Detailed Model Research Articles

Medium-term stochastic hydrothermal scheduling with short-term operational effects for large-scale power and water networks

The high integration of variable renewable sources in electric power systems entails a series of challenges inherent to their intrinsic variability. A critical challenge is to correctly value the water available in reservoirs in hydrothermal systems, considering the flexibility that it provides. In this context, this paper proposes a medium-term multistage stochastic optimization model for the hydrothermal scheduling problem solved with the stochastic dual dynamic programming algorithm. The proposed model includes operational constraints and simplified mathematical expressions of relevant operational effects that allow more informed assessment of the water value by considering, among others, the flexibility necessary for the operation of the system. In addition, the hydrological uncertainty in the model is represented by a vector autoregressive process, which allows capturing spatio-temporal correlations between the different hydro inflows. A calibration method for the simplified mathematical expressions of operational effects is also proposed, which allows a detailed short-term operational model to be correctly linked to the proposed medium-term linear model. Through extensive experiments for the Chilean power system, the results show that the difference between the expected operating costs of the proposed medium-term model, and the costs obtained through a detailed short-term operational model was only 0.1%, in contrast to the 9.3% difference obtained when a simpler base model is employed. This shows the effectiveness of the proposed approach. Further, this difference is also reflected in the estimation of the water value, which is critical in water shortage situations.

A 3D simulation model to study all-inorganic CsPbX3 (X = Br and I) perovskites-based light-emitting diodes with different hole-transporting layers

The development of numerical models is essential for optimizing perovskite light-emitting diodes (PeLEDs) and explaining their physical mechanism for further efficiency improvement. This study reports, for the first time, on a detailed device modelling of an all-inorganic perovskite LED consisting of CsPbX3 (X = Br and I) as light emitting layer (LEL) with different hole transporting layers (HTLs), employing COMSOL Multiphysics simulation package. Therefore, a 3D simulation model is served to investigate the appropriate HTLs that meet the design requirements of a PeLED in terms of band off-set engineering. For this purpose, a series of all-inorganic halide perovskites with different HTLs such as PEDOT: PSS, CuSCN and MoO3 are simulated under the same theoretical settings, and the performances of LEDs are compared with each other. This is done through studying their electronic properties using current density–voltage (J-V) curves and internal quantum efficiency (IQE) measurements. The results obtained from the J-V curves reveal that all the CsPbBr3-based samples with different HTLs exhibit the same turn-on voltage (V on) of approximately 4.2 V, while this value increases to 5.8 V for the CsPbI3-based samples. Compared with the PeLEDs based on CsPbI3, the PeLEDs based on CsPbBr3 indicate lower V on due to the formation of shorter charge carrier injection barriers at their interfaces. Furthermore, among the various simulated structures, the highest IQE is obtained for perovskite CsPbI3-based LED with MoO3 HTL (5.21%). The effect of different parameters on the performance of the proposed configurations are also investigated, and it turns out that the thickness of LELs and lifetime of charge carriers have a decisive role to play in the efficiency of PeLEDs. This theoretical study not only successfully explains the working principle of PeLEDs but also clearly shows researchers how to produce high-performance LEDs in the laboratory by knowing the physical properties of materials and accurately adjusting energy band alignments.

Climatologically invariant scale invariance seen in distributions of cloud horizontal sizes

Abstract. Cloud area distributions are a defining feature of Earth's radiative exchanges with outer space. Cloud perimeter distributions n(p) are also interesting because the shared interface between clouds and clear sky determines exchanges of buoyant energy and air. Here, we test using detailed model output and a wide range of satellite datasets a first-principles prediction that perimeter distributions follow a scale-invariant power law n(p) ∝ p-(1+β), where the exponent β = 1 is evaluated for perimeters within moist isentropic atmospheric layers. In model analyses, the value of β is closely reproduced. In satellite data, β is remarkably robust to latitude, season, and land–ocean contrasts, which suggests that, at least statistically speaking, cloud perimeter distributions are determined more by atmospheric stability than Coriolis forces, surface temperature, or contrasts in aerosol loading between continental and marine environments. However, the satellite-measured value of β is found to be 1.26 ± 0.06 rather than β = 1. The reason for the discrepancy is unclear, but comparison with a model reproduction of the satellite perspective suggests that it may owe to cloud overlap. Satellite observations also show that scale invariance governs cloud areas for a range at least as large as ∼ 3 to ∼ 3 × 105 km2, and notably with a corresponding power law exponent close to unity. Many prior studies observed a much smaller range for power law behavior, and we argue this difference is due to inappropriate treatments of the statistics of clouds that are truncated by the edge of the measurement domain.

First activity and interactions in thalamus and cortex using raw single-trial EEG and MEG elicited by somatosensory stimulation.

One of the primary motivations for studying the human brain is to comprehend how external sensory input is processed and ultimately perceived by the brain. A good understanding of these processes can promote the identification of biomarkers for the diagnosis of various neurological disorders; it can also provide ways of evaluating therapeutic techniques. In this work, we seek the minimal requirements for identifying key stages of activity in the brain elicited by median nerve stimulation. We have used a priori knowledge and applied a simple, linear, spatial filter on the electroencephalography and magnetoencephalography signals to identify the early responses in the thalamus and cortex evoked by short electrical stimulation of the median nerve at the wrist. The spatial filter is defined first from the average EEG and MEG signals and then refined using consistency selection rules across ST. The refined spatial filter is then applied to extract the timecourses of each ST in each targeted generator. These ST timecourses are studied through clustering to quantify the ST variability. The nature of ST connectivity between thalamic and cortical generators is then studied within each identified cluster using linear and non-linear algorithms with time delays to extract linked and directional activities. A novel combination of linear and non-linear methods provides in addition discrimination of influences as excitatory or inhibitory. Our method identifies two key aspects of the evoked response. Firstly, the early onset of activity in the thalamus and the somatosensory cortex, known as the P14 and P20 in EEG and the second M20 for MEG. Secondly, good estimates are obtained for the early timecourse of activity from these two areas. The results confirm the existence of variability in ST brain activations and reveal distinct and novel patterns of connectivity in different clusters. It has been demonstrated that we can extract new insights into stimulus processing without the use of computationally costly source reconstruction techniques which require assumptions and detailed modeling of the brain. Our methodology, thanks to its simplicity and minimal computational requirements, has the potential for real-time applications such as in neurofeedback systems and brain-computer interfaces.

A detailed occupant activity classification model in a residential environment using building monitoring data: Considering occupant characteristics

In this study, we developed a detailed occupant activity classification model to detect occupant activities using various non-intrusive monitoring data in buildings (i.e., indoor environmental data, energy consumption data, motion·door sensor data, and state of indoor climate control devices). To collect building monitoring data, we conducted experiments with five subjects (Person A to E) in an experimental testbed. In total, ten occupant activities (i.e., Sleeping, Eating, Personal hygiene and care, Working, Cooking, Cleaning up after meals, Indoor cleaning, Exercising, Resting, and Away) were selected to predict occupant activity experimentally. Three classification algorithms, including random forest (RF), K-nearest neighbor, and support vector machine, were employed to develop the detailed occupancy activity classification models. In this study, we found that the key variables influencing each occupant activity classification varied significantly for each individual. The lifestyle and characteristics of each occupant were reflected in the building monitoring data. Accordingly, the performance of the model varied depending on each occupant’s behavior. In the case of Person A, all classification algorithms displayed a high performance when only environmental datasets were considered; the accuracy was 0.98, and the f1-score was also above 0.98. Person B’s classification model required various building monitoring data to ensure optimal performance; the highest accuracy and f1-score was 0.86 using RF. In contrast, Person D’s classification model showed a low performance when all monitoring datasets were used; the accuracy was 0.43, and the f1-score was 0.41 using RF. These results support that occupants’ characteristics affect the feature variables in an activity classification model, and these feature variables determine the performance of the model of each occupant.

Whole-heart modelling with valves in a fluid–structure interaction framework

Computational modelling of whole-heart function is a useful tool to study heart mechanics and haemodynamics. Many existing heart models focus on electromechanical aspect without considering physiological valves and use simplified fluid models instead. In this study we develop a four-chamber heart model featuring realistic chamber geometry, detailed valve modelling, hyperelasticity with fibre architecture and fluid–structure interaction analysis. Our model is used to investigate heart behaviours with different modelling assumptions including restricted/free valve annular dynamics, and with/without heart-pericardium interactions. Our simulation results capture the interactions between valve leaflet and surrounding flow, typical left ventricular flow vortices, typical venous and transvalvular flow waveform, and physiological heart deformations such as atrioventricular plane movement. The improvement of ventricular filling and atrial emptying at early diastole is evident with free annulus. In addition, we find that the added pericardial forces on the heart have a predominant effect on atrial wall deformation especially during atrial contraction, and further help with the atrial filling process. Most importantly, the current study provides a framework for comprehensive multi-physics whole-heart modelling considering all heart valves and fluid–structure interactions.

Simultaneous Water Sorption and Crystallization in ASDs 1: Stability Studies Lasting for Two Years.

One way to increase the slow dissolution rate and the associated low bioavailability of newly developed active pharmaceutical ingredients (APIs) is to dissolve the API in a polymer, leading to a so-called amorphous solid dispersion (ASD). However, APIs are often supersaturated in ASDs and thus tend to crystallize during storage. The kinetics of the crystallization process is determined by the amount of water the ASD absorbs during storage at relative humidity (RH), storage temperature, polymer type, and the drug load of the ASD. Here, the crystallization kinetics and shelf life of spray-dried ASDs were investigated for ASDs consisting of nifedipine (NIF) or celecoxib (CCX) as the APIs and of poly(vinylpyrrolidone-co-vinyl acetate) or hydroxypropyl methylcellulose acetate succinate as polymers. Samples were stored over 2 years at different RHs covering conditions above and below the glass transition of the wet ASDs. Crystallization kinetics and onset time of the crystallization were qualitatively studied by using powder X-ray diffraction and microscopic inspection and were quantitatively determined by using differential scanning calorimetry. It was found that the NIF ASDs crystallize much faster than CCX ASDs at the same drug load and at the same storage conditions due to both higher supersaturation and higher molecular mobility in the NIF ASDs. Experimental data on crystallization kinetics were correlated using the Johnson-Mehl-Avrami-Kolmogorov equation. A detailed thermodynamic and kinetic modeling will be performed in Part 2 of this paper series.

Allylic-alkylic C-C bond decomposition in 1-butene and 1-pentene

1-Butene and 1-pentene are critical intermediates in the oxidation and pyrolysis of larger alkanes and alcohols. Their thermal decomposition plays important role in fuel consumption under combustion conditions, yet seldom investigated. In this work, we studied the title reactions behind reflected shock waves over temperatures of 1178 – 1416 K and pressures near 1.2 bar. We monitored absorbance time-history of the product allyl radicals by employing a sensitive UV diagnostic scheme at a wavelength of 220 nm. We extracted the target rate coefficients by simulating the measured absorbance profiles with detailed kinetic models. Our determined rate coefficients of 1-butene (k1) and 1-pentene (k2) thermal decomposition may be expressed as (unit s−1):k1(1C4H8→CH3+aC3H5)=1.52×1016e(−37937T)k2(1C5H10→C2H5+aC3H5)=3.84×1014e(−31434T)We believe the current work provides the first direct measurements of these reactions. Our 1-butene thermal decomposition rate coefficients exhibit a positive Arrhenius temperature dependence. Around 1350 K and 1 bar, 1-butene decomposition proceeds ∼ 4.5 times faster than n-butane, and ∼ 66 times faster than 1,3-butadiene. Our work extends the literature measurements of 1-butene decomposition to temperatures higher than 1310 K. Literature measurements above 1000 K underestimate our rate coefficients by ∼ 20 %. Additionally, our 1-butene rate coefficients provide an analogy to bio-derived molecules containing similar allylic-alkylic C-C bonds, such as methyl-3-hexenoate. For 1-pentene thermal decomposition, we observed the product allyl radicals starting from 1178 K compared to 1213 K in 1-butene measurements. Thus 1-pentene decomposition proceeds 2 – 6 times faster than 1-butene, and shows a gentler Arrhenius temperature dependence. The current literature models exhibit a range of rate values that vary by an order of magnitude, resulting in noticeable discrepancies. Our study addresses this issue by providing much-needed clarity and precision. Furthermore, we implemented our rate coefficients in literature kinetic models and evaluated their influence on ignition delay time (IDT) and speciation measurements. The results indicate that our rate coefficients generally improved the model performance.

Development of an Artificial Intelligence Model to Predict Combustion Properties, With a Focus on Auto-Ignition Delay

Abstract Hydrogen-compatible gas turbines are one way to decarbonize electricity production. However, burning and handling hydrogen is not trivial because of its high reactivity and tendency to detonate. Mandatory safety parameters, such as auto-ignition delay times, can be estimated thanks to predictive detailed kinetic models, but with significant calculation times that limit coupling with fluid mechanic codes. An auto-ignition prediction tool was developed based on an artificial intelligence (AI) model for fast computations and an implementation into an explosion model. A dataset of ignition delay times (IDTs) was generated automatically using a recent detailed kinetic model from National University of Galway (NUIG) selected from the literature. Generated data cover a wide operating range and different compositions of fuels. Clustering problems in sample points were avoided by a quasi-random Sobol sequence, which covers uniformly the entire input parameter space. The different algorithms were trained, cross-validated, and tested using a database of more than 70,000 ignitions cases of natural gas/hydrogen blends calculated with the full kinetic model by using a common split of 70/30 for training, testing. The AI model shows a high degree of robustness. For both the training and testing datasets, the average value of the correlation coefficient was above 99.91%, and the mean absolute error (MAE) and the mean square error (MSE) were around 0.03 and lower than 0.04, respectively. Tests showed the robustness of the AI model outside the ranges of pressure, temperature, and equivalence ratio of the dataset. A deterioration is, however, observed with increasing amounts of large alkanes in the natural gas.

MODELLING THE EVOLUTION OF CASTING DEFECT CLOSURE BY RADIAL SHEAR ROLLING

This paper studies the behavior of a transverse through defect in an ingot during complex vortex flow of metal deformed by the radial shift rolling (RSR) method. The aim of the work was to investigate the applicability of the RSR method for the deformation treatment of oxide-dispersed strengthened steel (ODS-steel) ingots into a final semi-finished product. Vortex flow of metal promotes dispersion of alloying solid oxides and refinement of steel structure. However, the question of development or closure of possible casting defects of ODS-steel ingot remains open. For this purpose, an ODS-steel ingot with a through-hole modelling the defect was rolled according to the assumed process scheme. Also, to visualize the evolution of the defect in more detail during the rolling process, a simulation of thesame defect development in an aluminum bar has been performed in 2 mm steps. The results of both rolls showed good correlation. Based on many cross-sections of the experimental bars, detailed 3D models of the defect evolution were built. An average reduction of 50 % in defect volume was found. Welding of the outer areas occurs immediately, but the central areas remain unclosed, stretching proportionally with the drawing of the rod during rolling. For this reason, RSR cannot be used with large cavity defects in the axial zone, but this method is guaranteed to weld surface defects even at low reductions.

Complex rupture evolution of the 2007 Martinique earthquake: a non-double-couple event in the Caribbean Sea

SUMMARY A large non-double-couple component of a tectonic earthquake indicates that its rupture likely was complex and likely involved multiple faults. Detailed source models of such earthquakes can add to our understanding of earthquake source complexity. The 2007 Martinique earthquake in the Caribbean Sea is one of the largest recent earthquakes with a known large non-double-couple component. It was an intermediate depth intraslab earthquake within the South American plate where it is subducting beneath the Caribbean plate. We applied potency density tensor inversion (PDTI) to teleseismic P waves generated by the 2007 Martinique earthquake to model its source processes and focal mechanism distribution. We identified two focal mechanisms: a strike-slip mechanism with a north–south tension axis (T-axis), and a downdip extension (DDE) mechanism with an east–west T-axis. Rupture by the DDE mechanism was predominant in the northern part of the source region and strike-slip rupture in the southern part. These two focal mechanisms had approximately parallel pressure axes (P-axes) and approximately orthogonal T-axes. The seismic moments released by both types of rupture were almost equal. These results indicate that the 2007 Martinique earthquake had a large non-double-couple component. We identified five subevents with two predominant directions of rupture propagation: two strike-slip subevents propagated to the southeast and three DDE subevents propagated to the east. Although the directions of propagation were consistent for each focal mechanism, each subevent appears to have occurred in isolation. For example, the rupture of one DDE subevent propagated from the edge of the source region back towards the hypocentre. Complex ruptures that include multiple subevents may be influenced by high pore fluid pressure associated with slab dehydration. Our results show that PDTI can produce stable estimates of complex seismic source processes and provide useful information about the sources of complex intermediate depth intraslab earthquakes for which fault geometry assumptions are difficult.

Cubic elasticity of porous materials produced by additive manufacturing: experimental analyses, numerical and mean-field modelling

Although the elastic properties of porous materials depend mainly on the volume fraction of pores, the details of pore distribution within the material representative volume are also important and may be the subject of optimisation. To study their effect, experimental analyses were performed on samples made of a polymer material with a predefined distribution of spherical voids, but with various porosities due to different pore sizes. Three types of pore distribution with cubic symmetry were considered and the results of experimental analyses were confronted with mean-field estimates and numerical calculations. The mean-field ‘cluster’ model is used in which the mutual interactions between each of the two pores in the predefined volume are considered. As a result, the geometry of pore distribution is reflected in the anisotropic effective properties. The samples were produced using a 3D printing technique and tested in the regime of small strain to assess the elastic stiffness. The digital image correlation method was used to measure material response under compression. As a reference, the solid samples were also 3D printed and tested to evaluate the polymer matrix stiffness. The anisotropy of the elastic response of porous samples related to the arrangement of voids was assessed. Young’s moduli measured for the additively manufactured samples complied satisfactorily with modelling predictions for low and moderate pore sizes, while only qualitatively for larger porosities. Thus, the low-cost additive manufacturing techniques may be considered rather as preliminary tools to prototype porous materials and test mean-field approaches, while for the quantitative and detailed model validation, more accurate additive printing techniques should be considered. Research paves the way for using these computationally efficient models in optimising the microstructure of heterogeneous materials and composites.

A method to bridge energy and process system optimization: Identifying the feasible operating space for a methanation process in power-to-gas energy systems

The growing adoption of renewable energy is driving the integration of new, complex process technologies into energy systems, presenting operational optimization challenges. Simple models are necessary for computational tractability, yet detailed models are essential for feasible operating decisions. This work introduces a method for optimally operating an energy system with integrated complex process technologies. The proposed method bridges the mathematical modeling and optimization at energy and process system levels. Simple surrogate models and detailed process models are used to represent complex process technologies at their respective system levels. The resulting operational problems are solved through a sequential iterative approach. Applied to a power-to-gas energy system with an integrated methanation process, our method outperforms a benchmark method by capturing interactions between energy and process system levels, and among different process variables. The proposed method integrates more detailed constraints and ramping costs, yielding an optimal, feasible operating strategy within tractable computational time.

Impacts of multiple demand-side management strategies on microgrids planning: a literature survey

Abstract Demand-side management (DSM) schemes play a crucial role in managing renewable energy generation and load fluctuations by utilizing demand–response programmes (DRPs). This paper aims to provide a detailed overview of DRPs that help microgrid operators to keep costs and reliability within acceptable ranges. Additionally, this review paper provides a detailed economic load model for DRPs based on initial load, demand–response (DR) incentive, DR penalty and elasticity coefficients. This article also aims to guide researchers in identifying research gaps in DSM applications in microgrids by comparing various DSM schemes from different countries and regions in terms of DSM strategies, objective functions and optimization techniques. Furthermore, this study analyses the impact of DRPs on microgrid configuration from the perspective of utilities and customers, considering technical and economic performance metrics. As a result, it can be concluded that none of the studied cases provides models or guidelines for choosing appropriate DSM schemes that consider different consumer interests or load-type features. Furthermore, a few researchers have addressed the features of a modern price-based DR strategy, renewable generation-based dynamic pricing DR, which offers higher customer satisfaction than traditional DRPs.

Unravelling Size-Dependent and Coupled Properties in Mechanical Metamaterials: A Couple-Stress Theory Perspective.

The lack of material characteristic length scale prevents classical continuum theory (CCT) from recognizing size effect. Additionally, the even-order material property tensors associated with CCT only characterize the materials' centrosymmetric behavior and overlook their intrinsic chirality and polarity. Moreover, CCT is not reducible to 2D and 1D space without adding couples and higher-order deformation gradients. Despite several generalized continuum theories proposed over the past century to overcome the limitations of CCT, the broad application of these theories in the field of mechanical metamaterials has encountered significant challenges. These obstacles primarily arise from a limited understanding of the material coefficients associated with these theories, impeding their widespread adoption. Implementing a bottom-up approach based on augmented asymptotic homogenization, a consistent and self-sufficient effective couple-stress theory for materials with microstructures in 3D, 2D, and 1D spaces is presented. Utilizing the developed models, material properties associated with axial-twist, shear-bending, bending-twist, and double curvature bending couplings in mechanical metamaterials are characterized. The accuracy of these homogenized models is investigated by comparing them with the detailed finite element models and experiments performed on 3D-printed samples. The proposed models provide a benchmark for the rational design, classification, and manufacturing of mechanical metamaterials with programmable coupleddeformation modes.

Oxygen Chemistry in Polymer Fouling: Insights from Multiphase Detailed Kinetic Modeling

Oxygen Chemistry in Polymer Fouling: Insights from Multiphase Detailed Kinetic Modeling

Integrated sand management: modeling of sand erosion in a mature oil field in Malaysia

Sand production can lead to various problems, including erosion in production flow lines that may lead to total production loss for extended periods and costly workover operations. The extent of erosive damage is determined by many factors, among which the flow and sand rates are the most significant. Three main issues must be addressed to ensure an efficient production operation: Sand erosion estimation, Sand monitoring (settling and deposition), and maintaining optimum production rates. If sand production exceeds certain levels, i.e., allowable sand rate, the erosion in the production network becomes problematic. Sand production has been problematic in some wells in Reservoir X. Core, and historical production data was used to build a comprehensive model using Schlumberger PIPESIM™ hydraulic modelling. The software allows for detailed modelling of the production network by which erosion rate, erosion hotspots, and deposition of the produced sand can be quantitatively analyzed. Considering an allowable erosion rate of 0.3 mm/year, the model outcomes indicate that sand erosion is critical in wells J-1, J-2, and L-2. The next step was identifying the hotspots where the produced sand is deposited in the abovementioned wells. The modelling results indicated that sand deposition is primarily severe in the teeline between the platforms. Moreover, the gas-oil ratio was identified as the most influential factor in the sand deposition. Lastly, a sensitivity analysis was conducted on the allowable flow rates and maximum (technical) allowable sand production and erosion rates to find optimum production rates from reservoir X.

Reduced-Order Model for Wind-Solar Multi-Microgrids Considering Time-Scale Coupling

A computationally efficient and accurate reduced-order model (ROM) that characterizes the time-scale coupling caused by the high penetration of power-electronic converters in microgrids (MGs) is crucial to system stability analysis. When such MGs are interconnected into multi-microgrids (MMGs), the coupling between fast and slow dynamics will be more complicated due to the interactions among each MG. The traditional ROM without concerning dynamic coupling may lead to low accuracy and even misjudgment of system stability. In this paper, by considering primary energy dynamics of renewable energy sources, e.g., doubly-fed induction generator (DFIG) and photovoltaic (PV), a small-signal model that characterizes the practical multi-timescale of MMGs is firstly presented. Then with the division of the entire MMGs into fast and slow dynamic subsystems based on the assumption of time-scale separating, the coupling dynamics are identified via the residues-based method. Finally, a ROM considering coupling dynamics and preserving physical meaning is proposed by reconfiguring the fast and slow dynamic subsystems based on singular perturbation. The accuracy of the proposed ROM is verified by comparing it with the detailed model under different control parameters, wind speeds and irradiance conditions. The time-domain simulation is employed to validate the dynamic response of the proposed ROM.

Estimation of Operation Time with Digital Twin in Manufacturing

There are two most important factors that are taken into consideration in businesses. One of them is time and the other is cost. In order to save time and cost, planning of production subcomponents involves a series of critical activities. Making more effective plans in the field of production has become possible with industry 4.0. Industry 4.0 includes the digitalization of production. One of the most popular topics in this process is the Digital Twin [DT]. The DT philosophy has enabled businesses to better understand the sub-processes of production. In this way, they can optimize them. With the development of this philosophy, more detailed models have been created. Enterprises keep their data under control in order to control, manage and optimize processes. These data are then utilized in the model building process. The aim of this study is to estimate the time from the moment a product enters the process to the moment it leaves the process by using the data obtained through time study etc. studies. Automated machine learning (AutoML) method is used to build the best model. Machine learning (ML) algorithms, which are popularly used in the literature, may not always give the best result. In order to prevent this, starting from the data preprocessing step, including hyperparameter optimization, the aim is to find the algorithm and parameters that give the best performance. It will contribute to DT studies by estimating the operation time. The study used a 115-row dataset from CNC machines. The dataset consists of velocity, motion and actual time. The actual time is tried to be estimated using motion/speed. It is aimed to achieve the best results with AutoML. lazy predict and tpot library were used in the study. As a result, an estimation of the duration of 100% was realized.

On the unique temperature-dependent interplay of a B-exciton and its trion in monolayer MoSe2.

Plasmonics in metal nanoparticles can enhance their near field optical interaction with matter, promoting emission into selected optical modes. Here, using Ga nanoparticles with carefully tuned plasmonic resonance in proximity to MoSe2 monolayers, we show selective photoluminescence enhancement from the B-exciton and its trion with no observable A-exciton emission. The nanoengineered substrate allows for the first direct experimental observation of the B-trion binding energy in semiconducting monolayers. Using temperature-dependent photoluminescence measurements, we show the following features of the MoSe2 B-exciton family: (i) the trion binding energy has an observable temperature dependence with a decreasing trend towards low temperatures and (ii) the exciton-trion emission ratio varies non-monotonically with temperature with a steep increase in the trion emission at lower temperatures. Using detailed models, we identify the particle size required for selective excitation and describe the underlying physical processes. This opens newer avenues for selectively promoting excitonic species and tuning the effective particle lifetimes in monolayer semiconductors. These results demonstrate the excellent plasmonic properties of Ga nanoparticles, which along with facile processing techniques makes it an attractive alternative to the prevalent noble metal plasmonics having applications in flexible/stretchable materials and textiles.