Accurate Performance Prediction Research Articles

Assessing acoustic performance of building material: A finite element model for 3D printed multilayer micro-perforated panels with compressed earth blocks

This study deals with 3D printing multilayered micro-perforated panels (M-MPPs) coupled with buildings materials as compressed earth block for noise reduction applications. The sound absorption coefficient α is utilized as a metric to assess the sound insulation capabilities across a frequency range spanning from 10 to 3000 Hz, then evaluated and validated by numerical and experimental methods. The FEM model developed makes it possible to predict the acoustic absorption of M-MPPs by tuning the frequency range and varying optimized acoustics parameters, considering hole-hole interaction and taking into account visco-thermal effects that are present in compressed earth blocks. It is shown that the shape of perforations and material properties including the porosity rate, arrangement in the design of multilayer micro-perforated structures are identified to play a significant role in the sound performance of the entire structure. In addition, the application of MPP coupled with compressed earth blocks improve the sound absorption capacity of the composite structure. The developed FEM leads to accurate prediction of performance, efficient optimization, and cost effectiveness. Finally, the present study reveals the importance of M-MPPs combined with compressed earth blocks (CEBs) as viable noise reduction materials, particularly relevant for engineering applications and development initiatives in emerging economies.

Bayesian Neural Networks for Solar Power Forecasts in Advanced Thermoelectric Systems

This study presents a Bayesian neural network for the comprehensive performance forecast of an advanced thermoelectric system featuring segmented thermocouples with variable area shape for optimal solar energy conversion. The training data is obtained from a three-dimensional numerical model developed in ANSYS software to account for the energy, exergy, and entropy flows in the thermoelectric module to maximize the power output, energy, and exergy efficiencies while minimizing the entropy generation and system irreversibilities. The thermodynamic model optimizes key parameters, including geometry (height, cross-section, and percentage skutterudite content) across realistic diverse heat fluxes and cooling coefficients. To facilitate a faster and more efficient optimization pipeline, various neural networks, leveraging algorithms like Levenberg-Marquardt, scaled conjugate gradient, and Bayesian regularization, are utilized for efficient data management and accurate performance prediction. The Bayesian neural network emerges as the most accurate and efficient model providing the lowest training and testing loss of ∼10-9. Furthermore, key findings highlight the importance of skutterudite content and solar concentration ratio. For instance, an optimal skutterudite content of 6.2% at 25 Suns produces maximum power of 29.7 W, while 37% skutterudite at 75 Suns minimizes exergy destruction. This study's methodology and findings contribute to designing more efficient clean energy systems and advancing sustainable energy goals.

Life prediction of lithium battery based on particle filter and BP neural network

Abstract This paper focuses on using Particle Filter (PF) and Back Propagation (BP) neural networks for RUL prediction. This fusion strategy uses particle filtering (PF) for battery system state estimation and prediction while considering various types of noise and uncertainties to assess the system state comprehensively. Furthermore, the strategy employs a BP neural network to learn and combine historical data patterns with particle filtering estimation, improving prediction accuracy and reliability. This study validates the proposed fusion method by comparing it with the original PF and BP prediction methods using Toyota Motor Corporation and Stanford University datasets. The experimental results demonstrate its superior performance and higher prediction accuracy.

Graph Relearn Network: Reducing performance variance and improving prediction accuracy of graph neural networks

Recent studies show that the predictive performance of graph neural networks (GNNs) is inconsistent and varies across different experimental runs, even with identical parameters. The prediction variability limits GNNs’ applicability, and the underlying reasons remain unclear. We identified a key factor contributing to this issue: the oscillation of the predicted classes of some nodes during GNN training. To address this problem, we propose a novel framework, known as Graph Relearn Network (GRN), designed to reduce prediction variance by iteratively refining the predictions of unstable nodes. The GRN framework operates in two phases: pre-predict and relearn. During the pre-predict phase, a graph-dense encoder is trained to pre-predict the node categories. In the relearn phase, the model intensively focuses on the unstable nodes to optimize the predictions. Extensive experiments on ten graph datasets demonstrate that the GRN significantly enhances the performance stability of GNNs (with std. reduced by up to 75%), and achieves state-of-the-art performance in prediction accuracy (increased by up to 11.97%). By mitigating the instability introduced by unstable nodes, GRN enhances both the performance stability and prediction accuracy of GNNs in node classification tasks.

Performance Analysis of Water Heat Pipe for Application of the Passive Cooling System in Nuclear Power Plants

Incorporating heat pipes into passive cooling systems in nuclear reactors offers the benefits of passive operation without external power, a simple design, and high thermal capacity. Accurate thermal performance prediction of the heat pipe is crucial for ensuring safe reactor design and operation. Prior studies on nuclear reactor systems utilizing heat pipes have focused on thermosyphons, which operate by gravity. However, to expand the range of heat pipe applications in reactor systems, experimental investigations of large-scale heat pipes driven by capillary pumping force are required. In this study, a water heat pipe with a 25.4-mm diameter and 4-m length was manufactured to provide thermal experimental results under extreme conditions, such as system rollover or loss-of-cooling accidents. A three-dimensional (3D) printing technique was used to fabricate the high-performance lattice capillary wick structure by combining cubic and diamond lattice structures. The 3D printed wick structure showed 21 to 165 times higher capillarity and enhanced surface properties compared to the screen mesh wick structure. Compared to wickless thermosyphons, the 3D printed wick heat pipe exhibited higher thermal conductivity, stable operation in both vertical and horizontal orientations, and faster startup under extreme conditions.

Bayesian expectation maximization-maximization for robust estimation in proton exchange membrane fuel cells: A comparative study

In this paper, we present a novel approach based on Bayesian Expectation Maximization-Maximization (BEMM) to address this challenge. Unlike traditional optimization methods, which may struggle with high-dimensional and nonlinear optimization problems, BEMM offers a robust framework that combines the benefits of Bayesian inference with the flexibility of expectation maximization and maximization techniques. By iteratively updating parameter estimates based on observed data and maximizing the likelihood of the model, BEMM effectively navigates the solution space to converge on accurate estimates of the unspecified variables in Proton Exchange Membrane Fuel Cells (PEMFCs)models. Through extensive experimentation and comparison with other metaheuristic techniques, including Arithmetic Optimization Algorithm (AOA), Gravitational Search Algorithm (GSA), Flower Pollination Algorithm (FPA), and Biogeography-Based Optimization (BBO), we demonstrate the superior performance of our BEMM approach. Our results show that BEMM outperforms these alternative methods in terms of precision, convergence speed, and stability across various scenarios involving different numbers of unspecified variables. The implications of our findings are significant for both researchers and practitioners in the field of PEMFC modeling and optimization. By providing a more efficient and reliable method for estimating model parameters, our approach can facilitate more accurate predictions of PEMFC performance, leading to better-informed decision-making in the design, operation, and optimization of PEMFC systems. Furthermore, the robustness and versatility of BEMM make it well-suited for a wide range of optimization problems beyond PEMFC modeling, highlighting its potential impact across various domains of engineering and scientific research. In the sensitivity analysis, as the population size increases from 10 to 40, there is a significant improvement in solution quality by approximately 100 %. However, beyond a population size of 40, the marginal gains diminish, with only marginal improvements of less than 1 % observed despite further increases in population size up to 200.

A Critical Review of Emerging Technologies for Flash Flood Prediction: Examining Artificial Intelligence, Machine Learning, Internet of Things, Cloud Computing, and Robotics Techniques

There has been growing interest in the application of smart technologies for hazard management. However, very limited studies have reviewed the trends of such technologies in the context of flash floods. This study reviews innovative technologies such as artificial intelligence (AI)/machine learning (ML), the Internet of Things (IoT), cloud computing, and robotics used for flash flood early warnings and susceptibility predictions. Articles published between 2010 and 2023 were manually collected from scientific databases such as Google Scholar, Scopus, and Web of Science. Based on the review, AI/ML has been applied to flash flood susceptibility and early warning prediction in 64% of the published papers, followed by the IoT (19%), cloud computing (6%), and robotics (2%). Among the most common AI/ML methods used in susceptibility and early warning predictions are random forests and support vector machines. However, further optimization and emerging technologies, such as computer vision, are required to improve these technologies. AI/ML algorithms have demonstrated very accurate prediction performance, with receiver operating characteristics (ROC) and areas under the curve (AUC) greater than 0.90. However, there is a need to improve on these current models with large test datasets. Through AI/ML, IoT, and cloud computing technologies, early warnings can be disseminated to targeted communities in real time via electronic media, such as SMS and social media platforms. In spite of this, these systems have issues with internet connectivity, as well as data loss. Additionally, Al/ML used a number of topographical variables (such as slope), geological variables (such as lithology), and hydrological variables (such as stream density) to predict susceptibility, but the selection of these variables lacks a clear theoretical basis and has inconsistencies. To generate more reliable flood risk assessment maps, future studies should also consider sociodemographic, health, and housing data. Considering future climate change impacts, susceptibility or early warning studies may be projected under different climate change scenarios to help design long-term adaptation strategies.

Predicting cochlear implant performance: Impact of demographic, audiologic, surgical factors, and cochlear health

Predicting cochlear implant performance: Impact of demographic, audiologic, surgical factors, and cochlear health Amit Walia, Matt Shew and Craig A. Buchman from Washington University School of Medicine, explain the challenges of understanding variables or factors informing CI performance and how this can be addressed. Cochlear implants (CIs) are highly effective for restoring hearing in individuals with moderate-to-profound hearing loss who do not gain sufficient benefit from hearing aids. Despite being one of modern medicine’s most successful advancements, there is wide inter-individual variability in speech perception outcomes (Figure 1). The drivers of this variability are not well understood, making it difficult to set realistic expectations for patients before surgery and to evaluate potential interventions that may enhance performance post-operatively. Accurate prediction of speech perception performance after surgery could significantly impact how we assess candidates, plan postoperative aural rehabilitation, choose surgical techniques, design and fit electrodes, and segment the patient population in future CI clinical trials.

Enhancing robustness and accuracy of supercritical CO2 compressor performance prediction in closed Brayton cycles: A thermodynamic properties-based numerical method

The optimization of the closed Brayton cycle results in the inlet condition of supercritical CO2 (sCO2) compressors being near the critical point. However, this proximity leads to decreased robustness and accuracy during numerical simulations. To enhance computational fluid dynamics (CFD) robustness and accuracy, a method utilizing Cubic B-spline smoothing is proposed to reconstruct thermodynamic properties using property look-up tables (LUTs). After revealing the characteristics and action mechanism of sCO2 thermodynamic properties on the solver, effects of the method on solution robustness, accuracy, and the relationship with resolution are systematically investigated. The results demonstrate that this method effectively addresses the deficiencies arising from the inability of original LUTs to adapt to the high resolution and the presence of pseudo-convergence. The residuals achieve the convergence requirement in half the iteration steps compared to the original method, resulting in an over one-fold reduction in calculation time. The solution robustness is significantly enhanced by over tenfold, and the calculation accuracy is improved. This improvement increases the sensitivity of friction loss and secondary flow loss to drastic changes in thermodynamic properties. As a result, a highly robust and accurate performance prediction scheme for centrifugal compressor components is established.

Modelling Anti-Corrosion Coating Performance of Metallic Bipolar Plates for PEM Fuel Cells: A Machine Learning Approach

Proton exchange membrane (PEM) fuel cells have significant potential for clean power generation, yet challenges remain in enhancing their performance, durability, and cost-effectiveness, particularly concerning metallic bipolar plates, which are pivotal for lightweight compact fuel cell stacks. Protective coatings are commonly employed to combat metallic bipolar plate corrosion and enhance water management within stacks. Conventional methods for predicting coating performance in terms of corrosion resistance involve complex physical-electrochemical modelling and extensive experimentation, with significant time and cost. In this study machine learning techniques are employed to model metallic bipolar plate coating performance, diamond-like-carbon coatings of varying thicknesses deposited on SS316L are considered, and coating performance is evaluated using potentiodynamic polarization and electrochemical impedance spectroscopy. The obtained experimental data is split into two datasets for machine learning modelling: one predicting corrosion current density and another predicting impedance parameters. Machine learning models, including extreme gradient boosting (XGB) and artificial neural networks (ANN), are developed, and optimized to predict coating performance attributes. Data preprocessing and hyperparameter tuning are carried out to enhance model accuracy. Results show that ANN outperforms XGB in predicting corrosion current density, achieving an R2 > 0.98, and accurately predicting impedance parameters with an R2 > 0.99, indicating that the models developed are very promising for accurate prediction of the corrosion performance of coated metallic bipolar plates for PEM fuel cells.

Enhanced Method for Estimating Unknown Parameters in Single Diode Model for Solar Photovoltaic Cells

Three models of equivalent circuit are frequently applied for modelling solar cells/modules: single, double and three diode models. Among these, the single diode (SD) model is currently the most widely applied for solar cell modelling. This model is characterized by five parameters, which can be estimated using various methods. In the present work, the unknown parameters of this model are determined using an improved hybrid method. Our research introduces an innovative approach for this purpose. Our method formulates and resolves the equations defining these critical parameters. Particularly, our main objective revolves around estimating the diode's saturation current (I0), series resistance, photocurrent (Iph) and parallel resistance (Rsh), ideality factor ( through these equations. We confirmed our method reliability by thoroughly simulating the I-V curves of six PV cells, showing the credibility and the precision of our approach. This research significantly advances solar cell modelling by offering an improved technique for estimating crucial parameters within the SD model, enhancing the accuracy and reliability of solar cell performance predictions.

A combinatorial prediction model for the performance of fuel cell air compressor with air bearings considering accelerated decay characteristics

As the core component in air supply system, the air compressor plays a key role in efficient operation of fuel cell system. In this research, accelerated endurance tests for air compressor with air bearings were conducted based on designed operating condition, and the Hybrid Variational Artificial Bee Colony (HyVABC) algorithm was proposed to achieve enhancement and balance of global optimization capability and local feature capture ability. On this basis, combinatorial prediction models were developed to realize accurate performance prediction of the compressor considering accelerated decay characteristics. The results indicate that the degradation degree of pressure ratio is the greatest at the flow rate of 133 g/s and the speed of 100000 rpm, with the degradation rate of 25.9% compared with that before break-in. The HyVABC algorithm obtains optimal values on 16 out of 20 test problems, which highlights its strong optimization performance, fast convergence speed and high robustness. The XGBoost-HyVABC model is chosen to describe mapping relationship between operating and performance parameters of the compressor, and absolute values of relative errors between predicted and test values are below 3%. All these provide efficient heuristic algorithm and accurate combinatorial model for accelerated decay performance prediction of fuel cell air compressors.

Machine learning and Bayesian optimization for performance prediction of proton-exchange membrane fuel cells

Proton-exchange membrane fuel cells (PEMFCs) are critical components of zero-emission electro-hydrogen generators. Accurate performance prediction is vital to the optimal operation management and preventive maintenance of these generators. Polarization curve remains one of the most important features representing the performance of PEMFCs in terms of efficiency and durability. However, predicting the polarization curve is not trivial as PEMFCs involve complex electrochemical reactions that feature multiple nonlinear relationships between the operating variables as inputs and the voltage as outputs. Herein, we present an artificial-intelligence-based approach for predicting the PEMFCs’ performance. In that way, we propose first an explainable solution for selecting the relevant features based on kernel principal component analysis and mutual information. Then, we develop a machine learning approach based on XGBRegressor and Bayesian optimization to explore the complex features and predict the PEMFCs’ performance. The performance and the robustness of the proposed machine learning based prediction approach is tested and validated through a real industrial dataset including 10 PEMFCs. Furthermore, several comparison studies with XGBRegressor and the two popular machine learning-based methods in predicting PEMFC performance, such as artificial neural network (ANN) and support vector machine regressor (SVR) are also conducted. The obtained results show that the proposed approach is more robust and outperforms the two conventional methods and the XGBRegressor for all the considered PEMFCs. Indeed, according to the coefficient of determination criterion, the proposed model gains an improvement of 6.35%, 6.8%, and 4.8% compared with ANN, SVR, and XGBRegressor respectively.

Enhancing predictive capabilities in fusion burning plasmas through surrogate-based optimization in core transport solvers

This work presents the PORTALS framework (Rodriguez-Fernandez et al 2022 Nucl. Fusion 62 076036), which leverages surrogate modeling and optimization techniques to enable the prediction of core plasma profiles and performance with nonlinear gyrokinetic simulations at significantly reduced cost, with no loss of accuracy. The efficiency of PORTALS is benchmarked against standard methods, and its full potential is demonstrated on a unique, simultaneous 5-channel (electron temperature, ion temperature, electron density, impurity density and angular rotation) prediction of steady-state profiles in a DIII-D ITER Similar Shape plasma with GPU-accelerated, nonlinear CGYRO (Candy et al 2016 J. Comput. Phys. 324 73–93). This paper also provides general guidelines for accurate performance predictions in burning plasmas and the impact of transport modeling in fusion pilot plants studies.

Systematic Review of Computational Fluid Dynamics Modelling and Simulation Techniques Employed in Vertical Axis Hydrokinetic Turbines

At present, Computational Fluid Dynamics (CFD) is being utilized to explore tidal and hydrokinetic turbine systems, advancing comprehension of turbulent flow phenomena and raising the accuracy of performance predictions for these fluid-driven turbines. Consequently, this utilization of CFD contributes to the optimization of efficiency in these devices. Turbines are subject to variations from the best design point due to the influence of fluctuating flow rates, despite the findings of recent research that have identified the ideal design points. In contrast to conventional literature reviews, systematic analysis offers numerous advantages. The methodological strategy applied in this study entailed cross-referencing the findings obtained from Web of Science (WOS) and Scopus databases to establish the comprehensiveness as well as reliability of researcher data. Improving the review process, elevating the prominence of the field of study, and establishing critical priorities to mitigate research bias are all potential strategies for enhancing the quality of these evaluations. This research divides its findings into three core themes: (1) Performance assessment for practical implications, (2) Numerical analysis for theoretical insights, and (3) Design parameter optimization for engineering relevance. These themes collectively provide a well-rounded examination of the research outcomes across practical, theoretical, and engineering dimensions. Finally, the research findings have the potential to act as a significant point of reference for informing the best design considerations pertaining to vertical axis turbines.

Rapid and accurate identification of effective metal organic frameworks for tetrafluoromethane/nitrogen separation by machine learning

BackgroundEffectively capturing tetrafluoromethane (CF4), a notorious greenhouse gas having a greenhouse warming potential 6630 times higher than carbon dioxide, is important to mitigate climate change. Metal organic frameworks (MOFs) are promising adsorbents to entrap CF4 with extreme high selectivity because they contain versatile functionalized ligands and tunable pores. However, the large population makes experimental methods unpractical to perform the large-scale screening and rational selection of efficient MOFs. MethodsIn this work, an intelligent method based on machine learning was developed to identify the important features of MOFs governing their CF4/N2 separation performances and establish the relationship between these features and performance metrics, including the CF4 adsorption capacity, the adsorption selectivity of CF4 over N2, and their trade-off. Significant findingsThe random forest (RF) machine learning algorithm was found to exhibit the highest accuracy in performance prediction. The heat of adsorption, the relative molecular mass of MOFs, and the density of MOFs were three critical features that influenced the CF4/N2 separation performances. These dominant features indicate that the pore geometry, framework geometry, and the interaction law between CF4 and MOFs significantly affected their CF4/N2 separation efficiency. Machine learning is thus a powerful tool to guide the design of CF4-selective MOFs and extend the applicability of machine learning among chemical and environmental communities.

Electrothermal Coupling Methodologies and Their Influence on the Optimization of Aircraft Electric Motors

This work presents a fully coupled electrothermal motor analysis and optimization framework, where the motor’s electromagnetic performance depends on its temperature distribution, and the temperature distribution simultaneously depends on its electromagnetic performance. Notably, we analytically compute derivatives through this nonlinear, multidisciplinary analysis using coupled adjoints, which enables the exploration of high-dimensional design spaces. We demonstrate the framework on a series of optimization problems that investigate the effect of feedback coupling and serve to highlight the framework’s utility and flexibility. Furthermore, our results indicate that modeling the motor’s electrothermal coupling is critical to accurate performance prediction. We show that designs optimized using a feedforward-coupled model are found to be either infeasible or nonoptimal under the fully coupled feedback analysis; both conditions are unacceptable for aircraft applications, in which performance is critical.

One-dimensional model of manifold microchannels for embedded cooling: Prediction of thermal performance and flow non-uniformity

A one-dimensional model has been developed to accurately predict the thermal performance and flow non-uniformity of the manifold microchannels (MMC) for embedded liquid cooling. The model consists of one-dimensional governing equations derived from the integral relations of momentum and energy over appropriately-defined two separate control volumes. To validate the model, a series of 3-D numerical simulation is conducted over the wide ranges of the Reynolds number (Rem,in) at the manifold inlet from 560 to 3190, the dimensionless hydraulic flow length (x+) from 0.012 to 0.123, and the dimensionless thermal flow length (x∗) from 0.002 to 0.023. It is shown that the model provides accurate predictions of the thermal performance and flow non-uniformity (CV) of MMC heat sinks within the root mean square percentage error (RMSPE) of 6% and 26% for 50 data points, respectively. The significant improvement of the prediction accuracy is made over the earlier models with an error reduction of 82%. Finally, a design guideline for the uniform flow distribution is suggested for the first time based on a newly proposed explicit correlation for predicting the flow non-uniformity: the dynamic pressure at the manifold inlet should be kept smaller than the pressure drop across the microchannels.

Improving accuracy in predictions about future performance

Within the academic context, students often expect to perform significantly better on an upcoming exam when compared to their actual performance. Unfortunately, students who are least accurate in their performance predictions are also the most at risk of being underprepared compared to their peers. Thus, efforts to enhance accuracy in performance predictions would benefit the student population that is most at risk of academic shortcomings and failure. Two studies examined whether incentives improve accuracy of academic performance predictions. In Study 1, 126 students in a 200-level undergraduate course provided estimates of how well they would perform on an upcoming exam. Two weeks later, the same students were randomly assigned to receive a reward incentive, punishment incentive, or no incentive for accuracy in performance predictions and their exam performance estimates were reassessed then compared to their actual scores on an exam completed one week later. In Study 2, 144 students from multiple 300-level courses provided performance predictions for an upcoming exam that were then compared to their actual exam scores for accuracy. For a subsequent exam, the same students were randomly assigned to varying levels of reward incentives (no incentive, modest incentive, large incentive) for prediction accuracy and their performance estimates were examined and compared to actual performance on a second exam. Findings from both studies indicated performance estimates are malleable and with the appropriate incentives, accuracy in predictions may be improved and contribute to better actual performance.

Deep learning based on small sample dataset: prediction of dielectric properties of SrTiO3-type perovskite with doping modification.

The perovskite crystal structure represents a semiconductor material poised for widespread application, underpinned by attributes encompassing heightened efficiency, cost-effectiveness and remarkable flexibility. Notably, strontium titanate (SrTiO3)-type perovskite, a prototypical ferroelectric dielectric material, has emerged as a pre-eminent matrix material for enhancing the energy storage capacity of perovskite. Typically, the strategy involves augmenting its dielectric constant through doping to enhance energy storage density. However, SrTiO3 doping data are plagued by significant dispersion, and the small sample size poses a formidable research hurdle, hindering the investigation of dielectric property and energy storage density enhancements. This study endeavours to address this challenge, our foundation lies in the compilation of 200 experimental records related to SrTiO3-type perovskite doping, constituting a small dataset. Subsequently, an interactive framework harnesses deep neural network models and a one-dimensional convolutional neural network model to predict and scrutinize the dataset. Distinctively, the mole percentage of doping elements exclusively serves as input features, yielding significantly enhanced accuracy in dielectric performance prediction. Lastly, rigorous comparisons with traditional machine learning models, specifically gradient boosting regression, validate the superiority and reliability of deep learning models. This research advances a novel, effective methodology and offers a valuable reference for designing and optimizing perovskite energy storage materials.