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The interface of an active liquid crystal can support travelling waves. We derive dispersion relations from a linear theory and compute the dynamical structure factor from continuum simulations.
This paper studied the effects of air void defects on the failure behavior of adhesively-bonded materials under global shear via micro-scale computational modeling. The numerical results indicated that interfacial void defects can largely facilitate interfacial debonding of a weaker adherend/adhesive interface under shear. However, this is not the case for a stronger adherend/adhesive interface, showing the reduction on the nominal shear strength of an adhesive joint is mainly due to internal void defects. The reduced bonding performance due to voids can be improved by leveraging an appropriate surface roughness. This preliminary investigation is a first step to better understand the micro-mechanics of interfacial failure in the adhesion of a roughened/patterned adherend via surface modification(s) and an adhesive, and also shows the importance of minimizing interfacial void defects in particular at a weaker adherend/adhesive interface via different techniques.
SAND2021-1654 O The Python Material Tensor code (PyMTensor) applies crystal symmetries to arbitrary material tensors. It uses symbolic algebra to exactly determine the zero tensor components and the relationships between the nonzero components for all crystallographic point groups. It is capable of computing components of higher-order material tensors needed when treating nonlinear effects. It has a simple and flexible interface for inputting tensor rank information and any known symmetries between tensor indices.Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
Virtual Engineering (VE) is a Python software framework designed to accelerate the research and development of engineering processes that are fundamentally defined by multiple unit operations executed in series. VE supports a wide variety of different multi-physics models and integrates them to simulate a complete end-to-end process. To automate the execution of this model sequence, VE provides (i) a robust method to communicate between models, (ii) a high-level, user-friendly interface to set model parameters and enable optimization, and (iii) an overall model-agnostic approach that allows new computational units to be swapped in and out of workflows. Although the VE framework was developed to support the biochemical conversion of biomass to fuel, we have designed each component to easily accommodate new domains and unit models.
This document serves as the final report for United States Department of Energy Basic Energy Sciences Grant DE-FG02-08ER15929, “Computational and Spectroscopic Investigations of the Molecular Scale Structure and Dynamics of Geologically Important Fluids and Mineral-Fluid Interfaces” (R. James Kirkpatrick, P.I., A. O. Yazaydin, co-P.I.). The research under this grant was intimately tied to that supported by the parallel the grant of the same title at Alfred (DOE DE-FG02-10ER16128; Geoffrey M. Bowers, P.I.).
We describe an object-oriented framework and user interface for developing models of adversarial scenarios, implemented as stochastic Petri nets. The framework is implemented using the statistical computing language R, and provides facilities for eliciting models from subject matter experts, displaying models graphically, simulating scenario execution, and presenting scenario outcomes and statistics.
Diatoms are important oceanic photosynthetic microorganisms, responsible for approximately 20% of all carbon fixation on Earth. Additionally, diatoms are attractive metabolic engineering candidates. This investigation leveraged Systems Biology techniques to advance our understanding of and potential to engineer diatoms for bioproducts of societal interest, such as biofuels. Systems Biology combines computational and experimental techniques to understand an organism at the whole-cell level as well as characterize the interface between the organism and its environment. Our research resulted in major contributions to the understanding of systems-level light-driven metabolism. Diatoms have a unique evolutionary history. As a result, existing understanding of photosynthetic metabolism may not be applicable to diatom physiology. We generated, validated, and published a high-quality metabolic model of diatom metabolism facilitating novel insights into these unique organisms. Additionally, we overcame a consistent challenge in the modeling of photosynthetic organisms by integrating light into metabolic models. This result allows realistic assessments of how the light environment affects cell physiology and metabolism, a necessary advancement for bioengineering. Next, we generated several methodologies for integrating large-scale datasets with models of metabolism. These “big data” approaches begin to address a constant challenge in biology, which is translating data into information and understanding. Finally, we built web-based databases for housing, distributing, and visualizing systems-level biology data. These projects created an online ecosystem for sharing, analyzing, and characterizing metabolic information and is currently being used by public and private institutions. Photosynthetic bioengineering has the promise to create high-energy products with minimal inputs. Systems Biology has a long history of facilitating bioengineering of cellular metabolism. However, photosynthetic organisms are only sparsely present in this history. This project and its outputs have accelerated the application of proven Systems Biology techniques to light-driven metabolism. The resulting methodologies will accelerate public and private endeavors leveraging photosynthetic systems in the bioengineering of bioproducts such as biofuels or carbon sequestration.
Bioaerosol and soil particles are ubiquitous in the environment. They are multicomponent and complex in nature displaying mixed inorganic and organic components. The way components are mixed in a bioaerosol sample is referred to as its mixing state. Soil particles are also a mixture of inorganic (mineral) and organic (soil organic matter) components. Bioaerosol particles contribute to a major fraction of coarse mode atmospheric particles, especially in the tropical areas, contributing up to 80 % of the particle mass concentration. The mixing state of particles is crucial to evaluate because it impacts several important environmental processes such as warm and cold cloud formation and radiation budget. Mixing states in aerosols are accompanied by chemical reactions across solid-liquid-gas interfaces. In this study, we utilized elemental compositions and microcopy images of thousands of atmospheric particles acquired by computer-controlled scanning electron microscope equipped with an energy-dispersive x-ray spectrometer to compute the mixing state of atmospheric particles. A 2D convolutional neural network (CNN), also known as convnet, was used to model the relationship between low resolution imaging data and higher resolution spectroscopy data, with the former as training input and the latter as target output. Two types of CNNs were implemented and tested; a basic CNN and an Inception-v3 network. For binary classification, the basic CNN achieved an accuracy of 84.29 % across all atom types, and the Inception-v3-like network achieved an accuracy 85.51 %. This study demonstrates the applicability of deep learning to handle large amounts of imaging/chemical spectroscopy data efficiently and evaluate particle mixing state from a range of environmental samples.
This report contains the problem formulation for the Grid Optimization (GO) Competition Challenge 3. The Grid Optimization Competition is run by a team of researchers from a number of organizations, including the sponsor Advanced Research Projects Agency - Energy (ARPA-E), lead organization Pacific Northwest National Laboratory (PNNL), and technical contributors from Los Alamos National Laboratory (LANL), National Renewable Energy Laboratory (NREL), Texas A&M University (TAMU), Georgia Institute of Technology (GT), University of Wisconsin (UW), and others. The GO Competition poses challenge problems in the field of power grid management, invites entrants to develop solvers for these problems, invokes the solvers on a set of problem instances using common hardware, ranks the solvers according to their performance, and awards prizes according to the rankings. The overall goal of the GO Competition is to spur innovative research on high impact and computationally challenging problems in power grid management from initial development through commercial deployment. Complete information about the GO Competition can be found online at [2]. The webpage covers previous Challenges, rules, timeline, registration information, data formats, scoring methods, computational platform information, information on supported solvers and languages, sponsor information, frequently asked questions, administrator contact information, publicly available problem instances, computer code for reading and evaluating problem and solution data, a sandbox for testing solvers, and a solver submission interface, results, and publications.
MCFM is a dedicated Monte-Carlo simulation program for collider phenomenology at highest energies. Designed during the Tevatron era, it has successfully incorporated the latest developments needed for LHC precision calculations and remained on the forefront of collider phenomenology. The Fortran code includes interfaces to modern PDF and loop reduction libraries but has been unchanged structurally compared to the earlier versions. Parallel computing has been enabled using OpenMP and MPI. MCFM provides numerically highly stable one-loop amplitudes and superior phase-space efficiency, leading to excellent performance in NXLO calculations using jettiness or qT subtraction techniques for IR regularization.
The U.S. Department of Energy’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program aims to develop predictive capabilities by applying computational methods to the analysis and design of advanced reactor and fuel cycle systems. This program has been providing engineering-scale support for the development of BISON, a high-fidelity and high-resolution fuel performance tool. Stress-based failure probability has been developed and analyzed to assess the integrity of tri-structural isotropic (TRISO) fuel particles during fuel life cycles. While simple, stress-based approaches to failure probability leveraging the Weibull statistical distribution entails a number of drawbacks when stress concentration occurs near crack tips, including finite element mesh size dependency. In this report, we use an interaction integral approach to the computation of stress intensity factors in functionally graded materials (FGM) for axisymmetric models. The inner pyrolytic carbon (IPyC) cracking induced silicon carbide (SiC) failure is one of the dominated failure modes in TRISO failure analysis. In this study, we consider a crack in the IPyC layer perpendicular to the SiC layer. The interface between these two TRISO layers is considered to be porous, which we simulate considering a transition of mechanical properties over the porous length. These aspects are considered in the computation of stress intensity factor (SIF) from a fracture mechanics approach and compared with the known stress-based failure probability approach.
The performance of CdTe solar cells can be very sensitive to the emitter/absorber interface, especially for high-efficiency cells with high bulk lifetime. Performance losses from acceptor-type interface defects can be significant when interface defect states are located near mid-gap energies. Numerical simulations show that the emitter/absorber band alignment, the emitter doping and thickness, and the defect properties of the interface (i.e., defect density, defect type, and defect energy) can all play significant roles in the interface recombination. In particular, a type I heterojunction with small conduction-band offset (0.1 eV ≤ ΔE{sub C} ≤ 0.3 eV) can help maintain good cell efficiency in spite of high interface defect density, much like with Cu(In,Ga)Se{sub 2} (CIGS) cells. The basic principle is that positive ΔE{sub C}, often referred to as a “spike,” creates an absorber inversion and hence a large hole barrier adjacent to the interface. As a result, the electron-hole recombination is suppressed due to an insufficient hole supply at the interface. A large spike (ΔE{sub C} ≥ 0.4 eV), however, can impede electron transport and lead to a reduction of photocurrent and fill-factor. In contrast to the spike, a “cliff” (ΔE{sub C} < 0 eV) allows high hole concentration in the vicinity of the interface, which will assist interface recombination and result in a reduced open-circuit voltage. Another way to mitigate performance losses due to interface defects is to use a thin and highly doped emitter, which can invert the absorber and form a large hole barrier at the interface. CdS is the most common emitter material used in CdTe solar cells, but the CdS/CdTe interface is in the cliff category and is not favorable from the band-offset perspective. The ΔE{sub C} of other n-type emitter choices, such as (Mg,Zn)O, Cd(S,O), or (Cd,Mg)Te, can be tuned by varying the elemental ratio for an optimal positive value of ΔE{sub C}. These materials are predicted to yield higher voltages and would therefore be better candidates for the CdTe-cell emitter.
Oblique shock wave–interface interactions of gases and liquids have been extensively studied in shock tubes using optical methods to measure equation-of-state (EOS) parameters. However, this is difficult with solids due to their opaqueness to visible light. X ray phase contrast imaging (XPCI) has the penetrative strength to probe solids while still being sensitive to mass density and enhancing the visibility of material boundaries. We investigate the accuracy and repeatability of measuring the mean value of the average mass density (areal density divided by thickness) over region S (B S ) and flow deflection angle (θ) from XPCI images of a sample. To that end, a Hough transform-based method for measuring θ is developed. To measure B S , the XPCI image intensity probability density function (PDF) is modeled accounting for the spatial distribution of x ray energy, scintillator response, and pulse-to-pulse variation in the x ray intensity. In addition, a Monte Carlo-based algorithm for computing the B S PDF is developed. Both methods are validated on an impact-generated oblique shock wave interacting at a solid polymer-to-polymer interface. Furthermore this is accomplished through a comparison to hydrodynamic simulations using well-established EOS. Under the modeling framework for the XPCI image intensity, B S is computed with an accuracy of < 0.1% and precision of 3%–5%, while θ has an uncertainty of 0.2°, respectively. This shows that the XPCI-based model that is developed here could be an invaluable tool for high-fidelity testing of hydrodynamic models in shock polar configurations.
Weibull stress-based methods for failure probability assessment have been developed and analyzed to assess the integrity of tristructural isotropic (TRISO) fuel particles during fuel life cycles and accident operating conditions. While simple, these methods entail a number of drawbacks when stress concentrates near crack tips, including finite element mesh size dependency when the Weibull stress is averaged over the finite element domain. Fracture mechanics approaches involving the use of interaction integrals eliminate this lack of mesh convergence and produce consistent fracture predictions. In this work, we use an interaction integral approach to computing stress intensity factors for a crack in the inner pyrolytic carbon layer perpendicular to the silicon carbide layer, which is simplified representation of a failure mode in TRISO particles. Further, the interface between these two TRISO layers has been shown to become porous, which we simulate by considering a transition of mechanical properties over such porous length, i.e. the layers are modeled as a functionally graded material. Aspects such as porosity and thermal and irradiation eigenstrains are considered in computing the stress intensity factor from a fracture mechanics approach and compared with the known Weibull stress failure approach. The methodology introduced in this paper enables a more general fracture probability assessment in TRISO particles and eliminates the need to identify best suited parameters when using local or averaged stress-based failure criterion. Finally, the numerical sensitivity studies show how parameters such as the porous transition zone length, the material stiffness, and creep affect the probability of TRISO fuel particle failure.
We have combined two seafloor mapping techniques to efficiently map benthic habitat conditions. Multibeam echosounder (MBES) surveys provide detailed bathymetry and backscatter mosaics. Sediment profile and plan view imaging (SPI/PV) rapidly provides dense ground truth data on seabed physical and biological features. A key innovation of this project has been the development of a computer-assisted image processing system that standardizes the SPI data generation process. A unique combination of machine- and deep-learning techniques for grain-size classification, sediment–water interface delineation, and object detection (e.g., infauna and feeding voids) is described. An automated SPI image analysis system provides a standardized and cost-effective approach for image processing. Combining MBES with SPI/PV camera technology provides a first tier approach to rapidly documenting seabed physical and biological conditions in high resolution at MHK sites. When coupled with computer automated data extraction from the imagery, this approach provides a standardized and cost-effective method to establish and monitor benthic habitat conditions over time.
Proton transfer has been widely regarded as a key step in many electrochemical and biological processes. However, direct molecular evidence has long been lacking. In this work, we chose the electrochemical oxidation of acetaminophen (APAP) as a model system and utilized in situ liquid time-of-flight secondary ion mass spectroscopy (ToF-SIMS) to molecularly examine proton solvation and transfer in this process. In addition, we successfully captured and identified the transient radical intermediate, providing solid molecular evidence to resolve an important debate in electron transfer-proton transfer oxidation mechanism of APAP. Moreover, the potential-dependent behaviors of both inert ions and electroactive species during the dynamic potential scanning were chemically monitored in real time and the mass diffusion mechanism regarding the electroactive and non-electroactive species was revealed under polarized conditions. The results are well consistent with our computer simulation. The observations in this work greatly improved the comprehensive understanding of proton transfer and mass dynamics occurring at the electrode-electrolyte interface involved in complex electrochemical processes.
As a promising new design concept, the converter-interfaced combined heat and power (CHP) system is coupled to the bulk grid through a rectifier and a grid-ready tied inverter. Compared to the traditional directly-coupled CHP system, it removes the requirement for oversizing the CHP generator, limits the short-circuit contribution of the generator and simplifies the grid integration process of CHP system. This paper evaluates the economic benefits of this concept by calculating the annualized Return-on-Investment (ROI) and comparing it to the directly-coupled system. The economic analysis includes timeseries simulations to compute energy transactions with the bulk grid as well as sizing the equipment to calculate the capital and operational costs. Obtained results indicate that in majority of user cases evaluated, the converter-interfaced CHP systems can provide better ROI than directly-coupled systems. Given the additional technical benefits provided by inverter-based distributed energy resources (DERs), the proposed concept is proved to be technically viable and economically feasible.
Neutron scattering is a powerful tool to elucidate the structure and dynamics of systems that are important to geochemists, including ion association in complex aqueous solutions, solvent-exchange reactions at mineral–water interfaces, and reaction and transport of fluids in nanoporous materials. This article focusses on three techniques: neutron diffraction, which can reveal the atomic-level structure of aqueous solutions and solids; quasi-elastic neutron scattering, which measures the diffusional dynamics at mineral–water interfaces; and small-angle neutron scattering, which can show how properties of nanoporous systems change during gas, liquid, and solute imbibition and reaction. Furthermore, the usefulness and applicability of the experimental results are extended by rigorous comparison to computational simulations.