Implementation of fractional open circuit voltage mppt algorithm in a low cost microcontroller

The solar or the Photovoltaic (PV) cell is a source of electric energy which is eco-friendly, free and though it is a renewable source of energy, it is relatively costlier and inefficient nowadays which includes the difficulties related to complete harnessing of solar power. The MPPT or the maximum power point tracking of the PV panel for every type of environmental and climatic circumstances is the vital strategy to get the maximum power output thus increasing the efficiency of solar power extraction mechanism. This project recommends an innovative and a more efficient method for the maximum power point tracking of photovoltaic systems that is the Fractional Open Circuit (FOC) Algorithm which estimates the MPPT by manipulating the Open Circuit voltage of the Photovoltaic Cell. The method considerably improves the tracking speed and accuracy of the maximum power point tracking when we relate the results with other techniques. This project gives a detailed analysis and report of the maximum power point tracking using the FOC voltage technique

The Use of Artificial Intelligence to Naturally Produced Movement

Between keyframes, modern animation software allows for partial automation of the action. Anyway, the formation of scenes including many collaborating characters actually requires the greater part of the work to be hand-done by illustrators and any programmed conduct in the liveliness arrangement will in general be permanently set up and lacking independence. This paper depicts our "FreeWill" model which tends to these restrictions by proposing and executing an extendable mental engineering intended to oblige objectives, activities, and information, consequently investing enlivened characters with some level of independent insightful way of behaving

An Enhanced Maximum-Entropy Based Meshfree Method: Theory and Applications

This thesis develops an enhanced meshfree method based on the local maximum-entropy (max-ent) approximation and explores its applications. The proposed method offers an adaptive approximation that addresses the tensile instability which arises in updated-Lagrangian meshfree methods during severe, finite deformations. The proposed method achieves robust stability in the updated-Lagrangian setting and fully realizes the potential of meshfree methods in simulating large-deformation mechanics, as shown for benchmark problems of severe elastic and elastoplastic deformations. The improved local maximum-entropy approximation method is of a general construct and has a wide variety of applications. This thesis presents an extensive study of two applications - the modeling of equal-channel angular extrusion (ECAE) based on high-fidelity plasticity models, and the numerical relaxation of nonconvex energy potentials. In ECAE, the aforementioned enhanced maximum-entropy scheme allows the stable simulation of large deformations at the macroscale. This scheme is especially suitable for ECAE as the latter falls into the category of severe plastic deformation processes where simulations using mesh-based methods (e.g. the finite element method (FEM)) are limited due to severe mesh distortions. In the second application, the aforementioned max-ent meshfree method outperforms FEM and FFT-based schemes in numerical relaxation of nonconvex energy potentials, which is essential in discovering the effective response and associated energy-minimizing microstructures and patterns. The results from both of these applications show that the proposed method brings new possibilities to the subject of computational solid mechanics that are not within the reach of traditional mesh-based and meshfree methods.</p

Inverse designing surface curvatures by deep learning

Smooth and curved microstructural topologies found in nature - from soap films to trabecular bone - have inspired several mimetic design spaces for architected metamaterials and bio-scaffolds. However, the design approaches so far have been ad hoc, raising the challenge: how to systematically and efficiently inverse design such artificial microstructures with targeted topological features? Here, we explore surface curvature as a design modality and present a deep learning framework to produce topologies with as-desired curvature profiles. The inverse design framework can generalize to diverse topological features such as tubular, membranous, and particulate features. Moreover, we demonstrate successful generalization beyond both the design and data space by inverse designing topologies that mimic the curvature profile of trabecular bone, spinodoid topologies, and periodic nodal surfaces for application in bio-scaffolds and implants. Lastly, we bridge curvature and mechanics by showing how topological curvature can be designed to promote mechanically beneficial stretching-dominated deformation over bending-dominated deformation.Comment: 23 pages, 12 figure

Enhanced local maximum-entropy approximation for stable meshfree simulations

We introduce an improved meshfree approximation scheme which is based on the local maximum-entropy strategy as a compromise between shape function locality and entropy in an information-theoretical sense. The improved version is specifically designed for severe, finite deformation and offers significantly enhanced stability as opposed to the original formulation. This is achieved by (i) formulating the quasistatic mechanical boundary value problem in a suitable updated-Lagrangian setting, (ii) introducing anisotropy in the shape function support to accommodate directional variations in nodal spacing with increasing deformation and eliminate tensile instability, (iii) spatially bounding and evolving shape function support to restrict the domain of influence and increase efficiency, (iv) truncating shape functions at interfaces in order to stably represent multi-component systems like composites or polycrystals. The new scheme is applied to benchmark problems of severe elastic and elastoplastic deformation that demonstrate its performance both in terms of accuracy (as compared to exact solutions and, where applicable, finite element simulations) and efficiency. Importantly, the presented formulation overcomes the classical tensile instability found in most meshfree interpolation schemes, as shown for stable simulations of, e.g., the inhomogeneous extension of a hyperelastic block up to 100% or the torsion of a hyperelastic cube by 200° –both in an updated Lagrangian setting and without the need for remeshing

Automated discovery of generalized standard material models with EUCLID

We extend the scope of our approach for unsupervised automated discovery of material laws (EUCLID) to the case of a material belonging to an unknown class of behavior. To this end, we leverage the theory of generalized standard materials, which encompasses a plethora of important constitutive classes. We show that, based only on full-field kinematic measurements and net reaction forces, EUCLID is able to automatically discover the two scalar thermodynamic potentials, namely, the Helmholtz free energy and the dissipation potential, which completely define the behavior of generalized standard materials. The a priori enforced constraint of convexity on these potentials guarantees by construction stability and thermodynamic consistency of the discovered model; balance of linear momentum acts as a fundamental constraint to replace the availability of stress-strain labeled pairs; sparsity promoting regularization enables the automatic selection of a small subset from a possibly large number of candidate model features and thus leads to a parsimonious, i.e., simple and interpretable, model. Importantly, since model features go hand in hand with the correspondingly active internal variables, sparse regression automatically induces a parsimonious selection of the few internal variables needed for an accurate but simple description of the material behavior. A fully automatic procedure leads to the selection of the hyperparameter controlling the weight of the sparsity promoting regularization term, in order to strike a user-defined balance between model accuracy and simplicity. By testing the method on synthetic data including artificial noise, we demonstrate that EUCLID is able to automatically discover the true hidden material model from a large catalog of constitutive classes, including elasticity, viscoelasticity, elastoplasticity, viscoplasticity, isotropic and kinematic hardening

Review of Extreme Multilabel Classification

Extreme multilabel classification or XML, is an active area of interest in machine learning. Compared to traditional multilabel classification, here the number of labels is extremely large, hence, the name extreme multilabel classification. Using classical one versus all classification wont scale in this case due to large number of labels, same is true for any other classifiers. Embedding of labels as well as features into smaller label space is an essential first step. Moreover, other issues include existence of head and tail labels, where tail labels are labels which exist in relatively smaller number of given samples. The existence of tail labels creates issues during embedding. This area has invited application of wide range of approaches ranging from bit compression motivated from compressed sensing, tree based embeddings, deep learning based latent space embedding including using attention weights, linear algebra based embeddings such as SVD, clustering, hashing, to name a few. The community has come up with a useful set of metrics to identify correctly the prediction for head or tail labels.Comment: 46 pages, 13 figure

Characterization of sewage and design of a UASB reactor for its treatment

Wastewater treatment is becoming ever more critical due to fading water resources, increasing wastewater disposal expenses and firmer discharge regulations that have lowered permissible contaminant levels in waste streams. The ultimate goal of wastewater management is the protection of the environment in a manner commensurate with public health and socio-economic concerns. This project is devoted for the characterization of general parameters of domestic waste water collected from the campus of NIT Rourkela, and study and design of an anaerobic treatment plant, the Upflow Anaerobic Sludge Blanket reactor (UASB). The UASB model showed promising results in removal of BOD, heavy metals, pH, turbidity and decrease in microbe content. A laboratory scale Upflow Anaerobic Sludge Blanket reactor study was done with waste water generated from hostels in NIT Rourkela as substrate. The reactor was fed with waste water in the presence of sludge generated from the same waste water. The substrate was recirculated within the reactor to ensure a continuous steady upflow. Samples were collected from the reactor every week and were analyzed for concentration of various parameters and were plotted against time. Treated waste water found from UASB model reduces turbidity of water decreases from 57.1 to 37.6 NTU, pH of treated water increases s from 7.9 to 8.9,BOD of treated sample decreases with time from 6.6 to 1.5 mg/L, concentration of metals decreases with time as Potassium concentration decreases from 2.066 to 1.351 mg/L, Calcium concentration decreases from 2.391 to 1.075 mg/L, Zinc concentration decreases from 0.251 to 0.162 mg/L, Iron concentration decreases from 0.517 to 0.239 mg/L, Copper concentration decreases from 0.107 to 0.056 mg/L, Lead concentration decreases from 0.033 to 0.202 mg/L, Arsenic concentration decreases from 0.09 to 0.048 mg/L, magnesium concentration decreases from 6.439 to 6.145 mg/L