Adaptive Grid Research Articles

P-1227. Population Pharmacokinetics (PK) of Cefiderocol (FDC) during Acute Pulmonary Exacerbations (APE) in Adult Persons with Cystic Fibrosis (CF)

Abstract Background FDC has potent activity against many Gram-negative rods associated with CF APE. Due to metabolic changes seen in CF as well as frequent infection with resistant bacteria, persons with CF (PwCF) frequently have altered PK and may require higher doses. Herein, we describe the population PK and safety of FDC in adult PwCF admitted with an APE. Methods Adult PwCF admitted with an APE received at least 3 doses of FDC according to their calculated creatinine clearance (CrCL) by Cockroft-Gault; all doses were infused over 3 hours. Subsequent blood sampling (0, 1.5, 3. 3.25, 3.5, 4, 5, 6,and 8h) was performed after the 3rd - 6th dose. FDC concentrations were analyzed by a validated LC/MSMS method.Protein binding (PB) was determined by ultrafiltration. FDC concentrations were fitted using the non-parametric adaptive grid algorithm in Pmetrics for R. The free time above the MIC (fT>MIC) for each PwCF was assessed at MICs of 4, 8, and 16 mg/L. Total AUC24h was calculated to compare overall exposures with non-CF patients. Tolerability and adverse events were monitored throughout the study. Results Nine PwCF were included; six received 2g q8h and three 2g q6h based on CrCL. The mean (range) age, weight, and CrCL were 33 (22-58) years, 62 (45-78) kg, and 117 (71-164) mL/min, respectively. A two-compartment model best fit the data (AIC 514) with mean estimates for clearance, volume of the central compartment, and inter compartmental transfer constants (K12 and K21) of 5.69 ± 1.45 L/h, 7.42 ± 3.74 L, 2.54 ± 1.76 h-1 and 2.72 ± 3.04 h-1, respectively. PB was 45% (38-51). The 2g q8h regimen for PwCF with CrCL 60-120 mL/min achieved a mean (range) fT > MIC of 100% (100-100), 92% (78-100), and 73% (50-100) at MICs of 4, 8, and 16mg/L, respectively, with AUC24h of 1210 (790-1460) mg/L*h. In those with CrCL >120mL/min, the 2g q6h attained 100% fT> MIC in all PwCF up to 8 mg/L and 83% (71-92) fT > MIC at 16 mg/L, with AUC24h of 1241 (950-1578) mg/L*h. One adverse event of nausea was reported. Conclusion Among these 9 PwCF experiencing an APE with normal or augmented renal clearance, FDC using label prescribed dosing regimens according to CrCL was well tolerated and achieved optimal fT >MIC exposure in all patients for pathogens up to MICs of 8mg/L. The AUC24h was also similar to previously reported estimates in non-CF patients. Disclosures Christina Konig, PhD, Gilead Inc.: Honoraria|Pfizer Inc.: Honoraria|Shionogi Inc.: Honoraria Ryan K. Shields, PharmD, MS, Allergan: Advisor/Consultant|Cidara: Advisor/Consultant|Entasis: Advisor/Consultant|GSK: Advisor/Consultant|Melinta: Advisor/Consultant|Melinta: Grant/Research Support|Menarini: Advisor/Consultant|Merck: Advisor/Consultant|Merck: Grant/Research Support|Pfizer: Advisor/Consultant|Roche: Grant/Research Support|Shionogi: Advisor/Consultant|Shionogi: Grant/Research Support|Utility: Advisor/Consultant|Venatorx: Advisor/Consultant|Venatorx: Grant/Research Support Andrew J. Fratoni, PharmD, InsightRX: Grant/Research Support James D. Finklea, MD, Cystic Fibrosis Foundation: Grant/Research Support David P. Nicolau, PharmD, CARB-X: Grant/Research Support|Innoviva: Grant/Research Support|Innoviva: Honoraria|Merck: Advisor/Consultant|Merck: Grant/Research Support|Merck: Honoraria|Pfizer: Advisor/Consultant|Pfizer: Grant/Research Support|Pfizer: Honoraria|Shionogi: Advisor/Consultant|Shionogi: Grant/Research Support|Shionogi: Honoraria|Venatorx: Grant/Research Support Joseph L. Kuti, PharmD, Abbvie: Advisor/Consultant|bioMerieux: Grant/Research Support|Merck: Grant/Research Support|Pfizer: Grant/Research Support|Shionogi Inc: Advisor/Consultant|Shionogi Inc: Grant/Research Support|Shionogi Inc: Honoraria|Venatorx: Grant/Research Support

Optimizing Smart Grids for Distributed Energy Resource Integration and Management

Incorporating Distributed Energy Resources, which comprise solar, wind energy, and batteries, in traditional energy supply represents a substantial opportunity and challenge regarding the maximization of efficient management of energy along with its proper stability at a grid level. Hence, rising penetrations call for advanced smart grids offering real-time monitoring, analytics based on forecasting capabilities, and control at local decentralised nodes due to high variation and uncertainties brought about by renewables. This study aims to optimize smart grids for seamless integration of DERs by harnessing advanced technologies such as the Internet of Things, machine learning algorithms, and blockchain-enabled energy transaction systems. The methodology includes data collection from a distributed grid network through IoT-enabled sensors, predictive modelling of energy demand and supply through machine learning, and implementation of a decentralized trading mechanism using blockchain technology. Prototyping a smart grid system and testing its efficiency, reliability, and scalability were simulated-based evaluations. Results include a 25% improvement in energy utilization efficiency, a 30% reduction in grid balancing costs, and improving system resilience to such power fluctuations. The study also opens a way to demonstrate the possibility of blockchain usage while allowing energy transactions to be transparent and secure to reduce administrative overhead. Findings on these issues highlight how intelligent technologies play an important role in the transition toward more sustainable and resilient energy systems. This research contributes to the broader effort of creating adaptive smart grids capable of accommodating the growing complexity of modern energy networks.

Finite Differences on Sparse Grids for Continuous-Time Heterogeneous Agent Models

We present a finite difference method working on sparse grids to solve higher dimensional heterogeneous agent models. If one wants to solve the arising Hamilton–Jacobi–Bellman equation on a standard full grid, one faces the problem that the number of grid points grows exponentially with the number of dimensions. Discretizations on sparse grids only involve O(N(logN)d−1) degrees of freedom in comparison to the O(Nd) degrees of freedom of conventional methods, where N denotes the number of grid points in one coordinate direction and d is the dimension of the problem. While one can show convergence for the used finite difference method on full grids by using the theory introduced by Barles and Souganidis, we explain why one cannot simply use their results for sparse grids. Our numerical studies show that our method converges to the full grid solution for a two-dimensional model. We analyze the convergence behavior for higher dimensional models and experiment with different sparse grid adaptivity types.

Coupling immersed boundary method with wall function for high Reynolds number compressible flows on adaptive Cartesian grids

In this paper, a high Reynolds number compressible flow simulation method based on the immersed boundary method (IBM) is developed on the adaptive Cartesian grids to address two problems: one is the ability of large-scale Cartesian grid generation, and the other is the resolution of thin shear layers. For the former, an efficient automatic parallel generation method of adaptive Cartesian grids is proposed based on the k-d tree theory. The method has good parallel scalability and computational efficiency, e.g., the generation of 1.41 × 109 cells by 2048 cores takes only 0.40 min. For the latter, an IBM-wall function coupling method is designed to simulate geometrical configurations with high Reynolds numbers in the parallel framework. Numerical experiments show that the presented method can yield results consistent with the use of body-fitted grids. In conclusion, the presented method can realize a fully automatic simulation of high Reynolds number compressible flows, significantly reducing the labor cost in the computational fluid dynamics process, and can improve the accuracy and efficiency of engineering applications.

Research on acoustic temperature field hotspot tracking based on RBFNN adaptive grid

Research on acoustic temperature field hotspot tracking based on RBFNN adaptive grid

Stability and Fault Detection Algorithm of Grid-Connected Inverter in Complex Power Grid Environment

In order to improve the stability, this paper designs a control strategy based on adaptive grid impedance. By monitoring the grid impedance in real time and dynamically adjusting the inverter control parameters, the influence of grid impedance change on inverter stability is effectively reduced. At the same time, LCL filter is introduced, which significantly filters out the higher harmonics in the inverter output current and improves the output waveform quality. In addition, a voltage compensation device is applied to quickly generate a compensation voltage signal when the grid voltage fluctuates to ensure the stability of the inverter output voltage. In the aspect of fault detection, a data-driven fault detection algorithm is proposed, which combines machine learning and real-time monitoring technology to realize early detection and accurate diagnosis of inverter faults by collecting and analyzing inverter operation data. The experimental results show that the algorithm performs well in the detection of power switch device faults, sensor faults and other types, and has high classification accuracy. The research shows that the control strategy based on adaptive grid impedance, the addition of LCL filter and the application of voltage compensation device have significantly improved the stability of grid-connected inverter in complex grid environment. The proposed data-driven fault detection algorithm provides strong support for rapid fault location and accurate treatment of inverter.

Cefiderocol pharmacokinetics during acute pulmonary exacerbations in hospitalized adult persons with cystic fibrosis.

Persons with CF (pwCF) present altered pharmacokinetics (PK) and are often infected with multidrug-resistant (MDR) bacteria. Herein, we describe the PK of cefiderocol, a siderophore cephalosporin with potent activity against MDR Gram-negative rods, in hospitalized adult pwCF with acute pulmonary exacerbation (APE). PwCF received ≥3 doses of 2 g cefiderocol (3 h infusion) with frequency determined according to their estimated glomerular filtration rate (eGFR). Blood sampling collected at steady state. Concentrations were fitted using the non-parametric adaptive grid algorithm in Pmetrics for R. Ten pwCF were enrolled; nine completed the study with six receiving 2 g q8 h and three 2 g q6 h. A two-compartment model best fitted the data. Mean (SD) PK parameters were clearance, 5.66 (1.28) L/h; volume of central compartment, 5.81 (3.52) L, and intercompartment transfer constants, k12, 4.29 (3.46) and k21, 2.25 (2.76) h-1. Protein binding was 48% (35-57). The 2 g q8 h regimen achieved a mean free time above the MIC (fT >MIC) of 99% (94-99), 90% (69-100), and 64% (41-81) at MICs of 4 (susceptible), 8 (intermediate), and 16 (resistant) mg/L, respectively, with AUC24h of 1,191 (781-1,496) mg/L*h. In pwCF with eGFR >120 mL/min, 2 g q6 h attained 100% fT >MIC up to 8 mg/L and 87% (83-92) at 16 mg/L, with AUC24h of 1,279 (1,054-1,590) mg/L*h. Among these nine pwCF with APE with normal or augmented renal clearance, cefiderocol using label prescribed dosing regimens according to eGFR was well tolerated and achieved optimal fT >MIC exposure for pathogens up to MICs of 8 mg/L and AUC24h estimates similar to previously reported estimates in non-CF patients.

Robust and Fast Point Cloud Registration for Robot Localization Based on DBSCAN Clustering and Adaptive Segmentation.

This paper proposes a registration approach rooted in point cloud clustering and segmentation, named Clustering and Segmentation Normal Distribution Transform (CSNDT), with the aim of improving the scope and efficiency of point cloud registration. Traditional Normal Distribution Transform (NDT) algorithms face challenges during their initialization phase, leading to the loss of local feature information and erroneous mapping. To address these limitations, this paper proposes a method of adaptive cell partitioning. Firstly, a judgment mechanism is incorporated into the DBSCAN algorithm. This mechanism is based on the standard deviation and correlation coefficient of point cloud clusters. It improves the algorithm's adaptive clustering capabilities. Secondly, the point cloud is partitioned into straight-line point cloud clusters, with each cluster generating adaptive grid cells. These adaptive cells extend the range of point cloud registration. This boosts the algorithm's robustness and provides an initial value for subsequent optimization. Lastly, cell segmentation is performed, where the number of segments is determined by the lengths of the adaptively generated cells, thereby improving registration accuracy. The proposed CSNDT algorithm demonstrates superior robustness, precision, and matching efficiency compared to classical point cloud registration methods such as the Iterative Closest Point (ICP) algorithm and the NDT algorithm.

AI-Augmented Finite Difference Methods for Solving PDES: Advancing Numerical Solutions in Mathematical Modeling

This study explores the integration of artificial intelligence (AI) with finite difference methods (FDM) to enhance the numerical solution of partial differential equations (PDEs) in physics, engineering, and data science. Traditional FDM approaches, though effective for approximating solutions to PDEs, face limitations in handling high-dimensional, nonlinear, or computationally intensive problems due to constraints in grid size and stability. AI techniques, particularly machine learning (ML) and deep learning (DL), offer promising enhancements, including adaptive grid refinement, optimized time-stepping, and model selection, which significantly improve accuracy and computational efficiency. Using Python-based implementations, this research investigates AI-augmented FDM for various PDEs, including the heat equation, wave equation, Laplace’s equation, and Burger’s equation. Simulation results demonstrate that AI-enhanced FDM not only achieves robust performance but also reduces computational costs by focusing resources on high-error regions in real time. These findings highlight the potential of AI-driven techniques to revolutionize numerical modeling in applications such as fluid dynamics, climate modeling, and wave propagation. This interdisciplinary approach opens avenues for scalable and efficient solutions to complex PDEs, with implications for diverse fields like healthcare, finance, and geophysics. Future research will focus on extending these methods to more intricate PDEs and exploring their application in real-world, resource-constrained scenarios.

Simulation and Prediction of Springback in Sheet Metal Bending Process Based on Embedded Control System.

Amidst the accelerating pace of automation in sheet metal bending, the need for small-batch, multi-varietal, efficient, and adaptable production modalities has become increasingly pronounced. To address this need and to enhance the efficacy of the bending process, this study presents the design and development of an embedded soft PLC (Programmable Logic Controller) rooted in the Codesys development platform and leveraging the ARM Cortex-A55 architecture. This controller employs the EtherCAT communication protocol to facilitate seamless and efficient interactions with fully electric servo-driven CNC (Computerized Numerical Control) bending machinery. To mitigate the challenge of bending springback errors, a finite element simulation model is constructed and refined through the application of ALE (Arbitrary Lagrangian-Eulerian) adaptive grid technology, thereby bolstering simulation precision. Subsequently, an enhanced WOA-BP (Whale Optimization Algorithm-Backpropagation) model, integrating Latin hypercube sampling and neural network techniques, is deployed to anticipate and counteract these springback errors. Experimental outcomes demonstrate that the proposed methodology effectively constrains the final forming angle deviation to within 0.3°, significantly enhancing the reliability and precision of the bending system. This achievement not only underscores the technical feasibility but also contributes to advancing the frontier of sheet metal bending automation.

Comparison of optimization algorithms for automated method development of gradient profiles.

Optimization algorithms play an important role in method development workflows for gradient elution liquid chromatography. Their effectiveness has not been evaluated for chromatographic method development using standardized comparisons across factors such as sample complexity, chromatographic response functions (CRFs), gradient complexity, and application type. This study compares six optimization algorithms - Bayesian optimization (BO), differential evolution (DE), a genetic algorithm (GA), covariance-matrix adaptation evolution strategy (CMA-ES), random search, and grid search - for the development of gradient elution LC methods. Utilizing a multi-linear retention modeling framework, these algorithms were assessed across diverse samples, CRFs, and gradient segments, considering two observation modes: dry (in silico, deconvoluted), and wet (search-based, requiring peak detection). The optimization algorithms were evaluated based on their data (i.e. number of iterations) and time efficiency. Of the algorithms compared in this study, DE proved to be a highly competitive method for dry optimization purposes in terms of both data and time efficiency. BO outperformed all other algorithms in terms of data efficiency and was found to be most effective for search-based optimization, which requires a low number of iterations (<200). However, BO was found to be impractical for dry optimization requiring a large iteration budget due to its unfavorable computational scaling. It was observed that both the CRF and the sample have a strong influence on the efficiency of the algorithms, emphasizing the need for better benchmark samples and highlighting the importance of assessing CRF-induced complexity in the optimization landscape.

Research on the Quality of Adaptive Grids for Calculating Separate Flows on a Two-Fluid Turbulence Model

Research on the Quality of Adaptive Grids for Calculating Separate Flows on a Two-Fluid Turbulence Model

Study of Pathfinding Approach Based on A* With Adaptive Occupancy Grid

This paper presents the results of a study on the A* search algorithm applied to a two-dimensional map with obstacles. Since, in typical cases, A* is implemented on a map divided into cells of equal size, a scientific interest has lies in investigating the efficiency of this algorithm on a map with dynamically variable cell size. Such a map representation increases the ‘resolution’ of constructing a better trajectory near obstacles. For this purpose, the paper proposes an approach to representing the search space as a dynamic adaptive grid using a QuadTree structure. Additionally, a modification of the A* algorithm has been proposed and investigated, which involves selecting the best cell in the neighborhood of the agent’s current position and performing pathfinding from a starting point to a goal. The paper considers maps of various sizes and complexities for numerical experiments and compares the classical and modified A* algorithms. It has been shown that the proposed modification of the A* algorithm demonstrates better computational properties than the classical version of the algorithm on an adaptive grid.

Optimising hydrofoils using automated multi-fidelity surrogate models

ABSTRACT Lifting hydrofoils are gaining importance, since they drastically reduce the wetted surface area of a ship, thus decreasing resistance. To attain efficient hydrofoils, the geometries can be obtained from an automated optimisation process. However, hydrofoil simulations are computationally demanding, since fine meshes are needed to accurately capture the pressure field and the boundary layer on the hydrofoil. Simulation-based optimisation can therefore be very expensive. To speed up the fully automated hydrofoil optimisation procedure, we propose a multi-fidelity framework which takes advantage of both an efficient low-fidelity potential flow solver dedicated to hydrofoils and a high-fidelity RANS solver enhanced with adaptive grid refinement and dedicated foil-aligned overset meshes, to attain high accuracy with a limited computational budget. Both solvers are shown to be reliable for automatic simulation, and remarkable correlation between potential-flow and RANS results is obtained. Two different multi-fidelity frameworks are compared for a realistic hydrofoil: only RANS based and potential-RANS based. According to the optimisation results, the drag is able to be reduced by 17% and 8% in these frameworks, within a realistic time frame. Thus, industrial optimisation of hydrofoils appears possible. Finally, critical areas of future improvement regarding the robustness and efficiency of the optimisation procedure are discussed in this study.

Computational fluid dynamics for naval hydrodynamics

This article describes key issues which have to be addressed to apply Computational Fluid Dynamics to Naval Hydrodynamics. The specific aspects of Naval Hydrodynamics are discussed and illustrated by recent simulations and comparisons with available experiments. Free-surface flows with or without waves and even violent phenomena such as ventilation or cavitation can be modelled with mixture-fluid surface capturing. Turbulence modelling of thick boundary layers and vortical flows requires anisotropic RANS models or hybrid RANS/LES in case of strongly separated flows. Moreover, fluid–structure interaction in the form of rigid or flexible body motion and multi-body systems is crucial to represent ship manoeuvring and propulsion. Finally, the paper underlines the central role played by anisotropic adaptive grid refinement in the accurate simulation of marine flows.

Dynamic adaptive and fully unstructured tetrahedral gridding: Application to CO2 sequestration with consideration of full fluid compressibility

Numerical simulations of complex subsurface flow problems and geomechanics will advance enormously by dynamic adaptive gridding. Only a small part of a large domain where sharp changes occur may require fine gridding. In this work, we introduce a methodology to carry out dynamic adaptive gridding for large-scale flow problems. The algorithm allows 2D and 3D unstructured gridding with consideration of full fluid compressibility in single-phase, and two-phase compositional flow. We divide a triangular element into four and a tetrahedron element into eight which creates hanging nodes at each level of refinement. The handing nodes are eliminated by splitting extra elements. As a result, a transition region exists between the fine and coarse grid regions of the domain. We have applied the method to CO2 sequestration in subsurface aquifers. The conditions are selected such that gravity fingers develop from density increase by the dissolution of CO2 in the aqueous phase. The selected examples include large domains where neither systematic studies nor dynamic adaptive gridding have been reported in the past. Results from comparison with uniform gridding reveal a speedup of up to three orders of magnitude in 3D.

Incorporating numerical method for analyzing the conduction heat transfer during solidification loading nanoparticles

This study examines the freezing within a tank with a curved cold surface, integrating an innovative use of fins. The addition of fins and the careful introduction of nanomaterial knowingly enhance solidification efficiency, with potential applications in areas such as cryopreservation and thermal energy storage. To further optimize the solidification process, various types and concentrations of nano-powders were dispersed into the water, serving as heat transfer enhancers. The mathematical model was refined through specific assumptions, leading to the derivation of two principal equations. These equations were then effectively solved using the Galerkin method, offering a novel optimization approach for solidification. The enhancement in conductivity due to the modifications plays a key role in improving the productivity of the system. Furthermore, the addition of nanoparticles resulted in a notable extension of freezing time around 26.71 %. The use of an adaptive grid method during grid generation, coupled with the successful verification of the final model, strengthens the reliability of the research. By increasing the parameter "m" (shape factor) from 4.8 to 8.6, the solidification time was reduced by approximately 6.96 %, demonstrating a significant improvement in the system's efficiency.

High-order discontinuous Galerkin method with immersed boundary treatment for compressible flows on parallel adaptive Cartesian grids

Adaptive mesh refinement (AMR) technology and high-order methods are important means to improve the quality of simulation results and have been hotspots in the computational fluid dynamics community. In this paper, high-order discontinuous Galerkin (DG) and direct DG (DDG) finite element methods are developed based on a parallel adaptive Cartesian grid to simulate compressible flow. On the one hand, a high-order multi-resolution weighted essentially nonoscillatory limiter is proposed for DG and DDG methods. This limiter can enhance the stability of DG/DDG methods for compressible flows dominated by shock waves. It is also compact, making it suitable for the implementation of AMR with frequent refinement/coarsening. On the other hand, a coupling method of DG and immersed boundary method is proposed to simulate flow around objects. Due to the compactness of DG, the physical quantities of image points can be directly obtained through the DG/DDG polynomial of the corresponding cells. It avoids the wide interpolation stencil of traditional IBM and makes it more suitable for the parallel adaptive Cartesian grid framework in this paper. Finally, the performance of the proposed method is verified through typical two- and three-dimensional cases. The results indicate that the method proposed in this paper has low numerical dissipation in smooth areas and can effectively handle compressible flow dominated by discontinuities. Moreover, for transonic flow over a sphere, the error of results between the proposed method and direct numerical simulation is within 1%, fully validating the accuracy of the method presented in this paper.

Distributed photovoltaic reactive power control strategy based on improved multiobjective particle swarm algorithm

AbstractDistributed power supply access to the distribution network, although it can effectively support the band voltage, will also cause problems such as voltage overruns at the point of grid connection and large network losses, so this paper establishes a reactive power optimization model containing three objectives: network loss, voltage fluctuation rate, and static reactive power generator (SVG) installation capacity in distributed photovoltaic power generation scenarios by taking advantage of the characteristics of SVG that both absorb and send out reactive power. A multiobjective particle swarm algorithm with an adaptive grid and roulette mechanism is introduced to ensure the uniformity and diversity of the Pareto boundaries under the constraint that the output of each device does not exceed the constraints, and to obtain the optimal set of solutions capable of coping with the stochastic fluctuations of distributed power sources. When the algorithm is compared with three other algorithms, such as nondominated sorting genetic algorithm‐II, the results show that it reduces the network loss by about 25% and significantly improves the voltage fluctuation rate.

Single- and Multinozzle Plume Impingement to Detumble Space Debris

The management of space debris is increasingly crucial for ensuring the safety of space missions by mitigating collision risks. Most proposed debris management systems involve the capture and deorbiting of debris. However, it is also essential to eliminate any residual rotation and nutation of the debris to perform capture without accidents. The current work examines the effectiveness of thruster plume impingement for despinning space debris. A proof-of-concept study of plume impingement dynamics is conducted on a rectangular prism target using single- and multiple-nozzle arrangements for different target offsets and orientations. The three-dimensional simulation is carried out using the open-source Direct Simulation Monte Carlo code SPARTA. Staging and dynamic grid adaptation techniques are incorporated to obtain high-resolution results for the multiscale problem. It is observed that the plume–plume interaction in the multinozzle configuration leads to the beneficial formation of a relatively narrow beam of flow interacting with the target; that is, a multinozzle plume exerts a more concentrated directed force, which imparts greater countertorque across a broader spectrum of target orientations than a single-nozzle plume.