Numerical Performance Research Articles

Numerical modeling and performance analysis of an open sorption energy storage system based on zeolite/water in building heating

The performance of sorption energy storage is influenced by operating conditions. Based on a zeolite/water reactor, a mathematical model of an open sorption energy storage system is established and the effects of several operating parameters are studied. Increasing the temperature in the charging process enhances mass transfer. Increasing the air mass flow rate increases the heat transfer coefficient but decreases the temperature and reduces the mass transfer coefficient. A higher mass transfer coefficient is beneficial for energy storage. Inlet air humidity has little effect on the heat transfer coefficient but increases the mass transfer coefficient, thereby promoting energy release. Meanwhile, reactor inlet air temperature is the main factor affecting the charging process and the initial water uptake is an important parameter in the discharge process. Higher inlet moisture and lower initial water uptake can achieve better thermal performance. An 80 kg/h air mass flow rate and 6 of collector cascade numbers are recommended, which can achieve 5.84 kWh of the cumulative charged energy and 48.2 % charging efficiency. Moreover, the long-term stable heat supply with a discharging efficiency of 49.0 % can be achieved at air 75 kg/h of mass flow rate and 8 g/kg of inlet air moisture.

Weak convergence of tamed exponential integrators for stochastic differential equations

We prove weak convergence of order one for a class of exponential based integrators for SDEs with non-globally Lipschitz drift. Our analysis covers tamed versions of Geometric Brownian Motion (GBM) based methods as well as the standard exponential schemes. The numerical performance of both the GBM and exponential tamed methods through four different multi-level Monte Carlo techniques are compared. We observe that for linear noise the standard exponential tamed method requires severe restrictions on the step size unlike the GBM tamed method.

A Succinct Review on the Numerical and Experimental Performance Evaluation Techniques for Composite Marine Propellers

A Succinct Review on the Numerical and Experimental Performance Evaluation Techniques for Composite Marine Propellers

Developing a lightweight corrugated sandwich panel based on tea oil camellia shell: correlation of experimental and numerical performance

This study presents an experimental and numerical comparison between the mechanical performance of a lightweight corrugated sandwich panel based on the tea oil camellia shell (TOCS). Hence, TOCS was mixed in two groups with Poplar particles and fibers. After that, in the experimental part, the conventional mechanical tests, including the 3-point bending test, flatwise compression, dowel bearing, and screw resistance, and in the numerical part, finite element analysis (FEA), including the normal, maximum principal, and equivalent (von Mises) stress by Ansys Mechanical software carried out. The specimens for experimental and numerical tests were prepared in transverse and longitudinal directions. Before that, the engineering data (shear modulus, Young's modulus, and Poisson's ratio) for improving the FEA simulation were obtained from TOCS-based flat panels fabricated with a mixture of Poplar particles and fibers. The results of FEA are used to compare the mechanical behavior and failure mechanism with the results of experimental tests. According to the mean values of bending stiffness and maximum bending moment, sandwich panels made with 100% particles demonstrated an advantage in both directions. Nevertheless, the compression strength and screw resistance showed the same trend, but the dowel bearing showed higher values for panels made with fibers. The observed results of equivalent (von Mises) stress indicated a coloration with the results of failure mechanisms.

An algebraic global linelet preconditioner for incompressible flow solvers

Given the wide range of scales present in fluid dynamics, highly anisotropic meshes are often employed to resolve boundary layer flow at high Reynolds numbers. However, such meshes result in distorted elements, which can deteriorate the convergence of the preconditioned conjugate gradient solver (PCG) used to solve the discrete pressure equations. To address this issue, the Linelet Preconditioner is commonly used to accelerate the convergence rate of the PCG by building line segments (linelets) along the direction of the strongest couplings and applying a specific operation to each linelet. Under the current state of the art, linelets are constrained to operate independently within each domain partition. In these scenarios, PCG convergence was observed to deteriorate when the number of domain partitions is increased. This work proposes a communication step to be integrated into the preconditioning step, enabling different linelets to exchange information. The proposed method is observed to improve the convergence rate of linear systems with high stiffness due to highly stretched elements. Also, an algorithm is developed to generate the preconditioning matrix by purely algebraic considerations, in contrast to the usual approach of building linelets based on geometrical considerations. Furthermore, since the current approach is agnostic to the domain partition, it does not impose any constraint on the domain decomposition. Hence, the proposed method is a robust preconditioner with a numerical performance almost independent of the quality of the domain decomposition, providing a way for a more balanced load distribution.

Development of numerical and experimental performance evaluation techniques for high-reliability turbine flow meters of ice-making water-dispenser

A turbine flowmeter for sensing the water supply in an ice-making system of a refrigerator requires high sensitivity to the flow rate. This research aims to develop a comprehensive performance evaluation technique by constructing an experimental setup and establishing a numerical technique to simulate the rotational motion of the impeller in response to the injection of water into the flowmeter. An experimental rig was built to secure the measurement accuracy according to the fluid temperature and pressure changes and verify the reliability under various flow conditions from low to high. A numerical method based on the six-degree of freedom method was used to confirm its validity, which is different from a moving reference frame, which numerically analyzes the rotational speed of the turbine by backtracking the rotational speed to correspond to the flow conditions by giving it a constant rotational speed. By simulating water injection into the flowmeter, generating kinetic energy in the impeller according to the flow rate, and subsequently rotating the impeller due to the generated torque, the numerical analysis cost was reduced, allowing direct numerical analysis of the impeller’s acceleration motion. The results were evaluated in terms of angular velocity and pulse signal, and the applicability and reliability of the numerical technique were verified by comparing the numerical results with experimental results. The aerodynamic performance of the impeller was analyzed to understand the phenomena as the impeller rotates. The results of this study can be utilized to conduct future research to improve the performance of flow meters, thus proving its value as a preliminary study.

Optimal Design of I-PD and PI-D Industrial Controllers Based on Artificial Intelligence Algorithm

This research aims to apply Artificial Intelligence (AI) methods, specifically Artificial Immune Systems (AIS), to design an optimal control strategy for a multivariable control plant. Two specific industrial control approaches are investigated: I-PD (Integral-Proportional Derivative) and PI-D (Proportional-Integral Derivative) control. The motivation for using these variations of PID controllers is that they are functionally implemented in modern industrial controllers, where they provide precise process control. The research results in a novel solution to the control synthesis problem for the industrial system. In particular, the research deals with the synthesis of I-P control for a two-loop system in the technological process of a distillation column. This synthesis is carried out using the AIS algorithm, which is the first application of this technique in this specific context. Methodological approaches are proposed to improve the performance of industrial multivariable control systems by effectively using optimization algorithms and establishing modified quality criteria. The numerical performance index ISE justifies the effectiveness of the AIS-based controllers in comparison with conventional PID controllers (ISE1 = 1.865, ISE2 = 1.56). The problem of synthesis of the multi-input multi-output (MIMO) control system is solved, considering the interconnections due to the decoupling procedure.

Leveraging Narrative Feedback in Programmatic Assessment: The Potential of Automated Text Analysis to Support Coaching and Decision-Making in Programmatic Assessment.

Current assessment approaches increasingly use narratives to support learning, coaching and high-stakes decision-making. Interpretation of narratives, however, can be challenging and time-consuming, potentially resulting in suboptimal or inadequate use of assessment data. Support for learners, coaches as well as decision-makers in the use and interpretation of these narratives therefore seems essential. We explored the utility of automated text analysis techniques to support interpretation of narrative assessment data, collected across 926 clinical assessments of 80 trainees, in an International Medical Graduates' licensing program in Australia. We employed topic modelling and sentiment analysis techniques to automatically identify predominant feedback themes as well as the sentiment polarity of feedback messages. We furthermore sought to examine the associations between feedback polarity, numerical performance scores, and overall judgments about task performance. Topic modelling yielded three distinctive feedback themes: Medical Skills, Knowledge, and Communication & Professionalism. The volume of feedback varied across topics and clinical settings, but assessors used more words when providing feedback to trainees who did not meet competence standards. Although sentiment polarity and performance scores did not seem to correlate at the level of single assessments, findings showed a strong positive correlation between the average performance scores and the average algorithmically assigned sentiment polarity. This study shows that use of automated text analysis techniques can pave the way for a more efficient, structured, and meaningful learning, coaching, and assessment experience for learners, coaches and decision-makers alike. When used appropriately, these techniques may facilitate more meaningful and in-depth conversations about assessment data, by supporting stakeholders in interpretation of large amounts of feedback. Future research is vital to fully unlock the potential of automated text analysis, to support meaningful integration into educational practices.

Comparison of Clinical Outcomes After Medial Patellofemoral Ligament Reconstruction With Allograft Versus Autograft: A Matched-Cohort Analysis.

Disruption of the medial patellofemoral ligament (MPFL) may lead to recurrent lateral patellar dislocation and patellofemoral chondral injury. Despite significant previous work investigating numerous performance parameters, the optimal graft choice for MPFL reconstruction for patellar instability remains unclear. To compare functional outcomes scores, subjective recurrent instability, and revision rates between autograft and allograft in MPFL reconstruction. Cohort study; Level of evidence, 3. Patients who underwent MPFL reconstruction with autograft between 2013 and 2018 were identified. A 2:1 comparison group of patients who underwent MPFL reconstruction with allograft was matched by sex, age (±3 years), and body mass index (BMI) (±3 kg/m2). Patient characteristics, preoperative radiograph measurements, and intraoperative data were compared between the groups, as were patient-reported outcome measures, including International Knee Documentation Committee (IKDC) score, Lysholm score, Single Assessment Numerical Evaluation (SANE), and visual analog scale (VAS) for pain. Subjective recurrent instability and revision rate were also compared between groups. The autograft group was composed of 30 patients (13 male, 17 female) with a mean age of 24.4 years and mean BMI of 25.0 kg/m2, and the allograft group was composed of 60 matched patients (25 male, 35 female) with a mean age of 24.1 years and mean BMI of 25.1 kg/m2. The autograft and allograft groups reported similar IKDC scores (73.0 vs 73.7; P = .678), Lysholm scores (77.5 vs 80.7; P = .514), SANE (72.0 vs 75.8; P = .236), and VAS pain (30.7 vs 26.6; P = .482), as well as similar rates of postoperative patellar subluxations (20.0% vs 19.3%; P = .867) and dislocations (10.0% vs 15.0%; P = .805). Both allograft and autograft were found to be viable options for MPFL reconstruction. There were no significant group differences in failure rates, patient-reported outcomes, pain, or complications between autograft and allograft MPFL reconstruction in this series.

Improving prediction of linear regression models by integrating external information from heterogeneous populations: James-Stein estimators.

We consider the setting where (1) an internal study builds a linear regression model for prediction based on individual-level data, (2) some external studies have fitted similar linear regression models that use only subsets of the covariates and provide coefficient estimates for the reduced models without individual-level data, and (3) there is heterogeneity across these study populations. The goal is to integrate the external model summary information into fitting the internal model to improve prediction accuracy. We adapt the James-Stein shrinkage method to propose estimators that are no worse and are oftentimes better in the prediction mean squared error after information integration, regardless of the degree of study population heterogeneity. We conduct comprehensive simulation studies to investigate the numerical performance of the proposed estimators. We also apply the method to enhance a prediction model for patella bone lead level in terms of blood lead level and other covariates by integrating summary information from published literature.

Autoethnographic Memoirs of Rimba Resonance Vibes Ensemble Festival Performances

Marimba music performance practice has emerged globally in hundreds of marimba ensembles since the Kwanongoma marimba was designed in Bulawayo in the 1960s. The purpose of the study is to explore, the situation of marimba performance within Zimbabwe from an emic perspective. The country has a fledgling economy which is a push factor leading to the problem of about eighty-five percent of the population intending to relocate to other countries. This has not spared the marimba music industry as numerous marimba builders, performers and tutors have already, or intend to migrate. In this autoethnography I present perspectives on my marimba music performance experiences at local and regional festivals in Zimbabwe and South Africa. I conduct a qualitative autoethnographic exploration of Rimba Resonance Vibes Ensemble’s performances, describe the culture of Zimbabwe marimba music performance practices, and reflect on various permutations of the practice in which the art has been kept alive in Zimbabwe in the wake of a brain drain. The findings show multiple challenges that we face as marimba musicians. There is a glaring need for agency and advocacy to sustain the art of marimba music performance by bands based in Zimbabwe.

An N - policy M/M/1 queueing model for energy saving mechanism in Networks

The conservation of power in wireless sensor networks (WSNs) is critical due to the difficulty of replacing or recharging batteries in remote sensor nodes. Additionally, sensor node failure is inevitable in WSNs. In order to overcome these difficulties, this study proposes an N-policy M/M/1 queuing system model with an unstable server. This model offers insightful information for improving performance, maximizing energy use, and prolonging the lifespan of WSNs. The study looks into how different parameters and N-values affect the system's performance by examining the average system size under various scenarios. Important performance parameters are taken into consideration in the suggested model, including the total system size and the average number of data packets in the busy, idle, and down stages. These measurements aid in the comprehension of the behavior of the system and direct the N-policy optimization process to reduce setup, holding, server downtime, and operating state expenses. The analytical results are supported by numerical representations that show system size is reduced by higher repair and service rates and increased by higher breakdown rates. The cost function is estimated using MATLAB simulations, which also find the ideal N-value to reduce the overall predicted cost per unit of time. The findings demonstrate that an ideal N-policy can greatly enhance system performance and energy efficiency, guaranteeing the WSN functions well even in the face of node failure and power limitations. Based on in-depth numerical analysis and performance evaluation, this paper offers a thorough framework for improving WSN efficiency and reliability through strategic N-policy implementation.

Numerical Analysis of Flow Velocity and Performance Test of Cross-Flow Turbine (500 W)

The purpose of this study is to analyze the flow velocity and assess the performance of a cross-flow turbine through numerical simulations and performance tests. Using computational fluid dynamics (CFD), the flow patterns and velocity distributions around the turbine blades to gain insights into the turbine's hydrodynamic behavior. In the experimental phase, constructed a prototype of the cross-flow turbine and tested it in a water flume, measuring key performance metrics such as power output and efficiency.The test were performed under design flow condition. The numerical results were validated with performance tests. The numerical analysis revealed critical areas of flow separation and turbulence, which were consistent with the observed experimental data.

Variation in Flow Characteristics and Power Performance Due to Axial Distance Optimization in the Design of Counter-Rotating Tidal Turbines

Counter-rotating turbines, designed to enhance the performance efficiency of tidal turbines, exhibit variable operational characteristics depending on the axial distance between the front and rear blades. This study encompassed both numerical analyses and performance experiments to establish the optimal design by examining the relationship between flow field alterations and the performance of a counter-rotating tidal turbine with varied axial distances. The blades of the counter-rotating tidal turbine, based on a 10-kW single turbine, were designed utilizing the Blade Element Momentum technique. The turbine blades were assessed for changes in output performance attributed to flow separation by analyzing the velocity distribution and separation points within the flow, demonstrating a maximum power coefficient of 40.3% at a design Tip Speed Ratio of 3. At y/D = 0.3. The counter-rotating tidal turbine achieved a maximum power coefficient of 47%, with performance enhancements of the rear blades driven by the accelerated wake of the front blades. Furthermore, the pressure coefficients of the blades, influenced by their shape, inflow velocity, and angle, were detailed separately for the suction and pressure sides. The study also explored the correlation between the flow characteristics and the output performance of each blade by analyzing the distribution of pressure coefficients.

Cooperative Communication Based Protocols for Underwater Wireless Sensors Networks: A Review.

Underwater wireless sensor networks are gaining popularity since supporting a broad range of applications, both military and civilian. Wireless acoustics is the most widespread technology adopted in underwater networks, the realization of which must face several challenges induced by channel propagation like signal attenuation, multipath and latency. In order to address such issues, the attention of researchers has recently focused on the concept of cooperative communication and networking, borrowed from terrestrial systems and to be conveniently recast in the underwater scenario. In this paper, we present a comprehensive literature review about cooperative underwater wireless sensor networks, investigating how nodes cooperation can be exploited at the different levels of the network protocol stack. Specifically, we review the diversity techniques employable at the physical layer, error and medium access control link layer protocols, and routing strategies defined at the network layer. We also provide numerical results and performance comparisons among the most widespread approaches. Finally, we present the current and future trends in cooperative underwater networks, considering the use of machine learning algorithms to efficiently manage the different aspects of nodes cooperation.

Optimizing inorganic SnS/ZrS2 heterojunction solar cells: Numerical analysis and performance insights

The SnS and ZrS2 have emerged as promising photovoltaic materials. This paper gives the first numerical analysis of inorganic SnS/ZrS2 heterojunction solar cells using SCAPS-1D. The study focuses on the effect of several parameters, such as thickness, doping charge carrier concentration, and energy bandgap, on fundamental solar cell properties in both the window and active layers. Our findings show that a variety of parameters influence solar cell performance, including built-in voltage, minority charge carrier lifetime, depletion breadth, charge carrier collection length, photogenerated current, and recombination rate. The maximum efficiency (η) attained in our simulated devices was 32.13 %, with FF of 84.51 % and Voc of 0.796 V. To achieve this efficiency, particular SnS parameters were used, such as a band gap of 1.00 eV, thickness of 5.0 μm, and doping charge carrier concentration of 1020 cm−3. In ZrS2, characteristics such as a band gap of 1.2 eV, thickness of 0.2 μm, and doping charge carrier content optimized 1020 cm−3 contribute to the observed efficiency. Our simulation results indicate that inorganic SnS/ZrS2 heterojunction devices have potential for solar device production that is cost-effective, large-scale, and high-efficiency. High performance enables a new path toward clean energy.

Numerical simulation and performance evaluation of compression members in long-span single-layer spatial grid structures constrained by steel bars

The collapse of long-span single-layer spatial grid structures can be triggered by the instability of compression members. To improve the stability of axially compressed members, this study proposes two novel compression members that are constrained using steel bars based on the design concept of existing constraint measures. A validated FE analysis is adopted to investigate the compressive mechanical behaviors, including parametric analysis. Members with different constraint measures are applied to single-layer latticed domes, and their feasibility in improving the progressive collapse resistance of the domes is evaluated. The results show that restraint measures using steel bars can significantly improve the stability of compression members, and when the ends of the restraint body are able to slide, the bearing capacity and ductility are greatly improved. The improvement can be maximized by reasonably designing various parameters of the constraint body. In addition, using different measures to constrain the compression members with higher key grades and thus improve the progressive collapse resistance of single-layer latticed domes is found to be feasible. However, the study finds that the failure mechanism of the constrained members sliding at the ends of the constraint body cannot be fully realized following dome collapse.

Systematic approach to measure the performance of microchannel-plate photomultipliers

In this paper, we present our approach to systematically measure numerous performance parameters of microchannel plate photomultiplier tubes. The experimental setups, the analyses and selected results are discussed. Although the techniques used may be different in other locations, the document is intended as a guide for comparable measurements with other types of microchannel plate photomultiplier tubes. Measurements are shown for the following performance parameters: spectral and spatial quantum efficiency, collection efficiency, gain as a function of voltage, position and magnetic field, time resolution, rate capability and lifetime. By using a dedicated 3-axis stepper and an FPGA-based data acquisition system, also inner PMT parameters are measured as a function of the active area, such as relative detection efficiency, dark count rate, time resolution, recoil electron and afterpulse distributions, as well as charge sharing and electronic crosstalk. In addition, some of the parameters are investigated inside a strong magnetic field. For many of these measurements, the change of most setup parameters and the subsequent analysis can be controlled semi-automatically by software scripts.

FETILDA: Evaluation Framework for Effective Representations of Long Financial Documents

In the financial sphere, there is a wealth of accumulated unstructured financial data, such as the textual disclosure documents that companies submit on a regular basis to regulatory agencies, such as the Securities and Exchange Commission. These documents are typically very long and tend to contain valuable soft information about a company’s performance that is not present in quantitative predictors. It is therefore of great interest to learn predictive models from these long textual documents, especially for forecasting numerical key performance indicators. In recent years, there has been great progress in natural language processing via pre-trained language models (LMs) learned from large corpora of textual data. This prompts the important question of whether they can be used effectively to produce representations for long documents, as well as how we can evaluate the effectiveness of representations produced by various LMs. Our work focuses on answering this critical question, namely, the evaluation of the efficacy of various LMs in extracting useful soft information from long textual documents for prediction tasks. In this article, we propose and implement a deep learning evaluation framework that utilizes a sequential chunking approach combined with an attention mechanism. We perform an extensive set of experiments on a collection of 10-K reports submitted annually by U.S. banks, and another dataset of reports submitted by U.S. companies, to investigate thoroughly the performance of different types of language models. Overall, our framework using LMs outperforms strong baseline methods for textual modeling as well as for numerical regression. Our work provides better insights into how utilizing pre-trained domain-specific and fine-tuned long-input LMs for representing long documents can improve the quality of representation of textual data and, therefore, help in improving predictive analyses.

Dynamic analysis of the three-phase magneto-electro-elastic (MEE) structures with the finite element method enriched by proper enrichment functions

In this study, a carefully designed enriched finite element method (EFEM) is presented to improve the solution accuracy of the conventional FEM by analyzing the dynamic behavior of the magnetic-electric-elastic (MEE) composite structures, which are frequently used in designing various smart and intelligent devices. By formulating the proper EFEM with ideal numerical performance, different enrichment functions are considered and the corresponding solution quality of different versions of the EFEM is compared and examined in great detail. When the Lagrange polynomial basis functions together with the harmonic trigonometric functions are used as enrichment functions, the obtained EFEM shows extremely powerful and ideal numerical performance, which is obviously better than the other versions of EFEM and the conventional FEM, in studying the free vibration and harmonic frequency responses of the MEE structures. Nearly exact numerical solutions for three-phase physical fields of MEE structures can be generated by the proposed EFEM even if very coarse mesh patterns are used. Intensive numerical studies are conducted to confirm and verify the excellent properties of the proposed EFEM in performing dynamic analysis of the MEE structures.