Explore the vast range of titles available.

An efficient battery pack-level thermal management system was crucial to ensuring the safe driving of electric vehicles. To address the challenges posed by insufficient heat dissipation in traditional liquid cooled plate battery packs and the associated high system energy consumption. This study proposes three distinct channel liquid cooling systems for square battery modules, and compares and analyzes their heat dissipation performance to ensure battery safety during high-rate discharge. The results demonstrated that the extruded multi-channel liquid cooled plate exhibits the highest heat dissipation efficiency. Subsequently, response surface experiments were conducted to analyze the width parameters of various flow channels in the liquid cooled plate Finally, the Design of Experiment (DOE) was employed to conduct optimal Latin hypercube sampling on the flow channel depth ( H ), mass flow ( Q ), and inlet and outlet diameter ( d ), combined with a genetic algorithm for multi-objective analysis. The T max of the battery module decreased by 6.84% from 40.94°C to 38.14°C and temperature mean square deviation decreased ( TSD ) by 62.13% from 1.69 to 0.64. Importantly, the battery thermal management model developed in this study successfully met heat dissipation requirements without significantly increasing pump energy consumption.

Ocean heat content (OHC) is key to estimating the energy imbalance of the Earth system. Over the past two decades, an increasing number of OHC studies were conducted using oceanic objective analysis (OA) products. Here we perform an intercomparison of OHC from eight OA products with a focus on their robust features and significant differences over the Argo period (2005–19), when the most reliable global-scale oceanic measurements are available. For the global ocean, robust warming in the upper 2000m is confirmed. The 0–300-m layer shows the highest warming rate but is heavily modulated by interannual variability, particularly El Niño–Southern Oscillation. The 300–700- and 700–2000-m layers, on the other hand, show unabated warming. Regionally, the Southern Ocean and midlatitude North Atlantic show a substantial OHC increase, and the subpolar North Atlantic displays an OHC decrease. A few apparent differences in OHC among the examined OA products were identified. In particular, temporal means of a few OA products that incorporated other ocean measurements besides Argo show a global-scale cooling difference, which is likely related to the baseline climatology fields used to generate those products. Large differences also appear in the interannual variability in the Southern Ocean and in the long-term trends in the subpolar North Atlantic. These differences remind us of the possibility of product-dependent conclusions on OHC variations. Caution is therefore warranted when using merely one OA product to conduct OHC studies, particularly in regions and on time scales that display significant differences.

PurposeProject managers work to ensure successful project completion within the shortest period and at the lowest cost. One of the main tasks of a project manager in the planning phase is to generate the project time–cost curve, and furthermore, to determine the most appropriate schedule for the construction process. Numerous existing time–cost tradeoff analysis models have focused on solving a simple project representation without regarding for typical activity and project characteristics. This study aims to present a novel approach called “multiple-objective social group optimization” (MOSGO) for optimizing time–cost decisions in generalized construction projects.Design/methodology/approachIn this paper, a novel MOGSO to mimic the time–cost tradeoff problem in generalized construction projects is proposed. The MOSGO has slightly modified the mechanism operation from the original algorithm to be a free-parameter algorithm and to enhance the exploring and exploiting balance in an optimization algorithm. The evidential reasoning technique is used to rank the global optimal obtained non-dominated solutions to help decision makers reach a single compromise solution.FindingsTwo case studies of real construction projects were investigated and the performance of MOSGO was compared to those of widely considered multiple-objective evolutionary algorithms. The comparison results indicated that the MOSGO approach is a powerful, efficient and effective tool in finding the time–cost curve. In addition, the multi-criteria decision-making approaches were applied to identify the best schedule for project implementation.Research limitations/implicationsAccordingly, the first major practical contribution of the present research is that it provides a tool for handling real-world construction projects by considering all types of construction project. The second important implication of this study derives from research finding on the hybridization multiple-objective and multi-criteria techniques to help project managers in facilitating the time–cost tradeoff (TCT) problems easily. The third implication stems from the wide-range application of the proposed model TCT.Practical implicationsThe model can be used in early stages of the construction process to help project managers in selecting an appropriate plan for whole project lifecycle.Social implicationsThe proposal model can be applied to multi-objective contexts in diversified fields. Moreover, the model is also a useful reference for future research.Originality/valueThis paper makes contributions to extant literature by: introducing a method for making TCT models applicable to actual projects by considering general activity precedence relations; developing a novel MOSGO algorithm to solving TCT problems in multi-objective context by a single simulation; and facilitating the TCT problems to project managers by using multi-criteria decision-making approaches.

There exists complicated competitive and synergetic relationships among the objectives in the multi-objective problems, which is hard to quantify and brings difficulty for decision making. Existing studies focus on the tradeoff analysis qualitatively and are lack of quantitative calculation. This study proposes a tradeoff analysis index called Conflict Evaluation Index (CEI) for quantitative many-objective conflict evaluation and tradeoff analysis using Pareto optimal solutions. The index is applied into a six-objective reservoir operation problem. In the application, a reservoir operation optimization model including two electricity objectives and four water supply objectives is established and Pareto optimal solutions are obtained with ε-NSGAII. CEI values of any two objectives are calculated under four water demand scenarios. The results show that the conflict degrees among six objectives become more fierce with the increase of water demands and the major conflict is shifted from electricity objectives to water supply objectives. Besides, the CEI values are applied to determine the objective weights and recommend the best solutions. Objectives of intensive conflict are assigned a large weight, and solutions with better performance in those objectives are recommended. The application illustrates that the proposed index is rational and can be instrumental for insightful many-objective analysis and informed decision making.

Background Living organisms need to allocate their limited resources in a manner that optimizes their overall fitness by simultaneously achieving several different biological objectives. Examination of these biological trade-offs can provide invaluable information regarding the biophysical and biochemical bases behind observed cellular phenotypes. A quantitative knowledge of a cell system’s critical objectives is also needed for engineering of cellular metabolism, where there is interest in mitigating the fitness costs that may result from human manipulation. Results To study metabolism in photoheterotrophs, we developed and validated a genome-scale model of metabolism in Rhodopseudomonas palustris , a metabolically versatile gram-negative purple non-sulfur bacterium capable of growing phototrophically on various carbon sources, including inorganic carbon and aromatic compounds. To quantitatively assess trade-offs among a set of important biological objectives during different metabolic growth modes, we used our new model to conduct an 8-dimensional multi-objective flux analysis of metabolism in R. palustris . Our results revealed that phototrophic metabolism in R. palustris is light-limited under anaerobic conditions, regardless of the available carbon source. Under photoheterotrophic conditions, R. palustris prioritizes the optimization of carbon efficiency, followed by ATP production and biomass production rate, in a Pareto-optimal manner. To achieve maximum carbon fixation, cells appear to divert limited energy resources away from growth and toward CO 2 fixation, even in the presence of excess reduced carbon. We also found that to achieve the theoretical maximum rate of biomass production, anaerobic metabolism requires import of additional compounds (such as protons) to serve as electron acceptors. Finally, we found that production of hydrogen gas, of potential interest as a candidate biofuel, lowers the cellular growth rates under all circumstances. Conclusions Photoheterotrophic metabolism of R. palustris is primarily regulated by the amount of light it can absorb and not the availability of carbon. However, despite carbon’s secondary role as a regulating factor, R. palustris’ metabolism strives for maximum carbon efficiency, even when this increased efficiency leads to slightly lower growth rates.

PurposeThe main purpose of the expert system presented in the paper is to support proper decision-making to perform the operation of the complex and crucial technical system in a rational way.Design/methodology/approachThe proposed system was developed using the universal concepts of a semi-Markov process, quality space and a multi-objective analysis. The maintenance and operation processes of a machine were modelled in the form of a semi-Markov process, the quality space was used to exclude the operation and maintenance process of critical quality and finally, thanks to implementation of a multi-objective analysis, the assessment system was build.FindingsBy generating each flow of the process, the expert system supports optimization of a technical system operation to choose the best maintenance strategy. Application of the expert system created based on a real industrial system is presented at the end of the paper.Research limitations/implicationsThe limitations of the proposed approach can be found in the parts of simulation and assessment. As the number of states to be taken into consideration increases, the time of calculation gets longer as well. As regards the assessment, ranges of the criteria argument have to be determined. Unfortunately, in some industrial systems, they are difficult to define or they are infinite and should be artificially limited.Practical implicationsThe system provides three most important benefits as compared to other solutions. The first benefit is the system ability to make a choice of the best strategy from the perspective of the accepted criteria. The second advantage is the ability to choose the best operation and maintenance strategy from the point of view of a decision-maker. And the third is that the decision-maker can be completely sure that the chosen way of operation is not of critical quality.Originality/valueThe novelty of the proposed solution involves the system approach to the expert system design, thanks to the described procedure which is flexible and can be easily implemented in different technical systems which have a crucial impact on reliability and safety of their operation. It is the unique combination of probability-based simulation, multi-dimensional quality considerations and multi-objective analysis.

To address the difficulty in the characterization of the geomechanical properties of reservoirs in petroleum engineering using the traditional formula, due to the complexity of the reservoir, this study proposes a framework of inverse analysis to characterize the geomechanical properties of reservoirs formed through hydraulic fracturing by combining the XGBoost, multi-objective particle swarm optimization (MOPSO), and numerical models. XGBoost was used to generate a surrogate model to approximate the physical model, and the numerical model was used to generate a dataset for XGBoost. MOPSO is regarded as an optimal technology to deal with the conflict between multi-objective functions in inverse analysis. On comparing the results between the actual geomechanical properties and those obtained by using traditional inverse analysis, the proposed framework accurately characterizes the geomechanical parameters of reservoirs obtained through hydraulic fracturing. This provides a feasible, scientific, and promising way to characterize reservoir formation in petroleum engineering, as well as a reference for other fields of engineering.

Water resources managers need to make decisions in a constantly changing environment because the data relating to water resources are uncertain and imprecise. The Robust Optimization and Probabilistic Analysis of Robustness (ROPAR) algorithm is a well-suited tool for dealing with uncertainty. Still, the failure to consider multiple uncertainties and multi-objective robustness hinders the application of the ROPAR algorithm to practical problems. This paper proposes a robust optimization and robustness probabilistic analysis method that considers numerous uncertainties and multi-objective robustness for robust water resources allocation under uncertainty. The copula function is introduced for analyzing the probabilities of different scenarios. The robustness with respect to the two objective functions is analyzed separately, and the Pareto frontier of robustness is generated. The relationship between the robustness with respect to the two objective functions is used to evaluate water resources management strategies. Use of the method is illustrated in a case study of water resources allocation in the Huaihe River basin. The results demonstrate that the method opens a possibility for water managers to make more informed uncertainty-aware decisions.

In the last decades in Italian mountainous regions, forests are invading abandoned pastures and cultivated surfaces that often played a key ecological role for biodiversity conservation, a complex land-use change phenomenon. To improve sustainable regional planning and management it is increasingly important to quantify the phenomenon and classify those areas according their ecological vocation. This study explores multi-objective and multi-criteria assessment for the identification of the most suitable areas for agricultural purposes between those surfaces that have been invaded by forests. Free and Open-Source Software for Geospatial (FOSS4G) software has been used to carry out the research. The work here presented has been funded by the EIT Climate-KIC SATURN project (2018-2021) and was carried out in the pilot areas of the Province of Trento (Italy). Geospatial data set was georeferenced and managed with GRASS and QGIS and the files were collected combining data freely available at the Autonomous Province of Trento geocatalogue as well as others self-produced during the project. The comparative analysis and methodology were carried out by means of QGIS 3.16 Geographic Information System which has been used to complete the analysis to develop a methodology that can be widely used by territorial operators and Public Administrations. To validate the model and verify the results, on-site inspections were carried out. The developed model can be used in different environmental assessments for territorial planning purposes.

Determining an oceanographic parameter on regular grid positions, using a set of data at random locations both in space and time, is the most sought after typical problem since long in the field of oceanography. This is usually called the gridding problem, and the outcome is useful for many applications such as data analysis, graphical display, forcing or initialization of models, etc. In the present study temperature and salinity profiles data obtained from Argo profiling floats were used, and data on regular grids were generated. Data-interpolating variational analysis (DIVA) method was chosen for generating the gridded product. Extensive analysis was done to obtain correct choices of correlation length ( L ) and signal-to-noise ratio ( λ ), which results in an optimal gridded product. The gridded data obtained for different choices of L and λ were later validated with datasets deliberately set aside before performing the analyses. For each combination of L and λ , the resultant gridded data was also validated with subsurface data from OMNI buoys. Based on the statistics of comparison with OMNI, the best-fit choice for L and λ was concluded. Later, a comparative analysis was performed with the obtained gridded products from DIVA against the gridded product obtained from objective analysis (OA) to demonstrate the method's reliability. The resultant optimal combination of L and λ will be used for generating Argo gridded data, which will be subsequently used for generating value-added products like mixed layer depth, ocean heat content, D20, etc., and will be made available on INCOIS Live Access Server.