Optimization Stage Research Articles

Design of a quint-band passive NGDC by using Taguchi’s optimization method

This article presents a quint-band passive negative group delay circuit (Q-NGDC) by using Taguchi’s optimization method for the first time. This Q-NGDC mainly consists of a semi-circle and a multi-stub resonator, and it is evolved from a simple NGDC (Type I) to a well-designed one (Type VI) through three optimization stages. It is found that, with the application of Taguchi’s method (TM) in conjunction with full-wave simulations, the efficiency of the designed passive multi-band NGDC has been improved significantly. The proposed Q-NGDC was fabricated and tested, and the experimental results show that the five operation bands have been achieved at the center frequencies of 1.76, 2.80, 3.84, 4.42, and 5.28 GHz, with the corresponding group delay values of −1.85, −2.15, −1.45, −1.6, and −1.1 ns. The measured transmission coefficients are −4.4, −7.8, −9.7, −9.3, and − 8.9 dB. Furthermore, the proposed NGDC shows the best figure of merit (FOM) at each resonant frequency with a relatively small size. The proposed NGDC can be potentially applied to WCDMA, 5G communication, Zigbee (IEEE802.15.4), and Wi-Fi (IEEE802.11n) systems.

Ultrasensitive and highly selective electrochemical determination of mesalazine in pharmaceutical, biological and environmental samples with the use of Co-Mordenite/Mesoporous Carbon modified Glassy Carbon Electrode

In this paper, an ultrasensitive and highly selective analytical method for the voltammetric determination of the anti-inflammatory drug mesalazine (MES) at the Co-exchanged mordenite/mesoporous carbon modified glassy carbon electrode (Co-MOR/MC-GCE) is presented. The combination of unique properties of zeolite and mesoporous carbon, affirmed by conducting morphology and textural parameters studies, resulted in the fabrication of the unique platform sensing, which exhibited a significant enhancement effect of MES oxidation peak current. In the voltammetric determination of mesalazine by the means of differential pulse voltammetry (DPV), univariate optimization stage of the instrumental parameters and the pH value of the supporting electrolyte was performed. In 0.04 mol L−1 Britton-Robinson buffer solution (pH 2.0), the oxidation peak of MES at 0.41 V vs. Ag | AgCl | 3 M KCl reference electrode revealed a linear response in the range of 0.99 – 99.01 µg L−1 (6.47·10−9 – 6.47·10−7 mol L−1) with the limit of detection (LOD) equal to 0.27 µg L−1 (1.76·10−9 mol L−1). The developed protocol was successfully applied for the direct determination of mesalazine in pharmaceuticals, environmental waters, and biological fluids (human urine and serum) with recovery of 103.4 – 119.3%.

Effect of harvesting stage on postharvest quality of black pepper piper nigrum l. cultivars grown in Morogoro, Tanzania

Optimal harvest maturity stage determines the on-shelf quality of black pepper (Piper nigrum L.). Small-scale farmers in Morogoro harvest black pepper before reaching full maturity to catch up with high market prices offered early in the season. Unfortunately, traders are complaining on the quality of black pepper from the area. This study evaluated the effect of harvesting stages on post-harvest quality of pepper cultivars grown in the Morogoro district. Pepper cultivars; Babu kati, Babu kubwa, and Ismailia at premature and mature stages were dried on coconut leaves mats to suitable drying stage. Treatments in a 2 x 3 factorial experiment following a complete randomized design (CRD) were replicated thrice. Statistical significance of variables was generated by ANOVA and mean separation by Tukey HSD test at p ≤ 0.05. Results showed a significant (p<0.001) difference between harvest stages and cultivars on berry colour, moisture content, berry firmness, dry recover and weight loss percentage. Pepper berries at pre-mature stage across cultivars had higher (79.11%) initial moisture content than mature berries (71.6%). Mature berries had higher dry recovery percentage (28.84%) than pre mature (15.78%). Mature “Ismailia” berries had the highest firmness (10.5kg/mm2) than premature (5.8 kg/mm2). During sun drying, colour values L*(brightness), and b* (yellowness) decreased significantly while greenness decreased as a* values increased from more negative values to zero in both harvesting stages as pepper berries darkened. Colour changes were sharper on mature than pre-mature harvested pepper corns. These findings will help farmers to make right decision on when to harvest for quality black pepper product. Farmers are recommended to harvest black pepper when mature for better firmness and colour development during drying. Further studies are required to determine the sensory quality and volatile oil contents of the cultivars when harvested premature and mature stages.

Piston Detection of Optical Sparse Aperture Systems Based on an Improved Phase Diversity Method

The piston error has a significant effect on the imaging resolution of the optical sparse aperture system. In this paper, an improved phase diversity method based on particle swarm optimization and the sequential quadratic programming algorithm is proposed, which can overcome the drawbacks of the traditional phase diversity method and particle swarm optimization, such as the instability that results from polychromatic light conditions and premature convergence. The method introduces factor β in the stage of calculating the objective function, and combines the advantages of a heuristic algorithm and a nonlinear programming algorithm in the optimization stage, thus enhancing the accuracy and stability of piston detection. Simulations based on a dual-aperture optical sparse aperture system verified that the root mean square error obtained by the method can be guaranteed to be within 0.001λ (wavelength), which satisfies the requirement of practical imaging. An experimental test was also conducted to demonstrate the performance of the method, and the test results showed that the quality of the image after piston detection and correction improved significantly compared to images with the co-phase error.

Insight into the effects of different ripeness levels on the quality and flavor chemistry of Noni fruit (Morinda citrifolia L.)

Different ripeness plays a key role in the final quality and flavor of Noni products, but the variations of quality and flavor during Noni ripening are still unclear. In this study, physicochemical analysis combined with electronic nose, electronic tongue and gas chromatography-mass spectrometry (GC–MS) were used to investigate the effect of different ripeness levels on Noni fruit quality and flavor chemistry. The results showed that the optimum stage of nutrient accumulation is 7–8 ripe (7M−8M), in which especially the higher contents were 144Mg/100 g vitamin C, 11.08 g/kg polyphenols, 15.32 g/kg flavonoids and 2420Mg/kg potassium. Octanoic acid and hexanoic acid with an unfriendly odor began to decrease significantly after 8 ripe (8M), the lowest contents were 256.76 and 231.26 ng/g at 9 ripe (9M). Sour, astringent and fresh tastes are the dominant tastes of Noni fruit. Therefore, the optimum harvesting time of Noni fruit is 8M.

AI & experimental-based discovery and preclinical IND-enabling studies of selective BMX inhibitors for development of cancer therapeutics

The current work aims to design and provide a preliminary IND-enabling study of selective BMX inhibitors for cancer therapeutics development. BMX is an emerging target, more notably in oncological and immunological diseases. In this work, we have employed a predictive AI-based platform to design the selective inhibitors considering the novelty, IP prior protection, and drug-likeness properties. Furthermore, selected top candidates from the initial iteration of the design were synthesized and chemically characterized utilizing 1H NMR and LC-MS. Employing a panel of biochemical (enzymatic) and cancer cell lines, the selected molecules were tested against these assays. In addition, we used artificial intelligence to predict and evaluate several critical IND-focused physicochemical and pharmacokinetics values of the selected molecules. A secondary objective of the current work was also to validate the sole role of BMX in animal models known to be mediated by BMX. More than 50 molecules were designed in the present study employing five novel discovered scaffolds. Two molecules were nominated for further IND-focused studies. Compound II showed promising in-vitro activity against BMX in both enzymatic assays compared to other kinases and in cancer cell lines with known BMX overexpression. Interestingly, compound II showed very favorable physicochemical and pharmacokinetics properties as predicted by the used platforms. The animal study further confirmed the sole role of BMX in the disease model. The current work provides promising data on a selective BMX inhibitor as a potential lead for therapeutics development, and the asset is currently in the optimization stage. Notably, the current study shows a framework for a combined approach employing both AI and experimentation that can be used by academic labs in their research programs to more streamline programs into IND-focused to be bridged easily for further clinical development with industrial partners.

ILP-based Substrate Routing with Mismatched Via Dimension Consideration for Wire-bonding FBGA Package Design

With the rapidly growing demand for system-level integration, package substrates have become one of the most important carriers in semiconductor industry. Fine pitch ball grid array (FBGA) packaging is a widely used technology thanks to its relative cost-effectiveness compared to other advanced packaging technologies. In addition, it is also widely used in space-constrained applications, such as mobile and handheld devices. These packaging substrate interconnections are usually customized by layout engineers taking many complex and stringent design rules into consideration. However, fully net-by-net manual design for FBGA is time-consuming and error-prone. In this article, we propose an integer linear programming (ILP)-based router for wire-bonding FBGA packaging design. Our ILP formulation not only can handle design-dependent constraints but also take the problem of mismatched via dimension into account, which is caused by the mechanical processes and greatly increases design complexity. In addition to the ILP formulation for substrate routing, three optimization stages and several ILP constraint reduction techniques are also developed to boost the run time of ILP solver. Experimental results indicate that the proposed framework can achieve high routing completion rates, which could effectively reduce the cycle time of substrate layout design. In addition, in combination with the proposed optimization strategies, 278× speedup can be achieved compared to the ILP constraint optimized router.

Multi-objective optimization design of coupled wall structure with hybrid coupling beams using hybrid machine learning algorithms

A new hybrid coupling beam incorporating buckling restrained energy dissipaters and viscous dampers was previously recommended. The beam can effectively reduce the floor acceleration and interstory drift ratio of coupled wall structure, and has great application prospects. However, the optimal design of a structure with hybrid coupling beams needs to consider multiple objectives, and these objectives often conflict with each other. At the same time, multiple parameters need to be optimized, resulting in a corresponding increase in the time-cost. This usually brings challenges to structural design. To address this issue, this study presents a hybrid intelligent optimization framework based on multi-objective particle swarm optimization (MOPSO) and machine learning techniques. The developed framework uses machine learning technology to quickly predict the seismic performance of structures with hybrid coupling beams, and obtain a design solution set through multi-objective particle swarm optimization. The proposed framework is demonstrated by considering a case study of a 12-story coupled wall structure with hybrid coupling beams, and it is shown that compared with the initial stage of optimization, the seismic performance of optimized structure is simultaneously improved at close cost. The floor acceleration and interstory drift ratio of the optimized structure are reduced by 11.5% and 38.7%, respectively. Finally, a formula is proposed to quantify the optimization result of pareto solution, so as to make an optimal structural design scheme. In practice, the proposed framework can provide guidance for realizing rapid and accurate about multi-objective optimization of the coupled wall structure with hybrid coupling beams.

Weed Detection in Rainfed Maize Crops Using UAV and PlanetScope Imagery

Weed invasion of crop fields, such as maize, is a major threat leading to yield reductions or crop right-offs for smallholder farming, especially in developing countries. A synoptic view and timeous detection of weed invasions can save the crop. The sustainable development goals (SDGs) have identified food security as a major focus point. The objectives of this study are to: (1) assess the precision of mapping maize-weed infestations using multi-temporal, unmanned aerial vehicle (UAV), and PlanetScope data by utilizing machine learning algorithms, and (2) determine the optimal timing during the maize growing season for effective weed detection. UAV and PlanetScope satellite imagery were used to map weeds using machine learning algorithms—random forest (RF) and support vector machine (SVM). The input features included spectral bands, color space channels, and various vegetation indices derived from the datasets. Furthermore, principal component analysis (PCA) was used to produce principal components (PCs) that served as inputs for the classification. In this study, eight experiments are conducted, four experiments each for UAV and PlanetScope datasets spanning four months. Experiment 1 utilized all bands with the RF classifier, experiment 2 used all bands with SVM, experiment 3 employed PCs with RF, and experiment 4 utilized PCs with SVM. The results reveal that PlanetScope achieves accuracies below 49% in all four experiments. The best overall performance was observed for experiment 1 using the UAV based on the highest mean accuracy score (>0.88), which included the overall accuracy, precision, recall, F1 score, and cross-validation scores. The findings highlight the critical role of spectral information, color spaces, and vegetation indices in accurately identifying weeds during the mid-to-late stages of maize crop growth, with the higher spatial resolution of UAV exhibiting a higher precision in the classification accuracy than the PlanetScope imagery. The most optimal stage for weed detection was found to be during the reproductive stage of the crop cycle based on the best F1 scores being indicated for the maize and weeds class. This study provides pivotal information about the spatial distribution of weeds in maize fields and this information is essential for sustainable weed management in agricultural activities.

Artificial Intelligence Methods Applied to Catalytic Cracking Processes

Fluidic Catalytic Cracking (FCC) is a complex petrochemical process affected by many highly non-linear and interrelated factors. Product yield analysis, flue gas desulfurization prediction, and abnormal condition warning are several key research directions in FCC. This paper will sort out the relevant research results of the existing Artificial Intelligence (AI) algorithms applied to the analysis and optimization of catalytic cracking processes, with a view to providing help for the follow-up research. Compared with the traditional mathematical mechanism method, the AI method can effectively solve the difficulties in FCC process modeling, such as high-dimensional, nonlinear, strong correlation, and large delay. AI methods applied in product yield analysis build models based on massive data. By fitting the functional relationship between operating variables and products, the excessive simplification of mechanism model can be avoided, resulting in high model accuracy. AI methods applied in flue gas desulfurization can be usually divided into two stages: modeling and optimization. In the modeling stage, data-driven methods are often used to build the system model or rule base; In the optimization stage, heuristic search or reinforcement learning methods can be applied to find the optimal operating parameters based on the constructed model or rule base. AI methods, including data-driven and knowledge-driven algorithms, are widely used in the abnormal condition warning. Knowledge-driven methods have advantages in interpretability and generalization, but disadvantages in construction difficulty and prediction recall. While the data-driven methods are just the opposite. Thus, some studies combine these two methods to obtain better results.

Thermal characterization of a high-temperature industrial bread-baking oven: A comprehensive experimental and numerical study

This study presents a complete experimental campaign and the first three-dimensional numerical model developed for a high-temperature bread-baking oven. A typical baking tunnel oven prototype is designed and constructed for the study. The prototype has appropriate temperature sensors to investigate temperature profiles at several points inside the baking chamber. A specific device is developed to measure the heat flux received by bread during baking, showing values ranging between 6.6 and 29.3 kW/m2. Computational fluid dynamic software models the non-premixed combustion of diesel fuel occurring at the burner and the airflow characteristics inside the baking chamber. Numerical results show the heterogeneity of temperatures (450–1000 °C) inside the tunnel oven and agree with the experimentally measured values (maximal variation of 9 %) also shows complex airflow dynamics and the existence of multiple velocity zones. The model is applied to the specific case of Lebanese bread baking. An energy efficiency analysis of the oven reveals an efficiency range of around 16 %. This foundational work sets the stage for future research and optimization of oven design, energy recovery systems, and bread-baking processes. Also, the insights gained from this study will be integral in developing future numerical models for flatbread baking coupled with large deformation.

Extended ray-mapping method based on differentiable ray-tracing for non-paraxial and off-axis freeform illumination lens design.

The ray-mapping method has been widely used for designing freeform illumination lenses. However, in non-paraxial or off-axis situations, it remains challenging to obtain an integrable ray-mapping, often requiring a complex iterative correction process for the initial mapping. To address this challenge, we propose an extended ray-mapping method that incorporates differentiable ray-tracing into the design pipeline of the ray-mapping method. This enables accurate surface construction according to ray-mapping and efficient shape correction based on irradiance distribution. The proposed method involves two optimization stages. In the first stage, the freeform surface is preliminarily optimized to closely match the optimal transport mapping. The obtained freeform surface is then further optimized in the second stage to minimize the divergence between the target and simulated irradiance distributions. Additionally, the mean curvature of the freeform surface is also constrained in the second stage to facilitate the fabrication of the final freeform surface. Non-paraxial illumination lenses and off-axis illumination lenses have been designed using the proposed method within ten minutes, and simulations demonstrate that the approach is effective and robust.

Multi-objective multi-fidelity optimisation for position-constrained human-robot collaborative disassembly planning

ABSTRACT Human-robot collaborative disassembly lines are widely used by remanufacturing companies to disassemble end-of-life (EOL) products. When disassembling large-sized EOL products, each workstation on a disassembly line is generally divided into multiple operating positions, so that different operators can disassemble the same product at their respective positions at the same time, thereby greatly improving efficiency. This paper focuses on a position-constrained human-robot collaborative disassembly planning (PC-HRCDP) problem for the above-mentioned lines, including three subproblems of disassembly sequence planning, disassembly line balancing and robot path planning. A multi-objective mixed integer programming model for PC-HRCDP is developed to solve small-scale instances. Furthermore, a multi-objective multi-fidelity optimisation (MO-MFO) algorithm is proposed to solve large-scale instances. Comprehensive experiments are conducted based on 10 problem instances generated in this study. Experimental results show that the proposed MO-MFO is better than a high-fidelity optimisation algorithm in terms of running time. In addition, benefiting from the strategy of MO-MFO to allocate the limited high-fidelity computational budget to solutions in the two stages of multi-objective optimisation and optimal sampling, MO-MFO is significantly better than the existing representative multi-fidelity optimisation algorithms in terms of the hyper-volume and the inverted generational distance.

Effects of tree canopy on herbaceous understorey throughout the developmental cycle of a temperate mountain primary forest

Temporal dynamics of temperate montane primary forests were described based on data from Central-European nature reserves in the 20th century as a long-term cycle involving tree regeneration, growth, maturation, and disintegration. However, the lack of chronosequences focusing on the herbaceous understory has limited our understanding of plant community dynamics within this developmental cycle. To address questions regarding whether the herbaceous understory follows the cyclic trajectory of the tree layer and if the properties of the tree layer drive the dynamics of the herbaceous understory, we investigated a 19-year chronosequence in ten plots (with five sampling periods) within an unmanaged primary beech-fir forest in the Western Carpathians. The vegetation data from the resampled plots, including herb species frequencies and dendrometric variables, covered a significant portion of the developmental cycle of a single plant community type. The results confirmed the cyclical nature of herbaceous understory development in terms of species composition and diversity trajectories, demonstrating transitions between developmental stages. Diversity was highest during the disintegration stage, with a rapid decline towards the growth stage, followed by a slow recovery as it progressed towards the optimum stage. Temporal responses of individual species, along with ordination analyses, allowed for the separation of taxa associated with the disintegration, growth, and optimum stages, as well as the most stable ones throughout the entire cycle. The proportion of therophytes slightly increased after gap-opening events during disintegration, geophytes were most represented during the growth stage, and hemicryptophytes culminated during the optimum stage. The composition of herb species was significantly affected by the above-ground tree biomass (AGTB), which explained 21.4% of the temporal variability. The sum of the diameter at breast height (Sum of DBH) described an additional 7.2%. The relationship between the frequency of the herbaceous understory and AGTB followed a “U” shape, while the relationship with Sum of DBH was negative and linear. The relationship between the herbaceous understory and the tree overstory is likely driven by competition for light, water, and nutrient resources. These findings provide new insights into the temporal relationships between the herbaceous understory and tree layer development, and they may inspire future studies and management strategies aimed at conserving or enhancing biodiversity in temperate forests.

How similar is the zoning of different vegetation indices: Defining the optimal framework for monitoring grapevines’ growth within vigorous vineyards

The spatial patterns of grapevines’ vigor across vigorous vineyards were assessed through distinct vegetation indices (VI) for comparison purposes. This survey also aids the selection of the most sensitive VI to monitor the vineyards during a given optimal stage of the last period of the growing season. The canopy reflectance (ρ) of grapevines cv. Cabernet Franc and Cabernet Sauvignon (Vitis vinifera L.) was measured throughout their berry development and ripening stages. Georeferenced data of ρ at the red (680 nm), red edge (730 nm), and near-infrared (780 nm) wavebands were taken using a hand-held active sensor to compute the normalized difference red edge index (NDRE), and the normalized difference vegetation index (NDVI). Spatial predictions of both VI were estimated by geostatistical interpolation technique (kriging) and then categorized into homogeneous zones (HZ) through the Jenks natural breaks optimization method to delimit low and high vigor regions. Statistics of the inter-rater reliability between each VI zoning were calculated afterward. The relative dispersion of data around their mean was assumed as a criterion to assess the sensibility of each VI in detecting vigor variability under a given condition. The most appropriate stage to monitor the vineyards was determined based on the peak of vegetative growth inferred from the values of the VI we presumed as most sensitive. Differences between the HZ defined from the best combination of VI and stage in terms of yield, cane weight, and crop load were also examined. We demonstrated that NDRE was more sensitive than NDVI in detecting the vigor variability of high-density vegetation due to the relative dispersion of its datasets and the saturation effect of NDVI at the late stages of the grapevine growing season. The lowest and higher agreement between the VI zoning was usually detected at the berry touch and berry softening growth stages, respectively. Although there were distinct levels of concordance between NDRE and NDVI maps, their overall agreement was higher than 70% in most surveys. The disagreement in quantity or spatial allocation of vigor categories provided by each VI relied on the vineyard, growth stage, and growing season. Nevertheless, the early definition of HZ from NDRE monitoring carried out at veraison could be suitable to identify differences in cane weight at pruning, with a moderate effect size.

Optimal debris removal using debris-tether-tug system considering attitude–orbit coupling effects

Towing space debris with the utilization of a tethered space tug, also referred to as debris-tether-tug (DTT) system, is considered to be one of the most promising methods to remove space debris. However, as the space tug only carries limited amounts of fuel, the number of debris that a DTT system can remove is limited. To increase the capacity of the DTT system, fuel consumption per towing cycle should be minimized. This study proposes an optimal deorbit scheme with two stages: optimal transfer and swing control. First, a dynamics model of the DTT system is formulated considering attitude–orbit coupling effects and tether flexibility. In the fuel optimal transfer stage, the dynamics model as well as two simplified models is employed to calculate the optimal transfer orbit, subject to a continuous thrust from the tug. By comparing the optimal results, it can be found that the attitude–orbit coupling effect of the DTT system is non-negligible, whereas the tether flexibility can be ignored with a large tether elasticity coefficient during the optimal orbit transfer mission. For the swing control stage at the destination, a control law is applied by regulating the velocity of tether releasement to increase the debris velocity. The parametric analysis of the control law suggests that the tether should be completely deployed before separation to achieve a larger orbit altitude increment. Appropriate parameters can be assigned to attain maximum orbit altitude increment in the swing control based on this analysis. The conclusions could serve as an effective reference for transfer orbit calculation and swing control design in future deorbit missions using the DTT system.

An Innovative H-Type Flux Switching Permanent Magnet Linear Generator for Thrust Force Enhancement

In this paper, two H-type flux switching permanent magnet linear generators with outer-translator and inner-translator configurations are discussed and compared to a more conventional flux switching topology. The stators consist of H-Type modules housing circumferential coils and are surrounded by two annular permanent magnets. In conventional flux switching machines, the windings are orientated perpendicular to the direction of motion and the conductors twist around the magnets. In H-type topologies, the orientation of the windings is in the same plain as the magnets and parallel to the direction of motion, resulting in an increase in flux linkage. The proposed topologies are designed for a low operating speed and a large magnetic gap, as found in wave energy converters. All topologies are optimized using the Taguchi optimization approach with the goals of reducing force ripple and increasing the average thrust force and efficiency. The 2D finite element method (FEM) is used in the optimization stage to calculate the optimized parameters of the presented generators, after which the optimized structures are simulated using 3D FEM, and the results are extracted. The results of the optimization show that the H-type topologies deliver a 20% higher shear stress whilst offering an easier to assemble structure.

A currency trading system based on simplified models using fuzzy multi-criteria hierarchical optimization

This paper is based on some assumptions validated using real data and the most used Forex trading platform Meta Trader 4. First, we assume that any reasonable and relatively simple models, reflecting some trading hypotheses, can be profitable for a certain period. Having a sufficient set of such models optimized for selected currency pairs, we can use the model that provides the greatest profit in the current trading period. The second proposal is to use a fuzzy multiple-criteria approach at the training optimization stage using historical data in order to overcome or reduce the negative effect of overfitting. Here for the first time, the problem of fuzzy multiple-criteria optimization of trading was formulated and solved based on the output parameters of the developed simple single-criteria crisp models. This provides significantly greater profit than that obtained using single-criteria crisp models. The third proposal is to use the hierarchical structure of fuzzy local criteria to solve the multiple-criteria problem. It is shown that this additionally provides a significant increase in profit. The profitability and riskless of the developed trading models are studied based on real quotations of currency pairs USDJPY, EURUSD and AUDUSD using H4 timeframe.

Phenology-Based Maximum Light Use Efficiency for Modeling Gross Primary Production across Typical Terrestrial Ecosystems

The maximum light use efficiency (LUE) (ε0) is a key essential parameter of the LUE model, and its accurate estimation is crucial for quantifying gross primary production (GPP) and better understanding the global carbon budget. Currently, a comprehensive understanding of the potential of seasonal variations of ε0 in GPP estimation across different plant functional types (PFTs) is still lacking. In this study, we used a phenology-based strategy for the estimation of ε0 to find the optimal photosynthetic responses of the parameter in different phenological stages. The start and end of growing season (SOS and EOS) from time series vegetation indices and the camera-derived greenness index were extracted across seven PFT flux sites using the methods of the hybrid generalized additive model (HGAM) and double logistic function (DLF). Optimal extractions of SOS and EOS were evaluated, and the ε0 was estimated from flux site observations during the optimal phenological stages with the light response equation. Coupled with other obligatory parameters of the LUE model, phenology-based GPP (GPPphe-based) was estimated over 21 site-years and compared with vegetation photosynthesis model (VPM)-based GPP (GPPVPM) and eddy covariance-measured GPP (GPPEC). Generally, GPPphe-based basically tracked both the seasonal dynamics and inter-annual variation of GPPEC well, especially at forest, cropland, and wetland flux sites. The R2 between GPPphe-based and GPPEC was stable between 0.85 and 0.95 in forest ecosystems, between 0.75 and 0.85 in cropland ecosystems, and around 0.9 in wetland ecosystems. Furthermore, we found that GPPphe-based was significantly improved compared to GPPVPM in cropland, grassland, and wetland ecosystems, implying that phenology-based ε0 is more appropriate in the GPP estimation of herbaceous plants. In addition, we found that GPPphe-based was significantly improved over GPPVPM in cropland, grassland, and wetland ecosystems, and the R2 between GPPphe-based and GPPEC was improved by up to 0.11 in cropland ecosystems and 0.05 in wetland ecosystems compared to GPPVPM, and RMSE was reduced by up to 5.90 and 2.11 g C m−2 8 day−1, respectively, implying that phenology-based ε0 in herbaceous plants is more appropriate for GPP estimation. This work highlights the potential of phenology-based ε0 in understanding the seasonal variation of vegetation photosynthesis and production.

LES Investigation of Kerosene Spray Flame Emission Characteristics in a Staged Combustor

ABSTRACT Staged combustion technology is widely applied in modern low-emission energy and power systems. This study employs large eddy simulations (LES) to investigate the flame dynamics and emission characteristics in a staged two-phase partially premixed model combustor. To improve the suitability of the thickened flame model (TFM) for turbulent flames in the thin-reaction-zone regime, a new model for efficiency function is formulated such that the effects of small-scale turbulence on flame propagation are taken into account. The enhanced LES-TFM approach is then integrated with an efficient NOx model, in which an additional NOx transport equation is solved with pre-tabulated NOx formation source terms. Five LES runs are performed to investigate the evolution of flame dynamics and emission characteristics with staging ratios at the scaled cruise operating conditions, in which the optimal staging ratio is critical for NOx reduction. Results show that the trend of flame morphology and NOx emission predictions with staging ratios are consistent with the experimental results. The flame zones in the staged model combustor shift from a pilot-stage-only flame to a pilot-primary-stage flame, then to a primary-stage-only flame with decreasing fuel staging ratio. Due to the redistribution of the equivalence ratio and the NOx emission characteristics trade-off between the two-stage flows, there is an optimal staging ratio for NOx reduction in the staged model combustor. The proposed LES-TFM-NOx approach, which has both the benefits of efficient flame dynamics modeling with the global reaction mechanism and the advantages of prediction accuracy with the detailed mechanism for NOx emission, demonstrates its advantages in simulating flame and emission characteristics in low-emission combustion devices.