Optimal Configuration Research Articles

Hierarchical Optimization Framework for Layout Design of Star–Tree Gas-Gathering Pipeline Network in Discrete Spaces

The gas-gathering pipeline network is a critical infrastructure for collecting and conveying natural gas from the extraction site to the processing facility. This paper introduces a design optimization model for a star–tree gas-gathering pipeline network within a discrete space, aimed at determining the optimal configuration of this infrastructure. The objective is to reduce the investment required to build the network. Key decision variables include the locations of stations, the plant location, the connections between wells and stations, and the interconnections between stations. Several equality and inequality constraints are formulated, primarily addressing the affiliation between wells and stations, the transmission radius, and the capacity of the stations. The design of a star–tree pipeline network represents a complex, non-deterministic polynomial (NP) hard combinatorial optimization problem. To tackle this challenge, a hierarchical optimization framework coupled with an improved genetic algorithm (IGA) is proposed. The efficacy of the genetic algorithm is validated through testing and comparison with other traditional algorithms. Subsequently, the optimization model and solution methodology are applied to the layout design of a pipeline network. The findings reveal that the optimized network configuration reduces investment costs by 16% compared to the original design. Furthermore, when comparing the optimal layout under a star–star topology, it is observed that the investment needed for the star–star topology is 4% higher than that needed for the star–tree topology.

Improving the performance of PV/diesel microgrids via integration of a battery energy storage system: the case of Bilgo village in Burkina Faso

BackgroundPV/diesel microgrids are getting more popular in rural areas of sub-Saharan Africa, where the national grid is often unavailable. Most of the time, for economic purposes, these hybrid PV/diesel power plants in rural areas do not include any storage system. This is the case in the Bilgo village in Burkina Faso, where a PV/diesel microgrid without any battery storage system has been set up. This power plant is composed of three diesel generators operating in parallel (two of 16 kW and one of 24 kW), coupled with a photovoltaic field of 30 kWp. It was observed that for such power plants, the grid management is not always efficient due to constantly fluctuating solar output and loads. This inconsistency in energy output raises the question if integrating battery energy storage systems could improve the grid's performance. While many studies in the literature focus on hybrid energy systems, only a few of them have tackled the optimization of existing and operational systems.MethodsThis study investigated three scenarios based on the existing microgrid's characteristics: conventional standalone diesel generators, PV/diesel without battery storage and PV/diesel with a battery storage system which are the main technologies used for off-grid rural electrification in Burkina Faso. The levelized cost of electricity (LCOE) was used to assess the economic performance of each scenario, and the calculations were made using the HOMER software.ResultsIt was found that the best among the scenarios considered is the PV/diesel/battery configuration which has the lowest LCOE of US$ 0.524/kWh. The battery storage system for the optimal configuration has a capacity of 182 kWh with about 8 h of autonomy.ConclusionsIt can be inferred from this study that a storage unit is necessary for an optimal management of a PV/diesel microgrid. Indeed, the storage unit significantly reduces the operating and maintenance costs associated with running diesel generators, as well as the excess electricity. The storage system also allows for a greater reduction in CO2 emissions compared to systems without storage.

Optimal wind and solar sizing in a novel hybrid power system incorporating concentrating solar power and considering ultra-high voltage transmission

The coordinated operation of concentrating solar power (CSP) and traditional thermal power can facilitate the integration of variable wind and solar renewable energy (VRE) into the grid supported by ultra-high voltage (UHV) transmission line, forming a novel HRES. The optimal sizing of VRE in this novel HRES has been rarely investigated. This paper thus proposed a framework serving the capacity configuration optimization for VRE in aforementioned HRES. The lifecycle economic and technical performance of the HRES was evaluated to guide capacity planning. The operational characteristics of the thermal power and CSP were investigated via short-term scheduling optimization. The methodology was applied to a HRES base located in Qinghai Province, China. Results indicate a maximum capacity ratio of VRE to flexible power of 1.74:1 without power curtailments. Further expansion of VRE leads to an escalation in power curtailments. The optimal VRE sizing is determined as a combination of 4000 MW solar power and 1000 MW wind power, which achieves a levelized cost of energy as low as 0.363 CNY/kWh, corresponding to a capacity factor of 0.32. The role of CSP in mitigating VRE fluctuations in HRES is more significant than that of thermal power due to its superior flexibility and low operating costs. The inter-day heat transfer of CSP can further facilitate the stability of the HRES output between consecutive days. These findings provide valuable insights for capacity configuration and operation of the new HRES, which will promote the utilization of renewable energy and cleaner production of the power system.

Component, design, and prospects of self-consistent energy systems for transport infrastructures

ABSTRACT This paper introduces a Transportation Self-Consistent Energy System (TSCES) to meet the increasing electricity requirements of transport infrastructure in various traffic environments, including highway, railway, and waterway. The TSCES consists of four primary components: power source, load, energy storage, and interconnected microgrid. The power source component can be categorized into natural energy endowment and energy-electricity conversion components. Load components can be classified, quantified, and forecasted based on diverse transportation facilities, traffic volume, and importance levels. Energy storage components necessitate thorough consideration of the technical attributes of various energy storage methods, along with the functional requirements of the traffic environment, power sources, and loads. Interconnected microgrid components establish connections among all components, followed by configuration optimization and choosing appropriate network topological structures based on techno-economic conditions, ensuring that all components adhere to constraints imposed by transportation space, regulations, and policy indicators. Additionally, the design procedure and architectural patterns of TSCES are presented, which indicates that the TSCES has excellent adaptability and research application value in the field of transportation.

Analysis of power load tracking and regulation performance in a distributed multi-energy coupled system with nuclear and solar sources

This paper presents a novel distributed multi-energy coupled system that combines solar PV, nuclear power, and energy storage systems to address the power supply challenges in remote regions. Through parameter analysis and multi-objective optimization, the study aims to minimize the need for frequent reactor adjustments during system operation. The key findings indicate that by regulating the rotational speed of the main-compressor, it is possible to meet the power load requirements for a single nuclear power system. The battery capacity and the number of PV panels have a significant impact on the adjustment frequency of the reactor and the power output of the nuclear power system, respectively. The optimal battery capacity is determined to be 183 kWh, while the optimal number of PV panels is 327. These configurations result in an average power output of 20.86 kW for the PV system and 3458.28 kW for nuclear power system. To maximize the utilization of PV energy and minimize reactor adjustments, an energy dispatch strategy is proposed. With the optimal configuration, the adjustment frequency of the reactor decreases to 339 and 276 during the winter and summer months, respectively. Overall, this paper offers a feasible configuration and energy dispatch method for regional power supply.

Optimizing thermal efficiency: Advancements in flat plate heat exchanger performance through baffle integration

Compact heat exchangers have been gaining popularity in many industrial applications. Various types of passive turbulising structures such as corrugations, protrusions and ribs are introduced in the flow path to increase the effective heat transfer area and the level of turbulence in the flow path. This study investigates the impact of introduction of baffles on the performance of PHEs in terms of flow characteristics, pressure drop, and heat transfer. Two distinct types of baffle structures, namely wedge and aerofoil configurations, were introduced at varying numbers - 1, 3, and 5. An extensive experimentation is conducted for a FPHE in a thermal power plant of 500 × 2 MW and various flow and thermal parameters are measured. Computational Fluid Dynamics is utilized in this study to find the optimum baffle configuration. A detailed validation study is executed to obtain the correct computational algorithm, that is the right mesh count, optimum turbulence model, and precise numerical algorithm by comparing the numerical results with the available experimental results. Wedge- type baffles create increased turbulence and pressure drop, while aerofoil-type baffles minimize stagnation and exhibit lower pressure drop. Both baffle configurations lead to a substantial increase in heat transfer, with the 5-wedge-baffle setup showing the highest up to a 55% enhancement of Nusselt number. The Performance Evaluation Criterion (PEC) of wedge and aerofoil type is about 1.24 to 1.3 and 1.22 to 1.24 to that of conventional one respectively.

Efficient low-grade heat harvesting with flexible paper-based thermoelectric generators fabricated by facile process

The study presents the fabrication and characterization of paper-based thermoelectric generators (PTGs) using p-type Bi0.5Sb1.5Te3 (BST) and n-type Bi2Te2.7Se0.3 (BTS) thermoelectric (TE) materials, deposited via a novel cotton swab powder deposition technique. This method eliminates the need for complex equipment or high-temperature processes, reducing fabrication complexity and enabling large-scale production. The key advancements of this method are its simplicity, cost-effectiveness, and practical applicability, ensuring uniform coating and good material adherence to the paper substrate. The PTGs, consisting of five p-n pairs interconnected with various materials, were laminated for enhanced stability and humidity protection. Experimental measurements under temperature gradients(ΔTs) of 5–30 °C revealed an optimal configuration achieving an open-circuit voltage(Voc) of 42.60 mV, internal resistance(Rint) of 42.50kΩ, short-circuit current(Isc) of 1.00µA, and maximum power output(Pmax) of 10.65nW for the carbon paste/copper sheet interconnected PTG at ΔT of 30 °C. The obtained normalized power density is 5.68nWcm-2K-1pair-1, which is one of the best among the reported Bi2Te3-based PTGs. A preliminary demonstration on an arm showed the PTG generating an 11.95 mV output voltage in hot weather conditions (ΔT ≈ 8 °C), effectively harnessing body heat for wearable electronics. This work highlights a simple, cost-effective, and scalable method for fabricating PTGs for sustainable energy harvesting in low-power wearable applications. Future efforts will focus on optimizing materials, fabrication techniques, and novel designs to enhance efficiency and scalability.

Suboptimal Analysis of the Differential System of the Conceptual Trailer Air Brake Valve

Motivation: To increase the efficiency of the brake valve by adding a corrective member. Background: The speed of response and smooth transition between modes of operation in the braking system are the primary research questions. Objective and research question: Will the optimal selection of the input parameters of the differentiating part of a conceptual brake valve ensure the speed of operation and enable a smooth transition from the accelerating mode to the tracking mode? Methods: The mathematical model of the differentiating part of the brake valve uses the lumped method, and the solution was obtained by numerical means. Results: Within the assumed range of variation of spring stiffness and control piston bore throughput, the distribution maps of action times and piston lift were determined, and the optimal configuration of the analyzed input parameters was obtained by a genetic algorithm. Future research: future activities will focus on the development of a system of smooth variation of the throughput of the connecting chamber of the differential part of the valve. Conclusions: The determined maps of functional parameter distributions are the basis for the selection of components of the braking system; optimization indicates the directions of modification of the valve in order to obtain an acceptable performance system.

Efficient energy conversion mechanism and energy storage strategy for triboelectric nanogenerators

Energy management strategy is the essential approach for achieving high energy utilization efficiency of triboelectric nanogenerators (TENGs) due to their ultra-high intrinsic impedance. However, the proven management efficiency in practical applications remains low, and the output regulation functionality is still lacking. Herein, we propose a detailed energy transfer and extraction mechanism addressing voltage and charge losses caused by the crucial switches in energy management circuits. The energy conversion efficiency is increased by 8.5 times through synergistical optimization of TENG and switch configurations. Furthermore, a TENG-based power supply with energy storage and regularization functions is realized through system circuit design, demonstrating the stable powering electronic devices under irregular mechanical stimuli. A rotating TENG that only works for 21 s can make a hygrothermograph work stably for 417 s. Even under hand driving, various types of TENGs can consistently provide stable power to electronic devices such as calculators and mini-game consoles. This work provides an in-depth energy transfer and conversion mechanism between TENGs and energy management circuits, and also addresses the technical challenge in converting unstable mechanical energy into stable and usable electricity in the TENG field.

Deciphering Optimal Radar Ensemble for Advancing Sleep Posture Prediction through Multiview Convolutional Neural Network (MVCNN) Approach Using Spatial Radio Echo Map (SREM).

Assessing sleep posture, a critical component in sleep tests, is crucial for understanding an individual's sleep quality and identifying potential sleep disorders. However, monitoring sleep posture has traditionally posed significant challenges due to factors such as low light conditions and obstructions like blankets. The use of radar technolsogy could be a potential solution. The objective of this study is to identify the optimal quantity and placement of radar sensors to achieve accurate sleep posture estimation. We invited 70 participants to assume nine different sleep postures under blankets of varying thicknesses. This was conducted in a setting equipped with a baseline of eight radars-three positioned at the headboard and five along the side. We proposed a novel technique for generating radar maps, Spatial Radio Echo Map (SREM), designed specifically for data fusion across multiple radars. Sleep posture estimation was conducted using a Multiview Convolutional Neural Network (MVCNN), which serves as the overarching framework for the comparative evaluation of various deep feature extractors, including ResNet-50, EfficientNet-50, DenseNet-121, PHResNet-50, Attention-50, and Swin Transformer. Among these, DenseNet-121 achieved the highest accuracy, scoring 0.534 and 0.804 for nine-class coarse- and four-class fine-grained classification, respectively. This led to further analysis on the optimal ensemble of radars. For the radars positioned at the head, a single left-located radar proved both essential and sufficient, achieving an accuracy of 0.809. When only one central head radar was used, omitting the central side radar and retaining only the three upper-body radars resulted in accuracies of 0.779 and 0.753, respectively. This study established the foundation for determining the optimal sensor configuration in this application, while also exploring the trade-offs between accuracy and the use of fewer sensors.

Adjustment strategies and chaos in duopoly supply chains: The impacts of carbon trading markets and emission reduction policies

Low-carbon policies serve as a core incentive mechanism to promote carbon reduction in duopoly supply chains. However, their directive impact may cause product prices and carbon emission adjustment speeds to accelerate too quickly, thereby triggering market instability. Current research is noticeably lacking in discussions on optimal configurations of supply chain management strategies and policy implementation under market equilibrium conditions. Therefore, this paper utilizes game theory and chaos theory to explore the dynamic operational strategies and optimal profits of duopoly supply chains under a single carbon trading mechanism and considering carbon reduction incentives. The findings indicate: (1) Price adjustment parameters significantly affect both parties' equilibrium strategies and system stability; (2) An increase in carbon prices and carbon reduction incentive intensity both narrow the stable areas for pricing and reduction strategies; (3) Both manufacturers and remanufacturers experience higher optimal pricing, reduced carbon reductions on their part, and more carbon reductions on the part of their counterparts due to the escalating costs of carbon reduction; (4) While enhancing carbon reduction incentives significantly aids both parties in achieving carbon reductions, as the reduction effect gradually diminishes, it also compresses their profit margins, whereas an increase in carbon quotas can linearly enhance business profits.

Optimal configuration method of photovoltaic energy storage in distribution network based on source-load coordination

Abstract To enhance the configurability of photovoltaic energy storage within distribution network systems and foster synchronized development of power sources and loads, a source-load coordinated approach for optimal photovoltaic energy storage configuration in distribution networks is introduced. An alternative multi-objective framework for optimal allocation of photovoltaic energy storage capacity in distribution networks is formulated, which is the optimal goal of maximum economic benefit of photovoltaic energy storage, the optimal goal of minimum network loss and the optimal goal of source-network load coordination. The constraints of the system’s power flow, energy storage charging and discharging capabilities, and an optimized allocation strategy for energy storage are established, and the objective function is solved with full consideration of source-network load coordination factors. The energy storage optimization is updated in the iterative process. Based on the update results, the process for optimal allocation of photovoltaic energy storage in the distribution network has been devised to attain the most efficient allocation. Experimental results indicate a minimal discrepancy between the actual and specified energy storage output, along with a reduced average output power resulting from the optimized photovoltaic energy storage configuration, which shows excellent performance in energy storage optimization configuration.

Large-depth-range 3D measurement based on optimized multi-focal projection system

High-speed and large-depth-range 3D measurement technology is of great importance in a variety of fields. Conventional binary defocusing methods can achieve high measurement speed, but their depth range is limited because the defocusing effect is insufficient near the focal plane in the ordinary projection system. To address this problem, we propose an optimized multi-focal projection system by introducing a cylindrical lens with optimal parameter configuration. Compared to traditional methods, the proposed method could overcome the constraint of defocusing region to obtain high-quality sinusoidal fringe patterns over large depth range without sacrificing the measurement speed. In this paper, the principle of multi-focal projection system and the related scheme of parameter configuration are presented in order to illustrate the role of the cylindrical lens and system parameters on modulating the distribution of defocusing kernel and removing the limitations of defocusing region. Experimental results show that the proposed multi-focal projection system with optimal parameter configuration increased the depth range from 150 mm to 725 mm over the conventional single-focal system, achieving greater depth range and better measurement results.

Research on stiffness matching of support structure of high-precision equipment on satellite

Abstract This paper takes the support structure of high-precision equipment on satellites as the research object, and combining it with the characteristics of an actual satellite platform, analyses the stiffness matching between the platform and the load mounting point represented by the axial connecting relay. On this basis, the support structure of two different types of installation points is quantitatively evaluated, the optimal configuration is selected through the analysis result, and the design improvement measures are proposed. It provides a new design idea for the support structure of the same type of high-precision equipment on satellite.

Enhancing machining efficiency in hybrid metal matrix composites through EDM parameter optimization via grey relational analysis

In summary, this study utilized stir casting to develop three distinct aluminium hybrid composites incorporating boron carbide and alumina. These composites were identified as Al7075 + 12%B4C + 6%Al2O3, Al7075 + 6%B4C + 6%Al2O3, and Al7075 + 6%B4C + 12%Al2O3. The novelty of this research lies in the unique combination of materials and their proportions, offering enhanced properties yet unexplored in this specific configuration. The research focused on machining these composite materials using EDM and employed a Taguchi L27 orthogonal array design to plan the machining tests. Key process parameters considered were gap voltage (V), duty cycle (%), current (A), and different percentages of reinforcement content. This aspect adds a significant novel dimension to the study, as the impact of EDM machining parameters on hybrid composites with this particular composition has not been extensively explored previously. The study applied Grey Relational Analysis to evaluate the impact of EDM machining process parameters on critical output responses, specifically material removal rate (MRR) and tool wear rate (TWR). SEM images revealed redeposited molten material, craters, and surface cracks, emphasizing the importance of controlling discharge energy and current levels for desired surface quality. An optimal configuration was identified peak current 2 A, pulse duration 100 μs, duty factor 20%, Composition 2 for improved surface attributes. These qualitative and quantitative insights inform process optimization strategies for EDM machining of aluminium hybrid composites.

Exploration of environmentally friendly processes for converting CO2 into propanol through direct hydrogenation

This study firstly explores six process configurations for the conversion of CO2 to propanol via direct hydrogenation. The variations in the proposed configurations lie in the technologies used for off-gas treatment (such as pressure swing adsorption, oxyfuel combustion, autothermal reforming, and chemical absorption) and the intensification of separation (including the incorporation of the hydration reaction of ethylene oxide) within the process. Energy efficiency analysis, techno-economic analysis (in minimum required selling price, MRSP), and life cycle assessment (on global warming potential, GWP) were conducted to evaluate all proposed schemes. Overall, this study suggests that enhancing the selectivity towards propanol and implementing a suitable off-gas treatment strategy are crucial for this process. Based on the findings, we recommend Scheme 4, which involves auto-thermal reforming for off-gas treatment, as the optimal configuration. It leads to an energy efficiency of 45.33 %. Despite the higher MRSP (3.12 USD/kg when using grey H2, 7.45 USD/kg when using green H2, commercial process: 1.4 to 1.6 USD/kg), it significantly reduces GWP (3.19 kg-CO2-eq/kg when using grey H2, 1.59 kg-CO2-eq/kg when using green H2) created from the conventional process (6.77 kg-CO2-eq/kg). Given appropriate economic incentives, the proposed process could serve as a more environmentally friendly option for propanol production.

Seismic Isolators Layout Optimization Using Genetic Algorithm Within the Pymoo Framework

In most previous studies, seismic base isolation system optimization has mainly focused on determining isolation layer parameters. However, the subsequent steps of isolator device selection and positioning can significantly impact overall system performance. To address these shortcomings, we propose an alternative optimization approach demonstrated through two models: regular and irregular 8-storey reinforced concrete structures. This approach utilizes the Pymoo framework and commercially available isolators to find optimal isolator layout configurations in two steps. First, using the equivalent lateral force (ELF) procedure, an initial population of seismic isolators meeting shear strain, base shear coefficient, and buckling requirements was randomly selected from suppliers' elastomeric bearing catalogs. Second, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was used to improve the seismic response of the models under the fast nonlinear analysis (FNA) method by minimizing peak roof acceleration, inter-story drift ratio, displacement of the isolated base layer, as well as maximizing the fundamental period. The results underscore the effectiveness of this approach in improving seismic response. Compared to fixed-base structures, the optimal solutions achieved more than double the fundamental period, reduced peak roof acceleration by over 70%, and diminished base shear force by approximately 50%. This methodology can serve as a reference for future research across various structure types, including hybrid isolation systems and steel structures. Doi: 10.28991/CEJ-2024-010-08-07 Full Text: PDF

A general design framework of flexible thermoelectric devices bridging power requirements for wearable electronics

Wearable thermoelectric generators (WTEGs) promise sustainable power for wearable electronics with various strategies proposed for on-body applications. However, there is still a lack of power-demand-oriented system design that directly provides WTEG configurations tailored to targeted end electronics, resulting in sufficient power only under extreme ambience. To address this, we propose a straightforward, power-demand-oriented design framework for WTEGs that bridges the power requirements of end electronics. As we input the properties of thermoelectric materials, specific operational conditions (temperature and heat transfer coefficient), expected power output and required flexibility, the design framework can directly determine the geometric features of thermoelectric pillars and fill factor. The effectiveness has been widely verified using data from the literature, showing a mean absolute deviation of 7.1 %. This framework shows high working efficiency and significantly shortens the design process, which is the very first ever-reported design tool for WTEGs. As a case, we use the framework and design a skin-conformable thermoelectric textile (TET) with optimal structure configurations and enhanced heat transfer capabilities, achieving a high normalized power density of 4.48 μW cm−2 K−2. As expected, the TET, with a maximum power density of 231 μW cm−2 successfully powered a series of on-body electronics when attached to the forearm at a breezy ambient temperature of ∼283 K. On the other hand, the TET exhibits a cooling effect of 5.73 K. Our work provides a thermal design guide for WTEGs, shedding light on the direct connection between WTEG configurations and end electronics.

Innovative biomass waste heat utilization for green hydrogen production: A comparative and optimization study of steam and organic rankine cycles

Today, designing a green hydrogen production process under an optimal, efficient, and economical configuration is one of the priorities of energy systems engineers. This research aims to explore the application of electricity produced by the steam Rankine cycle (S/RC) and Organic Rankine cycle (ORC) through biomass-based waste heat utilization in an alkaline electrolyzer (AE) system for the production of green hydrogen. The S/RC unit utilizes high-temperature waste heat, while the ORC system utilizes medium to low-temperature ones from the S/RC to improve overall system efficiency and hydrogen output. The study developed eight distinct configurations of the combined system, employing two organic fluids across four ORC designs. It aimed to assess the influence of integrating a recuperator or/and an OFOH (open feed organic heater), as well as varying the organic fluids, on the ORC unit's hydrogen production capability. A detailed thermodynamic, thermo-economic, and eco-economic analysis was conducted to assess key metrics. The environmental analysis quantified the carbon dioxide (CO2) emission reductions achieved through hydrogen production, using the emission rate from the steam methane reforming method as a benchmark. This approach underscored the environmental benefits of the AE system for hydrogen production. Further, the study quantified the financial gains from CO2 reduction, referred to as carbon credit gain (CCG), through an eco-economic analysis. From the outcomes, the highest hydrogen yield and the lowest LCOH (levelized cost of hydrogen) value were 94.65 tons/year and 1.724 US$/kg, respectively, related to Case (IV)-a (biomass waste heat-based S/RC- Regenerative& recuperator ORC system-AE system under R245fa). Lastly, optimization process for maximizing hydrogen production via the optimization methodology was established.

Advances in neural architecture search.

Automated machine learning (AutoML) has achieved remarkable success in automating the non-trivial process of designing machine learning models. Among the focal areas of AutoML, neural architecture search (NAS) stands out, aiming to systematically explore the complex architecture space to discover the optimal neural architecture configurations without intensive manual interventions. NAS has demonstrated its capability of dramatic performance improvement across a large number of real-world tasks. The core components in NAS methodologies normally include (i) defining the appropriate search space, (ii) designing the right search strategy and (iii) developing the effective evaluation mechanism. Although early NAS endeavors are characterized via groundbreaking architecture designs, the imposed exorbitant computational demands prompt a shift towards more efficient paradigms such as weight sharing and evaluation estimation, etc. Concurrently, the introduction of specialized benchmarks has paved the way for standardized comparisons of NAS techniques. Notably, the adaptability of NAS is evidenced by its capability of extending to diverse datasets, including graphs, tabular data and videos, etc., each of which requires a tailored configuration. This paper delves into the multifaceted aspects of NAS, elaborating on its recent advances, applications, tools, benchmarks and prospective research directions.