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At least 145 records · Page 8

Impact Forces From Artificial and Real Birds on a Large Diameter Hopkinson Bar

Forces were measured from normal impacts with two different artificial bird recipes and two species of real birds on a very rigid flat surface. The tests were conducted by launching the soft body projectiles axially into a Hopkinson bar in accordance with the SAE AS6940 test standard at three different nominal impact conditions: (a) a 1 kg projectile at an impact velocity of 49 m/sec; (b) a 1.8 kg projectile at an impact velocity of 110 m/sec; and (c) a 1.8 kg projectile at an impact velocity of 310 m/sec. At each condition two artificial birds and one real bird projectile were tested with at least three test repeats. Simulations were conducted to assess the effects of projectile orientation and velocity on predicted forces. The Hopkinson bar consisted of an Aluminum 6061 solid cylindrical bar with a diameter of 305 mm and a length of 7.31 m, made up of two 3.66 m long sections that were in axial contact with each other. The bar was instrumented for strain measurement at two locations, 457.2 mm and 609.6 mm (1.5 and 2 diameters) from the impacted face. Forces were calculated from the measured strain. In addition, digital image correlation (DIC) was used to measure the velocity of the free end of the bar. The real bird projectiles were 1 kg Mallard ducks and 1.8 kg Golden Comet chickens that were prepared per ASTM F330-21. Prior to testing, the real birds were placed in muslin bags to minimize tumbling during flight. Two artificial bird formulations were tested, one produced by the University of Dayton Research Institute (UDRI) and the other by the German Aerospace Center, DLR. The UDRI projectile was a homogeneous, relatively soft solid, formulated with a mixture of water, gelatin and phenolic micro-balloons to achieve a nominal density of 0.95 g/cc. The DLR projectile consisted of a printed plastic shell with internal plastic ribs and filled with a gel material. The projectiles were designed such that the overall average density was the desired value of 0.95 g/cc. Initial testing showed that contact between the two Hopkinson bar segments was not sufficient to allow full transmission of the waves through the interface. For the 49 m/sec and 110 m/sec tests this resulted in reflections returning to the strain measurement site prior to the end of the impact. For these tests only a portion of the force pulse could be accurately measured. For the highest velocity tests at 310 m/sec the force pulse was complete by the time reflected waves interfered with the response. Despite this limitation, useful force data were obtained for all velocities. Impact forces were very sensitive to the impact orientation and velocity. The force history generally consisted of an initial region with varying amplitude followed by a relatively constant amplitude response. The steady state region was similar among all projectiles and similar to what would be predicted for an inviscid fluid with a density of 0.95 g/cc. The initial part of the force response exhibited varying degrees of test-to-test repeatability associated with each projectile type. Forces calculated from DIC velocity measurements of the free end of the bar compared favorably those from the strain measurements. In general, the artificial projectile impact forces were quite repeatable from test to test, with some exceptions which could be explained by issues associated with either the projectile itself or with how the projectile exited the gun barrel. For all impact velocities the initial portion of the impact force from the bird projectiles exhibited large test-to-test variability. In this paper, impact forces for three types of projectiles at three impact velocities will be presented with an emphasis on the test-to-test repeatability of the results. Based on test and simulation results a proposed method for normalizing the impact force to minimize effects of differences in projectile orientation, impact velocity and density will also be discussed.

Bird strike↗

Identification and assignment of rotational spectra using artificial neural networks

A method of identifying molecular parameters may include receiving observed transition frequencies, generating transition frequency sets and a spectral parameter sets, training one or more artificial neural networks by analyzing the transition frequency sets and the spectral parameter sets, analyzing the observed transition frequencies using the one or more trained artificial neural networks to predict estimated spectral parameters, and identifying molecular parameters by analyzing the estimated spectral parameters. A molecular parameter identification system may include a rotational spectrometer, a user interface, and a spectrum analysis application that may retrieve observed transition frequencies, identify a Hamiltonian type by a neural network analyzing the observed transition frequencies, select a second trained artificial neural network based on the identified Hamiltonian type, analyze observed transition frequencies using the second artificial neural network to identify estimated spectral parameters, and identify molecular parameters.

Prozuments, Kirills↗

Artificial Photosynthesis for Sustainable Fuel and Chemical Production

The apparent incongruity between the increasing consumption of fuels and chemicals and the finite amount of resources has led us to seek means to maintain the sustainability of our society. Artificial photosynthesis, which utilizes sunlight to create high-value chemicals from abundant resources, is considered as the most promising and viable method. This Minireview describes the progress and challenges in the field of artificial photosynthesis in terms of its key components: developments in photoelectrochemical water splitting and recent progress in electrochemical CO 2 reduction. Finally, advances in catalysis, concerning the use of renewable hydrogen as a feedstock for major chemical production, are outlined to shed light on the ultimate role of artificial photosynthesis in achieving sustainable chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Artificial intelligence to unlock real-world evidence in clinical oncology: A primer on recent advances

Purpose: Real world evidence is crucial to understanding the diffusion of new oncologic therapies, monitoring cancer outcomes, and detecting unexpected toxicities. In practice, real world evidence is challenging to collect rapidly and comprehensively, often requiring expensive and time-consuming manual case-finding and annotation of clinical text. In this Review, we summarise recent developments in the use of artificial intelligence to collect and analyze real world evidence in oncology. Methods: We performed a narrative review of the major current trends and recent literature in artificial intelligence applications in oncology. Results: Artificial intelligence (AI) approaches are increasingly used to efficiently phenotype patients and tumors at large scale. These tools also may provide novel biological insights and improve risk prediction through multimodal integration of radiographic, pathological, and genomic datasets. Custom language processing pipelines and large language models hold great promise for clinical prediction and phenotyping. Conclusions: Despite rapid advances, continued progress in computation, generalizability, interpretability, and reliability as well as prospective validation are needed to integrate AI approaches into routine clinical care and real-time monitoring of novel therapies.

60 APPLIED LIFE SCIENCES↗

Artificial Photosynthesis: Beyond Mimicking Nature

In this Editorial, Guest Editors Holger Dau, Etsuko Fujita, and Licheng Sun introduce the Special Issue of ChemSusChem on “Artificial Photosynthesis for Sustainable Fuels”. Here, they discuss the need for non-fossil based fuels, introduce both biological and artificial photosynthesis, and outline various important concepts in artificial photosynthesis, including molecular and solid-state catalysts for water oxidation and hydrogen evolution, catalytic CO 2 reduction, and photoelectrochemical systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

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↗

Frustration and thermalization in an artificial magnetic quasicrystal

Here, artificial frustrated systems offer a playground to study the emergent properties of interacting systems. Most work to date has been on spatially periodic systems, known as artificial spin ices when the interacting elements are magnetic. Here we have studied artificial magnetic quasicrystals based on quasiperiodic Penrose tiling patterns of interacting nanomagnets. We construct a low-energy configuration from a step-by-step approach that we propose as a ground state. Topologically induced emergent frustration means that this configuration cannot be constructed from vertices in their ground states. It has two parts, a quasi-one-dimensional ‘skeleton’ that spans the entire pattern and is capable of long-range order, surrounding ‘flippable’ clusters of macrospins that lead to macroscopic degeneracy. Magnetic force microscopy imaging of Penrose tiling arrays revealed superdomains that are larger for more strongly coupled arrays, especially after annealing the array above its blocking temperature.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Interaction modifiers in artificial spin ices

The modification of geometry and interactions in two-dimensional magnetic nanosystems has enabled a range of studies addressing the magnetic order, collective low-energy dynamics and emergent magnetic properties in, for example, artificial spin-ice structures. The common denominator of all these investigations is the use of Ising-like mesospins as building blocks, in the form of elongated magnetic islands. Here, we introduce a new approach: single interaction modifiers, using slave mesospins in the form of discs, within which the mesospin is free to rotate in the disc plane1. We show that by placing these on the vertices of square artificial spin-ice arrays and varying their diameter, it is possible to tailor the strength and the ratio of the interaction energies. We demonstrate the existence of degenerate ice-rule-obeying states in square artificial spin-ice structures, enabling the exploration of thermal dynamics in a spin-liquid manifold. Furthermore, we even observe the emergence of flux lattices on larger length scales, when the energy landscape of the vertices is reversed. In conclusion, the work highlights the potential of a design strategy for two-dimensional magnetic nano-architectures, through which mixed dimensionality of mesospins can be used to promote thermally emergent mesoscale magnetic states.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Direct observation of a dynamical glass transition in a nanomagnetic artificial Hopfield network

Spin glasses, generally defined as disordered systems with randomized competing interactions, are a widely investigated complex system. Theoretical models describing spin glasses are broadly used in other complex systems, such as those describing brain function, error-correcting codes5 or stock-market dynamics. This wide interest in spin glasses provides strong motivation to generate an artificial spin glass within the framework of artificial spin ice systems. Here we present the experimental realization of an artificial spin glass consisting of dipolar coupled single-domain Ising-type nanomagnets arranged onto an interaction network that replicates the aspects of a Hopfield neural network. Using cryogenic X-ray photoemission electron microscopy (XPEEM), we performed temperature-dependent imaging of thermally driven moment fluctuations within these networks and observed characteristic features of a two-dimensional Ising spin glass. Specifically, the temperature dependence of the spin glass correlation function follows a power-law trend predicted from theoretical models on two-dimensional spin glasses. As a result, we observe clear signatures of the hard-to-observe rugged spin glass free energy in the form of sub-aging, out-of-equilibrium autocorrelations and a transition from stable to unstable dynamics.

36 MATERIALS SCIENCE↗

Energy-relaxation pathways in finite-sized artificial square-ice structures

The energy-relaxation pathway in artificial spin ice structures refers to the series of individual moment reorientations the system undergoes as it relaxes from an initial state to a final low-energy state. The probability of the artificial spin ice structure reaching a ground state depends on the energy of the intermediate states as approximated by an Arrhenius-type relation. In this work, we report that by modifying the geometry of artificial square-ice structures, we can influence the probability of observing the ground state in different configurations. Furthermore, our findings are supported by dipolar energy calculations for various energy-relaxation pathways and the corresponding experimental data obtained by magnetic force microscopy.

Analog computation↗

Tailoring ferromagnetic resonance in bicomponent artificial spin ices

Magnonic crystals are artificial media in which magnetic properties such as saturation magnetization or relative permeability are periodically modulated in space, allowing for effective control of the magnonic bandstructure by geometrical structuring. From this end, artificial spin ice - arrays of interacting nanomagnets - can be viewed as reprogrammable twodimensional magnonic crystal. Here, we introduce a new kind of bicomponent artificial spin ice that is made of two dissimilar ferromagnetic metals arranged on complementary lattice sites. It is shown that the resonant dynamics can effectively be controlled based on the distinct magnetic properties of the two materials. We use broadband ferromagnetic resonance to measure the spin-wave eigenmodes of the different sublattices. Here, we compare the ferromagnetic resonance spectra of the single component arrays with the spectrum of a bicomponent system that can be interpreted using a superposition principle. Furthermore, we show that the switching behavior of the sublattices affects one another.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Artificial Replication of Field Soiling Losses on PV Modules

In this paper, we experimentally demonstrate an improved replication of field soiling losses using an indoor artificial soiling chamber and tests on anti-soiling coated PV modules and coupons. The primary focus is to use site-specific soil collected from module surface and replicate the natural soiling processes including dust concentration in the air, slow and gradual dust accumulation and sedimentation on the module surface during the dominant soiling season of the site of interest. The experiments were conducted on two sample sets having different anti-soiling properties. The first set contains commercial modules with two different surface properties retrieved after three years of exposure from a single PV plant in a mid-Atlantic location; the other set contain glass coupons with three different coating materials that were installed and exposed over 4 months, at Lemoore, California. Major field-representative factors considered here for the close replication in the chamber include: the use of dust collected from modules surfaces at the outdoor sites to give the same dust chemistry; dust particle size distribution and concentration; the charge size of dust (< 0.15 g per injection); field humidity, and module temperature. The effectiveness of antisoiling coatings (or surface properties) for both sample sets were ranked in the artificial testing and were found to be closely matching with the field rank orders of the respective sites and sample sets. This paper provides the rank ordering results to objectively demonstrate the replication of field soiling losses in the artificial soiling chamber.

artificial soiling↗

Artificial Intelligence for Power Electronics in Electric Vehicles: Challenges and Opportunities

We report progress in the field of power electronics within electric vehicles has generally been driven by conventional engineering design principles and experiential learning. Power electronics is inherently a multidomain field where semiconductor physics and electrical, thermal, and mechanical design knowledge converge to achieve a practical realization of desired targets in the form of conversion efficiency, power density, and reliability. Due to the promising nature of artificial intelligence in delivering rapid results, engineers are starting to explore the ways in which it can contribute to making power electronics more compact and reliable. Here, we conduct a brief review of the foray of artificial intelligence in three distinct subtechnologies within a power electronics system in the context of electric vehicles: semiconductor devices, power electronics module design and prognostics, and thermal management design. The intent is not to report an exhaustive literature review, but to identify the state of the art and opportunities for artificial intelligence to play a meaningful role in power electronics design from a mechanical and thermal standpoint, as well as to discuss a few promising future research directions.

33 ADVANCED PROPULSION SYSTEMS↗

Temperature and magnetic-field driven dynamics in artificial magnetic square ice

Artificial spin ices are often spoken of as being realisations of some of the celebrated vertex models of statistical mechanics, where the exact microstate of the system can be imaged using advanced magnetic microscopy methods. The fact that a stable image can be formed means that the system is in fact athermal and not undergoing the usual finite-temperature fluctuations of a statistical mechanical system. In this paper we report on the preparation of artificial spin ices with islands that are thermally fluctuating due to their very small size. The relaxation rate of these islands was determined using variable frequency focused magneto-optic Kerr measurements. We performed magnetic imaging of artificial spin ice under varied temperature and magnetic field using X-ray transmission microscopy which uses X-ray magnetic circular dichroism to generate magnetic contrast. Furthermore, we have developed an on-membrane heater in order to apply temperatures in excess of 700 K and have shown increased dynamics due to higher temperature. Due to the ‘photon-in, photon-out' method employed here, it is the first report where it is possible to image the microstates of an ASI system under the simultaneous application of temperature and magnetic field, enabling the determination of relaxation rates, coercivties, and the analysis of vertex population during reversal.

36 MATERIALS SCIENCE↗

Programs and Code for Geothermal Exploration Artificial Intelligence

The scripts below are used to run the Geothermal Exploration Artificial Intelligence developed within the "Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning" project. It includes all scripts for pre-processing and processing, including: - Land Surface Temperature K-Means classifier - Labeling AI using Self Organizing Maps (SOM) - Post-processing for Permanent Scatterer InSAR (PSInSAR) analysis with SOM - Mineral marker summarizing - Artificial Intelligence (AI) Data splitting: creates data set from a single raster file - Artificial Intelligence Model: creates AI from a single data set, after splitting in Train, Validation and Test subsets - AI Mapper: creates a classification map based on a raster file

15 GEOTHERMAL ENERGY↗

Hopkinson Bar Impact Force Measurement for Application to Development of an Artificial Bird

A test method has been published for measuring forces from bird cadavers and artificial birds to demonstrate equivalency to support the use of artificial birds in aircraft certification testing. The test method involves a large diameter Hopkinson bar as the technique for measuring and comparing forces. The method involves the assumption that a force on one end of the bar results in a one-dimensional plane wave that travels down the bar. To avoid reflected waves from the end of the bar overlapping the input pulse, and due to practical limitations on the length of the bar, strain transducers are located relatively close to the impacted face of the bar, raising questions about the validity of the assumption of one-dimensional wave propagation. In addition, the large diameter of the bar can introduce dispersion effects in the propagated pulse. These must be assessed to determine the impact on the accuracy of impact force measurements and, if significant, dispersion correction techniques must be used. In this study analytical, experimental, and computational methods are used to analyze the wave propagation characteristics for impact forces expected from realistic bird impacts. While wave dispersion and effects of non-concentric impacts are present, the resulting errors are relatively small. For the response we expect to see in bird impacts up to velocities of approximately 300 m/sec the large diameter Hopkinson bar test appears to be an acceptable method for measuring the impact forces and comparing the response of real and proposed artificial birds.

Birdstrike↗

Adapting to Artificial Intelligence

There is no need to be afraid of the emerging advances in artificial intelligence, says Argonne’s Marius Stan, Program Lead for Intelligent Materials Design. Although artificial intelligence may upend the job market in some ways, he says, it may also create new industries and positions as employees begin to designing and making robots. “It’s the problem and the solution,” Stan says. Meanwhile, artificial intelligence is playing a significant role in defining new research methods that bring us to resolution or discovery more quickly than ever before. Argonne is using or developing many of these technologies as AI extends to new branches of science and technology, sprouting new buds that represent various facets of human function and cognition — from learning to language processing to visual recognition.

97 MATHEMATICS AND COMPUTING↗

Identification of NPP transients using artificial intelligence - 335

If the accidents happen in nuclear power plants (NPPs), the NPP operators identify the transients through observing the behaviors of the major parameters. However, the integrity of the instrumentation signals is not ensured under the accident circumstances and the major parameters essential in identifying the transients can rapidly change although the instruments generate accurate signals. Thus, to successfully control the accidents by taking necessary measures relatively faster, it is very important to provide the operators with the information that can identify the transients and accidents accurately. In this study, the transients of severe accidents in NPPs are identified and classified using artificial intelligence methodologies. Several transients are identified using support vector classification (SVC) and probabilistic neural network (PNN) as artificial intelligence methodologies. The used data for the transients are the simulation data using the modular accident analysis program (MAAP) code for the optimized power reactors (OPR1000s). The time-integrated values of chosen simulated sensor signals are used as the input variables for identifying the transients. As a result of this study, the proposed models identified the transients quite precisely. Therefore, the quite accurate identification of the transients indicates the excellence of these artificial intelligence methods and they will produce useful supporting information for the operators even under extreme transient circumstances in NPPs. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗