Engineering topics

Egan, Hilary

Publications and source records attributed to Egan, Hilary.

13 records

DOE OSTI2024

Decision Points and Practical Considerations for AI Projects

In this presentation, I will present business-relevant decisions, risks, and considerations for practical implementations of AI projects. I will use energy efficiency and renewable energy AI projects at NREL as examples and case-studies highlighting the journey from concept to implementation. First, I present challenges, questions, and trade-offs related to system inputs: the data. Next, I will examine issues with system behavior and trust, presenting examples, risks, and mitigation strategies. Finally, I will discuss challenges to effective widespread deployment of AI systems including energy, compute, and time requirements.

AI↗

DOE OSTI2024

Enabling Scale-Up Through Multi-Fidelity Adaptive Computing

We present ideas from our ongoing work in adaptive computing - an optimization framework that allows us to strategically deploy various fidelity level experiments and simulations to guide decision making. The framework aims to enable uncertainty quantified scale-up of simulations and experiments, which causes increased complexity. A key feature of the framework is the integration of user-specified local model trustworthiness estimates. Adaptive sampling strategies allow us to optimally exploit the multiple fidelity level information and trustworthiness measures to arrive at the best decisions within a highly limited budget of objective function evaluations.

adaptive sampling↗

DOE OSTI2024

Reverse Osmosis Simulation Data

This dataset consists of computational fluid dynamics (CFD) output for various spacer configurations in a feed-water channel in reverse osmosis (RO) applications. Feed-water channels transport brine solution to the RO membrane surfaces. The spacers embedded in the channels help improve membrane performance by disrupting the concentration boundary layer growth on membrane surfaces. Refer to the "Related Work" resource below for more details. This dataset considers a feed-water channel of length 150mm. The inlet brine velocity and concentration are fixed at 0.1m/s and 100kg/m3 respectively. The diameter of the cylindrical spacers is fixed as 0.3mm and six varying inter-spacer distances of 0.75mm, 1mm, 1.5mm, 2mm, 2.5mm, and 3mm are simulated. The dataset comprising the steady, spatial fields of solute concentration, velocity, and density near each spacer is placed in the folder corresponding to the spacer configuration considered. We run two sets of CFD simulations and include the outputs from both sets for each configuration: (1) with a coarser mesh, producing low-resolution (LR) data of spatial resolution 20x20, and (2) with a finer mesh, producing high-resolution (HR) data of spatial resolution 100x100. These data points can be treated as images with the quantities of interest as their channels and can be used to train machine learning models to learn a mapping from the LR images as inputs to the HR images as outputs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Machine Learning for Advanced Building Construction

High-efficiency retrofits can play a key role in reducing carbon emissions associated with buildings if processes can be scaled-up to reduce cost, time, and disruption. Here we demonstrate an artificial intelligence/computer vision (AI/CV)-enabled framework for converting exterior build scans and dimensional data directly into manufacturing and installation specifications for overclad panels. In our workflow point clouds associated with LiDAR-scanned buildings are segmented into a facade feature space, vectorized features are extracted using an iterative random-sampling consensus algorithm, and from this representation an optimal panel design plan satisfying manufacturing constraints is generated. This system and the corresponding construction process is demonstrated on a test facade structure constructed at the National Renewable Energy Laboratory (NREL). We also include a brief summary of a techno-economic study designed to estimate the potential energy and cost impact of this new system.

build scans↗

DOE OSTI2023

Autonomy Loops for Monitoring, Operational Data Analytics, Feedback, and Response in HPC Operations

Many High Performance Computing (HPC) facilities have developed and deployed frameworks in support of continuous monitoring and operational data analytics (MODA) to help improve efficiency and throughput. Because of the complexity and scale of systems and workflows and the need for low-latency response to address dynamic circumstances, automated feedback and response have the potential to be more effective than current human-in-the-loop approaches which are laborious and error prone. Progress has been limited, however, by factors such as the lack of infrastructure and feedback hooks, and successful deployment is often site- and case-specific. In this position paper we report on the outcomes and plans from a recent Dagstuhl Seminar, seeking to carve a path for community progress in the development of autonomous feedback loops for MODA, based on the established formalism of similar (MAPE-K) loops in autonomous computing and self-adaptive systems. By defining and developing such loops for significant cases experienced across HPC sites, we seek to extract commonalities and develop conventions that will facilitate interoperability and interchangeability with system hardware, software, and applications across different sites, and will motivate vendors and others to provide telemetry interfaces and feedback hooks to enable community development and pervasive deployment of MODA autonomy loops.

autonomy loops↗

DOE OSTI2023

Machine Learning for Advanced Building Construction: Preprint

High-efficiency retrofits can play a key role in reducing carbon emissions associated with buildings if processes can be scaled-up to reduce cost, time, and disruption. Here we demonstrate an artificial intelligence/computer vision (AI/CV)- enabled framework for converting exterior build scans and dimensional data directly into manufacturing and installation specifications for overclad panels. In our workflow point clouds associated with LiDAR-scanned buildings are segmented into a facade feature space, vectorized features are extracted using an iterative random-sampling consensus algorithm, and from this representation an optimal panel design plan satisfying manufacturing constraints is generated. This system and the corresponding construction process is demonstrated on a test facade structure constructed at the National Renewable Energy Laboratory (NREL). We also include a brief summary of a techno-economic study designed to estimate the potential energy and cost impact of this new system.

building retrofits↗

DOE OSTI2023

Green Computing Opportunities & Strategy

Computation is critical to emerging fields of data intensive research, enabling new methodologies, approaches and tools. As the rate of hardware efficiency gains slows, computational time and energy costs increase. To match pace with computation demand, new approaches are needed to keep the opportunity for impact open. Charles Tripp, lead of the Green Computing Catalyzer, discusses research efforts to improve software efficiency to enable faster, less energy-intensive computing.

algorithmic efficiency↗

DOE OSTI2023

Data Center Facility Monitoring with Physics Aware Approach

U.S. Department of Energy's National Renewable Energy Laboratory (NREL) hosts one of the world's most energy-efficient HPC data centers; this system uses component-level warm-water liquid cooling to efficiently remove heat from the data center and capture it for reuse in the building or rejection to the atmosphere. Given the complexity of this system, building data-driven tools for holistically monitoring and operating the entire data center is a priority for ensuring maximal efficiency and resiliency. In this advanced smart facility, over one million metrics are recorded per minute using state-of-the-art streaming data architecture and software to capture and process the state of the system in real time. Here we detail two efforts to effectively analyze, visualize, and interpret this large volume streaming data. We have developed a novel, flexible system for identifying and visualizing individual metric anomalies and component performance across the data center through automatic metadata extraction and physically-motivated visualization for quick interpretation. Additionally, to directly connect system maintenance to data stream processing we explore a physics informed multi-metric drift and anomaly detection application to detect scale-build up in heat exchangers.

anomaly detection↗

DOE OSTI2021

Artificial Intelligence for Data Center Operations (AI Ops)

HPC data centers such as the one at NREL's ESIF will increasingly need to rely on automation to keep pace with exascale growth in compute capability and to manage and optimize the data center environment and facility resources. Artificial intelligence (AI) and machine learning (ML) approaches provide the means to improve HPC data center efficiency (energy, operational, and managerial efficiency) and resiliency by learning historical trends and training models to operate on real-time data collected from both IT and facilities sources. The goal of coupled improvement of data center resiliency and energy efficiency through automated data collection and AI has led to a multi-year, multi-staged collaboration between NREL and Hewlett-Packard Enterprise's Advanced Technology Group, referred to as Artificial Intelligence for Data Center Operations (AIOps). The extended efforts within the AIOps project include a common goal of building capabilities for an advanced smart facility and demonstration of data collection and AI modeling techniques in the ESIF data center.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

NREL HPC Eagle Jobs Data

3 months of Eagle jobs data including Slurm metadata and derived power use characterization.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2020

The High-energy Radiation Environment around a 10 Gyr M Dwarf: Habitable at Last?

Recent work has demonstrated that high levels of X-ray and UV activity on young M dwarfs may drive rapid atmospheric escape on temperate, terrestrial planets orbiting within the habitable zone. However, secondary atmospheres on planets orbiting older, less active M dwarfs may be stable and present more promising candidates for biomarker searches. In order to evaluate the potential habitability of Earth-like planets around old, inactive M dwarfs, we present new Hubble Space Telescope and Chandra X-ray Observatory observations of Barnard’s Star (GJ 699), a 10 Gyr old M3.5 dwarf, acquired as part of the Mega-MUSCLES program. Despite the old age and long rotation period of Barnard’s Star, we observe two FUV (δ 130 ≈ 5000 s; E 130 ≈ 10 29.5 erg each) and one X-ray (E X ≈ 10 29.2 erg) flares, and we estimate a high-energy flare duty cycle (defined here as the fraction of the time the star is in a flare state) of ~25%. A publicly available 5 Å to 10 μm spectral energy distribution of GJ 699 is created and used to evaluate the atmospheric stability of a hypothetical, unmagnetized terrestrial planet in the habitable zone (r HZ ~ 0.1 au). Both thermal and nonthermal escape modeling indicate (1) the quiescent stellar XUV flux does not lead to strong atmospheric escape: atmospheric heating rates are comparable to periods of high solar activity on modern Earth, and (2) the flare environment could drive the atmosphere into a hydrodynamic loss regime at the observed flare duty cycle: sustained exposure to the flare environment of GJ 699 results in the loss of ≈87 Earth atmospheres Gyr -1 through thermal processes and ≈3 Earth atmospheres Gyr -1 through ion loss processes. These results suggest that if rocky planet atmospheres can survive the initial ~5 Gyr of high stellar activity, or if a second-generation atmosphere can be formed or acquired, the flare duty cycle may be the controlling stellar parameter for the stability of Earth-like atmospheres around old M stars.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

Planetary magnetic field control of ion escape from weakly magnetized planets

ABSTRACT Intrinsic magnetic fields have long been thought to shield planets from atmospheric erosion via stellar winds; however, the influence of the plasma environment on atmospheric escape is complex. Here we study the influence of a weak intrinsic dipolar planetary magnetic field on the plasma environment and subsequent ion escape from a Mars-sized planet in a global three-dimensional hybrid simulation. We find that increasing the strength of a planet’s magnetic field enhances ion escape until the magnetic dipole’s standoff distance reaches the induced magnetosphere boundary. After this point increasing the planetary magnetic field begins to inhibit ion escape. This reflects a balance between shielding of the Southern hemisphere from ‘misaligned’ ion pickup forces and trapping of escaping ions by an equatorial plasmasphere. Thus, the planetary magnetic field associated with the peak ion escape rate is critically dependent on the stellar wind pressure. Where possible we have fit power laws for the variation of fundamental parameters (escape rate, escape power, polar cap opening angle, and effective interaction area) with magnetic field, and assessed upper and lower limits for the relationships.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

Stellar influence on heavy ion escape from unmagnetized exoplanets

Abstract Planetary habitability is in part determined by the atmospheric evolution of a planet; one key component of such evolution is escape of heavy ions to space. Ion-loss processes are sensitive to the plasma environment of the planet, dictated by the stellar wind and stellar radiation. These conditions are likely to vary from what we observe in our own Solar system when considering a planet in the habitable zone around an M-dwarf. Here, we use a hybrid global plasma model to perform a systematic study of the changing plasma environment and ion escape as a function of stellar input conditions, which are designed to mimic those of potentially habitable planets orbiting M-dwarfs. We begin with a nominal case of a solar wind experienced at Mars today, and incrementally modify the interplanetary magnetic field orientation and strength, dynamic pressure, and Extreme Ultraviolet input. We find that both ion-loss morphology and overall rates vary significantly, and in cases where the stellar wind pressure was increased, the ion loss began to be diffusion or production limited with roughly half of all produced ions being lost. This limit implies that extreme care must be taken when extrapolating loss processes observed in the Solar system to extreme environments.

79 ASTRONOMY AND ASTROPHYSICS↗