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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

The Past, Present and Future of Structural Health Monitoring: An Overview of Three Ages

This paper presents an overview of the discipline of structural health monitoring (SHM), organised in terms of three proposed ages. The first age is delineated by the prehistory of SHM and the period where nondestructing testing methods evolved into an organised set of principles built upon physics-based models; this age ended when the model-based approaches reached an impasse in terms of their ability to properly deal with real-world problems. The second age of SHM began with a transition to data-based methods based on statistical pattern recognition, which provided a holistic approach to SHM problems for the first time. This age arguably ended when the methods foundered in situations where the necessary training data were scarce. It is argued here that the third age began with the development of population-based SHM, which has been designed to overcome the problem of data scarcity. As there is very limited space in a single article to provide a comprehensive overview, an appendix has been provided here that gives a very systematic bibliography of SHM reviews—a meta-bibliography.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

MRI-based treatment planning with pseudo CT generated through atlas registration

Purpose: To evaluate the feasibility and accuracy of magnetic resonance imaging (MRI)-based treatment planning using pseudo CTs generated through atlas registration. Methods: A pseudo CT, providing electron density information for dose calculation, was generated by deforming atlas CT images previously acquired on other patients. The authors tested 4 schemes of synthesizing a pseudo CT from single or multiple deformed atlas images: use of a single arbitrarily selected atlas, arithmetic mean process using 6 atlases, and pattern recognition with Gaussian process (PRGP) using 6 or 12 atlases. The required deformation for atlas CT images was derived from a nonlinear registration of conjugated atlas MR images to that of the patient of interest. The contrasts of atlas MR images were adjusted by histogram matching to reduce the effect of different sets of acquisition parameters. For comparison, the authors also tested a simple scheme assigning the Hounsfield unit of water to the entire patient volume. All pseudo CT generating schemes were applied to 14 patients with common pediatric brain tumors. The image similarity of real patient-specific CT and pseudo CTs constructed by different schemes was compared. Differences in computation times were also calculated. The real CT in the treatment planning system was replaced with the pseudo CT, and the dose distribution was recalculated to determine the difference. Results: The atlas approach generally performed better than assigning a bulk CT number to the entire patient volume. Comparing atlas-based schemes, those using multiple atlases outperformed the single atlas scheme. For multiple atlas schemes, the pseudo CTs were similar to the real CTs (correlation coefficient, 0.787–0.819). The calculated dose distribution was in close agreement with the original dose. Nearly the entire patient volume (98.3%–98.7%) satisfied the criteria of chi-evaluation (<2% maximum dose and 2 mm range). The dose to 95% of the volume and the percentage of volume receiving at least 95% of the prescription dose in the planning target volume differed from the original values by less than 2% of the prescription dose (root-mean-square, RMS < 1%). The PRGP scheme did not perform better than the arithmetic mean process with the same number of atlases. Increasing the number of atlases from 6 to 12 often resulted in improvements, but statistical significance was not always found. Conclusions: MRI-based treatment planning with pseudo CTs generated through atlas registration is feasible for pediatric brain tumor patients. The doses calculated from pseudo CTs agreed well with those from real CTs, showing dosimetric accuracy within 2% for the PTV when multiple atlases were used. The arithmetic mean process may be a reasonable choice over PRGP for the synthesis scheme considering performance and computational costs.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

SU-C-304-04: A Compact Modular Computational Platform for Automated On-Board Imager Quality Assurance

Purpose: Traditionally, the assessment of X-ray tube output and detector positioning accuracy of on-board imagers (OBI) has been performed manually and subjectively with rulers and dosimeters, and typically takes hours to complete. In this study, we have designed a compact modular computational platform to automatically analyze OBI images acquired with in-house designed phantoms as an efficient and robust surrogate. Methods: The platform was developed as an integrated and automated image analysis-based platform using MATLAB for easy modification and maintenance. Given a set of images acquired with the in-house designed phantoms, the X-ray output accuracy was examined via cross-validation of the uniqueness and integration minimization of important image quality assessment metrics, while machine geometric and positioning accuracy were validated by utilizing pattern-recognition based image analysis techniques. Results: The platform input was a set of images of an in-house designed phantom. The total processing time is about 1–2 minutes. Based on the data acquired from three Varian Truebeam machines over the course of 3 months, the designed test validation strategy achieved higher accuracy than traditional methods. The kVp output accuracy can be verified within +/−2 kVp, the exposure accuracy within 2%, and exposure linearity with a coefficient of variation (CV) of 0.1. Sub-millimeter position accuracy was achieved for the lateral and longitudinal positioning tests, while vertical positioning accuracy within +/−2 mm was achieved. Conclusion: This new platform delivers to the radiotherapy field an automated, efficient, and stable image analysis-based procedure, for the first time, acting as a surrogate for traditional tests for LINAC OBI systems. It has great potential to facilitate OBI quality assurance (QA) with the assistance of advanced image processing techniques. In addition, it provides flexible integration of additional tests for expediting other OBI quality assurance tests, such as 2D/3D image quality, making completely automated QA possible. Research Funding from Varian Medical Systems Inc. . Dr. Sasa Mutic receives compensation for providing patient safety training services from Varian Medical Systems, the sponsor of this study.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Design of a Robust Memristive Spiking Neuromorphic System with Unsupervised Learning in Hardware

Spiking neural networks (SNN) offer a power efficient, biologically plausible learning paradigm by encoding information into spikes. The discovery of the memristor has accelerated the progress of spiking neuromorphic systems, as the intrinsic plasticity of the device makes it an ideal candidate to mimic a biological synapse. Despite providing a nanoscale form factor, non-volatility, and low-power operation, memristors suffer from device-level non-idealities, which impact system-level performance. To address these issues, this article presents a memristive crossbar-based neuromorphic system using unsupervised learning with twin-memristor synapses, fully digital pulse width modulated spike-timing-dependent plasticity, and homeostasis neurons. Additionally, the implemented single-layer SNN was applied to a pattern-recognition task of classifying handwritten-digits. The performance of the system was analyzed by varying design parameters such as number of training epochs, neurons, and capacitors. Furthermore, the impact of memristor device non-idealities, such as device-switching mismatch, aging, failure, and process variations, were investigated and the resilience of the proposed system was demonstrated.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Studying CPU and memory utilization of applications on Fujitsu A64FX and Nvidia Grace Superchip

ARM-based manycore CPU architectures are well-positioned to provide the rising memory throughput requirements of modern data intensive scientific applications in High Performance Computing (HPC). The Fujitsu A64FX CPU platform is based on the ARM v8.2A architecture, and is the processor of the flagship Japanese supercomputer - "Fugaku", which was previously ranked as the #1 supercomputer in the world according to the Top500 list. The Nvidia Grace superchip features 144 Neoverse V2 cores based on the ARMv9 architecture with 4x128b SVE2, providing exceptional computational power. The chip supports up to 480GB of memory, making it ideal for AI, machine learning, and scientific computing workloads. In this paper, we conduct a thorough performance exploration of a variety of parallel bandwidth-sensitive benchmarks and applications compiled with the native Fujitsu compiler on a Fugaku A64FX compute node and ARM (LLVM) Compiler on an NVIDIA Grace superchip compute node, engaging all the computational cores per cluster using OpenMP multithreading (assuming the cores can drive the available bandwidth). Our ultimate goals are to study the resource utilization of scientific applications and benchmarks on A64FX and Grace superchip, considering graph application scenarios ( GAP Benchmark suite) and eleven appli- cation proxies from the Rodinia heterogeneous benchmark suite (considering domains such as Data Mining, Bioinformatics, Fluid Dynamics, Pattern Recognition, etc.). Through exhaustive performance monitoring, we quantify the resource utilization of diverse OpenMP-based HPC applications on both the Fujitsu A64FX and the Nvidia Grace Superchip platforms.

benchmarking, Performance Analysis, High performan↗
DOE OSTI2025

Quantum Transfer Learning to Boost Dementia Detection

Dementia is a devastating condition with profound implications for individuals, families, and healthcare systems. Early and accurate detection of dementia is critical for timely intervention and improved patient outcomes. While classical machine learning and deep learning approaches have been explored extensively for dementia prediction, these solutions often struggle with high-dimensional biomedical data and large-scale datasets, quickly reaching computational and performance limitations. To address this challenge, quantum machine learning (QML) has emerged as a promising paradigm, offering faster training and advanced pattern recognition capabilities. This work aims to demonstrate the potential of quantum transfer learning (QTL) to enhance the performance of a weak classical deep learning model applied to a binary classification task for dementia detection. Besides, we show the effect of noise on the QTL-based approach, investigating the reliability and robustness of this method. Using the OASIS 2 dataset, we show how quantum techniques can transform a suboptimal classical model into a more effective solution for biomedical image classification, highlighting their potential impact on advancing healthcare technology.

Bhowmik, Sounak [University of Tennessee, Knoxvill↗
DOE OSTI2017

Machine Learning in Multimodal Medical Imaging

Machine learning techniques have been increasingly applied in the medical imaging field for developing computer-aided diagnosis and prognosis models. Multimodal medical imaging can provide us with separate yet complementary structure and function information of a patient study and hence has transformed the way we study living bodies. Therefore, using machine learning techniques to deal with multimodal medical images is much more challenging due to the diversity of biophysical-biochemical mechanisms. In these years, researchers mainly adapt modern machine learning and pattern recognition techniques such as supervised, unsupervised, semi-supervised, and deep learning to solve multimodal medical imaging related problems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Quantum Cascade Laser Infrared Spectroscopy for Online Monitoring of Hydroxylamine Nitrate

We describe a new approach for high sensitivity and real-time online measurements to monitor the kinetics in the processing of nuclear materials and other chemical reactions. Mid infrared (Mid-IR) quantum cascade laser (QCL) high-resolution spectroscopy was used for rapid and continuous sampling of nitrates in aqueous and organic reactive systems, using pattern recognition analysis and high sensitivity to detect and identify chemical species. In this standoff or off-set method, the collection of a sample for analysis is not required. To perform the analysis, a flow cell was used for in situ sampling of a liquid slipstream. A prototype was designed based on attenuated total reflection (ATR) coupled with the QCL beam to detect and identify chemical changes and be deployed in hostile environments, either radiological or chemical. The limit of detection (LOD) and the limit of quantification (LOQ) at 3 σ for hydroxylamine nitrate ranged from 0.3 to 3 and from 3.5 to 10 g·L −1 , respectively, for the nitrate system at three peaks with wavelengths between 3.8 and 9.8 μ m.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Beyond PCA: Additional Dimension Reduction Techniques to Consider in the Development of Climate Fingerprints

Abstract Dimension reduction techniques are an essential part of the climate analyst’s toolkit. Due to the enormous scale of climate data, dimension reduction methods are used to identify major patterns of variability within climate dynamics, to create compelling and informative visualizations, and to quantify major named modes such as El Niño–Southern Oscillation. Principal components analysis (PCA), also known as the method of empirical orthogonal functions (EOFs), is the most commonly used form of dimension reduction, characterized by a remarkable confluence of attractive mathematical, statistical, and computational properties. Despite its ubiquity, PCA suffers from several difficulties relevant to climate science: high computational burden with large datasets, decreased statistical accuracy in high dimensions, and difficulties comparing across multiple datasets. In this paper, we introduce several variants of PCA that are likely to be of use in climate sciences and address these problems. Specifically, we introduce non-negative , sparse , and tensor PCA and demonstrate how each approach provides superior pattern recognition in climate data. We also discuss approaches to comparing PCA-family results within and across datasets in a domain-relevant manner. We demonstrate these approaches through an analysis of several runs of the E3SM climate model from 1991 to 1995, focusing on the simulated response to the Mt. Pinatubo eruption; our findings are consistent with a recently identified stratospheric warming fingerprint associated with this type of stratospheric aerosol injection.

Weylandt, Michael↗
DOE OSTI2015

VizBin - an application for reference-independent visualization and human-augmented binning of metagenomic data

Background: Metagenomics is limited in its ability to link distinct microbial populations to genetic potential due to a current lack of representative isolate genome sequences. Reference-independent approaches, which exploit for example inherent genomic signatures for the clustering of metagenomic fragments (binning), offer the prospect to resolve and reconstruct population-level genomic complements without the need for prior knowledge. Results: We present VizBin, a Java™-based application which offers efficient and intuitive reference-independent visualization of metagenomic datasets from single samples for subsequent human-in-the-loop inspection and binning. The method is based on nonlinear dimension reduction of genomic signatures and exploits the superior pattern recognition capabilities of the human eye-brain system for cluster identification and delineation. We demonstrate the general applicability of VizBin for the analysis of metagenomic sequence data by presenting results from two cellulolytic microbial communities and one human-borne microbial consortium. The superior performance of our application compared to other analogous metagenomic visualization and binning methods is also presented. Conclusions: VizBin can be applied de novo for the visualization and subsequent binning of metagenomic datasets from single samples, and it can be used for the post hoc inspection and refinement of automatically generated bins. Due to its computational efficiency, it can be run on common desktop machines and enables the analysis of complex metagenomic datasets in a matter of minutes. The software implementation is available at https://claczny.github.io/ VizBin under the BSD License (four-clause) and runs under Microsoft Windows™, Apple Mac OS X™ (10.7 to 10.10), and Linux.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Application of a convolutional neural network in permeability prediction: A case study in the Jacksonburg-Stringtown oil field, West Virginia, USA

Permeability is a critical parameter for understanding subsurface fluid flow behavior, managing reservoirs, enhancing hydrocarbon recovery, and sequestering carbon dioxide. In general, permeability is measured in the laboratory based on subsurface core samples, calculated from well logs or estimated from well tests. However, laboratory measurements and well tests are expensive, time-consuming, and usually limited to a few core samples or wells in a hydrocarbon field or carbon storage site. Machine-learning techniques are good options for generating a rapid, robust, and cost-effective permeability prediction model because of their strengths to recognize the potential interrelationships between input and output variables. Convolutional neural networks (CNN), as a good pattern recognition algorithm, are widely used in image processing, natural language processing, and speech recognition, but are rarely used with regression problems and even less often in reservoir characterization. We have developed a CNN regression model to estimate the permeability in the Jacksonburg-Stringtown oil field, West Virginia, which is a potential carbon storage site and enhanced oil recovery operations field. We also evaluate the concept of the geologic feature image, which is converted from geophysical well logs. Five variables, including two commonly available conventional well logs (the gamma rays [GRs] and bulk density) and three well-log-derived variables (the slopes of the GR and bulk density curves, and shale content), are used to generate a geologic feature image. The CNN treats the geologic feature image as the input and the permeability as the desired output. In addition, the permeability predicted using traditional backpropagation artificial neural networks, which are optimized by genetic algorithms and particle swarm optimization, is compared with the permeability estimated using our CNN. Our results indicate that the CNN regression model provides more accurate permeability predictions than the traditional neural network.

Geochemistry & Geophysics↗
DOE OSTI2021

2021 Smoky Mountains Conference Data Challenge Synthetic-to-Real Domain Adaptation for Autonomous Driving Dataset

The dataset is comprised of both real and synthetic images from a vehicle's forward-facing camera. Each camera image is accompanied by a corresponding pixel-level semantic segmentation image (all files are .png files). In total, the dataset contains 5600 images in the training/validation set and 1400 images in the testing set. The training dataset contains mostly synthetic RGB images collected with a wide range of weather and lighting conditions using the CARLA simulator [1]. In addition, the training data also includes a small pre-selected subset of data from the Cityscapes training dataset – which is comprised of RGB-segmentation image pairs from driving scenarios in various European cities [2]. The testing data is split into three sets. The first set contains synthetic CARLA images with weather/lighting conditions that were not present in the training set. The second set is a subset of the Cityscapes testing dataset. Finally, the third set is an unknown testing set which will not be revealed to the participants until after the submission deadline. [1] Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., and Koltun, V. (2017, October). CARLA: An open urban driving simulator. In Conference on robot learning (pp. 1-16). PMLR. [2] Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., ... and Schiele, B. (2016). The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3213-3223).

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Stabilization of the 81-channel coherent beam combination using machine learning

We develop a rapidly converging algorithm for stabilizing a large channel-count diffractive optical coherent beam combination. An 81-beam combiner is controlled by a novel, machine-learning based, iterative method to correct the optical phases, operating on an experimentally calibrated numerical model. A neural-network is trained to detect phase errors based on interference pattern recognition of uncombined beams adjacent to the combined one. Due to the non-uniqueness of solutions in the full space of possible phases, the network is trained within a limited phase perturbation/error range. This also reduces the number of samples needed for training. Simulations have proven that the network can converge in one step for small phase perturbations. When the trained neural-network is applied to a realistic case of 360 degree full range, an iterative scheme exploits random walking at the beginning, with the accuracy of prediction on phase feedback direction, to allow the neural-network to step into the training range for fast convergence. This neural-network-based iterative method of phase detection works tens of times faster than the commonly used stochastic parallel gradient descent approach (SPGD) using a single-detector and random dither when both are tested with random phase perturbations.

Wang, Dan↗
DOE OSTI2019

Identifying Muon Neutrino Charged-Current Interactions in the MicroBooNE Detector

The MicroBooNE experiment is a short-baseline neutrino experiment at the Fermi National Accelerator Laboratory (FNAL) in the US, receiving a highly pure muon neutrino beam from the Booster Neutrino Beam (BNB) and taking data since October 2015. The main physics goal of MicroBooNE is to clarify the nature of the low-energy excess of electron-like events observed by the MiniBooNE Cherenkov detector, which due to its detector technology is unable to resolve whether the observed excess shower-like events are due to electrons or photons. Instead, MicrooBooNE employs cutting-edge liquid argon time projection chamber (LArTPC) technology, which offers excellent spatial and calorimetric resolution, which makes it possible to reconstruct complex neutrino interactions and to efficiently distinguish different final state particle types. This thesis presents a fully-automated event selection of interactions of the types ν μ +Ar → μ - + X and ν μ + Ar → μ - + p + X developed using the Pandora multi-algorithm approach to pattern recognition for LArTPC experiments. The analysis in this work is performed on a subset of the MicroBooNE Run 1 dataset corresponding to 4.52 × 10 19 protons on target acquired by the MicroBooNE detector between February and October 2016. The analysis quantifies the main signal and background interaction channels that produce a reconstructed neutrino interaction with one and two particles in the final state, and quantifies the extent to which different aspects of the Pandora reconstruction affect the selection performance. MicroBooNE is a surface detector, and therefore cosmic-ray tracks are the main observed background. A direction fitting procedure has been implemented in this analysis to formulate directional probabilities, using the Bethe equation and the high-resolution calorimetric information of MicroBooNE, to reduce this cosmic-ray background. This functionality is used to enhance the efficiency of the Pandora cosmic ray tagging logic, and the impact of this procedure on the reconstruction and event selection performance is analysed. The result of this study is two fully-automated selections of ν μ +Ar → μ - + X (57.91% pure) and ν μ + Ar → μ - + p + X (78.30% pure) with efficiencies of 64.35% and 62.65%, respectively.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2014

Using High Performance Computing to Examine the Processes of Neurogenesis Underlying Pattern Separation and Completion of Episodic Information

Adult neurogenesis in the hippocampus region of the brain is a neurobiological process that is believed to contribute to the brain's advanced abilities in complex pattern recognition and cognition. Here, we describe how realistic scale simulations of the neurogenesis process can offer both a unique perspective on the biological relevance of this process and confer computational insights that are suggestive of novel machine learning techniques. First, supercomputer based scaling studies of the neurogenesis process demonstrate how a small fraction of adult-born neurons have a uniquely larger impact in biologically realistic scaled networks. Second, we describe a novel technical approach by which the information content of ensembles of neurons can be estimated. Finally, we illustrate several examples of broader algorithmic impact of neurogenesis, including both extending existing machine learning approaches and novel approaches for intelligent sensing.

Aimone, James Bradley↗
DOE OSTI2014

In-Situ MVA of CO 2 Sequestration Using Smart Field Technology

Capability of underground carbon dioxide storage to confine and sustain injected CO 2 for a long period of time is the main concern for geologic CO 2 sequestration. If a leakage from a geological CO 2 sequestration site occurs, it is crucial to find the approximate amount and the location of the leak, in a timely manner, in order to implement proper remediation activities. An overwhelming majority of research and development for storage site monitoring has been concentrated on atmospheric, surface or near surface monitoring of the sequestered CO 2 . This study aims to monitor the integrity of CO 2 storage at the reservoir level. This work proposes developing in-situ CO 2 Monitoring and Verification technology based on the implementation of Permanent Down-hole Gauges (PDG) or “Smart Wells” along with Artificial Intelligence and Data Mining (AI&DM). The technology attempts to identify the characteristics of the CO 2 leakage by de-convolving the pressure signals collected from Permanent Down-hole Gauges (PDG). Citronelle field, a saline aquifer reservoir, located in the U.S. was considered as the basis for this study. A reservoir simulation model for CO 2 sequestration in the Citronelle field was developed and history matched. PDGs were installed, and therefore were considered in the numerical model, at the injection well and an observation well. Upon completion of the history matching process, high frequency pressure data from PDGs were generated using the history matched numerical model using different CO 2 leakage scenarios. Since pressure signal behaviors were too complicated to de-convolute using any existing mathematical formulations, a Machine Learning-based technology was introduced for this purpose. An Intelligent Leakage Detection System (ILDS) was developed as the result of this effort using the machine learning and pattern recognition technologies. The ILDS is able to detect leakage characteristics in a short period of time (less than a day from its occurrence) demonstrating the capability of the system in quantifying leakage characteristics subject to complex rate behaviors. The performance of ILDS is examined under different conditions such as multiple well leakages, cap rock leakage, availability of an additional monitoring well, presence of pressure drift and noise in the pressure sensor and uncertainty in the reservoir model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Light Water Reactor Sustainability Program Potential to Extend the Range of Established Online Monitoring Technologies, Such as Guided Waves in Nuclear Power Plant Systems

The Light Water Reactor Sustainability Program began to evaluate technologies that could be used to perform online monitoring of piping and other secondary system structural components in commercial NPPs. These online monitoring systems have the potential to identify when a more detailed inspection is needed using real-time measurements, rather than at a pre-determined inspection interval. This transition to condition-based, risk informed automated maintenance will contribute to a significant reduction of operations and maintenance costs that account for most nuclear power generation costs. This report describes the current state of research related to ultrasonic guided wave testing and its application to defect detection in commercial nuclear power plants. The report analyses the applicability of the guided wave technology to secondary piping systems and also study the potential for expanding the range of guided wave technology to include bent piping and other piping components. The ultrasonic guided waves can inspect long stretches of straight piping, however more complex geometries such as elbows, welds, and tees are causing spurious reflections and coherent noise which significantly decrease the sensitivity of the technique. The report analyses two approaches to suppression of coherent noise in guided wave systems: advanced signal processing and pattern recognition based on independent component analysis and blind source separation and hardware-based technique of piezoelectric paint which can be used to facilitate the propagation of mechanical stress waves through piping. It is demonstrated on simulated guided wave data that the independent component analysis is capable of separating different coherent noise components and segregating them from useful signal. The piezoelectric paint approach seem to be a viable alternative to the signal processing methods, however it requires significantly more complicated logistics and initial financial outlay. The future efforts will be concentrated on applying the independent component analysis to real-world guided wave data recorded by the guided wave monitoring system installed on the shell of the low-pressure feedwater heater 13A at Exelon Corporation’s Braidwood Nuclear Generating Station. Also, the joint EPRI-INL research efforts will focus on experimental evaluation of piezoelectric paint as a technique to extend the range of guided wave technology.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Data Driven Smart Proxy for CFD Application of Big Data Analytics & Machine Learning in Computational Fluid Dynamics, Report Two: Model Building at the Cell Level

To ensure the usefulness of simulation technologies in practice, their credibility needs to be established with Uncertainty Quantification (UQ) methods. In this project, smart proxy is introduced to significantly reduce the computational cost of conducting large number of multiphase CFD simulations, which is typically required for non-intrusive UQ analysis. Smart proxy for CFD models are developed using pattern recognition capabilities of Artificial Intelligence (AI) and Data Mining (DM) technologies. Several CFD simulation runs with different inlet air velocities for a rectangular fluidized bed are used to create a smart CFD proxy that is capable of replicating the CFD results for the entire geometry and inlet velocity range. The smart CFD proxy is validated with blind CFD runs (CFD runs that have not played any role during the development of the smart CFD proxy). The developed and validated smart CFD proxy generates its results in seconds with reasonable error (less than 10%). Upon completion of this project, UQ studies that rely on hundreds or thousands of smart CFD proxy runs can be accomplished in minutes. Following figure demonstrates a validation example (blind CFD run) showing the results from the MFiX simulation and the smart CFD proxy for pressure distribution across a fluidized bed at a given time-step (the layer number corresponds to the vertical location in the bed).

97 MATHEMATICS AND COMPUTING↗