Adaptive Time Research Articles

Approach for sizing by optimization of an electrical drive considering a multiphysics and multidynamics behaviour

PurposeThis paper aims to describe a modelling and solving methodology of a (static converter–electric motor–control) system for its sizing by optimization, considering the dynamic thermal heating of the machine.Design/methodology/approachThe electrical drive sizing model is composed of two simulators (electrical and thermal) that are co-simulated with a master−slave relationship for the time step management. The computation is stopped according to simulation criteria.FindingsThis paper details a methodology to represent and size an electrical drive using a multiphysics and multidynamics approach. The thermal simulator is the master and calls the electrical system simulator at a fixed exchange time step. The two simulators use a dedicated dynamic time solver with adaptive time step and event management. The simulation automatically stops on pre-established criteria, avoiding useless simulations.Research limitations/implicationsThis paper aims to present a generic methodology for the sizing by optimization of electrical drives with a multiphysics approach, so the precision and computation time highly depend on the modelling method of each components. A genetic multiobjective optimization algorithm is used.Practical implicationsThe methodology can be applied to size electrical drives operating in a thermally limited zone. The power electronics converter and electrical machine can be easily adapted by modifying their sub-model, without impacting the global model and simulation principle.Originality/valueThe approach enables to compute a maximum operating duration before reaching thermal limits and to use it as an optimization constraint. These system considerations allow to over constrain the electrical machine, enabling to size a smaller machine while guaranteeing the same output performances.

Digital Twin Modeling for Hydropower System Based on Radio Frequency Identification Data Collection

The safe and steady operation of hydropower generation systems is crucial for electricity output in the grid. However, hydropower stations have complicated interior structures, making defect detection difficult without disassembly inspections. The application of digital modeling to hydropower stations will effectively promote the intelligent transformation of hydropower stations as well as reduce the maintenance costs of the system. This study provides a model of the power generating and transmission system for hydropower plants, with an emphasis on primary equipment and measured data. The model utilizes PSCAD to digitalize state response in hydropower plants with various short-circuit faults. The fault information is identified and learned using the Adaptive Time–Frequency Memory (AD-TFM) deep learning model. It is demonstrated that our proposed method can effectively obtain the fault information through radio frequency identification (RFID). The accuracy of the traditional method is 0.90, while the results for AD-TFM show a fault classification accuracy of 0.92, which is more than enough to identify multiple fault types compared to the existing methods.

Adaptive finite time tracking control for the perturbed nonlinear systems by using backstepping technique and a fast tracking differentiator

Adaptive finite time tracking control for the perturbed nonlinear systems by using backstepping technique and a fast tracking differentiator

Adaptive Sliding Window–Dynamic Time Warping-Based Fluctuation Series Prediction for the Capacity of Lithium-Ion Batteries

Accurately predicting the capacity of lithium-ion batteries is crucial for improving battery reliability and preventing potential incidents. Current prediction models for predicting lithium-ion battery capacity fluctuations encounter challenges like inadequate fitting and suboptimal computational efficiency. This study presents a new approach for fluctuation prediction termed ASW-DTW, which integrates Adaptive Sliding Window (ASW) and Dynamic Time Warping (DTW). Initially, this approach leverages Empirical Mode Decomposition (EMD) to preprocess the raw battery capacity data and extract local fluctuation components. Subsequent to this, DTW is employed to forecast the fluctuation sequence through pattern-matching methods. Additionally, to boost model precision and versatility, a feature recognition-based ASW technique is used to determine the optimal window size for the current segment and assist in DTW-based predictions. The study concludes with capacity fluctuation prediction experiments carried out across various lithium-ion battery models. The results demonstrate the efficacy and extensive applicability of the proposed method.

Intrafraction organ movement in adaptive MR-guided radiotherapy of abdominal lesions – dosimetric impact and how to detect its extent in advance

IntroductionMagnetic resonance guided radiotherapy (MRgRT) allows daily adaptation of treatment plans to compensate for positional changes of target volumes and organs at risk (OARs). However, current adaptation times are relatively long and organ movement occurring during the adaptation process might offset the benefit gained by adaptation. The aim of this study was to evaluate the dosimetric impact of these intrafractional changes. Additionally, a method to predict the extent of organ movement before the first treatment was evaluated in order to have the possibility to compensate for them, for example by adding additional margins to OARs.Materials & methodsTwenty patients receiving adaptive MRgRT for treatment of abdominal lesions were retrospectively analyzed. Magnetic resonance (MR) images acquired at the start of adaptation and immediately before irradiation were used to calculate adapted and pre-irradiation dose in OARs directly next to the planning target volume. The extent of organ movement was determined on MR images acquired during simulation sessions and adaptive treatments, and their agreement was evaluated. Correlation between the magnitude of organ movement during simulation and the duration of simulation session was analyzed in order to assess whether organ movement might be relevant even if the adaptation process could be accelerated in the future.ResultsA significant increase in dose constraint violations was observed from adapted (6.9%) to pre-irradiation (30.2%) dose distributions. Overall, OAR dose increased significantly by 4.3% due to intrafractional organ movement. Median changes in organ position of 7.5 mm (range 1.5–10.5 mm) were detected within a median time of 17.1 min (range 1.6–28.7 min). Good agreement was found between the range of organ movement during simulation and adaptation (66.8%), especially if simulation sessions were longer and multiple MR images were acquired. No correlation was determined between duration of simulation sessions and magnitude of organ movement.ConclusionIntrafractional organ movement can impact dose distributions and lead to violations of OAR tolerance doses, which impairs the benefit of daily on-table plan adaptation. By application of simulation images, the extent of intrafractional organ movement can be predicted, which possibly allows to compensate for them.

Optimal Battery Energy Storage Dispatch for the Day-Ahead Electricity Market

This work presents an innovative application of optimal control theory to the strategic scheduling of battery storage in the day-ahead electricity market, focusing on enhancing profitability while factoring in battery degradation. This study incorporates the effects of battery degradation on the dynamics in the optimisation framework. Considering this cost in economic analysis and operational strategies is essential to optimise long-term performance and economic viability. Neglecting degradation costs can lead to suboptimal operation and dispatch strategies. We employ a continuous-time representation of the dynamics, in contrast with many other studies that use a discrete-time approximation with rather coarse intervals. We adopt an equivalent circuit model coupled with empirical degradation parameters to simulate a battery cell’s behaviour and degradation mechanisms with good support from experimental data. Utilising direct collocation methods with mesh refinement allows for precise numerical solutions to the complex, nonlinear dynamics involved. Through a detailed case study of Belgium’s day-ahead electricity market, we determine the optimal charging and discharging schedules under varying objectives: maximising net revenues, maximising profits considering capacity degradation, and maximising profits considering both capacity degradation and internal resistance increase due to degradation. The results demonstrate the viability of our approach and underscore the significance of integrating degradation costs into the market strategy for battery operators, alongside its effects on the battery’s dynamic behaviour. Our methodology extends previous work by offering a more comprehensive model that empirically captures the intricacies of battery degradation, including a fine and adaptive time domain representation, focusing on the day-ahead market, and utilising accurate direct methods for optimal control. This paper concludes with insights into the potential of optimal control applications in energy markets and suggestions for future research avenues.

Relative foveal dark adaptation: a potential method for assessing macular health.

Dark adaptation measures photoreceptor recovery following intense light stimulation. Time to recovery reflects retinal function. We describe a novel method of relative foveal dark adaptation using an iPhone. Data from a small number of healthy subjects were studied to assess reproducibility, effects of age, and consider potential clinical utility. Relative foveal dark adaption was studied in 6 normal subjects across ages from 20 to 81 years and across differing testing conditions. Foveal bleaching is produced by fixating a bright white circle on an iPhone for variable times. After foveal bleaching an annular surround appears to complete a bullseye stimulus with surround initially brighter than centre. As the fovea recovers the centre regains brightness. Relative foveal dark adaptation, the time for the visual anchor to shift from surround to centre, was studied across a range of bleaching times, ages, and testing conditions. Dispersion of dark adaptation times grows with increasing age. Foveal bleaching for 30 s was as effective as longer times. Testing times with a 30 s bleach were less than 1 min. Foveal dark adaptation was reproducible within each subject and was unaffected by ambient room lighting, pupil size, and light attenuation. Repeat, immediately sequential testing was similarly reproducible except after long bleaching. This method of dark adaptation is intuitive, repeatable, and relatively unaffected by testing condition. Testing times are brief, requiring only an iPhone screen positioned at reading distance. Relative foveal dark adaptation may be a useful tool to assess macular health.

Resistance to uncertainty and the degree of actual feeling of loneliness as factors of adaptation of a first-year student to study at a university

A person’s experience of uncertainty and loneliness is an existential topic that attracts the attention of researchers long before psychology was formed into an independent science. The issues of studying the states of uncertainty and loneliness are relevant at the present time. Note that despite the stability or turbulence of social processes, there are periods in every person’s life in which uncertainty and loneliness will be present a priori. Such periods include the time of adaptation of a first-year student to study at a university. The article presents the results of a study of patterns describing the connectedness of the processes of experiencing loneliness and uncertainty in first-year students studying in the first year of P.G. Demidov YarSU. It is proved that these two processes are inextricably linked both with each other and with the adaptation of a first-year student at a new stage of his life: the transition to higher education, the success of which largely depends on a person’s ability to cope with the impact of uncertainty and loneliness on him, which, in turn, are almost always present at this stage.

Ammonia-induced constraints on butyrate degradation in anaerobic digestion: Impact of ammonia levels and pH conditions, and recovery behaviour

The accumulation of butyrate is a recurrent phenomenon in anaerobic digesters utilizing animal manure as the primary feedstock. Despite its prevalence, the factors governing the anaerobic degradation of butyrate remain inadequately explored. In this study, a series of experiments were carried out under different total ammonia concentrations (TAN, 0.18–20 g N/L) and pH conditions (7.0–8.0) to investigate the inhibition of butyrate anaerobic degradation by different ammonia species (NH4+ and NH3) and to assess the recoverability following severe ammonia inhibition. The findings indicate that at pH 7.5, butyrate degradation experienced remarkable inhibition when TAN exceeded 8.0 g N/L, while no discernible impact was observed at pH 7.0–8.0 and 4.0 g TAN/L. Additionally, the lag phase for butyrate degradation extended with increasing TAN concentration. Notably, the activity of butyrate-degrading bacteria exhibited full recovery from severe ammonia inhibition (TAN 20 g N/L or NH3 779.2 mg N/L), provided prolonged adaption time was allowed. The analysis using a modified Monod inhibition model highlighted that NH4+ contributed more to inhibition than NH3 at TAN concentrations of 2.0–20.0 g N/L. Therefore, simply reducing pH levels would not adequately counteract ammonia inhibition. Implementing an extended hydraulic retention time emerges as an effective measure to reduce butyrate accumulation in anaerobic digestion systems, particularly the feedstock being nitrogen-rich materials (e.g., animal manure).

The Effect of Groundstroke Forehand Exercise on Speed and Agility in 14 to 16-year-old Tennis Athletes

This research addresses gaps by developing a holistic training model that integrates essential elements for optimal tennis performance. The study explores how physical conditioning can enhance agility, speed, and overall performance of junior athletes aged 14 to 16, focusing on forehand drive. The aim is to provide insights for coaches and players by integrating physical and technical training to improve performance. The scope includes evaluating the effectiveness of a forehand drive training program supplemented with speed and agility exercises for junior tennis players. An experimental one-group pre-test-post-test design was employed. Eleven tennis athletes from the Nusalima Tennis Academy participated. Speed was measured using a 20-meter sprint, and agility was calculated using the 505 Agility Test. The training program spanned 12 sessions over four weeks. Statistical analysis included mean, standard deviation, normality tests, and t-tests. Pre-test and post-test results showed significant improvements in speed and agility. The t-test values indicated statistical significance, confirming the effectiveness of the forehand drive training model. Players demonstrated enhanced neuromuscular adaptation, muscle strength, coordination, and reaction time. The study concludes that the forehand drive training model significantly improves speed and agility in junior tennis players. The findings support the theory that focused and structured training enhances physical abilities and performance. Additionally, the research highlights the long-term health benefits of intensive tennis training, such as improved cardiovascular health and overall fitness. The training model can be widely applied to develop well-rounded athletes in tennis programs.

Time is NTCP: Should we maximize patient throughput or perform online adaptation on proton therapy systems?

BackgroundCompared to conventional radiotherapy (XT), proton therapy (PT) may improve normal tissue complication probabilities (NTCP). However, PT typically requires higher adaptation rates due to an increased sensitivity to anatomical changes. Systematic online adaptation may address this issue, but it requires additional replanning time, decreasing patient throughput. Therefore, less patients would benefit in such case from PT for a given machine capacity, with results in worse NTCP. AimTo investigate the trade-off between PT patient throughput and NTCP gain as a function of the time needed for adaptation. MethodsA retrospective database of 14 lung patients with two repeated 4DCTs was used to compare NTCP values between XT and PT for NTCP2ym (2-year mortality), NTCPdysphagia and NTCPpneumonitis. Four scenarios were considered for PT: no adaptation using clinical robustness parameters (4D robust optimization, 3 % range error and PTV-equivalent setup errors); systematic online adaptation with clinical robustness parameters; setup errors reduced to 4 mm and to 2 mm. Dose was accumulated on the planning CT. The number of patients treated with PT depended on the extra time needed for adaptation, assuming an 8-hours capacity (assuming 14 patients a day; thus minimum 34.2 min per treatment session if there is no or instantaneous adaptation). ResultsBaseline NTCP gains (PT against XT without adaptation) equaled 6.9 %, 6.1 %, and 7.7 % for NTCP2ym, NTCPdysphagia and NTCPpneumonitis, respectively. Using instantaneous online adaptation and setup errors of 2 mm, the overall gains were then 10.7 %, 13.6 % and 12.4 %. Taking into account loss of capacity, 13.7 min was the maximum extra-time allowed to complete adaptation and maintain an advantage on all three metrics for the 2-mm setup error scenario. ConclusionThis study highlights the critical importance of keeping short online adaptation times when using systems with limited capacity like PT.

Adaptive fuzzy neural network-based finite time prescribed performance control for uncertain robotic systems with actuator saturation

Adaptive fuzzy neural network-based finite time prescribed performance control for uncertain robotic systems with actuator saturation

Factors influencing speech improvement following maxillary complete-arch implant-supported restorations: A retrospective study.

This study explored factors affecting speech improvement in patients with an edentulous maxilla after the delivery of a complete-arch implant-supported fixed dental prosthesis (IFDP). Patients who had received IFDP for edentulous maxilla were enrolled, and various potential speech improvement-related factors were considered, including patient demographics, anterior residual bone volume, preoperative facial features, preoperative acoustic parameters, and adaptation time. Acoustic analysis and perceptual ratings were used to assess three fricatives [s], [f], and [ɕ]. Correlation and regression analyses were conducted to assess the association between changes in fricatives and potential factors (α = .05). The study included 50 patients (18 females and 32 males, aged 50.62 ± 15.71 years, range 19-76). Significant correlations were found among the change in the center of gravity (ΔCoG) of [s] and anterior residual bone volume, zygomatic implants number and proportion (p < .05). These correlations were largely mirrored in the perceptual score (ΔPS) changes. After controlling for age, sex, preoperative acoustic parameters, and adaptation time, the ΔCoG and ΔPS of fricatives were mainly correlated with the anterior residual bone volume, preoperative acoustic parameters, and adaptation time. Speech improvements post-IFDP delivery are mainly related to preoperative speech characteristics, anterior residual bone volume, and adaptation time. The residual bone volume's impact on consonants varies with specific articulatory gestures. This study provides insights into forecasting speech outcomes following IFDP restoration and provides recommendations and methods for data collection in developing future prediction models.

Adaptive Real Time Efficient Control Algorithm for Buildings Under Wind and Earthquake Forces

The study aims to tackle two key issues: the dependency of optimal controllers on available training data and their limited capability in managing multi-component structural responses. Real Time Fuzzy Logic Efficient Control system (RTFLEC) was developed to address these challenges. This system integrates a fuzzy logic inference system with a multi-verse optimization algorithm and utilizes real time training for real-time optimal controller derivation. It incorporates decentralized systems and optimal controller memory concepts and employs a magnetorheological damper (MR) for adjustable vibration control. Two case studies were conducted to evaluate the RTFLEC system's effectiveness. The first study focused on a three-story shear building under seismic loads, while the second analyzed a 76-story building facing multi-directional wind loads. The results showed that RTFLEC system reduced structural drifts by 28.33% and 51.5% during near-field and far-field earthquakes, respectively. It also decreased structural acceleration by 27.66% and 53% during near-field and far-field earthquakes, respectively. For wind-induced structures, it reduced story displacement by 37.5%, 14.5%, and 12% against across-wind, along-wind, and rotational forces, respectively. RTFLEC system demonstrated robust performance against uncertainties in external excitations, providing exceptional structural responses. Its key advantage lies in real-time adaptability, requiring minimal training data and computational processing. These findings highlight its significant potential in enhancing the performance of adaptive vibration control systems under unpredictable real-world loads.

Deep neural network based time–frequency decomposition for structural seismic responses training with synthetic samples

AbstractTime–frequency decomposition is a powerful tool in assessing the dynamic behaviors of structures. Traditional time–frequency decomposition methods struggle with adaptability, and are limited in handling the structural responses with strong nonlinearity and closely spaced modes. In this study, a cutting‐edge approach based on deep neural network (DNN) is proposed to achieve a precise and adaptive time–frequency decomposition of nonlinear structural responses under seismic excitations. Remarkably, despite being trained on synthetic samples, the proposed method demonstrates outstanding performance in decomposing time–frequency components across various seismic response scenarios. Compared to variational mode decomposition (VMD) and synchroextracting transform (SET), the proposed method exhibited superior precision in time–frequency decomposition and excellent efficiency in parameter optimization. Moreover, the applicability of the proposed method to real‐world complex structures has been verified, which also shows promising generalization capabilities. Future research will aim to enhance the network performance by incorporating additional learning samples with diverse nonlinear characteristics.

Switched Model Predictive Control for Nonholonomic Mobile Robots Under Adaptive Dwell Time.

In this article, switched model predictive control (MPC) is proposed for nonholonomic mobile robots with adaptive dwell time and a dual-terminal set. The dual-terminal set is used to reduce on-line complexity of the switched MPC for the nonholonomic mobile robots with multiple constraints. By a switched signal with the adaptive dwell time, cost functions are switched to improve control performance under multiple constraints. The switched MPC with feasibility and stability can adjust a tradeoff between control performance and computational complexity for the closed-loop system. Simulation results are given to illustrate superiority of the switched MPC for nonholonomic mobile robots.

The Timing of Detectable Increases in Seasonal Soil Moisture Droughts Under Future Climate Change

AbstractGlobal warming exacerbates the increase of soil moisture drought by accelerating the water cycle, posing potential threats to food security and ecological sustainability. The design of drought prevention and mitigation policies should be based on the reliable detection of the future change signal in droughts, so it is critical to know when the signal can be detected (Time of Emergence, ToE) in the background noise of the climate system. While the ToE framework has been successfully applied for temperature‐related signal detection, the ToE for changes in drought has not been well studied. Based on 66 Coupled Model Intercomparison Project Phase 6 model ensemble members under four Shared Socio‐economic Pathways, we conduct a global ToE analysis of seasonal soil moisture drought characteristics and discuss the impact of different warming levels. Six subregions with robust increase in soil moisture droughts are identified. For drought frequency, most of the subregion's ToE is centered around 2080, however for drought intensity it is much earlier and can even reach around 2040 in AMZ. For drought frequency and drought intensity, approximately 14%–22% and 47%–49% of global land areas would reach ToE in 21st century. The global land areas with ToE of increasing droughts would increase by at least 1/5 when global warming level is kept to 2°C rather than 1.5°C above pre‐industrial conditions. This suggests that limiting global warming can significantly delay the emergence time of increases in seasonal soil moisture droughts, allowing additional adaptation time for the drought‐related sectors.

Modeling the thermally adaptive performance of building exterior walls with linear temperature-dependent thermal conductivity

The concept of adapting to variable external thermal environments by varying the thermophysical properties of building envelopes has gained increased attention in recent years. This study aims to evaluate the thermally adaptive performance of exterior walls incorporating linear temperature-dependent thermal conductivity-based insulation (LTTI). A variant resistance–capacitance nodal model was developed and validated against Fluent simulations, demonstrating high accuracy and time efficiency. The study evaluates the thermally adaptive indices (i.e., the adaptive time lag, adaptive decrement factor, and thermally adaptive ratio) for LTTI-based walls with at different temperature-dependent slopes under the Beijing climate. The results show that the dynamic thermal indices follow a parabolic pattern, whereas the monthly adaptive indices exhibit a folded line pattern with the extreme value in July. The maximum value for the adaptive time lag and decrement factor reached 3.8 h and 0.003 at −1.0 mW·m−1·K−2, while minimum extreme value is observed at 1.0 mW·m−1·K−2 with −3.8 h and −0.003, respectively. Furthermore, the study determines the relationship between the heating, cooling, and overall thermally adaptive ratios and the seasonal month and the temperature-dependent slope. At a temperature-dependent slope of 1.0 mW·m−1·K−2, the heating, cooling, and overall thermally adaptive ratios were approximately 0.162, −0.045, and 0.160, respectively. These findings suggest that LTTIs with a positive slope adapt to the thermal environment in Beijing, while those with a negative slope exhibit the opposite, anti-adaptive performance. These results could provide insights into the development of energy-efficient and thermally adaptive building envelopes based on LTTI materials.

Climate change adaptation projects of transport facilities: is a demonstrator necessary?

AbstractTransport facilities are increasingly threatened by the climate-change related natural disasters in recent years. In order to incentivize the transport facility operators to engage in climate change adaptation, governments may provide subsidies to the adaptation investment. Due to the substantial uncertainties in the outcomes of the adaptation projects, the government can set up an “experiment” project, which can be used as a demonstrator. In this paper, we develop real-option game models to investigate the transport facility operators’ adaptation investment under static stochastic demand and time-varying stochastic demand. Meanwhile, the government’s optimal decisions on whether to set up a demonstrator and the related subsidy policies are also examined. We find that under static stochastic demand, the government prefers to set up a demonstrator if the marginal benefit of the adaptation capacity is larger than its marginal cost, or the demonstration effect is high enough. Under time-varying stochastic demand, the government prefers to set up a demonstrator when the demand volatility is low. Moreover, increasing demand volatility requires less (or more) subsidy, when the demonstrator is (or is not) set up. Increasing demand volatility leads to postponement of the adaptation timing and more adaptation investment, irrespective of whether there is a demonstrator.

Stochastic Roll Dynamics of Smooth and Impacting Vessels in Random Waves

Ship instability is frequently associated with extreme roll motion. In this paper smooth and vibro-impact dynamics of vessels rolling under random wave excitations including nonlinearities is investigated. The vessel is represented by a single degree of freedom roll model. The random excitation due to waves and ice is modeled by a sum of random pulses with Poisson arrival rate. Stochastic P-bifurcation analysis is carried out by examining the variation with parameter of the joint probability density functions (jpdf) of the response computed numerically by the solution of the corresponding Fokker–Planck–Kolmogorov (FPK) equation. The finite element (FE), finite difference (FD), path integral (PI) and radial basis neural (RBN) network formulation methods are used in the solution of the FPK equation. The D-bifurcation analysis is analyzed by computing the largest Lyapunov exponent. The mean upcrossing rate is computed using Rice’s formula. The random wave excitation is also modeled as the response of a second order linear filter to white noise and the random response of the ship is investigated by solution of the corresponding FPK equation of the four dimensional Markov system by the FD method. Non-smooth coordinate transformations like the Zhuravlev and Ivanov transformations are used converting the discontinuous systems to equivalent smooth systems in the impact dynamics analysis. The adaptive time stepping method (ATSM) approach is used to accurately locate the discontinuity point and a Brownian tree approach is used to direct the integration along the correct Brownian path. Different models for nonlinear damping and the restoring moment are considered and their effects on ship stochastic response such as chaotic motion, unbounded rotational motion, impact modulation motion and ship capsizing are investigated.