EXPERIMENTAL INVESTIGATIONS OF CONVECTIVE HEAT TRANSFER OVER AN AIRFOIL SURFACE

As experimentation becomes more complex, the need for the co-operation in it of technical elements from outside becomes greater and the modern laboratory tends increasingly to resemble the factory and to employ in its service increasing numbers of purely routine workers. This experimentation involves calculation of flow and Convective heat transfer characteristics of an airfoil. Firstly we are placing the airfoil in the wind tunnel having pressure distribution measurement equipment. There we are placing Digital 2 –component force measuring Transducer by which we are getting the lift and drag values acting on the airfoil .so from the above information we are going to calculate the coefficient of drag so that we can know design considerations so as to reduce the drag and lift force acting on the airfoil shaped bodies. Another parameter we are analyzing here is the temperature distribution at various points which requires an airfoil drilled at different points and counter sunken with respective screws for thermocouples insertion. Thermocouples are used to measure the reading of the temperature distribution at given points .Initially the reading is taken without any heat input to the airfoil specimen, after giving the heat energy externally we are going to determine the value of convective heat transfer from the airfoil element to the surroundings. So according to this we are going to temperature distribution of the airfoil

Parameters, Properties, and Process: Conditional Neural Generation of Realistic SEM Imagery Towards ML-assisted Advanced Manufacturing

The research and development cycle of advanced manufacturing processes traditionally requires a large investment of time and resources. Experiments can be expensive and are hence conducted on relatively small scales. This poses problems for typically data-hungry machine learning tools which could otherwise expedite the development cycle. We build upon prior work by applying conditional generative adversarial networks (GANs) to scanning electron microscope (SEM) imagery from an emerging manufacturing process, shear assisted processing and extrusion (ShAPE). We generate realistic images conditioned on temper and either experimental parameters or material properties. In doing so, we are able to integrate machine learning into the development cycle, by allowing a user to immediately visualize the microstructure that would arise from particular process parameters or properties. This work forms a technical backbone for a fundamentally new approach for understanding manufacturing processes in the absence of first-principle models. By characterizing microstructure from a topological perspective we are able to evaluate our models' ability to capture the breadth and diversity of experimental scanning electron microscope (SEM) samples. Our method is successful in capturing the visual and general microstructural features arising from the considered process, with analysis highlighting directions to further improve the topological realism of our synthetic imagery

TopTemp: Parsing Precipitate Structure from Temper Topology

Technological advances are in part enabled by the development of novel manufacturing processes that give rise to new materials or material property improvements. Development and evaluation of new manufacturing methodologies is labor-, time-, and resource-intensive expensive due to complex, poorly defined relationships between advanced manufacturing process parameters and the resulting microstructures. In this work, we present a topological representation of temper (heat-treatment) dependent material micro-structure, as captured by scanning electron microscopy, called TopTemp. We show that this topological representation is able to support temper classification of microstructures in a data limited setting, generalizes well to previously unseen samples, is robust to image perturbations, and captures domain interpretable features. The presented work outperforms conventional deep learning baselines and is a first step towards improving understanding of process parameters and resulting material properties

Neural Lumped Parameter Differential Equations with Application in Friction-Stir Processing

Lumped parameter methods aim to simplify the evolution of spatially-extended or continuous physical systems to that of a "lumped" element representative of the physical scales of the modeled system. For systems where the definition of a lumped element or its associated physics may be unknown, modeling tasks may be restricted to full-fidelity simulations of the physics of a system. In this work, we consider data-driven modeling tasks with limited point-wise measurements of otherwise continuous systems. We build upon the notion of the Universal Differential Equation (UDE) to construct data-driven models for reducing dynamics to that of a lumped parameter and inferring its properties. The flexibility of UDEs allow for composing various known physical priors suitable for application-specific modeling tasks, including lumped parameter methods. The motivating example for this work is the plunge and dwell stages for friction-stir welding; specifically, (i) mapping power input into the tool to a point-measurement of temperature and (ii) using this learned mapping for process control

HEURISTIC OPTIMIZATION OF BAT ALGORITHM FOR HETEROGENEOUS SWARMS USING PERCEPTION

In swarm robotics, a group of robots coordinate with each other to solve a problem. Swarm systems can be heterogeneous or homogeneous. Heterogeneous swarms consist of multiple types of robots as opposed to Homogeneous swarms, which are made up of identical robots. There are cases where a Heterogeneous swarm system may consist of multiple Homogeneous swarm systems. Swarm robots can be used for a variety of applications. Swarm robots are majorly used in applications involving the exploration of unknown environments. Swarm systems are dynamic and intelligent. Swarm Intelligence is inspired by naturally occurring swarm systems such as Ant Colony, Bees Hive, or Bats. The Bat Algorithm is a population-based meta-heuristic algorithm for solving continuous optimization problems. In this paper, we study the advantages of fusing the Meta-Heuristic Bat Algorithm with Heuristic Optimization. We have implemented the Meta- Heuristic Bat Algorithm and tested it on a heterogeneous swarm. The same swarm has also been tested by segregating it into different homogeneous swarms by subjecting the heterogeneous swarm to a heuristic optimization

Neoantigen quality predicts immunoediting in survivors of pancreatic cancer.

Cancer immunoediting1 is a hallmark of cancer2 that predicts that lymphocytes kill more immunogenic cancer cells to cause less immunogenic clones to dominate a population. Although proven in mice1,3, whether immunoediting occurs naturally in human cancers remains unclear. Here, to address this, we investigate how 70 human pancreatic cancers evolved over 10 years. We find that, despite having more time to accumulate mutations, rare long-term survivors of pancreatic cancer who have stronger T cell activity in primary tumours develop genetically less heterogeneous recurrent tumours with fewer immunogenic mutations (neoantigens). To quantify whether immunoediting underlies these observations, we infer that a neoantigen is immunogenic (high-quality) by two features-'non-selfness'  based on neoantigen similarity to known antigens4,5, and 'selfness'  based on the antigenic distance required for a neoantigen to differentially bind to the MHC or activate a T cell compared with its wild-type peptide. Using these features, we estimate cancer clone fitness as the aggregate cost of T cells recognizing high-quality neoantigens offset by gains from oncogenic mutations. With this model, we predict the clonal evolution of tumours to reveal that long-term survivors of pancreatic cancer develop recurrent tumours with fewer high-quality neoantigens. Thus, we submit evidence that that the human immune system naturally edits neoantigens. Furthermore, we present a model to predict how immune pressure induces cancer cell populations to evolve over time. More broadly, our results argue that the immune system fundamentally surveils host genetic changes to suppress cancer

Stress relaxation in pre-stressed aluminum core–shell particles: X-ray diffraction study, modeling, and improved reactivity

Stress relaxation in aluminum micron-scale particles covered by alumina shell after pre-stressing by thermal treatment and storage was measured using X-ray diffraction with synchrotron radiation. Pre-stressing was produced by annealing Al particles at 573K followed by fast cooling. While averaged dilatational strain in Al core was negligible for untreated particles, it was measured at 4.40×10-5 and 2.85×10-5 after 2 and 48 days of storage. Consistently, such a treatment leads to increase in flame propagation speed for Al+CuO mixture by 37% and 25%, respectively. Analytical model for creep in alumna shell and stress relaxation in Al core-alumina shell structure is developed and activation energy and pre-exponential multiplier are estimated. The effect of storage temperature and annealing temperature on the kinetics of stress relaxation was evaluated theoretically. These results provide estimates for optimizing Al reactivity with the holding time at annealing temperature and allowable time for storage of Al particles for different environmental temperatures

Tissue-Specific Features of the T Cell Repertoire After Allogeneic Hematopoietic Cell Transplantation in Human and Mouse

T cells are the central drivers of many inflammatory diseases, but the repertoire of tissue-resident T cells at sites of pathology in human organs remains poorly understood. We examined the site-specificity of T cell receptor (TCR) repertoires across tissues (5 to 18 tissues per patient) in prospectively collected autopsies of patients with and without graft-versus-host disease (GVHD), a potentially lethal tissue-targeting complication of allogeneic hematopoietic cell transplantation, and in mouse models of GVHD. Anatomic similarity between tissues was a key determinant of TCR repertoire composition within patients, independent of disease or transplant status. The T cells recovered from peripheral blood and spleens in patients and mice captured a limited portion of the TCR repertoire detected in tissues. Whereas few T cell clones were shared across patients, motif-based clustering revealed shared repertoire signatures across patients in a tissue-specific fashion. T cells at disease sites had a tissue-resident phenotype and were of donor origin based on single-cell chimerism analysis. These data demonstrate the complex composition of T cell populations that persist in human tissues at the end stage of an inflammatory disorder after lymphocyte-directed therapy. These findings also underscore the importance of studying T cell in tissues rather than blood for tissue-based pathologies and suggest the tissue-specific nature of both the endogenous and posttransplant T cell landscape

Relationship Between Gross Happiness Indices and Socioeconomic Diversity: A Case Study of Visakhapatnam District of Andhra Pradesh

The first World Happiness Report was published in 2012 and also the United Nations General Assembly declared 20th March as the 'International Day of Happiness'. As per the World Happiness Report (2021), India is observed to be placed at 139th place out of 149 countries and Finland securing the first place is noticed to be the happiest country. Moreover, Indian rank has been observed to slip down continuously from 111 in 2013 to 133 in 2018, 140 in 2019 and 144 in 2020.It is pertinent to note that our neighbour countries are well ahead in the happiness rankings compared to India, where in China stood at 82rd rank, Nepal at 85th, Bangladesh at 99th and Pakistan at 103rd and Sri Lanka at 126th rank. For the first time the Happiness Report of India (Rajesh K Pillania, 2020)was published during the September 2020 and according to it among the big states Punjab, Gujarat and Telangana are at the top three whereas Odisha, Uttarakhand and Chhattisgarh are at the bottom three. Among the South Indian states, Puducherry, Telangana, and Andhra Pradesh are the top three in happiness rankings. However, Andhra Pradesh raked as 5th state among the big states