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At least 883 records · Page 49

Build Less Code, Deliver More Science: An Experience Report on Composing Scientific Environments using Component-based and Commodity Software Platforms

Modern scientific software is daunting in its diversity and complexity. From massively parallel simulations running on the world’s largest supercomputers, to visualizations and user support environments that manage ever growing complex data collections, the challenges for software engineers are plentiful. While high performance simulators are necessarily specialized codes to maximize performance on specific supercomputer architectures, we argue the vast majority of supporting infrastructure, data management and analysis tools can leverage commodity open source and component-based technologies. This approach can significantly drive down the effort and costs of building complex, collaborative scientific user environments, as well as increase their reliability and extensibility. In this paper we describe our experiences in creating an initial user environment for scientists involved in modeling the detailed effects of climate change on the environment of selected geographical regions. Our approach composes the user environment using the Velo scientific knowledge management platform and the MeDICi Integration Framework for scientific workflows. These established platforms leverage component-based technologies and extend commodity open source platforms with abstractions and capabilities that make them amenable for broad use in science. Using this approach we were able to deliver an operational user environment capable of running thousands of simulations in a 7 month period, and achieve significant software reuse.

Gorton, Ian↗

Characterizing Tradeoffs in Memory, Accuracy, and Speed for Chemistry Tabulation Techniques

Chemistry tabulation is a common approach in practical simulations of turbulent combustion at engineering scales. Linear interpolants have traditionally been used for accessing precomputed multidimensional tables but suffer from large memory requirements and discontinuous derivatives. Higher-degree interpolants address some of these restrictions but are similarly limited to relatively low-dimensional tabulation. Artificial neural networks (ANNs) can be used to overcome these limitations but cannot guarantee the same accuracy as interpolants and introduce challenges in reproducibility and reliable training. These challenges are enhanced as the physics complexity to be represented within the tabulation increases. Here, we assess the efficiency, accuracy, and memory requirements of Lagrange polynomials, tensor product B-splines, and ANNs as tabulation strategies. We analyze results in the context of nonadiabatic flamelet modeling where higher dimension counts are necessary. While ANNs do not require structuring of data, providing benefits for complex physics representation, interpolation approaches often rely on some structuring of the table. Interpolation using structured table inputs that are not directly related to the variables transported in a simulation can incur additional query costs. This is demonstrated in the present implementation of heat losses. We show that ANNs, despite being difficult to train and reproduce, can be advantageous for high-dimensional, unstructured datasets relevant to nonadiabatic flamelet models. Furthermore we demonstrate that Lagrange polynomials show significant speedup for similar accuracy compared to B-splines.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Monte Carlo simulations for water adsorption in porous materials: Best practices and new insights

Technologies based on water adsorption such as water harvesting from air have tremendous potential in mitigating important global crises such as water scarcity. An important challenge to the deployment of such technologies is finding optimal adsorbent materials. Given the large materials space of available adsorbents, large-scale computational screening can be extremely helpful for this task. This work explores the methods and details associated with such screening procedures and recommends best practices. We also shed light on the limitations of traditionally used and inexpensive to compute prescreening approaches involving geometric and energetic features to predict water adsorption behavior of porous materials. Such approaches can provide general trends to predict adsorption behavior but may lead to the overlook of potentially important structures due to the complex nature of water adsorption. Finally, this study offers insights for future water adsorption simulations to facilitate the development of optimal water adsorbents.

42 ENGINEERING↗

Towards engineered branch placement: Unreal™ match between vapour-liquid-solid glancing angle deposition nanowire growth and simulation

The vapour-liquid-solid glancing angle deposition (VLS-GLAD) process is capable of producing complex nanotree structures with control over azimuthal branch orientation and height. We have developed a thin film growth simulation including ballistic deposition, simplified surface diffusion, and droplet-mediated cubic crystal growth for the VLS-GLAD process using the Unreal{sup TM} Development Kit. The use of a commercial game engine has provided an interactive environment while allowing a custom physics implementation. Our simulation's output is verified against experimental data, including a volumetric film reconstruction produced using focused ion beam and scanning-electron microscopy (SEM), crystallographic texture, and morphological characteristics such as branch orientation. We achieve excellent morphological and texture agreement with experimental data, as well as qualitative agreement with SEM imagery. The simplified physics in our model reproduces the experimental films, indicating that the dominant role flux geometry plays in the VLS-GLAD competitive growth process responsible for azimuthally oriented branches and biaxial crystal texture evolution. The simulation's successful reproduction of experimental data indicates that it should have predictive power in designing novel VLS-GLAD structures.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Simulant Basis for the Standard High Solids Vessel Design

The Waste Treatment and Immobilization Plant (WTP) is working to develop a Standard High Solids Vessel Design (SHSVD) process vessel. To support testing of this new design, WTP engineering staff requested that a Newtonian simulant and a non-Newtonian simulant be developed that would represent the Most Adverse Design Conditions (in development) with respect to mixing performance as specified by WTP. The majority of the simulant requirements are specified in 24590-PTF-RPT-PE-16-001, Rev. 0. The first step in this process is to develop the basis for these simulants. This document describes the basis for the properties of these two simulant types. The simulant recipes that meet this basis will be provided in a subsequent document.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Predicting Microstructural Void Nucleation in Discontinuous Fiber Composites through Coupled in-situ X-ray Tomography Experiments and Simulations

Composite materials have become widely used in engineering applications, in order to reduce the overall weight of structures while retaining their required strength. In this work, a composite material consisting of discontinuous glass fibers in a polypropylene matrix is studied at the microstructural level through coupled experiments and simulations, in order to uncover the mechanisms that cause damage to initiate in the microstructure under macroscopic tension. Specifically, we show how hydrostatic stresses in the matrix can be used as a metric to explain and predict the exact location of microvoid nucleation that occurs during damage initiation within the composite’s microstructure. Furthermore, this work provides evidence that hydrostatic stresses in the matrix can lead to coupled microvoid nucleation and early fiber breakage, and that small fragments of fibers can play an important role in the process of microvoid nucleation. These results significantly improve our understanding of the mechanics that drive the initiation of damage in the complex microstructures of discontinuous fiber reinforced thermoplastics, while also allowing scientists and engineers to predict the microstructural damage behavior of these composites at sub-fiber resolution and with high accuracy.

36 MATERIALS SCIENCE↗

Technical Advisor to the American Indian Research and Education Initiatives (AIREI).

The American Indian Research & Education Initiative (AIREI) is a pilot program that started in 2011 and is funded by the US Department of Energy (DOE) Economic Impact & Diversity and National Nuclear Security Administration in partnership with the American Indian Higher Education Consortium (AIHEC) and the American Indian Science and Engineering Society. AIREI brings science, technology, engineering, and mathematics (STEM) research and education funding to Tribal Colleges and Universities (TCU) and other US universities. AIREI has funded eight schools, including four pairs of tribal colleges and mainstream universities, in order for student and faculty research teams to bring energy projects to tribal lands. The research team from Southwest Indian Polytechnic Institute (SIPI) and Northern Arizona University (NAU) has performed a student-centric research and analysis feasibility study of a potential utility-scale solar power plant on the Jemez Pueblo reservation trust land. The research team from Navajo Technical University (NTU) and Arizona State University (ASU) has assessed the effectiveness of solar photovoltaic (PV) system designs in meeting the electricity demands of Navajo Tribal homes and public buildings in addition to the development of a solar technology curriculum that incorporates the outcomes of this study, helping to advance PV system design and installations on local Tribal lands. The Little Big Horn College (LBHC) and Montana State University-Bozeman (MSUB) team has developed fast growing strains of nitrogen-fixing cyanobacteria to help advance carbon capture and sequestration (CCS) technologies. The research supported the Crow Nation reservation as it evaluates opportunities for coal-to-liquid fuel and CCS projects. The Sinte Gleska University (SGU) and South Dakota School of Mines (SDSM) team developed computer modeling and simulation technologies to evaluate the feasibility of oil and gas development from the Niobrara Formation on the Rosebud Sioux reservation. Through this project, the students developed skills in applied energy-related research involving computer simulation, chemistry, geology, and petroleum engineering. AIREI supports collaboration between these universities and connects the teams with the technological expertise and mentorship opportunities provided through Sandia National Laboratories (Sandia). AIHEC consists of 37 American Indian tribally controlled colleges around the nation and provides technical assistance through professional development workshops, strategic planning meetings, and information sharing strategies.

99 GENERAL AND MISCELLANEOUS↗

Massively Parallel Capability in Sierra/SD for Simulation Vibration with Piezoelectrics

Sierra/SD is an engineering structural dynamics code that provides Sandia and other customers a tool to model structural and acoustic physics on large complex physical systems using massively parallel processing. This report provides a detailed overview on Sierra/SD’s most recent physics package: coupled electro-mechanical physics. This capability uses the finite element method to model coupled electro-mechanical physics exhibited by piezoelectric materials. This report provides an applications overview, theory overview, and verification examples demonstrating the electro-mechanical physics modeling capabilities of Sierra/SD.

97 MATHEMATICS AND COMPUTING↗

Quasiparticle excitations in a one-dimensional interacting topological insulator: Application for dopant-based quantum simulation

Here, we study the effects of electron-electron interactions on the charge excitation spectrum of the spinful Su-Schrieffer-Heeger (SSH) model, a prototype of a one-dimensional bulk obstructed topological insulator. In view of recent progress in the fabrication of dopant-based quantum simulators we focus on experimentally detectable signatures of interacting topology in finite lattices. To this end we use Lanczos-based exact diagonalization to calculate the single-particle spectral function in real space which generalizes the local density of states to interacting systems. Its spatial and spectral resolution allows for the direct investigation and identification of edge states. By studying the noninteracting limit, we demonstrate that the topological in-gap states on the boundary are robust against both finite-size effects as well as random bond and onsite disorder which suggests the feasibility of simulating the SSH model in engineered dopant arrays in silicon. While edge excitations become zero-energy spinlike for any finite interaction strength, our analysis of the spectral function shows that the single-particle charge excitations are gapped out on the boundary. Despite the loss of topological protection, we find that these edge excitations are quasiparticlelike as long as they remain within the bulk gap. Above a critical interaction strength of U c ≈5t these quasiparticles on the boundary lose their coherence which is explained by the merging of edge and bulk states. This is in contrast to the many-body edge excitations which survive the limit of strong coupling, as established in the literature. Our findings show that for moderate repulsive interactions the nontrivial phase of the interacting SSH model can be detected through remnant signatures of topological single-particle states using single-particle local measurement techniques such as scanning tunneling spectroscopy.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Review of recent activities with MOOSE, an open-source finite element & finite volume multi-fidelity simulation framework

Modeling and simulation are an increasing part of engineering. This is undoubtedly driven by the high costs of constructing experimental facilities, but also enabled by the exponential increase in computing powers over the last decades, which allows computational models to be closer than ever to reality. One of the main drivers for the development of MOOSE is supporting advanced nuclear reactor simulations. A challenging aspect of modeling advanced nuclear reactors is the plurality of physics involved, including neutronics, thermal hydraulics and fuel performance. These physics are all coupled to some extent and are generally solved in a sequential but iterative fashion. The United States (U.S.) national laboratories have been developing MOOSE, an open source multiphysics framework since its inception at the Idaho National Laboratory (INL) in 2008. This framework enables seamless coupling of multiphysics simulations and facilitates the implementation of new physics and material governing laws. It is continuously expanded with novel numerical methods and new pre-implemented physics module. Numerous applications, developed within the Department of Energy (DOE) laboratories, academia, and industry, including outside of nuclear engineering, have been developed to study specialized physics problems. International collaborations are welcome on this open-source modeling and simulation project.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

Opportunities and challenges in process modeling and simulation of electrochemical systems

Electrochemical technologies have garnered intense interest in both academic research and industrial applications due to their potential to increase energy efficiency and reduce carbon footprint. However, electrochemical process fundamentals have been absent from the chemical process simulators available to millions of chemical engineers worldwide. To expedite process research and development of electrochemical technologies in the chemical industry, it is imperative that essential electrochemical process fundamentals be incorporated into process simulators to support modeling and simulation of electrochemical processes. Here, this study examines three process fundamentals key to the research and development of electrochemical processes: electrochemical reaction kinetics, electrolyte thermodynamics, and heat and mass transfer. It further illustrates application of these process fundamentals with a case study modeling an electrochemical process for the conversion of acrylonitrile to adiponitrile. The modeling example highlights the roles of applied voltage on reaction rates, and electron flow rates on the performance of electrochemical processes. It suggests that the applied voltage and electron flow rates are two unique concepts that should be included in simulators for electrochemical processes.

09 BIOMASS FUELS↗

Numerical Simulation with Experimental Validation of the Draping Behavior of Woven Fabrics

Woven fabric composites are extensively used in molding complex geometrical shapes due to their high conformability compared to other fabrics. Preforming is an important step in the overall process. In this step, the two-dimensional fabric is draped to become the three-dimensional shape of the part prior to resin injection. During preforming, the orientation of the tows may change significantly compared to the initial orientations. Accurate prediction of the tow orientations after molding is important for evaluating the structural performance of the final part. This paper investigates the fiber angle changes for carbon fiber woven fabrics during draping over a truncated pyramid tool designed and fabricated at the General Motors Research Labs. This aspect of study is a subset of the broad study conducted under the purview of a Department of Energy project funded to GM in developing state of the art computational tools for integrated manufacturing and structural performance prediction of carbon fiber composites. Fabric bending, picture frame testing, and bias-extension evaluations were carried out to determine the material parameters for these fabrics. The PAM-FORM computer program was used to model the draping behavior of these fabrics. Following deformation, fiber angle changes at different locations on the truncated pyramid were measured experimentally. The predicted angles matched the experimental results well as measured along the centerline and at several different locations on the deformed fabric. Details of the test methods used as well as the numerical results with various simulation parameters will be provided.

42 ENGINEERING↗

Plasma-Assisted Pre-Chamber Ignition System for Highly Dilute Stoichiometric Heavy-Duty Natural Gas Engines (Final Technical Report)

This project explored advanced ignition technologies to significantly enhance efficiency and reduce operating costs for heavy-duty natural gas engines operating at stoichiometric conditions, while meeting ultra-low NOx emission standards. The main goal was to develop and validate a plasma-assisted pre-chamber ignition system that could deliver at least a 2% increase in brake thermal efficiency (BTE) and a 4% decrease in total cost of ownership (TCO) compared to a typical multi-cylinder engine with three-way catalyst aftertreatment, ensuring compatibility with the expected 2027 EPA/CARB regulations. In the first half of the project, the research team concentrated on developing and testing plasma-assisted pre-chamber ignition using nanosecond pulsed discharges. Extensive experiments were conducted in an optically accessible rapid-compression and expansion machine, a constant-volume chamber, and an optical single-cylinder engine. Experiments were coupled with CFD simulations. The work produced unique insights into pre-chamber flame formation, jet ignition, dilution effects, and flame quenching at pressures, temperatures, and dilution levels relevant to engines. Although plasma-assisted ignition showed promise in controlled lab settings, the research also identified fundamental and practical challenges when applying this technology to real engine conditions. Midway through the project, a crucial pivot was made, guided by three key findings. First, the power electronics required for nanosecond plasma discharges were found to be too costly for commercial use, undermining the project’s cost-of-ownership goals. Second, nanosecond plasma ignition was highly sensitive to turbulent flow in the pre-chamber, resulting in lower ignition reliability than traditional spark under engine-like conditions. Third, achieving a truly diffuse low-temperature plasma at high pressures near top dead center was not possible, reducing the anticipated chemical enhancement benefits. These results collectively suggested that continuing with plasma-assisted ignition was unlikely to meet both efficiency and cost objectives. In response, the project shifted focus to a more realistic approach: enhancing traditional spark-based pre-chamber ignition with significantly less spark energy. Using insights gained earlier in the project, the team redesigned the pre-chamber to maintain high dilution tolerance and quick combustion, even with lower ignition energy. Testing confirmed that with optimized pre-chamber design and combustion timing, a lower-energy spark could reliably ignite highly diluted stoichiometric mixtures, reduce burn time, and boost thermal efficiency. Final engine testing and techno-economic analysis verified that this revised approach successfully achieved the project goals. The optimized pre-chamber ignition system provided over a 2% increase in calculated brake thermal efficiency compared to the baseline engine. Notably, the lower ignition energy and simplified hardware reduced component stress, extended maintenance intervals, and lowered the total cost of ownership. When used with stoichiometric operation and traditional three-way aftertreatment, the system remained compatible with near-zero NOx emissions targets without increasing cost or complexity in the emissions control system. In summary, although the project deviated from its initial plasma-assisted ignition idea, the work produced a more practical and commercially viable solution. The results show that precisely optimized, low-energy pre-chamber spark ignition can significantly improve efficiency and reduce overall ownership costs for heavy-duty natural gas engines. This directly aligns with DOE goals for cleaner, more efficient, and cost-effective transportation technologies.

03 NATURAL GAS↗

A comprehensive numerical investigation on spray models for Direct-Injection Spark-Ignition engines

Gasoline direct-injection spark-ignition (DISI) engines generate a large portion of their unburned hydrocarbon (UHC) and soot emissions during the cold-start phase. A predictive computational fluid dynamics (CFD) modeling framework can be used to understand the physical processes that characterize fuel spray evolution and fuel-film formation at cold start conditions, which can help to reduce engine-out particulate emissions. This study systematically evaluated spray submodels and developed a set of simulation best practices for physical-numerical submodels with the goal of enabling accurate simulations of liquid spray behavior in a DISI engine. Three comprehensive experimental datasets containing free-spray projected liquid volume (PLV), liquid volume fraction (LVF), and near-field X-ray radiography data were used to validate the simulation results and evaluate the spray submodels. Systematic analysis delved into injected parcel distribution, droplet collision, spray breakup, and evaporation via a detailed assessment of the relevant spray submodels. Moreover, the effects of turbulence models and the initial turbulent flow properties on the liquid spray evolution were examined. Based on extensive calibration efforts, a set of simulation best practices for the free spray was developed and validated against the PLV/LVF data. Simulation results indicated that the uniform distribution for parcel initialization, coupled with appropriate droplet collision submodels, provides an improved spray morphology compared to the cluster distribution. The findings also underscored the importance of calibrating the Kelvin-Helmholtz Rayleigh-Taylor (KH-RT) breakup model constants and droplet heat transfer coefficient scaling factor to achieve favorable agreement regarding measured liquid penetration and spray widths. In conclusion, this study marks a substantial stride towards accurately predicting fuel film evolution and soot formation within DISI engine performance.

ECN Spray G↗

Smoothing-based compressed state Kalman filter for joint state-parameter estimation: Applications in reservoir characterization and CO 2 storage monitoring

The operation of most engineered hydrogeological systems relies on simulating physical processes using numerical models with uncertain parameters and initial conditions. Predictions by such uncertain models can be greatly improved by Kalman-filter techniques that sequentially assimilate monitoring data. Each assimilation constitutes a nonlinear optimization, which is solved by linearizing an objective function about the model prediction and applying a linear correction to this prediction. However, if model parameters and initial conditions are uncertain, the optimization problem becomes strongly nonlinear and a linear correction may yield unphysical results. In this paper, we investigate the utility of one-step ahead smoothing, a variant of the traditional filtering process, to eliminate nonphysical results and reduce estimation artifacts caused by nonlinearities. Here, we present the smoothing-based compressed state Kalman filter (sCSKF), an algorithm that combines one step ahead smoothing, in which current observations are used to correct the state and parameters one step back in time, with a nonensemble covariance compression scheme, that reduces the computational cost by efficiently exploring the high-dimensional state and parameter space. Numerical experiments show that when model parameters are uncertain and the states exhibit hyperbolic behavior with sharp fronts, as in CO 2 storage applications, one-step ahead smoothing reduces overshooting errors and, by design, gives physically consistent state and parameter estimates. We compared sCSKF with commonly used data assimilation methods and showed that for the same computational cost, combining one step ahead smoothing and nonensemble compression is advantageous for real-time characterization and monitoring of large-scale hydrogeological systems with sharp moving fronts.

42 ENGINEERING↗

Process Anomaly Detection for Sparsely Labeled Events in Nuclear Power Plants

An essential aspect of online monitoring, subtle anomaly detection increases the detection lead time for equipment failure and enables a nuclear power plant (NPP) to mitigate unexpected partial or full outages, resulting in significant cost saving to the plant. Once an anomaly is detected by plant staff, its cause and severity are investigated. Because the vast majority of anomalies require some level of investigation, including some that require time-consuming examination, before they are passed over to the engineering organization for further analysis, plants are often equipped with tools to assist the staff in performing anomaly detection. Those tools operate as a black box and are often based on statistical methods that establish sensor correlations using preconfigured mathematical models and flag correlation deviations as anomalies. Due to the number of anomalies detected at a given NPP on a daily basis, a significant number of flagged anomalies usually await examination for days or weeks. A primary cause of this backlog is that the methods used by the tools generate many false positives. Though this is usually attributed to oversensitive model settings due to very narrow normal operation bands, it can also be associated with the model development being inadequate for the process being monitored, or with missing model inputs that could have explained misclassified positives. The performance of anomaly detection tools impacts their plant acceptance and utilization, especially when the effort to address false positives generated by the tool depletes the value or cost saved by using that tool. Thus, means to advance anomaly detection performance have been investigated by the Department of Energy’s Light Water Reactor Sustainability program. Previous and ongoing efforts have targeted unsupervised machine-learning (ML) methods, which do not require the labeling of any data fed into the ML model. By contrast, in supervised anomaly detection methods, every data point is labeled as either a normal or abnormal process condition, and the model is trained to replicate the classification process. Supervised methods usually outperform unsupervised methods, due to the added value in differentiating normal from anomalous states of the monitored process. An NPP’s corrective action program requires it to track and document, via a dedicated report, the resolution of any issues that occur within the plant. Once created, each report is reviewed by a plant screening committee, and several classifications and decisions are made. Recently, a collaborating NPP developed an artificial intelligence and ML-based classifier to categorize a condition report (CR) into classes that can serve to label the data as normal or anomalous. Applying CRs as labels represents a semi-supervised use case. Semi-supervised ML assumes that labels exist for some data points (i.e., labeled anomalies, in this case) but not for the rest. In this effort, semi-supervised ML methods were used to fuse data from CRs with anomaly detection methods in order to test the hypothesis that partially labeled anomalies would improve the accuracy of the anomaly detection methods. Specifically, two methods were used. The first is the deep Semi-supervised Anomaly Detection (deep SAD) method, which can handle labels ranging from fully unsupervised to fully supervised cases. The second is a newly designed ML method developed specifically for this effort and referred to as the high-order feature (HOF)-based method. To evaluate these two methods in controlled environments, synthetic data generators were developed and used. The first datasets used a spring-mass-damper (SMD) system simulator commonly found in mechanical engineering references. This was used to create two use cases: a one- and a three-mass system. Anomalies were introduced by changing the spring and damper coefficients while the system was actuated by random forces. The second datasets used the commercial Dymola-Modelica software to build a simplified nuclear reactor model. Anomalies were added in the form of corrupted sensor readings and/or control commands. The deep SAD method was tested using the SMD system, while the HOF method was tested using both datasets. Application of the deep SAD semi-supervised ML method demonstrated that labels can generate increased confidence in detecting true anomalies. This helped increase the number of true positives and decrease the number of false negatives—something that would aid in addressing the backlog of possible anomalies. Application of the HOF method demonstrated that labels can aid in down selecting from a candidate set of features to a more optimal subset in order to better differentiate between normal and anomalous conditions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

The structure and propagation of laminar flames under autoignitive conditions

The laminar flame speed s l is an important reference quantity for characterising and modelling combustion. Experimental measurements of laminar flame speed require the residence time of the fuel/air mixture (τ f ) to be shorter than the autoignition delay time (τ). This presents a considerable challenge for conditions where autoignition occurs rapidly, such as in compression ignition engines. As a result, experimental measurements in typical compression ignition engine conditions do not exist. Simulations of freely propagating premixed flames, where the burning velocity is found as an eigenvalue of the solution, are also not well posed in such conditions, since the mixture ahead of the flame can autoignite, leading to the so called “cold boundary problem”. In this paper, a numerical method for estimating a reference flame speed, s R , is proposed that is valid for laminar flame propagation at autoignitive conditions. Two isomer fuels are considered to test this method: ethanol, which in the considered conditions is a single-stage ignition fuel; and dimethyl ether, which has a temperature-dependent single- or two-stage ignition and a negative temperature coefficient regime for τ. Calculations are performed for the flame position in a one-dimensional computational domain with inflow-outflow boundary conditions, as a function of the inlet velocity U I and for stoichiometric fuel–air premixtures. The response of the flame position, L F , to U I shows distinct stabilisation regimes. For single-stage ignition fuels, at low U I the flame speed exceeds U I and the flame becomes attached to the inlet. Above a critical U I value, the flame detaches from the inlet and L f becomes extremely sensitive to U I until, for sufficiently high U I , the sensitivity decreases and L f corresponds to the location expected from a purely autoignition stabilised flame. The transition from the attached to the autoignition regimes has a corresponding peak dL f /dU I value which is proposed to be a unique reference flame speed s R for single-stage ignition fuels. For two-stage ignition fuels, there is an additional stable regime where a high-temperature flame propagates into a pool of combustion intermediates generated by the first stage of autoignition. This results in two peaks in dL f /dU I and therefore two reference flame speed values. The lower value corresponds to the definition of s R for single-stage ignition fuels, while the higher value exists only for two-stage ignition fuels and corresponds to a high temperature flame propagating into the first stage of autoignition and is denoted s R ' . Finally, a transport budget analysis for low- and high-temperature radical species is also performed, which confirms that the flame structures at U I = s R and U I = s R ' do indeed correspond to premixed flames (deflagrations), as opposed to spontaneous ignition fronts which do not have a unique propagation speed.

33 ADVANCED PROPULSION SYSTEMS↗

Argonne News Brief: Making Sense of Noise

The Argonne Leadership Computing Facility at Argonne National Laboratory helped Joe Nichols, of the University of Minnesota, to create high fidelity simulations of jet turbulence to determine how and where noise is produced. The results may lead to novel engineering designs that reduce noise over commercial flight paths and on aircraft carrier decks.

42 ENGINEERING↗