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Asymptotic-preserving methods for hyperbolic and transport equations with random inputs and diffusive scalings
In this paper we develop a set of stochastic numerical schemes for hyperbolic and transport equations with diffusive scalings and subject to random inputs. The schemes are asymptotic preserving (AP), in the sense that they preserve the diffusive limits of the equations in discrete setting, without requiring excessive refinement of the discretization. Our stochastic AP schemes are extensions of the well-developed deterministic AP schemes. To handle the random inputs, we employ generalized polynomial chaos (gPC) expansion and combine it with stochastic Galerkin procedure. We apply the gPC Galerkin scheme to a set of representative hyperbolic and transport equations and establish the AP property in the stochastic setting. We then provide several numerical examples to illustrate the accuracy and effectiveness of the stochastic AP schemes.
Stable large-scale solver for Ginzburg–Landau equations for superconductors
Understanding the interaction of vortices with inclusions in type-II superconductors is a major outstanding challenge both for fundamental science and energy applications. At application-relevant scales, the long-range interactions between a dense configuration of vortices and the dependence of their behavior on external parameters, such as temperature and an applied magnetic field, are all important to the net response of the superconductor. Capturing these features, in general, precludes analytical description of vortex dynamics and has also made numerical simulation prohibitively expensive. Here we report on a highly optimized iterative implicit solver for the time-dependent Ginzburg-Landau equations suitable for investigations of type-II superconductors on massively parallel architectures. Its main purpose is to study vortex dynamics in disordered or geometrically confined mesoscopic systems. In this work, we present the discretization and time integration scheme in detail for two types of boundary conditions. We describe the necessary conditions for a stable and physically accurate integration of the equations of motion. Using an inclusion pattern generator, we can simulate complex pinning landscapes and the effect of geometric confinement. We show that our algorithm, implemented on a GPU, can provide static and dynamic solutions of the Ginzburg-Landau equations for mesoscopically large systems over thousands of time steps in a matter of hours. Using our formulation, studying scientifically-relevant problems is a computationally reasonable task.
Iron phosphate glasses: Bulk properties and atomic scale structure
Bulk properties such as glass transition temperature, density and thermal expansion of iron phosphate glass compositions, with replacement of Cs by Ba, are investigated as a surrogate for the transmutation of 137Cs to 137Ba, relevant to the immobilisation of Cs in glass. These studies are required to establish the appropriate incorporation rate of 137Cs in iron phosphate glass. Density and glass transition temperature increases with the addition of BaO indicating the shrinkage and reticulation of the iron phosphate glass network. The average thermal expansion coefficient reduces from 19.8 × 10-6 K-1 to 13.4 × 10-6 K-1, when 25 wt. % of Cs2O was replaced by 25 wt. % of BaO in caesium loaded iron phosphate glass. In addition to the above bulk properties, the role of Ba as a network modifier in the structure of iron phosphate glass is examined using various spectroscopic techniques. The FeII content and average coordination number of iron in the glass network was estimated using Mössbauer spectroscopy. The FeII content in the un-doped iron phosphate glass and barium doped iron phosphate glasses was 20, 21 and 22 ± 1% respectively and the average Fe coordination varied from 5.3 ± 0.2 to 5.7 ± 0.2 with increasing Ba content. The atomic scale structure was further probed by Fe K-edge X-ray absorption spectroscopy. The average coordination number provided by extended X-ray absorption fine structure spectroscopy and X-ray absorption near edge structure was in good agreement with that given by the Mössbauer data.
Atomic solid state energy scale: Universality and periodic trends in oxidation state
The atomic solid state energy (SSE) scale originates from a plot of the electron affinity (EA) and ionization potential (IP) versus band gap (E{sub G}). SSE is estimated for a given atom by assessing an average EA (for a cation) or an average IP (for an anion) for binary inorganic compounds having that specific atom as a constituent. Physically, SSE is an experimentally-derived average frontier orbital energy referenced to the vacuum level. In its original formulation, 69 binary closed-shell inorganic semiconductors and insulators were employed as a database, providing SSE estimates for 40 elements. In this contribution, EA and IP versus E{sub G} are plotted for an additional 92 compounds, thus yielding SSE estimates for a total of 64 elements from the s-, p-, d-, and f-blocks of the periodic table. Additionally, SSE is refined to account for its dependence on oxidation state. Although most cations within the SSE database are found to occur in a single oxidation state, data are available for nine d-block transition metals and one p-block main group metal in more than one oxidation state. SSE is deeper in energy for a higher cation oxidation state. Two p-block main group non-metals within the SSE database are found to exist in both positive and negative oxidation states so that they can function as a cation or anion. SSEs for most cations are positioned above −4.5 eV with respect to the vacuum level, and SSEs for all anions are positioned below. Hence, the energy −4.5 eV, equal to the hydrogen donor/acceptor ionization energy ε(+/−) or equivalently the standard hydrogen electrode energy, is considered to be an absolute energy reference for chemical bonding in the solid state. - Highlights: • Atomic solid-state energies are estimated for 64 elements from experimental data. • The relationship between atomic SSEs and oxidation state is assessed. • Cations are positioned above and absolute energy of −4.5 eV and anions below.
Determining Individual Phase Properties in a Multi-phase Q&P Steel using Multi-scale Indentation Tests
A new inverse method was developed to predict the stress-strain behaviors of constituent phases in a multi-phase steel using the load-depth curves measured in nanoindentation tests combined with microhardness measurements. A power law hardening response was assumed for each phase, and an empirical relationship between hardness and yield strength was assumed. Adjustment was made to eliminate the indentation size effect and indenter bluntness effect. With the newly developed inverse method and statistical analysis of the hardness histogram for each phase, the average stress-strain curves of individual phases in a quench and partitioning (Q&P) steel, including austenite, tempered martensite and untempered martensite, were calculated and the results were compared with the phase properties obtained by in-situ high energy X-ray diffraction (HEXRD) test. It is demonstrated that multi-scale instrumented indentation tests together with the new inverse method are capable of determining the individual phase flow properties in multi-phase alloys.
In-Situ Thermal Imaging for Single Layer Build Time Alteration in Large-Scale Polymer Additive Manufacturing
Ideally, objects are constructed as quickly as possible; however, short build times for a layer can present an issue. Short layer build times do not provide enough time for the material to cool sufficiently. As a result, an object cannot support its own weight nor deal with overhanging features. Additionally, the generation of support structures may be impractical either due to geometry or, in the case of large-scale polymer additive manufacturing, due to being difficult to remove. To address these issues, researchers at Oak Ridge National Laboratory’s Manufacturing Demonstration Facility investigated the use of a thermal camera mounted to a gantry to collect data on an object under construction. This data provided feedback for an in-situ control system to adjust layer build times. Adjustments were made in the form of additional waiting to allow the material to cool to specific thermal thresholds. This additional cooling time allowed the construction of objects with low layer build times or overhanging features that would have otherwise failed.
Real-Time Defect Correction in Large-Scale Polymer Additive Manufacturing via Thermal Imaging and Laser Profilometer
Defects can result in a failed part and are costly in terms of time and material. This cost is even greater in the context of large-scale additive manufacturing where the objects can be very large. As a result, a great deal of research has focused on defect identification and mitigation. To address defects during object construction, researchers at Oak Ridge National Laboratory’s Manufacturing Demonstration Facility investigated an in-situ control system comprised of two sensors: a thermal camera and laser profilometer. This control system adjusted material flow and build speed to mitigate three types of defects: low layer times, underfill, and overfill. Several test objects were constructed. The control system was found to adjust build parameters to handle low layer times of approximately 15 seconds and height deviations from -100% underfill (the absence of a layer) to 50% overfill. Within two layers, height deviations could be returned to within 10% of the expected layer height. Further, preliminary results suggest the system can compensate for uneven build surfaces.
A visual analytics system for optimizing the performance of large-scale networks in supercomputing systems
Not Available
Integrating computation and experiment to investigate photoelectrodes for solar water splitting at the microscopic scale.
CONSPECTUS: Photoelectrochemical water-splitting is a promising and sustainable way to store the energy of the sun in chemical bonds and use it to produce hydrogen gas, a clean fuel. The key components in photoelectrochemical cells (PECs) are photoelectrodes, including a photocathode that reduces water to hydrogen gas and a photoanode that oxidizes water to oxygen gas. Materials used in photoelectrodes for PECs must effectively absorb sunlight, yield photogenerated carriers, and exhibit electronic properties that enable the efficient shuttling of carriers to the surface to participate in relevant water-splitting reactions. Discovering and understanding the key characteristics of optimal photoelectrode materials is paramount to the realization of PEC technologies. Oxide-based photoelectrodes can satisfy many of these materials requirements, including stability in aqueous environments, band edges with reasonable alignment with the redox potentials for water splitting, and ease of synthesis. However, oxide photoelectrodes generally suffer from poor charge transport properties and considerable bulk electron-hole separation, and they have relatively large band gaps. Numerous strategies have been proposed to improve these aspects and understand how these improvements are reflected in the photoelectrochemical performance. Unfortunately, the structural and compositional complexity of multinary oxides accompanied by the inherent complexity of photoelectrochemical processes makes it challenging to understand the individual effects of composition, structure, and defects in the bulk and on the surface on a material's photoelectrochemical properties. The integration of experiment and theory has great potential to increase our atomic-level understanding of structure-composition-property relationships in oxide photoelectrodes. In this Account, we describe how integrating experiment and theory is beneficial for achieving scientific insights at the microscopic scale. We highlight studies focused on understanding the role of (i) bulk composition via solid-state solutions, intercalation, and comparison with isoelectronic compounds, (ii) dopants for both the anion and cation and their interactions with oxygen vacancies, and (iii) surface/interface structure in the photocurrent generation and photoelectrochemical performance in oxide photoelectrodes. In each instance, we outline strategies and considerations for integrating experiment and theory and describe how this integration led to valuable insights and new directions in uncovering structure-composition-property relationships. Our aim is to demonstrate the unique value of combining experiment and theory in studying photoelectrodes and to encourage the continued effort to bring experiment and theory in closer step with each other.
Effect of Temperature on the Pilot-Scale Catalytic Pyrolysis of Loblolly Pine
A pilot-scale biomass catalytic pyrolysis unit with a nominal throughput of one tonne of biomass per day (1TPD) has been in operation since 2013 to investigate the process parameters that have the largest influence on biocrude yield, oxygen content, and chemical composition. A parametric study was conducted to investigate the effect of pyrolysis temperature, ranging from 433 to 581 °C, on biocrude yield and quality. A locally sourced loblolly pine feedstock and a commercially available, spray dried, nonzeolitic γ-Al 2 O 3 catalyst were used in individual experiments conducted at each pyrolysis temperature to achieve a minimum of 4 h of steady-state continuous operation. Typically, 600–800 kg of biomass was fed over a 12-h period, with one experiment extended to almost 29 h (1144-kg biomass fed) and one experiment interrupted by a process upset after just 7 h (411-kg biomass fed). Comprehensive analysis of collected gas, liquid, and solid products were used to calculate carbon balances (77% to 107%) for each experiment. The biocrude yield ranged from 12 to 18 wt % C and, in general, decreased with increasing pyrolysis temperature. The average steady-state biocrude yield as a function of temperature translated to a biocrude production rate between 40 and 50 gallons per dry ton. The biocrude oxygen content varied between 21 and 31 wt %, on a dry basis, and as expected, decreased with increasing pyrolysis temperature. The identified components in the semivolatile biocrude products are mostly methoxyphenols and other multiphenolic compounds. The multiphenolic compounds are demethoxylated at pyrolysis temperatures above 500 °C, producing biocrudes with higher concentrations of monophenols and polycyclic aromatic hydrocarbons. The concentration of anhydrosugars, like levoglucosan in the biocrudes, decreased with increasing pyrolysis temperature from ∼15 to ∼1 vol %.
An Implementation of Replica Exchange with Dynamical Scaling for Efficient Large-Scale Simulations
An implementation of the replica exchange with dynamical scaling (REDS) method in the commonly used molecular dynamics program GROMACS is presented. REDS is a replica exchange method that requires fewer replicas than conventional replica exchange while still providing data over a range of temperatures and can be used in either constant volume or constant pressure ensembles. Details for running REDS simulations are given, and an application to the human islet amyloid polypeptide (hIAPP) 11-25 fragment shows that the model efficiently samples conformational space.
Brønsted Acid Scaling Relationships Enable Control over Product Selectivity from O2 Reduction with a Mononuclear Cobalt Porphyrin Catalyst
The selective reduction of O2, typically with the goal of forming H2O, represents a long-standing challenge in the field of catalysis. Macrocyclic transition-metal complexes, and cobalt porphyrins in particular, have been the focus of extensive study as catalysts for this reaction. Here, we show that the mononuclear Co-tetraarylporphyrin complex, Co(porOMe) (porOMe = meso-tetra(4-methoxyphenyl)porphyrin), catalyzes either 2e-/2H+ or 4e-/4H+ reduction of O2 with high selectivity simply by changing the identity of the Bronsted acid in dimethylformamide (DMF). The thermodynamic potentials for O2 reduction to H2O2 or H2O in DMF are determined and exhibit a Nernstian dependence on the acid pKa, while the CoIII/II redox potential is independent of the acid pKa. The reaction product, H2O or H2O2, is defined by the relationship between the thermodynamic potential for O2 reduction to H2O2 and the CoIII/II redox potential: selective H2O2 formation is observed when the CoIII/II potential is below the O2/H2O2 potential, while H2O formation is observed when the CoIII/II potential is above the O2/H2O2 potential. Mechanistic studies reveal that the reactions generating H2O2 and H2O exhibit different rate laws and catalyst resting states, and these differences are manifested as different slopes in a linear free energy correlation between the log(rate) vs. effective overpotential for the reactions. This work shows how the manipulation of thermodynamic scaling relationships may be used to control product selectivity, and it provides a mechanistic basis for the pursuit of molecular catalysts that achieve low overpotential reduction of O2 to H2O.
Identifying and Breaking Scaling Relations in Molecular Catalysis of Electrochemical Reactions
Improving molecular catalysis for important electrochemical proton-coupled electron transfer (PCET) reactions, such as the interconversions of H+/H2, O2/H2O, CO2/CO, and N2/NH3, is an ongoing challenge. Synthetic modifications to the molecular catalysts are valuable but often show trade-offs between turnover frequency (TOF) and the effective overpotential required to initiate catalysis (?eff). Herein, we derive a new approach for improving efficiencieshigher TOF at lower ?eff by changing the concentrations and properties of the reactants and products, rather than by modifying the catalyst. The dependence of TOF on ?eff is shown to be quite different upon changing, for instance, the pKa of the acid HA versus the concentration or partial pressure of a reactant or product. Using the electrochemical reduction of dioxygen catalyzed by iron porphyrins in DMF as an example, decreasing [HA] 10-fold lowers ?eff by 59 mV and decreases the TOF by a factor of 10. Alternatively, a 10- fold decrease in Ka(HA) also lowers ?eff by 59 mV but only decreases the TOF by a factor of 2. This approach has been used to improve a catalytic TOF by 104 vs the previously reported scaling relationship developed via synthetic modifications to the catalyst. The analysis has the potential to predict improved efficiency and product selectivity of any molecular PCET catalyst, based on its mechanism and rate law.
An Optimized Parameterization of Sub‐Grid Scale Advection for Convection Permitting Models
Convection‐permitting models (CPMs) explicitly resolve deep convection yet under‐resolve the organized lateral exchanges among drafts and their environment that control entrainment/detrainment, precipitation efficiency, and mesoscale structure. In this work, we introduce the Optimized Advection Scheme (OAS), which introduces a small rotation of the Cartesian frame of reference for the horizontal winds relative to other variables used in advection that induces cross‐gradient transport to mimic under‐resolved convective mixing. The rotation angle is selected to minimize the Kullback–Leibler divergence between the simulated and satellite observed precipitation intensity distributions, yielding a physically consistent perturbation that is computationally inexpensive and portable. Optimized Advection Scheme is implemented in WRF and evaluated over Amazon (April 2014). It shifts precipitation–precipitable‐water joint distributions toward lighter rain, reduces overly intense rates, and improves mesoscale convective system (MCS) lifetime and propagation. Mechanistically, the added cross‐gradient transport promotes convective detrainment and environmental mixing, which cools and moistens the mid‐troposphere, weakens downward momentum transport, alleviates excessive downwelling shortwave biases, and warms the surface temperature. The optimized rotation angle yields comparable improvements at 4‐km and 1‐km grid spacing, demonstrating resolution‐independent benefits across the CPM gray zone. By targeting the dynamical root of under‐mixed convective circulations, rather than tuning model microphysics or closures, OAS delivers robust, scale‐aware improvements in precipitation statistics, cloud vertical structure, and characteristics of MCS (MCSs), offering a practical pathway to more reliable CPM simulations for weather and climate applications.