The use of cluster analysis techniques in space flight project cost risk estimation
Explore the source record for details and available documents.
cost cost risk cluster analysis↗
SEARCH · Search NASA
Results for “cost cost risk cluster analysis”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
The use of cluster analysis techniques in spaceflight project cost risk estimation
Project cost risk is the uncertainty in final project cost, contingent on initial budget, requirements and schedule. For a proposed mission, a dynamic simulation model relying for some of its input on a simple risk elicitation is used to identify and quantify systemic cost risk.
cluster analysis↗
Integrated cost risk and cluster analyses
Explore the source record for details and available documents.
cost risk analysis cluster analysis↗
Feasibility Analyses for the Microgrid of the Mountain in the Cordillera Central Region of Puerto Rico
Distributed energy resource (DER) development can benefit communities through improvement of access to reliable electricity and a resilient energy system. Many place-specific considerations must be accounted for when designing electrical systems with co-located generation and loads. This paper presents an overview of four studies focusing on different aspects for regional DER development in the Cordillera Central region of Puerto Rico, including a grid and load stability study with the introduction of solar energy generation, microgrid design based on the solar resource in the area, resilience considerations during power outage scenarios, and qualitative risk aspects for components in the microgrid. Additionally, a conceptual substation microgrid analysis is conducted using the Microgrid Design Toolkit (MDT) to evaluate trade-offs between cost and energy availability, providing insights into optimal design strategies. A microgrid resilience assessment is performed using the Resilient Nodal Cluster Analysis Tool (ReNCAT) to identify critical loads and evaluates their accessibility during outages, emphasizing the importance of community needs. Finally, a qualitative risk assessment utilizing Hazard and Operability Analysis (HAZOP) methods highlights potential hazards and mitigations for microgrid components, ensuring robust system design and operation.
24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA Instrument Cost/Schedule Model
NASA's Office of Independent Program and Cost Evaluation (IPCE) has established a number of initiatives to improve its cost and schedule estimating capabilities. 12One of these initiatives has resulted in the JPL developed NASA Instrument Cost Model. NICM is a cost and schedule estimator that contains: A system level cost estimation tool; a subsystem level cost estimation tool; a database of cost and technical parameters of over 140 previously flown remote sensing and in-situ instruments; a schedule estimator; a set of rules to estimate cost and schedule by life cycle phases (B/C/D); and a novel tool for developing joint probability distributions for cost and schedule risk (Joint Confidence Level (JCL)). This paper describes the development and use of NICM, including the data normalization processes, data mining methods (cluster analysis, principal components analysis, regression analysis and bootstrap cross validation), the estimating equations themselves and a demonstration of the NICM tool suite.
JCL↗
Software forecasting as it is really done: A study of JPL software engineers
This paper presents a summary of the results to date of a Jet Propulsion Laboratory internally funded research task to study the costing process and parameters used by internally recognized software cost estimating experts. Protocol Analysis and Markov process modeling were used to capture software engineer's forecasting mental models. While there is significant variation between the mental models that were studied, it was nevertheless possible to identify a core set of cost forecasting activities, and it was also found that the mental models cluster around three forecasting techniques. Further partitioning of the mental models revealed clustering of activities, that is very suggestive of a forecasting lifecycle. The different forecasting methods identified were based on the use of multiple-decomposition steps or multiple forecasting steps. The multiple forecasting steps involved either forecasting software size or an additional effort forecast. Virtually no subject used risk reduction steps in combination. The results of the analysis include: the identification of a core set of well defined costing activities, a proposed software forecasting life cycle, and the identification of several basic software forecasting mental models. The paper concludes with a discussion of the implications of the results for current individual and institutional practices.
Griesel, Martha Ann↗
K-Means Cluster Study for Radiofrequency Propagation Characterization
The objective of this study is to design a simple method for mining radio frequency (RF) propagation data. The study explored the characteristics of a large dataset of propagation experiments conducted over the span of years and using several ground stations around the world. Furthermore, this study developed simple predictive models that can be used for link characterization and overall propagation behavior description, without the need for physical measurements on-site. It is understood that such statistical learning has several drawbacks in terms of accuracy and precision. K-means clustering was used to characterize the data set in a way never explored before in an attempt to create useful tools that reduce cost, time and risk. K-means clustering was used to characterize the data set. Cosine distance was used as a method to determine the optimal number for clustering each feature. Dependence and independence analysis was performed to explore intra and inter-sensitivity between the presented features, with respect to each other and time. Several predicative models were generated and evaluated with respect to a test set to assess a measure of prediction accuracy and precision. A simple method for data analysis was developed and tested as the basis for further studies and future refinement to produce optimal performing models.
Cognitive↗
Analyzing human errors in flight mission operations
A long-term program is in progress at JPL to reduce cost and risk of flight mission operations through a defect prevention/error management program. The main thrust of this program is to create an environment in which the performance of the total system, both the human operator and the computer system, is optimized. To this end, 1580 Incident Surprise Anomaly reports (ISA's) from 1977-1991 were analyzed from the Voyager and Magellan projects. A Pareto analysis revealed that 38 percent of the errors were classified as human errors. A preliminary cluster analysis based on the Magellan human errors (204 ISA's) is presented here. The resulting clusters described the underlying relationships among the ISA's. Initial models of human error in flight mission operations are presented. Next, the Voyager ISA's will be scored and included in the analysis. Eventually, these relationships will be used to derive a theoretically motivated and empirically validated model of human error in flight mission operations. Ultimately, this analysis will be used to make continuous process improvements continuous process improvements to end-user applications and training requirements. This Total Quality Management approach will enable the management and prevention of errors in the future.
Bruno, Kristin J.↗
A Total Factor Productivity Based Structure for Tactical Cluster Assessment: Empirical Investigation in the Airline Industry
In this paper we analyze and assess the efficiency of the United States (U.S.) airline industry through the total factor productivity (TFP) method. While airlines use various resources to produce a heterogeneous group of outputs, this article focuses on certain fundamental outputs as final products of selected airlines. The results from this analysis indicate that the national airlines (US. domestic carriers) have higher TFP as compared to the major airlines. While major airlines have drastically cut costs in the past few years, they also need to improve efficiency or risk going out of business. In this paper, we investigate the efficiency and productivity of a selection of U.S. airlines for the years 1996 through 2001. These years have been chosen as a good example of years in which the industry experienced normal growth and generally positively returns. Subsequent to 2001 the industry experienced two severe external shocks, namely, the September 11, 2001. terrorist attacks and the Iraq war. These anomalous shocks make the years after 2001 inconsistent with respect to the type of index developed in this article.
Vasigh, Bijan↗
Residential Vehicle-to-Home Backup Power Capabilities: Key Findings from a ComEd Beneficial Electrification R&D Pilot
This report summarizes key findings from a collaborative technical study of residential, non-grid-tied vehicle-to-home (V2H) backup power systems in Commonwealth Edison’s (ComEd’s) service territory. The work integrates (1) a feeder-level technoeconomic analysis (TEA) using historical outage-event data and simulated electric-vehicle (EV) driving/charging profiles to estimate potential reliability and customer interruption-cost impacts under V2H and vehicle-to-grid (V2G) adoption scenarios; (2) controlled laboratory performance testing of a representative V2H backup ecosystem to characterize transfer-to-backup behavior, sustained power delivery, efficiency trends, and repeatable reliability limitations; and (3) a cybersecurity assessment aligned with NIST Cybersecurity Framework (CSF) 2.0 and ISO/SAE 21434 to evaluate interface-level risk drivers and identify program-relevant mitigations. Results indicate that V2H can provide measurable resilience value, but outcomes are strongly context dependent on outage patterns and the share of events that are “V2H-applicable.” Typical transfer-to-backup behavior clustered on the order of minutes, but rare long-delay edge cases were observed (including an event approaching 30 minutes) and should be treated as a reliability risk. High-power testing showed that peak-rated output is not necessarily continuously deliverable; stable operation may require operation below nameplate ratings and attention to thermal and installation constraints. The cybersecurity assessment highlights a broad attack surface spanning commissioning, home networks, embedded services, and cloud/OTA pathways, motivating minimum controls for secure onboarding, signed updates, patch cadence, and coordinated vulnerability response for any scaled deployment.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Enabling the Discovery of Recurring Anomalies in Aerospace System Problem Reports using High-Dimensional Clustering Techniques
This paper describes the results of a significant research and development effort conducted at NASA Ames Research Center to develop new text mining techniques to discover anomalies in free-text reports regarding system health and safety of two aerospace systems. We discuss two problems of significant importance in the aviation industry. The first problem is that of automatic anomaly discovery about an aerospace system through the analysis of tens of thousands of free-text problem reports that are written about the system. The second problem that we address is that of automatic discovery of recurring anomalies, i.e., anomalies that may be described m different ways by different authors, at varying times and under varying conditions, but that are truly about the same part of the system. The intent of recurring anomaly identification is to determine project or system weakness or high-risk issues. The discovery of recurring anomalies is a key goal in building safe, reliable, and cost-effective aerospace systems. We address the anomaly discovery problem on thousands of free-text reports using two strategies: (1) as an unsupervised learning problem where an algorithm takes free-text reports as input and automatically groups them into different bins, where each bin corresponds to a different unknown anomaly category; and (2) as a supervised learning problem where the algorithm classifies the free-text reports into one of a number of known anomaly categories. We then discuss the application of these methods to the problem of discovering recurring anomalies. In fact the special nature of recurring anomalies (very small cluster sizes) requires incorporating new methods and measures to enhance the original approach for anomaly detection. ?& pant 0-
Srivastava, Ashok, N.↗
Exploring the Spatial Relationship Between Demographic Indicators and the Built Environment of a City
In addition to global and regional drivers of urbanization, neighborhood development in urban areas across the United States has been shown to be influenced by various local socio-economic factors. These factors, despite varying across socio-economic groups, have large implications regarding a population’s vulnerability to extreme climate events, including heat waves resulting in adverse health impacts. Additionally, the demographics of an urban area can shape its infrastructural characteristics, causing different populations groups to face varying levels of risks and benefits. As a result, the urban morphology and socio-economic characteristics of a city are deeply intertwined; however, their interactions on a finer scale are not yet fully understood. This research aims to better understand the relationships between various socio-economic factors and the built environment of a city, considering variability in building types, and temperature patterns. This research focuses on the city of Las Vegas, NV, and uses spatial data analysis to understand the correlation between of socio-economic characteristics, building morphology, building characteristics, and temperature data to understand the correlation between these various factors. Results of these research shows there is a distinct pattern of clustering of socio-economic characteristics with the city and there is a distinct correlation between age and cost, socio-economic characteristics, and locations of high heat distribution within the city.
Singh, Ridhima↗
Astro2020 APC White Paper Project: The Simons Observatory
The Simons Observatory (SO) is a ground-based cosmic microwave background (CMB) experiment sited on Cerro Toco in the Atacama Desert in Chile that promises to provide breakthrough discoveries in fundamental physics, cosmology, and astrophysics. Supported by the Simons Foundation, the Heising-Simons Foundation, and with contributions from collaborating institutions, SO will see first light in 2021 and start a five year survey in 2022. SO has 287 collaborators from 12 countries and 53 institutions, including 85 students and 90 postdocs. The SO experiment in its currently funded form (‘SO-Nominal’) consists of three 0.4 m Small Aperture Telescopes (SATs) and one 6 m Large Aperture Telescope (LAT). Optimized for minimizing systematic errors in polarization measurements at large angular scales, the SATs will perform a deep, degree-scale survey of 10% of the sky to search for the signature of primordial gravitational waves. The LAT will survey 40% of the sky with arc-minute resolution. These observations will measure (or limit) the sum of neutrino masses, search for light relics, measure the early behavior of Dark Energy, and refine our understanding of the intergalactic medium, clusters and the role of feedback in galaxy formation. With up to ten times the sensitivity and five times the angular resolution of the Planck satellite, and roughly an order of magnitude increase in mapping speed over currently operating (“Stage 3”) experiments, SO will measure the CMB temperature and polarization fluctuations to exquisite precision in six frequency bands from 27 to 280 GHz. SO will rapidly advance CMB science while informing the design of future observatories such as CMB-S4. Construction of SO-Nominal is fully funded, and operations and data analysis are funded for part of the planned five-year observations. We will seek federal funding to complete the observations and analysis of SO-Nominal, at the $25M level. The SO has a low risk and cost efficient upgrade path – the 6 m LAT can accommodate almost twice the baseline number of detectors and the SATs can be duplicated at low cost. We will seek funding at the $75M level for an expansion of the SO (‘SO-Enhanced’) that fills the remaining focal plane in the LAT, adds three SATs, and extends operations by five years, substantially improving our science return. By this time SO may be operating as part of the larger CMB-S4 project. This white paper summarizes and extends material presented in, which describes the science goals of SO-Nominal, and which describe the instrument design.
Abitbol, Maximilian H.↗
Analysis of Association Between Remotely Sensed (RS) Data and Soil Transmitted Helminthes Infection Using Geographical Information Systems (GIS): Boaco, Nicaragua
Soil-transmitted helminths are intestinal nematodes that can infect all members of a population but specially school-age children living in poverty. Infection can be significantly reversed with anthelmintic drug treatments and sanitation improvement. Implementation of effective public health programs requires reliable and updated information to identify areas at higher risk and to calculate amount of drug required. Geo-referenced in situ prevalence data will be overlaid over an ecological map derived from RS data using ARC Map 9.3 (ESRI). Prevalence data and RS data matching at the same geographical location will be analyzed for correlation and those variables from RS data that better correlate with prevalence will be included in a multivariate regression model. Temperature, vegetation, and distance to bodies of water will be inferred using data from Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat TM and ETM+. Elevation will be estimated with data from The Shuttle Radar Topography Mission (SRTM). Prevalence and intensity of infections are determined by parasitological survey (Kato Katz) of children enrolled in rural schools in Boaco, Nicaragua, in the communities of El Roblar, Cumaica Norte, Malacatoya 1, and Malacatoya 2). This study will demonstrate the importance of an integrated GIS/RS approach to define sampling clusters without the need for any ground-based survey. Such information is invaluable to identify areas of high risk and to geographically target control programs that maximize cost-effectiveness and sanitation efforts.
MorenoMadrinan, Max J.↗
Virtualization - A Key Cost Saver in NASA Multi-Mission Ground System Architecture
With science team budgets being slashed, and a lack of adequate facilities for science payload teams to operate their instruments, there is a strong need for innovative new ground systems that are able to provide necessary levels of capability processing power, system availability and redundancy while maintaining a small footprint in terms of physical space, power utilization and cooling.The ground system architecture being presented is based off of heritage from several other projects currently in development or operations at Goddard, but was designed and built specifically to meet the needs of the Science and Planetary Operations Control Center (SPOCC) as a low-cost payload command, control, planning and analysis operations center. However, this SPOCC architecture was designed to be generic enough to be re-used partially or in whole by other labs and missions (since its inception that has already happened in several cases!)The SPOCC architecture leverages a highly available VMware-based virtualization cluster with shared SAS Direct-Attached Storage (DAS) to provide an extremely high-performing, low-power-utilization and small-footprint compute environment that provides Virtual Machine resources shared among the various tenant missions in the SPOCC. The storage is also expandable, allowing future missions to chain up to 7 additional 2U chassis of storage at an extremely competitive cost if they require additional archive or virtual machine storage space.The software architecture provides a fully-redundant GMSEC-based message bus architecture based on the ActiveMQ middleware to track all health and safety status within the SPOCC ground system. All virtual machines utilize the GMSEC system agents to report system host health over the GMSEC bus, and spacecraft payload health is monitored using the Hammers Integrated Test and Operations System (ITOS) Galaxy Telemetry and Command (TC) system, which performs near-real-time limit checking and data processing on the downlinked data stream and injects messages into the GMSEC bus that are monitored to automatically page the on-call operator or Systems Administrator (SA) when an off-nominal condition is detected. This architecture, like the LTSP thin clients, are shared across all tenant missions.Other required IT security controls are implemented at the ground system level, including physical access controls, logical system-level authentication authorization management, auditing and reporting, network management and a NIST 800-53 FISMA-Moderate IT Security plan Risk Assessment Contingency Plan, helping multiple missions share the cost of compliance with agency-mandated directives.The SPOCC architecture provides science payload control centers and backup mission operations centers with a cost-effective, standardized approach to virtualizing and monitoring resources that were traditionally multiple racks full of physical machines. The increased agility in deploying new virtual systems and thin client workstations can provide significant savings in personnel costs for maintaining the ground system. The cost savings in procurement, power, rack footprint and cooling as well as the shared multi-mission design greatly reduces upfront cost for missions moving into the facility. Overall, the authors hope that this architecture will become a model for how future NASA operations centers are constructed!
Ground System Architecture↗
Launch Order, Launch Separation, and Loiter in the Constellation 1 1/2-Launch Solution
The NASA Constellation Program (CxP) is developing a two-element Earth-to-Orbit launch system to enable human exploration of the Moon. The first element, Ares I, is a human-rated system that consists of a first stage based on the Space Shuttle Program's solid rocket booster (SRB) and an upper stage that consists of a four-crew Orion capsule, a service module, and a Launch Escape System. The second element, Ares V, is a Saturn V-plus category launch system that consists of the core stage with a cluster of six RS-68B engines and augmented with two 5.5-segment SRBs, a Saturn-derived J-2X engine powering an Earth Departure Stage (EDS), and the lunar-lander vehicle payload, Altair. Initial plans called for the Ares V to be launched first, followed the next day by the Ares I. After the EDS performs the final portion of ascent and subsequent orbit circularization, the Orion spacecraft then performs a rendezvous and docks with the EDS and its Altair payload. Following checkout, the integrated stack loiters in low Earth orbit (LEO) until the appropriate Trans-Lunar Injection (TLI) window opportunity opens, at which time the EDS propels the integrated Orion Altair to the Moon. Successful completion of this 1 1/2-launch solution carries risks related to both the orbital lifetime of the assets and the probability of achieving the launch of the second vehicle within the orbital lifetime of the first. These risks, which are significant in terms of overall system design choices and probability of mission success, dictated a thorough reevaluation of the launch strategy, including the order of vehicle launch and the planned time period between launches. The goal of the effort described in this paper was to select a launch strategy that would result in the greatest possible expected system performance, while accounting for launch risks and the cost of increased orbital lifetime. Discrete Event Simulation (DES) model of the launch strategies was created to determine the probability of a second launch not occurring in a timely fashion (i.e., before the assets waiting in LEO expire). An overview of the launch strategy evaluation process is presented, along with results of specific cases that were analyzed. A high-level comparison of options is then presented, along with the conclusion derived from the analysis.
Stromgren, Chel↗
Utilization of Machine Learning Techniques for Managing the Tracking and Data Relay Satellite Constellation
National Aeronautics and Space Administration’s (NASA) Goddard Space Flight Center (GSFC) operates a constellation of ten geosynchronous Tracking and Data Relay Satellites (TDRS). The TDRS constellation consists of multiple geosynchronous communication relay satellites located around the equator so they can provide continual coverage of any mission in low earth orbit. The TDRS are located primarily in three oceanic regions around the earth. NASA’s White Sands Complex provides the ground communication support for TDRS located over the Atlantic and Pacific Oceans. Another TDRS ground station in Guam supports the TDRS over the Indian Ocean. With these satellites the TDRS network can provide continuous coverage of satellites in low-earth orbit. The NASA Space Network (SN) project office at GSFC manages the constellation of spacecraft. Major customers of the TDRS constellation include, but are not limited to, the International Space Station and the Hubble Space Telescope. The TDRS constellation has three generations of satellites and has been active for over 30 years providing reliable communication links between customer satellites and corresponding ground stations. However, one of the major concerns for TDRS, and in any space mission, is to ensure the health and safety of the spacecraft. Generally, engineers use telemetry data to monitor and analyze the performance and state of health of the spacecraft. Telemetry data contains hundreds of parameters that monitor each important component in the spacecraft, which can be utilized to recognize and characterize the behavior of the spacecraft. Each parameter contains considerable information to represent time-dependent properties of each spacecraft subsystem and component. During the entire life of a TDRS spacecraft, thousands of gigabytes of telemetry data are transmitted in real-time from the spacecraft to the ground station at the White Sands Complex in Las Cruces, New Mexico, and recorded as historical data sets for engineers to process and analyze the events that occurred on-orbit. These parameters contain the function of multiple spacecraft subsystems, such as the attitude control system (ACS), Thermal, Electrical Power Subsystem (EPS), etc. . The first and second generations have exceeded their required lifetime and NASA is keen to manage these spacecrafts carefully in order to maximize the remaining life using the spacecraft telemetry. The challenge is to know when the risk of losing a spacecraft in geosynchronous orbit exceeds the benefit of continued operations for customer support. In the TDRS fleet, the EPS is the most critical subsystem related to spacecraft operations. Failure of the EPS would strand a spacecraft in geosynchronous orbit. Since EPS provides power to the spacecraft, component failures ultimately lead to the inability to support the spacecraft loads and the communications payload. For instance, TDRS-8 has several anomalies in EPS including the Bus Voltage Limiter (BVL) shunt current, solar array loss of circuits, and failed battery cells. Any of these anomalies can cause critical issues to the spacecraft. Therefore, developing a system to analyze and perform early detection of a potential anomaly is an important issue in telemetry data analysis. In recent years, Telemetry Mining (TM) has been proposed to process telemetry data by using Data Mining (DM) techniques such as classification, clustering, regression and anomaly detection. Anomaly detection, also known as outlier detection, has been widely used in many data mining areas such as remote sensing, medical data processing and digital image processing. The goal of anomaly detection is to detect abnormal data, which contains a relatively low probability of occurrence among the entire data set. Early detection of anomalies is one of the most significant issues in managing the spacecraft configuration. If anomalies can be detected early enough, then the redundant resources can be used to extend the life of the operational spacecraft. We present an unsupervised anomaly detection method to process the EPS data extracted from TDRS-8. This is different from traditional analytical methods, which use telemetry data to illustrate behavior and physical meaning of each spacecraft component. TM connects multiple parameters as a vector and then conducts data analysis on this high dimension telemetry vector. This method is looking at the properties of a high dimensional vector that is able to consider the relationship between different parameters in the anomaly detection problem. This kind of method performs much better than the traditional limit checking method. In addition, we propose a new approach of real-time anomaly detection to process telemetry data in real-time, which can then be applied to spacecraft monitoring with high reliability, low cost and high accuracy.
Machine Learning (ML)↗
Hydrogen Leak Modeling for Development of Smart Distributed Monitoring Under Unintended Releases
Hydrogen is a versatile and clean energy carrier that can be produced from various renewable sources such as wind, solar, and hydropower. Hydrogen has the potential to play a crucial role in decarbonizing industrial processes that are currently reliant on fossil fuels and provide long-duration and/or seasonal energy storage to enable electricity decarbonization. Hydrogen can also be used as a fuel for fuel cell vehicles, providing a zero-emission alternative to traditional internal combustion engines. DOE launched the Hydrogen Energy Earthshot (Hydrogen Shot) in June 2021 to reduce the cost of clean hydrogen by 80% to $1 per 1 kilogram in 1 decade ("1 1 1"). While promising, Hydrogen is highly-flammable, and in the presence of oxygen, it can form explosive mixtures. . Therefore, understanding leak scenarios is essential to evaluate and mitigate the safety risks associated with potential hydrogen leaks. An increased understanding of leak behavior, and having tools to model leaks, can help assess how hydrogen would disperse in different environments, influencing emergency response plans and safety measures, and identify potential issues with materials and design systems that can withstand the challenges posed by hydrogen. Recently, researchers have attempted to study hydrogen leaks for development of risk management strategies. However, the focus has been on closed or semi-closed spaces like storage rooms, vehicles, garages, and fueling stations - all promising locations for future hydrogen infrastructure. In this presentation, the modeling environment extends the span of research further by modeling hydrogen leak in an outdoor, open space. We will present the key challenges with modeling hydrogen leaks in an uncontrollable environment, how they were handled, and how modeling results informed sensor selection and placement. A Hydrogen research facility at the National Renewable Energy Laboratory (NREL) was used as a case study to model hydrogen leaks. In the future, Hydrogen wide area detection methodologies will be developed and tested at this site to monitor for unintended and operational hydrogen releases. The data generated from modeling will be used to develop a predictive model to detect hydrogen leak location based on concentration measured by sensors in this open space. Furthermore, the facility was also chosen because controlled hydrogen releases can be performed. A computational fluid dynamics (CFD) based modeling approach was taken to model hydrogen leak. The full-scale hydrogen facility was modeled with a large ambient domain. The electrolyzer at the facility can produce a controlled release rate of 27 kg-H2/hr. Site-specific atmospheric and weather condition data such as wind direction, wind speed at various altitudes, and temperature were used as inputs to the model. To capture the variability of weather conditions, a subset of the weather conditions experienced during daytime hours without precipitation over the course of three months was generated; using established data clustering techniques, a total of 100 condition sets were chosen. The results show statistical distributions and ranges of hydrogen concentrations at locations throughout the domain. These distributions are compared to experimental data from a constant mass flow, controlled hydrogen release at the facility. The stochastic wind conditions of the release make direct validation difficult, therefore, statistical comparison approaches were used. Wind conditions are found to significantly impact the release behavior, including direction and concentration. Sensor selection and placement is proposed for the facility and is now based on release behavior predicted for the facility given its weather patterns; this is much more informed than without the modeling results. The methodology and analysis procedure can be translated to other facilities using modified geometries and site-specific weather conditions. Hydrogen holds great promise as a renewable energy fuel, but ensuring safety in its production, storage, and use is paramount. Studying potential leak scenarios in an open space will help develop sensors to detect hydrogen on a large spectrum of concentration and eventually build a smart distributed monitoring system.
CFD↗