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At least 721 records · Page 40

Modeling of Powder Bed Manufacturing Defects

Powder bed additive manufacturing offers unmatched capabilities. The deposition resolution achieved is extremely high enabling the production of innovative functional products and materials. Achieving the desired final quality is, however, hampered by many potential defects that have to be managed in due course of the manufacturing process. Defects observed in products manufactured via powder bed fusion have been studied experimentally. In this effort we have relied on experiments reported in the literature and—when experimental data were not sufficient—we have performed additional experiments providing an extended foundation for defect analysis. There is large interest in reducing the effort and cost of additive manufacturing process qualification and certification using integrated computational material engineering. A prerequisite is, however, that numerical methods can indeed capture defects. A multiscale multiphysics platform is developed and applied to predict and explain the origin of several defects that have been observed experimentally during laser-based powder bed fusion processes. The models utilized are briefly introduced. The ability of the models to capture the observed defects is verified. The root cause of the defects is explained by analyzing the numerical results thus confirming the ability of numerical methods to provide a foundation for rapid process qualification.

36 MATERIALS SCIENCE↗

Elucidating the temperature dependence of TRIP in Q&P steels using synchrotron X-Ray diffraction, constituent phase properties, and strain-based kinetics models

Understanding the deformation-induced martensitic transformation (DIMT) is critical for interpreting the structure-property relationships that govern the performance of transformation-induced plasticity (TRIP) assisted steels. However, modern TRIP-assisted steels often exhibit DIMT kinetics that are not easily captured by existing empirical models based on bulk tensile strain. We address this challenge by combined bulk uniaxial tensile tests and in-situ high energy synchrotron X-ray diffraction, which resolved the phase volume fractions, stress-strain response, and microstructure evolution of each constituent phase. A modification of the Olson-Cohen model is implemented, which describes the martensitic transformation kinetics as a function of the estimated partitioned strain in austenite, rather than the bulk tensile strain. This DIMT kinetic model is used as a framework to clarify the root cause of an insufficiently understood toughness trough reported for TRIP-assisted steels during deformation at elevated temperatures. Here, the importance of the temperature-dependent toughness is discussed, based on the opportunity to modify deformation processes to tailor the DIMT kinetics and mechanical properties during forming and in service.

36 MATERIALS SCIENCE↗

Revenue prediction for integrated renewable energy and energy storage system using machine learning techniques

Revenue estimation for integrated renewable energy and energy storage systems is important to support plant owners or operators’ decisions in battery sizing selection that leads to maximized financial performances. A common approach to optimizing revenues of a hybrid hydro and energy storage system is using mixed-integer linear programming (MILP). Although MILP models can provide accurate production cost estimations, they are typically very computationally expensive. To provide a fast yet accurate first-step information to hydropower plant owners or operators who consider integrating energy storage systems, we propose an innovative approach to predicting optimal revenues of an integrated energy generation and storage system. In this study, we examined the performance of two prediction techniques: Generalized Additive Models (GAMs) and machine learning (ML) models developed based on artificial neural networks (ANN). Predictive equations and models are generated based on optimized solutions from a market participation optimization model, the Conventional Hydropower Energy and Environmental Resource System (CHEERS) model. The two predicting techniques reduce the computational time to evaluate annual revenue for one set of battery configurations from 3 h to 1 to 4 min per run while also being implementable with significantly less data. The model validation prediction errors of developed GAMs and ML models are generally below 5%; for model testing predictions, the ML models consistently outperform the regression equations in terms of root mean square errors. This new approach allows plant owners, operators, or potential investors to quickly access multiple battery configurations under different energy generation and market scenarios. This new revenue prediction method will therefore help reduce the barriers, and thereby promoting the deployment of battery hybridization with existing renewable energy sources.

13 HYDRO ENERGY↗

Data assimilation for combustion ignition delay time simulation using schlieren image velocimetry

This study sought to improve the accuracy of simulating spray penetration and combustion ignition delay by means of data assimilation (DA). The simulations were conducted using the Reynolds-averaged Navier–Stokes (RANS) equations and assimilating the schlieren image data. In DA, an ensemble square root filter (EnSRF) was used to build the statistical model, making the simulation results more accurate without any change in the governing equations. Recognizing that the spray-cone injection angle has a large effect on penetration, we created ensemble members with different injection angles. And we applied the two-component velocity distribution calculated via SIV and updated both velocity and temperature by using a DA statistical model derived from RANS ensemble simulations. The ignition delay time is generally known to vary even under the same experimental conditions because it is influenced by many factors. In this study, we attempted the transient DA-assisted RANS simulation to predict the ignition delay time even when the temporal resolution and accuracy of the observation data ware insufficient. Our trials offer an example of how a combination of techniques can be effectively used to assimilate experimental data obtained under restricted conditions.

Combustion simulation↗

Understanding the Microphysical Control and Spatial‐Temporal Variability of Warm Rain Probability Using CloudSat and MODIS Observations

Abstract By combining measurements from MODIS and the CloudSat radar, we develop a parameterization scheme to quantify the combined microphysical controls by liquid water path (LWP) and cloud droplet number concentration (CDNC) of the probability of precipitation (PoP) in marine low cloud over tropical oceans. We demonstrate that the spatial‐temporal variation of grid‐mean in‐cloud can be largely explained by the variation of the joint probability density function of LWP and CDNC in the phase space specified by the bivariate PoP (LWP and CDNC) function. Through a series of sensitivity tests guided by this understanding, we find that in the Southeastern Pacific and Atlantic the stratocumulus to cumulus transition of the is mainly due to the variation of CDNC while the annual cycle is mainly due to the variation of LWP. The results of this study provide a viable way to diagnose the root cause of warm rain problems in global climate models.

54 ENVIRONMENTAL SCIENCES↗

Selecting durable building envelope systems with machine learning assisted hygrothermal simulations database

Hygrothermal simulations provide insight into the energy performance and moisture durability of building envelope components under dynamic conditions. The inputs required for hygrothermal simulations are extensive, and carrying out simulations and analyses requires expert knowledge. An expert system, the Building Science Advisor (BSA), has been developed to predict the performance and select the energy-efficient and durable building envelope systems for different climates. The BSA consists of decision rules based on expert opinions and thousands of parametric simulation results for selected wall systems. The number of potential wall systems results in millions, too many to simulate all of them. We present how machine learning can help predict durability data, such as mold growth, while minimizing the number of simulations needed to run. The simulation results are used for training and validation of machine learning tools for predicting wall durability. We tested Artificial Neural Network (ANN) and Gradient Boosted Decision Trees (GBDT) for their applicability and model accuracy. Models developed with both methods showed adequate prediction performance (root mean square error of 0.195 and 0.209, respectively). Finally, we introduce how the information supports guidance for envelope design via an easy-to-use web-based tool that does not require the end-user to run hygrothermal simulations.

Salonvaara, Mikael↗

Imaging plant metabolism in situ

Abstract Mass spectrometry imaging (MSI) has emerged as an invaluable analytical technique for investigating the spatial distribution of molecules within biological systems. In the realm of plant science, MSI is increasingly employed to explore metabolic processes across a wide array of plant tissues, including those in leaves, fruits, stems, roots, and seeds, spanning various plant systems such as model species, staple and energy crops, and medicinal plants. By generating spatial maps of metabolites, MSI has elucidated the distribution patterns of diverse metabolites and phytochemicals, encompassing lipids, carbohydrates, amino acids, organic acids, phenolics, terpenes, alkaloids, vitamins, pigments, and others, thereby providing insights into their metabolic pathways and functional roles. In this review, we present recent MSI studies that demonstrate the advances made in visualizing the plant spatial metabolome. Moreover, we emphasize the technical progress that enhances the identification and interpretation of spatial metabolite maps. Within a mere decade since the inception of plant MSI studies, this robust technology is poised to continue as a vital tool for tackling complex challenges in plant metabolism.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Spin liquid to spin glass crossover in the random quantum Heisenberg magnet

We study quantum SU($M$) spins with all-to-all and random Heisenberg exchange interactions of root-mean-square strength $J$. The $M \rightarrow \infty$ model has a spin liquid ground state with the spinons obeying the equations of the Sachdev-Ye-Kitaev (SYK) model. Numerical studies of the SU(2) model with $S=1/2$ spins show spin glass order in the ground state, but also display SYK spin liquid behavior in the intermediate frequency spin spectrum. We employ a $1/M$ expansion to describe the crossover from fractionalized fermionic spinons to a confining spin glass state with weak spin glass order $q_{EA}$. The SYK spin liquid behavior persists down to a frequency $\omega_\ast \sim J q_{EA}$, and for $\omega < \omega_\ast$, the spectral density is linear in $\omega$, thus quenching the extensive zero temperature entropy of the spin liquid. Furthermore, the linear $\omega$ spectrum is qualitatively similar to that obtained earlier using bosonic spinons for large $q_{EA}$. We argue that the extensive SYK spin liquid entropy is transformed as $T \rightarrow 0$ to an extensive complexity of the spin glass state.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Increased microbial growth, biomass, and turnover drive soil organic carbon accumulation at higher plant diversity

Species-rich plant communities have been shown to be more productive and to ­exhibit increased long-term soil organic carbon (SOC) storage. Soil microorganisms are central to the conversion of plant organic matter into SOC, yet the relationship between plant diversity, soil microbial growth, turnover as well as carbon use efficiency (CUE) and SOC accumulation is unknown. As heterotrophic soil microbes are primarily carbon limited, it is important to understand how they respond to increased plant-derived carbon inputs at higher plant species richness (PSR). We used the long-term grassland biodiversity experiment in Jena, Germany, to examine how microbial physiology responds to changes in plant diversity and how this affects SOC content. The Jena Experiment considers different numbers of species (1–60), functional groups (1–4) as well as functional identity (small herbs, tall herbs, grasses, and legumes). We found that PSR accelerated microbial growth and turnover and increased microbial biomass and necromass. PSR also accelerated microbial respiration, but this effect was less strong than for microbial growth. In contrast, PSR did not affect microbial CUE or biomass-specific respiration. Structural equation models revealed that PSR had direct positive effects on root biomass, and thereby on microbial growth and microbial biomass carbon. Finally, PSR increased SOC content via its positive influence on microbial biomass carbon. We suggest that PSR favors faster rates of microbial growth and turnover, likely due to greater plant productivity, resulting in higher amounts of microbial biomass and necromass that translate into the observed increase in SOC. We thus identify the microbial mechanism linking species-rich plant communities to a carbon cycle process of importance to Earth's climate system.

59 BASIC BIOLOGICAL SCIENCES↗

The MARS Code System User’s Guide Version 15(2016)

This paper is a user’s guide to the current version of the MARS15 Monte Carlo code. MARS allows inclusive and, in many cases, exclusive simulations of three-dimensional hadronic and electromagnetic cascades and modeling of heavy ion, muon and low energy neutron-photon transport in accelerator, detector, spacecraft and shielding components. Particles can have energies from a fraction of an electronvolt up to about 100 TeV. The code has undergone substantial improvements since the last documented version MARS13(95) and since previous releases of MARS15; all these as well as other specific features of the MARS code system are explained in detail. Descriptions of general input and output with commentary and recommendations are given. Examples are given for running the program with distributed sources, complex compounds, arbitrary geometries, and magnetic fields. A built-in graphical-user interface allows the visualization and detailed analysis of the geometry description. Enhanced options are described for the MARS code being coupled with: the DPMJET hadron event generator, the MCNP code for low-energy neutron transport, the ANSYS system for thermal and stress analysis, physics analysis and graphics packages, the MAD accelerator lattice description via a universal MAD-MARS beam line builder, the STRUCT program for tracking particles in accelerator lattices with beam loss recording, the DETRA code for nuclide decay and transmutation calculations, the EGS5 code for precise modeling of low-energy electromagnetic showers, and the ROOT system for description and visualization of arbitrary geometries. Use of the code in a multistage mode, coupled with event generators (DPMJET), with the STRUCT program for tracking particles in accelerator lattices with beam loss recording, and with physics analysis and graphics packages is demonstrated with typical input and output examples.

43 PARTICLE ACCELERATORS↗

Lubricant pressure rippling in dynamic Hertzian contacts induced by surface roughness

A numerical analysis of the pressure distribution of a lubricant in contact with a rough surface was conducted. The magnitude of the pressures was determined by their root mean square value for the contact of two dimensional cylinders. The pressure was found to vary in the following manner: (1) the location in the contact, (2) the spectrum or frequency content of the surface roughness, (3) the mean plateau film thickness, and (4) the root mean square value of the surface roughness. Mathematical models are developed to show the relationships of the parameters.

Tallian, T. E.↗

Identification of state variables and dynamic inflow from rotor model dynamic tests

The paper describes methods for extracting unknown state variables and parameters from dynamic rotor model tests given transient cyclic pitch stirring inputs, blade root flap-bending measurements, and the form of the dynamic rotor equations, including a rotor dynamic inflow description, when none of the physical parameters are known. A simplified version of the maximum likelihood method seems best suited for this purpose. The measurement equation error covariance matrix is assumed constant during each iteration, but updated for the subsequent iteration. A detailed analysis of the suitability of the derived techniques for studying various rotor dynamic inflow effects is provided.

Banerjee, D.↗

On intracluster Faraday rotation. II - Statistical analysis

The comparison of a reliable sample of radio source Faraday rotation measurements seen through rich clusters of galaxies, with sources seen through the outer parts of clusters and therefore having little intracluster Faraday rotation, indicates that the distribution of rotation in the former population is broadened, but only at the 80% level of statistical confidence. Employing a physical model for the intracluster medium in which the square root of magnetic field strength/turbulent cell per gas core radius number ratio equals approximately 0.07 microgauss, a Monte Carlo simulation is able to reproduce the observed broadening. An upper-limit analysis figure of less than 0.20 microgauss for the field strength/turbulent cell ratio, combined with lower limits on field strength imposed by limitations on the Compton-scattered flux, shows that intracluster magnetic fields must be tangled on scales greater than about 20 kpc.

Lawler, J. M.↗

Application of unsteady aeroelastic analysis techniques on the national aerospace plane

A presentation provided at the Fourth National Aerospace Plane Technology Symposium held in Monterey, California, in February 1988 is discussed. The objective is to provide current results of ongoing investigations to develop a methodology for predicting the aerothermoelastic characteristics of NASP-type (hypersonic) flight vehicles. Several existing subsonic and supersonic unsteady aerodynamic codes applicable to the hypersonic class of flight vehicles that are generally available to the aerospace industry are described. These codes were evaluated by comparing calculated results with measured wind-tunnel aeroelastic data. The agreement was quite good in the subsonic speed range but showed mixed agreement in the supersonic range. In addition, a future endeavor to extend the aeroelastic analysis capability to hypersonic speeds is outlined. An investigation to identify the critical parameters affecting the aeroelastic characteristics of a hypersonic vehicle, to define and understand the various flutter mechanisms, and to develop trends for the important parameters using a simplified finite element model of the vehicle is summarized. This study showed the value of performing inexpensive and timely aeroelastic wind-tunnel tests to expand the experimental data base required for code validation using simple to complex models that are representative of the NASP configurations and root boundary conditions are discussed.

Pototzky, Anthony S.↗

Measured and predicted structural behavior of the HiMAT tailored composite wing

A series of load tests was conducted on the HiMAT tailored composite wing. Coupon tests were also run on a series of unbalanced laminates, including the ply configuration of the wing, the purpose of which was to compare the measured and predicted behavior of unbalanced laminates, including - in the case of the wing - a comparison between the behavior of the full scale structure and coupon tests. Both linear and nonlinear finite element (NASTRAN) analyses were carried out on the wing. Both linear and nonlinear point-stress analyses were performed on the coupons. All test articles were instrumented with strain gages, and wing deflections measured. The leading and trailing edges were found to have no effect on the response of the wing to applied loads. A decrease in the stiffness of the wing box was evident over the 27-test program. The measured load-strain behavior of the wing was found to be linear, in contrast to coupon tests of the same laminate, which were nonlinear. A linear NASTRAN analysis of the wing generally correlated more favorably with measurements than did a nonlinear analysis. An examination of the predicted deflections in the wing root region revealed an anomalous behavior of the structural model that cannot be explained. Both hysteresis and creep appear to be less significant in the wing tests than in the corresponding laminate coupon tests.

Nelson, Lawrence H.↗

Adaptive clutter rejection filters for airborne Doppler weather radar applied to the detection of low altitude windshear

An optimum adaptive clutter rejection filter for use with airborne Doppler weather radar is presented. The radar system is being designed to operate at low-altitudes for the detection of windshear in an airport terminal area where ground clutter returns may mask the weather return. The coefficients of the adaptive clutter rejection filter are obtained using a complex form of a square root normalized recursive least squares lattice estimation algorithm which models the clutter return data as an autoregressive process. The normalized lattice structure implementation of the adaptive modeling process for determining the filter coefficients assures that the resulting coefficients will yield a stable filter and offers possible fixed point implementation. A 10th order FIR clutter rejection filter indexed by geographical location is designed through autoregressive modeling of simulated clutter data. Filtered data, containing simulated dry microburst and clutter return, are analyzed using pulse-pair estimation techniques. To measure the ability of the clutter rejection filters to remove the clutter, results are compared to pulse-pair estimates of windspeed within a simulated dry microburst without clutter. In the filter evaluation process, post-filtered pulse-pair width estimates and power levels are also used to measure the effectiveness of the filters. The results support the use of an adaptive clutter rejection filter for reducing the clutter induced bias in pulse-pair estimates of windspeed.

Keel, Byron M.↗

Simple Forest Canopy Thermal Exitance Model

We describe a model to calculate brightness temperature and surface energy balance for a forest canopy system. The model is an extension of an earlier vegetation only model by inclusion of a simple soil layer. The root mean square error in brightness temperature for a dense forest canopy was 2.5 C. Surface energy balance predictions were also in good agreement. The corresponding root mean square errors for net radiation, latent, and sensible heat were 38.9, 30.7, and 41.4 W/sq m respectively.

Smith J. A.↗

Carbon Dynamics in Vegetation and Soils

The overall goals of CD-08 team in Phase I were to quantify the contributions of different components of the carbon cycle to overall ecosystem carbon balance in Amazonian tropical forests and to undertake process studies at a number of sites along the eastern LBA transect to understand how and why these fluxes vary with site, season, and year. We divided this work into a number of specific tasks: (1) determining the average rate (and variability) of tree growth over the past 3 decades; (2) determining age demographics of tree populations, using radiocarbon to determine tree age; (3) assessing the rate of production and decomposition of dead wood debris; (4) determining turnover rates for organic matter in soils and the mean age of C respired from soil using radiocarbon measurements; and (5) comparing our results with models and constructing models to predict the potential of tropical forests to function as sources or sinks of C. This report summarizes the considerable progress made towards our original goals, which have led to increased understanding of the potential for central Amazon forests to act as sources or sinks of carbon with altered productivity. The overall picture of tropical forest C dynamics emerging from our Phase I studies suggests that the fraction of gross primary production allocated to growth in these forests is only 25-30%, as opposed to the 50% assumed by many ecosystem models. Consequent slow tree growth rates mean greater mean tree age for a given diameter, as reflected in our measurements and models of tree age. Radiocarbon measurements in leaf and root litter suggest that carbon stays in living tree biomass for several years up to a decade before being added to soils, where decomposition is rapid. The time lags predicted from 14C, when coupled with climate variation on similar time scales, can lead to significant interannual variation in net ecosystem C exchange.

Trumbore, Susan↗