Engineering topics

Brunhart-Lupo, Nicholas J

Publications and source records attributed to Brunhart-Lupo, Nicholas J.

8 records

DOE OSTI2019

Chapter 21: The Utility of Virtual Reality for Science and Engineering

In our daily usage of the large-scale immersive virtual environment at the National Renewable Energy Laboratory (NREL), we have observed how this VR system can be a useful tool to enhance scientific and engineering workflows. On multiple occasions, we have observed scientists and engineers discover features in their data using immersive environments that they had not seen in prior investigations of their data on traditional desktop displays. We have embedded more information into our analytics tools, allowing engineers to explore complex multivariate spaces. We have observed natural interactions with 3D objects and how those interactions seem to catalyze understanding. And we have seen improved collaboration with groups of stakeholders. In this chapter, we discuss these practical advantages of immersive visualization in the context of several real-world examples.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Coupling Visualization, Simulation, and Deep Learning for Ensemble Steering of Complex Energy Models

We describe a new framework that allows users to explore and steer ensembles of energy systems simulations by coupling multiple energy models and interactive visualization through a dataflow API. Through the visual interface, users can interactively explore complex parameter spaces populated by hundreds, or thousands, of simulation runs and interactively spawn new simulations to 'fill in' regions of interest in the parameter space. The computational and visualization capabilities reside within a general-purpose dataflow architecture for connecting producers of multidimensional timeseries data, such as energy simulations, with consumers of that data, whether they be visualizations, statistical analyses, or datastores. Fast computation and agile dataflow can enhance the engagement with energy simulations, allowing users to populate the parameter space in real time. However, many energy simulations are far too slow to provide an interactive response. To support interactive feedback, we are creating reduced-form simulations developed through machine learning techniques, which provide statistically sound estimates of the results of the full simulations at a fraction of the computational cost. These reduced-form simulations have response times on the order of seconds, suitable for real-time human-in-the-loop design and analysis. The approximation methods apply to a wide range of computational models, including supply-chain models, electric power grid simulations, and building models. Such reduced-form representations do not replace or re-implement existing simulations, but instead supplement them by enabling rapid scenario design and exploration for large ensembles of simulations. The improved understanding, facilitated by the reduced-form models, dataflow API, and visualization tools, allows researchers to better allocate computational resources to capture informative relationships within the system as well as provide a low-cost method for validating and quality-checking large-scale modeling efforts.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Contextual Compression of Large-Scale Wind Turbine Array Simulations

Data sizes are becoming a critical issue particularly for HPC applications. We have developed a user-driven lossy wavelet-based storage model to facilitate the analysis and visualization of large-scale wind turbine array simulations. The model stores data as heterogeneous blocks of wavelet coefficients, providing high-fidelity access to user-defined data regions believed the most salient, while providing lower-fidelity access to less salient regions on a block-by-block basis. In practice, by retaining the wavelet coefficients as a function of feature saliency, we have seen data reductions in excess of 94 percent, while retaining lossless information in the turbine-wake regions most critical to analysis and providing enough (low-fidelity) contextual information in the upper atmosphere to track incoming coherent turbulent structures. Our contextual wavelet compression approach has allowed us to deliver interative visual analysis while providing the user control over where data loss, and thus reduction in accuracy, in the analysis occurs. We argue this reduced but contextualized representation is a valid approach and encourages contextual data management.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Contextual Compression of Large-Scale Wind Turbine Array Simulations: Preprint

Data sizes are becoming a critical issue particularly for HPC applications. We have developed a user-driven lossy wavelet-based storage model to facilitate the analysis and visualization of large-scale wind turbine array simulations. The model stores data as heterogeneous blocks of wavelet coefficients, providing high-fidelity access to user-defined data regions believed the most salient, while providing lower-fidelity access to less salient regions on a block-by-block basis. In practice, by retaining the wavelet coefficients as a function of feature saliency, we have seen data reductions in excess of 94 percent, while retaining lossless information in the turbine-wake regions most critical to analysis and providing enough (low-fidelity) contextual information in the upper atmosphere to track incoming coherent turbulent structures. Our contextual wavelet compression approach has allowed us to deliver interactive visual analysis while providing the user control over where data loss, and thus reduction in accuracy, in the analysis occurs. We argue this reduced but contexualized representation is a valid approach and encourages contextual data management.

17 WIND ENERGY↗

DOE OSTI2017

Coupling Visualization, Simulation, and Deep Learning for Ensemble Steering of Complex Energy Models: Preprint

We have developed a framework for the exploration, design, and planning of energy systems that combines interactive visualization with machine-learning based approximations of simulations through a general purpose dataflow API. Our system provides a visual inter- face allowing users to explore an ensemble of energy simulations representing a subset of the complex input parameter space, and spawn new simulations to 'fill in' input regions corresponding to new enegery system scenarios. Unfortunately, many energy simula- tions are far too slow to provide interactive responses. To support interactive feedback, we are developing reduced-form models via machine learning techniques, which provide statistically sound esti- mates of the full simulations at a fraction of the computational cost and which are used as proxies for the full-form models. Fast com- putation and an agile dataflow enhance the engagement with energy simulations, and allow researchers to better allocate computational resources to capture informative relationships within the system and provide a low-cost method for validating and quality-checking large-scale modeling efforts.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

High Performance Interactive System Dynamics Visualization

This brochure describes a system dynamics simulation (SD) framework that supports an end-to-end analysis workflow that is optimized for deployment on ESIF facilities(Peregrine and the Insight Center). It includes (I) parallel and distributed simulation of SD models, (ii) real-time 3D visualization of running simulations, and (iii) comprehensive database-oriented persistence of simulation metadata, inputs, and outputs.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

High Performance Interactive System Dynamics Visualization

This presentation describes a system dynamics simulation (SD) framework that supports an end-to-end analysis workflow that is optimized for deployment on ESIF facilities(Peregrine and the Insight Center). It includes (I) parallel and distributed simulation of SD models, (ii) real-time 3D visualization of running simulations, and (iii) comprehensive database-oriented persistence of simulation metadata, inputs, and outputs.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Simulation Exploration through Immersive Parallel Planes

We present a visualization-driven simulation system that tightly couples systems dynamics simulations with an immersive virtual environment to allow analysts to rapidly develop and test hypotheses in a high-dimensional parameter space. To accomplish this, we generalize the two-dimensional parallel-coordinates statistical graphic as an immersive 'parallel-planes' visualization for multivariate time series emitted by simulations running in parallel with the visualization. In contrast to traditional parallel coordinate's mapping the multivariate dimensions onto coordinate axes represented by a series of parallel lines, we map pairs of the multivariate dimensions onto a series of parallel rectangles. As in the case of parallel coordinates, each individual observation in the dataset is mapped to a polyline whose vertices coincide with its coordinate values. Regions of the rectangles can be 'brushed' to highlight and select observations of interest: a 'slider' control allows the user to filter the observations by their time coordinate. In an immersive virtual environment, users interact with the parallel planes using a joystick that can select regions on the planes, manipulate selection, and filter time. The brushing and selection actions are used to both explore existing data as well as to launch additional simulations corresponding to the visually selected portions of the input parameter space. As soon as the new simulations complete, their resulting observations are displayed in the virtual environment. This tight feedback loop between simulation and immersive analytics accelerates users' realization of insights about the simulation and its output.

97 MATHEMATICS AND COMPUTING↗