Computer CPU Research Articles

Thermal enhancement of heat sinks with bio-inspired textured surfaces

The current requirements of the electronics industry for heat sink design are to increase heat dissipation and to reduce the occupied space and weight. Research to improve the thermal efficiency and reduce the size of heat sinks has mainly been conducted by modifying their geometry to increase the heat transfer area. In the field of tribology research, one way to modify a surface is to apply texture, which involves reducing the contact area but increasing the external area, which in turn could improve the performance of heat dissipation devices. In this paper, bio-textured surfaces are fabricated on straight fin heat sinks and numerically evaluated by thermal finite element analysis. The simulation results are validated by experimental measurements on a single-fin heat sink. The potential of surface texturing in heat sinks is demonstrated here through a numerical parametric analysis of a full-scale heat sink. The results show a reduction in operating temperatures of more than 26 %, reduction in thermal resistance around 34 % and increase between 21 % and 40 % of heat sink effectiveness for textured surface heat sinks compared to smooth fin heat sinks, depending on the convection coefficient. These results suggest that the design methodology proposed here is more relevant for heat sinks used in high power dissipation applications such as computer CPUs and GPUs.

A multiline fitting method for measuring ethylene concentration based on WMS-2f/1f

Coal spontaneous combustion risk assessment is a global technical challenge for the sustainable development of deep mining technology, and C2H4 is a key indicator for early warning of coal spontaneous combustion. Tunable diode laser absorption spectroscopy (TDLAS) has the advantages of high selectivity, high sensitivity, high accuracy and real-time on-line measurement, and it can detect multiple gases simultaneously, so it has significant advantages in the accurate detection of coal spontaneous combustion indicator gases. To address the problem of cross-interference between the near-infrared absorption lines of CH4 and C2H4, which are the indicator gases of spontaneous combustion in coal, a multi-line fitting method was proposed in this study to calibrate the concentration of C2H4. The high-precision Environics2000 automatic standard gas dispenser from the United States, which has a built-in CPU computer control and data control and processing system, was used. Its gas concentration accuracy: ± 1.0%, gas flow accuracy: ± 1.0%, gas repeatability accuracy: ± 1.0%, flow linearity accuracy: ± 0.5%, and inlet operating pressure: minimum 10 psig (0.67 bar) ~ 75 psig (5.04 bar). The measured and simulated WMS-2f/1f signals were multilinearly fitted using a multilinear fitting algorithm and wavelength modulation spectroscopy (WMS), and the measurement of C2H4 concentration was achieved based on the extracted spectral line information. The results show that the maximum relative error of C2H4 concentration measurement is 2.40%, which is 54% lower than that of the conventional 2f peak measurement method, thus demonstrating the effectiveness of the multilinear fitting algorithm in the inversion of C2H4 concentration under the interference of absorption lines. In addition, this study has far-reaching implications for the application of TDLAS technology in the accurate detection of coal spontaneous combustion indicator gases.

A 2D Hydraulic Simulation Model Including Dynamic Piping and Overtopping Dambreach

Numerical simulation of unsteady free surface flows using depth averaged equations that consider the presence of initial discontinuities has been often reported for situations dealing with dam break flow. The usual approach is to assume a sudden removal of the gate at the dam location. Additionally, in order to prevent any kind of dam risk in earthen dams, it is very interesting to include the possibility of a progressive dam breach leading to dam overtopping or dam piping so that predictive hydraulic models benefit the global analysis of the water flow. On the other hand, when considering a realistic large domain with complex topography, a fine spatial discretization is mandatory. Hence, the number of grid cells is usually very large and, therefore, it is necessary to use parallelization techniques for the calculation, with the use of Graphic Processing Units (GPU) being one of the most efficient, due to the leveraging of thousands of processors within a single device. The aim of the present work is to describe an efficient GPU-based 2D shallow water flow solver (RiverFlow2D-GPU) supplied with the formulation of internal boundary conditions to represent dynamic dam failure processes. The results obtained indicate that it is able to develop a transient flow analysis taking into account several scenarios. The efficiency of the model is proven in two complex domains, leading to >76× faster simulations compared with the traditional CPU computation.

A deep learning-based method detects dust from solar PV panels through Unmanned Aerial Vehicles

As the number of solar photovoltaic (PV) panels increases, dust detection on the panels becomes particularly important. In this paper, we propose a deep learning-based method that detects dust from solar PV panels through Unmanned Aerial Vehicles. The model utilizes the improved YOLOv5 method to detect PV panel dust on aerial images. The model is a lightweight model that requires fewer computing resources and time and can work in real time on a regular CPU computer. Moreover, in this paper, a prediction head is added to YOLOv5 to cope with significant changes in target scales due to unmanned aerial vehicles capturing images at different altitudes. And the model introduces new tricks to help detect dust targets in images with large coverage areas. After experimental validation, the proposed method outperforms the state-of-the-art in terms of detection accuracy, detection speed, F1 score, etc., and is more suitable for the inspection of dust on PV panels of Unmanned Aerial Vehicles.

Simulative study on the performance of polymeric composites containing phase change capsules for chip heat dissipation

Polymeric composites containing phase change capsules (PCC) have both heat storage capacity and thermal reliability, thus having important applications. The simulative study facilitates systematic investigations on the effects of the mass fraction and phase change characteristic of the capsules as well as the thermal conductivity of the composites on the application performance of the phase change composites. Herein, simulative researches were conducted on the performance of the polymeric composites containing the [email protected]2 capsules for chip heat dissipation. Specifically, a physical model was built according to a widely-employed computer CPU cooling system, which was validated by the experimental results obtained from the polydimethylsiloxane (PDMS)-based composites containing the [email protected]2 nanocapsules. It is found that the mass fraction of [email protected]2 capsules has an obvious effect on the equilibrium temperature of the chip, while the melting point and enthalpy of the capsules affect the temperature rise rate of the chip. Subsequently, in order to make the PDMS-based composite containing the [email protected]2 capsules to meet the heat dissipation requirements of the chip at different working powers, predictions on enhancements in its thermal conductivity were performed. The relationship between the working power of the chip and the required thermal conductivity of PCC was thus acquired. This work sheds light on designing polymeric composites containing phase change capsules for chip heat dissipation.

Wavelet lifting method for the numerical solution of micropolar lubrication problem of porous journal bearing

The pivotal aim of this paper is to develop the wavelet lifting method for the numerical solution of the micropolar lubrication problem of porous journal bearings. To ensure the applicability of the proposed method initially, test problems are considered and the numerical findings are presented in figures and tables elucidate that, the proposed method is more efficient and promising with the consumption of lesser computational CPU time. Subsequently, the effect of micropolar fluid on the porous journal bearing is analyzed by solving the corresponding non-dimensional Reynolds equation of the lubrication problem is considered. The obtained results for pressure distribution and load carrying capacity with varied parameters show that the squeeze film pressure and load carrying capacity are significantly increased by the micropolar fluid effect as compared to the Newtonian case.

Snapshot for Power Grids IoT: Adaptive Measurement for Resilience Intelligent Internet of Things

With the wide application of Internet of Things (IoT) devices in the power grids, the sophisticated feedback on their operating status is of great significance for improving efficiency and reducing accidents. For exquisite management of the resilient intelligent IoT with flexible increase and decrease of devices and heterogeneous operating systems, this paper proposes an adaptive measurement method ’MRAM’, which can snapshot the multidimensional resource view (MRV) of all devices in the jurisdiction. Extensible gateway platform based on CPU, FPGA and cloud computing is applied in MRAM, which liberates the local resources of monitored IoT devices. MRAM improves the LSTM algorithm called ELSTM. ELSTM can accommodate the current IoT devices’ state for detecting the mutation of MRV. The newly collected resources determined by ELSTM whether MRAM enters an abnormal state to drive the adaptive measurement state machine. According to the state machine which endeavors that the MRV is updated timely, MRAM adjusts the measurement granularity in real time. Simulations and experiments have tested the convergence time and occupied bandwidth of MRAM deployed in power grids. These evaluations confirmed MRAM’s practicality and robustness, as well as the MRV is genuine management data for the upper-layer power grids applications. A real environment is built to test the performance of this method as well. MRAM has high measurement accuracy and the precision of mutation detection is 98.41%. It converges the update MRV of second level under the condition of IoT devices and the cloud’s low consumption of memory and CPU utilization.

FastAiAlloc: A real-time multi-resources allocation framework proposal based on predictive model and multiple optimization strategies

In cloud platforms, a common task is to allocate computational resources, e.g., memory and CPU, requested by applications and users. The allocation of these resources, which is an optimal load-balancing task, is considered an NP-Hard problem, being a challenging research area. There are many works proposed in the literature to address this problem. They use several strategies to deal with this optimization problem such as evolutionary algorithms, exact programming and also heuristics. However, some steps of the allocation process are not considered by these works, which are sometimes treated separately and not in an integrated manner. These steps include the applications and users resource consumption request profile as part of the optimization process and defining adequate metrics to check the optimized allocation. To integrate these steps, this work proposes a framework based on the following strategies, widely used in the literature: Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Linear Programming, besides our Heuristic approach. Furthermore, we restricted the resource allocation optimization to a real-time scenario, facilitating its use in an industrial process. In addition, Key Performance Indicators (KPIs) are proposed to carry out a comparative study in a public dataset. Our experiments showed that our proposed framework achieved better results than the baseline one, e.g., using a random allocation. In some cases, we observed an increase of almost 60% in performance compared with the baseline. In addition, when trying to balance memory and CPU consumption in the cluster, the linear approach performed the best, while the GA achieved the best result in allocating different user profiles across the cluster.

A second order ensemble algorithm for computing the Navier-Stokes equations

We present an efficient, second order ensemble algorithm for computing the Navier-Stokes equations multiple times with different model parameters. The algorithm is based on the Crank-Nicolson Leap-frog (CNLF) scheme and the ensemble timestepping incorporating the artificial compressibility method and the recent scalar auxiliary variable (SAV) idea for developing unconditionally stable schemes for nonlinear flows. The computation of the velocity and pressure is decoupled in the algorithm resulting in smaller linear systems to be solved at each time step. All realizations of the flow corresponding to different model parameters share the same coefficient matrix so that efficient block solvers can be used to reduce the computational cost. The proposed algorithm is efficient in terms of both computer storage and CPU time. We prove the algorithm is long time stable without any timestep conditions. Ample numerical experiments are performed for various flow problems to validate the efficiency and effectiveness of the algorithm.

A flexible forcing immersed boundary scheme-based one-step simplified lattice Boltzmann method for two-dimensional fluid-solid interaction problems

In this study, we coupled the one-step simplified lattice Boltzmann method (OSLBM) [1] with flexible forcing immersed boundary (FFIB) scheme [2] and proposed a computationally efficient FFIB-OSLBM solver for complex two-dimensional fluid-solid interaction problems. The adopted OSLBM algorithm has the benefits of lower virtual memory requirement and easy implementation of physical boundary conditions over the traditional lattice Boltzmann (LBM) solver and is also computationally more efficient than the simplified lattice Boltzmann method (SLBM) [3]. Unlike in SLBM, the macroscopic variable is updated in one-step in OSLBM, thus avoiding the predictor and corrector steps and reducing the computational time. Furthermore, the adopted FFIB algorithm uses an implicit approach to calculate the body force density, which accurately satisfies the no-slip velocity condition at the boundary of the immersed solid. Moreover, this method requires less computational effort since it discards the time-consuming, complex matrix inversion process associated with the conventional immersed boundary (IB) method. We have conducted the numerical accuracy test and noticed that the proposed FFIB-OSLBM solver is nearly second-order accurate in space, which is higher than the typical coupled IB-LBM solvers. In addition, the applicability of the present solver is tested through a set of benchmark problems, and the results obtained from the present solver show an excellent agreement with the data available in the references. Compared to the FFIB-LBM [2], the present solver consumes at least 20% less computational memory and CPU time for all the validation problems investigated in this study.

Speed-up Measures and Associated Problems for Large-scale DEM-Models

This contribution discusses the possibilities to increase the efficiency of large Discrete Element Method (DEM) simulations. Simulations were conducted to test particle upscaling, decreasing shear modulus and using GPU instead of CPU for the computation. The conducted simulations modelled a simple ore extraction process from a defined outlet of a stope in a cave mine. The analysis is based on the influence of the approaches on the computation speed-up and the accuracy of the result. It was found that the real shear modulus could be decreased in the simulation by a factor of 103 without interfering strongly with the result, provided that the decreased shear modulus is not smaller than 108 Pa. However, a reduction of 102 was found to bring the highest speed-up to the present application.For the particle upscaling in this contribution without parameter calibration, the upscaling factor should not exceed 3. Higher upscaling factors affect the results significantly. However, the flow dynamics was already differing for an upscaling factor of 2, even though the flow zones were comparable. Using a GPU instead of a CPU is only recommended if the simulation contains a high number of particles. The speed-up for a simulation with almost 250,000 particles was over 8, but the advantage diminished for less particles. Furthermore, differences in the results between CPU and GPU computation could be found, which could be a starting point for future work. In summary, this research significantly aids in the development of more efficient DEM simulations for large-scale applications, such as cave mining.

GANBISS: a new GPU accelerated N-body code for binary star systems

We present a GPU accelerated N-body integrator using the Bulirsch–Stoer method, called GANBISS (GPU accelerated n-body code for binary star systems). It is designed to simulate the dynamical evolution of planetesimal disks in binary star systems which contain some thousand disk objects. However, it can also be used for studies of non-interacting massless bodies where up to 50 million objects can be studied in a simulation. GANBISS shows the energy and angular momentum conservation behavior of non-symplectic integration methods. The code is written in CUDA C and can be run on NVIDIA GPUs of compute capability of at least 3.5. A comparison of GPU and CPU computations indicates a speed-up of the GPU performance of up to 100 times—depending on the number of disk objects.

An Authenticated Group Shared Key Mechanism Based on a Combiner for Hash Functions over the Industrial Internet of Things

The Industrial Internet of Things (IIoT) provides internet connectivity for instruments, digital machines, and any other manufactured object to enable intelligent industrial operations to achieve high productivity. Securing communications between IIoT devices remains a critical and challenging issue due to the resource-constrained and processing capabilities of sensing devices. Moreover, the traditional group shared key might implement complex mathematical operations that are not suitable for the limited recourse capability of the IIoT device. Furthermore, the standard Diffie–Hellman (DH) and elliptic curve Diffie–Hellman (ECDH), which are the most suited for tiny devices, only work between a pair of IIoT devices, while they are not designed to work among a group of IIoT devices. This paper proposes an authenticated group shared key (AGSK) mechanism that allows a set of industrial objects to establish a common session key over the IIoT. The proposed AGSK utilizes the combiner for the hash function and digital signature, which is implemented in IIoT devices. Additionally, the random oracle model has been used to prove the security of AGSK, while the IIoT adversary model has been used to analyze the AGSK countermeasures against cyberattacks. The results of the performance evaluation showed that the efficiency of the AGSK was reduced by 41.3% for CPU computation time, 45.7% for storage cost, and 40% less power consumption compared to the baseline group key management algorithms.

Numerical Simulation of Diversion Tunnel Excavation Using GPU-Accelerated Anisotropic Random Fields

In this study, a combined anisotropic random field and GPU-accelerated Cholesky decomposition algorithm is proposed for diversion tunnel excavation. With the MATLAB programming control be used to combine the advantages of GPU and CPU computing, the efficient method of generating large numerical model random fields is realized. Based on the geological structure characteristics of red-layered soft rocks in central Yunnan, the anisotropic rock random field and tunnel excavation with different rotation angles are simulated. The results show that the variability of rotational anisotropy random fields and mechanical parameters of the red-bedded soft rocks in central Yunnan has a large influence on the deformation and stress state of the surrounding rocks at the excavation face of the tunnel. This study can provide theoretical and technical support for the design and construction of relevant rock engineering in the red-layered soft rock area in central Yunnan.

Heat transfer and thermal analysis in a semi-spherical fin with temperature-variant thermal properties: an application of Probabilists’ Hermite collocation method

Finned surfaces are utilized in several industrial and technological applications including radiators in cars, air conditioning systems, and computer CPU heat sink to dissipate heat through convection or radiation. In this regard, the heat transfer enhancement utilizing the fins has received more attention in the scientific field. Motivated by this, the present research explicates temperature variation through a convective-radiative semi-spherical fin (SSF) with internal heating by taking into consideration of linear and nonlinear temperature-dependent heat transfer coefficient and thermal conductivities. Furthermore, the efficiency and heat transfer aspects of the fin are elaborated. The Rosseland approximation is used in this study to analyze radiation heat exchange. The developed energy equation is converted to a nonlinear ordinary differential equation (ODE) using dimensionless terms, and the probabilists’ Hermite collocation method (PHCM) is used to solve this problem. The governing parameters have been varied in their appropriate ranges to illustrate their impact on the thermal profile, and the results have been portrayed graphically. Some notable key findings of the current analysis are that as the scale of the convection–conduction and the radiation number upsurges, the thermal distribution through SSF drops. On the other hand, thermal distribution escalates for heat generation and thermal conductivity parameters.

Predicting total organic carbon from well logs based on deep spatial-sequential graph convolutional network

The total organic carbon (TOC) is a key geologic parameter for unconventional reservoirs. Conventional empirical [Formula: see text] methods cannot handle the nonlinear relationships between the characteristics of TOC and its well-log responses. Increased data availability has the potential to speed up deep learning applications, which can reasonably propagate the integrated information from well logs to indirectly observable geologic properties, such as TOC. Although the existing convolutional neural network (CNN) has found superior performance to [Formula: see text] for predicting TOC, CNNs feature-learning capability is still constrained by the fact that it can only extract log-specific sequential features of the input logs. However, the cross-log topological association features are potentially essential for the nonlinear mapping between well logs and TOC. Thus, we introduce a novel deep spatial-sequential graph convolutional network (SSGCN) for predicting the TOC by jointly leveraging the cross-log topological association features and log-specific sequential features. Through further use of the previously unaccounted topological interactions, our SSGCN dramatically outperforms the sequence-based CNN. In the southeast Sichuan Basin, SSGCN exhibits beneficial mapping not demonstrated previously: its models achieve a better cross-validation performance within the same gas field wells and a greater generalizability in another gas field well. Our SSGCN method can predict TOC of shale gas field well with the best [Formula: see text] being 0.87 within 1 s on the CPU of a desktop computer, which increases the efficiency of obtaining the TOC parameter. From this study, we recommend graph and sequential convolutions for designing deep learning architectures in the well-log analysis.

Performance comparison of CPU and GPGPU calculations using three simple case studies

In this work, we have prepared and analyzed three case studies comparing CPU and GPGPU calculations. After briefly introducing the topic of parallel programming by means of contemporary CPU and GPGPU technologies, we provide an overview of selected existing works closely related to the topic of the paper. For each of the case studies, a set of programs has been implemented using the following technologies: pure CPU, CPU SIMD, CPU multi-threaded, CPU multi-threaded with SIMD instructions, and GPU - Cuda. We also illustrate the basic idea of the operation of selected algorithms using code snippets. Subsequently, the particular implementations are compared, and obtained results are evaluated and discussed.

Study on Verification Approach and Multicontact Points Issue When Modeling Cyperus esculentus Seeds Based on DEM

In this paper, the Multisphere (MS) models of three varieties of Cyperus esculentus seeds are modeled based on DEM. In addition, for comparison, other particle models based on automatic filing in EDEM software are also introduced. Then, the direct shear test, piling test, bulk density test, and rotating hub test are used to verify the feasibility of particle models of Cyperus esculentus seeds that we proposed. By comparing the simulated results and experimental results, combined with the CPU computation time, the proposed particle models achieved better simulation accuracy with fewer filing spheres. According to simulation results, some limitation was present when using one single verification test; varieties of verification tests used could improve the verification reliability, and a more appropriate particle model could be selected. Additionally, the issue of multicontact points in the MS model was studied. The Hertz Mindlin (no slip) (HM) model and Hertz Mindlin new restitution (HMNR) model were both considered in simulations for comparison. The rotating hub test and particle–wall impact test were used, and the influences of multiple contact points on the motion behavior of individual particles and particle assemblies were analyzed. Simulation results showed that the multiple contact points affected the motion behavior of individual particles; in contrast, the influence of multiple contact points on the motion behavior of the particle assembly was insignificant. Moreover, the relationships between moisture content of seeds and Young’s modulus, Young’s modulus, and the number of contact points were also considered. Young’s modulus decreased with increasing moisture content. The number of contact points increased with a decreasing Young’s modulus.

Legendre wavelet operational matrix method for the analysis of thermal radiation effect on natural convection of a vertical plate embedded in a saturated porous medium

In this paper, the heat transfer characteristics of natural convection around a vertical flat surface embedded in a saturated porous medium are studied by taking into account the thermal radiation effect. With the aid of the similarity transformations, the governing equations of the problem are reduced to the system of ordinary differential equations over the infinite domain. Moreover, the Legendre wavelet operational matrix method (LWOMM) is hired to find the numerical solution to the considered problem. Initially, to ensure the efficiency of the proposed strategy, a third-order nonlinear boundary value problem having the exact solution is considered a test problem. The numerical findings elucidate that the solutions obtained by the LWOMM are in good agreement with the exact solution as compared with the solution obtained by the Haar wavelet operational matrix method, with the consumption of less computational CPU time. Also, the temperature and velocity profiles are discussed graphically.

Method to Achieve High Speed and High Recognition Rate of Goal from Long Distance for CanSat

A structure is proposed for high-speed goal recognition by SSD MobileNet using the Coral USB Accelerator. It was confirmed that the goal recognition rate from a long distance is equal to or better than that of conventional methods. Using this method, two CanSat contests were won. The aim of the CanSat competitions is to guide the CanSat to a distance of 0 m from the goal. It is initially guided by GPS but must eventually employ an image recognition model to identify the nearby goal. Zero-meter guidance to the goal was achieved in the Tanegashima Rocket Contest 2018 using a method that recognized the color of the goal by the color of the image, but it was vulnerable to changes in lighting conditions, such as weather changes. Therefore, a deep-learning method for CanSat goal recognition was applied for the first time at ARLISS 2019, and the zero-distance goal was achieved, winning the competition. However, it took more than 10 s for recognition owing to the CPU calculations, making it time consuming to reach the goal. The conventional method uses image classification to recognize the location of a goal by preparing multiple regions of interest (ROIs) in the image and repeating the recognition operations for each ROI. However, this method has a complex algorithm and requires the recognition of more ROIs to recognize goals over long distances, which is computationally time consuming. Object detection is an effective method to identify the location of the target object in an image. However, even if the lightest SSD MobileNet V1 and V2 and a hardware accelerator are used, the computation time may not be short enough because the computer is a Raspberry Pi Zero, the weakest class of Linux computers. In addition, if SSD MobileNet V1 and V2 do not have a sufficiently high recognition rate at long distances from the goal compared with conventional methods, it will be difficult to adapt them to a CanSat. To clarify this, SSD MobileNet V1 and V2 were applied to a Raspberry Pi Zero connected to a Coral USB Accelerator, and the recognition rate and recognition time were investigated at long distances from the goal. It was found that the recognition rate was equivalent to or better than that of the conventional method, even at long distances from the goal, and that the recognition time was sufficiently short (approximately 0.2 s). The effectiveness of the proposed method was evaluated at the Noshiro Space Event 2021 and Asagiri CanSat Drop Test (ACTS) 2021, and the 0-m goal was achieved at both events.