73 research outputs found
GPU-accelerated depth map generation for X-ray simulations of complex CAD geometries
No full textInteractive x-ray simulations of complex computer-aided design (CAD) models can provide valuable insights for better interpretation of the defect signatures such as porosity from x-ray CT images. Generating the depth map along a particular direction for the given CAD geometry is the most compute-intensive step in x-ray simulations. We have developed a GPU-accelerated method for real-time generation of depth maps of complex CAD geometries. We preprocess complex components designed using commercial CAD systems using a custom CAD module and convert them into a fine user-defined surface tessellation. Our CAD module can be used by different simulators as well as handle complex geometries, including those that arise from complex castings and composite structures. We then make use of a parallel algorithm that runs on a graphics processing unit (GPU) to convert the finely-tessellated CAD model to a voxelized representation. The voxelized representation can enable heterogeneous modeling of the volume enclosed by the CAD model by assigning heterogeneous material properties in specific regions. The depth maps are generated from this voxelized representation with the help of a GPU-accelerated ray-casting algorithm. The GPU-accelerated ray-casting method enables interactive (> 60 frames-per-second) generation of the depth maps of complex CAD geometries. This enables arbitrarily rotation and slicing of the CAD model, leading to better interpretation of the x-ray images by the user. In addition, the depth maps can be used to aid directly in CT reconstruction algorithms.This proceeding may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This proceeding appeared in Grandin, Robert J., Gavin Young, Stephen D. Holland, and Adarsh Krishnamurthy. "GPU-accelerated depth map generation for X-ray simulations of complex CAD geometries." In AIP Conference Proceedings, vol. 1949, no. 1, p. 190002. AIP Publishing LLC, 2018, and may be found at
DOI: 10.1063/1.5031636.
Copyright 2018 Author(s).
Posted with permission
Incorporation of composite defects from ultrasonic NDE into CAD and FE models
No full textFiber-reinforced composites are widely used in aerospace industry due to their combined properties of high strength and low weight. However, owing to their complex structure, it is difficult to assess the impact of manufacturing defects and service damage on their residual life. While, ultrasonic testing (UT) is the preferred NDE method to identify the presence of defects in composites, there are no reasonable ways to model the damage and evaluate the structural integrity of composites. We have developed an automated framework to incorporate flaws and known composite damage automatically into a finite element analysis (FEA) model of composites, ultimately aiding in accessing the residual life of composites and make informed decisions regarding repairs. The framework can be used to generate a layer-by-layer 3D structural CAD model of the composite laminates replicating their manufacturing process. Outlines of structural defects, such as delaminations, are automatically detected from UT of the laminate and are incorporated into the CAD model between the appropriate layers. In addition, the framework allows for direct structural analysis of the resulting 3D CAD models with defects by automatically applying the appropriate boundary conditions. In this paper, we show a working proof-of-concept for the composite model builder with capabilities of incorporating delaminations between laminate layers and automatically preparing the CAD model for structural analysis using a FEA software.This proceeding may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This proceeding appeared in Bingol, Onur Rauf, Bryan Schiefelbein, Robert J. Grandin, Stephen D. Holland, and Adarsh Krishnamurthy. "Incorporation of composite defects from ultrasonic NDE into CAD and FE models." AIP Conference Proceedings 1806, no. 1, (2017): 150004. , and may be found at DOI: 10.1063/1.4974728. Posted with permission.</p
Resource Optimal Executable Quantum Circuit Generation Using Approximate Computing
No full textQuantum Computing is an emerging technology that combines the principles of computer science and quantum mechanics to solve computationally challenging problems significantly faster than classical computers. In this paper, we present a proof-of-principle procedure for generating hardware-executable quantum circuits for Noisy Intermediate-Scale Quantum (NISQ) devices that follows the paradigm of approximate computing.Our approach starts from the reference circuit and trans-forms it into an executable circuit with tuneable parameters by replacing the high-level quantum operations by approximate decompositions into hardware-native gates. An inner optimization loop over the rotation gates’ angles ensures that the so-created circuit behaves in the same way as the reference one in terms of its expectation-value landscape. This technique is complemented by compiler-based optimizations to further reduce or aggregate gate groups of the optimized circuit. This three-step procedure is embedded into an outer genetic algorithm framework that inspects many different circuit designs with placements of single- and multi-qubit gates according to the hardware’s lattice structure, and returns a set of approximate quantum circuits that can be executed on NISQ devices directly.We have validated our approach for superconducting quantum systems from IBM and Rigetti for various benchmark algorithms. In nearly all cases, our approach outperforms the vendors’ quantum-compiler frameworks and produces significantly smaller circuits with up to 50% reduction in the number of gates.Accepted author manuscriptNumerical Analysi
Resource Optimal Executable Quantum Circuit Generation Using Approximate Computing
No full textQuantum computing is an emerging technology that combines the principles of both computer science and quantum mechanics to solve computationally challenging problems significantly faster than the current classical computers. In this thesis, a proof of concept to generate hardware-executable quantum circuits for Noisy Intermediate-Scale Quantum (NISQ) devices that follow the paradigm of approximate computing is presented. We adopt a multi-level optimization approach and consider the placement of native quantum gates that can be physically implemented on one of the existing quantum systems (IBM, Rigetti) as a circuit topology optimization problem and the adjustment of parameters of these native quantum gates as a continuous design optimization problem. The outer topology problem is solved with the aid of a genetic algorithm, whereas the gradient-descent method is used for the inner design optimization.Our approach starts from the reference circuit and transforms it into an executable circuit with tunable parameters by replacing the high-level quantum operations with a set of approximate decompositions, e.g., single qubit rotation quantum gates or a combination of single qubit rotation gates (matrix multiplication). Later, the inner design optimization ensures that the circuit created is tuned to be executed to achieve an expectation value close to that of the reference circuit. This is complemented by compiler-based optimizations to further reduce or aggregate the quantum gates of the optimized circuit. This three-step pipeline is embedded into an outer genetic algorithm framework that inspects many different circuit designs (the approximate-decomposition replacements are by no means unique) and returns a set of unique approximate quantum circuits that can be executed on hardware.We have tested our circuit generation approach for superconducting quantum systems such as IBM and Rigetti hardware for many different benchmark circuits such as Quantum Fourier Transform, Toffoli, quantum adder, multi-controlled operations and three oracle-based algorithms, namely, Grover's search algorithm, Deutsch and Bernstein-Vazirani algorithm. In nearly all cases, our approach outperforms the quantum compiler frameworks by IBM and Rigetti even on their highest optimization level and produces significantly smaller circuits, up to 2x reduction in the number of gates. In addition, the circuits generated using the proposed approach provide better (a) performance (on average, improved by 24.25%) and (b) reliability (on average, improved by 23.50%) compared to the IBM generated circuits when executed on the ibmq_5_yorktown physical quantum device.Computer Engineerin
Preface: The Irago Conference 2017: A 360-degree Outlook on Critical Scientific and Technological Challenges for a Sustainable Society
No full textTHB‑Diff: a GPU‑accelerated diferentiable programming framework for THB‑splines
No full textWe have developed a differentiable programming framework for truncated hierarchical B-splines (THB-splines), which can be used for several applications in geometry modeling, such as surface fitting and deformable image registration, and can be easily integrated with geometric deep learning frameworks. Differentiable programming is a novel paradigm that enables an algorithm to be differentiated via automatic differentiation, i.e., using automatic differentiation to compute the derivatives of its outputs with respect to its inputs or parameters. Differentiable programming has been used extensively in machine learning for obtaining gradients required in optimization algorithms such as stochastic gradient descent (SGD). While incorporating differentiable programming with traditional functions is straightforward, it is challenging when the functions are complex, such as splines. In this work, we extend the differentiable programming paradigm to THB-splines. THB-splines offer an efficient approach for complex surface fitting by utilizing a hierarchical tensor structure of B-splines, enabling local adaptive refinement. However, this approach brings challenges, such as a larger computational overhead and the non-trivial implementation of automatic differentiation and parallel evaluation algorithms. We use custom kernel functions for GPU acceleration in forward and backward evaluation that are necessary for differentiable programming of THB-splines. Our approach not only improves computational efficiency but also significantly enhances the speed of surface evaluation compared to previous methods. Our differentiable THB-splines framework facilitates faster and more accurate surface modeling with local refinement, with several applications in CAD and isogeometric analysis.This article is published as Moola, Ajith, Aditya Balu, Adarsh Krishnamurthy, and Aishwarya Pawar. "THB-Diff: a GPU-accelerated differentiable programming framework for THB-splines." Engineering with Computers (2023): 1-17. doi: https://doi.org/10.1007/s00366-023-01929-1. © The Author(s) 2023. This open access article is licensed under a Creative Commons Attribution 4.0. (http://creativecommons.org/licenses/by/4.0/
Automated data-driven exploration of chemical space for catalysts
No full textCatalysts play an essential role in the daily lives of humans. These catalysts are used in many industries to make processes more energetically favourable. Climate change is pushing humanity towards the usage of more green energy and catalysts play an important role in this transition.For example, in the hydrogenation reaction used for the storage of H2, where the catalyst is involved in the storage and removal of H2 on a storage medium like CO2. The properties of the catalyst involved in this (de)hydrogenation reaction can affect the selectivity and yield of the reaction. Designing a catalyst that maximizes the property (yield for example) that we are interested in for a specific reaction, is an essential asset to tune catalyzed processes. Computational screening of many catalysts has attracted the attention of academia and industry due to constant developments in the field of computational chemistry.In these computational methods, predictive models together with DFT and/or DFTB methods can be used to correlate a set of reaction descriptors with catalyst properties. The model has a higher probability to find novel molecules with a high activity when more (reliable) training data is used and when the search spac eof the model is confined to a local chemical space. This means that newly added molecules for screening should be structurally closely related to the molecule that was used to build the model. Unfortunately, large data sets are not readily available for transition-metal containing complexes although these complexes are widely applied in the field of homogeneous catalysis. In this research a Python-based workflow, ChemSpaX, that is aimed at automating local chemical space exploration for any type of molecule is introduced. Thisworkflow enables the user to place fragments on molecules based on 3Dinformation, while staying close to the quality of the initial structure. This enables data-driven property calculations and prediction models, which could eventually be extended towards the automated design of new catalysts. Various representative applications of ChemSpaX are presented in which data-driven xTBand DFT property calculations are done. The found correlations between catalyst properties are shown and it is shown that ChemSpaX generates structures that have a reasonable quality for usage in data-driven prediction models for high-throughput screening. Applied Science
Effect of motion and motivation on task performance, workload and motion sickness
No full textFollowing the literature review, our goal was to study the effect and interaction of motion sickness and motivation on cognitive performance in a reading comprehension task and the associated workload with the task. We chose UCKAT reading tasks for our cognitive task, monetary incentive and ranks as our motivator and a multisine sickening motion profile on a simulator asour motion variable. We exposed participants to 4 conditions, employing a within-subject experiment design, manipulating our independent variables motion and motivation. We collected motion sickness data via the motion sickness susceptibility questionnaire, misery scale and motionsickness assessment questionnaire; motivation data via the situational motivation scale; workload data via the NASA TLX workload scale and task performance data via the total score obtained, the total time spent on the task and the average time spent per question. We found that our motion profile caused motion sickness in participants, with some evidence for habituation. We also found some evidence for training effects present in our data. Performance decrements, associated workload and motivation scores across the 4 conditions were statistically similar and we could not conclusively prove our hypotheses. Further analysis showed that amotivation scores almost showed significant effect on task performance which does match anecdotal evidence. MSAQ scores also negatively affected how much time people could spend on a cognitive task. We found that workload scores of participants increased significantly with increase in motion sickness which could giveus an insight on performing cognitive tasks under sickness. Overall, our experiment design could not show the trends that we had hypothesized, and we obtained partial results via our secondary analysis. Our findings indicate that further attention is to be given to the motivation variable to make it more robust. Further, a much large sample size is needed to better test our hypotheses, with perhaps, a mixed subject design for our study. Our study also showed an unexpected interaction of lateral and londitudinal motion profiles, causing significantly higher levels of sickness than what was predicted using existing models, which warrants further research into the same
Effect of metal oxide supports on active-Cu for CO/CO2 hydrogenation to methanol
No full textIncreasing tensions over global warming, talks about a sustainable future and a huge imbalance in closure of the carbon cycle indicate a response for developing efficient conversion of CO2 and syngas obtained from renewable sources. Thermochemical conversion of carbon oxides (CO and CO2) in combination with hydrogen to produce methanol in the presence of catalyst provides a pathway to close this carbon cycle. Steady state activity tests were carried out in a small integral reactor for methanol synthesis from a mixture of either CO/H2 or CO2/H2. The temperature was varied from 200 to 300°C, while the total pressure was held constant for CO/H2 at 85 bar and CO2/H2 at 60 bar keeping stoichiometric flow of hydrogen at GHSV of 24,000 hr¡1. Four different metal oxides namely ZnO, ZrO2,MgO and CeO2 were investigated for support effects on active Cu along with different combinations among them while keeping commercial catalyst as the benchmark. Catalysts were prepared using urea hydrolysis method. It was found that ZrO2 and MgO show higher selectivity however the latter does not exhibit comparable conversion as the commercial catalyst for CO2 hydrogenation. Detailed GHSV study for Cu-ZrO2 paint a completely different picture showing higher methanol selectivity (64%) with increasing space velocity (at GHSV of 32,000 hr¡1). In case of COhydrogenation, commercial catalyst performs the best, albeit displaying signs of carbon deposition at higher temperature (280°, 300°C). This situation is circumvented by employing ZnO/MgO combination as a support. Cu-CeO2 exhibited characteristics of an excellent water gas shift catalyst. This led to a novel configuration of mixed bed consisting of Cu-CeO2 with commercial catalyst. Results indicate that this combination improves themethanol yield by atleast 30% as compared to commercial catalyst at a high GHSV of 24,000 hr¡1.Chemical Engineerin
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