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Sustainable aviation fuels have the potential for reducing emissions and environmental impact. To help identify viable sustainable aviation fuels and accelerate research, several machine learning models have been developed to predict relevant physiochemical properties. However, many of the models have limited applicability, leverage data from complex analytical techniques with confined spectral ranges, or use feature decomposition methods that have limited interpretability. Using liquid-phase Fourier Transform Infrared (FTIR) spectra, this study presents a structured method for creating accurate and interpretable property prediction models for neat molecules, aviation fuels, and blends. Liquid-phase FTIR spectra measurements can be collected quickly and consistently, offering high reliability, sensitivity, and component specificity using less than 2 mL of sample. The method first decomposes FTIR spectra into fundamental building blocks using Non-negative Matrix Factorization (NMF) to enable scientific analysis of FTIR spectra attributes and fuel properties. The NMF features are then used to create five ensemble models for predicting final boiling point, flash point, freezing point, density at 15C, and kinematic viscosity at -20C. All models were trained using experimental property data from neat molecules, aviation fuels, and blends. The models accurately predict properties while enabling interpretation of relationships between compositional elements of a fuel, such as functional groups or chemical classes, and its properties. To support sustainable aviation fuel research and development, the models and data are available on an interactive web tool., Comment: 13 pages, 8 figures, Submitted to Fuel

Due to the expected disparity in quantum vs. classical clock speeds, quantum advantage for branch and bound algorithms is more likely achievable in settings involving large search trees and low operator evaluation costs. Therefore, in this paper, we describe and experimentally validate an exact classical branch and bound solver for quadratic unconstrained binary optimization (QUBO) problems that matches these criteria. Our solver leverages cheap-to-implement bounds from the literature previously proposed for Ising models, including that of Hartwig, Daske, and Kobe from 1984. We detail a variety of techniques from high-performance computing and operations research used to boost solver performance, including a global variable reordering heuristic, a primal heuristic based on simulated annealing, and a truncated computation of the recursive bound. We also outline a number of simple and inexpensive bound extrapolation techniques. Finally, we conduct an extensive empirical analysis of our solver, comparing its performance to state-of-the-art QUBO and MaxCut solvers, and discuss the challenges of a speedup via quantum branch and bound beyond those faced by any quadratic quantum speedup.

A coupling between Earths rotation and orbital angular momentum (OAM), known as the Sagnac effect, is observed in entangled neutrons produced using a spin echo interferometer. After correction for instrument systematics the measured coupling is within 5% of theory, with an uncertainty of 7.2%. The OAM in our setup is transverse to the propagation direction and scales linearly with wavelength (4 A - 12.75 A), hence the coupling can be varied, without mechanically rotating the device. Therefore, the systematic error is lower than in previous experiments. The detected transverse OAM of our beam corresponds to 4098 +- 295 hbar A-1, 5 orders of magnitude lower than in previous neutron experiments, thereby demonstrating the feasibility of using the Sagnac effect to definitively measure neutron OAM and paving the way towards observations of the quantum Sagnac effect

A machine learning (ML) method is generalizable if it can make predictions on inputs which differ from the training dataset. For predictions of wave-induced ship responses, generalizability is an important consideration if ML methods are to be useful in design evaluations. Furthermore, the size of the training dataset has a significant impact on the practicality of a method, especially when training data is generated using high-fidelity numerical tools which are expensive. This paper considers a hybrid machine learning method which corrects the force in a low-fidelity equation of motion. The method is applied to two different case studies: the nonlinear responses of a Duffing equation subject to irregular excitation, and high-fidelity heave and pitch response data of a Fast Displacement Ship (FDS) in head seas. The generalizability of the method is determined in both cases by making predictions of the response in irregular wave conditions that differ from those in the training dataset. The influence that low-fidelity physics-based terms in the hybrid model have on generalizability is also investigated. The predictions are compared to two benchmarks: a linear physics-based model and a data-driven LSTM model. It is found that the hybrid method offers an improvement in prediction accuracy and generalizability when trained on a small dataset.

The Monte Carlo Computational Summit was held on the campus of the University of Notre Dame in South Bend, Indiana, USA on 25--26 October 2023. The goals of the summit were to discuss algorithmic and software alterations required for successfully porting respective code bases to exascale-class computing hardware, compare software engineering techniques used by various code teams, and consider the adoption of industry-standard benchmark problems to better facilitate code-to-code performance comparisons. A large portion of the meeting included candid discussions of direct experiences with approaches that have and have not worked. Participants reported that identifying and implementing suitable Monte Carlo algorithms for GPUs continues to be a sticking point. They also report significant difficulty porting existing algorithms between GPU APIs (specifically Nvidia CUDA to AMD ROCm). To better compare code-to-code performance, participants decided to design a C5G7-like benchmark problem with a defined figure of merit, with the expectation of adding more benchmarks in the future. Problem specifications and results will eventually be hosted in a public repository and will be open to submissions by all Monte Carlo transport codes capable of running the benchmark problem. The participants also identified the need to explore the intermediate and long-term future of the Monte Carlo neutron transport community and how best to modernize and contextualize Monte Carlo as a useful tool in modern industry. Overall the summit was considered to be a success by the organizers and participants, and the group shared a strong desire for future, potentially larger, Monte Carlo summits., Comment: conference report

In spring 2022, WestEd conducted a literature review to summarize the major frameworks used in career intentions research and the evidence supporting each framework, as well as to develop an initial set of constructs to guide the development of a brief, culturally sensitive computing career intentions survey measuring individual, situational, and societal factors. In summer 2022, WestEd developed a draft computing career intentions survey aligned to seven constructs, and then conducted preliminary validation testing of the survey through a series of cognitive interviews and a small-scale field test with 50 respondents. This technical report is a companion to the 2023 paper presented at the 19th ACM Conference on International Computing Education Research (ICER) (Kao et al., 2023). This technical report contains additional details about the field testing of the survey that did not fit within the page limits of the ICER paper. This report contains the results of two studies: (1) a large-scale field test to more robustly establish the instrument's reliability and test for differential item functioning (DIF), and (2) a test of the survey's test--retest reliability with a subsample of participants from the largescale field test.

Simulating complex astrophysical reacting flows is computationally expensive -- reactions are stiff and typically require implicit integration methods. The reaction update is often the most expensive part of a simulation, which motivates the exploration of more economical methods. In this research note, we investigate how the explicit Runge--Kutta--Chebyshev (RKC) method performs compared to an implicit method when applied to astrophysical reactive flows. These integrators are applied to simulations of X-ray bursts arising from unstable thermonuclear burning of accreted fuel on the surface of neutron stars. We show that the RKC method performs with similar accuracy to our traditional implicit integrator, but is more computationally efficient when run on CPUs., Comment: accepted to RNAAS

A hybrid data-driven method, which combines low-fidelity physics with machine learning (ML) to model nonlinear forces and moments at a reduced computational cost, is applied to predict the roll motions of an appended ONR Tumblehome (ONRT) hull in waves. The method is trained using CFD data of unforced roll decay time series--a common data set used in parameter identification for ship roll damping and restoring moments. The trained model is then used to predict wave excited roll responses in a range of wave frequencies and the results are compared to CFD validation data. The predictions show that the method improves predictions of roll responses, especially near the natural frequency.

Due to the limited connectivity of gate model quantum devices, logical quantum circuits must be compiled to target hardware before they can be executed. Often, this process involves the insertion of SWAP gates into the logical circuit, usually increasing the depth of the circuit, achieved by solving a so-called qubit assignment and routing problem. Recently, a number of integer linear programming (ILP) models have been proposed for solving the qubit assignment and routing problem to proven optimality. These models encode the objective function and constraints of the problem, and leverage the use of automated solver technology to find hardware-compliant quantum circuits. In this work, we propose constraint programming (CP) models for this problem and compare their performance against ILP for circuit depth minimization for both linear and two-dimensional grid lattice device topologies on a set of randomly generated instances. Our empirical analysis indicates that the proposed CP approaches outperform the ILP models both in terms of solution quality and runtime.

Monte Carlo Application Toolkit (MCATK) commonly uses surface tracking on a structured mesh to compute scalar fluxes. In this mode, higher fidelity requires more mesh cells and isotopes and thus more computational overhead -- since every time a particle changes cells, new cross-sections must be found for all materials in a given cell -- even if no collision occurs in that cell. We implement a hybrid version of Woodcock (delta) tracking on this imposed mesh to alleviate the number of cross-section lookups. This algorithm computes an energy-dependent microscopic majorant cross section is computed for the problem. Each time a particle enters a new cell, rather than computing a true macroscopic cross-section over all isotopes in the cell, the microscopic majorant cross-section is simply multiplied by the total number density of the cell to obtain a macroscopic majorant cross-section for the cell. Delta tracking is then performed within that single cell. This increases performance with minimal code changes, speeding up the solve time by a factor of 1.5 -- 1.75 for k-eigenvalue simulations and 1.2 -- 1.6 for fixed source simulations in a series of materially complex criticality benchmarks., Comment: 8 pages, 4 figures, M&C 2023 ANS conference

To find deterministic solutions to the transient $S_N$ neutron transport equation, iterative schemes are typically used to treat the scattering (and fission) source terms. We explore the one-cell inversion iteration scheme to do this on the GPU and make comparisons to a source iteration scheme. We examine convergence behavior, through the analysis of spectral radii, of both one-cell inversion and source iterations. To further boost the GPU parallel efficiency, we derive a higher-order discretization method, simple corner balance (in space) and multiple balance (in time), to add more work to the threads and gain accuracy. Fourier analysis on this higher-order numerical method shows that it is unconditionally stable, but it can produce negative flux alterations that are critically damped through time. We explore a whole-problem (in all angle and all cell) sparse linear algebra framework, for both iterative schemes, to quickly produce performant code for GPUs. Despite one-cell inversion requiring additional iterations to convergence, those iterations can be done faster to provide a significant speedup over source iteration in quadrature sets at or below $S_{128}$. Going forward we will produce a two-dimensional implementation of this code to experiment with memory and performance impacts of a whole-problem framework including methods of synthetic acceleration and pre-conditioners for this scheme, then we will begin making direct comparisons to traditionally implemented source iteration in production code., Comment: 11 pages, 4 figures, M&C 2023 ANS conference

We discuss the current development of MC/DC (Monte Carlo Dynamic Code). MC/DC is primarily designed to serve as an exploratory Python-based MC transport code. However, it seeks to offer improved performance, massive scalability, and backend portability by leveraging Python code-generation libraries and implementing an innovative abstraction strategy and compilation scheme. Here, we verify MC/DC capabilities and perform an initial performance assessment. We found that MC/DC can run hundreds of times faster than its pure Python mode and about 2.5 times slower, but with comparable parallel scaling, than the high-performance MC code Shift for simple problems. Finally, to further exercise MC/DC's time-dependent MC transport capabilities, we propose a challenge problem based on the C5G7-TD benchmark model., Comment: 11 pages, 9 figures, M&C 2023 ANS conference

Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least $4m$. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the $6.5m$ James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit., Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figures

The Astropy Project supports and fosters the development of open-source and openly-developed Python packages that provide commonly needed functionality to the astronomical community. A key element of the Astropy Project is the core package $\texttt{astropy}$, which serves as the foundation for more specialized projects and packages. In this article, we summarize key features in the core package as of the recent major release, version 5.0, and provide major updates for the Project. We then discuss supporting a broader ecosystem of interoperable packages, including connections with several astronomical observatories and missions. We also revisit the future outlook of the Astropy Project and the current status of Learn Astropy. We conclude by raising and discussing the current and future challenges facing the Project., Comment: 43 pages, 5 figures. To appear in ApJ. The author list has two parts: the authors that made significant contributions to the writing and/or coordination of the paper, followed by maintainers of and contributors to the Astropy Project. The position in the author list does not correspond to contributions to the Astropy Project as a whole

Smoldering combustion plays a key role in wildfires in forests, grasslands, and peatlands due to its common occurrence in porous fuels like peat and duff. As a consequence, understanding smoldering behavior in these fuels is crucial. Such fuels are generally composed of cellulose, hemicellulose, and lignin. Here we present an updated computational model for simulating smoldering combustion in cellulose and hemicellulose mixtures. We used this model to examine changes in smoldering propagation speed and peak temperatures with varying fuel composition and density. For a given fuel composition, increases in density decrease the propagation speed and increase mean peak temperature; for a given density, increases in hemicellulose content increase both propagation speed and peak temperature. We also examined the role of natural fuel expansion with the addition of water. Without expansion, addition of moisture content reduces the propagation speed primarily due to increasing (wet) fuel density. However, with fuel expansion similar to that observed in peat, the propagation speed increases due to the overall drop in fuel density. Finally, we studied the influence of fuel composition on critical moisture content of ignition and extinction: mixtures dominated by hemicellulose have 10% higher critical moisture content due to the increase in peak temperature., Comment: 25 pages, 16 figures. Combustion Theory and Modelling (2022)

The high cost of chemistry integration is a significant computational bottleneck for realistic reactive-flow simulations using operator splitting. Here we present a methodology to accelerate the solution of the chemical kinetic ordinary differential equations using single-instruction, multiple-data vector processing on CPUs using the OpenCL framework. First, we compared several vectorized integration algorithms using chemical kinetic source terms and analytical Jacobians from the pyJac software against a widely used integration code, CVODEs. Next, we extended the OpenFOAM computational fluid dynamics library to incorporate the vectorized solvers, and we compared the accuracy of a fourth-order linearly implicit integrator -- both in vectorized form and a corresponding method native to OpenFOAM -- with the community standard chemical kinetics library Cantera. We then applied our methodology to a variety of chemical kinetic models, turbulent intensities, and simulation scales to examine a range of engineering and scientific scale problems, including (pseudo) steady-state as well as time-dependent Reynolds-averaged Navier--Stokes simulations of the Sandia flame D and the Volvo Flygmotor bluff-body stabilized, premixed flame. Subsequently, we compared the performance of the vectorized and native OpenFOAM integrators over the studied models and simulations and found that our vectorized approach performs up to 33--35x faster than the native OpenFOAM solver with high accuracy., Comment: 43 pages, 6 figures

Natural materials typically interact weakly with the magnetic component of light which greatly limits their applications. This has led to the development of artificial metamaterials and metasurfaces. However, natural atoms, where only electric dipole transitions are relevant at optical frequencies, can cooperatively respond to light to form collective excitations with strong magnetic, as well as electric, interactions, together with corresponding electric and magnetic mirror reflection properties. By combining the electric and magnetic collective degrees of freedom we show that ultrathin planar arrays of atoms can be utilized as atomic lenses to focus light to subwavelength spots at the diffraction limit, to steer light at different angles allowing for optical sorting, and as converters between different angular momentum states. The method is based on coherently superposing induced electric and magnetic dipoles to engineer a quantum nanophotonic Huygens' surface of atoms, giving full $2\pi$ phase control over the transmission, with close to zero reflection.

In this paper we present a catalog of 4584 eclipsing binaries observed during the first two years (26 sectors) of the TESS survey. We discuss selection criteria for eclipsing binary candidates, detection of hither-to unknown eclipsing systems, determination of the ephemerides, the validation and triage process, and the derivation of heuristic estimates for the ephemerides. Instead of keeping to the widely used discrete classes, we propose a binary star morphology classification based on a dimensionality reduction algorithm. Finally, we present statistical properties of the sample, we qualitatively estimate completeness, and discuss the results. The work presented here is organized and performed within the TESS Eclipsing Binary Working Group, an open group of professional and citizen scientists; we conclude by describing ongoing work and future goals for the group. The catalog is available from http://tessEBs.villanova.edu and from MAST., Comment: 32 pages, 21 figures, accepted to ApJ Supplement Series; comments welcome

Diffusive transport of mass occurs at small scales in turbulent premixed flames. As a result, multicomponent mass diffusion, which is often neglected in direct numerical simulations (DNS) of premixed combustion, has the potential to impact both turbulence and flame characteristics at small scales. In this study, we evaluate these impacts by examining enstrophy dynamics and the internal structure of the flame for lean premixed hydrogen-air combustion, neglecting secondary Soret and Dufour effects. We performed three-dimensional DNS of these flames by implementing the Stefan-Maxwell equations in the code NGA to represent multicomponent mass transport, and we simulated statistically planar lean premixed hydrogen-air flames using both mixture-averaged and multicomponent models. The mixture-averaged model underpredicts the peak enstrophy by up to 13% in the flame front. Comparing the enstrophy budgets of these flames, the multicomponent simulation yields larger peak magnitudes compared to the mixture-averaged simulation in the reaction zone, showing differences of 17% and 14% in the normalized stretching and viscous effects terms. In the super-adiabatic regions of the flame, the mixture-averaged model overpredicts the viscous effects by up to 13%. To assess the effect of these differences on flame structure, we reconstructed the average local internal structure of the turbulent flame through statistical analysis of the scalar gradient field. Based on this analysis, we show that large differences in viscous effects contribute to significant differences in the average local flame structure between the two models., Comment: 15 pages, 6 figures

The partial differential equations describing compressible fluid flows can be notoriously difficult to resolve on a pragmatic scale and often require the use of high performance computing systems and/or accelerators. However, these systems face scaling issues such as latency, the fixed cost of communicating information between devices in the system. The swept rule is a technique designed to minimize these costs by obtaining a solution to unsteady equations at as many possible spatial locations and times prior to communicating. In this study, we implemented and tested the swept rule for solving two-dimensional problems on heterogeneous computing systems across two distinct systems. Our solver showed a speedup range of 0.22-2.71 for the heat diffusion equation and 0.52-1.46 for the compressible Euler equations. We can conclude from this study that the swept rule offers both potential for speedups and slowdowns and that care should be taken when designing such a solver to maximize benefits. These results can help make decisions to maximize these benefits and inform designs., Comment: 18 pages, 11 figures

Constraint programming (CP) is a paradigm used to model and solve constraint satisfaction and combinatorial optimization problems. In CP, problems are modeled with constraints that describe acceptable solutions and solved with backtracking tree search augmented with logical inference. In this paper, we show how quantum algorithms can accelerate CP, at both the levels of inference and search. Leveraging existing quantum algorithms, we introduce a quantum-accelerated filtering algorithm for the $\texttt{alldifferent}$ global constraint and discuss its applicability to a broader family of global constraints with similar structure. We propose frameworks for the integration of quantum filtering algorithms within both classical and quantum backtracking search schemes, including a novel hybrid classical-quantum backtracking search method. This work suggests that CP is a promising candidate application for early fault-tolerant quantum computers and beyond., Comment: published in Quantum

Implementing multicomponent diffusion models in numerical combustion studies is computationally expensive; to reduce cost, numerical simulations commonly use mixture-averaged diffusion treatments or simpler models. However, the accuracy and appropriateness of mixture-averaged diffusion has not been verified for three-dimensional, turbulent, premixed flames. In this study we evaluated the role of multicomponent mass diffusion in premixed, three-dimensional high Karlovitz-number hydrogen, n-heptane, and toluene flames, representing a range of fuel Lewis numbers. We also studied a premixed, unstable two-dimensional hydrogen flame due to the importance of diffusion effects in such cases. Our comparison of diffusion flux vectors revealed differences of 10-20% on average between the mixture-averaged and multicomponent diffusion models, and greater than 40% in regions of high flame curvature. Overall, however, the mixture-averaged model produces small differences in diffusion flux compared with global turbulent flame statistics. To evaluate the impact of these differences between the two models, we compared normalized turbulent flame speeds and conditional means of species mass fraction and source term. We found differences of 5-20% in the mean normalized turbulent flame speeds, which seem to correspond to differences of 5-10% in the peak fuel source terms. Our results motivate further study into whether the mixture-averaged diffusion model is always appropriate for DNS of premixed turbulent flames., Comment: 50 pages, 7 figures. arXiv admin note: text overlap with arXiv:1808.05463

Ground-based measurements of frozen precipitation are heavily influenced by interactions of surface winds with gauge-shield geometry. The Multi-Angle Snowflake Camera (MASC), which photographs hydrometeors in free-fall from three different angles while simultaneously measuring their fall speed, has been used in the field at multiple mid-latitude and polar locations both with and without wind shielding. Here we show results of computational fluid dynamics (CFD) simulations of the airflow and corresponding particle trajectories around the unshielded MASC and compare these results to Arctic field observations with and without a Belfort double Alter shield. Simulations in the absence of a wind shield show a separation of flow at the upstream side of the instrument, with an upward velocity component just above the aperture, which decreases the mean particle fall speed by 55%(74%) for a wind speed of 5 m/s(10 m/s). MASC-measured fall speeds compare well with Ka-band Atmospheric Radiation Measurement (ARM) Zenith Radar (KAZR) mean Doppler velocities only when winds are light (<5 m/s) and the MASC is shielded. MASC-measured fall speeds that do not match KAZR measured velocities tend to fall below a threshold value that increases approximately linearly with wind speed but is generally <0.5 m/s. For those events with wind speeds <1.5 m/s, hydrometeors fall with an orientation angle mode of 12 degrees from the horizontal plane, and large, low-density aggregates are as much as five times more likely to be observed. We conclude that accurate MASC observations of the microphysical, orientation, and fall speed characteristics of snow particles require shielding by a double wind fence and restriction of analysis to events where winds are light (<5 m/s). Hydrometeors do not generally fall in still air, so adjustments to these properties' distributions within natural turbulence remain to be determined., Comment: 26 pages including 15 figures and 3 tables

PHOEBE 2 is a Python package for modeling the observables of eclipsing star systems, but until now has focused entirely on the forward-model -- that is, generating a synthetic model given fixed values of a large number of parameters describing the system and the observations. The inverse problem, obtaining orbital and stellar parameters given observational data, is more complicated and computationally expensive as it requires generating a large set of forward-models to determine which set of parameters and uncertainties best represent the available observational data. The process of determining the best solution and also of obtaining reliable and robust uncertainties on those parameters often requires the use of multiple algorithms, including both optimizers and samplers. Furthermore, the forward-model of PHOEBE has been designed to be as physically robust as possible, but is computationally expensive compared to other codes. It is useful, therefore, to use whichever code is most efficient given the reasonable assumptions for a specific system, but learning the intricacies of multiple codes presents a barrier to doing this in practice. Here we present the 2.3 release of PHOEBE (publicly available from http://phoebe-project.org) which introduces a general framework for defining and handling distributions on parameters, and utilizing multiple different estimation, optimization, and sampling algorithms. The presented framework supports multiple forward-models, including the robust model built into PHOEBE itself., Comment: accepted for publication in ApJS

A toroidal dipole represents an often overlooked electromagnetic excitation distinct from the standard electric and magnetic multipole expansion. We show how a simple arrangement of strongly radiatively coupled atoms can be used to synthesize a toroidal dipole where the toroidal topology is generated by radiative transitions forming an effective poloidal electric current wound around a torus. We extend the protocol for methods to prepare a delocalized collective excitation mode consisting of a synthetic lattice of such toroidal dipoles and a non-radiating, yet oscillating charge-current configuration, dynamic anapole, for which the far-field radiation of a toroidal dipole is identically canceled by an electric dipole.

Massive stars play an important role in the mechanical and chemical evolution of galaxies. Understanding the internal processes of these stars is vital to our understanding of their evolution and eventual end products. Deformations from spherical geometry are common for massive stars; however, the tools that are currently available for the study of these systems are almost exclusively one-dimensional. We present a new spectroscopic analysis tool tailored for massive stars that deviate from spherical symmetry. This code (entitled SPAMMS) is a spectroscopic patch model that takes the three-dimensional surface geometry of the system into account to produce spectral profiles at given phases and orientations. In using the Wilson-Devinney-like code PHOEBE in combination with the non local thermodynamic equilibrium (NLTE) radiative transfer code FASTWIND, we created a three-dimensional mesh that represents the surface geometry of our system and we assigned FASTWIND emergent intensity line profiles to each mesh triangle, which take the local parameters such as temperature, surface gravity, and radius into account. These line profiles were then integrated across the visible surface, where their flux contribution and radial velocity are taken into account, thus returning a final line profile for the visible surface of the system at a given phase. We demonstrate that SPAMMS can accurately reproduce the morphology of observed spectral line profiles for overcontact systems. Additionally, we show how line profiles of rapidly-rotating single stars differ when taking rotational distortion into account, and the effects that these can have on the determined parameters. Finally, we demonstrate the code's ability to reproduce the Rossiter-Mclaughlin and Struve-Sahade effects., Comment: 10 pages, 10 figures, accepted for publication in A&A

Traditionally, the effects of interstellar extinction on binary star light curves have been treated as a uniform reduction in the observed brightness of the system that is independent of orbital phase. However, unless the orbital plane of the system coincides with the plane of the sky, or if the two stars are completely identical and present with minimal mutual irradiation and tidal/rotational distortions, then this is unlikely to be an accurate representation of the effect of interstellar extinction. Here, we present an updated treatment of interstellar extinction as incorporated in the PHOEBE 2.2 release (publicly available from http://phoebe-project.org) and assess the importance of using such an approach in the modeling of different types of binary systems. We also present the incorporation of PHOENIX model atmospheres into the PHOEBE 2.2 release, providing increased fidelity on computed observables down to lower temperatures than previously available. The importance of these new code developments is then highlighted via an extincted toy model of the eclipsing white-dwarf-subdwarf binary SDSS~J235524.29+044855.7 -- demonstrating that, in the age of LSST as well as complementary space-based photometric missions, a proper accounting for extinction and as well as the use of realistic model atmospheres will be essential in deriving accurate binary parameters., Comment: 10 pages, 5 figures. Accepted for publication in ApJS

A major limitation of current generations of quantum annealers is the sparse connectivity of manufactured qubits in the hardware graph. This technological limitation generated considerable interest, motivating efforts to design efficient and adroit minor-embedding procedures that bypass sparsity constraints. In this paper, starting from a previous equational formulation by Dridi et al. (arXiv:1810.01440), we propose integer programming (IP) techniques for solving the minor-embedding problem. The first approach involves a direct translation from the previous equational formulation to IP, while the second decomposes the problem into an assignment master problem and fiber condition checking subproblems. The proposed methods are able to detect instance infeasibility and provide bounds on solution quality, capabilities not offered by currently employed heuristic methods. We demonstrate the efficacy of our methods with an extensive computational assessment involving three different families of random graphs of varying sizes and densities. The direct translation as a monolithic IP model can be solved with existing commercial solvers yielding valid minor-embeddings, however, is outperformed overall by the decomposition approach. Our results demonstrate the promise of our methods for the studied benchmarks, highlighting the advantages of using IP technology for minor-embedding problems., Comment: 16 pages, 6 figures

This paper describes the motivation and design of a 10-week graduate course that teaches practices for developing research software; although offered by an engineering program, the content applies broadly to any field of scientific research where software may be developed. Topics taught in the course include local and remote version control, licensing and copyright, structuring Python modules, testing and test coverage, continuous integration, packaging and distribution, open science, software citation, and reproducibility basics, among others. Lectures are supplemented by in-class activities and discussions, and all course material is shared openly via GitHub. Coursework is heavily based on a single, term-long project where students individually develop a software package targeted at their own research topic; all contributions must be submitted as pull requests and reviewed/merged by other students. The course was initially offered in Spring 2018 with 17 students enrolled, and will be taught again in Spring 2019., Comment: 7 pages, accepted at ICCS 2019

Applications that exploit the architectural details of high-performance computing (HPC) systems have become increasingly invaluable in academia and industry over the past two decades. The most important hardware development of the last decade in HPC has been the General Purpose Graphics Processing Unit (GPGPU), a class of massively parallel devices that now contributes the majority of computational power in the top 500 supercomputers. As these systems grow, small costs such as latency---due to the fixed cost of memory accesses and communication---accumulate in a large simulation and become a significant barrier to performance. The swept time-space decomposition rule is a communication-avoiding technique for time-stepping stencil update formulas that attempts to reduce latency costs. This work extends the swept rule by targeting heterogeneous, CPU/GPU architectures representing current and future HPC systems. We compare our approach to a naive decomposition scheme with two test equations using an MPI+CUDA pattern on 40 processes over two nodes containing one GPU. The swept rule produces a factor of 1.9 to 23 speedup for the heat equation and a factor of 1.1 to 2.0 speedup for the Euler equations, using the same processors and work distribution, and with the best possible configurations. These results show the potential effectiveness of the swept rule for different equations and numerical schemes on massively parallel computing systems that incur substantial latency costs., Comment: 24 pages, 9 figures. Accepted for publication by the Journal of Supercomputing

Recurrent neural networks (RNNs) have been successfully used on a wide range of sequential data problems. A well known difficulty in using RNNs is the \textit{vanishing or exploding gradient} problem. Recently, there have been several different RNN architectures that try to mitigate this issue by maintaining an orthogonal or unitary recurrent weight matrix. One such architecture is the scaled Cayley orthogonal recurrent neural network (scoRNN) which parameterizes the orthogonal recurrent weight matrix through a scaled Cayley transform. This parametrization contains a diagonal scaling matrix consisting of positive or negative one entries that can not be optimized by gradient descent. Thus the scaling matrix is fixed before training and a hyperparameter is introduced to tune the matrix for each particular task. In this paper, we develop a unitary RNN architecture based on a complex scaled Cayley transform. Unlike the real orthogonal case, the transformation uses a diagonal scaling matrix consisting of entries on the complex unit circle which can be optimized using gradient descent and no longer requires the tuning of a hyperparameter. We also provide an analysis of a potential issue of the modReLU activiation function which is used in our work and several other unitary RNNs. In the experiments conducted, the scaled Cayley unitary recurrent neural network (scuRNN) achieves comparable or better results than scoRNN and other unitary RNNs without fixing the scaling matrix.

A general framework for dealing with irradiation effects in the bolometric sense --- specifically, reflection with heat absorption and the consequent redistribution of the absorbed heat, for systems of astrophysical bodies where the boundaries are used as support for the description of the processes, is presented. Discussed are its mathematical and physical properties, as well as its implementation approximations, with a focus on three plausible redistribution processes (uniform, latitudinal, and local redistribution). These are tested by extending PHOEBE 2.1 ( http://phoebe-project.org ), the open-source package for modeling eclipsing binaries, and applied to a toy model of the known two-body eclipsing systems., Comment: 33 pages, 7 figures, accepted in ApJS

Effects of wave-driven Langmuir turbulence on the air-sea flux of carbon dioxide (CO$_2$) are examined using large eddy simulations featuring actively reacting carbonate chemistry in the ocean mixed layer at small scales. Four strengths of Langmuir turbulence are examined with three types of carbonate chemistry: time-dependent, instantaneous equilibrium chemistry, and no reactions. The time-dependent model is obtained by reducing a detailed eight-species chemical mechanism using computational singular perturbation analysis, resulting in a quasi-steady-state approximation for hydrogen ion (H$^+$), i.e., fixed pH. The reduced mechanism is then integrated in two half-time steps before and after the advection solve using a Runge--Kutta--Chebyshev scheme that is robust for stiff systems of differential equations. The simulations show that, as the strength of Langmuir turbulence increases, CO$_2$ fluxes are enhanced by rapid overturning of the near-surface layer, which rivals the removal rate of CO$_2$ by time-dependent reactions. Equilibrium chemistry and non-reactive models are found to bring more and less carbon, respectively, into the ocean as compared to the more realistic time-dependent model. These results have implications for Earth system models that either neglect Langmuir turbulence or use equilibrium, instead of time-dependent, chemical mechanisms., Comment: 25 pages, 9 figures

Accurately predicting key combustion phenomena in reactive-flow simulations, e.g., lean blow-out, extinction/ignition limits and pollutant formation, necessitates the use of detailed chemical kinetics. The large size and high levels of numerical stiffness typically present in chemical kinetic models relevant to transportation/power-generation applications make the efficient evaluation/factorization of the chemical kinetic Jacobian and thermochemical source-terms critical to the performance of reactive-flow codes. Here we investigate the performance of vectorized evaluation of constant-pressure/volume thermochemical source-term and sparse/dense chemical kinetic Jacobians using single-instruction, multiple-data (SIMD) and single-instruction, multiple thread (SIMT) paradigms. These are implemented in pyJac, an open-source, reproducible code generation platform. A new formulation of the chemical kinetic governing equations was derived and verified, resulting in Jacobian sparsities of 28.6-92.0% for the tested models. Speedups of 3.40-4.08x were found for shallow-vectorized OpenCL source-rate evaluation compared with a parallel OpenMP code on an avx2 central processing unit (CPU), increasing to 6.63-9.44x and 3.03-4.23x for sparse and dense chemical kinetic Jacobian evaluation, respectively. Furthermore, the effect of data-ordering was investigated and a storage pattern specifically formulated for vectorized evaluation was proposed; as well, the effect of the constant pressure/volume assumptions and varying vector widths were studied on source-term evaluation performance. Speedups reached up to 17.60x and 45.13x for dense and sparse evaluation on the GPU, and up to 55.11x and 245.63x on the CPU over a first-order finite-difference Jacobian approach. Further, dense Jacobian evaluation was up to 19.56x and 2.84x times faster than a previous version of pyJac on a CPU and GPU, respectively., Comment: 53 pages, 13 figures

Implementing multicomponent diffusion models in reacting-flow simulations is computationally expensive due to the challenges involved in calculating diffusion coefficients. Instead, mixture-averaged diffusion treatments are typically used to avoid these costs. However, to our knowledge, the accuracy and appropriateness of the mixture-averaged diffusion models has not been verified for three-dimensional turbulent premixed flames. In this study we propose a fast,efficient, low-memory algorithm and use that to evaluate the role of multicomponent mass diffusion in reacting-flow simulations. Direct numerical simulation of these flames is performed by implementing the Stefan-Maxwell equations in NGA. A semi-implicit algorithm decreases the computational expense of inverting the full multicomponent ordinary diffusion array while maintaining accuracy and fidelity. We first verify the method by performing one-dimensional simulations of premixed hydrogen flames and compare with matching cases in Cantera. We demonstrate the algorithm to be stable, and its performance scales approximately with the number of species squared. Then, as an initial study of multicomponent diffusion, we simulate premixed, three-dimensional turbulent hydrogen flames, neglecting secondary Soret and Dufour effects. Simulation conditions are carefully selected to match previously published results and ensure valid comparison. Our results show that using the mixture-averaged diffusion assumption leads to a 15% under-prediction of the normalized turbulent flame speed for a premixed hydrogen-air flame. This difference in the turbulent flame speed motivates further study into using the mixture-averaged diffusion assumption for DNS of moderate-to-high Karlovitz number flames., Comment: 36 pages, 14 figures

Smoldering combustion plays an important role in forest and wildland fires. Fires from smoldering combustion can last for long periods of time, emit more pollutants, and be difficult to extinguish. This makes the study of smoldering in woody fuels and forest duff important. Cellulose, hemicellulose, and lignin are the major constituents in these type of fuels, in different proportions for different fuels. In this paper, we developed a 1-D model using the open-source software Gpyro to study the smoldering combustion of cellulose and hemicellulose mixtures. We first validated our simulations against experimentally obtained values of propagation speed for mixtures with fuel compositions including 100%, 75%, 50%, and 25% cellulose, with the remaining proportion of hemicellulose. Then, we studied the effects of varying fuel composition, density, and moisture content on smoldering combustion. We find that propagation speed of smoldering increased with decreases in density and increases in hemicellulose content, which we attribute to the role of oxygen diffusion. Propagation speed increased with moisture content for pure cellulose up to a certain limiting value, after which the propagation speed dropped by up to 70%. The mean peak temperature of smoldering increased with increases in hemicellulose content and density, and decreased with increasing moisture content., Comment: 16 pages, 5 figures

Binary systems where the axis of rotation (spin) of one or both components is tilted w.r.t. the axis of revolution are called misaligned binary systems. The angle of misalignment, obliquity, has been measured for a handful of stars and extrasolar planets to date. Here we present a mathematical framework for a complete and rigorous treatment of misalignment and introduce an extension to the public PHOEBE code that implements this framework. We discuss misalignment for the Roche geometry and introduce methods for computing stellar shapes, equilibrium (generalized Lagrange) points of the potential and minimal requirements for lobe existence. Efficient parametrization of misalignment is proposed in the plane-of-sky coordinates and implementation details in PHOEBE are given alongside the proof-of-concept toy model, comparison with a known misaligned binary DI Her, and comparison with a misaligned planetary system Kepler-13. We provide important mathematical details of the model in the Appendix. This paper accompanies the release of PHOEBE 2.1, which will be available soon from its website http://phoebe-project.org., Comment: 20 pages, 10 figures, published in ApJS; release of PHOEBE 2.1 is available at http://phoebe-project.org

Design and optimization of higher efficiency, lower-emission internal combustion engines are highly dependent on fuel chemistry. Resolving chemistry for complex fuels, like gasoline, is challenging. A solution is to study a fuel surrogate: a blend of a small number of well-characterized hydrocarbons to represent real fuels by emulating their thermophysical and chemical kinetics properties. In the current study, an existing gasoline surrogate formulation algorithm is further enhanced by incorporating novel chemometric models. These models use infrared spectra of hydrocarbon fuels to predict octane numbers, and are valid for a wide array of neat hydrocarbons and mixtures of such. This work leverages 14 hydrocarbon species to form tailored surrogate palettes for the Fuels for Advanced Combustion Engine (FACE) gasolines, including candidate component species not previously considered: n-pentane, 2-methylpentane, 1-pentene, cyclohexane, and o-xylene. We evaluate the performance of "full" and "reduced" surrogates for the 10 fuels for advanced combustion engine (FACE) gasolines, containing between 8-12 and 4-7 components, respectively. These surrogates match the target properties of the real fuels, on average, within 5 %. This close agreement demonstrates that the algorithm can design surrogates matching the wide array of target properties: octane numbers, density, hydrogen-to-carbon ratio, distillation characteristics, and proportions of carbon-carbon bond types. We also compare our surrogates to those available in literature (FACE gasolines A, C, F, G, I and J). Overall, the approach demonstrated here offers a promising method to better design surrogates for gasoline-like fuels with a wide array of properties., Comment: 48 pages, 7 figures

The National Football League's (NFL) 2011 collective bargaining agreement (CBA) with its players placed a number of contact and quantity limitations on practices and workouts. Some coaches and others have expressed a concern that this has led to poor conditioning and a subsequent increase in injuries. We sought to assess whether the 2011 CBA's practice restrictions affected the number of overall, conditioning-dependent, and/or non-conditioning-dependent injuries in the NFL or the number of games missed due to those injuries. The study population was player-seasons from 2007-2016. We included regular season, non-illness, non-head, game-loss injuries. Injuries were identified using a database from Football Outsiders. The primary outcomes were overall, conditioning-dependent and non-conditioning-dependent injury counts by season. We examined time trends in injury counts before (2007-2010) and after (2011-2016) the CBA using a Poisson interrupted time series model. The number of game-loss regular season, non-head, non-illness injuries grew from 701 in 2007 to 804 in 2016 (15% increase). The number of regular season weeks missed exhibited a similar increase. Conditioning-dependent injuries increased from 197 in 2007 to 271 in 2011 (38% rise), but were lower and remained relatively unchanged at 220-240 injuries per season thereafter. Non-conditioning injuries decreased by 37% in the first three years of the new CBA before returning to historic levels in 2014-2016. Poisson models for all, conditioning-dependent, and non-conditioning-dependent game-loss injury counts did not show statistically significant or meaningful detrimental changes associated with the CBA. We did not observe an increase in injuries following the 2011 CBA. Other concurrent injury-related rule and regulation changes limit specific causal inferences about the practice restrictions, however., Comment: 22 pages, 4 figures, 5 tables; conference presentation at JSM 2018; lay version at FootballOutsiders.com

Contact binary stars have been known to have a peculiar and somewhat mysterious hydro- and thermodynamical structure since their discovery, which directly affects the radiation distribution in their atmospheres. Over the past several decades, however, observational data of contact binaries have been modeled through a simplified approach, involving the artificial concatenation of the two components of the contact envelope and populating their respective surfaces with either blackbody atmospheres or plane-parallel model atmospheres of single stars. We show the implications this approach has on the reliability of the system parameter values and propose a method to overcome these issues with a new generalized radiative transfer code, COBAIN (COntact Binary Atmospheres with INterpolation). The basic principles of COBAIN are outlined and their application to different geometries and polytropic stellar structures is discussed. We present initial tests on single non-rotating, uniformly rotating and differentially rotating stars, as well as on simplified polytropic structural models of contact binaries. We briefly discuss the final goal of this ambitious project, which is the computation of model atmosphere tables under the correct assumptions for contact binary stars, to be used in modern binary star analysis codes., Comment: 57 pages, 50 figures

Recently, the makespan-minimization problem of compiling a general class of quantum algorithms into near-term quantum processors has been introduced to the AI community. The research demonstrated that temporal planning is a strong approach for a class of quantum circuit compilation (QCC) problems. In this paper, we explore the use of constraint programming (CP) as an alternative and complementary approach to temporal planning. We extend previous work by introducing two new problem variations that incorporate important characteristics identified by the quantum computing community. We apply temporal planning and CP to the baseline and extended QCC problems as both stand-alone and hybrid approaches. Our hybrid methods use solutions found by temporal planning to warm start CP, leveraging the ability of the former to find satisficing solutions to problems with a high degree of task optionality, an area that CP typically struggles with. The CP model, benefiting from inferred bounds on planning horizon length and task counts provided by the warm start, is then used to find higher quality solutions. Our empirical evaluation indicates that while stand-alone CP is only competitive for the smallest problems, CP in our hybridization with temporal planning out-performs stand-alone temporal planning in the majority of problem classes., Comment: 9 pages, 2 figures, Proceedings of the 28th International Conference of Automated Planning and Scheduling 2018 (ICAPS-18)

It has long been recognized that the finite speed of light can affect the observed time of an event. For example, as a source moves radially toward or away from an observer, the path length and therefore the light travel time to the observer decreases or increases, causing the event to appear earlier or later than otherwise expected, respectively. This light travel time effect (LTTE) has been applied to transits and eclipses for a variety of purposes, including studies of eclipse timing variations (ETVs) and transit timing variations (TTVs) that reveal the presence of additional bodies in the system. Here we highlight another non-relativistic effect on eclipse or transit times arising from the finite speed of light---caused by an asymmetry in the transverse velocity of the two eclipsing objects, relative to the observer. This asymmetry can be due to a non-unity mass ratio or to the presence of external barycentric motion. Although usually constant, this barycentric and asymmetric transverse velocities (BATV) effect can vary between sequential eclipses if either the path length between the two objects or the barycentric transverse velocity varies in time. We discuss this BATV effect and estimate its magnitude for both time-dependent and time-independent cases. For the time-dependent cases, we consider binaries that experience a change in orbital inclination, eccentric systems with and without apsidal motion, and hierarchical triple systems. We also consider the time-independent case which, by affecting the primary and secondary eclipses differently, can influence the inferred system parameters, such as the orbital eccentricity., Comment: 24 pages, 12 figures, published in ApJ

As recently realized experimentally [L\'eonard et al., Nature 543, 87 (2017)], one can engineer models with continuous symmetries by coupling two cavity modes to trapped atoms, via a Raman pumping geometry. Considering specifically cases where internal states of the atoms couple to the cavity, we show an extended range of parameters for which continuous symmetry breaking can occur, and we classify the distinct steady states and time-dependent states that arise for different points in this extended parameter regime., Comment: 10 pages, 7 figures

Optical cavity QED provides a platform with which to explore quantum many-body physics in driven-dissipative systems. Single-mode cavities provide strong, infinite-range photon-mediated interactions among intracavity atoms. However, these global all-to-all couplings are limiting from the perspective of exploring quantum many-body physics beyond the mean-field approximation. The present work demonstrates that local couplings can be created using multimode cavity QED. This is established through measurements of the threshold of a superradiant, self-organization phase transition versus atomic position. Specifically, we experimentally show that the interference of near-degenerate cavity modes leads to both a strong and tunable-range interaction between Bose-Einstein condensates (BECs) trapped within the cavity. We exploit the symmetry of a confocal cavity to measure the interaction between real BECs and their virtual images without unwanted contributions arising from the merger of real BECs. Atom-atom coupling may be tuned from short range to long range. This capability paves the way toward future explorations of exotic, strongly correlated systems such as quantum liquid crystals and driven-dissipative spin glasses., Comment: 16 pages, 8 figures

Side-by-side comparison of detailed kinetic models using a new tool to aid recognition of species structures reveals significant discrepancies in the published rates of many reactions and thermochemistry of many species. We present a first automated assessment of the impact of these varying parameters on observable quantities of interest---in this case, autoignition delay---using literature experimental data. A recent kinetic model for the isomers of butanol was imported into a common database. Individual reaction rate and thermodynamic parameters of species were varied using values encountered in combustion models from recent literature. The effects of over 1600 alternative parameters were considered. Separately, experimental data were collected from recent publications and converted into the standard YAML-based ChemKED format. The Cantera-based model validation tool, PyTeCK, was used to automatically simulate autoignition using the generated models and experimental data, to judge the performance of the models. Taken individually, most of the parameter substitutions have little effect on the overall model performance, although a handful have quite large effects, and are investigated more thoroughly. Additionally, models varying multiple parameters simultaneously were evolved using a genetic algorithm to give fastest and slowest autoignition delay times, showing that changes exceeding a factor of 10 in ignition delay time are possible by cherry-picking from only accepted, published parameters. All data and software used in this study are available openly., Comment: 25 pages, 4 figures; More parameter sources found (Tables 8 and 10) and Supplementary Material expanded with list of models

This article describes the motivation, design, and progress of the Journal of Open Source Software (JOSS). JOSS is a free and open-access journal that publishes articles describing research software. It has the dual goals of improving the quality of the software submitted and providing a mechanism for research software developers to receive credit. While designed to work within the current merit system of science, JOSS addresses the dearth of rewards for key contributions to science made in the form of software. JOSS publishes articles that encapsulate scholarship contained in the software itself, and its rigorous peer review targets the software components: functionality, documentation, tests, continuous integration, and the license. A JOSS article contains an abstract describing the purpose and functionality of the software, references, and a link to the software archive. The article is the entry point of a JOSS submission, which encompasses the full set of software artifacts. Submission and review proceed in the open, on GitHub. Editors, reviewers, and authors work collaboratively and openly. Unlike other journals, JOSS does not reject articles requiring major revision; while not yet accepted, articles remain visible and under review until the authors make adequate changes (or withdraw, if unable to meet requirements). Once an article is accepted, JOSS gives it a DOI, deposits its metadata in Crossref, and the article can begin collecting citations on indexers like Google Scholar and other services. Authors retain copyright of their JOSS article, releasing it under a Creative Commons Attribution 4.0 International License. In its first year, starting in May 2016, JOSS published 111 articles, with more than 40 additional articles under review. JOSS is a sponsored project of the nonprofit organization NumFOCUS and is an affiliate of the Open Source Initiative., Comment: 22 pages, 8 figures

The interactions between acceptors in semiconductors are often treated in qualitatively the same manner as those between donors. Acceptor wave functions are taken to be approximately hydrogenic and the standard hydrogen molecule Heitler-London model is used to describe acceptor-acceptor interactions. But due to valence band degeneracy and spin-orbit coupling, acceptor states can be far more complex than those of hydrogen atoms, which brings into question the validity of this approximation. To address this issue, we develop an acceptor-acceptor Heitler-London model using single-acceptor wave functions of the form proposed by Baldereschi and Lipari, which more accurately capture the physics of the acceptor states. We calculate the resulting acceptor-pair energy levels and find, in contrast to the two-level singlet-triplet splitting of the hydrogen molecule, a rich ten-level energy spectrum. Our results, computed as a function of inter-acceptor distance and spin-orbit coupling strength, suggest that acceptor-acceptor interactions can be qualitatively different from donor-donor interactions, and should therefore be relevant to the control of two-qubit interactions in acceptor-based qubit implementations, as well as the magnetic properties of a variety of p-doped semiconductor systems. Further insight is drawn by fitting numerical results to closed-form energy-level expressions obtained via an acceptor-acceptor Hubbard model., Comment: 19 pages, 10 figures, text revised, figure quality improved, additional references added

Butanol has received significant research attention as a second-generation biofuel in the past few years. In the present study, skeletal mechanisms for four butanol isomers were generated from two widely accepted, well-validated detailed chemical kinetic models for the butanol isomers. The detailed models were reduced using a two-stage approach consisting of the directed relation graph with error propagation and sensitivity analysis. During the reduction process, issues were encountered with pressure-dependent reactions formulated using the logarithmic pressure interpolation approach; these issues are discussed and recommendations made to avoid ambiguity in its future implementation in mechanism development. The performance of the skeletal mechanisms generated here was compared with that of detailed mechanisms in simulations of autoignition delay times, laminar flame speeds, and perfectly stirred reactor temperature response curves and extinction residence times, over a wide range of pressures, temperatures, and equivalence ratios. The detailed and skeletal mechanisms agreed well, demonstrating the adequacy of the resulting reduced chemistry for all the butanol isomers in predicting global combustion phenomena. In addition, the skeletal mechanisms closely predicted the time-histories of fuel mass fractions in homogeneous compression-ignition engine simulations. The performance of each butanol isomer was additionally compared with that of a gasoline surrogate with an antiknock index of 87 in a homogeneous compression-ignition engine simulation. The gasoline surrogate was consumed faster than any of the butanol isomers, with tert-butanol exhibiting the slowest fuel consumption rate. While n-butanol and isobutanol displayed the most similar consumption profiles relative to the gasoline surrogate, the two literature chemical kinetic models predicted different orderings., Comment: 39 pages, 16 figures. Supporting information available via https://doi.org/10.1021/acs.energyfuels.6b01857

We report the discovery of KELT J041621-620046, a moderately bright (J$\sim$10.2) M dwarf eclipsing binary system at a distance of 39$\pm$3 pc. KELT J041621-620046 was first identified as an eclipsing binary using observations from the Kilodegree Extremely Little Telescope (KELT) survey. The system has a short orbital period of $\sim$1.11 days and consists of components with M$_1$ = $0.447^{-0.047}_{+0.052}\,M_\odot$ and M$_2$ = $0.399^{-0.042}_{+0.046}\,M_\odot$ in nearly circular orbits. The radii of the two stars are R$_1$ = $0.540^{-0.032}_{+0.034}\,R_\odot$ and R$_2$ = $0.453\pm0.017\,R_\odot$. Full system and orbital properties were determined (to $\sim$10% error) by conducting an EBOP global modeling of the high precision photometric and spectroscopic observations obtained by the KELT Follow-up Network. Each star is larger by 17-28% and cooler by 4-10% than predicted by standard (non-magnetic) stellar models. Strong H$\alpha$ emission indicates chromospheric activity in both stars. The observed radii and temperature discrepancies for both components are more consistent with those predicted by empirical relations that account for convective suppression due to magnetic activity., Comment: 12 Pages, 9 Figures, 4 Tables, Accepted for publication in ApJ

Fundamental experimental measurements of quantities such as ignition delay times, laminar flame speeds, and species profiles (among others) serve important roles in understanding fuel chemistry and validating chemical kinetic models. However, despite both the importance and abundance of such information in the literature, the community lacks a widely adopted standard format for this data. This impedes both sharing and wide use by the community. Here we introduce a new chemical kinetics experimental data format, ChemKED, and the related Python-based package for validating and working with ChemKED-formatted files called PyKED. We also review past and related efforts, and motivate the need for a new solution. ChemKED currently supports the representation of autoignition delay time measurements from shock tubes and rapid compression machines. ChemKED-formatted files contain all of the information needed to simulate experimental data points, including the uncertainty of the data. ChemKED is based on the YAML data serialization language, and is intended as a human- and machine-readable standard for easy creation and automated use. Development of ChemKED and PyKED occurs openly on GitHub under the BSD 3-clause license, and contributions from the community are welcome. Plans for future development include support for experimental data from laminar flame, jet stirred reactor, and speciation measurements., Comment: 22 pages, accepted for publication in the International Journal of Chemical Kinetics