Real-time Plume Research Articles

Interactive steering on in situ particle-based volume rendering framework

The development of supercomputers and multi-scale computational fluid dynamics (CFD) models based on adaptive mesh refinement (AMR) enabled fast, large-scale, and high fidelity CFD simulations. Interactive in situ steering is an effective tool for debugging, searching for optimal solutions, and analyzing inverse problems in such CFD simulations. We propose an interactive in situ steering framework for large-scale CFD simulations on GPU supercomputers. This framework employs in situ particle-based volume rendering (PBVR), in situ data sampling, and a file-based control that enables interactive and asynchronous communication of steering parameters, compressed visualization particle data, and sampled monitoring data between supercomputers and user PCs. The parallelized PBVR is processed on the host CPU to avoid interference with CFD simulations on the GPU. We apply the proposed framework to a real-time plume dispersion analysis code CityLBM, which computes the lattice Boltzmann method on the block AMR grid using GPU supercomputers. In the numerical experiment, we address an inverse problem to find a pollutant source from the observation data at monitoring points and demonstrate the effectiveness of the human-in-the-loop approach via the in situ steering framework.Graphical abstract

Study on Local Power Plant Emissions Using Multi-Frequency Differential Absorption LIDAR and Real-Time Plume Tracking

We present a new fiber-amplifier-based differential absorption light detection and ranging (DIAL) system for range-resolved detection of carbon dioxide (CO2) and water vapor (H2O) over a range of a few kilometers. The fiber amplifier chain is seeded with a 7.14 kHz fast-switching high-spectral purity wavelength source near 1572 nm to cover ten different frequencies across the CO2/H2O line pair in ≈1.4 ms. We demonstrate the system in a study of CO2 emissions from a local power plant in Boulder, CO, USA. We use real-time wind information to predict the plume location for tracking and modeling of the CO2 emission rate to compare with the reported data from the power plant over a 13 h period. There is overall agreement with the reported burn rate, but we see periods of bias towards underestimation of the CO2 emission rate. We attribute the dropout periods to uncertainties between the measured and the plant’s local wind speed data that impact both the tracking location and the plume model predictions. Upcoming studies that will make use of real-time Doppler wind data are expected to significantly decrease these uncertainties.

Real-time detection of deep-sea hydrothermal plume based on machine vision and deep learning

Recent years have witnessed an increase in applications of artificial intelligence (AI) in the detection of oceanic features with the tremendous success of deep learning. Given the unique biological ecosystems and mineral-rich deposits, the exploration of hydrothermal fields is both scientifically and commercially important. To achieve autonomous and intelligent sampling of the hydrothermal plume by using AUV, this paper proposes an innovative method for real-time plume detection based on the YOLOv5n deep learning algorithm designed with a light-weight neural network architecture to meet the requirements of embedded platforms. Ground truth labeler app LabelImg was used to generate the ground truth data from the plume dataset created by ourselves. To accurately and efficiently detect hydrothermal plumes using an embedded system, we improved the original structure of YOLOv5n in two aspects. First, SiLU activation functions in the model were replaced by ReLU activations at shallow layers and Hard-SiLU activations at deep layers to reduce the number of calculations. Second, an attention module termed Coordinate Attention (CA) was integrated into the model to improve its sensitivity to both channel and spatial features. In addition, a transfer learning training method was adopted to further improve the model’s accuracy and generalizability. Finally, we successfully deployed the proposed model in a low-cost embedded device (NVIDIA Jetson TX2 NX) by using the TensorRT inference engine. We then installed the Jetson TX2 NX into a hovering-type AUV as its vision processing unit and conducted a plume detection test in the water tank. The water tank experimental results demonstrated that the proposed method can achieve real-time onboard hydrothermal plume detection.

Real-time plume tracking using transfer learning approach

Deep learning has been used to track the real-time flammable plume of natural gas. However, a large volume of high-fidelity data is required to train the deep learning model for sufficient accuracy in congested industrial environments, which can be computationally prohibitive. This study proposes a transfer learning-based variable-fidelity approach for real-time plume tracking. A Gaussian dispersion model was applied to efficiently generate a large volume of low-fidelity data, which is then used to pre-train the deep learning model. A limited number of high-fidelity simulations were conducted by solving the Navier-Stokes equation to fine-tune the pre-trained model. A case study demonstrated our proposed approach could reduce high-fidelity computations by 72% while ensuring prediction accuracy with R2=0.96 for released plume area estimation in congested chemical facilities. Optimal number of frozen layers, learning rate and the number of high-fidelity simulations required were determined to ensure adequate efficiency for this approach. This study provides an efficient alternative to improve the generalization of deep learning for real-time plume area estimation for large-scale congested chemical plants.

Cover Picture: Real‐Time Plume Velocity Measurement of Solid Propellant Rocket Motors Using TDLAS Technique (Prop., Explos., Pyrotech. 4/2021)

The cover picture is based on the Full Paper Real-Time Plume Velocity Measurement of Solid Propellant Rocket Motors Using TDLAS Technique by Anchen Song et al., https://doi.org/10.1002/prep.202000091. The Full Paper can be found on page 636 ff.

Hydrothermal plume mapping as a prospecting tool for seafloor sulfide deposits: a case study at the Zouyu-1 and Zouyu-2 hydrothermal fields in the southern Mid-Atlantic Ridge

Seafloor hydrothermal polymetallic sulfide deposits are a new type of resource, with great potential economic value and good prospect development. This paper discusses turbidity, oxidation–reduction potential, and temperature anomalies of hydrothermal plumes from the Zouyu-1 and Zouyu-2 hydrothermal fields on the southern Mid-Atlantic Ridge. We use the known location of these vent fields and plume data collected in multiple years (2009, 2011, 2013) to demonstrate how real-time plume exploration can be used to locate active vent fields, and thus associated sulfide deposits. Turbidity anomalies can be detected 10 s of km from an active source, but the location precision is no better than a few kilometers because fine-grained particles are quasi-conservative over periods of many days. Temperature and oxidation–reduction potential anomalies provide location precision of a few hundred meters. Temperature anomalies are generally weak and difficult to reliably detect, except by chance encounters of a buoyant plume. Oxidation–reduction potential is highly sensitive (nmol concentrations of reduced hydrothermal chemicals) to discharges of all temperatures and responds immediately to a plume encounter. Real-time surveys using continuous tows of turbidity and oxidation–reduction potential sensors offer the most efficient and precise surface ship exploration presently possible.

Development of autonomous gamma dose logger for environmental monitoring

Continuous monitoring and archiving of background radiation levels in and around the nuclear installation is essential and the data would be of immense use during analysis of any untoward incidents. A portable Geiger Muller detector based autonomous gamma dose logger (AGDL) for environmental monitoring is indigenously designed and developed. The system operations are controlled by microcontroller (AT89S52) and the main features of the system are software data acquisition, real time LCD display of radiation level, data archiving at removable compact flash card. The complete system operates on 12 V battery backed up by solar panel and hence the system is totally portable and ideal for field use. The system has been calibrated with Co-60 source (8.1 MBq) at various source-detector distances. The system is field tested and performance evaluation is carried out. This paper covers the design considerations of the hardware, software architecture of the system along with details of the front-end operation of the autonomous gamma dose logger and the data file formats. The data gathered during field testing and inter comparison with GammaTRACER are also presented in the paper. AGDL has shown excellent correlation with energy fluence monitor tuned to identify (41)Ar, proving its utility for real-time plume tracking and source term estimation.

Implementing the lattice Boltzmann model on commodity graphics hardware

Modern graphics processing units (GPUs) can perform general-purpose computations inaddition to the native specialized graphics operations. Due to the highly parallel nature ofgraphics processing, the GPU has evolved into a many-core coprocessor that supports highdata parallelism. Its performance has been growing at a rate of squared Moore’s law, andits peak floating point performance exceeds that of the CPU by an order of magnitude.Therefore, it is a viable platform for time-sensitive and computationally intensiveapplications.The lattice Boltzmann model (LBM) computations are carried out via linear operations atdiscrete lattice sites, which can be implemented efficiently using a GPU-basedarchitecture. Our simulations produce results comparable to the CPU versionwhile improving performance by an order of magnitude. We have demonstratedthat the GPU is well suited for interactive simulations in many applications,including simulating fire, smoke, lightweight objects in wind, jellyfish swimmingin water, and heat shimmering and mirage (using the hybrid thermal LBM).We further advocate the use of a GPU cluster for large scale LBM simulations and for highperformance computing. The Stony Brook Visual Computing Cluster has been the platformfor several applications, including simulations of real-time plume dispersion in complexurban environments and thermal fluid dynamics in a pressurized water reactor.Major GPU vendors have been targeting the high performance computing market withGPU hardware implementations. Software toolkits such as NVIDIA CUDA provide aconvenient development platform that abstracts the GPU and allows access to itsunderlying stream computing architecture. However, software programming for a GPUcluster remains a challenging task. We have therefore developed the Zippy framework tosimplify GPU cluster programming. Zippy is based on global arrays combined with thestream programming model and it hides the low-level details of the underlying clusterarchitecture.

Field Evaluation of Mixing Length and Attenuation of Nutrients and Fecal Coliform in a Wastewater Effluent Plume

We present an alternative method to dye tracer studies for mapping wastewater effluent plumes in rivers. A case study of a wastewater treatment effluent plume that was mapped in two different months (October and April) showed good resolution in determining where the plume reached the opposite river bank, the degree of mixing in a given river reach, and where the river was fully mixed with respect to the plume. Both electrical conductivity and chloride were useful in mapping domestic wastewater effluent plume mixing. Mixing lengths obtained by field mapping were consistent with previous studies. Electrical conductivity field readings facilitated real-time plume delineation and sampling locations, and identified a non-point source plume during the mapping exercise. The apparent difference in dispersion of the plume between to the two mapping dates (despite similar upstream river discharge) suggests that the calibration of a water quality model based on a single dye test may be inadequate. The river distributions of effluent nutrients and fecal coliform were also mapped. Nutrient attenuation rates were faster than that of the chloride tracer, indicating processes other than mixing occur. Substantial differences were observed in nutrient and fecal coliform distributions between the two mapping dates.

"Terrestrial Gamma-Ray Surveys at Preoperational Nuclear Power Plants Using an In-Situ Ge(Li) Spectrometer"

As a prelude to in-situ measurements of gamma exposure rates from reactor plumes, baseline terrestrial gamma-ray surveys have been run at five preoperational nuclear power stations: Fort St. Vrain, Colorado; Fort Calhoun, Nebraska; Cooper Nuclear Station, Nebraska; Rancho Seco, California, and Diablo Canyon, California. A pressurized ion chamber was used to measure the total exposure from cosmic rays and terrestrial radionuclides. A portable Ge(Li) spectrometer was used to make in-situ measurements of the radionuclide soil concentrations and of the individual external gamma exposure rates at the TLD sites. Subsequent measurements after start-up will enable accurate determinations of deposition and buildup of reactor produced fission products to be made. The application of the in-situ Ge(Li) spectrometer for real-time plume dose measurements is being investigated.