High Rate Regime Research Articles

Modeling of heat conduction through rate equations

AbstractStarting from a classical thermodynamic approach, we derive rate-type equations to describe the behavior of heat flow in deformable media. Constitutive equations are defined in the material (Lagrangian) description where the standard time derivative satisfies the principle of objectivity. The statement of the Second Law is formulated in the classical form and the thermodynamic restrictions are then developed following a variant of the Coleman-Noll procedure where the entropy production too is given by a non-negative constitutive equation. Both the free energy and the entropy production are assumed to depend on a common set of independent variables involving, in addition to temperature, both temperature gradient and heat-flux vector together with their time derivatives. This approach results in rate-type constitutive function for the heat flux that are intrinsically consistent with the Second Law and easily amenable to analysis. In addition to providing already known models (e.g., Maxwell-Cattaneo-Vernotte’s and Jeffreys-like heat conductors), this scheme allows the formulation of new models of heat transport that are likely to apply also in nanosystems. This is consistent with the fact that higher-order time derivatives of the heat flux are in order when high-rate regimes occur.

Vertically Aligned Nanocomposite Thin Films Incorporating 3D-Architectures for Micro-Battery Applications

Vertically aligned nanocomposite (VAN) thin films have shown strong potential in oxide nanoionics but are yet to be explored in detail in solid-state battery systems [1,2]. VANs, typically grown by pulsed laser deposition (PLD), are chessboard-like structures comprised of pillars of one phase embedded in a matrix the second phase. They form by solid-state thermodynamic self-assembly, allowing the spontaneous formation of interdigitated battery components in a single deposition without additional processing steps such as etching. Their 3D architectures are attractive because they may allow enhancements in capacity, current and power densities [3]. Furthermore, VAN films grow with a preferential orientation, allowing for selective growth of the optimum crystallographic orientation for high performance by altering the substrate phase and/or orientation. Also, owing to their large interfacial surface areas, VAN could serve as models to study interfaces and solid-electrolyte interphase formation during cycling due to their high interfacial surface areas.In this poster, first we present highly crystalline and epitaxial vertically aligned nanocomposite PLD films comprised of a LixLa0.32±0.05(Nb0.7±0.1Ti0.32±0.05)O3±δ-Ti0.8±0.1Nb0.17±0.03O2±δ-anatase (LL(Nb,Ti)O-(Ti,Nb)O2) electrolyte/anode system, the first anode VAN battery system reported (Figure 1) [4]. This system exhibits an order of magnitude increased Li+ ionic conductivity over that in bulk Li3xLa1/3-xNbO3 (LLNO) and is comparable with the best available Li3xLa2/3-xTiO3 (LLTO) pulsed laser deposition films. Then, we will highlight the considerations and challenges that VANs pose, and how to overcome them [5]. Finally, we will also present our recently submitted work on VAN based cathodes [6]. Our carefully selected materials combination exhibits remarkable high-rate performance, achieving discharge capacities > 70 mAh g-1 up to ~200 C with clear redox behavior of the cathode-of-choice. The enhanced electrochemical performance is enabled by the 3D VAN architecture. Interestingly, we elucidate the relationship between pillar morphology and electrochemical performance, namely there is an optimum size and orientation of cathodic nanopillars such that high discharge capacity under high-rate regimes is possible. These works open up the possibility of incorporating VAN films into an all solid-state battery, either as electrodes or electrolytes, by the pairing of suitable materials.

A comparison of high strain rate response and adiabatic shear band formation in additively and traditionally manufactured Inconel 718

3D printing of Inconel 718 has been increasingly commercialized, as such research comparing the mechanical properties of additively manufactured (AM) and traditionally manufactured (TM) components in both quasi-static and high rate regimes should be conducted. Herein, the high strain rate material response, as well as the formation of adiabatic shear bands (ASBs) in AM and TM Inconel 718 are studied using a split Hopkinson pressure bar (SHPB) system. A top-hat cylindrical specimen geometry was used to convert the compressive load to shear and conduct high rate shear tests in the SHPB. The quasi-static and high rate stress-strain data reported herein indicates that TM components have at least a 70 MPa higher compressive yield strength. Interestingly, the AM specimens were more susceptible to ASB formation than their TM counterparts. The critical shear strain at which an ASB forms was higher for the TM specimens compared to the AM specimens. This was attributed to the TM material having a higher critical stress for dynamic recrystallization initiation, which is required to ASB formation. The microstructures of the shear zone in the top-hat specimens were examined using an optical microscope (OM), scanning electron microscope (SEM), and transmission electron microscope (TEM). Dynamic recrystallization was observed at the center of the shear affected zone (SAZ) using TEM from material lifted out from the ASB. Due to the plastic deformation in the shear region, the material strain hardens and therefore has a higher hardness than the bulk material. At the center of the shear region, a peak in nano-hardness of 585 HV was recorded on the ASB. The outcome of this research will lead to the prevention, containment, and control of shear instability in AM structures.

A constitutive relation generalizing the Navier–Stokes theory to high-rate regimes

We propose a constitutive equation for flows of gases in high-rate regimes where the Navier–Stokes theory breaks down. The model generalizes the Navier–Stokes relation and agrees well with that model in all lower rate flows examined. Our proposed constitutive relation is calibrated with the method of objective molecular dynamics (OMD) using families of compressible and incompressible flows of Lennard-Jones argon. The constitutive relation makes use of the higher-order objective strain rates due to Rivlin and Ericksen (J. Rat. Mech. Anal., vol. 4, 1955, pp. 323–425). The constitutive relation is fully frame-indifferent, and the macroscopic flows corresponding to the OMD simulations are exact solutions for the proposed model. The model is shown to agree with atomistic results much better than the Navier–Stokes equations in the transition regime. The success of our model indicates that it is not higher gradients that become important in the high-rate regime, but rather higher rates of change of the strain rate tensor. While somewhat more complicated to implement than the Navier–Stokes relation, the proposed model is expected to be compatible with existing methods of computational fluid dynamics and may extend those methods to higher rate regimes, while preserving their ability to handle large spatial scales.

Mechanical Behavior of Multi-Material Single-Lap Joints under High Rates of Loading Using a Split Hopkinson Tension Bar

In the presented research, a split Hopkinson tension bar (SHTB) was used to measure the mechanical response of multi-material single-lap joints in the high-rate loading regime. High-performance applications require high-quality measurements of the mechanical properties to define safe design rules. Servo-hydraulic machines are commonly used to investigate such small structures, but they are prone to produce oscillation-affected force measurements. To improve force–displacement measurements, an SHTB was chosen to investigate these joints. Three different kinds of joints were tested: multi-material bolted joints, multi-material bonded joints, and multi-material bonded/bolted joints. One substrate of the joints was made of aluminum (Al-2024-T3) and the other one was made of a laminated composite (TC250). A countersunk titanium bolt and a crash-optimized epoxy adhesive (Betamate 1496 V) were used to fasten the joints. A constant impedance mounting device was implemented to limit wave reflections and to improve the signal quality. Quasi-static experiments at a servo-hydraulic machine were performed to compare the data with the respective data from the high-rate loading conditions. The presented research shows that high-quality high-rate tests of multi-material single-lap joints can be achieved by employing an SHTB. With this high-quality measurement, a rate dependency of the mechanical behavior of these joints was identified. The dynamic increase (DI), which is the ratio of a high rate of loading over quasi-static loading, was measured for each of the joint types, where the dynamic increase in the max force was DI = 1.1 for the bolted, DI = 1.4 for the bonded, and DI = 1.6 for the bonded/bolted joints.

Neural Network Parameter Identification Based Constitutive Modeling of Superelastic Shape Memory Alloys

AbstractSuperelastic shape memory alloys (SMAs) are two‐phase polycrystal materials with hysteretic energy dissipation and deformation recovery capabilities. This feature opens numerous application possibilities, particularly using SMA wires in structural vibration control. However, vibrations, such as wind and earthquake induced, may occur in high rate regimes with alternating amplitudes. Experiments show that the thermodynamically coupled SMA behavior is highly sensitive to strain rate and amplitude. Accordingly, the prediction accuracy of the constitutive models depends notably on their thermodynamic parameters, such as latent heat, heat transfer coefficient and specific heat. The identification of these parameters requires extensive experiments. To circumvent this challenge, this study proposes for SMA wires a machine learning based parameter identification procedure using artificial neural networks (ANNs). In this approach, a recently improved uniaxial macroscopic constitutive SMA model is utilized as a forward physics based model to compute all possible responses in an expected parameter space. With the generated data, a multilayer ANN is trained as a data based inverse model. The Latin hypercube sampling and the backpropagation learning algorithms are applied. After training, the data based model is able to identify suitable thermodynamic parameters from SMA stress responses. With the identified parameters, the constitutive model replicates the strain rate and amplitude dependent SMA response effects. The experimental results are matched by the model with high accuracy.

Geopolitical uncertainty and sovereign bond yields of BRICS economies

PurposeThe politically unstable economies have high and volatile sovereign spread. The purpose of this paper is to investigate the impact of geopolitical uncertainty on sovereign bond yields.Design/methodology/approachThe sovereign yields at various maturities were decomposed into three factors, namely, level, slope and curvature, using the Dynamic Nelson Siegel model. The relationship between geopolitical uncertainty and the yield curve factors was examined using a quantile causality test.FindingsThe study found that at the extreme high-rate regime, geopolitical uncertainty causes the yield curve factors positively, indicating bond investors demand a higher return for geopolitical uncertainty. On the other hand, during extreme low-rate regime geopolitical causes the short- and medium-term factors negatively. The extreme low-rate regime indicates the period of economic slowdown. During this regime, the central banks try to reduce the short-term rates to stimulate growth.Originality/valueThis is one of the few papers that investigates the relationship between the geopolitical risk and sovereign bond yields at the various maturities and interest rate regimes. Understanding the relationship between the geopolitical risk and short-term rates would help the central banks the efficacy of their policy actions. The long-term rates are influenced by the global investor preferences; examining the relationship with the long-term rates would help the investors frame the trading strategies.

The study of activation impact during formation and testing of Ni(OH)2 electrochromic films in the presence of Al3+ and WO42- ions

The present study is devoted to an attempt to improve the characteristics of nickel hydroxide electrochromic films. The study of optical and electrochemical characteristics of the films, prepared through the codeposition of nickel and aluminum and additional modification during the cycling of the prepared samples was conducted. The modification was realized through the cycling of films in the solution of potassium hydroxide with small amounts of potassium tungstate. Over the course of the study, a series of films were obtained, which demonstrated different electrochemical and optical behavior. Thus, it was found that both the deposition method and the cycling regime have a significant effect on the characteristics of electrochromic films. It is shown that for the potentiodynamic regime, the best characteristics were demonstrated by the film without modifications or additives. On the other hand, all modified films, codeposited with aluminum, demonstrated better characteristics in galvanostatic regimes over the reference film. It would appear that this is due to the physico-chemical features of the films deposited in the presence of additives. In turn, the use of high-rate cycling regime leads to a decrease of electrochromic characteristics, namely higher degree of irreversibility. Despite the difference of currents in different galvanostatic regimes by 4 times, the coloration degree did not differ much. It was also found that the presence of 0.3 mM potassium tungstate in the cycling electrolyte resulted in a significant decrease in the electrochromic properties of the samples

Compound Poisson Noise Sources in Diffusion-Based Molecular Communication

Diffusion-based molecular communication (DMC) is one of the most promising approaches for realizing nano-scale communications for healthcare applications. The DMC systems in in-vivo environments may encounter biological entities that release molecules identical to the molecules used for signaling as part of their functionality. Such entities in the environment act as external noise sources from the DMC system’s perspective. In this paper, the release of molecules by external bio-inspired noise sources is particularly modeled as a compound Poisson process. The impact of compound Poisson noise sources (CPNSs) on the performance of a point-to-point DMC system is investigated. To this end, the noise from the CPNS observed at the receiver is characterized. Considering a simple on-off keying modulation and formulating symbol-by-symbol maximum likelihood (ML) detector, the performance of the DMC system in the presence of the CPNS is analyzed. For the special case of CPNS in a high-rate regime, the noise received from the CPNS is approximated as a Poisson process whose rate is normally distributed. In this case, it is proved that a simple single-threshold detector is an optimal ML detector. Our results reveal that in general, adopting the conventional simple homogeneous Poisson noise model may lead to overly optimistic performance predictions, if a CPNS is present.

An experimental and numerical survey into the potential of hybrid foams

Hybrid foams are composite materials consisting of two foamed constitutive materials. Compared to conventional mono material foams, hybrid foams have the advantage that their material properties can be adapted more easily to comply with any kinds of prescribed requirements deriving from the intended structural application. The present study is concerned with a survey of the mechanical properties of a variety of hybrid foams with both, interpenetrating and particulate microstructures. As constituent foams, polymeric, metallic and ceramic foams of different types are considered. In an experimental survey, the mechanical response of the hybrid foams is characterized under compression with loading rates covering the entire spectrum from quasi-static conditions up into the high rate regime. The experimental investigation is complemented by a numerical simulation using a hierarchical homogenization procedure to predict the effective material properties numerically. The experimental and numerical results reveal that the hybrid foam concept provides the opportunity to design lightweight cellular solids with effective mechanical properties in a wide range. The optimization potential is demonstrated in a case study considering the design of a material for a bird-strike protection system for commercial aviation.

Quantum Sphere-Packing Bounds With Polynomial Prefactors

We study lower bounds on the optimal error probability in classical coding over classical-quantum channels at rates below the capacity, commonly termed quantum sphere-packing bounds. Winter and Dalai have derived such bounds for classical-quantum channels; however, the exponents in their bounds only coincide when the channel is classical. In this paper, we show that these two exponents admit a variational representation and are related by the Golden-Thompson inequality, reaffirming that Dalai's expression is stronger in general classical-quantum channels. Second, we establish a sphere-packing bound for classical-quantum channels, which significantly improves Dalai's prefactor from the order of subexponential to polynomial. Furthermore, the gap between the obtained error exponent for constant composition codes and the best known classical random coding exponent vanishes in the order of $o(\log n / n)$, indicating our sphere-packing bound is almost exact in the high rate regime. Finally, for a special class of symmetric classical-quantum channels, we can completely characterize its optimal error probability without the constant composition code assumption. The main technical contributions are two converse Hoeffding bounds for quantum hypothesis testing and the saddle-point properties of error exponent functions.

Should the Interest Rate Level Influence Asset Allocation?

How does the interest rate environment inform the allocation between risky and safe assets? The authors focus on the level rather than change in rates because rates are highly persistent; changes are infrequent and unpredictable while low versus high rate regimes are durable. U.S. history from 1926 through 2016 shows that equities have lower risk and higher excess returns when rates are low. Despite all the concern about low yield, a low rates environment is actually a good thing for more-aggressive investors.

Analysis of volatility in driving regimes extracted from basic safety messages transmitted between connected vehicles

Driving volatility captures the extent of speed variations when a vehicle is being driven. Extreme longitudinal variations signify hard acceleration or braking. Warnings and alerts given to drivers can reduce such volatility potentially improving safety, energy use, and emissions. This study develops a fundamental understanding of instantaneous driving decisions, needed for hazard anticipation and notification systems, and distinguishes normal from anomalous driving. In this study, driving task is divided into distinct yet unobserved regimes. The research issue is to characterize and quantify these regimes in typical driving cycles and the associated volatility of each regime, explore when the regimes change and the key correlates associated with each regime. Using Basic Safety Message (BSM) data from the Safety Pilot Model Deployment in Ann Arbor, Michigan, two- and three-regime Dynamic Markov switching models are estimated for several trips undertaken on various roadway types. While thousands of instrumented vehicles with vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communication systems are being tested, nearly 1.4 million records of BSMs, from 184 trips undertaken by 71 instrumented vehicles are analyzed in this study. Then even more detailed analysis of 43 randomly chosen trips (N=714,340 BSM records) that were undertaken on various roadway types is conducted. The results indicate that acceleration and deceleration are two distinct regimes, and as compared to acceleration, drivers decelerate at higher rates, and braking is significantly more volatile than acceleration. Different correlations of the two regimes with instantaneous driving contexts are explored. With a more generic three-regime model specification, the results reveal high-rate acceleration, high-rate deceleration, and cruise/constant as the three distinct regimes that characterize a typical driving cycle. Moreover, given in a high-rate regime, drivers’ on-average tend to decelerate at a higher rate than their rate of acceleration. Importantly, compared to cruise/constant regime, drivers’ instantaneous driving decisions are more volatile both in “high-rate” acceleration as well as “high-rate” deceleration regime. The study contributes to analyzing volatility in short-term driving decisions, and how changes in driving regimes can be mapped to a combination of local traffic states surrounding the vehicle.

Deletion Codes in the High-Noise and High-Rate Regimes

The noise model of deletions poses significant challenges in coding theory, with basic questions like the capacity of the binary deletion channel still being open. In this paper, we study the harder model of worst case deletions, with a focus on constructing efficiently decodable codes for the two extreme regimes of high-noise and high-rate. Specifically, we construct polynomial-time decodable codes with the following tradeoffs (for any $ \varepsilon > 0$ ): 1) codes that can correct a fraction $1- \varepsilon $ of deletions with rate $ \mathop {\mathrm {poly}}\nolimits ( \varepsilon )$ over an alphabet of size $ \mathop {\mathrm {poly}}\nolimits (1/ \varepsilon )$ ; 2) binary codes of rate $1-\tilde {O}(\sqrt { \varepsilon })$ that can correct a fraction $ \varepsilon $ of deletions; and 3) Binary codes that can be list-decoded from a fraction $(1/2- \varepsilon )$ of deletions with rate $ \mathop {\mathrm {poly}}\nolimits ( \varepsilon )$ . This paper gives the first efficient constructions which meet the qualitative goals of correcting a deletion fraction approaching 1 over bounded alphabets, and correcting a constant fraction of bit deletions with rate approaching 1 over a fixed alphabet. The above-mentioned results bring our understanding of deletion code constructions in these regimes to a similar level as worst case errors.

Approaching Capacity at High Rates with Iterative Hard-Decision Decoding

A variety of low-density parity-check (LDPC) ensembles have now been observed to approach capacity with message-passing decoding. However, all of them use soft (i.e., non-binary) messages and a posteriori probability (APP) decoding of their component codes. In this paper, we show that one can approach capacity at high rates using iterative hard-decision decoding (HDD) of generalized product codes. Specifically, a class of spatially-coupled GLDPC codes with BCH component codes is considered, and it is observed that, in the high-rate regime, they can approach capacity under the proposed iterative HDD. These codes can be seen as generalized product codes and are closely related to braided block codes. An iterative HDD algorithm is proposed that enables one to analyze the performance of these codes via density evolution (DE).

HetNets With Random DTX Scheme: Local Delay and Energy Efficiency

Heterogeneous cellular networks (HetNets) are to be deployed for future wireless communications to meet the ever-increasing mobile traffic demand. However, the dense and random deployment of small cells and their uncoordinated operation raise important concerns about energy efficiency. On the other hand, discontinuous transmission (DTX) mode at the base station (BS) serves as an effective technology to improve energy efficiency of the overall system. In this paper, we investigate energy efficiency under a finite local delay constraint in the downlink HetNets with the random DTX scheme. Using a stochastic-geometry-based model, we derive the local delay and energy efficiency in a general case and obtain closed-form expressions in some special cases. These results give insights into the effect of key system parameters, such as path-loss exponents, BS densities, signal-to-interference-ratio (SIR) threshold, and mute probability on system performance. We also provide the low-rate and high-rate asymptotic behavior of the maximum energy efficiency. It is analytically shown that it is less energy efficient to apply the random DTX scheme in the low-rate regime. In the high-rate regime, however, a random DTX scheme is essential to achieve the finite local delay and higher energy efficiency. Finally, we extend the analysis to the load-aware DTX scheme, where the mute probability depends on the user activity level.

High-rate growth of single crystal diamond in microwave plasma in CH4/H2 and CH4/H2/Ar gas mixtures in presence of intensive soot formation

The growth of single crystal diamond plates in a microwave plasma using H2-CH4 and H2-CH4-Ar gas mixtures in a broad range of methane concentration (2–15%) is studied. The growth rates up to 60μm/h in H2-CH4 mixtures and up to 105μm/h in Ar-H2-CH4 mixtures are achieved at high CH4 content, without adding nitrogen, still obtaining transparent crystals. The thermal conductivity k of the SCs in the temperature range of 220–420K is measured by a laser flash technique. High thermal conductivity k≈2300W/m⋅K at room temperature is found for the sample grown at low growth rate in H2-CH4, this value reducing to k≈2000W/mK for the material produced in high rate regime at 15% CH4. The spatial profiles of Hα and C2 line intensities in the plasma were determined with optical emission spectroscopy (OES). Soot formation at high CH4 contents is observed at the plasma border both for Ar-free and Ar-containing mixtures, the soot yield being roughly proportional to diamond growth rate. The soot temperature Ts, as measured with OES, is almost constant (3800±300K) in H2-CH4 mixtures over the all methane concentration range explored, while for Ar-containing plasma the Ts is even higher (4100–4200K) at [CH4]<10%, reducing, however, below 3800K in CH4-reach mixtures. Raman spectra of collected soot correspond to crystalline graphite with high density of defects. The estimated carbon conversion efficiency from CH4 precursor to the soot can exceed 10% and should be taken into account in overall carbon balance in the CVD process.

Suppressing feedback in a distributed video coding system by employing real field codes

Single-view distributed video coding (DVC) is a video compression method that allows for the computational complexity of the system to be shifted from the encoder to the decoder. The reduced encoding complexity makes DVC attractive for use in systems where processing power or energy use at the encoder is constrained, for example, in wireless devices and surveillance systems. One of the biggest challenges in implementing DVC systems is that the required rate must be known at the encoder. The conventional approach is to use a feedback channel from the decoder to control the rate. Feedback channels introduce their own difficulties such as increased latency and buffering requirements, which makes the resultant system unsuitable for some applications. Alternative approaches, which do not employ feedback, suffer from either increased encoder complexity due to performing motion estimation at the encoder, or an inaccurate rate estimate. Inaccurate rate estimates can result in a reduced average rate-distortion performance, as well as unpleasant visual artifacts. In this paper, the authors propose a single-view DVC system that does not require a feedback channel. The consequences of inaccuracies in the rate estimate are addressed by using codes defined over the real field and a decoder employing successive refinement. The result is a codec with performance that is comparable to that of a feedback-based system at low rates without the use of motion estimation at the encoder or a feedback path. The disadvantage of the approach is a reduction in average rate-distortion performance in the high-rate regime for sequences with significant motion.

134 Fundamentals of ice formation during rapid cooling and warming

We have examined ice formation in aqueous solutions containing common cryoprotectants during cooling and warming at rates from ∼10 K/s to >10,000 K/s. For all solutes tested, the critical cooling rate below which ice forms varies exponentially with solute concentration. The exponential’s characteristic concentration for each solute correlates with the solute’s Stokes radius. We have developed a simple statistical mechanical extension of classical nucleation theory that assumes solutes must be excluded from the critical nucleus. This theory provides an excellent account of our data, and relates the characteristic concentration for each solute to the solute radius and the critical nucleation radius of ice in pure water. This microscopic theory of glass formation in the presence of solutes has consequences for cryobiology, for cloud physics, and for general glass forming systems. Ice formation on warming is of comparable or greater importance to ice formation on cooling in determining survival of cryopreserved samples. Measurements of solutions of several common cryoprotectants show that the critical warming rates below which ice transiently forms on warming are typically one to three orders of magnitude larger than critical cooling rates. Critical warming rates also depend upon cooling rates, due to the presence of small ice fractions in nominally vitrified samples. Current phenomenological models of ice nucleation and growth with current best estimates for their input parameters derived from data at large concentrations and small cooling rates overestimate both critical warming rates and critical cooling rates by orders of magnitude in the low concentration, high rate regime. In vitrification protocols, minimizing concentrations of potentially damaging cryoprotectants while minimizing ice formation thus requires ultrafast warming rates as well as fast cooling rates to minimize the required warming rates. Source of funding: NIH, NSF. Conflict of interest: None declared. ret6@cornell.edu

Adaptive Learning Vector Quantization for Online Parametric Estimation

This paper addresses the problem of parameter estimation in a quantized and online setting. A sensing unit collects random vector-valued samples from the environment. These samples are quantized and transmitted to a central processor which generates an online estimate of the unknown parameter. This paper provides a closed-form expression of the excess mean square error (MSE) caused by quantization in the high-rate regime i.e., when the number of quantization levels is supposed to be large. Next, we determine the quantizers which mitigate the excess MSE. The optimal quantization rule unfortunately depends on the unknown parameter. To circumvent this issue, we introduce a novel adaptive learning vector quantization scheme which allows to simultaneously estimate the parameter of interest and select an efficient quantizer.