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At least 289 records · Page 16

Front propagation formulation for large eddy simulation of turbulent premixed flames

The front propagation formulation (FPF) for large eddy simulation (LES) of turbulent premixed flames is presented and discussed. The FPF method adopts a regularized Dirac delta function to minimize the spurious propagation of under-resolved filtered flame fronts in LES, while reproducing the reaction characteristics of premixed flame fronts. Here, the extension of the FPF method to LES and the relation to existing sub-filter combustion models are discussed. An algebraic model for the regularized Dirac delta function is presented and validated using direct numerical simulation (DNS) data for statistically planar premixed flames propagating in homogeneous isotropic turbulence. A posteriori validation for a filtered 1-D planar laminar flame is performed to demonstrate the characteristics of FPF in reproducing the propagation speed and the thickness of the under-resolved flame front. LES of a turbulent Bunsen flame is also performed to demonstrate the performance of the proposed method.

42 ENGINEERING↗

Heat transfer in directly-irradiated high-temperature solid–gas flows laden with polydisperse particles

Heat transfer in directly-irradiated high-temperature solid–gas flows laden with polydisperse particles is investigated using a novel transient three-dimensional computational fluid dynamics model. The model couples particle–gas hydrodynamics of solid–gas flows laden with polydisperse particles, radiative heat transfer in non-grey absorbing, emitting and anisotropically-scattering multi-component participating media, conduction heat transfer in the gas phase, and interfacial convection heat transfer. The multiphase particle-in-cell method is used to predict high-fidelity solid–gas flow characteristics, such as the local discrete particle size distribution, with increased computational efficiency by combining the advantages of both Eulerian and Lagrangian methods. The multi-component radiative transfer model is implemented using an advanced collision-based Monte Carlo ray-tracing method. The number of the prescribed discrete particle components is found to be the key parameter affecting the computational accuracy and efficiency, which primarily depends on the size distribution of the particles. For the model particle–gas flow featuring free-falling Gamma-distributed ceramic particles exposed to concentrated solar irradiation, the particle volume fraction, radiative, fluid flow and thermal characteristics appear to converge with the increasing number of the discrete particle components. Five particle components are sufficient to obtain physically meaningful results. A further increase in the number of the particle components only slightly increases the accuracy of the numerical predictions at the expense of a rapidly increasing computational time. For five particle components, the particle vertical velocity at the receiver exit for particles with the diameter of 43.4 μm is 57% of that for the particles with the diameter of 202.8 μm. The temperatures of these two particle components increase from the initial ambient values by factors of 2 and 1.2, respectively, during the simulation time. In conclusion, the model developed allows for increased fidelity of particle–gas flow simulations with significant radiative effects.

97 MATHEMATICS AND COMPUTING↗

Learning-Based Load Control to Support Resilient Networked Microgrid Operations

Microgrids have proven to be an effective option for increasing the resiliency of critical end-use loads during extreme events. Building on past operational experiences, some microgrid operators are examining the potential to network microgrids to further improve resiliency. However, the frequency deviations experienced on isolated microgrids during transient events, such as switching operations, step increases in load, and loss of generation, are significantly larger than those typically seen on bulk transmission systems. The larger frequency deviations can cause a loss of inverter-connected assets, resulting in a loss of power to critical end-use loads. This paper presents a method of mitigating the impact of transient events by engaging end-use loads using Grid-Friendly Appliance TM (GFA) controllers. An online, i.e., real-time, device-level algorithm is presented, which adjusts individual GFA controller frequency set-points based on the operational characteristics of each end-use load, and on the changing grid dynamic characteristics. The presented method improves the dynamic stability of the networked microgrid operations while minimizing the interruptions to end-use loads. The presented work is validated with dynamic simulations using a modified version of the IEEE 123-node test system with three microgrids, using the GridLAB-D TM simulation environment.

Radhakrishnan, Nikitha↗

Refinement for single-nanoparticle structure determination from low-quality single-shot coherent diffraction data

With the emergence of X-ray free-electron lasers, it is possible to investigate the structure of nanoscale samples by employing coherent diffractive imaging in the X-ray spectral regime. In this work, we developed a refinement method for structure reconstruction applicable to low-quality coherent diffraction data. The method is based on the gradient search method and considers the missing region of a diffraction pattern and the small number of detected photons. We introduced an initial estimate of the structure in the method to improve the convergence. The present method is applied to an experimental diffraction pattern of an Xe cluster obtained in an X-ray scattering experiment at the SPring-8 Angstrom Compact free-electron LAser (SACLA) facility. It is found that the electron density is successfully reconstructed from the diffraction pattern with a large missing region, with a good initial estimate of the structure. The diffraction pattern calculated from the reconstructed electron density reproduced the observed diffraction pattern well, including the characteristic intensity modulation in each ring. Our refinement method enables structure reconstruction from diffraction patterns under difficulties such as missing areas and low diffraction intensity, and it is potentially applicable to the structure determination of samples that have low scattering power.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

End-Use Savings Shapes Measure Documentation: Dispatch Schedule Generation for Demand Flexibility Measures

This supplemental document describes the methodology used for determining the dispatch timing of various EUSS demand flexibility measures. Demand flexibility measures are designed to reduce/dispatch electricity demand in buildings during especially beneficial/critical times. The method used in this work utilizes predictions of building loads to generate a schedule that reflects the periods when the building's daily peak load occurs to support decision making in demand flexibility measures. The dispatch schedule generation method described in this document creates an hourly schedule that includes a load dispatch (peak) window for each day for a whole year based on load prediction, with options using different prediction methods: perfect prediction, bin-sampling method, fixed schedule, and outdoor air temperature (OAT)-based prediction method. The perfect prediction method performs a simulation to obtain the annual load profile as predicted load, representing the scenario of perfect load prediction. The bin-sampling method (1) categorizes days into representative bins by temperature characteristics, (2) performs simulations on sample days from each of those bins to create representative (or predicted) load, and (3) assigns representative loads for all days in a year based on the bin categorization. The fixed schedule method defines uniform start and end time of peak window with assumed fixed daily peak time, for all days in a season or a year. The OAT-based prediction method uses the statistics of OAT (minimum and maximum) as the indicators of peak load, with specified delay response time from building loads to temperature. Given the load prediction, daily peak periods are determined as a time window with specified length in each day that include the predicted daily peak load and with a secondary rule such as maximizing energy saving potential. The dispatch schedule generation method is not a standalone measure and is intended to be combined with other demand flexibility measures that could leverage the peak schedule and apply demand controls on specific systems or devices for demand response, such as measures described in "Measure Documentation - Thermostat Control for Load Shedding" and "Measure Documentation - Thermostat Control for Load Shifting".

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Basic factors for acquiring, correcting, and interpreting probe current-voltage characteristic in moderate-collisional plasma for determining electron energy distribution

Methods for inferring the electron distribution function (EDF) from a probe current–voltage trace are reviewed for moderate-collisional plasma. The explicit accounting of particle collisions, modeling sheath depletion, and electron refilling processes are rarely performed when analyzing probe measurements, yet they are critical for regime validity, EDF-profile accuracy, and space potential quantification precision. Furthermore, the advantages, limitations, and utilization of large-area wall probes in moderate-collisional plasma, capable of highly resolved indications of energetic electrons in plasma, are outlined.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Effects of chemical composition and physicochemical properties of poplar biomass on the performance of 3D printed poplar-reinforced PLA materials

Lignocellulosic biomass has been well-acknowledged as a filler for making 3D printed composites. The technical performances of composites were influenced by the characteristics of the components. The correlations between poplar biomass properties and the mechanical and thermal performances of the 3D printed poplar-plastic composites were investigated. The characteristics of poplar were modified by different pretreatment methods, including using hot water, dilute acid, and organic solvent (organosolv), and each treated poplar biomass was applied as a filler in a polylactic acid (PLA) polymer matrix to produce eco-friendly materials. These solvent pretreatments increased the hydrophobicity and surface area of poplar. Organosolv treated poplar showed the highest cellulose content and significantly increased Young's modulus of its biocomposites. Principal component analysis revealed that the specific surface area and water contact angle of biomass contributed to the thermal stability of biocomposites. Additionally, the degree of polymerization of cellulose and xylan content within the biomass correlated with the biocomposites' break stress. Notably, the crystallinity of biocomposites impacted the modulus of these materials. The reported relationships between biomass characteristics and 3D printed composite behaviors provide guidance for optimizing biomass processing in biocomposite applications.

Ji, Anqi [State Univ. of New York (SUNY), Syracuse↗

Investigation of inter-subject variation in ultrafine particle deposition across human nasal airways: A study involving children, adults, and the elderly

Occupational and environmental exposure to toxic nanoparticles, driven by the rapid expansion of nanotechnology, raises significant respiratory health concern. Numerous studies have explored airflow and particle dynamics in adult nasal airways, but understanding the impact of age-related anatomical changes in children and the elderly remains limited. This study systematically investigates age-related anatomical variations and associated influence on nasal airflow dynamics and ultrafine particle deposition characteristics. Using Computational Fluid-Particle Dynamics (CFPD) method, simulation was conducted under diverse inhalation conditions spanning a wide age range, including: two children (5 years old), two young adults (in their twenties), and two elderly (over 77 years old). Our results reveal distinctive variations across age groups in anatomical dimensions, which affect distribution of wall shear stress where the elderly and children display unique patterns distinct from the young adults. While total deposition efficiency differs significantly between children and adults, filtration efficiency in the subregion with most deposition, main respiratory, remains consistent. However, inter-subject differences are observed in the vestibular and olfactory regions,emphasizing nuanced impact of age-related anatomical variations. Overall and subregional empirical equations for deposition efficiency were developed by incorporating the combined diffusion parameter, Sc a Δ b , corroborating the use of geometrical characteristic parameters for each specific subject in predicting nasal deposition efficiency across age groups. Our findings contribute to predictive nanoparticle exposure analysis in nasal airways across different age groups, thereby enhancing respiratory healthcare for individuals across the life span.

60 APPLIED LIFE SCIENCES↗

A lipidomics platform to analyze the fatty acid compositions of non-polar and polar lipid molecular species from plant tissues: Examples from developing seeds and seedlings of pennycress (Thlaspi arvense)

The lipidome comprises the total content of molecular species of each lipid class, and is measured using the analytical techniques of lipidomics. Many liquid chromatography-mass spectrometry (LC-MS) methods have previously been described to characterize the lipidome. However, many lipidomic approaches may not fully uncover the subtleties of lipid molecular species, such as the full fatty acid (FA) composition of certain lipid classes. Here, we describe a stepwise targeted lipidomics approach to characterize the polar and non-polar lipid classes using complementary LC-MS methods. Our “polar” method measures 260 molecular species across 12 polar lipid classes, and is performed using hydrophilic interaction chromatography (HILIC) on a NH2 column to separate lipid classes by their headgroup. Our “non-polar” method measures 254 molecular species across three non-polar lipid classes, separating molecular species on their FA characteristics by reverse phase (RP) chromatography on a C30 column. Five different extraction methods were compared, with an MTBE-based extraction chosen for the final lipidomics workflow. A state-of-the-art strategy to determine and relatively quantify the FA composition of triacylglycerols is also described. This lipidomics workflow was applied to developing, mature, and germinated pennycress seeds/seedlings and found unexpected changes among several lipid molecular species. During development, diacylglycerols predominantly contained long chain length FAs, which contrasted with the very long chain FAs of triacylglycerols in mature seeds. Potential metabolic explanations are discussed. The lack of very long chain fatty acids in diacylglycerols of germinating seeds may indicate very long chain FAs, such as erucic acid, are preferentially channeled into beta-oxidation for energy production.

59 BASIC BIOLOGICAL SCIENCES↗

Parallel simulated annealing with embedded machine learning and multifidelity models for reactor core design

This paper presents extensions to a penalty-free, parallel simulated annealing (SA) algorithm for multi-constrained combinatorial optimization with the aim of embedding multi-fidelity physics models into the annealing procedure. The method uses a low-fidelity, quickly executing model for rapid design space exploration and a high-fidelity model for detailed constraint resolution and on-the-fly bias correction. Machine learning models updated within the annealing procedure were used to bridge the gap between the multi-fidelity models, which led to accurate rapid exploration and efficient detailed constraint resolution. A software implementation of the new multi-fidelity optimization methods, called ML-PSA, was demonstrated on a continuous multi-fidelity optimization problem and a constrained combinatorial PWR lattice design problem. These problems demonstrate some of the features, parallel performance characteristics, and extensible nature of the multi-fidelity SA methods. This paper shows that the developed software and procedure are a general optimization tool that can be applied to a wide variety of scientific and engineering design optimization applications. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

A Compound Poisson Generator Approach to Point-source Inference in Astrophysics

Abstract The identification and description of point sources is one of the oldest problems in astronomy, yet even today the correct statistical treatment for point sources remains one of the field’s hardest problems. For dim or crowded sources, likelihood-based inference methods are required to estimate the uncertainty on the characteristics of the source population. In this work, a new parametric likelihood is constructed for this problem using compound Poisson generator (CPG) functionals that incorporate instrumental effects from first principles. We demonstrate that the CPG approach exhibits a number of advantages over non-Poissonian template fitting (NPTF)—an existing method—in a series of test scenarios in the context of X-ray astronomy. These demonstrations show that the effect of the point-spread function, effective area, and choice of point-source spatial distribution cannot, generally, be factorized as they are in NPTF, while the new CPG construction is validated in these scenarios. Separately, an examination of the diffuse-flux emission limit is used to show that most simple choices of priors on the standard parameterization of the population model can result in unexpected biases: when a model comprising both a point-source population and diffuse component is applied to this limit, nearly all observed flux will be assigned to either the population or to the diffuse component. A new parameterization is presented for these priors that properly estimates the uncertainties in this limit. In this choice of priors, CPG correctly identifies that the fraction of flux assigned to the population model cannot be constrained by the data.

79 ASTRONOMY AND ASTROPHYSICS↗

Improving energy performance metrics to maximize the benefits of disruptive technologies

Advances in appliance and equipment systems' technologies also cause changes in their technical characteristics that can affect energy consumption and associated greenhouse gas emissions. Energy performance evaluation methods that use testing and metrics can evolve to account for energy consumption contributed from new technological characteristics. However, there is no systematic effort to enable the advancement of test procedures and performance metrics to maximize the energy, climate, and financial benefits of disruptive technologies. Therefore, it is important for countries to regularly update their energy-efficiency policy programs, such as standards and labels, by improving test procedures and metrics to reflect the innovation in emerging technologies as well as to mitigate the risk of deploying obsolete technology. This paper shows how energy performance metrics have been improving in selected appliances and equipment and provides insights to design a systematic effort to improve metrics and test procedures at the speed of ongoing changes in technologies and markets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Dislocation Patterning in Deforming Crystals: Theory, Computational Predictions and Validation (Final Technical Report)

This project was awarded for an initial period of three years, followed by a no-cost extension for one year and a funded one-year renewal. During the first four years, the project focused on investigating the role of dislocation reactions and dislocation correlation in dislocation patterning in FCC metals. During the fifth year, the scope of research was expanded to investigate the effects of composition inhomogeneity on the mesoscale plastic response of Body-Centered Cubic (BCC) alloys. In addition to its impact on metal hardening during deformation, dislocation patterning provides the microstructure information required to understand phenomena like fracture and recrystallization in metals. The Continuum Dislocation Dynamics (CDD) framework was the methodology used during the first four years, followed by the use of Discrete Dislocation Dynamics (DDD) during the fifth year. CDD is a density-based formalism of dislocation dynamics in which the plastic deformation of crystals is predicted together with the mesoscale dislocation patterns by tracking the space-time evolution of dislocations as driven by the applied stress, short-range and long-range interactions of dislocations, and cross slip. CDD is a crystal mechanics approach in which the plastic constitutive part is replaced with the equations of dislocation dynamics, which is driven by the internal stress via a mobility laws, while the evolution of the density gives the eigenstrain required to update the internal stress itself. The governing equations are thus those of crystal mechanics cast as an eigenstrain problem and those of dislocation transport and reactions. Our investigations during the first four years were driven by the hypothesis that that dislocation patterning is triggered by spatiotemporal dislocation density and internal stress fluctuations, and that such fluctuations influence the collective dislocation dynamic through their effects on the short-range reaction rates and the collective dislocation mobility. This hypothesis was tested by modeling the influence of the dislocation reactions and dislocation correlations on collective dislocation dynamics. The main research components during the first four years were: 1) Reformulation of the CDD framework to integrate the dislocation correlations and dislocation reactions. 2) Simulation of the dislocation correlations and quantifying their contribution to the long-range stress of complex dislocation systems. 3) Computational implementation of the updated CDD model for FCC single crystals and development of an efficient CDD code. 4) Investigation of dislocation patterning in FCC crystals based on the updated CDD model. Some key findings from these investigations were: 1) The patterning of dislocations is initiated by cross slip and internal stress fluctuations, and the refinement of the pattern results from junction formation. 2) Patterning is more prominent under high stress. 3) The correlation stress was found to be a significant part of the mean field stress in continuum representations of dislocation dynamics. During the last year of the project, we have established a method for performing dislocation dynamics in inhomogeneous alloys and demonstrated the impact of composition inhomogeneity on the characteristics of initial yielding and dislocation character in BCC alloys. We also tested this method using an irradiated ferritic alloy in which the composition and dislocation loops effects are both present. In doing so, we have considered two aspects of the role of inhomogeneity, the creation of internal coherency stress due to the dependence of the lattice parameter on the local composition, and the dependence of the dislocation mobility on the local composition. These two aspects resulted in an unexpected behavior of the dislocation system, and, in turn, the yielding behavior of BCC alloys. Key findings include: 1) The composition inhomogeneity in BCC alloys alters the well-known role of screw dislocations by forcing a level of waviness on the average dislocation character, thus destroying the screw character dominance in the yielding observed in pure or homogeneous BCC metals. 2) In the case of irradiated alloys, while composition inhomogeneity by itself and dislocation loops by themselves result in some hardening, the superposition of the two mechanisms does not result in the sum of the two contributions via any known rule. The latter result contradicts the classical works related hardening via multiple mechanisms, which state that various hardening mechanisms can be added linearly or using some Pythagorean additive rule. We were able to rationalize this unexpected result by a closer loop at the origin of the hardening mechanisms themselves, which both involved long-range elastic interactions with dislocations. We reached the conclusion that the hardening resulting from the total stress associated with these two mechanisms (current work) differs from the sum of the effects of the individual stress fields (classical literature). We have also found a major flaw in the classical theory of spinodal hardening. The latter theory never considered the impact of composition undulation on the solute hardening. We have built a new general theory accounting for the effect of solute friction together with the composition undulations on the dislocation configuration at Critical Resolved Shear Stress (CRSS), which is yielding a new definition of the spinodal hardening and new different results. This work is the first to recognize the lack of the role of solute in spinodal hardening theory of alloys.

36 MATERIALS SCIENCE↗

Nomogram for Predicting Postoperative Portal Venous Systemic Thrombosis in Patients with Cirrhosis Undergoing Splenectomy and Esophagogastric Devascularization

Objectives. The aim of the study is to develop a nomogram for predicting postoperative portal venous systemic thrombosis (PVST) in patients with cirrhosis undergoing splenectomy and esophagogastric devascularization. Methods. In total, 195 eligible patients were included. Demographic characteristics were collected, and the results of perioperative routine laboratory investigations and ultrasound examinations were also recorded. Blood cell morphological traits, including the red cell volume distribution width (RDW), mean platelet volume, and platelet distribution width, were identified. Univariate and multivariate logistic regressions were implemented for risk factor filtration, and an integrated nomogram was generated and then validated using the bootstrap method. Results. A color Doppler abdominal ultrasound examination on a postoperative day (POD) 7 (38.97%) revealed that 76 patients had PVST. The results of the multivariate logistic regression suggested that a higher RDW on POD3 (RDW3) (odds ratio (OR): 1.188, 95% confidence interval (CI): 1.073–1.326), wider portal vein diameter (OR: 1.387, 95% CI: 1.203–1.642), history of variceal hemorrhage (OR: 3.407, 95% CI: 1.670–7.220), and longer spleen length (OR: 1.015, 95% CI: 1.001–1.029) were independent risk parameters for postoperative PVST. Moreover, the nomogram integrating these four parameters exhibited considerable capability in PVST forecasting. The nomogram’s receiver operating characteristic curve reached 0.83 and achieved a sensitivity and specificity of 0.711 and 0.848, respectively, at its cutoff. The nomogram’s calibration curve demonstrated that it was well calibrated. Conclusion. The nomogram exhibited excellent performance in PVST prediction and might assist surgeons in identifying vulnerable patients and administering timely prophylaxis.

Chen, Miao↗

Distribution Feeder Characteristics and Their Resiliency to Natural Hazards

This paper introduces a method and approach for initially screening actions to improve the reliability of distribution systems exposed to three natural hazards (wildlife, weather, and vegetation). The method is well-suited to needs and capabilities of smaller utilities because it relies on readily available information and does not depend on simulations involving detailed engineering models. The method involves first aggregating feeders into like types and then correlating them, separately by hazard, to standard measures of reliability, namely, System Average Interruption Frequency Index (SAIFI) and System Average Interruption Duration Index (SAIDI), and the individual constituents of these measures. The correlations provide both new insights into the efficacy of accepted reliability management practices while and re-confirm long-understood, accepted practices. Importantly, the method does not seek to replace the need for detailed engineering analysis. Instead, in view of the significant costs involved in conducting these more involved analyses, the method is intended to help smaller utilities prioritize their more limited resources to maximize the efficacy of actions they take to improve reliability.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Accelerated radiation transport modeling techniques for pencil beam computed tomography using gamma rays

Monte Carlo radiation transport modeling studies were performed for a compact, and high-resolution gamma-ray computed tomography system designed for imaging irradiated nuclear fuel. The system comprises a 60 Co source – chosen for its highly penetrating 1173 keV and 1332 keV gamma rays – a pair of high-aspect-ratio pencil beam collimators, and an inorganic scintillator detector. Here two acceleration methods are proposed to rapidly model a transmission type gamma-ray tomography system. The first, a variance reduction technique, is based on performing Monte Carlo simulations with a monodirectionally-biased source, sampled from a characteristic sub-volume of the full source volume. The second acceleration method is based on the deterministic calculations using the Beer–Lambert law and detector response characteristics. Comparison of simulations using acceleration approaches with analog simulations of the fully isotropic, full-volume equivalent, show that the Monte Carlo variance reduction technique gives quantitatively accurate predictions for large collimator aspect ratios while the deterministic calculations are semi-quantitative but converge close to the correct result as the collimator aspect ratio increases. As such, these techniques can be used to reduce the computational cost in generating simulated radiographs and tomographs by several orders of magnitude. Experimental validation efforts are currently underway and will be demonstrated in future work.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Predicting failure parameters of semiconductor devices subjected to stress conditions

A method for predicting failure parameters of semiconductor devices can include receiving a set of data that includes (i) characteristics of a sample semiconductor device, and (ii) parameters characterizing a stress condition. The method further includes extracting a plurality of feature values from the set of data and inputting the plurality of feature values into a trained model executing on the one or more processors, wherein the trained model is configured according to an artificial intelligence (AI) algorithm based on a previous plurality of feature values, and wherein the trained model is operable to output a failure prediction based on the plurality of feature values. Further, the method includes generating, via the trained model, a predicted failure parameter of the sample semiconductor device due to the stress condition.

Ahmed, Moinuddin↗