Parameter Window Research Articles

Study of microstructure evolution of friction stir welded novel (Al-Zn-Mg)-Fe (HE700) cast alloys for automotive applications

Light weighting of automobiles would improve fuel efficiency and reduce emissions. Newly developed high-strength, high-elongation (Al-Zn-Mg)-Fe alloys for structural shape casting applications have the potential to fulfill the demand for significant lightweighting of components. Joining such shape casting alloys to form a high integrity component assembly is a critical process in structural lightweighting. Friction stir welding has recently emerged as an effective joining method for aluminium alloys. In this work, friction stir welding was used to join these new-generation (Al-Zn-Mg)-Fe cast alloys. The critical process parameters such as rotation and traverse speed were optimized through a detailed microstructural study and mechanical property evaluation of the welds. Three different tool rotation speeds (600, 800, and 1000 rpm) and traverse speeds (25, 50, and 75 mm/min) were the independent parameters. The welds made with 800 rpm and 25 mm/min traverse speed showed the best properties with no weld/microstructural defects. Friction stir welding also refined the microstructure and uniformly distributed the agglomerated Al-Fe-based intermetallic particles. The microhardness and tensile properties (longitudinal direction) of the stir zone increased significantly (Ultimate tensile strength = 380 MPa, Yield strength = 250 MPa) with appreciable ductility (31%) due to the microstructural refinement despite the dissolution of the strengthening precipitates such as Guinier-Preston zones and ηꞌ phases. The study was able to establish a viable window of processing parameters for joining the new generation Al-Zn-Mg-Fe alloys for structural lightweight components.

A theoretic analysis of magnetoactive GES-based turbulent solar plasma instability

ABSTRACT A recently reported gravito-electrostatic sheath (GES) model is procedurally applied to study the turbumagnetoactive helioseismic oscillation features in the entire bi-fluidic solar plasma system. The bounded solar interior plasma (SIP, internally self-gravitating), and the unbounded solar wind plasma (SWP, externally point-gravitating) are coupled through the interfacial diffused solar surface boundary (SSB) due to an exact gravito-electrostatic interplay. A numerical platform on the developed theoretic formalism reveals the evolution of both dispersive and non-dispersive features of the modified GES mode fluctuations in new parametric windows. Different colourspectral profiles exhibit important features of the GES-based SIP–SWP perturbations elaborately. It is illustratively shown that the thermostatistical GES stability depends mainly on the radial distance, magnetic field, equilibrium plasma density, and plasma temperature. We see that their dispersive features are more pertinently pronounced in the self-gravitational domains (SIP) than the electrostatic counterparts (SWP). Besides, different characteristic parameters with accelerating (or decelerating) and stabilizing (or destabilizing) effects influencing the entire solar plasma stability are illustratively portrayed. We speculate that, in the SIP, the long-wave (gravitational-like) helioseismic fluctuations become highly dispersive showing more propagatory nature than the shorter ones (acoustic-like). The short waves show more propagatory propensity than the longer ones in the SSB and SWP regime. The reliability of our proposed investigation is bolstered along with the tentative applicability and future scope in light of the current solar observational scenarios, such as SOHO, STEREO, SDO, PSP, and SolO.

Adaptive thermal comfort model and active occupant behaviour in a mixed-mode apartment. A synergy to sustainability.

The adaptive comfort model was mainly developed for naturally ventilated buildings, but few recent studies explore its applicability in mixed-mode buildings. The present study uses the adaptive thermal comfort model (EN15251) in a dynamic thermal simulation (EnergyPlus) parametric analysis of a mixed-mode apartment, in an attempt to determine the energy savings. Two simulation models were created. The first one makes full use of mechanical heating and cooling systems when the required temperature is not reached. The second one adopts a hybrid approach, resembling the existing operation of residential buildings in Greece. The primary regulator of the indoor conditions is the occupant through his/her interaction with the building shell and only when this is not effective the mechanical systems are activated. The internal thermal gains in both models were determined based on the detailed recording of the real conditions in a typical apartment. Many design parameters (window size, thermal insulation position and thickness, orientation, airtightness, glazing properties and shading) along with different occupant behavioural patterns (derived from a questionnaire campaign) have been examined in sensitivity analysis. Machine learning algorithms, such as the Random Forest, were also incorporated to identify most important parameters. Results indicate that airtightness, occupant behaviour and shading are the most important parameters for primary energy consumption for cooling, while airtightness, window size and shading for the total of heating and cooling primary energy consumption.

An adaptive absorption spectroscopy with adjustable moving window width for suppressing nonlinear effects in absorbance measurements.

Absorption spectroscopy based on Lambert-Beer law has been widely used in material structure analysis, research in chemical reaction kinetics, and exploration of various physicochemical reaction mechanisms. However, serious nonlinearity between absorbance and measured concentration can occur in actual measurements. The idea of moving window is first introduced into the field of spectral nonlinearity in the paper. Combining with the characteristic absorption spectra of the substances to be measured, we propose an adaptive absorption spectroscopy (A-AS) with adjustable moving window parameters to effectively suppress the nonlinear effects in absorbance measurements. The validity of this method is verified by taking the differential optical absorption spectroscopy to detect SO2 as an example. The 210-230nm characteristic absorption band is traversed and divided by the moving window with adjustable parameters, and the estimated coefficient (k-value) of each band is calculated. On this basis, all k-values are initially and secondly screened to obtain the optimal kbest, and then the optimal concentration value is obtained by inversion. Compared with the broad-band method and narrow-band method, it shows excellent performance that the maximum error and standard deviation of A-AS is only 1.3% and 3.8 in the entire concentration range, suggesting good linearity and stability in both high and low concentration environments. Therefore, it is inferred that A-AS is universally adaptable and enables dynamic linear measurements over wide concentration range.

Low-Pressure Cold Spray Deposition Window Derived from a One-Dimensional Analytical Model

Low-pressure cold spray (LPCS) coating deposition requires the consideration of multiple parameters to define spraying conditions. The use of a parameter window provides an integral approach to achieving this goal. In this work, an LPCS deposition window for zinc powders was obtained through the development of a one-dimensional analytical model of fluid and particle interaction. The model considers powder particle injection downstream of the nozzle and follows the particle from injection to impact. The model equations relate the particle velocity (vp) to the process parameters, such as the gas pressure (P0) and temperature (T0), particle size (dp) and stand-off distance (SoD). The values of the particle velocity (vp) at the nozzle exit and during the “free-jet”, as well as the drag coefficient (Cd), were calculated using experimental spraying conditions for Cu and Al that have previously been documented in LPCS studies. The model’s accuracy and applicability to other materials were confirmed upon comparing the results with those in the aforementioned studies. Moreover, the definition of the model equations allowed for the identification of three new parameters: (γ) the maximum ideal particle velocity, (β) the capacity to accelerate the powder particle inside the nozzle and (α) the deceleration of the particle in the free-jet zone. These parameters have not previously been published and allow for comparative evaluation between LPCS processes.

Evaluation of the Impact of Window Parameters on Energy Demand and CO2 Emission Reduction for a Single-Family House

This article deals with the determination of the impact of selected parameters on energy consumption for heating and cooling purposes and CO2 emissions. Mathematical modelling combined with planning a computational experiment was adopted as the research method. The database for creating the models was developed using building energy simulations performed with DesignBuilder software. A single-family house with an area of 101 m2 was the subject of this study. Four deterministic mathematical models for the estimation of annual energy demand for heating, cooling, total final energy demand, and CO2 emissions were developed. Four parameters affecting the energy balance of the house: the area of the glazing system (three levels), U-value of windows (two-, three- and four-pane), U-value of external walls (0.1, 0.15, 0.2 W/m2K) and location (Warsaw, Berlin, Paris) were considered. The article discusses in detail the influence of individual factors on the energy demand and their common interactions. It was found that the level of thermal insulation of the glazing system plays the most important role in saving energy. This factor was the only one to show a stable and significant reduction in house energy demand, and thus a reduction in CO2 emissions for all four objective functions.

Chiral spin liquid state of strongly interacting bosons with a moat dispersion: A Monte Carlo simulation

We consider a system of strongly interacting bosons in two dimensions with moat band dispersion which supports an infinitely degenerate energy minimum along a closed contour in the Brillouin zone. The system has been theoretically predicted to stabilize a chiral spin liquid (CSL) ground state. In the thermodynamic limit and vanishing densities, n→0, chemical potential, μ, of the uniform CSL state was shown to scale with n as μ∼n2log2n. Here we perform a Monte Carlo simulation to find the parametric window for particle density, n≲k0282π, where k0 is the linear size of the moat (the radius for a circular moat), for which the scaling ∼n2log2n in the equation of state of the homogeneous CSL is preserved. We variationally show that the uniform CSL state is favorable in an interval beyond the obtained scale and present a schematic phase diagram for the system. Our results offer some density estimates for observing the low-density behavior of CSL in time-of-flight experiments with a recently Floquet-engineered moat band system of ultracold atoms in Bracamontes et al. (2022), and for the recent experiments on emergent excitonic topological order in imbalanced electron–hole bilayers.

Possibilities of Artificial Intelligence-Enabled Feedback Control System in Robotized Gas Metal Arc Welding

In recent years, welding feedback control systems and weld quality estimation systems have been developed with the use of artificial intelligence to increase the quality consistency of robotic welding solutions. This paper introduces the utilization of an intelligent welding system (IWS) for feedback controlling the welding process. In this study, the GMAW process is controlled by a backpropagation neural network (NN). The feedback control of the welding process is controlled by the input parameters; root face and root gap, measured by a laser triangulation sensor. The NN is trained to adapt NN output parameters; wire feed and arc voltage override of the weld power source, in order to achieve consistent weld quality. The NN is trained offline with the specific parameter window in varying weld conditions, and the testing of the system is performed on separate specimens to evaluate the performance of the system. The butt-weld case is explained starting from the experimental setup to the training process of the IWS, optimization and operating principle. Furthermore, the method to create IWS for the welding process is explained. The results show that the developed IWS can adapt to the welding conditions of the seam and feedback control the welding process to achieve consistent weld quality outcomes. The method of using NN as a welding process parameter optimization tool was successful. The results of this paper indicate that an increased number of sensors could be applied to measure and control the welding process with the developed IWS.

Research on the source-detector variance reduction method based on the AIS adjoint Monte Carlo method

The coupled variance reduction method based on adjoint calculation has a good variance reduction effect when solving the source-detector problem. This paper incorporates the Automatic Importance Sampling (AIS) method into the adjoint Monte Carlo method and then proposes a coupled variance reduction method based on the AIS adjoint Monte Carlo method. The result shows that the total flux deviation between the AIS adjoint Monte Carlo method and the original adjoint method is 0.13%, indicating good agreement and verifying the accuracy of the AIS adjoint method. In this paper, the AIS adjoint Monte Carlo method is applied to the calculation of a commercial reactor shielding example, and four sets of source bias and weight window parameters of the AIS adjoint calculation are used as calculation examples. The maximum statistical error is less than 5.00%, and the relative deviation from the measured value is within 20.00%. Generally, the calculation efficiency of AIS-CADIS is slightly better than that of CADIS.

Single-shot spatiotemporal plasma density diagnosis using an arbitrary time-wavelength-encoded biprism interferometer

Single-shot ultrafast imaging reveals the transient dynamics of ultrafast phenomena. Herein, we introduce a new approach for capturing ultrafast micron-scale dynamics using an arbitrary time-wavelength-encoded biprism interferometer (TWEBI). In TWEBI, a time-wavelength-encoded pulse train is generated using a set of cascaded frequency-doubling crystals with slightly different phase-matching angles and independent delay lines, which have the advantages of independent temporal resolution, time window, and frame interval parameters. Phase measurement of TWEBI is achieved using a plug-and-play biprism interferometer. In the experiment to capture the transient dynamic of femtosecond laser-induced plasma filament, we demonstrated that the TWEBI setup can capture a total of 12 frames in a single shot, and its effective frame rate is 5 trillion frames per second (Tfps), corresponding to a temporal resolution of 200 fs and a spatial resolution of 4 μm. Moreover, the time window of the TWEBI setup can be adjusted from sub-picosecond to nanosecond timescales. Thus, TWEBI has the advantages of high-precision temporal-spatial resolution, high frame rate, adjustable time window, and no reference arm, thus providing a practical and feasible diagnostic scheme for complex transient dynamics.

Context-aware query derivation for IoT data streams with DIVIDE enabling privacy by design

Integrating Internet of Things (IoT) sensor data from heterogeneous sources with domain knowledge and context information in real-time is a challenging task in IoT healthcare data management applications that can be solved with semantics. Existing IoT platforms often have issues with preserving the privacy of patient data. Moreover, configuring and managing context-aware stream processing queries in semantic IoT platforms requires much manual, labor-intensive effort. Generic queries can deal with context changes but often lead to performance issues caused by the need for expressive real-time semantic reasoning. In addition, query window parameters are part of the manual configuration and cannot be made context-dependent. To tackle these problems, this paper presents DIVIDE, a component for a semantic IoT platform that adaptively derives and manages the queries of the platform’s stream processing components in a context-aware and scalable manner, and that enables privacy by design. By performing semantic reasoning to derive the queries when context changes are observed, their real-time evaluation does require any reasoning. The results of an evaluation on a homecare monitoring use case demonstrate how activity detection queries derived with DIVIDE can be evaluated in on average less than 3.7 seconds and can therefore successfully run on low-end IoT devices.

PO-04-004 UTILITY OF OPEN-WINDOW MAPPING FOR CATHETER ABLATION OF ACCESSORY PATHWAY

Open window mapping (OWM) is a novel automated mapping methods for catheter ablation of an accessory pathway (AP), in which the local signal is annotated with the windows of interest parameters set to analyze both atrial and ventricular signals. We sought to determine the utility of the OWM to identify the location of APs in patients with APs. In this two-center study, 28 patients (20 male, 55 years old: IQR 22-67) who underwent high-density OWM with extended early-meets-late (EEML) algorithm (CARTO 3, Biosense Webstar, USA) using a 20-electrode, 5-spline catheter (Pentaray, Biosense Webster) were enrolled. The lower threshold of the EEML was set to adjust the EEML gap to match propagation mapping (Figure A) and the EEML gap of > 1cm was defined as a wide AP (Figure B). The median mapping points, mapping time, and lower threshold of EEML were 2399 points (1736-3798), 24 minutes (16-30), and 22 (18-25), respectively. APs were successfully eliminated in all 28 patients (22 APs in mitral annulus and six APs in tricuspid annulus), in 19 of which were eliminated by the first radiofrequency (RF) application (68%). OWM revealed wide or multiple APs in 10 patients (36%), in all of whom multiple RF applications were required to eliminate the APs. OWM facilitates to visualize the location and width of APs, which is particularly useful to predict whether linear or cluster ablation lesion sets are required for wide APs.

Studies on effect of wire EDM process parameters on machining characteristics of Inconel 825 plate

Wire-cut Electrical Discharge Machining (WEDM) is an emerging engineering process in the field of micro parts manufacturing to fabricate the complex shapes of micro products. This machining method has been magnificently employed in machining various materials starting from Al metal, a highly ductile material to a very high hard material like ceramics and Inconel’s. Inconel and Ti-based alloys treasure trove wider applications in these present industries. Those parts are producing difficulty in several machining processes and create difficult interactions with machining process parameters. Inconel 825 is a Ni-based strength metal and temperature resistant. Conventional machining of these results in large amounts of cutting forces, more strain-hardened and toughened chips, and extreme tool wear. In this work, an effort is made to study the influence of WEDM process parameters such as Ton, Toff and WF on the output variables such as the Cutting Width of Wire-cut EDM for Inconel 825. Preliminary trials were conducted by varying the process parameters in order to design a process parameter window. Factorial design matrix has been developed with L9 numbers of experiments and the corresponding output/responses such as Kerf Width and surface roughness (Ra) were measured and recorded. The responses have been used to measure the Ra and kerf values of all the specimens after cutting by Wire cut EDM machine. Use of ANOVA using identifies the influenced process parameter on machining (cutting) Inconel 825 output responses Kerf Width and surface roughness.

Development and Application of Particle Transport Monte Carlo Simulation Modeling Toolkit

In particle transport Monte Carlo simulation, the manual modeling method is not suitable for complex geometric models. A Monte Carlo Simulation Modeling Toolkit (pyMCMT) is developed based on Python language. The toolkit can generate input card in several classical format such as MCNP and GDML format automatically. In this paper, two typical applications are presented based on the pyMCMT toolkit: 1, constructing a multilayer circular geometric model in complex density distribution by pyMCMT toolkit in order to satisfy the requirement of building large-space fine model with the influence of blast wave. A two-dimensional adaptive model is built for non-uniform atmospheric induced by blast wave. The number of the cells in this model is reduced vastly with the same model resolution; 2, producing the optimized weight window parameter using the mesh-based weight window variance reduction combined with density iteration method which is realized to solve the high statistical fluctuation and low counting efficiency problem in deep penetration process. The successful application of pyMCMT shows that pyMCMT is a modularized, parameterized, customizable and easy-to-debug toolkit for particle transport Monte Carlo simulation modeling, which can improve the modeling efficiency tremendously.

Role of Hydrogen in Ethylene-Based Synthesis of Single-Walled Carbon Nanotubes.

We examined the effect of hydrogen on the growth of single-walled carbon nanotubes in the aerosol (a specific case of the floating catalyst) chemical vapor deposition process using ethylene as a carbon source and ferrocene as a precursor for a Fe-based catalyst. With a comprehensive set of physical methods (UV-vis-NIR and Raman spectroscopies, transmission electron microscopy, scanning electron microscopy, differential mobility analysis, and four-probe sheet resistance measurements), we showed hydrogen to inhibit ethylene pyrolysis extending the window of synthesis parameters. Moreover, the detailed study at different temperatures allowed us to distinguish three different regimes for the hydrogen effect: pyrolysis suppression at low concentrations (I) followed by surface cleaning/activation promotion (II), and surface blockage/nanotube etching (III) at the highest concentrations. We believe that such a detailed study will help to reveal the complex role of hydrogen and contribute toward the synthesis of single-walled carbon nanotubes with detailed characteristics.

Nonlinear pulsational mode dynamics in strongly correlated dust molecular clouds

We theoretically study the nonlinear pulsational mode dynamics in strongly correlated (viscoelastic) self-gravitating complex charge-fluctuating dust molecular clouds (DMCs) on the astrophysical spatiotemporal scales. A nonlinear normal mode (local) analysis results in a unique pair of extended Kortweg de-Vries-Burgers (KdV-B) equations on the conjugational gravito-electrostatic potential fluctuations in a mixed form. The KdV-B system is numerically analysed in a wide-range parametric window relevant to realistic astronomical DMC circumstances. It is found that the fluctuation dynamics evolves as solitary-chain patterns. The electrostatic fluctuation amplitude increases with the referral frame velocity; whereas, the gravitational fluctuations are insensitive to this velocity. The fluctuation dynamics are found to be independent of variation in the equilibrium dust density, equilibrium dust charge, dust mass, and so forth. The validation and reliability checkup of our results is highlighted in fair corroboration with the literature. Our results could be useful in understanding the mechanism behind bounded structure formation via the non-local self gravitational collapse dynamics.

Ink‐Assisted Laser‐Induced Heterogeneous Permanent/Erasable Nanostructures on Metals

AbstractStructural color fabrication attracting significant interest for science and art is usually restricted by costly and time‐consuming structuring processes or inerasable characteristics of structures. Herein, by developing a universal, low‐cost technical solution—the nanosecond laser irradiation of ink (Solvent Black 7) layers coated on metal—laser‐induced heterogeneous permanent/erasable nanostructures (LIHPEN) are produced successfully on multiple metals, especially noble metals, without ablation damage to intrinsic surfaces. LIHPEN consist of uniform laser‐induced periodic surface structures produced by surface plasmon polariton excitation of metals and can be extended into 2D hybrid micro/nanostructures by introducing the direct laser interference patterning technique. LIHPEN technology operated under a large processing parameter window can realize space‐selective erasability of prepared structures by controlling processing parameters, which determine the ink‐layer carbonization degree and thus the permanent or erasable characteristic. Because ink layers can be coated manually, in addition to realizing digital scanning patterns, LIHPEN technology can be integrated with traditional artistic techniques to solidify and color artworks without risk of information loss and leakage in digital copying. LIHPEN with good durability can exhibit vivid structural colors on metals, demonstrating its great potential in fields of artwork iridescent coloring, encryption, and anti‐counterfeiting, particularly those requiring customization, personalization, and rewritability.

Temperature-induced logical resonance in the Hodgkin–Huxley neuron

Logical resonance has been demonstrated to be present in the FitzHugh–Nagumo (FHN) neuron, namely, the FHN neuron can operate as a reliable logic gate within an optimal parameter window. Here we attempt to extend the results to the more biologically realistic Hodgkin–Huxley (HH) model of neurons. In general, biological organisms have an optimal temperature at which the biological functions are most effective. In view of this, we examine if there is an optimal range of temperature where the HH neuron can work like a specific logic gate, and how temperature influences the logical resonance. Here we use the success probability P to measure the reliability of the specific logic gate. For AND logic gate, P increases with temperature T, reaches the maximum in an optimal window of T, and eventually decreases, which indicates the occurrence of the temperature-induced logical resonance phenomenon in the HH neuron. Moreover, single and double logical resonances can be induced by altering the frequency of the modulating periodic signal under the proper temperatures, suggesting the appearance of temperature-controlled transition of logical resonance. These results provide important clues for constructing neuron-based energy-efficient new-fashioned logical devices.

Intelligent Parking Control Method Based on Multi-Source Sensory Information Fusion and End-to-End Deep Learning

To address the challenges of inefficient intelligent parking performance and reduced efficiency in complex environments, this study presents an end-to-end intelligent parking control model based on a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) architecture incorporating multi-source sensory information fusion to improve decision-making and adaptability. The model can produce real-time intelligent parking control decisions by extracting spatiotemporal features, including comprehensive 360-degree panoramic images and ultrasonic sensor distance measurements. To enhance the coverage of real-world environments in the dataset, a data collection platform was developed, leveraging the PreScan simulation platform in conjunction with actual parking environments. Consequently, a comprehensive parking environment dataset comprising various types was constructed. A deep learning model was devised to ameliorate horizontal and vertical control in intelligent parking systems, integrating Convolutional Neural Networks and Long Short-Term Memory in a parallel configuration. By meticulously accounting for parking environment characteristics, sliding window parameters were optimized, and transfer learning was employed for secondary model training to fortify the prediction accuracy. To ascertain the system’s robustness, simulation tests were performed. The ultimate results from the actual environment experiment revealed that the end-to-end intelligent parking model substantially surpassed the existing approaches, bolstering parking efficiency and effectiveness in complex contexts.

Analysis of the Effect of Exhaust Configuration and Shape Parameters of Ventilation Windows on Microclimate in Round Arch Solar Greenhouse

The round-arch solar greenhouse (RASG) is widely used in the alpine and high latitude areas of China for its excellent performance. Common high temperature and high humidity environments have adverse effects on plants. It is extremely important to explore a reasonable and efficient ventilation system. A three-dimensional numerical simulation model of greenhouse ventilation considering crop canopy airflow disturbance was established. A robust statistical analysis to determine the validity of the model was calculated to thoroughly validate its overall performance. Microclimate distribution characteristics of nine kinds of exhaust configuration in greenhouse in summer were analyzed comparatively. It was determined that the highest ventilation efficiency could be achieved by adopting the combined configuration of rolling film at the south corner of the greenhouse and pivoting the window at the north side of the roof. In winter, the opening angle of ventilation window at the north side of the roof was less than 40° to ensure the rapid cooling of the interior of the greenhouse without the crops being affected by the cold environment. Through optimization analysis, the ventilation configuration with a deviation angle of 25° and a width of 900 mm is more reasonable (10 m span). The research results provide theoretical guidance for the design of the ventilation structure in RASG and further improve the sustainable development of the facility’s plant production.