Dynamic Emulation Research Articles

Enabling dynamic emulation of high-dimensional model outputs: Demonstration for Mexico City groundwater management

Model emulation has become an integral tool in scenario analysis, risk assessment, and calibration of environmental models. Of particular interest is dynamic emulation – the approximation of model outputs from inputs or processes that vary in time. This paper presents a method for data-driven dynamic emulation of high-dimensional model outputs that overcomes the logistical challenges from assumptions in traditional multivariate statistics concerning output covariance. In this method, outputs are subjected to principal component analysis, and Gaussian random fields are fit along new orthogonal axes to accommodate spatial heterogeneity and serial correlation. The technique is demonstrated on a regional groundwater model of metropolitan Mexico City, where it successfully emulates spatial and temporal dynamics of land subsidence and aquifer level fluctuation resulting from two management scenarios. In doing so, we introduce methodological advances to emulation techniques, which facilitate the use of models with high-dimensional outputs in computationally expensive planning and optimization applications.

The influence of bicycle lean on maximal power output during sprint cycling

Competitive cyclists typically sprint out of the saddle and alternately lean their bikes from side to side, away from the downstroke pedal. Yet, there is no direct evidence as to whether leaning the bicycle or conversely, attempting to minimize lean, affects maximal power output during sprint cycling. Here, we modified a cycling ergometer so that it can lean from side to side but can also be locked to prevent lean. This modified ergometer made it possible to compare maximal 1-s crank power during non-seated, sprint cycling under three different conditions: locked (no lean), ad libitum lean, and minimal lean. We found that leaning the ergometer ad libitum did not enhance maximal 1-s crank power compared to the locked condition. However, trying to minimize ergometer lean decreased maximal 1-s crank power by an average of 5% compared to leaning ad libitum. IMU-derived measures of ergometer lean provided evidence that subjects leaned the ergometer away from the downstroke pedal during the ad-lib condition, as in overground cycling. This finding suggests that our ergometer provides a suitable emulation of bicycle-lean dynamics. Overall, we find that leaning a cycle ergometer ad libitum does not enhance maximal power output, but conversely, trying to minimize lean impairs maximal power output.

Flexible Time-Stepping Dynamic Emulation of AC/DC Grid for Faster-Than-SCADA Applications

Dynamic simulation of the integrated AC/DC grids plays a crucial role in the energy control center. In this work, a faster than supervisory control and data acquisition (FT-SCADA) emulation based on flexible time-stepping (FTS) algorithm is proposed for the energy control center to predict and mitigate the impacts after serious disturbances using field-programmable gate arrays (FPGAs). To gain a high acceleration over SCADA/real-time, the FTS-based dynamic emulation is applied to the AC grid, which is the IEEE 118-bus system where a 9 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> -order synchronous machine model is adopted. Meanwhile, the electromagnetic transient (EMT) emulation revealing the exact performance of the DC grid provides an insight into the impact on its AC counterpart. A power-voltage interface is inserted between the AC and DC grids since distinct emulation strategies are utilized, and the EMT-dynamic co-emulation is able to run concurrently on FPGA boards due to their massive parallelism. Three case studies are emulated to demonstrate the efficacy of the proposed algorithm, and a minimum of 101 times faster-than-SCADA/real-time can be achieved. Hence, following the occurrence of a disturbance, the FT-SCADA/RT emulator will generate an optimal solution in advance to maintain the stability of the hybrid AC/DC grid. The results of the FTS-based FT-SCADA/RT emulation are validated by the off-line transient stability simulation tool TSAT of the DSATools suite.

Design of rotor profile of a new roots vacuum pump

This paper presents a new rotor profile of roots vacuum pump composed of arc, involute and cycloid.It’ s whole tooth profile which is described exactly by combining the meshing principle of gears with parameter equation. The performance of meshing of two rotors is improved without profile interference and packet gas, which are the problems of conventional involute profiles. The work provides new theoretical basis for digital design of new rotor profile, its meshing analysis, dynamic emulation, kinematics and dynamics analysis.

Surrogate-based modeling for emulation of supercritical injector flow and combustion

High-fidelity simulations play an increasingly important role in understanding fundamental turbulence-chemistry interactions and combustion dynamics in practical propulsion and power-generation systems. These simulations are, however, too computationally expensive and unwieldy for the purposes of design and optimization, given the large group of design parameters and wide design space. In this paper, we present an efficient surrogate-based modeling strategy to emulate spatiotemporal flows and combustion at supercritical pressures with accuracy similar to that of large-eddy simulations (LES). A common kernel-smoothed proper orthogonal decomposition (CKSPOD)-based surrogate model is developed, incorporating computer experiments, projection-based model reduction, kriging, and uncertainty quantification. The surrogate model (emulator) is carefully trained using a database drawn from a set of LES-based simulations that are conducted at designated sampling points in a given design space. A common Gram matrix is built using a Hadamard product to transform reduced spatial basis functions to remedy phase deviations among different design settings. Kriging is then used to obtain temporal spatial functions and associated coefficients for flowfield reconstruction at a new design setting.The framework is examined with two case studies: an emulation of flow dynamics in a simplex swirl injector, and an emulation of mixing and combustion in a gas-centered liquid-swirl coaxial injector. The surrogate model not only faithfully captures the salient features of its LES counterpart, but also shortens the computation time dramatically, by up to five orders of magnitude. The developed surrogate model can be applied to a broad range of engineering systems and will provide support for future engineering innovation and design.

Threshold switching synaptic device with tactile memory function

Abstract The development of efficient construction of neuromorphic sensory systems for the next generation of neurorobots and electronic prostheses is highly expected but also challenging. Here, we present a simple two-terminal piezo-resistive threshold switching (PRTS) synaptic device for the emulation of synaptic dynamics that simultaneously exhibits volatile resistive switching (RS) and pressure-tunable synaptic behaviors. The designed synaptic device, which mimics the functions of the human tactile system, simplifies the complex circuit design needed to recognize an external pressure image in the pressure memory network and improves the efficiency and recognition rate of subsequent processing tasks compared with those of traditional pressure sensors. This study opens up the possibility to simplify the neurological circuits of tactile perception systems, with promising applications in electronic skin and prosthetics.

ERRANT: Realistic emulation of radio access networks

Mobile devices drastically changed how people use the Internet. We use smartphones to access a heterogeneous catalog of web services such as news, social networks, audio/video streaming. Differently from wired connections, mobile networks do not offer the same kind of performance stability yet. Thus, service providers have to handle different network scenarios, e.g., 3G or 4G, while promising good end-users’ quality of experience (QoE). To ensure that QoE is adequate, it is necessary to thoroughly test applications with a wide range of possible network conditions. For this, network emulation is of vital importance as it allows a tester to run experiments with a wide range of network conditions. However, when it comes to mobile networks, the variety of technical characteristics, coupled with the opaque network configurations, makes realistic emulation a challenging task. Most of the freely available emulation tools rely on a simple emulation, offering limited variability performances for each network condition.In this paper, we propose ERRANT, EmulatoR of Radio Access NeTworks, an open-source tool that emulates mobile networks with a high level of realism, following a data-driven approach. We use a large-scale dataset composed of 100 k speed test measurements collected from 4 network operators in 2 countries. We create 32 different network profiles based on different countries, operators, radio access technologies, and signal qualities. For each profile, we obtain both typical behavior and variability for latency, download and upload bandwidth. We use the profiles to create models by means of the Kernel Density Estimation. Then, ERRANT employs the tc-netem Linux tool and the models for emulation. In this way, ERRANT offers realistic network emulation, in which both typical behavior and network variability are accurately recreated.We validate ERRANT models with an independent dataset of HTTP downloads performed on the same mobile networks as of the profiles. Results show the effectiveness of ERRANT in the emulation of real mobile networks in terms of average behavior and obtained variability. We also show the limitations of a simple emulation, and of other freeware approaches versus ERRANT. Finally, we show two practical use cases to demonstrate the benefits of a dynamic emulation in understanding the performance of web browsing and video streaming. To run new measurement campaigns and create new models, we provide guidelines along with the required open-source code.

Detection of dynamic location primary user emulation on mobile cognitive radio networks using USRP

The study and the detection of possible network attacks are essential for wireless networks, in particular for mobile cognitive radio networks due to its characteristics such as the dynamic spectrum allocation and constant frequency hopping. The primary user emulation attack is one of the most significant attacks in cognitive radio, because it hazards the complete cognitive cycle. The techniques used for the detection of primary user emulation found in the literature are based on a fixed attacker location. However, in a mobile environment, the attacker usually has dynamic locations and this compromises the current applied security techniques and generates inefficient attack detection. Therefore, our work proposes a novel technique using cross-layer design for the detection of primary user emulation with mobility. This attack detection technique was tested with experiments using software-defined radio equipment and mobile phones at indoor scenarios with dynamic locations and with a mobile phone base station built up also with software-defined radio. The obtained results show that the combination of the three utilized techniques, energy detection, motion estimation, and application information analysis, are able to optimize the detection with around 100% of effectiveness for the primary user emulation attack with dynamic location. The proposed technique shows that the energy detection time is around 100 ms and for the processing time of the information analysis in the mobile phone is about 30 s. This result shows a practical and effective approach to detect primary emulation attacks. The proposed technique, to the best of the authors’ knowledge, has not been presented before in the literature with experiments neither with mobility conditions of the attacker as presented in our proposed work.

Consensus-based distribution of power packets and decentralized control for routing

A power packet distribution network is expected to be one of the advanced power distribution systems, providing high controllability in both energy management and failure management. Regarding network operations, the power packet transmission is governed by switching operation within each of the routers. Here, the power distribution through power packets exhibits consensus-like dynamical behaviors. These features lead to the question of a consensus dynamics on switching topology and routing controls for appropriate power flows. Our approach to the above subjects is based on the dynamical modeling and the emulation of dynamics through the decentralized control of routers. The simulations on a ring-structure network, of the power distribution, reveal that the dynamical solution of the unbiased distribution is feasible via the decentralized control, while in the biased case, the result shows two behavioral fragments, which is quite different from the dynamical solution. In this discussion, we propose a decentralized algorithm that contains only fundamental functions for the packet transmission and is able to be redesigned or extended for further applications.

Symmetry Breaking Bias and the Dynamics of a Quantum Phase Transition.

The Kibble-Zurek mechanism predicts the formation of topological defects and other excitations that quantify how much a quantum system driven across a quantum critical point fails to be adiabatic. We point out that, thanks to the divergent linear susceptibility at the critical point, even a tiny symmetry breaking bias can restore the adiabaticity. The minimal required bias scales like τ_{Q}^{-βδ/(1+zν)}, where β, δ, z, ν are the critical exponents and τ_{Q} is a quench time. We test this prediction by DMRG simulations of the quantum Ising chain. It is directly applicable to the recent emulation of quantum phase transition dynamics in the Ising chain with ultracold Rydberg atoms.

Emulating dynamic non-linear simulators using Gaussian processes

The dynamic emulation of non-linear deterministic computer codes where the output is a time series, possibly multivariate, is examined. Such computer models simulate the evolution of some real-world phenomenon over time, for example models of the climate or the functioning of the human brain. The models we are interested in are highly non-linear and exhibit tipping points, bifurcations and chaotic behaviour. However, each simulation run could be too time-consuming to perform analyses that require many runs, including quantifying the variation in model output with respect to changes in the inputs. Therefore, Gaussian process emulators are used to approximate the output of the code. To do this, the flow map of the system under study is emulated over a short time period. Then, it is used in an iterative way to predict the whole time series. A number of ways are proposed to take into account the uncertainty of inputs to the emulators, after fixed initial conditions, and the correlation between them through the time series. The methodology is illustrated with two examples: the highly non-linear dynamical systems described by the Lorenz and van der Pol equations. In both cases, the predictive performance is relatively high and the measure of uncertainty provided by the method reflects the extent of predictability in each system.

Short-term effects of selection, gap, patch and clear cutting on the beetle fauna in boreal spruce-dominated forests

Even-aged management based on clear cutting has simplified the structure of boreal forests, which has adversely affected ecosystem services, including biodiversity. A potential way of combining economic, ecological and social sustainability in forest management is emulation of natural disturbance dynamics. However, ecological effects of alternative cutting methods have been little studied in northern Europe. We studied the responses of beetles (Coleoptera) to four cutting treatments – selection, gap, patch and clear cutting – compared to uncut control stands. We collected beetles one year before, and one and three years after cutting using flight-intercept window traps to assess the effects of treatment, proportion of removed trees, size of harvested gaps, and the volume of dead wood on the species richness and composition of beetles. The total number of species increased sharply in the first year after cutting in all the four treatments. Three years after cutting, the difference in species richness between controls and treatments had decreased. Both species richness and community structure formed a continuum according to treatment intensity. Selection cuts were most similar to the controls, while clear cuts differed from all the other treatments. Irrespective of the treatment, species richness of saproxylic beetles increased with the volume of dead wood, while the number of non-saproxylic species increased with gap size. Contrary to expectation, the proportion of species that were found both before and after cutting did not differ significantly between the treatments. According to an indicator species analysis, two species were associated with controls, 11 with a combination of controls and selection, gap or patch cuts, 17 with patch and clear cuts, and 38 with clear cuts. Our findings suggest that increasing harvesting intensity rapidly increases the total species richness of beetles in the studied spruce forests, but that community recovery may be faster in the less intensive treatments. In the short term, most beetle species inhabiting mature managed forests seem to tolerate cutting disturbances creating open conditions. Clear cuts hosted a large number of species, including species of conservation concern, which were missing from other cutting treatments. Even a moderate increase in the level of dead wood in managed forests is likely to enhance saproxylic diversity. If the aim of management is to preserve mature-forest beetle communities, selection or gap cutting should be used instead of clear cutting.

Analysis of Passenger Car Turbocharged Diesel Engines Performance When Tested at Altitude and of the Altitude Simulator Device Used

According to current worldwide trends for homologation vehicles in real driving conditions is forced to test the engines in altitude and in highly dynamic driving cycles in order to approach nowadays and next future emissions standard. Up to now, there were two main options to perform this type of tests: round-robin tests of the whole vehicle or hypobaric chambers, both with high costs and low repeatability. In this paper a new device is described, which can emulate ambient conditions at whatever altitude between sea level and 5000 m high. Even it can be used to emulate ambient conditions at sea level when test bench is placed up to 2000 m high. The main advantages of the altitude simulation equipment are as follows: dynamic emulation of all the psychrometric variables affecting the vehicles during round-robin tests; lower space usage and low-energy consumption. The altitude simulator (AS) has been validated comparing with results from a hypobaric chamber at different altitudes. Previously a research about the dispersion in the measurements of both testing devices has been done for assessing the results of the comparison experiment. Final conclusion resulted in the same operating performance and emissions of the studied engine with both types of testing equipment for altitude simulation.

Climate policy implications of nonlinear decline of Arctic land permafrost and other cryosphere elements

Arctic feedbacks accelerate climate change through carbon releases from thawing permafrost and higher solar absorption from reductions in the surface albedo, following loss of sea ice and land snow. Here, we include dynamic emulators of complex physical models in the integrated assessment model PAGE-ICE to explore nonlinear transitions in the Arctic feedbacks and their subsequent impacts on the global climate and economy under the Paris Agreement scenarios. The permafrost feedback is increasingly positive in warmer climates, while the albedo feedback weakens as the ice and snow melt. Combined, these two factors lead to significant increases in the mean discounted economic effect of climate change: +4.0% ($24.8 trillion) under the 1.5 °C scenario, +5.5% ($33.8 trillion) under the 2 °C scenario, and +4.8% ($66.9 trillion) under mitigation levels consistent with the current national pledges. Considering the nonlinear Arctic feedbacks makes the 1.5 °C target marginally more economically attractive than the 2 °C target, although both are statistically equivalent.

Demonstrating Advantages of Neuromorphic Computation: A Pilot Study.

Neuromorphic devices represent an attempt to mimic aspects of the brain's architecture and dynamics with the aim of replicating its hallmark functional capabilities in terms of computational power, robust learning and energy efficiency. We employ a single-chip prototype of the BrainScaleS 2 neuromorphic system to implement a proof-of-concept demonstration of reward-modulated spike-timing-dependent plasticity in a spiking network that learns to play a simplified version of the Pong video game by smooth pursuit. This system combines an electronic mixed-signal substrate for emulating neuron and synapse dynamics with an embedded digital processor for on-chip learning, which in this work also serves to simulate the virtual environment and learning agent. The analog emulation of neuronal membrane dynamics enables a 1000-fold acceleration with respect to biological real-time, with the entire chip operating on a power budget of 57 mW. Compared to an equivalent simulation using state-of-the-art software, the on-chip emulation is at least one order of magnitude faster and three orders of magnitude more energy-efficient. We demonstrate how on-chip learning can mitigate the effects of fixed-pattern noise, which is unavoidable in analog substrates, while making use of temporal variability for action exploration. Learning compensates imperfections of the physical substrate, as manifested in neuronal parameter variability, by adapting synaptic weights to match respective excitability of individual neurons.

Dynamic Emulation of a PEM Electrolyzer by Time Constant Based Exponential Model

The main objective of this paper is to develop a dynamic emulator of a proton exchange membrane (PEM) electrolyzer (EL) through an equivalent electrical model. Experimental investigations have highlighted the capacitive effect of EL when subjecting to dynamic current profiles, which so far has not been reported in the literature. Thanks to a thorough experimental study, the electrical domain of a PEM EL composed of 3 cells has been modeled under dynamic operating conditions. The dynamic emulator is based on an equivalent electrical scheme that takes into consideration the dynamic behavior of the EL in cases of sudden variation in the supply current. The model parameters were identified for a suitable current interval to consider them as constant and then tested with experimental data. The obtained results through the developed dynamic emulator have demonstrated its ability to accurately replicate the dynamic behavior of a PEM EL.

Experimental emulation of quantum non-Markovian dynamics and coherence protection in the presence of information backflow

We experimentally emulate, in a controlled fashion, the non-Markovian dynamics of a pure dephasing spin-boson model at zero temperature. Specifically, we use a randomized set of external radio-frequency fields to engineer a desired noise power-spectrum to effectively realize a non-Markovian environment for a single NMR qubit. The information backflow, characteristic to the non-Markovianity, is captured in the nonmonotonicity of the decoherence function and von Neumann entropy of the system. Using such emulated non-Markovian environments, we experimentally study the efficiency of the Carr-Purcell-Meiboom-Gill dynamical decoupling (DD) sequence to inhibit the loss of coherence. Using the filter function formalism, we design optimized DD sequences that maximize coherence protection for non-Markovian environments and study their efficiencies experimentally. Finally, we discuss DD-assisted tuning of the effective non-Markovianity.

Dynamic temperature model for proton exchange membrane fuel cell using online variations in load current and ambient temperature

ABSTRACTThis paper presents a dynamic temperature model for a proton exchange membrane fuel cell (PEMFC) system. The proposed model overcomes the complexity of conventional models using first-order expressions consisting of load current and ambient temperature. The proposed model also incorporates a PEMFC cooling system, which depends upon the temperature difference between events. A dynamic algorithm is developed to detect load changing events and calculate instantaneous PEMFC temperature variations. The parameters of the model are extracted by employing the lightning search algorithm (LSA). The temperature characteristics of the NEXA 1.2 kW PEMFC system are experimentally studied to validate model performance. The results show that the proposed model output and the temperature data obtained from experiments for linear and abrupt changes in PEMFC load current are in agreement. The root-mean-square error between the model output and experimental results is less than 0.9. Moreover, the proposed model outperforms the conventional models and provides advantages such as simplicity and adaptability for low and high sampling data rates of input variables, namely, load current and ambient temperature. The model is not only helpful for simulations but also suitable for dynamic real-time controllers and emulators.

Transport in Floquet-Bloch Bands.

We report Floquet band engineering of long-range transport and direct imaging of Floquet-Bloch bands in an amplitude-modulated optical lattice. In one variety of Floquet-Bloch bands we observe tunable rapid long-range high-fidelity transport of a Bose condensate across thousands of lattice sites. Quenching into an opposite-parity Floquet-hybridized band allows Wannier-Stark localization to be controllably turned on and off using modulation. A central result of this work is the use of transport dynamics to demonstrate direct imaging of a Floquet-Bloch band structure. These results demonstrate that transport in dynamical Floquet-Bloch bands can be mapped to transport in quasistatic effective bands, opening a path to cold atom quantum emulation of ultrafast multiband electronic dynamics.

Simulation methods for stochastic storage problems: a statistical learning perspective

We consider solution of stochastic storage problems through regression Monte Carlo methods. Taking a statistical learning perspective, we develop the dynamic emulation algorithm (DEA) that unifies the different existing approaches in a single modular template. We then investigate the two central aspects of regression architecture and experimental design that constitute DEA. For the regression piece, we discuss various non-parametric approaches, in particular introducing the use of Gaussian process regression in the context of stochastic storage. For simulation design, we compare the performance of traditional design (grid discretization), against space-filling, and several adaptive alternatives. The overall DEA template is illustrated with multiple examples drawing from natural gas storage valuation and optimal control of back-up generator in a microgrid.