Dynamic response of a supersonic diffuser to bypass and spike oscillation
Investigation of amplitude and phase shift of static pressure variations in supersonic diffuser for separate oscillation of spike and bypass
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Investigation of amplitude and phase shift of static pressure variations in supersonic diffuser for separate oscillation of spike and bypass
Here we show analytically using a macrospin approximation that easy-plane spin Hall nano-oscillators excited by a spin current polarized perpendicularly to the easy plane have phase dynamics analogous to that of Josephson junctions. Similarly to Josephson junctions, they can reproduce the spiking behavior of biological neurons that is appropriate for neuromorphic computing. To take advantage of typical spin-orbit torques, we use a nanoconstriction geometry, in which the magnetostatic interaction and magnetocrystalline anisotropy are tuned to create an easy plane that includes the interface normal direction. We perform micromagnetic simulations of such oscillators realized in this geometry and show that the easy-plane spiking dynamics is preserved in this experimentally feasible architecture. Finally we simulate two elementary neural network blocks that implement operations essential for neuromorphic computing. First, we show that output spikes energies from two neurons can be summed and injected into a following layer neuron and second, we demonstrate that outputs can be multiplied by synaptic weights implemented by locally modifying the anisotropy.
The oscillating flow field around a spike-tipped body has been simulated by the numerical solution of the 3-D compressible Navier-Stokes equations at a Mach number of 3.0 and a nominal Reynolds number of 7.87 x 10 to the 6th/m. Computations were performed using a vectorized 3-D Navier-Stokes program on the STAR 100 computer. Numerical solutions confirmed the experimental result that the self-sustained oscillation occurs within a limited range of the protruded spike length to shoulder height ratio. The numerical result predicted correctly the discrete frequency range as well as the rms pressure intensity. The detailed flow structure is also presented and discussed.
This work reports that synchronization of Mott material-based nanoscale coupled spiking oscillators can be drastically different from that in conventional harmonic oscillators. We investigated the synchronization of spiking nanooscillators mediated by thermal interactions due to the close physical proximity of the devices. Controlling the driving voltage enables in-phase 1:1 and 2:1 integer synchronization modes between neighboring oscillators. Transition between these two integer modes occurs through an unusual stochastic synchronization regime instead of the loss of spiking coherence. In the stochastic synchronization regime, random length spiking sequences belonging to the 1:1 and 2:1 integer modes are intermixed. The occurrence of this stochasticity is an important factor that must be taken into account in the design of large-scale spiking networks for hardware-level implementation of novel computational paradigms such as neuromorphic and stochastic computing.
While the complementary metal-oxide semiconductor (CMOS) technology is the mainstream for the hardware implementation of neural networks, an alternative route is explored based on a new class of spiking oscillators called “thermal neuristors”, which operate and interact solely via thermal processes. Utilizing the insulator-to-metal transition (IMT) in vanadium dioxide, a wide variety of reconfigurable electrical dynamics mirroring biological neurons is demonstrated. Notably, inhibitory functionality is achieved just in a single oxide device, and cascaded information flow is realized exclusively through thermal interactions. To elucidate the underlying mechanisms of the neuristors, a detailed theoretical model is developed, which accurately reflects the experimental results. In conclusion, this study establishes the foundation for scalable and energy-efficient thermal neural networks, fostering progress in brain-inspired computing.
The same elements can form different compounds with widely different physical properties. Synthesis of a single-phase material is commonly achieved by controlling experimental conditions. Synthesizing materials that incorporate multiple specific spatially distributed chemical phases is often challenging, especially if different phases must be organized into well-defined spatial patterns. Here, we present an efficient solid reaction laser annealing (SRLA) approach to directly write regions of different local chemical compositions. We demonstrate the practical utility of our approach by locally writing microscale patterns of distinct chemical phases in vanadium oxide thin films. Specifically, we achieved the controlled local recrystallization of a uniform V 2 O 3 matrix into VO 2 , V 3 O 5 , and V 4 O 7 regions exhibiting sharp 1st- and 2nd-order metal–insulator phase transitions over a wide range of critical temperatures, i.e., a characteristic feature of select vanadium oxides that is extremely sensitive to even minute structural or compositional imperfections. We utilized the local chemical phase writing to pattern spiking oscillators with distinct electrical behavior directly in the thin film sample without employing elaborate lithography fabrication. Our laser tuning local chemical composition opens a pathway to synthesize a wide range of artificially micropatterned composite materials, with precision and control unattainable in conventional material synthesis methods.
Abstract In the pursuit of scalable and energy-efficient neuromorphic devices, recent research has unveiled a novel category of spiking oscillators, termed “thermal neuristors.” These devices function via thermal interactions among neighboring vanadium dioxide resistive memories, emulating biological neuronal behavior. Here, we show that the collective dynamical behavior of networks of these neurons showcases a rich phase structure, tunable by adjusting the thermal coupling and input voltage. Notably, we identify phases exhibiting long-range order that, however, does not arise from criticality, but rather from the time non-local response of the system. In addition, we show that these thermal neuristor arrays achieve high accuracy in image recognition and time series prediction through reservoir computing, without leveraging long-range order. Our findings highlight a crucial aspect of neuromorphic computing with possible implications on the functioning of the brain: criticality may not be necessary for the efficient performance of neuromorphic systems in certain computational tasks.
As renewable energy sources like solar and wind power become more integrated into the grid, coordinated control of behind-the-meter devices is crucial for enhancing grid flexibility and reliability and for meeting cost targets, with standardized models being developed to support this transition. The increasing flexibility and uncertainty of integrated renewable energy grids, along with interactions between various subsystems, make traditional steady-state modeling insufficient to capture transient and dynamic behaviors. Current models (e.g., composite load and battery equivalent models) focus on thermodynamic or electrical characteristics but overlook critical electromechanical interactions. This limits the ability to share performance information for grid services and hampers fast dynamic simulations. In addition, motor stalling is usually triggered by a fault event and attributed to the characteristics of the mechanical torque of the motor, resulting in absorption of a large amount of reactive power during the stalling period. Further, this significant withdrawal of reactive power will deteriorate the dynamic voltage stability of power grids and cause delayed voltage recovery. Therefore, an in-depth modeling of the thermodynamics or mechanical torque is essential to study the impacts of the realistic torque characteristics of those behind-the-meter devices on power system voltage stability. This study developed a dynamic multidomain model for building HVAC systems, such as air-source heat pumps, to simulate their thermal and electrical responses to grid transients. The model can accurately predict power metrics with a mean absolute percentage error of 10%, by validating against with power system computer-aided design performance data. Case studies demonstrate the model capability of capturing the transient response to sudden voltage changes, rapid load fluctuations, and system shutdowns respectively. During a sudden voltage drop (30% for 0.1s), a fully loaded heat pump’s motor speed dropped, continued declining, and shut down after 3.6s, with severe power oscillations and a torque spike. A partially loaded unit experienced temporary oscillations but stabilized. Under higher building loads, compressor speed increased from 64% to 100%, with power and torque rising before stabilizing. In safety-triggered shutdowns, power decreased after minor fluctuations, and torque briefly spiked before dropping to zero.
A dual-stream, low-boom supersonic inlet designed for use on a small, Mach 1.6 aircraft was tested experimentally in the 8- by 6-Foot Supersonic Wind Tunnel (SWT) at the NASA Glenn Research Center (GRC). The tests showed that the inlet had good recovery and stable operation over large mass flow range. The inlet went into buzz at mass flows well below that needed for engine operation, and the experiments generated a wealth of data during buzz. High frequency response pressure measurements and high-speed schlieren videos were recorded for many buzz events. The objective of the present work was to use computational fluid dynamics (CFD) to predict some of the experimental data taken during buzz, compare those predictions to the experimental data, and to use both datasets to explain the physics of the buzz cycle. The calculations were done with the Wind-US CFD code using a second-order time-accurate differencing scheme and the SST turbulence model. Computed Mach number contours were compared with schlieren images, and ensemble-averaged unsteady pressures were compared to data. The results showed that the buzz cycle consisted partly of spike buzz, an unsteady oscillation of the main shock at the spike tip while the inlet pressure dropped, and partly of choked flow while the inlet repressurized. Most of the results could be explained by theory proposed by Dailey in 1954, but did not support commonly used acoustic resonance explanations.
We report translating the surging interest in neuromorphic electronic components, such as those based on nonlinearities near Mott transitions, into large-scale commercial deployment faces steep challenges in the current lack of means to identify and design key material parameters. These issues are exemplified by the difficulties in connecting measurable material properties to device behavior via circuit element models. Here, the principle of local activity is used to build a model of VO 2 /SiN Mott threshold switches by sequentially accounting for constraints from a minimal set of quasistatic and dynamic electrical and high-spatial-resolution thermal data obtained via in situ thermoreflectance mapping. By combining independent data sets for devices with varying dimensions, the model is distilled to measurable material properties, and device scaling laws are established. The model can accurately predict electrical and thermal conductivities and capacitances and locally active dynamics (especially persistent spiking self-oscillations). The systematic procedure by which this model is developed has been a missing link in predictively connecting neuromorphic device behavior with their underlying material properties, and should enable rapid screening of material candidates before employing expensive manufacturing processes and testing procedures.
In mouse primary visual cortex (V1), familiar stimuli evoke significantly altered responses when compared with novel stimuli. This stimulus-selective response plasticity (SRP) was described originally as an increase in the magnitude of visual evoked potentials (VEPs) elicited in layer 4 (L4) by familiar phase-reversing grating stimuli. SRP is dependent on NMDA receptors (NMDARs) and has been hypothesized to reflect potentiation of thalamocortical (TC) synapses in L4. However, recent evidence indicates that the synaptic modifications that manifest as SRP do not occur on L4 principal cells. To shed light on where and how SRP is induced and expressed in male and female mice, the present study had three related aims: (1) to confirm that NMDAR are required specifically in glutamatergic principal neurons of V1, (2) to investigate the consequences of deleting NMDAR specifically in L6, and (3) to use translaminar electrophysiological recordings to characterize SRP expression in different layers of V1. We find that knock-out (KO) of NMDAR in L6 principal neurons disrupts SRP. Current-source density (CSD) analysis of the VEP depth profile shows augmentation of short latency current sinks in layers 3, 4, and 6 in response to phase reversals of familiar stimuli. Multiunit recordings demonstrate that increased peak firing occurs in response to phase reversals of familiar stimuli across all layers, but that activity between phase reversals is suppressed. Together, these data reveal important aspects of the underlying phenomenology of SRP and generate new hypotheses for the expression of experience-dependent plasticity in V1. SIGNIFICANCE STATEMENT Repeated exposure to stimuli that portend neither reward nor punishment leads to behavioral habituation, enabling organisms to dedicate attention to novel or otherwise significant features of the environment. The neural basis of this process, which is so often dysregulated in neurologic and psychiatric disorders, remains poorly understood. Learning and memory of stimulus familiarity can be studied in mouse visual cortex by measuring electrophysiological responses to simple phase-reversing grating stimuli. The current study advances knowledge of this process by documenting changes in visual evoked potentials (VEPs), neuronal spiking activity, and oscillations in the local field potentials (LFPs) across all layers of mouse visual cortex. In addition, we identify a key contribution of a specific population of neurons in layer 6 (L6) of visual cortex.
Results are presented of an investigation of the time-accurate simulation of supersonic unsteady flow oscillations over spike-tipped bodies using the multistage Runge-Kutta scheme coupled with a dynamic solution-adaptive grid algorithm modified for multiblock capabilities. The inviscid fluxes are described by a modified advective upwind split method to obviate the need for artificial dissipation. If a time-varying, solution-adaptive mesh algorithm is incorporated, resolution of the details of the unsteady spike-tipped body flow is improved. The adaptive algorithm is also shown to resolve multiple and diverse features of the flow simultaneously, with the adapted regions in the mesh convecting with these features as they translate.
A study was made regarding the beading and spiking phenomena observed in the M551 metals melting experiment conducted during the Skylab I mission in June 1973. An analysis was made of the beading phenomenon based on the Karman vortex shedding theory. The results tend to support the hypothesis that beading which occurred in the stainless and tantalum samples was a Karman vortex street formation. A dynamic model of cavity oscillation is discussed to explain the spiking phenomenon which was observed in the stainless steel and tantalum samples. Calculations of spiking frequency indicate that the intensity of spiking depends primarily on the vapor pressure and surface tension properties of the material, and is only slightly affected by the level of gravitation acceleration.
The transient gamma-ray burst observed on March 5, 1979 by the nine spacecraft forming the interplanetary gamma-ray burst network is discussed. Measurements reveal the event to be unlike any previously observed gamma-ray burst or X-ray burster, with a maximum intensity greater than several thousandths erg/sq cm per sec, a rise time of less than 200 microsec, a narrow and featureless initial spike and a regular 8-sec oscillation with a compound pulse shape. The source of the transient has been localized to a region including the supernova remnant N 49 in the Large Magellanic Cloud, at a distance of 55 pc, and a ratio of X-ray point-source steady state to transient emission of less than 10 to the -9th was obtained. The identification of the burst source with the supernova remnant at that distance would require an enormous source output if it was radiated isotropically, and substantially less if beaming is present. Alternatively, the event could be explained by a nearby, invisible neutron star.
The Quasi Biennial Oscillation in the Earth s upper atmosphere has an analog deep inside the Sun. As on Earth, the flow is east or west, it is at low latitude, and it reverses direction in a roughly periodic manner. The period in the solar case is 1.3 years. It was detected using solar oscillations similar to the way earthquakes are used to study the Earth's interior. But its cause was not known. We showed that global oscillations (g-modes) can supply enough angular momentum to drive zonal flows with the observed reversal period. This required a calculation of wave dissipation rates inside each flow and in the turbulent layer that separates any two flows of opposite sign. Heat that this process leaves behind causes a thermal spike inside the Sun at the same depth. This may explain an anomaly in observed sound speed that has had no sure explanation.
Deep Space Climate Observatory (DSCOVR) measurements of Earth’s reflected solar radiation from the Lissajous orbital position near the Lagrangian L1 point provide continuous monitoring of the Earth’s sunlit hemisphere. CERES-based angle models were used to convert the near-hourly reflected radiances of the EPIC images into a climate-style planetary albedo data-point over the sunlit hemisphere. Integration over the sunlit hemispheres averages out the meteorological weather noise, but retains the seasonal and planetary-scale variability. As the Earth rotates, this generates variations in the Earth’s planetary albedo that are precisely aligned in longitude, which constitutes the dayurnal cycle. This dayurnal variability in the planetary albedo arises from planetary-scale changes in cloud radiative properties that can be directly compared to similarly sampled climate GCM output data. Six years of EPIC data have been analyzed, showing characteristic patterns in the seasonal and dayurnal variability of the Earth’s planetary albedo. Much of the seasonal change in planetary albedo is associated with the changing DSCOVR viewing geometry and the change in solar declination. But throughout the year, the highest planetary albedos are observed over the Central Asia (Iraq) longitude, while the lowest planetary albedos occur over the Central Pacific longitude. For these longitudes, the relative seasonal changes in the planetary albedo are slowly varying and anti-correlated. Dayurnal amplitude maxima tend to occur during the July-September timeframe, with April-May and December exhibit distinct minima in the dayurnal amplitude. West Africa and the West Pacific longitudes likewise exhibit anti-correlated seasonal variability, while they also undergo anti-correlated short period oscillations. On the other hand, in nearby longitudes, there are short period spikes in planetary albedo of a few-days duration, as well as longer period oscillations that may range from a week to several months, that generally tend to be correlated.
One of the fundamental properties of an intermediate polar is the dynamical nature of the accretion flow as it encounters the white dwarf’s (WD’s) magnetosphere. Many works have presumed a dichotomy between disk-fed accretion, in which the WD accretes from a Keplerian disk, and stream-fed accretion, in which the matter stream from the donor star directly impacts the WD’s magnetosphere without forming a disk. However, there is also a third, poorly understood regime in which the accretion flow consists of a torus of diamagnetic blobs that encircles the WD. This mode of accretion is expected to exist at mass-transfer rates below those observed during disk-fed accretion, but above those observed during pure stream-fed accretion. We invoke the diamagnetic-blob regime to explain the exceptional Transiting Exoplanet Survey Satellite light curve of the intermediate polar TX Col, which transitioned into and out of states of enhanced accretion during Cycles 1 and 3. Power-spectral analysis reveals that the accretion was principally stream fed. However, when the mass-transfer rate spiked, large-amplitude quasi-periodic oscillations (QPOs) abruptly appeared and dominated the light curve for weeks. The QPOs have two striking properties: they appear in a stream-fed geometry at elevated accretion rates, and they occur preferentially within a well-defined range of frequencies (∼10–25 cycle day{sup −1}). We propose that during episodes of enhanced accretion, a torus of diamagnetic blobs forms near the binary’s circularization radius and that the QPOs are beats between the white dwarf’s spin frequency and unstable blob orbits within the WD’s magnetosphere. We discuss how such a torus could be a critical step in producing an accretion disk in a formerly diskless system.
Unijunction oscillator controlled by potentiometer to generate a spike that may be converted by transformer action to the simulated heart beat is used as a calibrator for heart monitoring equipment.