Optimization Process Research Articles

Parametric structure optimization of a main rotor blade as an application for FSI analysis in main rotor optimization process

Purpose Modern warfare and modern battlefield are very demanding. The recent conflicts showed that the usage of the helicopters is very limited and only the best constructions are able to provide support for the operations. The purpose of this research is to show the possibilities of new design tool for main rotor aerostructural optimization. It is a next chapter of the research that is aimed at finding new solutions for rotorcraft constructions. Design/methodology/approach This work presents a method of preliminary structure optimization of the main rotor blade using parametric modeling. It is the next step in the main rotor optimization studies. It is the next step after preparing the parametric model for the external shape CFD analysis. As a basis for parametric blade structure calculations, the analytical model is provided in this paper. The equations of rigid blade loads and, as a consequence of the strength elements, stresses are shown. The parametric blade modeling is conducted using the Graphic Integrated Programming language. The parametric design method is shown to be used for various blade planform models and different section airfoils. The structure of a blade is generated automatically after the user enters the parameters. The code-inbuilt analysis systems provide a quick inertia examination of the generated geometry, which is the basis for further optimization. The program calculates the blade loads and verifies them with the given material conditions and proposed safety factors. In the analysis, composite materials for the strength elements were proposed. Findings The results of this research showed the application of parametrization into the main rotor blade design loop. It was presented that the main rotor blade structure can be enhanced using fluid-structure interaction (FSI) methods. The time saving with the implementation the process into design loop is shown. Practical implications This work can be practically used in the main rotor blade design process. It provides the possibilities to check various blade aerodynamic configuration in a structure strength aspect. Originality/value To the best of the authors’ knowledge, there were no published research that combines the main rotor FSI analysis. The method, which is presented in the work, provides a new approach to a rotorcraft design. The application of the parametrization and combining it with the FSI method gives a novel solution for helicopters construction enhancement.

Optimizing Flow and Heat Transfer With Guiding Pin Fins in a Wedge-Shaped Cooling Channel for Gas Turbine Blade Trailing Edge

Abstract In this study, an innovative guiding pin-fin array optimization has been developed through three-dimensional numerical simulations to enhance the cooling efficiency of gas turbine blade trailing edge. The guiding pin-fin array is optimized using the Kriging surrogate model and Genetic Algorithm (GA), aiming to significantly improve the heat transfer rates and uniformity within the wedge-shaped channel. The design parameter chosen for optimization is the deflection angle of each guiding pin fin, and the optimization process is conducted in two rounds. The first-round optimization yields a first-optimized guiding pin-fin array, which exhibits superior overall heat transfer performance and reasonable pressure loss compared to conventional circular, oblong, and parallel pin-fin arrays. For the first-optimized guiding pin-fin (1st-OGP) channel at the Reynolds number of Re = 50,000, the total Nusselt number and pressure loss are 44.1% higher and 9.9% lower than those of the baseline circular pin-fin array (CP), respectively. An experimental validation using the transient liquid crystal (TLC) thermography method is carried out and proves the effectiveness of the optimization process. However, it is noted that the first-optimized guiding pin-fin exhibits even lower heat transfer at a low Reynolds number of Re = 10,000, particularly in the channel middle region, which is mainly due to the incapable turning flow control in the root region of the wedged channel. To address this issue and further improve the heat transfer performance at low Reynolds numbers, a second-round optimization is performed by specifically adjusting the deflection angle of the selected guiding pin fins near the root region of the wedged channel. This secondary optimization demonstrates significant heat transfer improvements over the whole studied Reynolds number range with a reasonably reduced consumption of computational resources. The total Nusselt number and pressure loss are 69.3% higher and 11.9% lower than those of the baseline circular pin-fin array, respectively, at Re = 50,000. The optimization process proposed in this paper produces a high-performance cooling structure design with elaborate guiding pin-fin arrangements in the wedge-shaped channel, which indicates high heat transfer enhancement and relatively lower pressure loss in the wedged channel for the turbine blade trailing edge.

Generative artificial intelligence and optimisation framework for concrete mixture design with low cost and embodied carbon dioxide

This research presents a generative Artificial Intelligence (AI) and design framework that integrates machine learning (ML) and optimisation methodologies to discover new concrete mixture designs. Unlike traditional ML models that predict based on existing data, this framework innovatively generates new concrete mix designs that meet specific requirements such as strength, cost-efficiency, and reduced embodied CO2. To propose a powerful and reliable generative AI model, several advanced ML algorithms were considered, e.g., CatBoost, XGBoost, and LGBM. These models were trained on a unique dataset consisting of 4,936 data points collected from five different batching plants and have not been published yet. Bayesian Optimisation was employed to fine-tune model hyperparameters, resulting in the most effective models attaining R2 values of 0.94 and 0.89 for raw and grouped data, respectively. To verify the trained generative AI model, a case study was conducted, in which the model was requested to provide designs of a mix with pre-determined strength and optimised cost and embodied CO2. The mix designs generated by the framework were successfully validated through experimental tests, corroborating the predictive outcomes. The research culminated in the development of a web application, a tool crafted to streamline the concrete mixture design and optimisation process. This generative AI design framework can be applied to many other aspects of material design and engineering problems.

An Analysis and Optimization of Distortion Effect Caused by Pupil Decentering in Optical Gun Scope

During the use of optical gun scopes, slight movements between the human eye and the instrument can cause the pupil to offset from the optical axis, resulting in a dynamic distortion effect. This affects the accuracy and stability of aiming. Based on the mechanism, this study established parameters of the centroid’s deviation of image spots for marginal field points under pupil decentering and centering conditions and their differences to quantitatively evaluate the distortion. These evaluation parameters were obtained by performing a double integral calculation of the ray aberration distribution function over the entire designed exit pupil. Based on this evaluation method, three optical design strategies for reducing the distortion were proposed: optimizing ray aberrations, optimizing centroid shift of image spots, and utilizing vignetting effects. An optimization process was established by combining increasing vignetting and suppressing centroid shift. For a gun scope with significant distortion, the distortion effect was significantly weakened by increasing the vignetting factor and optimizing the centroid shift of image spots. This proved the effectiveness of the proposed analysis, evaluation, and optimization design methods.

Design of a tooth-shaped magnetorheological brake with multiple coils

This research presents an optimized design for a tooth-shaped magnetorheological brake (MRB) with multiple coils. The MRB configuration consists of a disk immersed in magnetorheological fluid (MRF), where four coils act as electromagnets when an electric current is applied. The energized coils induce the alignment of suspended particles in the MRF, forming a chain-like structure that increases the fluid's viscosity. This rapid, strong, and controllable phenomenon enables the MRB to find applications in haptic systems, tension control, and industrial braking systems. To analyze the electromagnetic behavior of the brake, Finite Element Method (FEM) simulations are conducted using ANSYS APDL, and the optimization process is performed utilizing ANSYS Workbench software. The software provides optimized geometries for the brake, resulting in significant improvements in desired performance aspects compared to prior designs, etc.

Critical alterations in the brain and psyche

Critical alterations in the brain and psyche are often triggered by critical points and feedback effects in closely networked systems. Such crises can occur in the form of neurological disorders, such as epilepsy or mental disorders, such as bipolar disorder and depression. A central mechanism is the excitation-inhibition (EI) balance in the brain, which is responsible for an optimal processing of information. Disruptions in this balance can lead to pathological conditions. The concept of attractors, which represent the stable conditions in neuronal networks, helps to explain the consolidation of memories, behavioral patterns and mental states. These attractor states can be triggered by external stimuli and may become anchored in pathological contexts. Advances in measurement technologies and methods of artificial intelligence enable a deeper analysis of neuronal dynamics and open up new pathways for targeted therapeutic interventions for the treatment of mental disorders.

Simulation-based design: minimizing energy consumption in residential buildings through optimal thermal insulation

Purpose This study aims to optimize the energy consumption of residential buildings in mild and humid climates. It investigates the use of thermal insulation to reduce thermal load through energy simulation analysis. Design/methodology/approach A residential building located in Rasht city, Iran (a mild and humid climate zone), is simulated using DesignBuilder software. Subsequently, the minimum thermal resistance for external walls and roof is analyzed along with its impact on building energy consumption and carbon emissions. Findings The simulation results indicated a 26.5% reduction in heat loss through the walls and a 14.2% reduction through the roof due to optimal thermal insulation. Furthermore, optimal insulation led to a 19.2% reduction in cooling system energy use, a 12% reduction in heating system energy use and a combined 15.3% reduction in total energy consumption for cooling and heating. Originality/value This optimization process leads to several benefits: reduced costs associated with thermal and cooling energy losses in buildings, improved building performance against atmospheric factors and, ultimately, a reduction in energy consumption across the building industry. This research can be valuable to various stakeholders, including the construction industry and building sector, municipalities and engineering systems, building owners and contractors and environmental organizations. By implementing these findings, they can improve the state of modern building insulation and achieve greater energy efficiency.

Techno-financial analysis of 100 % renewable electricity for the south west region of the UK by 2050

In the UK, the transition to 100 % Renewable Energy Systems is a critical strategy in addressing climate change, aligning with the UK government's 2050 net-zero objective. This study explores the potential for such a transformation within the South West region of the UK by 2050. EnergyPLAN16.2 software was utilised for scenario modelling and the fmincon algorithm in MATLAB for optimisation analysis. The focus was on two scenarios involving hybrid renewable energy systems incorporating onshore wind, solar, offshore wind, and two energy storage systems which are lithium-ion battery energy storage and hydrogen fuel cell energy storage. The optimisation process entailed two stages: determining renewable energy configurations for cost-efficient coverage of annual electricity demand and computing the minimum energy storage capacity to ensure a constant power supply. The results suggest an optimal configuration of 4,460 MW onshore wind, 9,880 MW solar, 6,982 MW offshore wind, and 2,273,662 MWh of lithium-ion battery energy storage for 100 % hybrid renewable energy systems in the South West region of the UK in 2050. This configuration achieves the lowest annual cost of 3208 M£/year, with a balanced wholesale electricity price ranging from 4.3p/kWh to 6.6p/kWh and a levelized cost of energy of 6.44p/kWh. Furthermore, hydrogen fuel cell storage systems can be another alternative, however, the present study showed it is not economical enough compared to lithium-ion battery storage systems. This research broadens the understanding of hybrid renewable energy systems implementation in large regions, offering valuable insights for sustainable energy planning in the South West region of the UK.

Predicting normalized difference vegetation index using a deep attention network with bidirectional GRU: a hybrid parametric optimization approach

AbstractScalable and accurate normalized difference vegetation index (NDVI) prediction is necessary to track the status of vegetation and the environment and to support proper ecological management. Herein, we present an innovative deep-learning approach to improve NDVI prediction performances by considering enhanced temporal modeling and hybrid optimization processes. The analysis is based on a core model that integrates a Bidirectional Gated Recurrent Unit (BiGRU) with the profound attention feature since the primary research incorporates the capability of complex temporal in addition to NDVI-time series value. The model performs better through a dual algorithm combining the waterwheel plant algorithm (WWPA) and statistical fractal search (SFS) named WWPASFS-BiGRU. The proposed approach is evaluated using real-world NDVI datasets, demonstrating its capability to outperform traditional models and state-of-the-art deep learning methods. Key performance metrics highlight the model’s accuracy, with a root mean square error (RMSE) as low as 0.00011, reflecting its superior predictive ability. Comparative experiments showcase the robustness of our model across different environmental conditions and geographical settings, affirming its applicability in diverse ecological forecasting scenarios. Additionally, extensive statistical validation, including ANOVA and Wilcoxon tests, confirms the model’s consistency and reliability. The effectiveness of the WWPASFS-BiGRU model is illustrated through applications in predicting NDVI trends across regions in Saudi Arabia, providing critical insights for ecosystem management and sustainable development planning.

FWI-WF: Robust full-waveform inversion using hybrid Wasserstein-1 and Fourier metrics

The performance of full-waveform inversion (FWI) in constructing high-resolution subsurface models is closely related to the design of mismatch functions. The least-squares norm ([Formula: see text]) is commonly used; however, it is prone to local minima when high-quality initial guess and low-frequency data are unavailable. The Wasserstein-1 ([Formula: see text]) metric captures time shifts more effectively, but it may be plagued by imprecise deep structures. The Fourier metric leverages power spectra from simulated and observed data, offering higher-resolution updates near solutions. We develop a progressive waveform inversion method called FWI-WF using [Formula: see text] and Fourier metrics. Specifically, in the early stage of inversion, we apply greater weight to the [Formula: see text] metric for constructing a good background model and avoiding falling into local minima. Then, the Fourier metric gradually dominates to refine edges and deep structures, providing high-resolution inversion results. During the optimization process, we use automatic differentiation to improve inversion efficiency. Experimental results on three baseline geologic models indicate that FWI-WF outperforms three state-of-the-art methods.

Insights into Catechin-Copper Complex Structure and Biologic Activity Modulation.

Compounds of natural origin found in varying quantities in plant-based products constitute a highly significant category, possessing structural significance as well as the capacity to regulate oxidative processes. The activity of these compounds may be modulated by the composition of the biological environment in which they operate, the pH of the environment, or the presence of metal cations in plants or plant extracts. A successful complexation reaction was mainly confirmed by FT-IR, observing the shift from the original transmittance of catechin bonds, especially O-H ones. This work shows the synthetic methodology and the optimization process that took place to synthesize a catechin-copper complex, which demonstrated antioxidant activity. It was tested for iron chelation ability, hydroxyl radicals, and the inhibition of lipoxygenase (15-LOX). An antidiabetic assay was performed by determining the inhibition of alpha-amylase and alpha-glucosidase, finding that the synthesized complex had similar inhibitory potential as pure catechin. The antibacterial tests showed results against Staphylococcus aureus and the antifungal properties of the complex against Candida albicans.

Manajemen Keuangan Madrasah dalam Pengadaan Sarana dan Prasarana Pendidikan

This study underscores the importance of effective financial management in the procurement of educational facilities and infrastructure at MA Al Washoya Ngoro Jombang, aiming to support optimal learning processes. Effective financial management is essential to ensure that the madrasah can provide adequate facilities in line with established educational standards. The objective of this research is to analyze how financial management is implemented at MA Al Washoya, focusing on planning, inventory, and evaluation related to the procurement of educational facilities and infrastructure.The research employs a qualitative descriptive approach, using data collection techniques such as observation, interviews, and documentation. The study examines the financial planning process involving the Waka Sarpras, fund allocation based on annual work meetings, and how inventory and financial evaluation are conducted to ensure proper and efficient use of funds.The findings reveal that financial management at MA Al Washoya is functioning reasonably well; however, there are areas requiring improvement, particularly in optimizing procurement and maintenance of existing facilities and infrastructure. The evaluation indicates that, although facilities and infrastructure have been planned and procured, better maintenance is needed to ensure their sustainability. The study recommends that the madrasah be more proactive in financial planning and asset maintenance strategies to ensure the achievement of high-quality and sustainable educational outcomes.

Physics-guided full waveform inversion using Encoder-Solver convolutional neural networks

Abstract Full Waveform Inversion (FWI) is an inverse problem for estimating the wave velocity distribution in a given domain, based on observed data on the boundaries. The inversion is computationally demanding because we are required to solve multiple forward problems, either in time or frequency domains, to simulate data that are then iteratively fitted to the observed data. We consider FWI in the frequency domain, where the Helmholtz equation is used as a forward model, and its repeated solution is the main computational bottleneck of the inversion process. To ease this cost, we integrate a learning process of an Encoder-Solver preconditioner that is based on convolutional neural networks (CNNs). The Encoder-Solver is trained to effectively precondition the discretized Helmholtz operator given velocity medium parameters. Then, by re-training the CNN between the iterations of the optimization process, the Encoder-Solver is adapted to the iteratively evolving velocity medium as part of the inversion. Without retraining, the performance of the solver deteriorates as the medium changes. Using our light retraining procedures, we obtain the forward simulations effectively throughout the process. We demonstrate our approach to solving FWI problems using 2D geophysical models with high-frequency data.

Pore-Scale multi-objective shape optimization of triply periodic minimal surface cellular architectures: Volumetric Nusselt number versus friction factor

Triply Periodic Minimal Surface (TPMS) cellular architectures, i.e., minimal surfaces that are periodic in three independent directions, have attracted interest in the engineering community for their potential in heat transfer applications with severe volume constraints due to their high surface-to-volume ratio, unique geometrical properties and smooth porous structure. Such performance can be conveniently controlled by tuning the TPMS unit type, relative density, and structural parameters. In this study, a multi-objective optimization process is carried out to investigate how pore-scale geometric characteristics affect the heat transfer performance of TPMS, and which combinations result in an optimal trade-off between heat dissipation capability and associated pressure drop in terms of the Nusselt number and friction factor. The analysis is performed using an in-house Genetic Algorithm routine that generates the structure, allowing for a wider and denser range of investigation, and incorporates a commercial finite element CFD software as a black box fitness function. Over 14,000 unique combinations between two heat transfer boundary conditions (constant wall heat flux, fixed wall temperature) and several flow inlet conditions are investigated. The optimization achieved an increase in performance of up to 245 % in terms of volumetric Nusselt number, which was accompanied, however, by a significant, although reasonable, increase in friction factor values. The results also show specific trends among the geometric characteristics studied, which are then contextualized through the use of CFD analysis. Finally, empirical correlations are derived to cluster the non-dominated solutions obtained, enabling the reader to choose the best cellular TPMS structure by selecting the desired level of trade-off between convection heat transfer and flow resistance.

Response Surface Methodology Optimization of Exosome-like Nanovesicles Extraction from Lycium ruthenicum Murray and Their Inhibitory Effects on Aβ-Induced Apoptosis and Oxidative Stress in HT22 Cells.

Exosome-like nanovesicles (ELNs) derived from plants are nanoscale vesicles isolated from edible plant sources. Lycium ruthenicum Murray (LRM) has garnered growing attention for its dietary value and therapeutic benefits. In this study, a PEG6000-based method was developed to isolate LRM-ELNs. Response surface methodology (RSM) was used to optimize the extraction conditions to obtain the optimal extraction efficiency. When PEG6000 concentration was at 11.93%, relative centrifugal force was 9720 g, and incubation time was 21.12 h, the maximum LRM-ELN yield was 4.24 g/kg. This optimization process yielded LRM-ELNs with a particle size of 114.1 nm and a surface charge of -6.36 mV. Additionally, LRM-ELNs mitigated Aβ-induced apoptosis in HT22 cells by enhancing mitochondrial membrane potential (MMP), lowering the Bax/Bcl-2 ratio, and reducing Cleaved Caspase-3 expression. Furthermore, LRM-ELNs alleviated Aβ-induced oxidative stress in HT22 cells by promoting the nuclear translocation of Nrf2 and upregulating the expression of HO-1 and NQO1. These findings indicate that LRM-ELNs exert protective effects against Aβ-induced damage in HT22 cells and may be considered as a potential dietary supplement for Alzheimer's disease prevention.

Application of chaotic teaching-learning-based optimization technique for estimating unknown parameters of proton exchange membrane fuel cell model.

Proton exchange membrane fuel cells (PEMFC) possess features like high specific power density, low operating temperature, and low operating pressure and thus are most widely used. The performance of PEMFC highly depends on its output voltage which further affects its efficiency. This research paper aims to improve this output voltage by minimizing the losses through parameter estimation technique for three well-known commercial PEMFC stacks, namely, 250W, BCS 500W, and Ballard Mark V. The multiobjective function framed for optimization serves two goals. One is to extract the unknown parameters of FC, and second is to achieve the minimum sum of squared errors (SSE) that occur due to the difference between experimental voltage and estimated voltage. Chaotic teaching-learning-based optimization (CTLBO) algorithm is used for optimization process. SSE values obtained for three commercial PEMFC stacks 250W, BCS 500W, and Ballard Mark V are 7.02267, 4.00150, and 0.8100, respectively. The applicability of this technique is further checked for different operating conditions of each model. The I-P (current-power) curve and I-V (current-voltage) curve obtained using estimated data closely matched the experimental data that showed the practical relevance and efficacy of the algorithm. A comparison is done among the SSE results obtained using CTLBO to other competitive algorithms mentioned in the literature. CTLBO showed its superiority over other algorithms in estimating the unknown parameters of PEMFC stack parameters.

High-efficient sample point transform algorithm for large-scale complex optimization

Decomposition algorithms and surrogate model methods are frequently employed to address large-scale, intricate optimization challenges. However, the iterative resolution phase inherent to decomposition algorithms can potentially alter the background vector, leading to the repetitive evaluation of samples across disparate iteration cycles. This phenomenon significantly diminishes the computational efficiency of optimization. Accordingly, a novel approach, designated the Sample Point Transformation Algorithm (SPTA), is put forth in this paper as a means of enhancing efficiency through a process of mathematical deduction. The mathematical deduction reveals that the difference between sample points in each iteration loop is a simple function related to the inter-group dependent variables. Consequently, the SPTA method achieves the comprehensive transformation of the sample set by establishing a surrogate model of the difference between the sample sets of two cycles with a limited number of sample points, as opposed to conducting a substantial number of repeated samplings. This SPTA is employed to substitute the most time-consuming step of direct calculation in the classical optimization process. To validate the calculation efficiency, a series of numerical examples were conducted, demonstrating an improvement of approximately 75 % while maintaining optimal accuracy. This illustrates the advantage of the SPTA in addressing large-scale and complex optimization problems.

A New Method Based on Belief Rule Base with Balanced Accuracy and Interpretability for Student Achievement Prediction

In the field of education, the accurate prediction of students’ future performance is essential for personalized instruction and efficient allocation of resources. Such predictions not only help education professionals develop targeted educational strategies but also identify students’ learning needs at an early stage so that timely interventions and support can be provided. To gain the trust of educational experts and ensure the practical application value of the prediction results, the prediction methods used must be highly interpretable. However, there are two problems with the current belief rule base (BRB) applied to student performance prediction. First, there is a current lack of effective strategies for enhancing the interpretability of the optimization process. Second, BRB models that overemphasize accuracy tend to exhibit characteristics of black-box models. To overcome these challenges, this paper proposes a new method based on BRB with balanced accuracy and interpretability (BRB-Bai) for student achievement prediction. First, an attribute selection method is proposed to filter out important features associated with student performance. Then, expert knowledge credibility is calculated, and four interpretability strategies are proposed to ensure the interpretability of the model and to achieve a balance between interpretability and accuracy on the basis of expert knowledge credibility. The effectiveness of the proposed model is demonstrated by conducting experiments on the student achievement dataset.

Metaheuristics-based multistage iterative optimization framework for decomposed high-speed rail systems planning problem

High-speed rail (HSR) systems planning and decision-making is a large-scale, multistage, intricate problem, where each stage represents a complex sub-problem. The solution approach to the multistage problem needs information exchange across the stages. A non-optimal planning and decision at any stage could yield a sub-optimal HSR system. This study proposes a novel multistage iterative HSR station location and alignment optimization framework (HSLAOF) to solve the main problem by decomposing it into the identification of (a) HSR potential regions and corridor, (b) station location, (c) shortest sequence/corridor of stations, and (d) collision-free alignment (horizontal and vertical) sub-problems. It integrates these sub-problems, which are solved separately using customized artificial intelligence- and motion planning-based metaheuristics such as particle swarm optimization for station location, ant colony optimization for shortest sequence of stations, path planner method for horizontal alignment and exploration, and exploitation-based ant colony optimization for vertical profile. In HSLAOF, the information from the upper and lower stages of the optimization process are exchanged and combined for a holistic optimized HSR system. Application of HSLAOF in the Mumbai-Ahmedabad HSR project yielded a solution that has 14.24 % lower total system cost, no environmental impact, 5.82 % better socio-economic impact, 21.14 % lesser noise and vibration impacted population, and 2.79 % higher station location utility than the one proposed by the experts. For predefined station location, the HSLAOF yielded alignment with 13.90 % lower total system cost and 24.00 % less noise and vibration impacted population than the one developed by experts. It required about 150 min to obtain a solution for each iteration.

Unlocking the wound healing potential of Anastatica hierochuntica L. (Kaff Maryam) Extract's nanosuspension: UPLC-MS/MS metabolic profiling, formulation development and statistical optimization

BackgroundAnastatica hierochuntica L. (Kaff Maryam) is a desert plant with documented therapeutic applications. Various medicinal uses have been reported for the plant extract such as antioxidant, anti-inflammatory, antifungal, antimicrobial, hypoglycemic, hypolipidemic, and hepatoprotective activities. The abundant plants constituents are promising and encourage the investigation of novel therapeutic uses. The principal objective of this study was to identify the bioactive constituents of A. hierochuntica (AH) by phytochemical profiling of its ethanolic extract, alongside an investigation of its wound healing efficacy after being formulated into nanosuspension for topical localized wound treatment. MethodA comprehensive metabolomics study of the ethanolic extract of A. hierochuntica aerial parts was assessed using UPLC MS/MS technique. Additionally, the preparation of AH nanosuspension was executed utilizing the antisolvent-precipitation method. A D-optimal experimental design was adopted to investigate the influence of variables such as: stabilizer ratio, antisolvent: solvent ratio, and stabilizer type on particle size (PS), polydispersity index (PDI), and zeta potential (ZP). The design also was employed in the optimization process. The selected optimal formula was further assessed for size characteristics, morphological identification by TEM, storage stability, FTIR, cytocompatibility with human skin fibroblast (HSF), and scratch assay on HSF to assess wound healing aptitude. ResultsChemical profiling of the aerial parts revealed an abundance of glucosinolates, flavonoids, flavolignans, and fatty acids. The optimized AH-nanosuspension showed small particle size (128.87 ± 26 nm), small PDI (0.136 ± 0.05), and high ZP absolute value (−37.53 ± 0.52 mV). Furthermore, TEM photography of the optimal nanosuspension formula exhibited circular non-aggregating nanosized particles. FTIR confirmed the successful formulation of the AH ethanolic extract into nanosuspension with no physicochemical interactions. Additionally, AH-nanosuspension demonstrated cytocompatibility with HSF with IC50 = 58.317 μg/mL. Moreover, both AH pure extract and AH-nanosuspension exhibited a remarkable cell migration rate and wound closure % which propounds an auspicious wound healing potential. ConclusionThe study accentuates the supremacy of the nanosized delivery approach of AH aerial parts ethanolic extract and warrants further in-vivo investigations to establish its clinical utility for wound management.