Computer Performance Analysis Research Articles

Volumetric Add-On Retrofit Strategy with Multi-Benefit Approach toward Nearly Zero Energy Buildings Target

Around 35% of the total housing stock of the European Union is more than half a century old. The shortage of funds for new construction, combined with rapidly changing economic, social, and technological factors, has led to significant obsolescence. Additionally, this situation makes it difficult to satisfy the owners’ energy, functional, and socio-economic needs. This research aims to develop an innovative retrofit approach that brings multiple benefits to assessing retrofit designs for social housing, with specific emphasis on volumetric envelope additions toward the nearly zero energy buildings target (nZEBs). To achieve the purpose of this study, the research through design methodology was chosen. The research methodology consisted of two phases: design and simulation. First, the design phase focused on re-designing and retrofitting social housing to address various aspects of the functional requirements in developing rational solutions. Second, the simulation phase focused on computational modeling and analysis of energy performance to assess the nZEBs target. The results show that the use of high-efficiency Heating, Ventilation, and Air Conditioning (HVAC) systems and improved material envelopes cut electricity consumption use by 43% and primary energy use by 40% compared to the base case. Photovoltaics (PV) production can meet the total electricity demand for six months. This approach can encourage residents and tenants to actively participate in the retrofit process and increase the real estate value of buildings through improvements in energy efficiency and housing function.

Effect of impeller rotational phase on the FDA blood pump velocity fields.

The Food and Drug Administration (FDA) blood pump is an open-source benchmark cardiovascular device introduced for validating computational and experimental performance analysis tools. The time-resolved velocity field for the whole impeller has not been established, as is undertaken in this particle image velocimetry (PIV) study. The level of instantaneous velocity fluctuations is important, to assess the flow-induced rotor vibrations which may contribute to the total blood damage. To document these factors, time-resolved two-dimensional PIV experiments were performed that were precisely phase-locked with the impeller rotation angle. The velocity fields in the impeller and in the volute conformed with the previous single blade passage experiments of literature. Depending on the impeller orientation, present experiments showed that volute outlet nozzle flow can fluctuate up to 34% during impeller rotation, with a maximum standard experimental uncertainty of 2.2%. Likewise, the flow fields in each impeller passage also altered in average 33.5%. Considerably different vortex patterns were observed for different blade passages, with the largest vortical structures reaching an average core radii of 7 mm. The constant volute area employed in the FDA pump design contributes to the observed velocity imbalance, as illustrated in our velocity measurements. By introducing the impeller orientation parameter for the nozzle flow, this study considers the possible uncertainties influencing pump flow. Expanding the available literature data, analysis of inter-blade relative velocity fields is provided here for the first-time to the best of our knowledge. Consequently, our research fills a critical knowledge gap in the understanding of the flow dynamics of an important benchmark cardiovascular device. This study prompts the need for improved hydrodynamic designs and optimized devices to be used as benchmark test devices, to build more confidence and safety in future ventricular assist device performance assessment studies.

Classical density functional theory in three dimensions with GPU-accelerated automatic differentiation: Computational performance analysis using the example of adsorption in covalent-organic frameworks

We show how classical density functional theory can greatly benefit from algorithmic advances in machine learning, especially neural networks. By exploiting GPU-accelerated backward automatic differentiation, we overcome the often cumbersome and error-prone implementation of functional derivatives for classical density functional theory computations. This provides an efficient and straightforward solution for computing functional derivatives, opening up a wide range of applications. We show the gain in computational performance by using backward automatic differentiation to compute the functional derivatives on GPUs, and exemplify the use of this easy-to-implement and highly extensible classical density functional theory framework to predict the adsorption isotherms of a methane/ethane mixture described by a Helmholtz energy functional based on the PC-SAFT equation of state in the covalent-organic framework 2,3-DhaTph. Together with this manuscript, we provide the full classical density functional theory code as supplementary material.

Computational analysis of thermal performance of temperature dependent density and Arrhenius-activation energy of chemically reacting nanofluid along polymer porous sheet in high temperature differences

An innovative technique to improve heat transmission is the use of nanofluids. Nanofluids have a significant thermal conductivity for better heat transport. For the thermal behavior of a porous polymer sheet, activation energy assessment is a useful technique for the advancement of the thermal properties of polymers. The governing model is developed for the numerical and physical analysis of heat transfer of porous polymer sheets. The present model is converted into a smooth format for the accuracy of results. The Keller box and Newton–Raphson approaches are used to calculate the thermal properties numerically. The novelty of this research is the depiction of the temperature distributions and heat transfer of chemically reacting thermophoretic nanomaterials along porous polymer stretching sheets. It is noted that the velocity and temperature of thermophoretic nanoparticles decreases and nanoparticle concentration increases as activation energy increases. It is noted that the velocity of nanoparticles increases and concentration decreases as the temperature difference increases. The enhanced heating transfer with maximum thermophoretic transportation was depicted under maximum reaction and activation energy. It is observed that the mass transfer of nanomaterials increases as the Brownian motion of thermophoretic nanomaterials enhances.

Solar radiation analysis: computational performance analysis for the foldable axis of a responsive external skin on the south façade

Solar radiation analysis: computational performance analysis for the foldable axis of a responsive external skin on the south façade

Lessons learned from an fMRI-guided rTMS study on performance in a numerical Stroop task.

The Stroop task is a well-established tool to investigate the influence of competing visual categories on decision making. Neuroimaging as well as rTMS studies have demonstrated the involvement of parietal structures, particularly the intraparietal sulcus (IPS), in this task. Given its reliability, the numerical Stroop task was used to compare the effects of different TMS targeting approaches by Sack and colleagues (Sack AT 2009), who elegantly demonstrated the superiority of individualized fMRI targeting. We performed the present study to test whether fMRI-guided rTMS effects on numerical Stroop task performance could still be observed while using more advanced techniques that have emerged in the last decade (e.g., electrical sham, robotic coil holder system, etc.). To do so we used a traditional reaction time analysis and we performed, post-hoc, a more advanced comprehensive drift diffusion modeling approach. Fifteen participants performed the numerical Stroop task while active or sham 10 Hz rTMS was applied over the region of the right intraparietal sulcus (IPS) showing the strongest functional activation in the Incongruent > Congruent contrast. This target was determined based on individualized fMRI data collected during a separate session. Contrary to our assumption, the classical reaction time analysis did not show any superiority of active rTMS over sham, probably due to confounds such as potential cumulative rTMS effects, and the effect of practice. However, the modeling approach revealed a robust effect of rTMS on the drift rate variable, suggesting differential processing of congruent and incongruent properties in perceptual decision-making, and more generally, illustrating that more advanced computational analysis of performance can elucidate the effects of rTMS on the brain where simpler methods may not.

Enhancing pore network extraction performance via seed-based pore region growing segmentation

Pore-scale modeling, aided by imaging advancements and computational power, is now a vital tool for comprehending fluid flow and transport in porous media. It allows detailed exploration of pore structures and analysis of fluid dynamics and mass transport at smallest scales. This work presents the Pore Region Growing (PREGO) Algorithm, a novel approach to address segmentation challenges encountered in large-sized 3D images used for pore network modeling. The algorithm overcomes limitations of traditional methods like the watershed algorithm, in both computational time and memory of the pore segmentation. Like the watershed method, the proposed work involves identifying seed points, then grows each to fill their respective basis. The algorithm differs from existing approaches in that it efficiently prioritizes and grows the seed points using a FIFO queue, resulting in well-defined segmentation with clear and smooth boundaries between neighboring regions. The output was validated by demonstrating the algorithm's robustness in predicting diffusive behavior and viscous fluid flow within the pore phase network, with excellent agreement in comparison to lattice Boltzmann simulations and experimental data. The proposed algorithm was also shown to accurately predict transport properties under varying porosity, and different types of real porous media images. The evaluation of the Intersection over Union (IoU) and Dice Similarity Coefficient (DSC) metrics revealed that the PREGO technique had a maximum error of 5.44%, whereas the Watershed method exhibited a comparatively higher error rate of 22.3%. Structural analysis of physical properties revealed PREGO captures pore and throat sizes in close agreement with existing approaches. Crucially, computational performance analysis demonstrated its efficiency, significantly reducing memory consumption by 50% and CPU time speed up of 3.35X compared to the standard watershed algorithm using a priority queue.

Multiphase simulation of sustainable nanoenhanced ionic liquid coolants for improved thermal performance in Ti–6Al–4V alloy drilling

Extensive research has been conducted by the manufacturing industry to enhance the efficiency of drilling processes by focusing on the utilization of nanoenhanced cutting fluids that possess excellent heat conductivity. Due to their eco-friendliness and adaptability of physical and chemical properties, ionic fluids offer enormous potential for application as cutting fluids. This study investigates the computational fluid dynamics analysis of the heat transfer performance of various ionanofluid pairs dispersed with nanoparticles as cutting fluids in the drilling process using Ansys Fluent software. For this purpose, 1-Hexyl-3-methyl-imidazolium-tetrafluoroborate is considered the ionic fluid, and its thermal behavior is examined by dispersing it with nanoparticles of copper, silver, and multiwalled carbon nanotubes (MWCNT) at different particle volume fractions and Reynolds numbers. The workpiece is composed of an alloy of titanium Ti–6Al–4V, while the drill bit is made of tungsten carbide-cobalt. It is observed that the ionic nanocoolant mist emanates from the spray tip and moves towards the drill bit-workpiece interface. Initially, the coolant's velocity is greatest close to the orifice, and as time passes, it approaches the drilling space. The data indicates that the spraying velocity of the coolant augments over time and that it disperses heat at the tool-chip interface. The results help us validate the flow and interaction of ionanocoolant with the drilling zone. With a rise in the volume fraction of added nanoparticles and Reynolds number, the results indicated a significant decrease in the drilling temperature. With a higher particle volume fraction, the MWCNT-ionic coolant combination decreases the drilling temperature of pure ionic liquid by 25.64 %. The copper, silver, and MWCNT ionanofluids enhance the average heat transfer coefficient of pure ionic coolant by 35.14 %, 47.42 %, and 62.75 %, respectively. In addition, MWCNT nanocoolants demonstrated improved thermal performance and heat removal rate in comparison to copper and silver ionanocoolants.

Optimizing the Performance of the Sparse Matrix-Vector Multiplication Kernel in FPGA Guided by the Roofline Model.

The widespread adoption of massively parallel processors over the past decade has fundamentally transformed the landscape of high-performance computing hardware. This revolution has recently driven the advancement of FPGAs, which are emerging as an attractive alternative to power-hungry many-core devices in a world increasingly concerned with energy consumption. Consequently, numerous recent studies have focused on implementing efficient dense and sparse numerical linear algebra (NLA) kernels on FPGAs. To maximize the efficiency of these kernels, a key aspect is the exploration of analytical tools to comprehend the performance of the developments and guide the optimization process. In this regard, the roofline model (RLM) is a well-known graphical tool that facilitates the analysis of computational performance and identifies the primary bottlenecks of a specific software when executed on a particular hardware platform. Our previous efforts advanced in developing efficient implementations of the sparse matrix-vector multiplication (SpMV) for FPGAs, considering both speed and energy consumption. In this work, we propose an extension of the RLM that enables optimizing runtime and energy consumption for NLA kernels based on sparse blocked storage formats on FPGAs. To test the power of this tool, we use it to extend our previous SpMV kernels by leveraging a block-sparse storage format that enables more efficient data access.

Spatial-temporal mapping of forest vegetation cover changes along highways in Brunei using deep learning techniques and Sentinel-2 images

Infrastructure development is a leading driver of forest cover loss in the tropics, resulting in a significant decrease in biodiversity. With recent advancements in digital image processing and satellite imagery techniques, this study presents an approach for tracking forest cover alterations along a recently constructed highway in Brunei. Specifically, this paper harnesses the power of cutting-edge deep learning (DL) models; U-Net and Deeplabv3+, to classify forest and non-forest features from Sentinel-2 satellite images captured along the Telisai–Lumut highway between 2018 and 2022. A computational performance analysis and network comparison revealed that the Deeplabv3+ had a remarkable semantic segmentation performance, with a mean intersection over union of 0.9247, followed by an ensemble of four networks, with a mean intersection over union of 0.9171. The best model consistently outperformed other models in segmenting forest and non-forest features, with an intersection over union (IOU) of 0.9463 and 0.9031, respectively. Moreover, deploying hyperparameter optimization allowed us to assess the model's sensitivity to different hyperparameters for better results. Furthermore, forest change statistics were computed using the best model's generated forest change maps, and the results indicated a 16.7% gain in the overall forest vegetation cover along the highway, demonstrating the validity of the results. These reproducible findings will provide forest managers and ecologists with near real-time information on the status, health, and extent of forest ecosystems. This study's results have critical implications for using DL to monitor forest cover changes and will be of great interest to the scientific community as they open new avenues for future research and underline the need for continued innovation in this important field.

Computational thermal performance analysis by LBM for cooling a hot oval object via magnetohydrodynamics non-Newtonian free convection by using magneto-ferrofluid

The subject of equipment cooling has always been one of the most important issues that has imbibed the attention of many researchers. In addition to this matter, the system thermal performance analysis in which the cooling process takes place is very momentous. Cooling of an electronic component in a limited space is one of the cases of investigating these phenomena. Due to the importance of this issue, via LBM, the cooling of a hot body through the process of natural convection has been simulated. The cooling chamber is a tilted trapezoidal and contains a non-Newtonian ferrofluid, while the whole set, is under the effect of the magnetic field in different positions. By calculating the amount of entropy produced and the average Nusselt value under the impact of heat absorption, the best thermal performance was determined in this present work. Magnetic field strength (0⩽Ha⩽90), buoyancy forces strength (104⩽Ra⩽106), the fluid behavioral index (0.8⩽n⩽1.2), heat absorption coefficient (-20⩽Δ⩽0), the angle of chamber placement (0⩽ζ⩽270), the position of applying magnetic field (0⩽R⩽2H3) have been evaluated as the main variables. Based on the outcomes, for applying the magnetic field in the position of the middle third of the enclosure, the greatest effect of increasing the Hartmann value is observed. The highest thermal performance coefficient of the system can be obtained for the highest Rayleigh value, the lowest Hartmann value in the absence of heat absorption for the shear thinning fluid. By changing the angle of chamber placement, the system thermal performance can be effectively affected, so that the worst performance was reported at an angle of 270 degrees. For the highest value of heat absorption coefficient, the change in the values of the Hartmann number, power-law index and the Rayleigh number is not noticeable.

Embedded MPC Strategies for ESP-Lifted Oil Wells: Hardware-in-the-Loop Performance Analysis of Nonlinear and Robust Techniques

This paper proposes embedded model predictive control strategies for oil-production processes equipped with electric submersible pump (ESP) installations. The novelty of this paper is the robustness and computational performance analysis of the Robust Infinite-Horizon Model Predictive Controller (RIHMPC) and Nonlinear Model Predictive Controller (NMPC) strategies, which have not yet been documented by the oil and gas exploration and production literature. The proposed method to embed the control laws is flexible with different hardware and is based on automatic code generation, which facilitates the project workflow. Hardware-in-the-loop simulation cases were used to compare the performance of both control strategies embedded in the Teensy 4.1 microcontroller, using key indices for real applications. The results showed that the RIHMPC strategy is a very promising alternative for real-time operation in ESP-lifted oil wells, with overall performance similar to the NMPC controller, even in noisy and plant–model mismatch scenarios, and using only linear models in its formulation.

Computational analysis of blind performance on natural ventilated double skin façade in winter

Computational analysis of blind performance on natural ventilated double skin façade in winter

Thermally Deposited Sb2Se3/CdS-Based Solar Cell: Experimental and Theoretical Analysis.

As a promising solar absorber material, antimony selenide (Sb2Se3) has gained popularity. However, a lack of knowledge regarding material and device physics has slowed the rapid growth of Sb2Se3-based devices. This study compares the experimental and computational analysis of the photovoltaic performance of Sb2Se3-/CdS-based solar cells. We construct a specific device that may be produced in any lab using the thermal evaporation technique. Experimentally, efficiency is improved from 0.96% to 1.36% by varying the absorber's thickness. Experimental information on Sb2Se3, such as the band gap and thickness, is used in the simulation to check the performance of the device after the optimization of various other parameters, including the series and shunt resistance, and a theoretical maximum efficiency of 4.42% is achieved. Further, the device's efficiency is improved to 11.27% by optimizing the various parameters of the active layer. It thus is demonstrated that the band gap and thickness of active layers strongly affect the overall performance of a photovoltaic device.

A Computational Analysis of the High-velocity Impact Performance of Lightweight 3D Hybrid Composite Armors

A Computational Analysis of the High-velocity Impact Performance of Lightweight 3D Hybrid Composite Armors

Design of Nonlinear Component of Block Cipher Using Gravesian Octonion Integers

Being the only nonlinear component in many cryptosystems, an S-box is an integral part of modern symmetric ciphering techniques that creates randomness and increases confidentiality at the substitution stage of the encryption. The ability to construct a cryptographically strong S-box solely depends on its construction scheme. The primary purpose of an S-box in encryption standards is to establish confusion between the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> -bit input into the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -bit output (both <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m, n &gt;= 2$ </tex-math></inline-formula> ). This article proposed a robust way to construct S-boxes based on the Gravesian octonion integers. We chunk the paper into threefold: firstly, a comprehensive technique for constructing S-box using affine mapping is described. The presented work is developed in such a way that for every valid input, it generates two S-boxes. Secondly, the strength of the newly generated S-box is evaluated by passing through a rigorous security analysis. Finally, a thorough comparison of the newly developed method with some well-known existing schemes is conducted. We mainly targeted some elliptic curve-based S-boxes in comparison by taking the same parameters in our scheme. The computational results and performance analysis reveal that the propose algorithm can construct a large number of distinct S-boxes that are cryptographically secured and create high resistance against various cryptanalysis attacks.

Computational analysis of vortex dynamics and aerodynamic performance in flying-snake-like gliding flight with horizontal undulation

This paper numerically studies the flow dynamics of aerial undulation of a snake-like model, which is adapted from the kinematics of the flying snake (Chrysopelea) undergoing a gliding process. The model applies aerial undulation periodically in a horizontal plane where a range of angle of attack (AOA) is assigned to model the real gliding motion. The flow is simulated using an immersed-boundary-method-based incompressible flow solver. Local mesh refinement mesh blocks are implemented to ensure the grid resolutions around the moving body. Results show that the undulating body produces the maximum lift at 45° of AOA. Vortex dynamics analysis has revealed a series of vortex structures including leading-edge vortices (LEV), trailing-edge vortices, and tip vortices around the body. Changes in other key parameters including the undulation frequency and Reynolds number are also found to affect the aerodynamics of the studied snake-like model, where increasing of undulation frequency enhances vortex steadiness and increasing of Reynolds number enhances lift production due to the strengthened LEVs. This study represents the first study of both the aerodynamics of the whole body of the snake as well as its undulatory motion, providing a new basis for investigating the mechanics of elongated flexible flyers.

New Heuristic Methods for Sustainable Energy Performance Analysis of HVAC Systems

Energy-efficient buildings have attracted vast attention as a key component of sustainable development. Thermal load analysis is a pivotal step for the proper design of heating, ventilation, and air conditioning (HVAC) systems for increasing thermal comfort in energy-efficient buildings. In this work, novel a methodology is proposed to predict the cooling load (LC) of residential buildings based on their geometrical characteristics. Multi-layer perceptron (MLP) neural network was coupled with metaheuristic algorithms to attain its optimum hyperparameter values. According to the results, the LC pattern can be promisingly captured and predicted by all developed hybrid models. Nevertheless, the comparison analysis revealed that the electrostatic discharge algorithm (ESDA) achieved the most powerful MLP model. Hence, utilizing the proposed methodology would give new insights into the thermal load analysis method and bridge the existing gap between the most recently developed computational intelligence techniques and energy performance analysis in the sustainable design of energy-efficient residential buildings.

Capture Based Trust Dependence framework for authorized node identification in mobile agent systems

A mobile agent is a self-learning machine entity that uses the system infrastructure to keep running in another remote zone, check and compile the results, interact with various locations and return to his home site after completing the relegated activities. Mobile Agent-based solutions for the testing community have grown in popularity and are now used in a variety of fields, including boardroom management, electronic commerce, renewable energy and power management. Addition to these applications, Broadband Interactive Sensors, network performance improvement, disseminated knowledge mining, multimedia, human monitoring, surveillance, affective computing, weather and environment, e-learning and semantic web administrations are only a few of the topics covered. In an extremely non trusty environment, focus should be taken to shield the portable operator from acquiring altered. Existing works on mobile agent frameworks with very surprising instruments does not offer complete security. In this paper a capture based trust dependence framework is proposed for identification of authorized or trust nodes inside mobile agents systems. Here we consider the mobile adhoc networks for computational analysis of network performance using network simulator. The framework provides efficient results in identification of authorized nodes.

Real-time physical field reconstruction for nanofluids convection using deep learning with auxiliary tasks

Nanofluids has become one of the most promising cooling fluids due to its great thermophysical properties, especially for microchannels. In recent years, to avoid prohibitively expensive simulations in design process or iterative optimization, massive data-driven and machine learning methods have been employed to construct surrogate models for thermophysical properties or concerned performance characteristics. Though the favorable accuracy is validated, this kind of surrogate models ignore the importance of physical fields, which are also an evaluating criterion in heat transfer design scenarios. In this article, we develop a deep learning framework to achieve the main task of real-time reconstruction mapping from design variables to physical fields. In addition, the auxiliary tasks of prediction mapping from physical fields to performance characteristics are tailored to improve the accuracy and generalization of reconstruction mapping. To validate the feasibility of this framework, we assess the ability of the generator alone to reconstruct physical fields and predict performance characteristics for the flow and heat transfer of nanofluid first. The results show that the introduction of auxiliary task can greatly improve the performance of main task. The computational performance analysis indicates that a well-trained deep learning model with GPU-accelerated has three orders of magnitude faster than CFD solver. This proposed approach was expected to provide a real-time predictor with a reasonable level of accuracy for nanofluid convective heat transfer.