Simple Model Research Articles

Dynamic simulation of a combined solar system including phase change material storage and its experimental validation

Thermal energy storage is a key issue in efficient energy system applications, especially in renewable energies. In this respect, phase change materials (PCM) have attracted interest as an active solution for efficient energy management, particularly in the building sector. This paper presents a modelling of a PCM thermal battery in solar system assisted by heat pump. The storage stores the heat produced by unglazed solar panels (Batisol®) and releases it into a heat pump. Then this heat is sent to two other PCM storages. In this way, the low-temperature heating and domestic hot water needs of a commercial building in winter are met. The storage consists of a block of PCM contained between two plates of heat transfer fluid. It acts as a plate heat exchanger that can be charged and discharged simultaneously. The objective of the study is to dynamically simulate the thermal behaviour of this storage for different hot inlet temperature profiles. A rectangular profile is used to validate the model using experimental results. Then, a solar system assisted by heat pump and three PCM storages is dynamically simulated. This 2D model would be useful to validate a simpler model for optimising the operational parameters of the system.

Coupled Multiphysics Modeling of Lithium-Ion Batteries for Automotive Crashworthiness Applications

Abstract Considerable advances have been made in battery safety models, but achieving predictive accuracy across a wide range of conditions continues to be challenging. Interactions between dynamically evolving mechanical, electrical, and thermal state variables make model prediction difficult during mechanical abuse scenarios. In this study, we develop a physics-based modeling approach that allows for choosing between different mechanical and electrochemical models depending on the required level of analysis. We demonstrate the use of this approach to connect cell-level abuse response to electrode-level and particle-level transport phenomena. A pseudo-two-dimensional model and simplified single-particle models are calibrated to electrical–thermal cycling data and applied to mechanically induced short-circuit scenarios to understand how the choice of electrochemical model affects the model prediction under abuse scenarios. These models are implemented using user-defined subroutines on ls-dyna finite element software and can be coupled with existing automotive crash safety models.

Unsteady nonlinear lifting line model for active gust load alleviation of airplanes

AbstractActive gust load alleviation is an important technology for designing future passenger airplanes to be lighter and thus more environmentally friendly. Unsteady Reynolds-averaged Navier–Stokes (URANS) simulations are typically used to accurately calculate gust loads, but because of their high computational cost, they can only be performed at a few selected operating points. In simpler potential theory models, stall is neglected, resulting in loss of accuracy. In this paper, a low-order unsteady aerodynamics wing model is presented, which is able to represent well compressible flow with stall. Furthermore, the model offers the possibility to modularly incorporate actuators, which allows the design and evaluation of active load alleviation systems. The model is based on a conventional unsteady 2D airfoil model including a dynamic stall model. The dynamic stall model requires viscous steady coefficients, e.g. from 2D steady RANS computations. This 2D airfoil model is coupled with a 3D steady-state lifting line model. The model is applied to the LEISA research airplane and extensively validated with URANS results. It performs well in calculating gust loads with and without simultaneous flap deflections, and provides significantly more accurate results in the case of stall than when stall is neglected.

Multi-layered solid element for seismic analysis of rubber bearings considering stress continuity based on finite particle method

Rubber bearings are crucial components in seismic isolation systems to mitigate the damaging effects of earthquakes on structures. Rubber bearings generally consist of alternate layers of steel and rubber bonded together. Simplified two-point spring models omit this layered construction and have limitations in adaptability and programmability, while refined multiple solid models lead to substantial computational costs. This study presents an effective and efficient approach to simulate rubber bearings by using multi-layered solid element based on the finite particle method (FPM). The traditional “Zigzag” shape function is extended into the 3D situation to reflect the serrated deformation within the layered construction. Based on the simplified assumption of stress continuity, an iterative approach is presented for the deformation gradient to achieve the force continuity between layers in dynamic nonlinear scenarios. The multi-layered solid models for both natural rubber bearings (NRBs) and lead rubber bearings (LRBs) are proposed based on this element. The errors of the hysteresis curves of rubber bearings are below 3.1 % between the numerical results obtained by using the multi-layered solid model and the experimental results. The computational speedup ratio of the proposed multi-layered solid model is verified to be 19 relative to the refined multiple solid model. The proposed method is applied to a base-isolated frame structure to demonstrate its effectiveness in structural seismic analysis.

Approximating Rayleigh Scattering in Exoplanetary Atmospheres using Physics-informed Neural Networks (PINNs)

Abstract This research introduces an innovative application of physics-informed neural networks (PINNs) to tackle the intricate challenges of radiative transfer (RT) modeling in exoplanetary atmospheres, with a special focus on efficiently handling scattering phenomena. Traditional RT models often simplify scattering as absorption, leading to inaccuracies. Our approach utilizes PINNs, noted for their ability to incorporate the governing differential equations of RT directly into their loss function, thus offering a more precise yet potentially fast modeling technique. The core of our method involves the development of a parameterized PINN tailored for a modified RT equation, enhancing its adaptability to various atmospheric scenarios. We focus on RT in transiting exoplanet atmospheres using a simplified 1D isothermal model with pressure-dependent coefficients for absorption and Rayleigh scattering. In scenarios of pure absorption, the PINN demonstrates its effectiveness in predicting transmission spectra for diverse absorption profiles. For Rayleigh scattering, the network successfully computes the RT equation, addressing both direct and diffuse stellar light components. While our preliminary results with simplified models are promising, indicating the potential of PINNs in improving RT calculations, we acknowledge the errors stemming from our approximations as well as the challenges in applying this technique to more complex atmospheric conditions. Specifically, extending our approach to atmospheres with intricate temperature-pressure profiles and varying scattering properties, such as those introduced by clouds and hazes, remains a significant area for future development.

Developing a crash severity model based on multi objective evolutionary feature selection approaches

The main purpose of this article is assessing the capability of multiobjective optimisation evolutionary algorithms (MOEAs) along with machine learning method on predicting the crash injury severity with the approach of reducing input variables. Dataset of crashes which were occurred in Iran was analysed over the period of 2016–2020. The data were classified into two classes in terms of injury severity: fatal and non-fatal datasets. At first, general prediction models were created by inserting 31 available input variables into support vector machine (SVM) method based on two imbalanced and obtained balanced datasets. A cluster based under sampling technique was used to obtained balanced data. Then evolutionary algorithms of two objectives optimisation algorithms namely non-dominated sorting genetic algorithm (NSGA-II), multi-objective evolutionary algorithm based on decomposition (MOEA/D), and pareto envelope-based selection algorithm (PESA-II) along with SVM were used to select features that significantly affect the severity of crashes and developed the simplified models. Then the simplified developed model was compared to the model which was prepared with random forest (RF) method in feature selection. It is shown that the simplified model developed by combined MOEAs and SVM is more appropriate to accurately predict the crash severity, considering the number of input variables in comparison to RF.

LUIE: Learnable physical model-guided underwater image enhancement with bi-directional unsupervised domain adaptation

Recently, learning-based underwater enhancement (UIE) methods have made considerable progress, significantly benefiting downstream tasks such as underwater semantic segmentation and underwater depth estimation. Most existing unsupervised UIE methods utilize the atmospheric image formation model to decompose underwater images into background color, transmission map, and scene radiance. However, they rely on simplified physical models for estimating the transmission map, over-simplifying its complex formation, which results in imprecise modeling of underwater scattering effects. Additionally, supervised UIE methods heavily depend on synthetic data or ground truth, leading to limited generalization capabilities due to the substantial domain gap presented in different underwater scenarios. To tackle these challenges, we propose a Learnable physical model-guided unsupervised domain adaptation framework for Underwater Image Enhancement, dubbed LUIE. LUIE learns to predict background light, depth, and scene radiance from an underwater image. We incorporate a learnable network to estimate the transmission map based on the predicted depth map. To minimize the inter-domain gap between synthetic and real underwater images, we introduce a bi-directional domain adaptation method that alternates the background light from each domain. Experimental results demonstrate the effectiveness of our proposed method compared to existing approaches, and high-level experiment results validate that our enhanced underwater results. Experiments in real-world settings on underwater ROVs platform with NVIDIA Jetson AGX Xavier further confirm the effectiveness and efficiency of our work.

Sustained oscillating regime in the two-dimensional magnetic Rayleigh–Taylor instability

There exists an oscillating stable solution to the single-mode two-dimensional Rayleigh–Taylor instability when a mean magnetic field B0 is imposed parallel to the interface, within the Boussinesq approximation. The characteristic frequency Ω and averaged deformation amplitude of the interface L¯ can be predicted by analyzing the stability of a background piecewise density profile. Comparisons with direct numerical simulations of the Navier–Stokes equations, under the magnetohydrodynamics approximation, yield satisfactory results, with deviations of ±5% for Ω and ±20% for L¯. By combining these theoretical predictions with numerical observations, simplified models are proposed to estimate the averaged amplitude and characteristic frequency of the oscillating solution.

A programmable microfluidic platform to monitor calcium dynamics in microglia during inflammation

Neuroinflammation is characterized by the elevation of cytokines and adenosine triphosphate (ATP), which in turn activates microglia. These immunoregulatory molecules typically form gradients in vivo, which significantly influence microglial behaviors such as increasing calcium signaling, migration, phagocytosis, and cytokine secretion. Quantifying microglial calcium signaling in the context of inflammation holds the potential for developing precise therapeutic strategies for neurological diseases. However, the current calcium imaging systems are technically challenging to operate, necessitate large volumes of expensive reagents and cells, and model immunoregulatory molecules as uniform concentrations, failing to accurately replicate the in vivo microenvironment. In this study, we introduce a novel calcium monitoring micro-total analysis system (CAM-μTAS) designed to quantify calcium dynamics in microglia (BV2 cells) within defined cytokine gradients. Leveraging programmable pneumatically actuated lifting gate microvalve arrays and a Quake valve, CAM-μTAS delivers cytokine gradients to microglia, mimicking neuroinflammation. Our device automates sample handling and cell culture, enabling rapid media changes in just 1.5 s, thus streamlining the experimental workflow. By analyzing BV2 calcium transient latency to peak, we demonstrate location-dependent microglial activation patterns based on cytokine and ATP gradients, offering insights contrasting those of non-gradient-based perfusion systems. By harnessing advancements in microsystem technology to quantify calcium dynamics, we can construct simplified human models of neurological disorders, unravel the intricate mechanisms of cell-cell signaling, and conduct robust evaluations of novel therapeutics.

Residual Sketch Learning for a Feature-Importance-Based and Linguistically Interpretable Ensemble Classifier.

Motivated by both the commonly used "from wholly coarse to locally fine" cognitive behavior and the recent finding that simple yet interpretable linear regression model should be a basic component of a classifier, a novel hybrid ensemble classifier called hybrid Takagi-Sugeno-Kang fuzzy classifier (H-TSK-FC) and its residual sketch learning (RSL) method are proposed. H-TSK-FC essentially shares the virtues of both deep and wide interpretable fuzzy classifiers and simultaneously has both feature-importance-based and linguistic-based interpretabilities. RSL method is featured as follows: 1) a global linear regression subclassifier on all original features of all training samples is generated quickly by the sparse representation-based linear regression subclassifier training procedure to identify/understand the importance of each feature and partition the output residuals of the incorrectly classified training samples into several residual sketches; 2) by using both the enhanced soft subspace clustering method (ESSC) for the linguistically interpretable antecedents of fuzzy rules and the least learning machine (LLM) for the consequents of fuzzy rules on residual sketches, several interpretable Takagi-Sugeno-Kang (TSK) fuzzy subclassifiers are stacked in parallel through residual sketches and accordingly generated to achieve local refinements; and 3) the final predictions are made to further enhance H-TSK-FC's generalization capability and decide which interpretable prediction route should be used by taking the minimal-distance-based priority for all the constructed subclassifiers. In contrast to existing deep or wide interpretable TSK fuzzy classifiers, benefiting from the use of feature-importance-based interpretability, H-TSK-FC has been experimentally witnessed to have faster running speed and better linguistic interpretability (i.e., fewer rules and/or TSK fuzzy subclassifiers and smaller model complexities) yet keep at least comparable generalization capability.

Novel approach for precise identification of vibration frequencies and damping ratios from free vibration decay time histories data of nonlinear single degree of freedom models

The significance of Single Degree of Freedom (SDOF) systems lies in their ability to serve as foundational elements for modeling more complex dynamic problems. By capturing essential dynamic behavior with simplicity, SDOF models enable efficient analysis and comprehension of complex systems, justifying the investigation of these simplified models. In nonlinear scenarios, SDOF models result in time series data wherein vibration frequencies vary over time. Classically, time–frequency or Hilbert transforms applied to temporal responses are frequently used to identify the evolution of frequencies and damping ratio over time. These techniques provide results that reflect the spectrum composition achieved for the analyzed time window and present difficulties in precisely determining the magnitude and the exact instant of an effective frequency or damping ratio variation. In this sense, this work introduces a new methodology capable of accurately identifying the vibration frequency as a function of time, i.e., the instantaneous frequency, along with the instantaneous damping ratio. At this initial stage, the focus is on validating the methodology by comparing its performance with the classical approach based on time–frequency transforms. The initial results obtained from synthetic free vibration decay responses of SDOF nonlinear models highlight the accuracy of our findings compared to those obtained from time–frequency transforms. The presented methodology holds promise for further advancement, with potential impacts including structural damage identification, modal identification and nonlinear dynamic analysis.

Reliability of toxicokinetic modelling for PFAS exposure assessment in contaminated water in northern Italy

IntroductionLong-term contamination of tap water and groundwater by perfluoroalkyl and polyfluoroalkyl substances (PFASs) has been documented in the Veneto region of northern Italy. This study aimed to assess the exposure of individuals residing in the contaminated area and to test several toxicokinetic (TK) models of varying complexities to identify an efficient method for predicting perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS) concentrations in human serum using observed data.The ultimate goal is to provide public health officials with guidance on selecting the appropriate TK model for specific contexts, a reliable and rapid tool to support human bio-monitoring (HBM) studies. MethodsTwo simpler empirical TK models and a more complex multi-compartment physiologically based toxicokinetic (PBTK) model were compared with individual and aggregate data from an HBM study. In addition, the PBPK model was modified by adjusting input parameters and introducing new terms into the equations within the original model code. These modifications aimed to optimize the results compared to the original model, with some versions incorporating adjustments to account for the influence of menstruation in women. All models were evaluated to understand their strengths and weaknesses, providing guidance on the appropriate model to use according to specific scenarios. ResultsThe results obtained from the tested models were quite similar, with significant improvements observed only in the modified models. Simpler models also provided satisfactory results in scenarios involving low PFOS serum concentrations and recent exposure cessation. In many cases, predictions demonstrated high accuracy, particularly at the aggregate level and for women. ConclusionsThese findings suggest that environmental protection agencies and health authorities may benefit from employing the tested models at the aggregate level as an initial step in HBM studies, rather than conducting more invasive and expensive screening campaigns.

Free-Drop Experimental and Simulation Study on the Ultimate Bearing Capacity of Stiffened Plates with Different Stiffnesses under Slamming Loads

Differing from previous studies on free-drop tests, this study focuses on the ultimate bearing capacity and failure mechanism of the ship’s bow under slamming loads. A prototype ship’s bow is selected to design two simplified stiffened plates with different stiffeners, and the lateral slamming loads used are equivalent to flare slamming loads. Free-drop tests of the two simplified models are conducted, and the test setups and procedures are provided. The experimental results of slamming pressures and structural responses are obtained. By comparing with the simulation results obtained by Arbitrary Lagrangian-Eulerian (ALE) fluid–structure coupling, the convergence study, symmetry, and independence verifications are carried out. Finally, the dynamic ultimate bearing capacity of stiffened plates with different stiffnesses under lateral slamming loads is studied. The results show that stiffeners enhance the ability of stiffened plates to resist plastic deformation under slamming loads, and T-section stiffeners can provide greater resistance to plastic deformation than other types.

COMPRESSIVE STRENGTH PREDICTION FOR SELF-COMPACTING CONCRETE USING ARTIFICIAL NEURAL NETWORKS

This study investigates the relationship between concrete constituent materials, slump values, density, strain and compressive strength. The database was set in the laboratory by performing a quality assurance test on the constituent materials to ascertain their suitability for concrete work. For material procurement and selection, the British Standard Department of Environment DOE technique of concrete trial mix designs was used. In this study, twelve concrete mixes were utilized. Two simplified models were developed using the feedback network Artificial Neural Networks architecture (ANNs) with R version 4.0.5 and R studio version 1.2.5033. In the two models, an independent layer comprising six nodes and a dependent layer comprising two nodes were taken. Having carried out the sensitivity analysis, the capacity of the developed equation was evaluated in terms of error metrics MSE and RMSE. The grading envelope for river sand showed that the graph fell perfectly into grading envelope zone 2 while other constituent materials tested were all confirmed suitable for concrete works. The results of compressive strength were obtained from varied water-cement ratios used for the concrete trial mix design which ranges between 0.45 to 0.6. The compressive strength results showed that the results increased consistently for each design mix. It is worthy of note that the strength of the mix fell between grades 15, 20, 25 and 35 respectively for both the 7-day and 28-day strength accordingly. Conversely, the ANN models predicted both 7-day and 28-day strength close to the laboratory value. It is concluded that this research has demonstrated economic viability which would enhance overall construction planning efficiency for future researchers, consultants and contractors in the building industries.

Comparative analysis of federated learning algorithms under extreme non-IID conditions for computer vision

Data heterogeneity presents enormous challenges for Federated Learning (FL) where data is characterized by non-independent and identically distributed (non-IID) patterns across participating clients. Although a prominent issue, there is a lack of comprehensive studies regarding the efficacy of FL algorithms under different configurations of hyperparameters and models. Three state-of-the-art algorithms were chosen to be the focus of this paper FedAvg, FedProx, and SCAFFOLD. Experiments of the paper study varying degrees of data imbalance and number of local training steps to see their effects on model training results. The algorithms and hyperparameters were evaluated using ResNet50, AlexNet, and DenseNet121 on the CIFAR-10 dataset. Experimental results reveal that FedAvg and SCAFFOLD generally outperform FedProx, except for the latter excelling in the scenario of extreme non-IID data distributions in combination with minimal local training. It was also observed that models with complex architectures like ResNet50 are more susceptible to data imbalances, while simpler models such as AlexNet prove to be more robust. This study provides valuable insights to how different configurations of hyperparameters affect FL in non-IID scenarios, contributing to future development of more efficient and robust FL algorithms.

Genome-wide discovery for biomarkers using quantile regression at biobank scale

Genome-wide association studies (GWAS) for biomarkers important for clinical phenotypes can lead to clinically relevant discoveries. Conventional GWAS for quantitative traits are based on simplified regression models modeling the conditional mean of a phenotype as a linear function of genotype. We draw attention here to an alternative, lesser known approach, namely quantile regression that naturally extends linear regression to the analysis of the entire conditional distribution of a phenotype of interest. Quantile regression can be applied efficiently at biobank scale, while having some unique advantages such as (1) identifying variants with heterogeneous effects across quantiles of the phenotype distribution; (2) accommodating a wide range of phenotype distributions including non-normal distributions, with invariance of results to trait transformations; and (3) providing more detailed information about genotype-phenotype associations even for those associations identified by conventional GWAS. We show in simulations that quantile regression is powerful across both homogeneous and various heterogeneous models. Applications to 39 quantitative traits in the UK Biobank demonstrate that quantile regression can be a helpful complement to linear regression in GWAS and can identify variants with larger effects on high-risk subgroups of individuals but with lower or no contribution overall.

Interpretable prediction model for decoupling hot rough rolling camber-process parameters

The presence of asymmetrical camber defects in reversible hot rough rolling processes significantly impacts the quality of plate shapes. Understanding and quantifying the intricate relationship between process parameters and camber is essential for developing cost-effective strategies to rectify plate shape defects. Previous research predominantly concentrated on specific couplings between process variables and camber, employing simplified mathematical models with physical mechanisms. However, these approaches may overlook critical factors inherent in the entire rough rolling process, which can substantially influence plate shape deformation. Furthermore, heavy reliance on expertise within the rolling domain may limit the capability and accuracy of coupling analysis models. In this paper, we introduce an Interpretable Prediction Model for Decoupling Camber-process Parameters (called IP-DCP), to investigate the process coupling within the entire rough rolling process. IP-DCP employs conditional modeling to integrate the intricate dependencies of process parameters during rough rolling, dynamically shaping composite features that enhance accurate camber predictions. Additionally, we introduce statistical interpretations for camber predictions using SHapley Additive exPlanation (SHAP) models. This enables a comprehensive comparison of critical process factors influencing camber and coupling analysis in various rolling scenarios, including the overall rough-rolled slab and specific steel grades such as Q335B and ST12. Extensive experiments have been conducted on actual rough roughing datasets, demonstrating the superior performance of our camber prediction model compared to baselines. Furthermore, we have conducted a quantitative analysis of their overall effect, main effect, and interaction effect. These coupling analysis results consistently align with established theoretical insights, showing their substantial significance in advancing precise process optimization strategies and cost-efficient measures for controlling camber.

Comparative analysis of circadian lighting models: melanopic illuminance vs. circadian stimulus

The influence of light exposure on human circadian rhythms has been widely recognized. This effect is mediated by a phototransduction process projected by the intrinsically photosensitive retinal ganglion cells (ipRGCs). The process also involves signal inputs from visual photoreceptors. However, the relative contributions of each photoreceptor to this process remain unclear; accordingly, two different types of circadian lighting models have been proposed: (i) the melanopic illuminance model based solely on ipRGC activation, including melanopic equivalent daylight D65 illuminance (m-EDI) and equivalent melanopic illuminance (EML), and (ii) the circadian stimulus (CS) model, which considers the participation of both ipRGC and visual photoreceptors. However, the two models can yield conflicting predictions. In this study, we assessed and compared the accuracies of these circadian lighting models by fitting a substantial amount of experimental data extracted from multiple laboratory studies. Upon evaluating the results across all exposure durations, data-fitting accuracy of the intricate CS model did not surpass that of the much simpler melanopic illuminance model. Consequently, the latter appears to be the more suitable model for lighting applications. Moreover, a recurring limitation of prior research was revealed: the lighting spectra were not tailored to effectively reflect the fundamental distinctions between the two types of models. Therefore, drawing clear conclusions regarding the accuracies of the models is challenging. To address this problem, we introduced a method for designing contrast-spectra pairs. This method can provide lighting spectra to highlight the difference in circadian illuminance based on one model while keeping the circadian illuminance of others constant.

Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer’s disease

Abstract Alzheimer’s disease (AD), a widely studied neurodegenerative disorder, poses significant research challenges due to its high prevalence and complex etiology. Age, a critical risk factor for AD, is typically assessed by comparing physiological and estimated brain ages. This study utilizes mouse models expressing human alleles of APOE and human nitric oxide synthase 2 (hNOS2), replicating genetic risks for AD alongside a human-like immune response. We developed a multivariate model that incorporates brain structural connectomes, APOE genotypes, demographic traits (age and sex), environmental factors such as diet, and behavioral data to estimate brain age. Our methodology employs a Feature Attention Graph Neural Network (FAGNN) to integrate these diverse datasets. Behavioral data are processed using a 2D convolutional neural network (CNN), demographic traits via a 1D CNN, and brain connectomes through a graph neural network equipped with a quadrant attention module that accentuates critical neural connections. The FAGNN model demonstrated a mean absolute error in age prediction of 31.85 days and a root mean squared error of 41.84 days, significantly outperforming simpler models. Our analysis further focused on the brain age delta, which assesses accelerated or delayed aging by comparing brain age, predicted by FAGNN, to the chronological age. A high-fat diet and the presence of the human NOS2 gene were identified as significant accelerators of brain aging in the old age group. Key neural connections identified by FAGNN, such as those between the cingulum, corpus callosum, striatum, hippocampus, thalamus, hypothalamus, cerebellum, and piriform cortex, were found to be significant in the aging process. Validation using diffusion MRI-based metrics, including fractional anisotropy and return-to-origin probability measures across these connections, revealed significant age-related differences. These findings suggest that white matter degradation in the connections highlighted by FAGNN plays a key role in aging. Our findings suggest that the complex interplay of APOE genotype with sex, immunity, and environmental factors modulates brain aging and enhance our understanding of AD risk in mouse models of aging.

Computation of SWCNT/MWCNT-doped thermo-magnetic nano-blood boundary layer flow with non-Darcy, chemical reaction, viscous heating and Joule dissipation effects

CNTs have been shown to exhibit exceptional electrical and thermal conductivities, chemical and mechanical stability and physiochemical reliability in wide-ranging applications covering biomedicine, energy and aerospace. Motivated by emerging applications in nano-drug delivery in medicine and radiative ablation therapy, a steady-state mathematical model is established for magnetohydrodynamic boundary-layer transport of chemically reactive carbon nanotubes (CNTs)-doped electrically conducting blood along a porous stretching wall of a blood vessel. Multiple and single walls CNTs are considered using the model developed by Xue (2005). Heat generation, radiative flux, wall mass (concentration) slip, viscous heating and Joule magnetic dissipation are incorporated. Chemical reaction effects are addressed utilizing homogenous first-order model. The Darcy-Forchheimer drag force model is utilized for bulk matrix and inertial drag effects in the porous medium. Radiative heat-transport is studied considering Rosseland's diffusion model. The governing partial differential conservation equations for mass, momentum, energy and species are formulated in a two-dimensional Cartesian coordinate system with allied wall and free-stream boundary conditions. Via scaling transformations, a nonlinear boundary value problem is derived. Numerical computations are achieved through bvp4c scheme. The impact of selected parameters on dimensionless quantities (velocity, temperature, concentration, skin friction, local Nusselt and Sherwood numbers) are computed and elaborated through tables and graphs. A good correlation of the numerical solutions with earlier simpler models from the literature is included. The simulations show that with augmentation in Hartmann magnetic number and Forchheimer inertial drag number, significant damping in the nano-doped blood flow is produced for both SWCNTs and MWCNTs. Temperature is elevated with increment in dissipation effect i. e. Eckert number. Higher values of mass (solutal) slip parameter induce a depletion in the concentration. Nusselt number i. e. heat transfer rate to the blood vessel wall is improved with greater values of solid volume fraction for both CNTs. The present model is relevant to radiative ablation therapy combined with nano-medical treatments in blood vessels wherein constant mechanical loading via blood pressure and flow can elevate internal stresses (circumferential wall stress and endothelial shear stress) and induce morphological alterations in blood vessel wall (stretching) and endothelium in conjunction with biochemical reactions.