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At least 397 records · Page 22

Investigation of Best-Practices and Computationally Inexpensive Radiative Exchange Models for Discrete Element Method Modeling of Aluminosilicate Particles in Concentrating Solar Power Environments

Chemically inert, aluminosilicate based particles have been investigated as both a thermal transport and sensible energy storage medium for concentrating solar power facilities. These particles will experience a wide range of operating temperatures (300-1000 K) and handling conditions (dense to dilute falling particle curtains, dense granular flows, or dense structures), requiring specially-designed and optimized infrastructures. The relative influence of collisional and frictional interactions between particles varies based on temperature-dependent particulate properties and greatly impacts the bulk, granular flow behavior. These underlying physics are captured using discrete element method modeling tools. However, this modeling method is computationally expensive as each particle position and interaction is tracked during the simulation. These modeling methods are further complicated by introducing temperature-dependent particle properties, high-temperature radiative exchange, and directional irradiation sources experienced by granular flows in concentrating solar power environments. In this study, coupled experimental and numerical slump testing of aluminosilicate particles was performed and computationally efficient radiative exchange models were evaluated to establish best-practices for discrete element method models for concentrating solar power environments. The three particle types investigated included Carbobead HSP 30 /60, Carbobead CP 30/60, and Granusil 4030. Existing modeling limitations and computationally-efficient multi-modal heat transfer models were evaluated using Aspherix®, a commercial discrete element method software. High-temperature (< 1073 K) slump testing of aluminosilicate particles was performed to investigate the deviation between experimentally-observed and numerically-predicted angles of repose introduced by computation-time reduction practices including the relaxation of the particle elastic modulus and coarse-graining. Coarse-graining is used to use a single modeled particle that is representative of a collection of smaller particles, decreasing the computational cost at the expense of geometric accuracy. Additionally, relaxation of the elastic modulus is used to reduce computational time at the expense of an increased, modeled particle overlap. Prior studies have determined that aluminosilicate particles retain a high elastic modulus at high temperatures (< 1073 K), requiring small simulation timesteps to ensure resolved contact forces resemble appropriate solid mechanics. A parametric study was performed to evaluate the influence of computation time improvements on the deviation between experimental and modeled angle of repose across high temperatures < 1073 K. Additionally, numerical case studies were performed on candidate particle systems at varying porosities and temperatures. These studies were performed to investigate the influence of computationally-efficient radiative-exchange modeling methods coupled to Aspherix® on modeled accuracy and computation time. The recently-developed distance-based approximation was evaluated in estimating radiative exchange between particles and participating surfaces located in close proximity. The distance based approximation was developed to use tabulated estimates of the radiative distribution factor between individual particles and surfaces in close proximity (< 40 particle radii). These methods were expanded to the aluminosilicate particles of interest, including the influence of particle size distributions. To capture radiative exchange between particles and surfaces not in close proximity (> 40 particle radii) and to capture the absorption of directional irradiation from concentrating solar resources, a volumetrically-averaged radiative distribution factor was calculated between the modeled granular flow and surfaces using Monte Carlo ray-tracing for participating media. Volume-averaged absorption and scattering coefficients were predicted using a volumetric discretization of the modeled domain with monodisperse approximations based on geometric optics and experimentally-determined scattering phase functions for aluminosilicate particles.

14 SOLAR ENERGY↗

Final Technical Report on Investigation of Short-range Ordering in Transition Metal Compounds by Diffuse Scattering

Future energy needs and sustainability require new materials with novel properties for such applications as energy production, storage and transport and microelectronics. Quantum materials with several competing interactions at the electronic level offer tremendous opportunity to discover, design and tune properties for such applications. Although it has been recognized that small deviations in atomic positions, driven by the competing electronic interactions, in crystalline materials can have significant impact on properties, it remains a challenge to accurately characterize such deviations (short-range order) due to the lack of advanced instruments and analysis tools to characterize them. This project used the powerful neutron and x-ray scattering instruments recently developed at the DOE user facilities to address this challenge. New methods and efficient analysis tools were employed to uncover the hidden ordering that is behind the unique properties of transition metal compounds. One example of hidden order revealed by this project comes from vanadium (IV) oxide (VO 2 ) and related compounds. The project found that the chemical bonds in VO 2 compete against each other, unlike most crystalline compounds where the chemical bonds cooperate to yield the ordered structure. This helps explain why different measurements yielded different, competing pictures about the nature of VO 2 , which has led to disagreement about where its physical properties come from. Another example from this project of hidden ordering comes from a class of compounds known as condensed Chevrel phases, which are superconducting compounds that are generally believed to be non-magnetic. The neutron scattering methods used in this project revealed evidence of magnetism, which usually does not coexist with superconductivity. In this case, magnetism is believed to be one part of a special type of electronic behavior that can occur in compounds that have one-dimensional bonding character.

36 MATERIALS SCIENCE↗

Bounds on Lorentz Violation Parameters in the DUNE Experiment

The Standard Model (SM) has been highly successful in describing fundamental interactions, yet phenomena like neutrino oscillations point to physics beyond its framework. Among the proposed extensions, Lorentz invariance violation (LIV) stands out as a compelling possibility. Long-baseline neutrino experiments such as DUNE, with its 1300 km baseline, offer a promising setting to test LIV effects with high precision. In this work, we evaluate DUNE s sensitivity to LIV by analyzing event rates and oscillation probabilities using the GLoBES framework. A custom implementation of LIV parameters was employed to derive bounds on the aa and cc matrices, identifying the parameter values that lead to a 10% deviation from the standard oscillation probability. Our results show partial agreement with previous studies for the aa parameters, while deviations in the cc parameters highlight the impact of differing methodologies and sensitivities. These findings reinforce the relevance of LIV searches and motivate further theoretical and experimental efforts in this direction.

Cruz, Thiago. [Tech. Fed. Parana U.]↗

Time-of-Flight Energy Measurements With BPMs

The energy of a bunched non-relativistic ion beam can be deduced from measuring the beam phases in neighbouring Beam Position Monitors (BPMs). This report discusses implementation of such a procedure at the PIP-II H- linac being constructed at Fermilab. The case when the flight time between BPMs is longer than the period of BPM frequency is considered in more detail. When absolute BPM phase calibration is not available, the BPM phase information can be used to track deviations of the beam energy from the de-sired value. Such energy deviation parameter is operationally implemented at the transfer line between 400 MeV Linac and the Booster, and its analog is expected to be used in the transfer line from PIP-II as well.

Shemyakin, Alexander V. [Fermilab] (ORCID:00000001↗

Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation

Background: Patient monitoring is vital in all stages of care. In particular, intensive care unit (ICU) patient monitoring has the potential to reduce complications and morbidity, and to increase the quality of care by enabling hospitals to deliver higher-quality, cost-effective patient care, and improve the quality of medical services in the ICU. Objective: We here report the development and validation of ICU length of stay and mortality prediction models. The models will be used in an intelligent ICU patient monitoring module of an Intelligent Remote Patient Monitoring (IRPM) framework that monitors the health status of patients, and generates timely alerts, maneuver guidance, or reports when adverse medical conditions are predicted. Methods: We utilized the publicly available Medical Information Mart for Intensive Care (MIMIC) database to extract ICU stay data for adult patients to build two prediction models: one for mortality prediction and another for ICU length of stay. For the mortality model, we applied six commonly used machine learning (ML) binary classification algorithms for predicting the discharge status (survived or not). For the length of stay model, we applied the same six ML algorithms for binary classification using the median patient population ICU stay of 2.64 days. For the regression-based classification, we used two ML algorithms for predicting the number of days. We built two variations of each prediction model: one using 12 baseline demographic and vital sign features, and the other based on our proposed quantiles approach, in which we use 21 extra features engineered from the baseline vital sign features, including their modified means, standard deviations, and quantile percentages. Results: We could perform predictive modeling with minimal features while maintaining reasonable performance using the quantiles approach. The best accuracy achieved in the mortality model was approximately 89% using the random forest algorithm. The highest accuracy achieved in the length of stay model, based on the population median ICU stay (2.64 days), was approximately 65% using the random forest algorithm. Conclusions: The novelty in our approach is that we built models to predict ICU length of stay and mortality with reasonable accuracy based on a combination of ML and the quantiles approach that utilizes only vital signs available from the patient’s profile without the need to use any external features. This approach is based on feature engineering of the vital signs by including their modified means, standard deviations, and quantile percentages of the original features, which provided a richer dataset to achieve better predictive power in our models.

59 BASIC BIOLOGICAL SCIENCES↗

Baryon $\sigma$ terms in SU(3) BChPT x 1/N c

ChPT and the 1/N c expansion provide systematic frameworks for the strong interactions at low energy. A combined framework of both expansions has been developed and applied for baryons with three light-quark-flavors. The small scale expansion of the combined approach is identified as the ξ-expansion, in which the power counting of the expansions is linked according to O(p) = O(1/N c ) = O(ξ). The physical baryon masses as well as lattice QCD baryon masses for different quark mass masses are analyzed to O(ξ 3 ) in that framework. σ terms are addressed using the Feynman Hellmann theorem. For the nucleon, a useful connection between the deviation of the Gell-Mann-Okubo relation and the σ term σ 8N associated with the scalar density $\bar{u}u+\bar{d}d-2\bar{s}s$ is identified. In particular, the deviation from the tree level relation σ 8N = $\frac{1}{3}$(2m N -m Σ -m &Xi; ), which gives rise to the so called σ-term puzzle, is studied in the ξ-expansion. A large correction non-analytic in ξ results for that relation, making plausible the resolution of the puzzle. Issues with the determination of the strangeness σ terms are discussed, emphasizing the need for lattice calculations at smaller m s for better understanding the range of validity of the effective theory. The analysis presented here leads to σ πN = 69(10) MeV and σ πΔ = 60(10) MeV.

Fernando, Ishara P.↗

The muon g-2 experiment at Fermilab

The E989 collaboration has recently published the most precise measurement of the muon anomalous magnetic moment $a_\mu$ with an uncertainty of $0.46\,\mathrm{ppm}$. The new experimental world average of $a_\mu$ deviates by 4.2 standard deviations from the Standard Model prediction provided by the Muon g-2 Theory Initiative. Emerging results from ab-initio lattice QCD calculations for hadron-physics contributions allow to scrutinize this tantalizing hint for physics beyond the Standard Model for the first time in a three way comparison. To extract the value of $a_\mu$ a frequency comparison experiment is performed with spin-polarized muons confined in a superbly controlled electric and magnetic field environment. A measurement of the difference between the Larmor and the cyclotron frequency, the so-called anomalous spin precession frequency, is combined with a high-precision measurement of the magnetic field environment which is performed using nuclear magnetic resonance techniques. I discussed the most recent result of the Fermilab Muon g-2 experiment's first physics data run performed in 2018. I reported on the experimental improvements implemented for the subsequent science runs that will allow the collaboration to achieve the ultimate goal of $0.14\,\mathrm{ppm}$ uncertainty on $a_\mu$.

Fertl, Martin↗

Data-Driven Chance-Constrained Design of Voltage Droop Control for Distribution Networks: Preprint

This paper addresses the design of local control methods for voltage control in distribution networks with high level of distributed energy resources (DERs). The designed control methods adapt the active and reactive power output of distributed energy resources proportional to the deviation of the local measured voltage magnitudes from a reference voltage, which is referred to as droop control. Thus, the design focuses on determining the droop characteristics which satisfy network-wide voltage magnitude constraints. The uncertainty and variability of DERs renders the design of optimal droop controls very challenging. Hence, this paper proposes chance constraints to limit the risk from intermittent DERs, by designing droop control coefficients that guarantee the satisfaction of network operational constraints with a specific probability. In addition, the proposed approach relies entirely on historical data rather than assuming knowledge of the probability distributions that characterize the uncertainty of DERs. The efficacy of the proposed method is demonstrated on a 37-bus distribution feeder.

chance-constrained optimization↗

Forming Interphase Microgrids in Distribution Systems Using Cooperative Inverters

Interphase-microgrids formed by three islanded single-phase feeders in distribution systems are proposed in this paper. Loss of utility caused by natural and human-made disasters may isolate each residential subdivision from the utility. In this case, the household loads are supplied by distributed generation (DG) units which may not be enough to meet the load demand. This can lead to deviations in voltage and frequency, and the deviations may vary for each phase. The proposed solution is to form an interphase-microgrid by seamlessly interconnecting the islanded single-phase feeders. This interphase-microgrid can effectively balance the load demands and DG generation in distribution systems after losing the utility supply. This paper presents different case studies to demonstrate the viability of forming interphase-microgrids for residential distribution systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

SPRUCE Sphagnum Phytobiome Responses to Whole Ecosystem Warming and Elevated Atmospheric CO2 in July, 2017-2021

This dataset reports the carbon (C) and nitrogen (N) isotopic composition of Sphagnum moss tissue collected from the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experimental site within the Marcell Experimental Forest in northern Minnesota, USA. Tissues were collected inside the SPRUCE experimental study plot, where air and peat warming are combined in whole-ecosystem warming treatments, from 2019-2021. Additionally, rates of nitrogen fixation and methane oxidation measured for Sphagnum from 2017, 2019, and 2021. Sphagnum tissue % water content was measured at all timepoints. This dataset was used to conduct analyses within Petro et al (2023). Sphagnum C and N concentrations and 13C and 15N isotopic natural abundance were determined for three replicate Sphagnum tissue samples collected from inside the SPRUCE enclosures in July 2019-2021. Elemental and isotope analyses were conducted at the University of Georgia – Center for Applied Isotope Studies (CAIS; https://cais.uga.edu/). Plant elemental analysis was performed by the micro-Dumas method, while isotopic natural abundance was measured by isotope ratio mass spectrometry. 13C natural abundance is expressed as the per mille (‰) deviation from the Pee Dee Belemnite standard (PDB) 13C:12C ratio (δ13C), while 15N natural abundance is expressed as the ‰ deviation from the N2 atmospheric 15N:14N ratio (δ15N). Rates of nitrogen fixation and methane oxidation were measured using serum bottle incubations performed with Sphagnum collected inside the SPRUCE enclosures in July 2017, 2019, and 2021. Rates were calculated according to the amount of 15N-N2 or 13C-CH4 (methane) incorporated into the Sphagnum tissue or incubation headspace over the 48-hour intion period. In 2017, incubations were performed in growth chambers set to the temperature of the experimental plots during Sphagnum sampling. In 2019 and 2021, the incubations were performed directly inside the experimental plots. The 13C and 15N isotopic natural abundance data and rate measurements presented in this dataset provide insight into the impacts of whole-ecosystem warming and elevated atmospheric carbon dioxide (CO2) on N and C cycling within the Sphagnum phytobiome. This dataset contains two data files in comma-separate values (*.csv) format. Additional metadata are provided: two data dictionaries and a file-level metadata file in comma-separate values (.csv) format and a user guide in PDF (*.pdf) format. Additional datasets on NH4-N availability, porewater concentrations of CH4 and CO2, and Sphagnum groundcover within the SPRUCE experimental plots are available. These are outlined in the Related Datasets section in the user guide.

delta 13 carbon↗

GCAM-Demeter LU dataset

The dataset includes the projected global gridded land cover (excluding the Antarctic) for the period of 2015-2100 at 0.05-degree resolution and 5-year time step under fifteen SSP-RCP scenarios driven by five GCMs (i.e., gfdl, hadgem, ipsl, miroc, and noresm), using the Global Change Analysis Model (GCAM) and a geospatial downscaling model (Demeter). The data has been stored in self-describing NetCDF format. The dataset also includes the mean and standard deviation of the results driven by the five GCMs. More specifically, the data in each year includes grid-explicit fraction (in percent) of each of the 32 plant functional types that are widely used in current Earth system models. The NetCDF files are named as “GCAM_Demeter_LU_SSP_RCP_Model_Year.nc”, where “SSP” and “RCP” denote the SSP and RCP scenarios, including 'ssp1_rcp26', 'ssp1_rcp45', 'ssp1_rcp60', 'ssp2_rcp26', 'ssp2_rcp45', 'ssp2_rcp60', 'ssp3_rcp45', 'ssp3_rcp60', 'ssp4_rcp26', 'ssp4_rcp45', 'ssp4_rcp60', 'ssp5_rcp26', 'ssp5_rcp45', 'ssp5_rcp60', and 'ssp5_rcp85'. “Year” denotes the year of the land use data, and “GCM” denotes the source driving forcing data from five global climate models (gfdl, hadgem, ipsl, miroc, and noresm), or the mean (“modelmean”) and the standard deviation (“modelstd”) of the results from the five GCMs.

54 ENVIRONMENTAL SCIENCES↗

GCAM-Demeter-LU

This GCAM-Demeter-LU is made available under the Open Data Commons Attribution License:http://opendatacommons.org/licenses/by/1.0/. The dataset includes the projected global gridded land cover (excluding the Antarctic) for the period of 2015-2100 at 0.05-degree resolution and 5-year time step under fifteen SSP-RCP scenarios driven by five GCMs (i.e., gfdl, hadgem, ipsl, miroc, and noresm), using the Global Change Analysis Model (GCAM) and a geospatial downscaling model (Demeter). The data has been stored in self-describing NetCDF format. The dataset also includes the mean and standard deviation of the results driven by the five GCMs. More specifically, the data in each year includes grid-explicit fraction (in percent) of each of the 32 plant functional types that are widely used in current Earth system models. The NetCDF files are named as “GCAM_Demeter_LU_SSP_RCP_Model_Year.nc”, where “SSP” and “RCP” denote the SSP and RCP scenarios, including 'ssp1_rcp26', 'ssp1_rcp45', 'ssp1_rcp60', 'ssp2_rcp26', 'ssp2_rcp45', 'ssp2_rcp60', 'ssp3_rcp45', 'ssp3_rcp60', 'ssp4_rcp26', 'ssp4_rcp45', 'ssp4_rcp60', 'ssp5_rcp26', 'ssp5_rcp45', 'ssp5_rcp60', and 'ssp5_rcp85'. “Year” denotes the year of the land use data, and “GCM” denotes the source driving forcing data from five global climate models (gfdl, hadgem, ipsl, miroc, and noresm), or the mean (“modelmean”) and the standard deviation (“modelstd”) of the results from the five GCMs.See https://github.com/JGCRI/chen_et_al_2020a for details on how to reproduce this data. Rights and permissions Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article

99 GENERAL AND MISCELLANEOUS↗

Evaluating the Benefits of Bayesian Hierarchical Methods for Analyzing Heterogeneous Environmental Datasets: A Case Study of Marine Organic Carbon Fluxes

Large compilations of heterogeneous environmental observations are increasingly available as public databases, allowing researchers to test hypotheses across datasets. Statistical complexities arise when analyzing compiled data due to unbalanced spatial sampling, variable environmental context, mixed measurement techniques, and other reasons. Hierarchical Bayesian modeling is increasingly used in environmental science to describe these complexities, however few studies explicitly compare the utility of hierarchical Bayesian models to simpler and more commonly applied methods. Here we demonstrate the utility of the hierarchical Bayesian approach with application to a large compiled environmental dataset consisting of 5,741 marine vertical organic carbon flux observations from 407 sampling locations spanning eight biomes across the global ocean. We fit a global scale Bayesian hierarchical model that describes the vertical profile of organic carbon flux with depth. Profile parameters within a particular biome are assumed to share a common deviation from the global mean profile. Individual station-level parameters are then modeled as deviations from the common biome-level profile. The hierarchical approach is shown to have several benefits over simpler and more common data aggregation methods. First, the hierarchical approach avoids statistical complexities introduced due to unbalanced sampling and allows for flexible incorporation of spatial heterogeneitites in model parameters. Second, the hierarchical approach uses the whole dataset simultaneously to fit the model parameters which shares information across datasets and reduces the uncertainty up to 95% in individual profiles. Third, the Bayesian approach incorporates prior scientific information about model parameters; for example, the non-negativity of chemical concentrations or mass-balance, which we apply here. We explicitly quantify each of these properties in turn. We emphasize the generality of the hierarchical Bayesian approach for diverse environmental applications and its increasing feasibility for large datasets due to recent developments in Markov Chain Monte Carlo algorithms and easy-to-use high-level software implementations.

54 ENVIRONMENTAL SCIENCES↗

Synthesis, Crystal and Electronic Structure of the New Ternary Compound Ca3InAs3

Crystals of a new ternary compound in the Ca-In-As family, Ca3InAs3, have been successfully synthesized via flux growth techniques. This is only the third known compound between the respective elements. As elucidated by single-crystal X-ray diffraction measurements, Ca3InAs3 crystallizes in the orthorhombic space group Pnma (No. 62, Pearson symbol oP28) with unit cell parameters a = 12.296(2) Å, b = 4.2553(7) Å, and c = 13.735(2) Å. The smallest building motifs of the structure are InAs4 tetrahedra, which are connected to one another by shared As corners, forming infinite [InAs2As2/2] chains. The latter propagate along the crystallographic b-axis. The As-In-As bond angles within the InAs4 tetrahedra deviate from the ideal 109.5° value and range from 98.12(2)° to 116.53(2)°, attesting to a small distortion from the regular tetrahedral geometry. Electronic structure calculations indicate the opening of a bandgap, consistent with the expected (Ca2+)3(In3+)(As3–)3 formula breakdown based on conventional oxidation numbers. The calculations also show that the Ca–As interactions are an intermediate between covalent and ionic, while providing evidence of strong covalent features of the In–As interactions. Weak s-p hybridization of In states was observed, supporting the experimentally found deviation of the InAs4 moiety from the ideal tetrahedral symmetry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Valuation of Wind Energy Turbines Using Volatility of Wind and Price

The limitedness of the nonrenewable local energy resources in Israel, even in the background of the later gas fields’ findings, continues to force the state to devote various efforts towards ‘green’ energy development. These efforts include installations, both for the solar and for wind energy, thus improving the diversity of energy sources. While the standard discounted cash flow (DCF) method using the net present value (NPV) criterion is extensively adopted to evaluate investments, the standard DCF method is inappropriate for the rapidly changing investment climate and for the managerial flexibility in investment decisions. In recent years, the real options analysis (ROA) technique has been widely applied in many studies for the valuation of renewable energy investment projects. Taking into account the above background, we apply, in this study, the real options analysis approach for the valuation of wind energy turbines and apply it to the analysis of wind energy economic potential in Israel, which is the context of our work. We hypothesize that due to nature of wind energy production uncertainties, the ROA method is better than the alternative. The novelty of this paper includes the following: real world wind statistics of the Merom Golan site in Israel (velocity 3.73 m/s, with a standard deviation of 2.03 m/s), a realistic power generation estimation (power generation of 1205.84 kW with a standard deviation of about 0.5% in annual value which is worth about 1.3 M$ per annum), and an economic model to evaluate the profitability of such a project. We thus discuss the existing challenges of diversifying renewable energy sources in Israel by adding wind installations. Our motivation is to introduce a method which will allow investors and officials to take into account uncertainties when deciding in investing in such wind installations. The outcomes of the paper, which are obtained using the method of Weibull statistics and the Black–Scholes ROA technique, include the result that market price volatility adds to the uncertainties much more than any wind fluctuations, provided that the analysis is integrated over a long enough time.

17 WIND ENERGY↗

LES and RANS Spray Combustion Analysis of OME 3-5 and n-Dodecane

Clean-burning oxygenated and synthetic fuels derived from renewable power, so-called e-fuels, are a promising pathway to decarbonize compression–ignition engines. Polyoxymethylene dimethyl ethers (PODEs or OMEs) are one candidate of such fuels with good prospects. Their lack of carbon-to-carbon bonds and high concentration of chemically bound oxygen effectively negate the emergence of polycyclic aromatic hydrocarbons (PAHs) and even their precursors like acetylene (C 2 H 2 ), enabling soot-free combustion without the soot-NO x trade-off common for diesel engines. The differences in the spray combustion process for OMEs and diesel-like reference fuels like n-dodecane and their potential implications on engine applications include discrepancies in the observed ignition delay, the stabilized flame lift-off location, and significant deviations in high-temperature flame morphology. For CFD simulations, the accurate modeling and prediction of these differences between OMEs and n-dodecane proved challenging. This study investigates the spray combustion process of an OME 3 − 5 mixture and n-dodecane with advanced optical diagnostics, Reynolds-Averaged Navier–Stokes (RANS), and Large-Eddy Simulations (LESs) within a constant-volume vessel. Cool-flame and high-temperature combustion were measured simultaneously via high-speed (50 kHz) imaging with formaldehyde (CH 2 O) planar laser-induced fluorescence (PLIF) representing the former and line-of-sight OH* chemiluminescence the latter. Both RANS and LES simulations accurately describe the cool-flame development process with the formation of CH 2 O. However, CH 2 O consumption and the onset of high-temperature reactions, signaled by the rise of OH* levels, show significant deviations between RANS, LES, and experiments as well as between n-dodecane and OME. A focus is set on the quality of the simulated results compared to the experimentally observed spatial distribution of OH*, especially in OME fuel-rich regions. The influence of the turbulence modeling is investigated for the two distinct ambient temperatures of 900 K and 1200 K within the Engine Combustion Network Spray A setup. The capabilities and limitations of the RANS simulations are demonstrated with the initial cool-flame propagation and periodic oscillations of CH 2 O formation/consumption during the quasi-steady combustion period captured by the LES.

CFD↗

Characterization of Mechanical Performance of Composites Fabricated Using Innovative Carbon Fiber Wet Laid Process

Recent innovation in production of optimized nonwoven wet laid (WL) carbon fiber (CF) mats raised the question of optimal translation of the performance and isotropy into composites formed through these dry preforms. This work explores the mechanical behavior of composites produced from WL-CF mats in conjunction with the microstructure predicted through Object Oriented Finite Element Analysis (OOF). The mats used for the composites were prepared in two dispersion regimes using 25.4 mm long CF. The mixing regimes discussed in the author’s previous work, are identified as Method 1 for the traditional processing regime and Method 2 for the innovative regime that provided optimal nonwoven WL-CF mats. Composite panels from Method 2 mats showed a normalized tensile strength increase of 52% over those from Method 1 panels. Reproducibility analysis of composites made from Method 2 mats demonstrated a standard deviation of 2% in fiber weight content, 2% in tensile modulus and 9% in tensile strength, while composites made from Method 1 mats demonstrated a standard deviation of 5% in fiber weight content, 5% in tensile modulus and 17% in tensile strength. Systematic study of the microstructure and its analysis through OOF confirmed the isotropy translation of mats produced through method 2 to the composites. This study validated the hypothesis that optimal nonwoven mats lead to a well-balanced composite with optimal performance and that non-optimal nonwoven mats do not pack into a well-balanced composite.

36 MATERIALS SCIENCE↗

Investigating the Linear Thermal Expansion of Additively Manufactured Multi-Material Joining between Invar and Steel

This work investigated the linear thermal expansion properties of a multi-material specimen fabricated with Invar M93 and A36 steel. A sequence of tests was performed to investigate the viability of additively manufactured Invar M93 for lowering the coefficient of thermal expansion (CTE) in multi-material part tooling. Invar beads were additively manufactured on a steel base plate using a fiber laser system, and samples were taken from the steel, Invar, and the interface between the two materials. The CTE of the samples was measured between 40 °C and 150 °C using a thermomechanical analyzer, and the elemental composition was studied with energy dispersive X-ray spectroscopy. The CTE of samples taken from the steel and the interface remained comparable to that of A36 steel; however, deviations between the thermal expansion values were prevalent due to element diffusion in and around the heat-affected zone. The CTEs measured from the Invar bead were lower than those from the other sections with the largest and smallest thermal expansion values being 10.40 μm/m-K and 2.09 μm/m-K. In each of the sections, the largest CTE was measured from samples taken from the end of the weld beads. An additional test was performed to measure the aggregate expansion of multi-material tools. Invar beads were welded on an A36 steel plate. The invar was machined, and the sample was heated in an oven from 40 °C and 160 °C. Strain gauges were placed on the surface of the part and were used to analyze how the combined thermal expansions of the invar and steel would affect the thermal expansion on the surface of a tool. There were small deviations between the expansion values measured by gauges placed in different orientations, and the elongation of the sample was greatest along the dimension containing a larger percentage of steel. On average, the expansion of the machined Invar surface was 42% less than the expansion of the steel surface. The results of this work demonstrate that additively manufactured Invar can be utilized to decrease the CTE for multi-material part tooling.

36 MATERIALS SCIENCE↗