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

Neu3 sialidase-mediated ganglioside conversion is necessary for axon regeneration and is blocked in CNS axons

This work was supported by the Medical Research Council, the Christopher and Dana Reeve Foundation, the John and Lucille van Geest Foundation, the Henry Smith Charity, the Commonwealth and Overseas scholarships, and the Hinduja Cambridge Trust.PNS axons have a high intrinsic regenerative ability, whereas most CNS axons show little regenerative response. We show that activation of Neu3 sialidase, also known as Neuraminidase-3, causing conversion of GD1a and GT1b to GM1 ganglioside, is an essential step in regeneration occurring in PNS (sensory) but not CNS (retinal) axons in adult rat. In PNS axons, axotomy activates Neu3 sialidase, increasing the ratio of GM1/GD1a and GM1/GT1b gangliosides immediately after injury in vitro and in vivo. No change in the GM1/GD1a ratio after axotomy was observed in retinal axons (in vitro and in vivo), despite the presence of Neu3 sialidase. Externally applied sialidase converted GD1a ganglioside to GM1 and rescued axon regeneration in CNS axons and in PNS axons after Neu3 sialidase blockade. Neu3 sialidase activation in DRGs is initiated by an influx of extracellular calcium, activating P38MAPK and then Neu3 sialidase. Ganglioside conversion by Neu3 sialidase further activates the ERK pathway. In CNS axons, P38MAPK and Neu3 sialidase were not activated by axotomy.Publisher PDFPeer reviewe