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At least 1,009 records · Page 56

Finite element model reduction application to parametric studies and optimization of rotorcraft structures

As a result of this work, a reduction procedure has been developed which can be applied to large finite element model of airframe type structures. This procedure, which is tailored to be used with MSC/NASTRAN finite element code, is applied to the full airframe dynamic finite element model of AH-64A Attack Helicopter. The applicability of the resulting reduced model to parametric and optimization studies is examined. Through application of the design sensitivity analysis, the viability and efficiency of this reduction technique has been demonstrated in a vibration reduction study.

Hashemi-Kia, M.↗

Flow In Streamer Boundaries, and Streamer Stability

Streamers can extend to many solar radii but the closed field regions, or helmets, reach no higher than 2-4 solar radii. The brightness boundary defining streamers is therefore a boundary between different flow regimes rather than between static plasma and expanding solar wind. It is reasonable to assume that this boundary divides fast coronal hole wind from slow wind. Flow inside this boundary can be studied using MHD models and is a type of stagnation flow. We describe such a model that is essentially analytic and show examples of flow solutions within the context and assumptions of the model. The flow affects the stability of the underlying helmet, which can be subject to a leakage out of the cusp that is similar to the small mass releases observed with the SOHO/LASCO coronagraph. It can also cause the helmet to be susceptible to being carried away in a coronal mass ejection. The model therefore also offers a way to study streamer stability.

Suess, S. T.↗

Neurovascular Responses to Simulated Deep Space Radiation in a Human Organ-on-a-Chip Model

A major health risk for human deep space exploration is central nervous system (CNS) damage by galactic cosmic ray radiation. Simulated galactic cosmic rays or their components, especially the high-linear energy transfer (LET) particles such as 56Fe ions, have been shown to cause CNS damage, neuroinflammation and cognitive dysfunction in rodent models, but their effects on human CNS remain to be investigated. CNS damage from any insult, including ionizing radiation, is partially mediated by the blood-brain barrier (BBB), which regulates interactions between CNS and the rest of the body. The main cellular regulators of BBB permeability are astrocytes, which also modulate neuroinflammation. However, there have been few studies on BBB and astrocyte functions in regulating CNS responses, especially in human tissue analogs. Therefore, we utilized a high-throughput 3D organ-on-a-chip system, seeded with human induced pluripotent stem cell-derived astrocytes and brain endothelial cells, or brain endothelial cells alone, to study human neurovascular responses to simulated deep space radiation. We investigated the permeability and morphology of vascular structures formed by endothelial cells, as well as oxidative stress and secreted cytokines and chemokine levels over 1-7 days after irradiation with 0.25 – 0.5 Gy 5-ion simplified simulated galactic cosmic rays or 0.3 – 0.8 Gy high-LET 600 MeV/n 56Fe particles, and compared the outcomes to low-LET X-ray irradiation. We observed that simulated deep space radiation caused delayed astrocyte activation in a pattern resembling CNS responses to brain injury, caused oxidative stress and the production of inflammatory cytokines, and compromised BBB integrity by damaging tight junctions, thus increasing vascular permeability. Furthermore, our results indicate that astrocytes have a dual role in regulating radiation responses: they exacerbate blood-brain barrier permeability early after irradiation, followed by switching to a more protective scar-like phenotype by reducing oxidative stress and pro-inflammatory cytokine and chemokine secretion. In a follow-up study using the same platform, we investigated the dose-rate effects of ionizing radiation, by exposing our model to chronic, low dose-rate, gamma radiation. Our model was significantly improved by adding additional cell types composing the BBB, modelling immune cell infiltration into the brain, and studying the effect of an antioxidant, to measure more complex outcomes and model more closely the effect of deep space radiation on the human BBB. In summary, our results present a human neurovascular model for space radiation studies and potential future automated payload adaptation, and suggest astrocyte regulatory mechanisms as targets for countermeasures to mitigate human neurovascular impairments during deep space exploration.

Ionizing radiation↗

High resolution gravity models combining terrestrial and satellite data

Spherical harmonic expansions to degree 360 have been developed that combine satellite potential coefficient information, terrestrial gravity data, satellite altimeter information as a direct tracking data type and topographic information. These models define improved representations of the Earth's gravitational potential beyond that available from just satellite or terrestrial data. The development of the degree 360 models, however, does not imply a uniform accuracy in the determination of the gravity field as numerous geographic areas are devoid of terrestrial data or the resolution of such data is limited to, for example, 100 km. This paper will consider theoretical and numerical questions related to the combination of the various data types. Various models of the combination process are discussed with a discussion of various correction terms for the different models. Various sources of gravity data will be described. The new OSU91 360 model will be discussed with comparisons made to previous 360 models and to other potential coefficient models that are complete to degree 50. Future directions in high degree potential coefficient models will be discussed.

Rapp, Richard H.↗

Air compressor load forecasting using artificial neural network

Air compressor systems are responsible for approximately 10% of the electricity consumed in United States and European Union industry. As many researches have proven the effectiveness of using Artificial Neural Network inair compressor performance prediction, there is still a need to forecast the air compressor electrical load profile. The objective of this study is to predict compressed air systems’ electrical load profile, which is valuable to industry practitioners as well as software providers in developing better practice and tools for load managementand look-ahead scheduling programs. Two artificial neural networks, Two-Layer Feed-Forward Neural Networkand Long Short-Term Memory were used to predict an air compressors electrical load. Compressors with three different control mechanisms are evaluated with a total number of 11,874 observations. Here, the forecasts were validated using out-of-sample datasets with 5-fold cross-validation. Models produced average coefficient ofdetermination values from 0.24 to 0.94, average root-mean-square errors from 0.05 kW - 5.83 kW, and meanabsolute scaled errors from 0.20 to 1.33. The results indicate that both artificial neural networks yield goodresults for compressors using variable speed drive (average R 2 = 0.8 and no naïve forecasting), only the longshort-term memory model gives acceptable results for compressors using on/off control (average R 2 = 0.82 and no naïve forecasting), and no satisfactory results are obtained for load/unload type air compressors (models constituting active forecasting).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Comparison of two propeller source models for aircraft interior noise studies

The sensitivity of the predicted synchrophasing (SP) effectiveness trends to the propeller source model issued is investigated with reference to the development of advanced turboprop engines for transport aircraft. SP effectiveness is shown to be sensitive to the type of source model used. For the virtually rotating dipole source model, the SP effectiveness is sensitive to the direction of rotation at some frequencies but not at others. The SP effectiveness obtained from the virtually rotating dipole model is not very sensitive to the radial location of the source distribution within reasonable limits. Finally, the predicted SP effectiveness is shown to be more sensitive to the details of the source model used for the case of corotation than for the case of counterrotation.

Mahan, J. R.↗

Toward a gradiometer analytic model

A new model is developed to model the data type, formulate the filter structure, and perform covariance for an orbiting gravity gradiometer, which is much more elaborate and realistic than the earlier gravity gradient model of Sonnabend and McEneaney (1988). The main new features of the new model are a general inertia tensor for the floated instrument, air drag and radiation pressure models using cubic power spectra, and more reasonable kinematics.

Sonnabend, Dave↗

Development of braided fiber seals for engine applications

A new type of braided fiber seal was developed for high temperature engine applications. Development work performed includes seal design, fabrication, leakage flow testing, and flow resistance modeling. This new type of seal utilizes the high flow resistance of tightly packed fibers and the conformability of textile structures. The seal contains a core part with aligned fibers, and a sheath with braided fiber layers. Seal samples are made by using the conventional braiding process. Leakage flow measurements are then performed. Mass flow rate versus the simulated engine pressure and preload pressure is recorded. The flow resistance of the seal is analyzed using the Ergun equation for flow through porous media, including both laminar and turbulent effects. The two constants in the Ergun equation are evaluated for the seal structures. Leakage flow of the seal under the test condition is found to be in the transition flow region. The analysis is used to predict the leakage flow performance of the seal with the determined design parameters.

Cai, Zhong↗

Type II Supernova Energetics and Comparison of Light Curves to Shock-Cooling Models

During the first few days after explosion, Type II supernovae (SNe) are dominated by relatively simple physics. Theoretical predictions regarding early-time SN light curves in the ultraviolet (UV) and optical bands are thus quite robust. We present, for the first time, a sample of 57 R-band SN II light curves that are well-monitored during their rise, with greater than 5 detections during the first 10 days after discovery, and a well-constrained time of explosion to within 13 days. We show that the energy per unit mass (E/M) can be deduced to roughly a factor of five by comparing early-time optical data to the 2011 model of Rabinak Waxman, while the progenitor radius cannot be determined based on R-band data alone. We find that SN II explosion energies span a range of EM = (0.2-20) x 10(exp 51) erg/(10 M stellar mass), and have a mean energy per unit mass of E/ M = 0.85 x 10(exp 51) erg(10 stellar mass), corrected for Malmquist bias. Assuming a small spread in progenitor masses, this indicates a large intrinsic diversity in explosion energy. Moreover, E/M is positively correlated with the amount of Ni-56 produced in the explosion, as predicted by some recent models of core-collapse SNe. We further present several empirical correlations. The peak magnitude is correlated with the decline rate (Delta m(sub15), the decline rate is weakly correlated with the rise time, and the rise time is not significantly correlated with the peak magnitude. Faster declining SNe are more luminous and have longer rise times. This limits the possible power sources for such events.

supernovae: general↗

Summary of Scale-Model Thrust-Reverser Investigation

An investigation was undertaken to determine the characteristics of several basic types of thrust-reverser. Models of three types, target, tailpipe cascade, and ring cascade, were tested with unheated air. The effects of design variables on reverse-thrust performance, reversed-flow boundaries, and thrust modulation characteristics were determined.

Povolny, John H↗

Enhancing Network Anomaly Detection Using Graph Neural Networks

In the world of Internet of Things (IoT) networks, where devices are constantly communicating, keeping them secure from cyber threats is critical. This paper introduces a novel approach to detecting unusual and potentially harmful activities in these networks using graph neural networks (GNNs). We combine two specific types of GNNs-GraphSAGE and graph attention networks (GAT)-to create a model that understands and represents the behaviors and interactions in a network. GraphSAGE creates an embedding of network activities by examining local data interactions, while GAT directs the model's focus to the most critical interactions. By integrating these two methods in a single model that considers different types of interactions (both host and flow nodes), we aim to create a system that accurately represents the current state of a network and can also spot anomalies effectively while reducing false positives and negatives. Our innovative approach has demonstrated promising results, achieving an accuracy of 98% on the UNSW-NB15 dataset, significantly outperforming standalone GraphSAGE and GAT models. This underscores its potential as a robust framework for securing IoT networks against cyber threats and anomalies.

Marfo, William↗

Semi-Empirical Prediction of Aircraft Low-Speed Aerodynamic Characteristics

This paper lays out a comprehensive methodology for computing a low-speed, high-lift polar, without requiring additional details about the aircraft design beyond what is typically available at the conceptual design stage. Introducing low-order, physics-based aerodynamic analyses allows the methodology to be more applicable to unconventional aircraft concepts than traditional, fully-empirical methods. The methodology uses empirical relationships for flap lift effectiveness, chord extension, drag-coefficient increment and maximum lift coefficient of various types of flap systems as a function of flap deflection, and combines these increments with the characteristics of the unflapped airfoils. Once the aerodynamic characteristics of the flapped sections are known, a vortex-lattice analysis calculates the three-dimensional lift, drag and moment coefficients of the whole aircraft configuration. This paper details the results of two validation cases: a supercritical airfoil model with several types of flaps; and a 12-foot, full-span aircraft model with slats and double-slotted flaps.

Olson, Erik D.↗

An Improved Zhang's Dynamic Water Balance Model Using Budyko-Based Snow Representation for Better Streamflow Predictions

Understanding the water balance of a catchment in relation to its regional climate forcings and catchment characteristics is critical for predicting current and future water resources amid changing climate and land cover. This study intends to improve Zhang's monthly water balance model (a physics-based conceptual hydrologic model) that reflects the physical partitioning process of the hydrological cycle at the basin level based on regional climate and catchment characteristics. The existing model does not include snow process and has confronted evident limitations in snow-affected areas, which is a critical aspect since snowmelt water has been a significant source of water resources for many regions, especially in the temperate and frigid zones. We introduce a snow module based on surface energy balance and Budyko-limits on melting and combine it with the existing water balance equations. Moreover, monthly parameterization is applied to the model to better explain the time-varying hydrological characteristics of a catchment. The proposed model involves five different monthly parameters, which determine the physical partitioning process of the hydrological cycle, and they are regionally calibrated and validated under Budyko-type constraints. The model is applied to 1,210 basins across the continental United States (CONUS), and the simulated streamflow is compared to the observed data. The proposed model significantly outperformed the original model, improving the median NSE by 31% (from 0.51 to 0.67) and increasing the number of catchments with an acceptable NSE by 58%. The spatial variability of the basin characteristics across the CONUS is also investigated based on the calibrated parameters.

54 ENVIRONMENTAL SCIENCES↗

Cr is not an acceptor in 𝛽⁢−G⁢a 2 ⁢O 3

The intensity of red C⁢r 3+ photoluminescence (PL) in monoclinic gallium oxide (𝛽⁢−G⁢a 2 ⁢O 3 ) is suppressed by 𝑛-type conductivity, an effect that has been attributed to a Cr deep acceptor level in the bandgap. In 𝑛-type material, such an acceptor level would be occupied, resulting in the C⁢r 2+ oxidation state and the absence of C⁢r 3+ PL. To test this model, 𝑛-type 𝛽⁢−G⁢a 2 ⁢O 3 crystals co-doped with Cr and Zr (a donor) were grown from the melt. The samples show C⁢r 3+ optical absorption bands and a high free-electron concentration of 4 × 10 18 c⁢m −3 . If Cr were an acceptor, then it would be fully compensated and therefore would not exhibit the C⁢r 3+ optical signature. Hybrid functional calculations indicate that Cr occupies the substitutional octahedral Ga(II) site and that the C⁢r 2+ state is energetically unfavorable, i.e., Cr is not an acceptor. Weak C⁢r 3+ PL was observed in the Cr/Zr co-doped samples. In conclusion, the quenching of PL may be caused by a transfer of energy to free electrons, a nonradiative process that would reduce the emission intensity.

carrier generation & recombination↗

Parameter Estimation for Electrode Degradation: Learning in the Face of Model-Experiment Discrepancies

Use of physics-based models to interpret battery degradation data over the course of cycling can provide deeper physical insight into the internal states of the system and how they evolve. We present a neural network trained on simulations generated by a previously published physics-based model for a lithium trivanadate (LVO) cathode to estimate parameters that evolve over the course of cycling. We focus on the robustness of the neural network through two case studies that probe different kinds of discrepancies between model and experiment: nonideal data and imperfect model. In the former, the experimental protocols do not meet the assumption made in the training data generated by the physics-based model, while in the latter, the physics-based model fails to describe all of the measured cathode behavior even under ideal conditions. When there is total model-experiment agreement, a neural network estimates parameters with improved accuracy compared to a maximum likelihood analysis using the same set of simulations. However, in both types of model-experiment discrepancy, the neural network returned biased parameter estimates. We introduce a data augmentation procedure into the neural network training to mitigate these effects and improve robustness, and employ it to estimate parameters for a cycling LVO cathode.

Mayilvahanan, Karthik S. (ORCID:0000000316282332)↗

Crater densities and crater ages of different terrain types on Ganymede

Alternative models of heliocentric and planetocentric crater-forming bombardments of Ganymede and Callisto are presently tested in view of the crater density and size frequency distribution measurements of several distinct Ganymede terrains. The results obtained are interpreted to indicate that the spatial distributions of most classes of craters on Ganymede, as well as the younger craters of Callisto, are more consistent with heliocentric than planetocentric bombardment. The sequence and type of surface modification on Ganymede were then inferred, with absolute ages being dependent on the assumption of heliocentric bombardment.

Murchie, Scott L.↗

Comparison between analytical models and finite-difference simulations in transmission-line tap resistors and L-type cross-Kelvin resistors

Approximate analytical models of the transmission-line tap resistor and the cross-Kelvin resistor are compared with computer-simulated pseudo-three-dimensional resistor network models. The analytical formulas are in good agreement with the simulations over a useful range of parameters and are readily applied to the extraction of the contact resistivity and the sheet resistances of the semiconducting layer under and outside the contacts. The extraction procedure, which is easily implemented on a personal computer, is carried out for the case of alloyed AuGeNi/GaAs contacts, illustrating the importance of (1) distinguishing between layer sheet resistance under and outside the contacts and (2) considering two-dimensional current flow in the semiconducting layer.

Scorzoni, Andrea↗