Dynamic Emulation Research Articles

Design and dynamic emulation of hybrid solar-wind-wave energy converter (SWWEC) for efficient power generation.

As research into wave energy converters progresses and new developers enter the field, there arises a growing requirement for a standardized modelling approach. This article presents a novel design and dynamic emulation for a hybrid solar-wind-wave energy converter (SWWEC) which is the combination of three very well-known renewable energies: solar, wind and wave energy. Photovoltaic (PV) panels and vertical axis wind turbine (VAWT) are installed on top of the floating WEC that harness the energies from the sun and wind respectively. The SWWEC is designed with a point absorber capture system. An electrical motor is used to dynamically emulate the performance of the SWWEC under real world conditions to drive the DC generator. The present paper shows the importance and necessity of the required control schemes for the proper control of generator side converters which is present in the offshore marine substation and the most required grid connected onshore converters. The better switching signal generation for the converter control and generated harmonics elimination techniques are also presented in the paper. Outcomes of the present study are discussed and verified.

Verification and Validation of Advanced Control Systems for a Spinal Joint Wear Simulator.

Wear simulation aims to assess wear rates and their dependence on factors like load, kinematics, temperature, and implant orientation. Despite its significance, there is a notable gap in research concerning advancements in simulator control systems and the testing of clinically relevant waveforms. This study addresses this gap by focusing on enhancing the conventional proportional-integral-derivative (PID) controller used in joint simulators through the development of a fuzzy logic-based controller. Leveraging a single-input multiple-output (SIMO) fuzzy logic control system, this study aimed to improve displacement control, augmenting the traditional proportional-integral (PI) tuning approach. The implementation and evaluation of a novel Fuzzy-PI control algorithm were conducted on the Leeds spine wear simulator. This study also included the testing of dailyliving (DL) profiles, particularly from the hip joint, to broaden the scope of simulation scenarios. While both the conventional PI controller and the Fuzzy-PI controller met ISO tolerance criteria for the spine flexion-extension (FE) profile at 1 Hz, the Fuzzy-PI controller demonstrated superior performance at higher frequencies and with DL profiles due to its real-time adaptive tuning capability. The Fuzzy-PI controller represents a significant advancement in joint wear simulation, offering improved control functionalities and more accurate emulation of real-world physiological dynamics.

Susceptible bacteria can survive antibiotic treatment in the mammalian gastrointestinal tract without evolving resistance

Antibiotic resistance and evasion are incompletely understood and complicated by the fact that murine interval dosing models do not fully recapitulate antibiotic pharmacokinetics in humans. To better understand how gastrointestinal bacteria respond to antibiotics, we colonized germ-free mice with a pan-susceptible genetically barcoded Escherichia coli clinical isolate and administered the antibiotic cefepime via programmable subcutaneous pumps, allowing closer emulation of human parenteral antibiotic dynamics. E.coli was only recovered from intestinal tissue, where cefepime concentrations were still inhibitory. Strikingly, "some" E.coli isolates were not cefepime resistant but acquired mutations in genes involved in polysaccharide capsular synthesis increasing their invasion and survival within human intestinal cells. Deleting wbaP involved in capsular polysaccharide synthesis mimicked this phenotype, allowing increased invasion of colonocytes where cefepime concentrations were reduced. Additionally, "some" mutant strains exhibited a persister phenotype upon further cefepime exposure. This work uncovers a mechanism allowing "select" gastrointestinal bacteria to evade antibiotic treatment.

Large scale integrated IGZO crossbar memristor array based artificial neural architecture for scalable in-memory computing

Neuromorphic systems based on memristor arrays have not only addressed the von Neumann bottleneck issue but have also enabled the development of computing applications with high accuracy. In this study, an artificial neural architecture based on a 10 × 10 IGZO memristor array is presented to emulate synaptic dynamics for performing artificial intelligence (AI) computing with high recognition accuracy rate. The large area 10 × 10 IGZO memristor array was fabricated using the photolithography method, resulting in stable and reliable memory operations. The bipolar switching at −2 V–2.5 V, endurance of 500 cycles, retention of >104 s, and uniform Vset/Vreset operation of 100 devices were achieved by modulating the oxygen vacancy in the IGZO film. The emulation of electric synaptic dynamics was also observed, including potentiation-depression, multilevel long-term memory (LTM), and multilevel short-term memory (STM), revealing highly linear and stable synaptic functions at different modulated pulse settings. Additionally, electrical modeling (HSPICE) with vector-matrix measurements and simulation of various artificial neural network (ANN) algorithms, such as convolution neural network (CNN) and spiking neural network (SNN), were performed, demonstrating a linear increase in current accumulation with high recognition rates of 99.33 % and 86.46 %, respectively. This work provides a novel approach for overcoming the von Neumann bottleneck issue and emulating synaptic dynamics in various neural networks with high accuracy.

An artificial neuromuscular junction for enhanced reflexes and oculomotor dynamics based on a ferroelectric CuInP2S6/GaN HEMT.

A neuromuscular junction (NMJ) is a particularized synapse that activates muscle fibers for macro-motions, requiring more energy than computation. Emulating the NMJ is thus challenging owing to the need for both synaptic plasticity and high driving power to trigger motions. Here, we present an artificial NMJ using CuInP2S6 (CIPS) as a gate dielectric integrated with an AlGaN/GaN-based high-electron mobility transistor (HEMT). The ferroelectricity of the CIPS is coupled with the two-dimensional electron gas channel in the HEMT, providing a wide programmable current range of 6 picoampere per millimeter to 5 milliampere per millimeter. The large output current window of the CIPS/GaN ferroelectric HEMT (FeHEMT) allows for amplifier-less actuation, emulating the biological NMJ functions of actuation and synaptic plasticity. We also demonstrate the emulation of biological oculomotor dynamics, including in situ object tracking and enhanced stimulus responses, using the fabricated artificial NMJ. We believe that the CIPS/GaN FeHEMT offers a promising pathway for bioinspired robotics and neuromorphic vision.

A 22-pJ/spike 73-Mspikes/s 130k-compartment neural array transceiver with conductance-based synaptic and membrane dynamics.

Neuromorphic cognitive computing offers a bio-inspired means to approach the natural intelligence of biological neural systems in silicon integrated circuits. Typically, such circuits either reproduce biophysical neuronal dynamics in great detail as tools for computational neuroscience, or abstract away the biology by simplifying the functional forms of neural computation in large-scale systems for machine intelligence with high integration density and energy efficiency. Here we report a hybrid which offers biophysical realism in the emulation of multi-compartmental neuronal network dynamics at very large scale with high implementation efficiency, and yet with high flexibility in configuring the functional form and the network topology. The integrate-and-fire array transceiver (IFAT) chip emulates the continuous-time analog membrane dynamics of 65 k two-compartment neurons with conductance-based synapses. Fired action potentials are registered as address-event encoded output spikes, while the four types of synapses coupling to each neuron are activated by address-event decoded input spikes for fully reconfigurable synaptic connectivity, facilitating virtual wiring as implemented by routing address-event spikes externally through synaptic routing table. Peak conductance strength of synapse activation specified by the address-event input spans three decades of dynamic range, digitally controlled by pulse width and amplitude modulation (PWAM) of the drive voltage activating the log-domain linear synapse circuit. Two nested levels of micro-pipelining in the IFAT architecture improve both throughput and efficiency of synaptic input. This two-tier micro-pipelining results in a measured sustained peak throughput of 73 Mspikes/s and overall chip-level energy efficiency of 22 pJ/spike. Non-uniformity in digitally encoded synapse strength due to analog mismatch is mitigated through single-point digital offset calibration. Combined with the flexibly layered and recurrent synaptic connectivity provided by hierarchical address-event routing of registered spike events through external memory, the IFAT lends itself to efficient large-scale emulation of general biophysical spiking neural networks, as well as rate-based mapping of rectified linear unit (ReLU) neural activations.

Emulating Non-Hermitian Dynamics in a Finite Non-Dissipative Quantum System.

We discuss the emulation of non-Hermitian dynamics during a given time window using a low-dimensional quantum system coupled to a finite set of equidistant discrete states acting as an effective continuum. We first emulate the decay of an unstable state and map the quasi-continuum parameters, enabling the precise approximation of non-Hermitian dynamics. The limitations of this model, including in particular short- and long-time deviations, are extensively discussed. We then consider a driven two-level system and establish criteria for non-Hermitian dynamics emulation with a finite quasi-continuum. We quantitatively analyze the signatures of the finiteness of the effective continuum, addressing the possible emergence of non-Markovian behavior during the time interval considered. Finally, we investigate the emulation of dissipative dynamics using a finite quasi-continuum with a tailored density of states. We show through the example of a two-level system that such a continuum can reproduce non-Hermitian dynamics more efficiently than the usual equidistant quasi-continuum model.

Inertia compensation for wind turbine emulator based on anticipation deviation suppression

The wind turbine emulator (WTE) is an important tool for dynamic emulation experiments of wind turbines. This paper studies the problem of high-frequency fluctuation of the rotating speed of the dynamic emulation platform when emulating the mechanical dynamics of large inertia wind turbine under fluctuating wind speed. Through the time domain analysis of the discrete mathematical model of the WTE, it is found that the torque compensation loop of the transmission system will incur the acceleration deviation because of the influence of the time delay, and this anticipatory deviation increases with the increase of the compensation multiple. As a result, the acceleration of the transmission chain oscillates, resulting in high-frequency fluctuations in the rotational speed. To this end, this paper proposes an inertia compensation scheme for the WTE based on the anticipation deviation suppression. Based on the principle of zero-pole configuration, a feedforward suppressor is designed to offset the zero-pole of the closed-loop transfer function of the transmission chain system, in order to eliminate the high-frequency fluctuation of the rotating speed caused by the torque deviation. The effectiveness of the proposed strategy is validated using the maximum power point tracking control experiment.

Quantum emulation of the transient dynamics in the multistate Landau-Zener model

Quantum simulation is one of the most promising near term applications of quantum computing. Especially, systems with a large Hilbert space are hard to solve for classical computers and thus ideal targets for a simulation with quantum hardware. In this work, we study experimentally the transient dynamics in the multistate Landau-Zener model as a function of the Landau-Zener velocity. The underlying Hamiltonian is emulated by superconducting quantum circuit, where a tunable transmon qubit is coupled to a bosonic mode ensemble comprising four lumped element microwave resonators. We investigate the model for different initial states: Due to our circuit design, we are not limited to merely exciting the qubit, but can also pump the harmonic modes via a dedicated drive line. Here, the nature of the transient dynamics depends on the average photon number in the excited resonator. The greater effective coupling strength between qubit and higher Fock states results in a quasi-adiabatic transition, where coherent quantum oscillations are suppressed without the introduction of additional loss channels. Our experiments pave the way for more complex simulations with qubits coupled to an engineered bosonic mode spectrum.

Inorganic Perovskite Quantum Dot-Mediated Photonic Multimodal Synapse

Artificial synapse is the basic unit of a neuromorphic computing system. However, there is a need to explore suitable synaptic devices for the emulation of synaptic dynamics. This study demonstrates a photonic multimodal synaptic device by implementing a perovskite quantum dot charge-trapping layer in the organic poly(3-hexylthiophene-2,5-diyl) (P3HT) channel transistor. The proposed device presents favorable band alignment that facilitates spatial separation of photogenerated charge carriers. The band alignment serves as the basis of optically induced charge trapping, which enables nonvolatile memory characteristics in the device. Furthermore, high photoresponse and excellent synaptic characteristics, such as short-term plasticity, long-term plasticity, excitatory postsynaptic current, and paired-pulse facilitation, are obtained through gate voltage regulation. Photosynaptic characteristics obtained from the device showed a multiwavelength response and a large dynamic range (∼103) that is suitable for realizing a highly accurate artificial neural network. Moreover, the device showed nearly linear synaptic weight update characteristics with incremental depression electric gate pulse. The simulation based on the experimental data showed excellent pattern recognition accuracy (∼85%) after 120 epochs. The results of this study demonstrate the feasibility of the device as an optical synapse in the next-generation neuromorphic system.

Surface Modification of a Titanium Carbide MXene Memristor to Enhance Memory Window and Low‐Power Operation

AbstractWith the demand for low‐power‐operating artificial intelligence systems, bio‐inspired memristor devices exhibit potential in terms of high‐density memory functions and the emulation of the synaptic dynamics of the human brain. The 2D material MXene attracts considerable interest for use in resistive‐switching memory and artificial synapse devices owing to its excellent physicochemical properties in memristor devices. However, few memristive and synaptic MXene devices that display increased switching performances are reported, with no significant results. Herein, the conductivity of MXene (Ti3C2Tx) is engineered via etching and oxidation to enhance the switching performance of the device. The exceptional properties of partially oxidized MXene memristors include large memory windows and low threshold biases, and the complex spike‐timing‐dependent plasticity synaptic rules are also emulated. The low threshold potential distribution, reliable retention time (104 s), and distinct resistance states with a high ON–OFF ratio (>104) are the main memory‐related features of this device. The experimentally determined switching potentials of the optimized device are also uniformly distributed, according to a statistical probability‐based approach. This investigation may promote the essential material properties for use in high‐density non‐volatile memory storage and artificial synapse systems in the field of innovative nanoelectronic devices.

Faster-Than-Real-Time Hardware Emulation of Transients and Dynamics of a Grid of Microgrids

Enhanced environmental standards are leading to an increasing proportion of microgrids (MGs) being integrated with renewable energy resources in modern power systems, which brings new challenges to simulate such a complex system. In this work, comprehensive modeling of a grid of microgrids for faster-than-real-time (FTRT) emulation is proposed, which can be utilized in the energy control center for contingencies analysis and dynamic security assessment. Electromagnetic transient (EMT) modeling is applied to the microgrid in order to reflect the detailed device processes of the converter and renewable energy sources, while the AC grid utilizes the transient stability modeling to reduce the computational burden and obtain a high acceleration value over real-time execution. Consequently, a dynamic power injection interface is proposed for the coexistence of the two simulation types. The reconfigurability and parallelism of the field-programmable gate arrays (FPGAs) enable the whole system to be executed in FTRT mode with 51 times acceleration over real-time. Meanwhile, three case studies are emulated and the results are validated by the off-line simulation tool Matlab/Simulink®.

Emulation of Mechanical Loads in Electric Drives – An Overview of Methods

Abstract The aim of the presented paper is to introduce the reader to hardware-in-the-loop (HIL) simulation in electric drives, the motivation for its usage and its benefits. Basic information about HIL simulation in general and the connection to electric drives is followed by an explanation of the main principle of the dynamic emulation of mechanical loads for electric drives, which is a special case of HIL simulation. Next, the description of the three main methods of load emulation and their mathematical background are presented in the order of historical appearance: the emulation with compensator and tracking controller, the emulation based on non-linear control and the emulation with feed-forward compensation with the use of inverse dynamics.

Faster-Than-Real-Time Hardware Emulation of Extensive Contingencies for Dynamic Security Analysis of Large-Scale Integrated AC/DC Grid

The rapid expansion of modern power systems has brought a tremendous computational challenge to dynamic security analysis (DSA) tools which consequently need to process extensive contingencies. In this work, hardware emulation is investigated to accelerate the DSA solution of a large-scale AC/DC system deployed on the field-programmable gate arrays (FPGAs) faster-than-real-time (FTRT) execution. Electromagnetic transient (EMT) modeling of the DC grid is conducted since the fast converter dynamics require a small time-step for accuracy; in contrast, the transient stability (TS) simulation is applicable to the AC grid which tolerates a much larger step size. To coordinate the 2 different types of simulation, an interface based on dynamic voltage injection is proposed to integrate the AC and DC grids, in addition to maintaining a low hardware latency. An emulation platform consisting of multiple FPGA boards is established so that with a proper allocation it has a sufficient capacity to accommodate the system under study which has 6 ACTIVSg 500-bus systems interconnected by a 6-terminal DC grid. The efficacy of the proposed FTRT hardware emulation platform is demonstrated by 2 case studies with more than 5500 contingencies analyzed in total, where an FTRT ratio of more than 208 is achieved for the hybrid AC/DC grid, while it is over 277 times for a single 500-bus system. Furthermore, the FTRT dynamic emulation results, including the security indices, are validated by the simulation tool DSATools/TSAT.

SVD perspectives for augmenting DeepONet flexibility and interpretability

Deep operator networks (DeepONets) are powerful and flexible architectures that are attracting attention in multiple fields due to their utility for fast and accurate emulation of complex dynamics. As their remarkable generalization capabilities are primarily enabled by their projection-based attribute, in this paper, we investigate connections with low-rank techniques derived from the singular value decomposition (SVD). We demonstrate that some of the concepts behind proper orthogonal decomposition (POD)-neural networks can improve DeepONet’s design and training phases. These ideas lead us to a methodology extension that we name SVD-DeepONet. Moreover, through multiple SVD analyses of scenario- and time-aggregated snapshots matrices, we find that DeepONet inherits from its projection-based attribute strong inefficiencies in representing dynamics characterized by symmetries. Inspired by the work on shifted-POD, we develop flexDeepONet, an architecture enhancement that relies on a pre-transformation network for generating a moving reference frame and isolating the rigid components of the dynamics. In this way, the physics can be represented on a latent space free from rotations, translations, and stretches, and an accurate projection can be performed to a low-dimensional basis. In addition to improving DeepONet’s flexibility and interpretability, the proposed perspectives increase its generalization capabilities and computational efficiencies. For instance, we show flexDeepONet can accurately surrogate the dynamics of 19 thermodynamic variables in a combustion chemistry application by relying on 95% fewer trainable parameters than that of the ‘vanilla’ architecture. As stressed in the paper, we argue that DeepONet and SVD-based methods can reciprocally benefit from each other. In particular, the flexibility of the former in leveraging multiple data sources and multifidelity knowledge in the form of both unstructured data and physics-informed constraints has the potential to greatly extend the applicability of methodologies such as POD and principal component analysis (PCA).

Faster-Than-Real-Time Hardware Emulation of Transients and Dynamics of a Grid of Microgrids

Faster-Than-Real-Time Hardware Emulation of Transients and Dynamics of a Grid of Microgrids

Digital Twin Development of a Dynamic Hardware Emulator

The increasing deployment of new technologies to contribute to the decarbonization of power systems is imposing new challenges in terms of system dynamics and stability. To deal with different operating and control issues in this sense, and support actual needs, advanced tools and solutions are required. Therefore, this paper presents a digital twin of a dynamic hardware emulator that can be used for controller hardware in the loop (CHIL) testing and is based on a small-scale laboratory system. To build the simulation model, the parameters of involved synchronous machines, excitation systems, prime movers and transmission lines have been identified and then compared to laboratory measurements to assess the accuracy of the digital twin. Static and dynamic accuracy have been investigated and an overall good accuracy can be shown with the help of quantification of errors. Furthermore, a case study is presented where the digital twin was used to design a controller to damp inter-area oscillations with the help of wide area measurements. This controller was then implemented and tested within the dynamic hardware emulator in the laboratory.

High-Bandwidth Mechanical Hardware-In-The-Loop Emulation of Structural Dynamics for More Efficient NVH Development and Testing

<div class="section abstract"><div class="htmlview paragraph">Numerical simulations offer a wide range of benefits. Therefore, they are widely used in research and development. One of the biggest benefits is the possibility of automated parameter variation. This allows testing different scenarios very quickly. Nevertheless, physical experiments in the laboratory or on a test rig are still, and will remain, necessary. Physical experiments offer benefits, e.g., for very complex and/or nonlinear systems and are required for the validation of numerical models. To enhance the quality of experimental NVH investigations and to make use of the benefits of numerical simulation during experimental investigations at the same time, numerical models can be integrated into physical test rigs using the mechanical hardware-in-the-loop (mHIL) method (also referred to as real-time dynamic substructuring, hybrid testing or active control of impedance). During experimental NVH testing, the device under test (DUT) is connected to a mHIL interface which in turn actively emulates the structural dynamics of the boundary condition based on a numerical target impedance model. The goal is to have realistic contact forces instead of blocked forces at the mounting points of the DUT.</div><div class="htmlview paragraph">This paper illustrates the development and characterization of a modular mHIL interface with high bandwidth for NVH testing of a vehicle’s front axle. The physical emulation of structural dynamics based on a target impedance model is demonstrated for synthetic test cases (e.g., emulation of different stiffness settings, or multi-resonant behavior) up to 1 kHz. Furthermore, emulation of the vehicle chassis’ structural dynamics in a single spatial direction is shown for an NVH investigation on a single-axis chassis dynamometer.</div></div>

Dynamic Performance Analysis, Optimization, and Verification of DC Auto-Transformer System With Rail Potential and Stray Current Emulator

DC auto-transformer (DCAT) traction power supply system has been studied to suppress the rail potential and stray current for urban rail transit (URT), which generally takes the constant train load to simplify the analysis. However, in practice, the train is a non-linear load with frequent accelerating and braking, which leads to the rail potential and stray current changing with the different train operating conditions. Therefore, the dynamic performance analysis and optimization of DCAT system are discussed in this paper. And the rail potential and stray current dynamic emulator (RSDE) for DCAT system verification is proposed because it’s difficult to test DCAT system directly in the actual URT. Based on the configurations and operation principles of both DCAT system and RSDE, the comparisons between DCAT system and the existing system on the rail potential and stray current are discussed. Meanwhile, considering the train dynamic performance, define the dynamic performance index (DPI) to evaluate the effects of DCAT system on the rail potential and stray current mitigation, and optimize DCAT system with DPI. Finally, the correctness and effectiveness of DCAT system on the rail potential and stray current mitigation are validated by simulation results and experimental verification.

Cloud-Based User Behavior Emulation Approach for Space-Ground Integrated Networks.

Cyber-physical systems (CPSs) based on space-ground integrated networks (SGINs) enable CPSs to break through geographical restrictions in space. Therefore, providing a test platform is necessary for new technical verification and network security strategy evaluations of SGINs. User behavior emulation technology can effectively support the construction of a test platform. Given the inherent dynamic changes, diverse behaviors, and large-scale characteristics of SGIN users, we propose user behavior emulation technology based on a cloud platform. First, the dynamic emulation architecture for user behavior for SGINs is designed. Then, normal user behavior emulation strategy driven by the group user behavior model in real time is proposed, which can improve the fidelity of emulation. Moreover, rogue user behavior emulation technology is adopted, based on traffic replay, to perform the security evaluation. Specifically, virtual Internet Protocol (IP) technology and the epoll model are effectively integrated in this investigation to resolve the contradiction between large-scale emulation and computational overhead. The experimental results demonstrate that the strategy meets the requirement of a diverse and high-fidelity dynamic user behavior emulation and reaches the emulation scale of 100,000-level concurrent communication for normal users and 100,000-level concurrent attacks for rogue users.