Optimal Profile Research Articles

Multi-objective optimization for multi-stage constant current charging for Li-ion batteries

Fast charging is a key challenge for the widespread adoption of electric vehicles (EVs), as it can make EVs more convenient and appealing to consumers. Therefore, different charging methods are proposed to enhance the performance of lithium-ion batteries (LIBs). Multi-stage constant current (MSCC) charging can improve LIB's performance in several aspects, including charging time, charged capacity, temperature rise, average temperature rise, and charging energy efficiency. However, achieving a multi-objective performance during LIB charging is challenging. In this paper, the Taguchi method is used to determine the multi-objective optimal (MOO) charging profile for the MSCC charging strategy. The Orthogonal experiments are designed and conducted to find the optimal solutions for each performance parameter for a five-stage constant current (5SCC) charging profile. A comparison is conducted among the performance-based selective charging profiles to validate the optimal charging profile. Furthermore, a comparison is made between the obtained MOO charging profile and the classical equivalent constant current constant voltage (CCCV) method. Experimental results demonstrated that the MOO charging profile reduces the charging time with comparable temperature rise to the CCCV, making it promising for the future alternative efficient EV charging. These findings highlight the effectiveness of the MSCC approach in improving LIB performance.

A methodological framework for optimizing the energy consumption of deep neural networks: a case study of a cyber threat detector

AbstractThe growing prevalence of deep neural networks (DNNs) across various fields raises concerns about their increasing energy consumption, especially in large data center applications. Identifying the best combination of optimization techniques to achieve maximum energy efficiency while maintaining system performance is challenging due to the vast number of techniques available, their complex interplay, and the rigorous evaluation required to assess their impact on the model. To address this gap, we propose an open-source methodological framework for the systematic study of the influence of various optimization techniques on diverse tasks and datasets. The goal is to automate experimentation, addressing common pitfalls and inefficiencies of trial and error, saving time, and allowing fair and reliable comparisons. The methodology includes model training, automatic application of optimizations, export of the model to a production-ready format, and pre- and post-optimization energy consumption and performance evaluation at inference time using various batch sizes. As a novelty, the framework provides pre-configured "optimization strategies" for combining state-of-the-art optimization techniques that can be systematically evaluated to determine the most effective strategy based on real-time energy consumption and performance feedback throughout the model life cycle. As an additional novelty, "optimization profiles" allow the selection of the optimal strategy for a specific application, considering user preferences regarding the trade-off between energy efficiency and performance. Validated through an empirical study on a DNN-based cyber threat detector, the framework demonstrates up to 82% reduction in energy consumption during inference with minimal accuracy loss.

Evaluation of crop model-based simplified marginal net return maximising nitrogen application rates on site-specific level in maize

AbstractCrop growth models such as DSSAT-CERES-Maize have proven to be useful for analysing plant growth and yield within homogenous land units. The paper presents results of newly developed model-based site-specific Soil Profile Optimisation (SPO) tools in combination with an updated version of an already published Nitrogen Prescription Model (NPM). Site-specific soil profiles were generated through an inverse modelling approach based on measured site-specific yield (point-based) and tops weight (above-ground biomass time-series) and evaluated. Site-specific soil profiles generated based only on measured yield variability were able to explain 72% (R2 0.72) of yield variability (dependent variable) based on selected soil profile input parameters (independent variable). Site-specific soil profiles generated based on measured yield and tops variability simultaneously (multiple target variable) explained 68% of yield variability (R2 0.68). The NPM uses the SPO generated site-specific soil profiles for economic evaluation of site-specific N application rates. NPM simulated N application rates, aiming at the maximisation of marginal net return (MNR) were 25% lower compared to the uniform N application rates with an assumed grain and N price of 0.17 and 0.3 Euro kg−1 respectively, under rainfed conditions over three years based on soil profiles generated via an inverse modelling approach only from measured yield variability (one target variable). N application rates were 28% lower when based on soil profiles generated from simultaneously included grain and tops variability in the inverse modelling approach. The results highlight the importance of site-specific fertilizer management when maximising MNR.

An Adapted NURBS Interpolator with a Switched Optimized Method of Feed-Rate Scheduling

With the increasing demand for processing precision in the manufacturing industry, feed-rate scheduling is a crucial component in achieving the processing quality of complex surfaces. A smooth feed-rate profile not only guarantees machining quality but also improves machining efficiency. Although the typical offline feed-rate scheduling method possesses good processing efficiency, it may not provide an optimal solution due to the NP-hard problem caused by the feed-rate scheduling of continuous curve segments, which easily results in excess kinetic limitations and feed-rate fluctuations in a real-time interpolation. Instead, the FIR (Finite Impulse Response) method is widely used to realize interpolation in real-time processing. However, the FIR method will filter out a large number of high-frequency signals, leading to a low-processing efficiency. Further, greater acceleration or deceleration is required to ensure the interpolation passes through the segment end at a predefined feed rate and the deceleration in the feed rate profile appears earlier, which allows the interpolation to easily exceed the kinetic limitation. At present, a simple offline or online method cannot realize the global optimization of the feed-rate profile and guarantee the machining efficiency. Moreover, the current feed-rate scheduling that considers both offline and online methods does not consider the situation that the call of offline data and online prediction data will lead to a decrease in the real-time performance of the CNC system. Further, real-time feed-rate scheduling data tend to dominate the whole interpolation process, thus reducing the effect of the offline feed-rate scheduling data. Hence, based on the tool path with C3 continuity (Cubic Continuously Differentiable), this paper first presents a basic interpolation unit relevant to the S-type interpolation feed-rate profile. Then, an offline local smooth strategy is proposed to smooth the feed-rate profile and reduce the exceeding of kinetic limitations and feed-rate fluctuations caused by frequent acceleration and deceleration. Further, a global online smoothing strategy based on the data generated by offline pre-interpolation is presented. What is more, FIR login and logout conditions are proposed to further smooth the feed-rate profile and improve the real-time performance and machining efficiency. The case study validates that the proposed method performs better in kinetic results compared with the typical offline and FIR methods in both the simulation experiment and actual machining experiments. Especially, in actual processing experiments, the proposed method obtains a 28% reduction in contour errors. Further, the proposed method compared with the FIR method obtains a 15% increase in machining efficiency but only a 4% decrease compared with the typical offline method.

Amplification of femtosecond pulses with AI-assisted spectral phase modulation

We report our investigation on ultrashort laser pulse optimization using an AI algorithm in a system consisting of a mode-locked oscillator, a spectral phase shaper, and a highly nonlinear amplifier. We analyzed the performance of the pulse optimization process as a function of two main parameters: the resolution of spectral phase modulation and the number of agents in the algorithm. We showed that the algorithm could find an optimum phase profile for the seed pulse, which allowed for a reduction of the FWHM of the amplified pulse by 10 fs (from 46 to 36 fs), and significantly reduced the intensity of the side-pulse by a factor of 4.6. Importantly, the algorithm used does not require any training and optimizes the pulse shape without any knowledge about the input pulse parameters or the parameters of the amplifier. We believe the proposed system might be a convenient test bed for evaluating various AI-based algorithms in a pulse optimization task.

The Development of a Machine Learning-Based Carbon Emission Prediction Method for a Multi-Fuel-Propelled Smart Ship by Using Onboard Measurement Data

Zero-carbon shipping is the prime goal of the seaborne trade industry at this moment. The utilization of ammonia and liquid hydrogen propulsion in a carbon-free propulsion system is a promising option to achieve net-zero emission in the maritime supply chain. Meanwhile, optimal ship voyage planning is a candidate to reduce carbon emissions immediately without new buildings and retrofits of the alternative fuel-based propulsion system. Due to the voyage options, the precise prediction of fuel consumption and carbon emission via voyage operation profile optimization is a prerequisite for carbon emission reduction. This paper proposes a novel fuel consumption and carbon emission quantity prediction method which is based on the onboard measurement data of a smart ship. The prediction performance of the proposed method was investigated and compared to machine learning and LSTM-model-based fuel consumption and gas emission prediction methods. The results had an accuracy of 81.5% in diesel mode and 91.2% in gas mode. The SHAP (Shapley additive explanations) model, an XAI (Explainable Artificial Intelligence), and a CO2 consumption model were employed to identify the major factors used in the predictions. The accuracy of the fuel consumption calculated using flow meter data, as opposed to power load data, improved by approximately 21.0%. The operational and flow meter data collected by smart ships significantly contribute to predicting the fuel consumption and carbon emissions of vessels.

OPTIMAL PROFILE FOR A FIN OF CONSTANT CROSS SECTION WITH MODELATION OF THE THERMOPHYSICAL PROPERTIES

The dependence of the temperature and composition on thermal conductivity of steel, and its influence in the optimal profile for a fin of constant cross section to be used in air cooled condenser is presented. In Air Cooled Condensers (ACC), air is used as refrigerant, which forces the need for fins to improve the heat transfer process leading to appreciable increments in the costs of the installation. Therefore, developing better fins profile will allow optimizing the operation, maximizing the heat transfer and minimizing the manufacturing cost of the fins. To this objective, in the present paper, three geometric parameters, thickness, height and surface of heat transfer of the fin, this parameters influence directly the optimal obtained profile for conditions of maximum heat flux and optimal Biot number. The study was accomplished for fins of constant cross section. In a future work, fins of variable cross sections will be considered.

Enhancing voltage profile in islanded microgrids through hierarchical control strategies

This paper presents a novel approach aimed at enhancing the voltage profile of an islanded microgrid (MG) through the utilization of Phasor Measurement Units (PMUs) and a hierarchical control system for Distributed Generators (DGs). Recognizing the criticality of maintaining an optimal voltage profile for essential loads within an MG, we assume that PMUs are strategically placed across the MG based on existing placement methodologies to ensure the observability of load buses. The synchrophasor measurements captured by these PMUs are leveraged within an offline-designed tertiary control layer of the hierarchical framework, which in turn sets the voltage reference for the secondary control layer of DGs across the MG. This approach effectively enhances the voltage profile of vital load buses within the MG. The proposed hierarchical strategy encompasses primary, secondary, and tertiary levels of control, responsible for stabilizing voltage and frequency, computing power references, and managing power flow, respectively. PMU measurements play a crucial role in the secondary control level by continuously monitoring deviations. By ensuring the effective placement of PMUs, the proposed framework enables efficient control, leading to notable improvements in the voltage profile and power management. To evaluate the effectiveness of our approach, five test networks were used: 14-bus, 30-bus, 39-bus, 57-bus, and 118-bus test networks. Simulation results demonstrate an impressive over 80 % enhancement in the MG's voltage profile achieved by our proposed method. Additionally, our heuristic strategy efficiently assigns the closest load bus to each DG while aggregating equidistant bus deviations. Collectively, our research underscores the significant impact of PMU measurements and a well-structured hierarchical voltage control system in substantially improving the voltage profile of MGs.

Investigation of Surface-Catalycity Effects on Hypersonic Glide Vehicle Trajectory Optimization

The optimization of a hypersonic glide vehicle’s reachable domain is studied using different models to constrain the stagnation-point heat transfer. The vehicle is modeled as a high-lift common aero vehicle that uses bank-angle modulation for crossrange maneuvering with a leading-edge thermal protection system made of an ultra-high-temperature ceramic. A constrained nonlinear optimization problem is formulated to determine the range of feasible trajectories that satisfy aerodynamic heating and loading constraints. The optimal control profile is determined using a modified particle swarm optimization routine that employs a penalty function approach to handle path and terminal constraints. Baseline trajectories are determined using an existing convective heat-flux correlation, and the effect of surface catalycity on the optimized trajectories is investigated by applying a correction factor, derived from high-fidelity computational fluid dynamics simulations, to the heat-flux correlation. Results demonstrate a high sensitivity of the optimized trajectories to the underlying aerothermodynamics model such that accounting for surface catalycity expands the reachability by an order of magnitude.

Optimization of higher-order S-curve motion profile using unitization method aiming to reduce the vibration of a lightly damped system

Optimization of higher-order S-curve motion profile using unitization method aiming to reduce the vibration of a lightly damped system

A machine learning-based framework for clustering residential electricity load profiles to enhance demand response programs

Load shapes derived from smart meter data are frequently employed to analyze daily energy consumption patterns, particularly in the context of applications like Demand Response (DR). Nevertheless, one of the most important challenges to this endeavor lies in identifying the most suitable consumer clusters with similar consumption behaviors. In this paper, we present a novel machine learning based framework in order to achieve optimal load profiling through a real case study, utilizing data from almost 5000 households in London. Four widely used clustering algorithms are applied specifically K-means, K-medoids, Hierarchical Agglomerative Clustering and Density-based Spatial Clustering. An empirical analysis as well as multiple evaluation metrics are leveraged to assess those algorithms. Following that, we redefine the problem as a probabilistic classification one, with the classifier emulating the behavior of a clustering algorithm, leveraging Explainable AI (xAI) to enhance the interpretability of our solution. According to the clustering algorithm analysis the optimal number of clusters for this case is seven. Despite that, our methodology shows that two of the clusters, almost 10% of the dataset, exhibit significant internal dissimilarity. As a result, these clusters have been excluded from consideration for DR programs. The scalability and versatility of our solution makes it an ideal choice for power utility companies aiming to segment their users for creating more targeted DR programs.

Enhancement of Wear Resistance of High-Load Pressing Screw in Smokeless Charcoal Production by Using Genetic Algorithm and Discrete Element Method

During the production of smokeless charcoal, the pressing screw of the sawdust compressor is exposed to the effects of friction and elevated temperature, resulting in rapid wear. This phenomenon not only diminishes productivity but also exerts adverse influences on the final product's quality. Consequently, the pursuit of research endeavors aiming at prolonging the lifespan of the pressing screw holds significant importance, not only in reducing production costs but also in enhancing the product's overall quality. This paper adopts an innovative approach by integrating theoretical calculations, numerical simulations with practical experiments to ascertain the optimal profile for the pressing screw. This methodology employs Genetic Algorithm and Discrete Element Method (DEM) simulations in conjunction with the EDEM software to simulate the working process and provide the optimal profile of the pressing screw. The analysis and simulation results indicate a substantial enhancement in the wear resistance of the pressing screw while ensuring the efficient movement of discrete materials during the pressing process. The results of this study not only indicate the main wear locations on the pressing screw but also suggest the optimal profile, providing a basis and control for wear assessment. Furthermore, the results of this research not only identifies the principal areas that are susceptible to wear on the pressing screw but also proposes optimal profile, threreby establish a solid foundation and methodology for wear assessment. These results will be pragmatically implemented in smokeless charcoal production factories, concurrently pave the way for further research and applications in this field.

AI-enabled cancer target prioritization with optimal profiles balancing novelty, confidence and commercial tractability

The identification of new biological targets is crucial to advance cancer therapy, but deciphering the fiendishly complex processes that drive and sustain disease can be tedious and resource intensive. To optimize and accelerate the drug discovery process, artificial intelligence (AI) platforms are emerging that enable fast and cost-effective identification and prioritization of novel and disease-specific therapeutic targets with optimal target profiles, balancing confidence, novelty and commercial tractability. AI-streamlined target profiling has the potential to significantly improve the commercial burden of traditional drug development, and provides an unbiased approach for novel target identification. Here, we discuss the AI-assessed target profile and clinical relevance of genes recently identified by our AI-driven target discovery platform as top priority cancer targets.

Extraction of geometric features and analysis of flow mechanism of high loaded compressor airfoils at low Reynolds number

When the aircraft cruising at high altitude, the aerodynamic performance of the compressor sharply decreases due to the enhanced boundary layer separation loss. Therefore, it is of great significance to design the high-performance airfoils suitable for low Reynolds numbers (Re) to improve the aerodynamic performance of compressor. In this paper, numerical simulations were carried out on a high-loaded subsonic compressor cascade V103 at low Re. Meanwhile, a multi-objective optimization of blade profile was performed to get optimal solutions, and data mining techniques were applied to extract valuable design knowledge from the optimal database. The results show that the Pareto-optimal airfoils achieve a maximum reduction of 27.32% in total pressure loss coefficient under design condition and 36.71% under near stall condition. Compared with the original airfoil, the curvature distribution law of the camber line with larger values at both ends and smaller values in the middle is advantageous for the performance of the compressor cascade under design condition. This distribution pattern causes a forward shift for the loading distribution of airfoils, accompanied by an earlier transition onset. Additionally, it reduces adverse pressure gradient at the rear part of suction surface, which suppresses the development and growth of laminar separation bubble and delays the separation of turbulent boundary layer. Under near stall condition, maintaining larger leading edge angle and uniform flow diffusion is beneficial for improving the performance of compressor cascade. This is because the larger leading edge angle reduces incidence angle, while the uniform flow diffusion inhibits a great flow separation.

Impact of flow intermittency on heat transfer enhancement in serpentine channels

With liquid cold plates being widely applied in industries such as battery energy storage systems, advanced heat transfer enhancement technologies are urgently needed to efficiently dissipate the ever-increasing heat load. The present work numerically and experimentally explores the potential of flow intermittency in a laminar serpentine channel for thermal performance improvement. The numerical analysis shows that the dynamic Dean vortex evolution induced by the intermittent mainstream disrupts the thermal boundary layer more effectively than the steady-flow vortices and enhances local Nusselt number at the U-turns by 117% maximally. Such secondary vortices are transported intermittently to the straight segments, resulting in a 55% increase in the area-averaged heat transfer by promoting mainstream-boundary flow mixing. The optimization of the flow intermittency profile is achieved by matching the pulse-on and deceleration stage durations with the characteristic times of secondary vortex growth and transport. The numerical results are qualitatively validated by the experimental measurement conducted in a water bath. The current study novelly demonstrates the design concept of enhancing heat transfer in curved channels by actively controlling the intermittent flow and proposes the design criteria for the intermittency profile to achieve optimal performance.

3D rolling contact finite element analysis of high-speed railway turnout considering ratchetting effect

The present work introduces a three-dimensional finite element model for the wheel-turnout-sleeper system, incorporating a cyclic constitutive model that accurately captures the nonlinear hardening behavior of rail steel under cyclic loadings. At the same time, a mixed implicit-explicit finite element method is employed to investigate the transient rolling contact behavior of wheel-turnout. Through dynamic analysis, the transient rolling contact status of the wheel-turnout, stress and strain responses of the turnout, and wheel-rail forces are investigated under varying crossing speeds and number of cycles. It is found that during the wheelset crossing, the contact stress remains relatively stable on the stock rail, and multi-point contact occurs at the wing rail and long point rail, and the contact stress on the surface of the long point rail is more significant. With an increase in crossing speed in the same cycle, there is a gradual rise in contact stress on the long point rail face. At the same crossing speed, with the increase in the number of cycles, the contact position between the wheelset and the long point rail gradually moves back, and the top width of the contact position gradually expands. Meanwhile, the fluctuation range of vertical displacement of the wheel center diminishes, and the wheel flange moves away from the long point rail. Additionally, a progressive accumulation of ratcheting strain is observed on the rail, and the ratcheting strain on the long point rail significantly exceeds that on the stock rail. The research results will help to provide a reference for the optimization and design of the critical cross-section profile of the long point rail and will have guiding significance for the maintenance and repair of turnouts.

The droplet race: Optimization of a wettability gradient surface

Droplet behavior influenced by wettability distribution is a pertinent field of research with applications in lab-on-a-chip and heat transfer devices among others. Some have proposed patterned surfaces with controlled variation of wettability to orient the direction of the droplet motion or to increase its velocity. These patterns are arrived upon with experience and knowledge of this phenomenon. In this research paper, the authors used a mathematical approach to the physical problem by using a gradient based optimizer for maximizing droplet velocity. Given some initial conditions, the optimizer marches toward the optimum wettability distribution profile. The droplet motion is modeled in two dimensions (i.e., on the xy-plane), on a plate having a wettability distribution in one dimension (i.e., along the x axis). The single component pseudopotential model allows for the quantification of the wettability distribution as a distribution of a pseudodensity of the solid nodes of the flat plate. Starting with several monotonous analytical profiles, a quadratic convex profile allows us to reach the maximum mean velocity for the threshold droplet displacement. Different sets of initial profiles, length of the plate (L), and diameter of the droplet (D) are tested. For smaller L/D ratio, the optimal wettability distributions exhibit non-trivial features: profiles can be non-monotonous, and wettability gradient could be locally null. With the increase in the L/D ratio, these specificities tend to be less prominent and optimal profiles converge to the quadratic convex one. The main innovation and significance of the paper is that mathematical optimization algorithms have been used conjointly with a multiphase lattice Boltzmann model solver to address for the first time the droplet race defined as: “what is the best wettability profile in order for a droplet to reach a desired location as quickly as possible?”

Fractional order modeling based optimal multistage constant current charging strategy for lithium iron phosphate batteries

AbstractThe primary power source for electric vehicles (EVs) is batteries. Due to the superior characteristics like higher energy density, power density, and life cycle of the lithium iron phosphate (LFP) battery is most frequently chosen among the various types of lithium‐ion batteries (LIBs). The main issues that users encounter are the time required to charge an EV battery and the safety of the EV battery during the charging period. The fast‐charging means, charging a battery with high currents which may lead to a rise in the temperature of a battery. The abrupt rise in battery temperature may cause changes in the internal chemical structures of the battery, reducing battery life even further. In this regard, an optimal charging profile design is of utmost importance in order to satisfy dual objectives simultaneously such as less charging time and improvement in life of the battery. To overcome the conflict between charging speed and rise in temperature an optimal multistage constant current (MSCC) based charging strategy has been investigated under different operating conditions. In addition, the proposed charging profiles have been studied using experimentation.

Optimal Cooperative Eco-Driving of Multitrain With TLET Comprehensive System

Aiming to minimize the total electricity usage in realistic operation of urban rail transit (URT) trains, this paper establishes the coupled model for the train-line-electric network-timetable (TLET) comprehensive system, and proposes a multi-train cooperative eco-driving method to achieve the system energy optimal. The dynamic power flow within the DC railway system with multiple trains is analyzed by modeling the traction power supply system (TPSS), and an improved power flow calculation (PFC) method is proposed to calculate the dynamic power flow distribution. To achieve the total substation energy minimization, a space-time-speed (STS) three-dimensional network is presented to transform continuous train motion process into discrete states on space-speed plane, and a multiple individuals dynamic programming (MIDP) algorithm is throughly developed to cooperatively optimize the multi-train trajectories along the time horizon, with which the optimal speed profiles and timetables of each train can be obtained simultaneously. Moreover, a computationally efficient heuristic algorithm together with a train-to-train (T2T) communication policy are developed as a comparative study. Numerical experiment with field data from Guangzhou Metro Line 8 is implemented, to illustrate the energy-saving performance of the proposed methods.

Optimization of low-temperature multi-stage submersible pump based on blade load

The multi-stage submersible pump is a power conveyor for low-temperature media, which is conveyed by the rotating of centrifugal impellers. In this study, the impellers of a multi-stage submersible pump were optimized to improve the efficiency under the premise of the constant total blade load and head. Based on the analysis of performance and flow for each stage of the 18-stage submersible pump, the optimization scheme composed of the first stage, the middle stage, and the last stage was determined. The blade outlet angle, average blade wrapping angle, and blade wrapping angle difference were selected as optimization parameters through Plackett–Burman experimental design and significance analysis, and the blade profile was redrawn by changing the blade load distribution. The performance prediction model was built based on the Kriging response surface model, and then, the global optimal blade profile was found by non-dominated Sorting Genetic Algorithm II. The efficiencies of the 3-stage submersible pump and 18-stage submersible pump with optimized impeller increased by 2.35% and 2.01%, respectively. Under the design condition, the flow rate loss and pressure pulsation at the impeller outlet decreased and the stator–rotor interaction between the impeller and guide vane was weakened. This will lead to a reduction in unstable flow such as secondary flow and vortices, and an improvement of flow stability at the impeller outlet.