Large Parameter Research Articles

Twistronics of Kekulé Graphene: Honeycomb and Kagome Flat Bands.

Kekulé-O order in graphene, which has recently been realized experimentally, induces Dirac electron masses on the order of m∼100 meV. We show that twisted bilayer graphene in which one or both layershave Kekulé-O order exhibits nontrivial flat electronic bands on honeycomb and kagome lattices. When only one layer has Kekulé-O order, there is a parameter regime for which the lowest four bands at charge neutrality form an isolated two-orbital honeycomb lattice model with two flat bands. The bandwidths are minimal at a magic twist angle θ≈0.7° and Dirac mass m≈100 meV. When both layers have Kekulé-O order, there is a large parameter regime around θ≈1° and m≳100 meV in which the lowest three valence and conduction bands at charge neutrality each realize isolated kagome lattice models with one flat band, while the next three valence and conduction bands are flat bands on triangular lattices. These flat band systems may provide a new platform for strongly correlated phases ofmatter.

(Invited) High-Nickel Layered Oxide Cathodes: Crack Vs. Surface Reactivity

The appetite for long driving range and high energy density is pushing the nickel content in layered oxide cathodes for lithium-ion batteries. The lying of Ni3+/4+:3d band slightly above the Co3+/4+:3d band enables to extract more lithium during charge without encountering oxygen release from the lattice in high-nickel cathodes compared to that in LiCoO2. However, as the nickel content increases in the lattice, the cells suffer from cycle and thermal instability. The cycle instability has largely been attributed in the literature to the formation of cracks caused by the occurrence of H2-H3 phase transition with large lattice parameter changes during high degree of charge. Several approaches, such as doping with other cations and microstructural control of the primary and secondary particles, have been extensively pursued to overcome the challenges and improve the cycle life.With a systematic investigation of a number of cathode compositions and their characterization after extensive cycling, this presentation will illustrate that surface reactivity of high-nickel cathodes with electrolyte plays a dominant and decisive role on cycle life. Even cathodes like LiNiO2 with 100% Ni without any cation doping or surface coating could be cycled with high capacities over a large number of cycles with an appropriate choice of electrolyte that suppresses surface reactivity. Electrolytes that are stable to slightly higher voltages, even by as low as 100 mV, compared to the conventional electrolytes alter the surface reconstruction pathway on cathode surface in contact with electrolyte. Electrolytes with better high-voltage stability favor the formation of mildly reduced LiNi2O4 spinel on the surface without much oxygen release from the surface. In contrast, electrolytes that have inferior stability to higher voltages lead to a drastic reduction of the cathode surface to highly reduced Ni3O4 spinel and NiO rock salt phases with significant oxygen loss from and volume change on the surface, which could cause crack initiation on the surface, followed by their propagation into the bulk. Interestingly, the fast 3-dimensional lithium-ion and electron transport in spinel LiNi2O4 supports a facile charge transfer through the cathode-electrolyte interphase (CEI), resulting in better cycle life, in contrast to the poor lithium-ion transport in both spinel Ni3O4 and rock salt NiO phases, resulting in poor charge-transfer through the CEI and inferior cycle life. With robust electrolyte formulations with better high-voltage stability, long cycle life could be achieved even with the repeated occurrence of the H2-H3 phase transitions over a large number of cycles.

Detection of diffuse H i emission in the circumgalactic medium of NGC 891 and NGC 4565 - II

ABSTRACT We probe the neutral circumgalactic medium (CGM) along the major axes of NGC 891 and NGC 4565 in 21-cm emission out to ≳100 kpc using the Green Bank Telescope (GBT), extending our previous minor axes observations. We achieve an unprecedented 5σ sensitivity of 6.1 × 1016 cm−2 per 20 km s−1 velocity channel. We detect H i with diverse spectral shapes, velocity widths, and column densities. We compare our detections to the interferometric maps from the Westerbork Synthesis Radio Telescope (WSRT) obtained as part of the HALOGAS survey. At small impact parameters, $\gt 31\!-\!43~{{\ \rm per\ cent}}$ of the emission detected by the GBT cannot be explained by emission seen in the WSRT maps, and it increases to $\gt 64\!-\!73~{{\ \rm per\ cent}}$ at large impact parameters. This implies the presence of diffuse circumgalactic H i. The mass ratio between H i in the CGM and H i in the disc is an order of magnitude larger than previous estimates based on shallow GBT mapping. The diffuse H i along the major axes pointings is corotating with the H i disc. The velocity along the minor axes pointings is consistent with an inflow and/or fountain in NGC 891 and an inflow/outflow in NGC 4565. Including the circumgalactic H i, the depletion time and the accretion rate of NGC 4565 are sufficient to sustain its star formation. In NGC 891, most of the required accreting material is still missing.

Algorithm–Hardware Co-Optimization and Deployment Method for Field-Programmable Gate-Array-Based Convolutional Neural Network Remote Sensing Image Processing

In recent years, convolutional neural networks (CNNs) have gained widespread adoption in remote sensing image processing. Deploying CNN-based algorithms on satellite edge devices can alleviate the strain on data downlinks. However, CNN algorithms present challenges due to their large parameter count and high computational requirements, which conflict with the satellite platforms’ low power consumption and high real-time requirements. Moreover, remote sensing image processing tasks are diverse, requiring the platform to accommodate various network structures. To address these issues, this paper proposes an algorithm–hardware co-optimization and deployment method for FPGA-based CNN remote sensing image processing. Firstly, a series of hardware-centric model optimization techniques are proposed, including operator fusion and depth-first mapping technology, to minimize the resource overhead of CNN models. Furthermore, a versatile hardware accelerator is proposed to accelerate a wide range of commonly used CNN models after optimization. The accelerator architecture mainly consists of a parallel configurable network processing unit and a multi-level storage structure, enabling the processing of optimized networks with high throughput and low power consumption. To verify the superiority of our method, the introduced accelerator was deployed on an AMD-Xilinx VC709 evaluation board, on which the improved YOLOv2, VGG-16, and ResNet-34 networks were deployed. Experiments show that the power consumption of the accelerator is 14.97 W, and the throughput of the three networks reaches 386.74 giga operations per second (GOPS), 344.44 GOPS, and 182.34 GOPS, respectively. Comparison with related work demonstrates that the co-optimization and deployment method can accelerate remote sensing image processing CNN models and is suitable for applications in satellite edge devices.

Patient-specific computational models of retinal prostheses

Retinal prostheses stimulate inner retinal neurons to create visual perception for blind patients. Implanted arrays have many small electrodes. Not all electrodes induce perception at the same stimulus amplitude, requiring clinicians to manually establish a visual perception threshold for each one. Phosphenes created by single-electrode stimuli can also vary in shape, size, and brightness. Computational models provide a tool to predict inter-electrode variability and automate device programming. In this study, we created statistical and patient-specific field-cable models to investigate inter-electrode variability across seven epiretinal prosthesis users. Our statistical analysis revealed that retinal thickness beneath the electrode correlated with perceptual threshold, with a significant fixed effect across participants. Electrode-retina distance and electrode impedance also correlated with perceptual threshold for some participants, but these effects varied by individual. We developed a novel method to construct patient-specific field-cable models from optical coherence tomography images. Predictions with these models significantly correlated with perceptual threshold for 80% of participants. Additionally, we demonstrated that patient-specific field-cable models could predict retinal activity and phosphene size. These computational models could be beneficial for determining optimal stimulation settings in silico, circumventing the trial-and-error testing of a large parameter space in clinic.

Flame stability characteristics of a flame spray pyrolysis burner

The stability characteristics of an established flame spray pyrolysis (FSP) burner are quantified in order to determine viable operating conditions and map the structure of FSP flames. The primary novelty of this work lies in exploring a large parameter space which is necessary to explain the dependence of visible flame emissions on input variables. Using high-speed flame luminescence imaging (1–120 kHz), we demonstrate that the input pilot heat release and the flowrates of liquid and dispersion gases are primary control variables that dictate combustion quality. Indeed, variation in these input conditions allows the qualitative identification of four flame types: (i) stable, (ii) lifted, (iii) unstable and (iv) low intensity. Quantification of these flame types is derived via statistical analysis of the temporal luminosity fluctuations. Key metrics such as the mean and standard deviation effectively describe the behaviour of the aforementioned flame types and act as classifying parameters for FSP flames. High normalised mean intensities and coefficients of variation are characteristic of stable flames. However, flame extinction events in the ignition region and throughout the entire flame are characteristic of unstable flame configurations. In this case, poor combustion efficiency and significant intensity fluctuations are quantified by significantly lower mean intensities and higher coefficients of variation. Further insight is given via the implementation of phase-Doppler anemometry (PDA). It is demonstrated how variation in the input flowrates - and therefore variation in gas and droplet-phase velocities - correlate with the identified flame structures. This analysis demonstrates how input parameters can be varied in order to configure the combustion mode and control combustion stability during FSP. Both of which are relevant for understanding the boundary conditions for nanoparticle growth using FSP.

INTEGRATING MOTION PRIORS FOR END-TO-END ATTENTION-BASED MULTI-OBJECT TRACKING

Abstract. Recent advancements in multi-object tracking (MOT) have heavily relied on object detection models, with attention-based models like DEtection TRansformer (DETR) demonstrating state-of-the-art capabilities. However, the utilization of attention-based detection models in tracking poses a limitation due to their large parameter count, necessitating substantial training data and powerful hardware for parameter estimation. Ignoring this limitation can lead to a loss of valuable temporal information, resulting in decreased tracking performance and increased identity (ID) switches. To address this challenge, we propose a novel framework that directly incorporates motion priors into the tracking attention layer, enabling an end-to-end solution. Our contributions include: I) a novel approach for integrating motion priors into attention-based multi-object tracking models, and II) a specific realisation of this approach using a Kalman filter with a constant velocity assumption as motion prior. Our method was evaluated on the Multi-Object Tracking dataset MOT17, initial results are reported in the paper. Compared to a baseline model without motion prior, we achieve a reduction in the number of ID switches with the new method.

Large time-stepping, delay-free, and invariant-set-preserving integrators for the viscous Cahn–Hilliard–Oono equation

Stabilization techniques are frequently used to construct efficient and stable schemes for stiff and nonlinear systems. However, the introduced errors may lead to severe delay effects when facing large time-steps or stabilization parameters. Considering a fourth-order-in-space viscous Cahn–Hilliard–Oono equation that consists of both short- and long-range interactions, and equipped with either a polynomial or logarithmic potential, we analyze its invariant sets by proposing a novel reformulation. Additionally, we devise a family of temporal integrators up to eighth-order, along with a suitable stabilization parameter, to ensure the preservation of these invariant sets for any time-step. To eliminate the lagging phenomenon, we propose rewriting the method into a parametric two-step Runge–Kutta (TSRK) formulation, quantifying the rescaled time-step, and finally performing a relaxation technique. We demonstrate that the s-stage, pth-order parametric relaxation TSRK integrators retain the order of the underlying TSRK Butcher tableau, and promote the strong-stability-preserving coefficient to at most s when p>2. To further reduce the stabilization parameter, we propose a parametric relaxation integrating factor TSRK framework that treats the stiff linear operator as an integrating factor. The proposed framework has mild restrictions on the underlying TSRK tableau, i.e., the coefficients are non-negative and the parametric abscissas are non-decreasing. Numerical experiments demonstrate the accuracy and structure-preserving property of the proposed schemes, and are used to investigate the effect of parameters on microphase separations in diblock copolymer melts.

A Novel Wind Power Outlier Detection Method with Support Vector Machine Optimized by Improved Harris Hawk

The accurate detection of wind power outliers plays a crucial role in wind power forecasting, while the inherited strong randomness and high fluctuations bring great challenges to this issue. This work investigates the way to improve the outlier detection accuracy based on support vector machine (SVM). Although SVM can achieve good results for outlier detection in theory, its performance is heavily dependent on the hyper-parameters. Parameter optimization is not an easy task due to its complex nonlinear multi-optimum nature; an improved Harris hawk optimization (IHHO) is proposed to optimize the parameters of SVM for more accurate outlier detection. HHO takes the cooperative behavior and chasing style of Harris’ hawks in nature called surprise pounce and can effectively search the optimal one in large parameter space, but it tends to fall into local optimum. To solve this issue, an improved Harris hawk optimization algorithm (IHHO) was proposed to obtain the optimal parameters of SVM. First, Hammersley sequence initialization is carried out to acquire good initial solutions. Then, a nonlinear factor control mode and an adaptive Gaussian–Cauchy mutation perturbation strategy are proposed to avoid getting trapped in local optima. In this way, a novel wind power outlier detection method named IHHO-SVM was constructed. The results on several wind power data with outliers show that IHHO-SVM outperforms SVM and HHO-SVM, which achieves the highest average F1 score of 96.63% and exhibits the smallest standard deviation. Compared to commonly used models for detecting outliers in wind power, such as isolation forest (IF), local outlier factor (LOF), SVM with grey wolf optimization (GWO-SVM), and SVM with particle swarm optimization (PSO-SVM), the proposed IHHO-SVM model shows the best overall performance with precision, recall, and F1 scores of 95.76%, 96.94%, and 96.35%, respectively.

Max-min greedy matching problem: Hardness for the adversary and fractional variant

Eden, Feige, and Feldman explored the concept of the max-min greedy matching problem, which can be conceived as a game played between an algorithm and an adversary. Both parties are presented with a bipartite graph consisting of items and players. Initially, the algorithm selects a priority order for the items, while the adversary, in turn, chooses a priority order for the players based on the algorithm's choice. These priority orders are then utilized in a greedy process to establish a matching between the items and players. During the process, when it is a player's turn, they select the highest priority item from their remaining available neighbors. The objective of the algorithm is to maximize the size of the resulting matching, while the adversary's goal is to minimize its size. Previous studies have demonstrated that the algorithm can employ a polynomial-time strategy to achieve a competitive ratio greater than 12.In this research, we present evidence that, from the adversary's standpoint, approximating the adversarial order minimum matching problem with a ratio better than 65 is NP-hard, assuming the small set expansion (SSE) hypothesis. Conversely, we introduce a fractional variant of the problem and analyze the interaction between the algorithm and the adversary when either or both parties are allowed to utilize fractional permutations. Notably, we discover that if the algorithm solely employs integral item permutations, then an optimal response from the adversary can also involve integral player permutations. Additionally, we establish that, in the fractional variant, the algorithm can adopt a round-robin strategy to achieve a competitive ratio of at least 1−1/e for input graphs with a sufficiently large granularity parameter m. Furthermore, we demonstrate that the analysis for the round-robin strategy is tight even for regular graphs.

Gænice: A general model for magnon band structure of artificial spin ices

Arrays of artificial spin ices exhibit reconfigurable ferromagnetic resonance modes that can be leveraged and designed for potential applications. However, analytical and numerical studies of the frequency response of artificial spin ices have been restricted in scope due to the need of take into account nonlocal dipole fields in theoretical calculations or by long computation times in micromagnetic simulations. Here, we introduce Gænice, a framework to compute magnon dispersion relations of arbitrary artificial spin ice configurations. Gænice makes use of a tight-binding approach to compute the magnon bands. It also provides the user control over the interaction terms included, e.g., external field, shape anisotropy, exchange, and dipole, making Gænice useful for computing ferromagnetic resonances for a variety of structures, such as multilayers and ensembles of weakly or non-interacting nanoparticles. Because it relies on a semi-analytical model, Gænice is computationally inexpensive and efficient. This makes it an attractive tool for the exploration of large parameter spaces. We expect Gænice to help guide the development of novel artificial spin ices geometries and specific micromagnetic simulations for full quantitative verification.

Exploration of fs-laser ablation parameter space for 2D/3D imaging of soft and hard materials by tri-beam microscopy

Tri-beam microscopes comprising a fs-laser beam, a Xe+ plasma focused ion beam (PFIB) and an electron beam all in one chamber open up exciting opportunities for site-specific correlative microscopy. They offer the possibility of rapid ablation and material removal by fs-laser, subsequent polishing by Xe-PFIB milling and electron imaging of the same area. While tri-beam systems are capable of probing large (mm) volumes providing high resolution microscopical characterisation of 2D and 3D images across exceptionally wide range of materials and biomaterials applications, presenting high quality/low damage surfaces to the electron beam can present a significant challenge, especially given the large parameter space for optimisation. Here the optimal conditions and artefacts associated with large scale volume milling, mini test piece manufacture, serial sectioning and surface polishing are investigated, both in terms of surface roughness and surface quality for metallic, ceramic, mixed complex phase, carbonaceous, and biological materials. This provides a good starting place for those wishing to examine large areas or volumes by tri-beam microscopy across a range of materials.

Flow pattern regime and unsteady characteristics of ventilated cavitating flow around the axisymmetric bodies with different headforms

This paper presents an experimental investigation of the flow pattern regime and unsteady characteristics of ventilated cavities with different headform shapes. The test model consists of a removable headform with three different forebodies (the conical, the blunt, and the hemispherical) and a common cylinder rear body. Experiments are conducted in a closed-loop cavitation tunnel. First, the flow pattern regimes on the ventilated cavity for different headforms are discussed in detail, and the dimensions of several flow patterns are measured. The results show that the cavity dimension and the regime are strongly dependent on the headform shape, and all typical flow patterns are introduced by schematic illustrations. Second, the ventilation hysteresis that happened during the flow pattern transition is pointed out. A quantitative gas leakage model is employed to explain the cause of hysteresis and flow pattern transition, and the results show the Strouhal number for different headforms is approximately ∼0.21. However, the blunt presented stronger gas leakage with a large constant parameter, K = 0.80. Finally, the unsteady characteristics of the ventilated cavity around different tested headforms are involved through descriptions of developments of the recirculating vortex and transparent cavity. In addition, an estimated cavitation number is applied to investigate the unsteady characteristics, and the maximum cavitation number and the strongest characteristic are obtained by the blunt headform due to the large drag coefficient and strong flow separation.

Dynamic Moore-Penrose Inversion With Unknown Derivatives: Gradient Neural Network Approach.

Finding dynamic Moore-Penrose inverses (DMPIs) in real-time is a challenging problem due to the time-varying nature of the inverse. Traditional numerical methods for static Moore-Penrose inverse are not efficient for calculating DMPIs and are restricted by serial processing. The current state-of-the-art method for finding DMPIs is called the zeroing neural network (ZNN) method, which requires that the time derivative of the associated matrix is available all the time during the solution process. However, in practice, the time derivative of the associated dynamic matrix may not be available in a real-time manner or be subject to noises caused by differentiators. In this article, we propose a novel gradient-based neural network (GNN) method for computing DMPIs, which does not need the time derivative of the associated dynamic matrix. In particular, the neural state matrix of the proposed GNN converges to the theoretical DMPI in finite time. The finite-time convergence is kept by simply setting a large parameter when there are additive noises in the implementation of the GNN model. Simulation results demonstrate the efficacy and superiority of the proposed GNN method.

Spatial and amplitude dynamics of neurostimulation: Insights from the acute intrahippocampal kainate seizure mouse model.

Neurostimulation is an emerging treatment for patients with drug-resistant epilepsy, which is used to suppress, prevent, and terminate seizure activity. Unfortunately, after implantation and despite best clinical practice, most patients continue to have persistent seizures even after years of empirical optimization. The objective of this study is to determine optimal spatial and amplitude properties of neurostimulation in inhibiting epileptiform activity in an acute hippocampal seizure model. We performed high-throughput testing of high-frequency focal brain stimulation in the acute intrahippocampal kainic acid mouse model of status epilepticus. We evaluated combinations of six anatomic targets and three stimulus amplitudes. We found that the spike-suppressive effects of high-frequency neurostimulation are highly dependent on the stimulation amplitude and location, with higher amplitude stimulation being significantly more effective. Epileptiform spiking activity was significantly reduced with ipsilateral 250 μA stimulation of the CA1 and CA3 hippocampal regions with 21.5% and 22.2% reductions, respectively. In contrast, we found that spiking frequency and amplitude significantly increased with stimulation of the ventral hippocampal commissure. We further found spatial differences with broader effects from CA1 versus CA3 stimulation. These findings demonstrate that the effects of therapeutic neurostimulation in an acute hippocampal seizure model are highly dependent on the location of stimulation and stimulus amplitude. We provide a platform to optimize the anti-seizure effects of neurostimulation, and demonstrate that an exploration of the large electrical parameter and location space can improve current modalities for treating epilepsy. In this study, we tested how electrical pulses in the brain can help control seizures in mice. We found that the electrode's placement and the stimulation amplitude had a large effect on outcomes. Some brain regions, notably nearby CA1 and CA3, responded positively with reduced seizure-like activities, while others showed increased activity. These findings emphasize that choosing the right spot for the electrode and adjusting the strength of electrical pulses are both crucial when considering neurostimulation treatments for epilepsy.

Machine learning based parameter sensitivity of regional climate models—a case study of the WRF model for heat extremes over Southeast Australia

Heatwaves and bushfires cause substantial impacts on society and ecosystems across the globe. Accurate information of heat extremes is needed to support the development of actionable mitigation and adaptation strategies. Regional climate models are commonly used to better understand the dynamics of these events. These models have very large input parameter sets, and the parameters within the physics schemes substantially influence the model’s performance. However, parameter sensitivity analysis (SA) of regional models for heat extremes is largely unexplored. Here, we focus on the southeast Australian region, one of the global hotspots of heat extremes. In southeast Australia Weather Research and Forecasting (WRF) model is the widely used regional model to simulate extreme weather events across the region. Hence in this study, we focus on the sensitivity of WRF model parameters to surface meteorological variables such as temperature, relative humidity, and wind speed during two extreme heat events over southeast Australia. Due to the presence of multiple parameters and their complex relationship with output variables, a machine learning (ML) surrogate-based global SA method is considered for the SA. The ML surrogate-based Sobol SA is used to identify the sensitivity of 24 adjustable parameters in seven different physics schemes of the WRF model. Results show that out of these 24, only three parameters, namely the scattering tuning parameter, multiplier of saturated soil water content, and profile shape exponent in the momentum diffusivity coefficient, are important for the considered meteorological variables. These SA results are consistent for the two different extreme heat events. Further, we investigated the physical significance of sensitive parameters. This study’s results will help in further optimising WRF parameters to improve model simulation.

Performance of the reconstruction of large impact parameter tracks in the inner detector of ATLAS

Searches for long-lived particles (LLPs) are among the most promising avenues for discovering physics beyond the Standard Model at the Large Hadron Collider (LHC). However, displaced signatures are notoriously difficult to identify due to their ability to evade standard object reconstruction strategies. In particular, the ATLAS track reconstruction applies strict pointing requirements which limit sensitivity to charged particles originating far from the primary interaction point. To recover efficiency for LLPs decaying within the tracking detector volume, the ATLAS Collaboration employs a dedicated large-radius tracking (LRT) pass with loosened pointing requirements. During Run 2 of the LHC, the LRT implementation produced many incorrectly reconstructed tracks and was therefore only deployed in small subsets of events. In preparation for LHC Run 3, ATLAS has significantly improved both standard and large-radius track reconstruction performance, allowing for LRT to run in all events. This development greatly expands the potential phase-space of LLP searches and streamlines LLP analysis workflows. This paper will highlight the above achievement and report on the readiness of the ATLAS detector for track-based LLP searches in Run 3.

Real-Time Lightweight Detection of Lychee Diseases with Enhanced YOLOv7 and Edge Computing

Lychee is an economically important crop with widespread popularity. However, lychee diseases significantly impact both the yield and fruit quality of lychee. Existing lychee disease detection models face challenges such as large parameter sizes, slow processing speeds, and deployment complexities. To address these challenges, this paper proposes an improved lightweight network, named YOLOv7-MGPC (YOLOv7-Mosaic-GhostNet-Pruning-CBAM), that enables real-time lychee disease detection. In this study, we collected datasets of lychee diseases, covering four types of leaf diseases, and employed Mosaic data augmentation for data preprocessing. Building upon the YOLOv7 framework, we replaced the original backbone network with the lightweight GhostNetV1 and applied channel pruning to effectively reduce the parameter overhead. Subsequently, an attention mechanism called CBAM was incorporated to enhance the detection accuracy. The resultant model was then deployed to edge devices (Nvidia Jetson Nano) for real-world applications. Our experiments showed that our enhanced YOLOv7 variant outperforms the original model by a large margin, achieving a speed increase from 120 frames/s to 217 frames/s while maintaining an accuracy of 88.6%. Furthermore, the parameter size was substantially reduced from 36.5 M to 7.8 M, which firmly demonstrates the effectiveness of our methods in enabling model deployment on edge devices for lychee disease detection.

An improved van der Pol wake oscillator model for a 1DOF circular cylinder undergoing vortex induced vibrations

An optimization procedure is presented for prescribing the empirical coupling parameters of a modified coupled van der Pol (VDP) wake oscillator and mass–spring–damper system for a one degree of freedom (1DOF) circular cylinder system undergoing vortex induced vibrations (VIV). This approach allows for the generalization of the traditional VDP model to cover a large VIV parameter range. The procedure is developed using experimental data covering the parameter space: 3<U∗<11, 8<m∗<20, 0.003<ζ<0.063, ReD=4000. The VDP model contains empirical coupling coefficients that describe the regime specific dynamic damping, ϵ, and wake coupling, β. Two calibration procedures are considered and the improvements over the traditional VDP model in predicting the VIV response are discussed. Subsequently, a regression fit is used to prescribe the two empirical coupling coefficients as functions of m∗, ζ and U∗. The generalized model results in a better prediction of the VIV response in terms of identification of lock-in regimes, lock-in frequency and amplitude response over several regimes when compared to the classical models. The use of a functional representation replaces the need for using tables and reduces user input for determining regime-specific coupling coefficients.

Designer biochar with specific functionalities for the sustainable mining of invaluable metals from sea water: Performance assessment, mechanism insights and cost benefit analysis

Resource efficiency and cleaner production are an integral component of sustainable development. The present research addresses three contemporary issues. Firstly, quest for a greener alterative to land based mining-seawater, secondly, translation of renewable biowastes into carbon negative biochar and thirdly, synthesis of organic salts of metals directly from metal laded biochar. Sea water is an untapped source of invaluable elements leading to rising momentum for various new technologies. Herein, neem cake is tailored specifically for selective recovery of magnesium and strontium ions from simulated sea water in both batch and continuous systems. Magnesium selective biochar is prepared by insitu pyrolysis at 700℃ using sodium bicarbonate and urea as green porogen and nitrogen source whereas strontium selective biochar is prepared by direct pyrolysis at 700℃ followed by encapsulating alginate microspheres within the biochar lattice. Batch studies revealed the maximum adsorption capacities and removal efficiencies of 34.08 mg/g and 95 % for strontium(S-BC) and 40.2 mg/g and 98 % for magnesium(M−BC). The analytics reveals biochars as fine-tuned mesoporous carbon dominant structure with several N and O functionalities suggesting complexation, H-bonding as the adsorption mechanism. The Sips and PNO models surpassed all isotherm and kinetic models and well describes the process kinetics as chemisorption along with film diffusion as main contributors respectively. In column studies, effect of different process parameters is investigated and it is observed that experimental data corelates well with the BDST model. Moreover, scale up approach is employed to predict large scale design parameters. Lastly, citric acid as a green chelating agent is utilized to recover metal ions in the form of citrates and regenerate spent biosorbent along with detailed cost benefit analysis, thereby endorsing the circular economy approach.