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The present work conducted in Summer 1987 continues investigations on Thermal Components for 1.8 K Space Cryogenics (Grant NAG 1-412 of 1986). The topics addressed are plug characterization efforts in a small pore size regime of sintered metal plugs, characterization in the nonlinear regime, temperature profiles in a heat supply unit for a fountain effect pump and modeling efforts.
A fundamental issue which directly impacts the scalability of current theoretical neural network models to massively parallel embodiments, in both software as well as hardware, is the inherent and unavoidable concurrent asynchronicity of emerging fine-grained computational ensembles and the possible emergence of chaotic manifestations. Previous analyses attributed dynamical instability to the topology of the interconnection matrix, to parasitic components or to propagation delays. However, researchers have observed the existence of emergent computational chaos in a concurrently asynchronous framework, independent of the network topology. Researcher present a methodology enabling the effective asynchronous operation of large-scale neural networks. Necessary and sufficient conditions guaranteeing concurrent asynchronous convergence are established in terms of contracting operators. Lyapunov exponents are computed formally to characterize the underlying nonlinear dynamics. Simulation results are presented to illustrate network convergence to the correct results, even in the presence of large delays.
A NASA-sponsored program is described for developing a high-efficiency low-power TWTA operating at 32 GHz and meeting the requirements for the Cassini Mission to study Saturn. The required RF output power of the helix TWT is 10 watts, while the dc power from the spacecraft is limited to about 30 watts. The performance level permits the transmission to earth of all mission data. Several novel technologies are incorporated into the TWT to achieve this efficiency including an advanced dynamic velocity taper characterized by a nonlinear reduction in pitch in the output helix section and a multistage depressed collector employing copper electrodes treated for secondary electron-emission suppression. Preliminary program results are encouraging: RF output power of 10.6 watts is obtained at 14-mA beam current and 5.2-kV helix voltage with overall TWT efficiency exceeding 40 percent.
Combining multiple optical resonators or engineering dispersion of complex media has provided an effective method for demonstrating topological physics controlling photons in unprecedented ways such as unidirectional light propagation and spatially localized modes between an interface or on a corner. Further, adding nonlinear responses to those topological photonic systems has enabled achieving diverse phases of photons in both space and time, allowing for more functionalities in photonic devices that provide a new playground for studying dynamic features of nonlinear topological systems. However, most methods for describing nonlinear topological photonic systems rely on linear topological theories, making it challenging to accurately characterize the topology of nonlinear systems. Thus, substantial efforts have focused on rigorously describing nonlinear topological phases and developing effective tools to analyze nonlinear topological effects. Meanwhile, coupled multimode optical waveguides with nonlinear dynamic responses provide an excellent platform for the statistical description of photons, opening a new paradigm called “optical thermodynamics”. This review will introduce the basic concepts of nonlinear topological photonics and the recent development of theoretical approaches focusing on data-driven approaches for creating phase diagrams as well as the spectral localizer framework and the pseudospectrum method for understanding optical nonlinearities in topological systems. In addition, the new concept of optical thermodynamics will be introduced with some recent theoretical works.
Nonlinear optical properties, such as bulk photovoltaic effects, possess great potential in energy harvesting, photodetection, rectification, etc. To enable efficient light–current conversion, materials with strong photo-responsivity are highly desirable. In this work, we predict that monolayer Janus transition metal dichalcogenides (JTMDs) in the 1T' phase possess colossal nonlinear photoconductivity owing to their topological band mixing, strong inversion symmetry breaking, and small electronic bandgap. 1T'JTMDs have inverted bandgaps on the order of 10 meV and are exceptionally responsive to light in the terahertz (THz) range. By first-principles calculations, we reveal that 1T' JTMDs possess shift current (SC) conductivity as large as 2300 nm μA V -2 , equivalent to a photo-responsivity of 2800 mA/W. The circular current (CC) conductivity of 1T' JTMDs is as large as ~10 4 nm μA V -2 . These remarkable photo-responsivities indicate that the 1T' JTMDs can serve as efficient photodetectors in the THz range. We also find that external stimuli such as the in-plane strain and out-of-plane electric field can induce topological phase transitions in 1T' JTMDs and that the SC can abruptly flip their directions. The abrupt change of the nonlinear photocurrent can be used to characterize the topological transition and has potential applications in 2D optomechanics and nonlinear optoelectronics.
Abstract Characterizing the sow physiological response to an increased heat load is essential for effective heat stress mitigation. The study objective was to characterize the effects of a 400-min heating episode on sow heart rate variability (HRV) at different reproductive stages. HRV is a commonly used noninvasive proxy measure of autonomic function. Twenty-seven sows were enrolled in the study according to their gestation stage at time of selection: 1) nonpregnant (NP; n = 7), 2) mid-gestation (MID; 57.3 ± 11.8 d gestation; n = 11), and 3) late-gestation (LATE; 98.8 ± 4.9 d gestation; n = 8). The HRV data utilized in the study were collected from each pig as the dry bulb temperature in the room increased incrementally from 19.84 ± 2.15 °C to 35.54 ± 0.43 °C (range: 17.1–37.5 °C) over a 400-min period. After data collection, one 5-min set of continuous heart rate data were identified per pig for each of nine temperature intervals (19–20.99, 21–22.99, 23–24.99, 25–26.99, 27–28.99, 29–30.99, 31–32.99, 33–34.99, and 35–36.99 °C). Mean inter-beat interval length (RR), standard deviation of r-r intervals (SDNN), root mean square of successive differences (RMSSD), high frequency spectral power (HF), sample entropy (SampEn), short-term detrended fluctuation analysis (DFAα1), and three measures (%REC, DET, LMEAN) derived from recurrence quantification analysis were calculated for each data set. All data were analyzed using the PROC GLIMMIX procedure in SAS 9.4. Overall, LATE sows exhibited lower RR than NP sows (P < 0.01). The standard deviation of r–r intervals and RMSSD differed between each group (P < 0.01), with LATE sows exhibiting the lowest SDNN and RMSSD and NP sows exhibiting the greatest SDNN and RMSSD. Late-gestation sows exhibited lower HF than both MID and NP sows (P < 0.0001), greater DFA values than NP sows (P = 0.05), and greater DET compared to MID sows (P = 0.001). Late-gestation also sows exhibited greater %REC and LMEAN compared to MID (P < 0.01) and NP sows (all P < 0.01). In conclusion, LATE sows exhibited indicators of greater autonomic stress throughout the heating period compared to MID and NP sows. However, temperature by treatment interactions were not detected as dry bulb increased. Future studies are needed to fully elucidate the effect of gestational stage and increasing dry bulb temperature on sow HRV.
The nonlinear response of laminated, resin matrix fibrous composites is modeled using orthotropic endochronic theory. The theory is formulated in terms of elastic constants and endochronic parameters characterizing the linear and nonlinear response, respectively, of unidirectional composites. All constants and parameters can be determined from normal (tension and/or compression) and shear tests on unidirectional and off-axis specimens. The nonlinear constitutive response relations for the unidirectional lamina are presented and the procedure for determining constants and parameters from test is described. The results are then used to predict the nonlinear response of unidirectional laminae and angle-ply laminates. Comparison between theory and experiment for compression loading of AS4/3502 graphite-epoxy, angle-ply laminates shows excellent correlation.
Rocks are heterogeneous materials that exhibit nonlinear elastic (anelastic) behavior at scales ranging from the laboratory to Earth. In the laboratory, typical, complex relationships exist between stress and strain that include hysteresis, finite relaxation times, strain rate, and history dependence. These behaviors are linked to important characteristics such as stress, porosity, permeability, material integrity, and material failure. In this work, we adopted a “pump-probe” type experiment common in laboratory studies, using solid earth tides as the low-frequency pump and empirical Green's function as the high-frequency probe. By probing the velocity at different points in the pump cycle, we constrained important information about the strain-modulus relationship. Near the San Andreas Fault, we observed strongly nonlinear elastic behavior that characterizes the damage zone. We also constrained important aspects of hysteretic behavior that are related to damage properties and possibly pore pressure. Away from the fault, the nonlinear behavior is diminished.
With the help of a theoretical model and finite-difference time-domain (FDTD) simulations based on the hydrodynamic–Maxwell model, we examine the effect of difference-frequency generation (DFG) in an array of L-shaped metal nanoparticles (MNPs) characterized by intrinsic plasmonic nonlinearity. The outcomes of the calculations reveal the spectral interplay between gain and loss in the vicinity of the fundamental frequency of the localized surface plasmon resonances. Subsequently, we identify different array thicknesses and pumping regimes facilitating parametric amplification and spontaneous parametric downconversion. Our results suggest that the parametric amplification regime becomes feasible on a scale of hundreds of nanometers and spontaneous parametric downconversion on the scale of tens of nanometers, opening up new exciting opportunities for developing building blocks of photonic metasurfaces.
An analysis is presented of the nonlinear effects of the gas motion as well as of the acoustic lining material on the transmission and attenuation of sound in a circular duct with a uniform cross-section and no mean flow. The acoustic material is characterized by an empirical, nonlinear impedance in which the instantaneous resistance is a nonlinear function of both the frequency and the acoustic velocity. The results show that there exist frequency bandwidths around the resonant frequencies in which the nonlinearity decreases the attenuation rate, and outside which the nonlinearity increases the attenuation rate, in qualitative agreement with experimental observations. Moreover, the effect of the gas nonlinearity increases with increasing sound frequency, whereas the effect of the material nonlinearity decreases with increasing sound frequency.
The discovery and development of CMOS-compatible, nonlinear optical materials is essential to produce integrated photonic devices with advanced functionalities. AlN is a strong candidate for on-chip device demonstration due to its intrinsic second-order optical nonlinearities, large bandgap, and well-established fabrication techniques. However, AlN is not easily phase matched for the largest coefficient d 33 ; the coefficients that could potentially be dispersion phase-matched, d 31 and d 15 , have weak nonlinearities. This work investigates ferroelectric Al 1-x B x N (x = 0 to 0.11) for viability as a large bandgap nonlinear optical material with unique suitability towards ultraviolet light generation using second harmonic generation. The linear and nonlinear optical properties are characterized accounting for material anisotropy. With increasing B concentration, a large enhancement from near negligible values to d 31 = 0.9 ± 0.1 pm/V and d 15 = 1.2 ± 0.1 pm/V is observed. This compares favorably to other large bandgap materials like β-Ba(BO 2 ) 2 , where the largest nonlinear coefficient is d 22 ~ 2.3 pm/V at 800 nm. This is accompanied by a change in the bandgap from 6.1 eV to 5.8 eV as B substitution goes from 0 to 11%. A periodically poled, quasi-phase-matched ferroelectric domain pattern with 400 nm domain size and a wall roughness of <16 nm is demonstrated.
Here, in this paper, we propose and study neural network-based methods for solutions of high-dimensional quadratic porous medium equation (QPME). Three variational formulations of this nonlinear PDE are presented: a strong formulation and two weak formulations. For the strong formulation, the solution is directly parameterized with a neural network and optimized by minimizing the PDE residual. It can be proved that the convergence of the optimization problem guarantees the convergence of the approximate solution in the $L^1$ sense. The weak formulations are derived following (Brenier in Examples of hidden convexity in nonlinear PDEs, 2020) which characterizes the very weak solutions of QPME. Specifically speaking, the solutions are represented with intermediate functions who are parameterized with neural networks and are trained to optimize the weak formulations. Extensive numerical tests are further carried out to investigate the pros and cons of each formulation in low and high dimensions. This is an initial exploration made along the line of solving high-dimensional nonlinear PDEs with neural network-based methods, which we hope can provide some useful experience for future investigations.
A method for adaptive (or, optionally, nonadaptive) filtering has been developed for estimating the states of complex process systems (e.g., chemical plants, factories, or manufacturing processes at some level of abstraction) from time series of measurements of system inputs and outputs. The method is based partly on the fundamental principles of the Kalman filter and partly on the use of recurrent neural networks. The standard Kalman filter involves an assumption of linearity of the mathematical model used to describe a process system. The extended Kalman filter accommodates a nonlinear process model but still requires linearization about the state estimate. Both the standard and extended Kalman filters involve the often unrealistic assumption that process and measurement noise are zero-mean, Gaussian, and white. In contrast, the present method does not involve any assumptions of linearity of process models or of the nature of process noise; on the contrary, few (if any) assumptions are made about process models, noise models, or the parameters of such models. In this regard, the method can be characterized as one of nonlinear, nonparametric filtering. The method exploits the unique ability of neural networks to approximate nonlinear functions. In a given case, the process model is limited mainly by limitations of the approximation ability of the neural networks chosen for that case. Moreover, despite the lack of assumptions regarding process noise, the method yields minimum- variance filters. In that they do not require statistical models of noise, the neural- network-based state filters of this method are comparable to conventional nonlinear least-squares estimators.
Many modern spacecraft are complex multi-body dynamic systems where the bodies are connected by several active control systems for pointing and isolation. Mission requirements indicate that many structural modes and possibly some nonlinear effects will require characterization. Thus, even characterization at the subsystem level will become more difficult than usual. System level characterization difficulties will be compounded by the fact that only limited ground testing will be possible on the full up system, and flight testing will be restricted by an extremely limited measurements set. The object of the present discussion is the application of matrix majorant theory to the problem of assessing dynamic system performance when knowledge of the system is uncertain. We show how majorants provide an effective tool to relate required performance output to system identification test quality in terms of residual uncertainty in input-output relations, parameter values, nonlinearities, and interactions. The underlying machinery consists of the block-norm matrix which is a nonnegative matrix each of whose elements is the norm of a block of a suitably partitioned matrix. A matrix which bounds the block-norm matrix in the sense of nonnegative matrices, i.e., element by element is known as a majorant.
Adhesives and adhesive joints are widely used in various industrial applications to reduce weight and costs, and to increase reliability. For example, advances in aerospace technology have been made possible, in part, through the use of lightweight materials and weight-saving structural designs. Joints, in particular, have been and continue to be areas in which weight can be trimmed from an airframe through the use of novel attachment techniques. In order to save weight over traditional riveted designs, to avoid the introduction of stress concentrations associated with rivet holes, and to take full advantage of advanced composite materials, engineers and designers have been specifying an ever-increasing number of adhesively bonded joints for use on airframes. Nondestructive characterization for quality control and remaining life prediction has been a key enabling technology for the effective use of adhesive joints. Conventional linear ultrasonic techniques generally can only detect flaws (delamination, cracks, voids, etc) in the joint assembly. However, more important to structural reliability is the bond strength. Although strength, in principle, cannot be measured nondestructively, a slight change in material nonlinearity may indicate the onset of failure. Furthermore, microstructural variations due to aging or under-curing may also cause changes in the third order elastic constants, which are related to the ultrasonic nonlinear parameter of the polymer adhesive. It is therefore reasonable to anticipate a correlation between changes in the ultrasonic nonlinear acoustic parameter and the remaining bond strength. It has been observed that higher harmonics of the fundamental frequency are generated when an ultrasonic wave passes through a nonlinear material. It seems that such nonlinearity can be effectively used to characterize bond strength. Several theories have been developed to model this nonlinear effect. Based on a microscopic description of the nonlinear interface binding force, a quantitative method was presented. Recently, a comparison between the experimental and simulated results based on a similar theoretical model was presented. A through-transmission setup for water immersion mode-converted shear waves was used to analyze the ultrasonic nonlinear parameter of an adhesive bond. In addition, ultrasonic guided waves have been used to analyze adhesive or diffusion bonded joints. In this paper, the ultrasonic nonlinear parameter is used to characterize the curing state of a polymer/aluminum adhesive joint. Ultrasonic through-transmission tests were conducted on samples cured under various conditions. The magnitude of the second order harmonic was measured and the corresponding ultrasonic nonlinear parameter was evaluated. A fairly good correlation between the curing condition and the nonlinear parameter is observed. The results show that the nonlinear parameter might be used as a good indicator of the cure state for adhesive joints.
Abstract We propose an on-chip gyroscope based on nonlinear multiresonant optics in a thin film χ (2) resonator that combines high sensitivity, compact form factor, and low power consumption simultaneously. We theoretically analyze a novel holistic metric – Fisher Information capacity of a multiresonant nonlinear photonic cavity – to fully characterize the sensitivity of our gyroscope under fundamental quantum noise conditions. Leveraging Bayesian optimization techniques, we directly maximize the nonlinear multiresonant Fisher Information. Our holistic optimization approach orchestrates a harmonious convergence of multiple physical phenomena – including noise squeezing, nonlinear wave mixing, nonlinear critical coupling, and noninertial signals – all encapsulated within a single sensor-resonator, thereby significantly augmenting sensitivity. We show that ∼ 470 × $\sim 470{\times}$ improvement is possible over the shot-noise limited linear gyroscope with the same footprint, intrinsic quality factors, and power budget.
In magnetic confinement fusion, toroidicity-induced Alfvén eigenmodes (TAEs) are well-studied, weakly stable solutions of the linearized ideal magnetohydrodynamic equations. Driven unstable by suprathermal populations of energetic particles, TAE pose a key vulnerability to the confinement of high-energy alpha particles generated by fusion reactions. Hence, it is paramount to understand TAE dynamics if a working reactor is to be realized. In this work, we detect and characterize signatures of nonstationary nonlinear coupling between TAE using a novel, time-resolved bispectral analysis; results are supported by analytic signal of band-passed data. Crucially, a stationary phase relationship between two TAE and a nascent low frequency fluctuation is observed precisely when the triple product of magnetic fluctuation amplitudes is enhanced. Local mode number and frequency spectrum, gleaned from beam-emission spectroscopy, corroborates simultaneous satisfaction of nonlinear matching conditions, and provides a tool to identify theorized pathways of energy transfer, e.g. TAE parametric instability.
Here, we report a first-principles study of the driven dissipative dynamics for Kerr oscillators in the mesoscopic regime. This regime is characterized by large Kerr nonlinearity, realized here using the nonlinear kinetic inductance of a large array of Josephson junctions. The experimentally measured nonlinear resonance lineshapes of the junction array modes show significant deviations from steady-state numerical predictions, and necessitate time-dependent numerical simulations indicative of strong measurement-induced dephasing in the system arising from the large cross-Kerr effect between array modes. Analytical and numerical calculations of switching rate corroborate this by showing the emergence of a slow time scale, which is much longer than the linear decay rate and is set by fluctuation-induced switching times in the bistable regime. Furthermore, our analysis shows that the usual quantum-activated escape treatment is inadequate for prediction of the switching rates at large frequency shifts caused by strong nonlinearities, necessitating a quantum treatment that utilizes the full system Liouvillian. Based on our analysis, we identify a universal crossover parameter that delineates the regimes of validity of semiclassical and quantum descriptions, respectively. Our work shows how dynamical switching effects in strongly nonlinear systems provide a platform to study quantum-to-classical transitions.