Inherent Fluctuations Research Articles

Optimal planning of IDR-integrated electricity-heat system considering techno-economic and carbon emission issues

This study presents an optimal electricity-heat system (EHS) planning framework to promote the accommodation of wind power while considering technical, economic and environmental criteria. To this end, the integrated demand response (IDR) is introduced as a flexibility resource to complement the inherent fluctuation of renewable energy sources and modeled by using price elasticity theory. Both the timing transferring and energy substitution potentials are considered in the proposed IDR program. Incorporating the effect of IDR into the EHS planning model, a two-stage stochastic programming model can be devised, in which the optimal EHS configuration design and associated operation control techniques are found simultaneously to minimize the system's total economic and carbon-emission costs over the planning horizon. The multi-scale uncertainties arising from both long-term demand growth and operation-level variability of renewables/load demands are captured collectively by using a scenario-based method. The suggested planning approach is illustrated on a real EHS test case and the results show that it is effective in practical applications.

Spectro-Temporal Combining in Bistate WiFi Backscatter Communication With Frequency Shift

In WiFi backscatter communication, the frequency shift technique allows a backscattered signal to appear not in the frequency channel of the carrier signal but in adjacent ones, thus avoiding noisy OFDM-based carrier signals and increasing the communication range. Through testbed experiments, we observe that frequency shift is effective in mitigating the impact of the inherent fluctuation of WiFi signals, particularly in bistate backscatter communication; however, due to the weak strength of the backscattered signal, other signals from incumbent transmitters may appear in the shifted frequency channels, significantly interfering with the backscattered signal. To combat this challenge in a way that is nondisruptive to incumbent transmitters, we propose a receiver-side spectro-temporal combining scheme in which spectrum combining is performed to suppress interference appearing in one of the shifted channels, while temporal combining is performed with transmission repetitions to suppress bit errors resulting from residual interference. The scheme’s on-the-fly spectrum combining and bit-sequence temporal combining require minimal buffer memory. Through system prototyping and testbed experiments, we demonstrate that the proposed scheme outperforms the conventional and temporal-combining-only cases in terms of the bit error rate and throughput under various conditions.

A perspective on the use of ecological momentary assessment and intervention to promote stroke recovery and rehabilitation

ABSTRACT Motivated by recent advances in technologies, ecological momentary assessment (EMA), and ecological momentary intervention (EMI) have seen a rise in behavioral medicine research that in real-time, informs the context for the behavior and prompts interventions to change that behavior in the natural setting when necessary. However, EMA and EMI have yet to be fully embraced in the field of stroke rehabilitation. Our objective is to provide a theoretically based perspective for the combined and synergistic use of EMA and EMI to promote person-centered, recovery-based durable changes in functional movement behaviors of stroke survivors. Research abounds for non-stroke populations with emerging evidence for the benefits of using real-time data capture techniques (i.e. EMA) coupled with EMI to better customize the content and timing of interventions to the inherent fluctuations in state and context that encompass the target behavior. We review existing EMA and EMI literature broadly in behavioral medicine and psychological science to identify how real-time repeated sampling technology has been used in the context of stroke rehabilitation and to delineate the pros and cons of this approach in general with non-stroke populations. We propose a coupled EMA and EMI strategy be used in conjunction with existing stroke recovery and rehabilitation practices. There is tremendous potential to effectively personalize recovery-promoting interventions to achieve durable behavior change, and importantly, shift the focus of rehabilitation practice from the health-care provider and clinical environment to the individual and their lived experience in the home and community.

Multi-photon enhancement of the Schwinger pair production mechanism under strong FEL radiation

In this work we study the production of electron-positron pairs from vacuum in presence of a field resulting from the collision of two counter-propagating FEL beams that undergo Gaussian amplitude fluctuations. Our work aims to the extension of previous works based on the standing wave hypothesis, by including the inherent stochastic amplitude fluctuations of the individual FEL beams. As shown, depending on the order of the process, a large non-linear enhancement in the number of created pairs can be expected over a big intensity window in the multi-photon regime. Vacuum pair creation in view of future plans on the production of ultra-strong and high energy radiation in FEL facilities is also discussed.

Short-Term Wind Speed Forecasting Based on Ensemble Online Sequential Extreme Learning Machine and Bayesian Optimization

Short-term wind speed forecasting is crucial to the utilization of wind energy, and it has been employed widely in turbine regulation, electricity market clearing, and preload sharing. However, the wind speed has inherent fluctuation, and accurate wind speed prediction is challenging. This paper aims to propose a hybrid forecasting approach of short-term wind speed based on a novel signal processing algorithm, a wrapper-based feature selection method, the state-of-art optimization algorithm, ensemble learning, and an efficient artificial neural network. Variational mode decomposition (VMD) is employed to decompose the original wind time-series into sublayer modes. The binary bat algorithm (BBA) is used to complete the feature selection. Bayesian optimization (BO) fine-tuned online sequential extreme learning machine (OSELM) is proposed to forecast the low-frequency sublayers of VMD. Bagging-based ensemble OSELM is proposed to forecast high-frequency sublayers of VMD. Two experiments were conducted on 10 min datasets from the National Renewable Energy Laboratory (NREL). The performances of the proposed model were compared with various representative models. The experimental results indicate that the proposed model has better accuracy than the comparison models. Among the thirteen models, the proposed VMD-BBA-EnsOSELM model can obtain the best prediction accuracy, and the mean absolute percent error (MAPE) is always less than 0.09.

Computational Surveillance of Microbial Water Quality With Online Flow Cytometry

Automated flow cytometry (FCM) adapted to real-time quality surveillance provides high4 temporal-resolution data about the microbial communities in a water system. The cell concentration calculated from FCM measurements indicates sudden increases in the number of bacteria, but can fluctuate significantly due to man-made and natural dynamics; it can thus obscure the presence of microbial anomalies. Cytometric fingerprinting tools enable a detailed analysis of the aquatic microbial communities, and could distinguish between normal and abnormal community changes. However, the vast majority of current cytometric fingerprinting tools use offline statistical computations which cannot detect anomalies immediately. Here, we present a computational model, entitled Microbial Community Change Detection (MCCD), which transforms microbial community characteristics into an online process control signal (herein called outlier score) that remains close to zero if the microbial community remains stable and increases with fluctuations in the community. The model is based on fingerprints and distance-based outlier calculations. We tested it in silico and in vitro by simulating accute contaminations to real-world water systems with large inherent microbial fluctuations. We showed that the outlier score was robust against these dynamic variations, while reliably detecting intentional contaminations. This model can be used with automated FCM to quickly detect potential microbiological contamination, and this especially when the time between treatment and distribution is very short.

Invasion front dynamics in disordered environments

Invasion occurs in environments that are normally spatially disordered, however, the effect of such a randomness on the dynamics of the invasion front has remained less understood. Here, we study Fisher’s equation in disordered environments both analytically and numerically. Using the Effective Medium Approximation, we show that disorder slows down invasion velocity and for ensemble average of invasion velocity in disordered environment we have bar{v}=v_0 (1-|xi |^2/6) where |xi | is the amplitude of disorder and v_0 is the invasion velocity in the corresponding homogeneous environment given by v_0=2sqrt{RD_0}. Additionally, disorder imposes fluctuations on the invasion front. Using a perturbative approach, we show that these fluctuations are Brownian with a diffusion constant of: D_{C}= dfrac{1}{8} xi ^2sqrt{RD_0 (1-|xi |^2/3)}. These findings were approved by numerical analysis. Alongside this continuum model, we use the Stepping Stone Model to check how our findings change when we move from the continuum approach to a discrete approach. Our analysis suggests that individual-based models exhibit inherent fluctuations and the effect of environmental disorder becomes apparent for large disorder intensity and/or high carrying capacities.

Stress energy correlator in de Sitter spacetime: Its conformal masking or growth in connected Friedmann universes

Semiclassical Physics in gravitational scenario, in its first approximation (1st order) cares only for the expectation value of stress energy tensor and ignores the inherent quantum fluctuations thereof. In the approach of stochastic gravity, on the other hand, these matter fluctuations are supposed to work as the source of geometry fluctuations and have the potential to render the results from 1st order semiclassical physics irrelevant. We study the object of central significance in stochastic gravity, i.e. the noise kernel, for a wide class of Friedmann space-times. Through an equivalence of quantum fields on de Sitter space-time and those on generic Friedmann universes, we obtain the noise kernel through the correlators of Stress Energy Tensor (SET) for fixed co-moving but large physical distances. We show that in many Friedmann universes including the expanding universes, the initial quantum fluctuations, the universe is born with, may remain invariant and important even at late times. Further, we explore the cosmological space-times where even after long times the quantum fluctuations remain strong and become dominant over large physical distances, which the matter driven universe is an example of. The study is carried out in minimal as well as non-minimal interaction settings. Implications of such quantum fluctuations are discussed.

Isolation of Cardiomyocytes from Fixed Hearts for Immunocytochemistry and Ploidy Analysis.

The adult mammalian heart is composed of various cell types including cardiomyocytes, endothelial cells and fibroblasts. Since it is difficult to reliably identify nuclei of cardiomyocytes on histological sections, many groups rely on isolating viable cardiomyocytes prior to fixation to perform immunostaining. However, these live cardiomyocyte isolation techniques require optimization to maximize the yield, viability and quality of the samples, with inherent fluctuations from sample to sample despite maximum optimization. Here, we report a reproducible protocol, involving fixation prior to enzymatic digestion of the heart, which leads to maximum yield while preserving the in vivo morphology of individual cardiomyocytes. We further developed an automated analysis platform to determine the number of nuclei and DNA content per nucleus for individual cardiomyocytes. After exposing the chest cavity, the heart was arrested in diastole by perfusion with 60 mM KCl in PBS. Next, the heart was fixed in 4% paraformaldehyde (PFA) solution, and then digested with 60 mg/mL collagenase solution. After digestions, cells were singularized by trituration, and the cardiomyocyte fraction was enriched via differential centrifugation. Isolated cardiomyocytes were stained for Troponin T and α-actinin to assess purity of the obtained population. Furthermore, we developed an image analysis platform to determine cardiomyocyte nucleation and ploidy status following DAPI staining. Image based ploidy assessments led to consistent and reproducible results. Thus, with this protocol, it is possible to preserve native morphology of individual cardiomyocytes to allow immunocytochemistry and DNA content analysis while achieving maximum yield.

Universal inherent fluctuations in statistical counting of large particles in slurry used for semiconductor manufacturing

In the chemical mechanical polishing process of semiconductor manufacturing, the concentration of ‘large’ particles (ge 0.5 μm) in the slurry, which is considerably larger in size than the main abrasives (approx 0.1 μm), is a critical parameter that strongly influences manufacturing defects, yields, and reliabilities of large-scale-integrated circuits. Various instruments, so-called particle counters, based on light scattering, light extinction, and holography techniques have been developed to measure and monitor the large particle concentration in semiconductor fabs in real time. However, sizeable fluctuation in the measured particle concentration complicates the statistical process control in the fabs worldwide. Here, we show that an inherent fluctuation exists in the counting of large particles, which is universal, independent of instrument type, and quantitatively determined by the instrument’s operation parameters. We analytically derive a statistical theory of the fluctuation based on Poisson statistics and validate the theory through experiments and Monte-Carlo simulation. Furthermore, we provide a strategy to enhance the measurement accuracy by statistically adjusting the instrumental parameters commonly involved in the particle counters. The present results and analyses could be useful for statistical process control in semiconductor fabs to prevent large particle-induced defects such as micro-scratches and pits on wafers.

Development of a hardware-in-the-loop simulator for small-scale concentrated solar combined heat and power system

• A HiL framework of a novel integrated solar mCHP plant is developed. • The HiL simulator is designed in Matlab/Simulink and coupled with the real controller of the plant. • The HiL simulator is used for control algorithms optimization and control strategy definition. • Optimal proportional and integral gains of the PI-based control are assessed for all the diverters. • The best control strategy to assure constant inlet temperature at the ORC consists in acting on the aperture of a diverter. In this work a hardware-in-the-loop (HiL) simulator of a novel micro combined heat and power system is presented and its use for control algorithm optimization is demonstrated and discussed. The plant under investigation consists of a concentrated Linear Fresnel Reflectors solar field, a 2 kWe/18 kWt Organic Rankine Cycle unit and an advanced latent heat thermal energy storage tank equipped with reversible heat pipes as developed by a consortium of universities and companies within the EU funded project Innova Microsolar. A smart control unit manages their integration and monitors the operation of each subsystem. In order to support the optimization of the control algorithms and the definition of the best control strategy of the micro-CHP plant at different working conditions, a simulation framework based on Matlab/Simulink has been developed by the authors and connected to the real control unit according to a HiL approach. Ad-hoc models of the different subsystems together with those of the components (i.e. valves and variable speed pumps) regulating the plant operation have been included. The use of the HiL simulator has permitted to optimize the control logic of the integrated plant prior to its future commissioning, thus helping to overcome some of the technical and reliability issues occurring during the setup of the real system. In particular, the HiL has allowed: (i) to define the proportional and integral gains of the diverters in order to assure a robust and fast response of the plant during the switch among the different operation modes; (ii) to prove the limits of acting on the oil pump flow rate in assuring the nominal oil temperature at the inlet of the ORC unit, due to the inherent fluctuations caused by this control strategy; and (iii) to assess the best control strategy which is obtained by acting on the aperture of the diverter which controls the oil mass flow rate to the ORC unit. Hence, the scientific approach here proposed can be extended also to many other complex energy conversion systems in order to significantly reduce the potential critical issues during their commissioning.

A Virtual Array Approach for Correcting Irregularities in eDPCA Imaging Radar

An imaging radar based on an electronically displaced-phase-center antenna (eDPCA) has been reported recently, which provides great application potentials in many areas. To overcome the degradation of the SAR image quality caused by the inherent pattern fluctuations of the eDPCA, we propose a modified virtual array method to correct the irregularities in the eDPCA-based system. The effectiveness of the proposed approach is validated through experimental examples on a simulated eDPCA imaging system at 157 GHz.

Economic feasibility of a wind-battery system in the electricity market with the fluctuation penalty

As the rapid development of wind energy and electricity markets, wind farms (WFs) in mounting numbers join the electricity markets such as spot and regulating markets, etc. The large-scale integration of WFs into the power system may threaten the safe and stable operation of power systems due to the inherent fluctuation nature of wind power, and as a consequence, the WFs may be penalized by the grid operator in the electricity market. In this case, WF owners will take actions to reduce the fluctuation and one of the promising strategies is to integrate battery storage system (BSS) into the WFs. Base on this idea, a novel framework of considering fluctuation penalty (FP) in the Danish spot market which is the electricity market investigated in this study is firstly proposed. The economic feasibility of integrating BSS into a WF in the spot market is analyzed and a bi-level programming is adopted to design the sizing of the BSS based on Return on Investment (ROI). This research also analyzes the impact of various fluctuation penalties and subsidies on the ROI. It is found that the net revenue of integrating BSS into a WF can increase by 15.8% and the FP can reduce by 64.9% in the spot market with the FP.

Strengthening mechanisms in high entropy alloys: Fundamental issues

High entropy alloys (HEAs), offering a multi-dimensional compositional space, provide almost limitless design opportunities surpassing the frontiers of structural materials development. However, an in-depth appraisal of the fundamental materials physics behind strengthening in HEAs is essential in order to leverage them to achieve greater flexibility in application oriented materials design. This viewpoint paper concentrates on issues regarding inherent compositional fluctuations in HEAs and corresponding impact on strengthening is highlighted. In particular, metal physics based design criteria in multi-phase HEAs are discussed and comparisons between multi-phase and single-phase HEAs are drawn.

Partially coherent Ince-Gaussian beams.

We report on the study and generation of Ince-Gaussian beams in the spatially partially coherent regime. The inherent random fluctuations both in time and space of these partially coherent fields make their characterization difficult. Our results show that the cross-correlation function (CCF) provides insight into the composition of the Ince-Gaussian beam, as well as into its spatial coherence structure and singularities. Our experimental findings are in very good agreement with the numerical simulations, particularly revealing a rich structure of nodal lines in the CCF.

A novel wavenets long short term memory paradigm for wind power prediction

Wind power prediction is essentially important for smooth integration of wind power into the national grid pertained to its inherent fluctuations. To facilitate the wind energy production and balance production versus market demand, a precise, efficient and robust forecast model is required to encounter highly nonlinear and intricate nature of the problem. In this study, a machine learning paradigm is presented by exploiting the strength of recurrent neural networks based Long Short Term Memory (LSTM), embedded with wavelet kernels, to encompass the dynamic behavior of temporal data. The novel idea of wavenets utilizing LSTM (WN-LSTM) with Gaussian, Morelet, Ricker and Shannon activation kernels for power prediction from various wind farms make it a unique hybrid forecast model to enrich the ultimate utilization of deep learning for vanishing gradient and wavelet transformations for nonlinear mapping. The proposed methodology, WN-LSTM, is implemented on seven wind farms based in Europe for short term wind power prediction and is evaluated in terms of various standard performance metrics, such as mean absolute error (MAE) and mean absolute percentage error (MAPE). The results are compared with the well-established existing techniques and a percentage improvement of upto 30% is observed on MAE. Multiple independent executions of the model are carried out to ensure its efficacy and robustness. Moreover, the interval prediction is complemented to quantitatively characterize the uncertainty as developing intervals, and the difference between means of predicted and actual power is 0.02 at 95% confidence level as illustrated from ANOVA based Tukey’s and Fisher’s tests.

Stochastic properties of thermoacoustic oscillations in an annular gas turbine combustion chamber driven by colored noise

Large-amplitude thermoacoustic oscillations are unwanted in gas turbines due to the detrimental damage to combustors. Thus predicting the onset of such oscillations and a better understanding of the unstable behaviors are important. In this work, the stochastic properties of thermoacoustic oscillations in the subthreshold region of thermoacoustic systems are investigated theoretically and numerically. The energy conversion from the unsteady heat release to sound is mainly achieved via two ways: one is caused by inherent turbulent fluctuations, and the other is due to the flame response to the acoustic waves. The turbulence-induced non-coherent heat release fluctuation is characterized by colored noise, and the coupling between the unsteady heat release and acoustic pressure is characterized by a 3rd order polynomial. Both stochastic averaging and stochastic normal form are utilized to approximate the responses of the system as a Markov process. The result shows that the correlation time τc and intensity D of the colored noise have significant but opposite effects on the dynamics of thermoacoustic oscillations, including the most probable amplitude, autocorrelation of the system as well as the correlation time. In addition, the resonance-like behaviors in signal-to-noise ratio (SNR) are observed, which denotes the emergence of coherence resonance (CR) in this annular combustion system. The optimal noise intensity, at which SNR is maximized, becomes sensitive to the variation of τc, as τc is larger than certain value. Lastly, the extent of the system coherent motions relies significantly on the proximity to the supercritical Hopf bifurcation point. Being closer to the stability boundary is found to increase the strength of system association. Meanwhile, SNR of the colored noise-induced motion becomes more distinguished, and the optimal noise intensity is shifted to a smaller value. This variation can serve as a precursor to predict the onset of thermoacoustic instability, as the correlation time remains unchanged.

Reducing Fluctuations in Slow-Extraction Beam Spill Using Transit-Time-Dependent Tune Modulation

Slow extraction of beam from synchrotrons or storage rings as required by many fixed target experiments is performed by controlled excitation and feeding of a structural lattice resonance. Due to the sensitive nature of this resonant extraction process, the temporal structure of the extracted beam is modulated by the minuscule current fluctuations present on the quadrupole magnet power supplies. Such a modulation lead to pile-ups in detectors and a significant reduction in accumulated event statistics. This contribution proposes and experimentally demonstrates that by an introduction of further modulation on quadrupole currents with a specific amplitude and frequency, the inherent power supply fluctuations are mitigated leading to a smoothening of the beam temporal structure. The slow extraction beam dynamics associated with this method are explained along with the operational results.

Statistical Modeling of the FSO Fronthaul Channel for UAV-Based Communications

In this paper, we investigate the statistics of the free space optics (FSO) communication channel between a hovering unmanned aerial vehicle (UAV) and a central unit. Two unique characteristics make UAV-based FSO systems significantly different from conventional FSO systems with static transceivers. First, for UAV-based FSO systems, the incident laser beam is not always orthogonal to the receiver lens plane. Second, both position and orientation of the UAV fluctuate over time due to dynamic wind load, inherent random air fluctuations in the atmosphere around the UAV, and internal vibrations of the UAV. On the contrary, for conventional FSO systems, the laser beam is always perpendicular to the receiver lens plane and the relative movement of the transceivers is limited. In this paper, we develop a novel channel model for UAV-based FSO systems by quantifying the corresponding geometric and misalignment losses (GML), while taking into account the non-orthogonality of the laser beam and the random fluctuations of the position and orientation of the UAV. In particular, for diverse weather conditions, we propose different fluctuation models for the position and orientation of the UAV and derive corresponding statistical models for the GML. We further analyze the performance of a UAV-based FSO link in terms of outage probability and ergodic rate and simplify the resulting analytical expressions for the high signal-to-noise ratio (SNR) regime. Finally, simulations validate the accuracy of the presented analysis and provide important insights for system design. For instance, we show that for a given variance of the fluctuations, the beam width should be properly adjusted to minimize the outage probability.

An Extended Building-In Reliability Methodology on Evaluating SRAM Reliability by Wafer-Level Reliability Systems

Advancement in integrated circuit (IC) brings a series of challenges on product reliability. In order to meet these challenges and fulfill customers’ requirements, we introduced the methodology of building-in reliability (BIR) into whole stages during IC manufacturing. The wafer-level reliability (WLR) is an essential element of the BIR system. Significant amount of costs, resources, and times are saved from the early detections on reliability deficiencies at design and development stages. After successful mass production, hidden reliability risks can also be timely identified before shipment. Currently, the process reliability has been successfully evaluated by WLR platform. However, the product reliability, which covers much wider reliability factors, has not been effectively covered by WLR. Currently, we still rely on the conventional package-level reliability (PLR) tests to assess product reliability. In this paper, we report how the classical static random access memory (SRAM) is transplanted into the WLR system, which serves as an efficient platform to detect reliability weakness. In our extended BIR methodology, by reasonable test structure and pattern designs, proper electric parameters selections, test methods optimizations, we can thoughtfully consider inherent failure mechanisms and process fluctuations in order to build quantitative reliability models, which are essential to our proposed system. The quantitative reliability model was not seen in the similar WLR approaches and this may explain why these previous proposals failed. Finally, we report the obtained robust correlations between single-bit cell wear-out and package-level SRAM degradations, which is further validated by conventional HTOL tests. Our approaches provide opportunities not only on early detecting reliability weakness, but also on reliability assurance after product deliveries. These two contributions are essential and especially crucial for advanced technology developments.