Reduce Calculation Time Research Articles

Direct aeroacoutic simulation of a ducted axial fan with acoustic liners using a volume penalization lattice Boltzmann solver on heterogeneous massively parallel platforms

Aeroacoustic noise of an axial fan is directly solved to understand its mechanism, using our in-house Lattice Boltzmann Method (LBM) solver. To make the LBM solver industrially successful, we must reduce calculation time while maintaining its accuracy. We adopt Volume Penalization method for moving boundaries to achieve independent parallelism, and to utilize GPU (Graphical Processing Units) for parallel computation. In this study, we present a comparison between numerical and experimental results of an axial fan in a lined duct. The acoustic liner is designed to reduce 2nd BPF noise, and the noise reduction of 9.8dB is observed in the experiment. With our LBM code, the noise reduction was reproduced within an accuracy of 2.5dB, and profound insights are provided through the detailed investigation of the numerical results.

S&Reg v2: a probabilistically complete sampling-based planner to solve multi-goal path finding problem via multi-task learning networks

In this paper, we present a variant of our previous research on multi-goal path finding problem, focusing on finding a feasible and closed path to visit a sequence of goals in an environment with obstacles. The newly proposed method, Segmentation & Regression v2 (S&Reg v2), employs multi-task learning networks to generate regions and estimates of lengths of local paths between pairwise goals. Importantly, the estimates are performed as weights for a complete graph to compute the visiting sequence. Subsequently, the path-finding process is executed following the sequence, and the predicted region works as a sampling domain to enhance the search speed. A hybrid sampler is designed by combining a uniform domain with the region domain, ensuring successful samples, even if the region is disconnected. Besides, a selection rule is introduced to balance the sampling domain during different searching stages. A proof of probabilistic completeness of the S&Reg v2 method is given. Simulations verify the superior performance of the S&Reg v2 method, demonstrating a reduction in calculation time ranging from 3.9% to 13.0%. Furthermore, a practical scenario validates the reliability of S&Reg v2, achieving a 15.0% improvement in success rate and a 9.7% reduction in calculation time.

Determination of the coefficient of friction in the penstock of a hydro-electric dam: Application of two approaches

ABSTRACT Several formulae of the coefficient of friction have emerged after that of Nikuradse such as the Colebrook–White formula adopted for the calculation of the coefficient of friction in penstocks. However, this requires enormous computing resources because it suffers from an implicit nature. Other authors have proposed explicit formulae for this in order to reduce calculation times and make applications easy. Among these authors, the most used are the formulae of Swamee–Jain and Haaland. In this work, we set out to study the evolution of friction on the top, bottom and side walls of a penstock using these two formulas. The objective is to find which one best characterizes the friction and gives results close to the implicit Colebrook–White formula. To carry out this work, the study was made by a numerical approach in FLUENT. It appears that the Swamee–Jain correlation gives values closer to that of Colebrook–White along the walls. On the side walls, Haaland's formula better describes the constancy of friction with a much larger range of values than Swamee–Jain. On the upper and lower walls, the friction has a linear character.

ENSURING ECOLOGICAL AND FIRE SAFETY DURING THE OPERATION OF MOTOR VEHICLES WITH PISTON PNEUMATIC AND CRYO ENGINES WHICH USING THE NITROGEN AS WORKING BODY

In the study, the purpose of which is to improve the mathematical apparatus based on the modified thermodynamic theory of disturbances for describing the thermophysical characteristics of nitrogen in the gaseous and liquid states as the working fluid of fire-safe and environmentally safe motor vehicles with piston pneumatic or cryoengines, a mathematical model was suggested to describe for describing the thermodynamic properties of gaseous and liquid of nitrogen, which is intended for the development of a new type of fire-safe and environmentally efficient vehicle with a piston pneumo- or cryoengine and its application is illustrated. The object of the study is the thermophysical characteristics of nitrogen in a liquid or gaseous aggregate state, as a working fluid of fire-safe and eco-safe motor vehicles with a piston pneumatic or cryoengine. The subject of the research is a mathematical apparatus based on the modified perturbation theory for describing the thermophysical characteristics of nitrogen in gaseous and liquid states. The scientific novelty of the research results is that a mathematical apparatus based on a modified thermodynamic theory of disturbances has been improved for a comprehensive description of all thermophysical characteristics of nitrogen, which is in both liquid and gaseous aggregate state, in terms of reducing calculation time and reducing the error of obtaining thermophysical characteristics compared to reference and experimental data. The practical significance of the research results is that the improved mathematical apparatus is marketable for providing accurate information to the composition of the initial data set in studies on the use of nitrogen in various aggregate states as the working fluid of fire-safe and eco-safe motor vehicles with a piston pneumatic or cryoengine.

A deep-level decomposed model to accelerate hydraulic simulations in large water distribution networks

As the size of water distribution network (WDN) models continues to grow, developing and applying real-time models or digital twins to simulate hydraulic behaviors in large-scale WDNs is becoming increasingly challenging. The long response time incurred when performing multiple hydraulic simulations in large-scale WDNs can no longer meet the current requirements for the efficient and real-time application of WDN models. To address this issue, there is a rising interest in accelerating hydraulic calculations in WDN models by integrating new model structures with abundant computational resources and mature parallel computing frameworks. This paper presents a novel and efficient framework for steady-state hydraulic calculations, comprising a joint topology-calculation decomposition method that decomposes the hydraulic calculation process and a high-performance decomposed gradient algorithm that integrates with parallel computation. Tests in four WDNs of different sizes with 8 to 85,118 nodes demonstrate that the framework maintains high calculation accuracy consistent with EPANET and can reduce calculation time by up to 51.93 % compared to EPANET in the largest WDN model. Further investigation found that factors affecting the acceleration include the decomposition level, consistency of sub-model sizes and sub-model structures. The framework aims to help develop rapid-responding models for large-scale WDNs and improve their efficiency in integrating multiple application algorithms, thereby supporting the water supply industry in achieving more adaptive and intelligent management of large-scale WDNs.

A method to shorten the time of high-dimensional matrix operations and convolution operations

Serverless computing, or Function-as-a-Service (FaaS), is a cloud computing execution model where the cloud provider dynamically manages the allocation and provisioning of servers which is a new way to solve the problem that the need of client.By serverless computing clients can easily solve their problems anytime,anywhere.However, the complex convolution operations and high-dimensional matrix operations can still consume a lot of resources such as computation time and memory occupancy in serverless computing. In this paper, firstly, the author selects two frequently used models in serverless computing, and then divide them into different numbers of partitions respectively to study the impact of partitioning methods on the computation time and memory occupancy. The author show that different partitioning methods can make an obviously impact on the computation time and memory occupancy,whitch shows that the proper partitioning of high-dimensional matrices and tensors can make a significantly reduction of calculation time and memory occupancy.

Improving computational efficiency in DSMC simulations of vacuum gas dynamics with a fixed number of particles per cell

The present study addresses the challenge of enhancing computational efficiency without compromising accuracy in numerical simulations of vacuum gas dynamics using the direct simulation Monte Carlo (DSMC) method. A technique termed ‘fixed particle per cell (FPPC)’ was employed, which enforces a fixed number of simulator particles across all computational cells. The proposed technique eliminates the need for real-time adjustment of particle weights during simulation, reducing calculation time. Using the SPARTA solver, simulations of rarefied gas flow in a micromixer and rarefied supersonic airflow around a cylinder were conducted to validate the proposed technique. Results demonstrate that applying the FPPC technique effectively reduces computational costs while yielding results comparable to conventional DSMC implementations. Additionally, the application of local grid refinement coupled with the FPPC technique was investigated. The results show that integrating local grid refinement with the FPPC technique enables accurate prediction of flow behaviour in regions with significant gradients. These findings highlight the efficacy of the proposed technique in improving the accuracy and efficiency of numerical simulations of complex vacuum gas dynamics at a reduced computational cost.

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.

Impact of occupant behavior on optimal multi-objective solutions for the design of low-energy buildings

The occupant behavior plays a fundamental role in the actual performance of the buildings, especially for very low energy buildings. Since inconsistencies are discovered post-occupancy, known as Energy Performance Gap, it is necessary to better represent the occupants when optimizing the design of buildings. In this context, a multi-objective optimization of a low energy building was carried out for three different French climates with two models of occupant behaviors. The first is a standard fixed scenario (SF) while the second is an agent-based tool (NoMASS) that predicts occupancy scenarios according to sociodemographic characteristics and estimates occupants’ interactions with windows, external shadings and lighting based on probabilistic models. The results obtained show that the occupant model significantly influences the expected performance of the building as NoMASS model estimates greater heating needs and improved summer comfort. However, for our case study, there is no influence of the accuracy of representation of occupant behavior on the optimal solutions of building design. The optimization could be carried out with a less detailed model, thus reducing calculation times while maintaining comparable optimal results. The performance of the optimal solutions obtained can then be refined by simulating with the most detailed model.

Enhanced-GA supports traffic signal optimizations and protects the urban environment

Much current research on traffic signal optimization often neglects the impact of environmental factors in urban areas. This can result in suboptimal solutions that do not consider the effects of traffic on air pollution and the overall urban environment. To address this issue, this article proposes a solution that combines Enhanced GA with a comprehensive framework for considering environmental factors in traffic signal optimization. By optimizing traffic signal timings and minimizing emissions, the proposed solution aims to reduce congestion and improve urban transportation networks' efficiency while protecting the environment. The proposed approach uses a set of optimization algorithms and assumptions to generate a comprehensive framework for traffic signal optimization. These algorithms and assumptions consider environmental factors such as air quality and the impact of traffic on the local ecosystem. Moreover, this article provided the enhanced genetic algorithm operators and suggested model formulation that could be applied in other research on traffic signal optimization directly to reduce calculation times and increase the efficiency of the novel suggested models

Semi-stationary and dynamic simulation models: A critical comparison of the energy and economic savings for the energy refurbishment of buildings

Dynamic simulation is a powerful tool for accurately evaluating the thermal demands of buildings and assessing the impact of energy refurbishment actions on their final consumption. Conversely, semi-stationary models are widely adopted in commercial applications for its simplified approach, which reduce calculation times, resulting in standardized results showing a certain deviation with respect to the real energy This paper presents the energy and economic comparison between the dynamic simulation and semi-stationary approaches for the calculation of primary energy demand of residential buildings. The semi-stationary method, used by the legislation to calculate the buildings energy label, is based on an energy performance parameter, not representative of the real energy demand. Conversely, an approach based on dynamic simulation provides a more reliable estimation of the primary energy demand. The main novelty of this paper is to numerically prove that the energy and economic savings calculated by means of software based on the current legislation may be overestimated. In this work, the dynamic simulation of the building-plant system is performed by TRaNsient SYstem Simulation (TRNSYS) program. Each building apartment is divided in thermal zones, where the internal heat gains are defined in detail. The semi-stationary simulation of the building-plant system is performed according to the Italian standard UNI TS 11300. The models allow one to evaluate the yearly primary energy demand, along with the energy bill and CO2 emissions. A specific case study is developed for a residential building located in Naples (Italy). The models are used to calculate the building energy demand for several scenarios, considering different thermal transmittances of the building elements. The results show that the semi-stationary method overestimates of primary energy saving, equal to 64.7 %, with respect to the one calculated with the dynamic approach, equal to 43.2 %.

Physics-based modeling of the effective gas transport properties of single jersey knitted fabrics based on images

Physics-based modeling of the effective gas transport properties of single jersey knitted fabrics based on images

Reduced Order Electrochemical-Thermal Modeling of Cylindrical Cells Containing Graphite-Silicon Anodes Considering Thermal Gradients and Hysteresis

Electrochemical thermal modeling of cylindrical cells presents unique challenges compared to other cell formats due to the effect of internal temperature gradients, which typically requires time-consuming simulations due to the number of mesh elements solved numerically. Adding to the difficulty, the emergence of silicon anodes induces voltage hysteresis that affects the cell behavior. In this paper, a reduced-order electrochemical-thermal model is developed for a 21700 cell, which is highlighted by three microcells considering the effects of internal temperature gradients, and an anodic stress model capturing the hysteresis effects caused by the silicon content. The electrochemical, thermal, and mechanical behaviors are investigated. During operations, a temperature gradient arises in the radial direction, resulting in a decrease in local resistance and an increase in reaction rate at the high-temperature core location. The presence of silicon causes a voltage hysteresis that is dominant in the low SOC range, which affects not only the irreversible but also the entropic heat generation. The proposed method achieves an 85% calculation time reduction compared with the existing literature method and a 95% reduction compared with the full order method, while maintaining the accuracy of the terminal voltage and heat generation rate predictions that are validated by experiments.

Coupling model predictive control and rules-based control for real-time control of urban river systems

Model Predictive Control (MPC) has the potential to enhance flood control in urban river systems. However, the computational burden of online optimization hinders the application of MPC to large-scale areas. To address this, we propose a method incorporating both Rules-Based Control (RBC) and MPC based on surrogate models. MPC optimizes the operation of gates and pumping stations in the area of interest, while RBC is implemented in a specific region that does not participate in online optimization, thereby reducing calculation time. We have successfully validated this approach in coastal urban river systems in China, reducing the decision variables from 109 to 24 and narrowing the simulation area from 165 km2 to 15 km2. During the Typhoon Lupit rainfall test, the total computation time and the highest online optimization time were only 33.9 % and 38.1 % of the full MPC system, respectively. Additionally, the method demonstrated impressive results, including a significant 94.62 % reduction in flood volume and a remarkable 96.5 % decrease in flood duration. These findings demonstrate that our method effectively addresses the challenge of long computation times for online optimization in urban river systems and presents a novel approach for implementing urban flood mitigation strategies.

Modification of extended Oxley’s predictive machining theory and determination of Johnson-Cook material model constants by inverse approach

The Johnson-Cook (J-C) flow stress model, renowned for its consideration of the combined effect of strain, strain-rate, and temperature on material property, forms the cornerstone of this research. Its simplicity and applicability in numerical simulation render it widely popular. The study addresses two primary objectives: first, to modify extended Oxley’s predictive machining theory by eliminating the iterative loop for calculating the tool-chip interface temperature, and second, to utilize an inverse approach for determining J-C material model constants specific to medium carbon steel. The modified approach successfully eliminates the iterative loop, thereby streamlining the determination process and effectively reducing calculation time by 25%–40%, thereby enhancing overall efficiency. Concurrently, the study employs an inverse approach to accurately determine the J-C constants (A, B, n, C, and m). Notably, the research avoids the need for expensive and time-consuming Split Hopkinson Pressure Bar (SHPB) tests, instead opting for low-cost axial tensile tests and numerical optimization techniques to obtain the J-C constants. Thus, this study provides a practical method for determining J-C constants, pivotal for accurate material representation in numerical simulations and metal cutting applications. Standard materials and machines are employed, ensuring ease of understanding for a wider audience. The predicted values for the J-C constants are in proximity to the experimental values obtained by using SHPB test. Validated findings, achieved with precision under specified metal cutting conditions, highlight the effectiveness and applicability of the proposed approach, providing valuable insights for optimizing machining processes and improving efficiency.

Systemic risk monitoring model from the perspective of public information arrival

We introduce a new technique called the market correlation deviation test, which aims to detect systemic anomalies and facilitate market monitoring by using high-frequency price data from the Chinese stock market. Our method achieves a significant reduction in calculation time (to one N of the traditional method, where N represents the number of stocks) while maintaining the accuracy of calculations. This reduction is achieved by converting the covariance calculation into a difference calculation. By comparing our approach with the conventional Barndorff-Nielson-Shephard (BNS) and Corsi-Pirino-Reno (CPR) models, we demonstrate superior accuracy in identifying anomalies and efficiency in monitoring. Notably, our method can measure local price synchronization behavior, often associated with the arrival of public information, and we also observe the impact of public information on pricing. To the best of our knowledge, this study is the first to directly observe and quantify the effect of public information on pricing.

Enhancing real-time cell culture monitoring: Automated Raman model optimization with Taguchi method.

Raman spectroscopy has found widespread usage in monitoring cell culture processes both in research and practical applications. However, commonly, preprocessing methods, spectral regions, and modeling parameters have been chosen based on experience or trial-and-error strategies. These choices can significantly impact the performance of the models. There is an urgent need for a simple, effective, and automated approach to determine a suitable procedure for constructing accurate models. This paper introduces the adoption of a design of experiment (DoE) method to optimize partial least squares models for measuring the concentration of different components in cell culture bioreactors. The experimental implementation utilized the orthogonal test table L25(56). Within this framework, five factors were identified as control variables for the DoE method: the window width of Savitzky-Golay smoothing, the baseline correction method, the order of preprocessing steps, spectral regions, and the number of latent variables. The evaluation method for the model was considered as a factor subject to noise. The optimal combination of levels was determined through the signal-to-noise ratio response table employing Taguchi analysis. The effectiveness of this approach was validated through two cases, involving different cultivation scales, different Raman spectrometers, and different analytical components. The results consistently demonstrated that the proposed approach closely approximated the global optimum, regardless of data set size, predictive components, or the brand of Raman spectrometer. The performance of models recommended by the DoE strategy consistently surpassed those built using raw data, underscoring the reliability of models generated through this approach. When compared to exhaustive all-combination experiments, the DoE approach significantly reduces calculation times, making it highly practical for the implementation of Raman spectroscopy in bioprocess monitoring.

Robust Tensor Hypercontraction of the Particle-Particle Ladder Term in Equation-of-Motion Coupled Cluster Theory.

One method of representing a high-rank tensor as a (hyper-)product of lower-rank tensors is the tensor hypercontraction (THC) method of Hohenstein et al. This strategy has been found to be useful for reducing the polynomial scaling of coupled-cluster methods by representation of a four-dimensional tensor of electron-repulsion integrals in terms of five two-dimensional matrices. Pierce et al. have already shown that the application of a robust form of THC to the particle-particle ladder (PPL) term reduces the cost of this term in couple-cluster singles and doubles (CCSD) from to with negligible errors in energy with respect to the density-fitted variant. In this work, we have implemented the least-squares variant of THC (LS-THC) which does not require a nonlinear tensor factorization, including the robust form (R-LS-THC), for the calculation of the excitation and electron attachment energies using equation-of-motion coupled cluster methods EOMEE-CCSD and EOMEA-CCSD, respectively. We have benchmarked the effect of the R-LS-THC-PPL approximation on excitation energies using the comprehensive QUEST database and the accuracy of electron attachment energies using the NAB22 database. We find that errors on the order of 1 meV are achievable with a reduction in total calculation time of approximately 5 ×.

“MALVA ALCEA”: ADVANCING UKRAINIAN NAVALCAPABILITIES WITH REACT NATIVE TECHNOLOGY

In the rapidly evolving domain of military operations, the advent of digital technologies has ushered in unprecedented capabilities and efficiencies. “The Digital Admiral “Malva Alcea” exemplifies this transformation, showcasing a React Native-based mobile application designed to revolutionize missile targeting calculations within the Ukrainian Navy. This paper delves into the critical need for innovation in the face of traditional, manual calculation methods that are both time-consuming and prone to human error. It charts the development journey of the application, highlighting the selection of React Native for its cross-platform capabilities and ease of integration with naval data systems. The paper provides a comprehensive overview of the application’s architecture, including its intuitive user interface, robust storage model, and the integration of a sophisticated mathematical model for missile targeting. Through user feedback and operational deployment, the application has demonstrated a significant reduction in calculation times and an improvement in accuracy, thus enhancing operational efficiency and decision-making in naval defense scenarios. Furthermore, the paper addresses the challenges encountered during development, such as ensuring data security and maintaining system adaptability for future expansions. It concludes by outlining future directions for the application, including potential integrations with larger situational awareness platforms and expansions to other operating systems, underscoring the pivotal role of digital innovations in advancing military capabilities and strategic operations. Keywords: React Native, Naval Defense Operations, Missile Targeting Calculations, Mobile Application Development, User Experience (UX), Data Security and Encryption, Redux State Management, Encrypted Storage, Operational Efficiency, Military Technology Innovation

Making Likelihood Calculations Fast: Automatic Differentiation Applied to RooFit

With the growing datasets of current and next-generation HighEnergy and Nuclear Physics (HEP/NP) experiments, statistical analysis has become more computationally demanding. These increasing demands elicit improvements and modernizations in existing statistical analysis software. One way to address these issues is to improve parameter estimation performance and numeric stability using Automatic Differentiation (AD). AD’s computational efficiency and accuracy are superior to the preexisting numerical differentiation techniques, and it offers significant performance gains when calculating the derivatives of functions with a large number of inputs, making it particularly appealing for statistical models with many parameters. For such models, many HEP/NP experiments use RooFit, a toolkit for statistical modeling and fitting that is part of ROOT. In this paper, we report on the effort to support the AD of RooFit likelihood functions. Our approach is to extend RooFit with a tool that generates overheadfree C++ code for a full likelihood function built from RooFit functional models. Gradients are then generated using Clad, a compiler-based source-codetransformation AD tool, using this C++ code. We present our results from applying AD to the entire minimization pipeline and profile likelihood calculations of several RooFit and HistFactory models at the LHC-experiment scale. We show significant reductions in calculation time and memory usage for the minimization of such likelihood functions. We also elaborate on this approach’s current limitations and explain our plans for the future.