-generation molten salt reactors.
We investigate how SiO x oxide interlayers prepared by different techniques (chemical, thermal) in combination with hydrogen released from an ALD Al 2 O 3 source layer govern passivation in 1) passivated contacts based on doped poly-Si layers and tunneling SiO 2 , and 2) wafer surface passivation by Al 2 O 3 . Profiles of O and H in these structures with engineered, buried SiOx interlayers were measured by Time-of-Flight SIMS (TOF-SIMS) at nanometer resolution. Passivated contacts perform best with thermally oxidized SiOx, while chemical SiO x causes poly-Si film blistering and performance degradation. ALD Al 2 O 3 acts as passivating H source, significantly improving B-doped and intrinsic poly-Si contacts for IBC cells. Fast-diffusing hydrogen from the Al 2 O 3 source layer appears to penetrate Si wafer thickness, improving the passivation of structures at the opposite side. In contrast to the passivated contacts, chemical SiOx interlayer promotes wafer surface passivation by ALD Al 2 O 3 , while similarly thin thermal SiO 2 suppresses passivation and built-in charge.
Gram-positive organisms with their thick envelope cannot be lysed by complement alone. Nonetheless, antibody-binding on the surface can recruit complement and mark these invaders for uptake and killing by phagocytes, a process known as opsonophagocytosis. The crystallizable fragment of immunoglobulins (Fcγ) is key for complement recruitment. The cell surface of S. aureus is coated with Staphylococcal protein A (SpA). SpA captures the Fcγ domain of IgG and interferes with opsonization by anti-S. aureus antibodies. In principle, the Fcγ domain of therapeutic antibodies could be engineered to avoid the inhibitory activity of SpA. However, the SpA-binding site on Fcγ overlaps with that of the neonatal Fc receptor (FcRn), an interaction that is critical for prolonging the half-life of serum IgG. This evolutionary adaptation poses a challenge for the exploration of Fcγ mutants that can both weaken SpA–IgG interactions and retain stability. Here, we use both wild-type and transgenic human FcRn mice to identify antibodies with enhanced half-life and increased opsonophagocytic killing in models of S. aureus infection and demonstrate that antibody-based immunotherapy can be improved by modifying Fcγ. Our experiments also show that by competing for FcRn-binding, staphylococci effectively reduce the half-life of antibodies during infection. These observations may have profound impact in treating cancer, autoimmune, and asthma patients colonized or infected with S. aureus and undergoing monoclonal antibody treatment.
The atomization of a liquid jet is a multi-scale and multi-physics problem of interest for many engineering applications. Particularly, it drives the fuel-oxidizer mixing and dictates the efficiency of combustion engines. High-fidelity multi-phase simulations remain challenging due to the excessive computational cost required to capture all the atomization scales. Thus, atomization models are necessary to represent sub-grid liquid structures. Here, in this work, a modified Σ-Y model in the context of Eulerian-Lagrangian Spray Atomization (ELSA) is used to transport the surface area density of the spray. The model's predictive performance is assessed under various operating conditions relevant to heavy-duty engines using the Engine Combustion Network (ECN) Spray C and Spray D research-grade injectors. The classic droplet collision formulation of the Σ-Y model alone does not replicate the response of the measured spray surface area to changes in injector, ambient pressure, and injection pressure, requiring individual tuning of the model's parameters. Instead, a transition between dense and dilute spray breakup mechanisms is proposed in terms of the average droplet spacing. The collision breakup mechanism represents the dilute spray, whereas the droplet size in the dense spray is driven by a competition between the integral scale of turbulence and the balance between the turbulent kinetic energy and the surface energy of the droplets. Such an approach minimizes the requirements for model tuning. Moreover, the role of the constants of a compressible standard k-ε RANS model is assessed in the injector's internal flow and external spray simulation framework, and an updated set is proposed. The results are validated against x-ray radiography and Ultra-Small Angle X-ray Scattering (USAXS) data and highlight the predictive capabilities of the proposed physics-driven Σ-Y model, which is compatible with engine simulation turnaround times.
Aqueous dissolution of silicate glasses and minerals plays a critical role in global biogeochemical cycles and climate evolution. The reactivity of these materials is also important to numerous engineering applications including nuclear waste disposal. The dissolution process has long been considered to be controlled by a leached surface layer in which cations in the silicate framework are gradually leached out and replaced by protons from the solution. This view has recently been challenged by observations of extremely sharp corrosion fronts and oscillatory zonings in altered rims of the materials, suggesting that corrosion of these materials may proceed directly through congruent dissolution followed by secondary mineral precipitation. Here we show that complex silicate material dissolution behaviors can emerge from a simple positive feedback between dissolution-induced cation release and cation-enhanced dissolution kinetics. This self-accelerating mechanism enables a systematic prediction of the occurrence of sharp dissolution fronts (vs. leached surface layers), oscillatory dissolution behaviors and multiple stages of glass dissolution (in particular the alteration resumption at a late stage of a corrosion process). In conclusion, our work provides a new perspective for predicting long-term silicate weathering rates in actual geochemical systems and developing durable silicate materials for various engineering applications.
The carrying capacity of prestressed joints may be increased by plastic deformation of one surface using a flexible tool (rotating wire brush) and simultaneous coating application to that surface. An aluminum coating increases the frictional coefficient in the joint. Impact-friction treatment or dynamic cold working by hammer faces creates a strengthened nanostructured layer.
The accurate numerical simulation of linear waves interacting with periodic layered media is a crucial capability in engineering applications. In this contribution we study the stable and high-order accurate numerical simulation of the interaction of linear, time-harmonic waves with a periodic, triply layered medium with irregular interfaces. In contrast with volumetric approaches, High-Order Perturbation of Surfaces (HOPS) algorithms are inexpensive interfacial methods which rapidly and recursively estimate scattering returns by perturbation of the interface shape. In comparison with Boundary Integral/Element Methods, the stable HOPS algorithm we describe here does not require specialized quadrature rules, periodization strategies, or the solution of dense non-symmetric positive definite linear systems. In addition, the algorithm is provably stable as opposed to other classical HOPS approaches. With numerical experiments we show the remarkable efficiency, fidelity, and accuracy one can achieve with an implementation of this algorithm.
The scheme of the propulsion unit with walking mechanism for a wheeled planetary rover is considered. The walking mechanism permits to convert the chassis in a transport position, displacement of rover’s center of mass so as to optimize the load on motor wheels. In wheeled motion the walking mechanism realize function of the active suspension for adaptation of the locomotion system to the surface features. On friable soils where wheeled locomotion is difficult or impossible, wheel-walking mode improves trafficability.
Abstract The surface energy imbalance, viz. that the turbulent dissipation does not fully account for available energy, has for long been an outstanding challenge in geophysical studies. In this study, we developed a novel approach based on nonequilibrium thermodynamics by representing the atmospheric boundary layer as a heat engine. In addition, an analytically tractable approach was used to estimate the ground heat flux based on Green's function approach, which in turn determines the available energy that drives the atmospheric heat engine. The proposed model was evaluated using heat fluxes measured by eight AmeriFlux eddy covariance towers with atmospheric temperature profiles recorded at adjacent radiosonde sites. The surface energy balance closure can be improved by ~11% over various landscapes, by including the estimated power production from the atmospheric heat engine.
Bisphenol A (BPA) is a high production volume chemical with potential estrogenic effects susceptible to abiotic degradation by MnO 2 . BPA transformation products and reaction mechanisms with MnO 2 have been investigated, but detailed process understanding of Mn(III)-mediated degradation has not been attained. Rapid consumption of BPA occurred in batch reaction vessels with 1 mM Mn(III) and 63.9 ± 0.7% of 1.76 ± 0.02 μmol BPA was degraded in 1 hour at circumneutral pH. BPA was consumed at 1.86 ± 0.09-fold higher rates in vessels with synthetic MnO 2 comprising approximately 13 mol% surface-associated Mn(III) versus surface-Mn(III)-free MnO 2 , and 10–35% of BPA transformation could be attributed to Mn(III) during the initial 10-min reaction phase. High-resolution tandem mass spectrometry (HRMS/MS) analysis detected eight transformation intermediates in reactions with Mn(III), and quantum calculations proposed 14 BPA degradation products, nine of which had not been observed during MnO 2 -mediated BPA degradation, suggesting mechanistic differences between Mn(III)- versus MnO 2 -mediated BPA degradation. Finally, the findings demonstrate that both Mn(III) and Mn(IV) can effectively degrade BPA and indicate that surface-associated Mn(III) increases the reactivity of synthetic MnO 2 , offering opportunities for engineering more reactive oxidized Mn species for BPA removal.
Here, we use a nonplanar aromatic molecule, contorted octabenzocircumbiphenyl (c-OBCB), to sort semiconducting single-walled carbon nanotubes (SWNTs) by their chiral angles. From absorption spectroscopy, photoluminescence excitation spectroscopy, and Raman spectroscopy studies, we find that c-OBCB preferentially binds and sorts for a number of semiconducting carbon nanotubes with chiral angles greater than 12°. Molecular dynamics simulations reveal that the contorted aromatic core of c-OBCB binds strongly to only certain SWNTs, especially those with matching curvature, and that this discriminatory binding interaction is reinforced by preferences of the side chains on the c-OBCB to stick to SWNT surface rather than interact with the solvent. This opens the door to side chain/solvent engineering to bias the selection of certain (m,n) SWNT variants. We also investigate the temperature dependence of hole mobility in field-effect transistors comprising c-OBCB-sorted semiconducting carbon-nanotube networks and find hole transport in these networks to be thermally activated.