Numerical investigations of brake cooling performance

In the modern world, tough legislation on lowered emissions, leads the manufacturers to apply innovative strategies which involve aerodynamic improvements, such as covered rims. A covered rim is a good solution from an aerodynamics point of view, but poses serious constraints on the cooling performances of the brake discs, as it somewhat a ects the cooling ability of the brake discs. To prevent critical situations that could lead to safety issues, such as decreased friction coefficients, brake hot-judder, increased wear, thermal cracking or even brake uid boiling, the heat must be dissipated and hence, there is a demand for efficient cooling of brakes. Traditionally, brake performance investigations were performed experimentally. However, with the computational power available today, these experiments can be simulated to save physical test time and resources. CAE simulations have shown good correlation with experimental results and can aid in incorporation of design changes at early stages of development. At Volvo Cars, these simulations are carried out using co-simulation where the aerodynamic and thermal solutions are calculated in parallel to get an estimate of the cooling performance. This work examines the possibility to run mono-simulations using the CFD tool Star-CCM+ to test different approaches and investigate important parameters for brake disc cooling performance. During the project, investigations were carried out pertaining to: Various factors affecting the cooling performance Applicability of different Heat Transfer Coefficient definitions Effects of changes in brake disc design and rotation direction Influence of parts around the brake disc Approaches for brake cooling simulations using Star-CCM+ Some important observations made during the course of the project suggests that: the Virtual Local Heat Transfer Coefficient can be used for early comparison investigations which saves simulation time, the performance behavior due to rotational velocity variation can be predicted by linearization of the Heat Transfer Coefficient and there is an optimal point in variation of the design parameters where the best cooling performance of a brake disc type is achieved. This work was carried out at Chalmers University and with the support and valuable feedback from the brakes department at Volvo Cars

Experimental analysis of the behavior and energy yield of different types of photovoltaic modules and brief comparisons with the responses of various types of irradiance sensors

openIn this thesis we've taken three types of Photovoltaic modules namely Bifacial, Half cut and Back contact commercially used currently. Their performances were measured in two ways: the first simply by noting the daily productions of each module technology, in the second case performance ratio measurement was done using two different types of pyranometer sensors namely Photovoltaic pyranometer and Thermopile pyranometer. These experimental activities were carried out under different time periods of the day in order to compare the effect of the Sun's irradiance level in the performance ratio.In this thesis we've taken three types of Photovoltaic modules namely Bifacial, Half cut and Back contact commercially used currently. Their performances were measured in two ways: the first simply by noting the daily productions of each module technology, in the second case performance ratio measurement was done using two different types of pyranometer sensors namely Photovoltaic pyranometer and Thermopile pyranometer. These experimental activities were carried out under different time periods of the day in order to compare the effect of the Sun's irradiance level in the performance ratio

Cooperative Deep Q -Learning Framework for Environments Providing Image Feedback

In this article, we address two key challenges in deep reinforcement learning (DRL) setting, sample inefficiency, and slow learning, with a dual-neural network (NN)-driven learning approach. In the proposed approach, we use two deep NNs with independent initialization to robustly approximate the action-value function in the presence of image inputs. In particular, we develop a temporal difference (TD) error-driven learning (EDL) approach, where we introduce a set of linear transformations of the TD error to directly update the parameters of each layer in the deep NN. We demonstrate theoretically that the cost minimized by the EDL regime is an approximation of the empirical cost, and the approximation error reduces as learning progresses, irrespective of the size of the network. Using simulation analysis, we show that the proposed methods enable faster learning and convergence and require reduced buffer size (thereby increasing the sample efficiency)

Energetics Based Spike Generation of a Single Neuron: Simulation Results and Analysis

Existing current based models that capture spike activity, though useful in studying information processing capabilities of neurons, fail to throw light on their internal functioning. It is imperative to develop a model that captures the spike train of a neuron as a function of its intracellular parameters for non-invasive diagnosis of diseased neurons. This is the first ever article to present such an integrated model that quantifies the inter-dependency between spike activity and intracellular energetics. The generated spike trains from our integrated model will throw greater light on the intracellular energetics than existing current models. Now, an abnormality in the spike of a diseased neuron can be linked and hence effectively analyzed at the energetics level. The spectral analysis of the generated spike trains in a time–frequency domain will help identify abnormalities in the internals of a neuron. As a case study, the parameters of our model are tuned for Alzheimer’s disease and its resultant spike trains are studied and presented. This massive initiative ultimately aims to encompass the entire molecular signaling pathways of the neuronal bioenergetics linking it to the voltage spike initiation and propagation; due to the lack of experimental data quantifying the inter dependencies among the parameters, the model at this stage adopts a particular level of functionality and is shown as an approach to study and perform disease modeling at the spike train and the mitochondrial bioenergetics level

Peptide-based direct electrochemical detection of receptor binding domains of SARS-CoV-2 spike protein in pristine samples

RNA isolation and amplification-free user-friendly detection of SARS-CoV-2 is the need of hour especially at resource limited settings. Herein, we devised the peptides of human angiotensin converting enzyme-2 (hACE-2) as bioreceptor at electrode interface for selective targeting of receptor binding domains (RBD) of SARS-CoV-2 spike protein (SP). Disposable carbon-screen printed electrode modified with methylene blue (MB) electroadsorbed graphene oxide (GO) has been constructed as cost-efficient and scalable platform for hACE-2 peptide-based SARS-CoV-2 detection. In silico molecular docking of customized 25 mer peptides with RBD of SARS-CoV-2 SP were validated by AutoDock CrankPep. N-terminal region of ACE-2 showed higher binding affinity of − 20.6 kcal/mol with 15 H-bond, 9 of which were < 3 Å. Electrochemical biosensing of different concentrations of SPs were determined by cyclic voltammetry (CV) and chronoamperometry (CA), enabling a limit of detection (LOD) of 0.58 pg/mL and 0.71 pg/mL, respectively. MB-GO devised hACE-2 peptide platform exert an enhanced current sensitivity of 0.0105 mA/pg mL(−1) cm(−2) (R(2) = 0.9792) (CV) and 0.45 nA/pg mL(−1) (R(2) = 0.9570) (CA) against SP in the range of 1 pg/mL to 1 µg/mL. For clinical feasibility, nasopharyngeal and oropharyngeal swab specimens in viral transport medium were directly tested with the prepared peptide biosensor and validated with RT-PCR, promising for point-of-need analysis

Impact of sex and comorbid diabetes on hospitalization outcomes in acute pancreatitis: A large United States population-based study

Backgrounds: Data on the association between comorbid diabetes mellitus (DM) and acute pancreatitis (AP) remains limited. Utilizing a large, nationwide database, we aimed to examine the impact of comorbid diabetes mellitus on patients admitted for acute pancreatitis. Methods: This was a retrospective case-control study of adult patients with AP utilizing the National Inpatient Sample from 2015–2018, using ICD–10 codes. Hospitalization outcomes of patients admitted for AP with comorbid DM were compared to those without comorbid DM at the time of admission. The primary outcome was a mortality difference between the cohorts. Multivariable-adjusted cox proportional hazards model analysis was performed. Data was analyzed as both sex aggregated, and sex segregated. Results: 940,789 adult patients with AP were included, of which 256,330 (27.3%) had comorbid DM. Comorbid DM was associated with a 31% increased risk of inpatient mortality (aOR: 1.31; p = 0.004), a 53% increased risk of developing sepsis (aOR: 1.53; p = 0.002), increased hospital length of stay (LOS) (4.5 days vs. 3.7 days; p < 0.001), and hospital costs (8486; p < 0.001). Whites admitted for AP with comorbid DM were at a 49% increased risk of mortality as compared to Hispanics ($9934 vs.$8486; p < 0.0001). Different comorbidities had sex-specific risks; men admitted for AP with comorbid DM were at a 28% increased risk of mortality (aOR: 1.28; p < 0.0001) as compared to women. Men with comorbid DM plus obesity or hypertension were also at increased risk of mortality as compared to women, whereas women with comorbid DM plus renal failure were at greater risk of mortality as compared to men. Conclusions: Comorbid DM appears to be a risk factor for adverse hospitalization outcomes in patients admitted for AP with male sex and race as additional risk factors. Future prospective studies are warranted to confirm these findings to better risk stratify this patient population

Therapeutic reprogramming of glioblastoma phenotypic states using multifunctional heparin nanoparticles

Glioblastomas (GB) are the most common and deadly primary malignant brain tumors due to their infiltrative growth and resistance to conventional therapies. GB cell plasticity and differentiation into drug‐resistant mesenchymal‐like (MES) states protect tumors from conventional treatments. This study introduces a novel precision medicine approach employing heparin‐based nanoparticles (HP‐NPs) engineered to cross the blood‐brain barrier and target MES‐like glioma stem cells (GSCs). Encapsulating doxorubicin (DOX) in HP‐NPs reduces drug‐mediated complement and coagulation cascades, enhancing hemocompatibility in human whole blood. In vitro, HP‐NPs demonstrate efficient uptake by patient‐derived GSCs. Preclinical evaluations in patient avatars indicate plain HP‐NPs outperform DOX‐loaded HP‐NPs in reducing GB progression. Transcriptomic studies show HP‐NPs downregulate heparin‐binding epidermal growth factor (HBEGF), shifting MES GSCs into less plastic astroglial‐like cells, impairing tumorigenesis. HP‐NPs are well‐tolerated and safe at therapeutic doses in healthy rats, offering a promising new paradigm in anticancer therapy to overcome GB recurrence and improve therapeutic outcomes