Cost-function characterization of systems
Cost function characterizations of dynamical control systems - Markov transition cost functions
MATHEMATICS↗
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Results for “characterization costs”
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.
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Reference system characterization and cost overview
A brief description of the necessary elements of the Satellite Power System is presented. This reference system provides the basis for making preliminary cost estimates.
Piland, R. O.↗
Robust Cost-Effective Characterization of Petrophysical Properties in Sedimentary Geothermal Applications Leveraged by Drilling Data and Cuttings
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Cuttings, drilling, porosity↗
Pumped Storage Hydropower Operation & Maintenance Cost Estimation
The National Laboratory of the Rockies (NLR) develops and hosts a pumped storage hydropower (PSH) cost model that is the most detailed bottom-up PSH cost model available to the public. It is available both as a spreadsheet and an interactive web tool, enabling users with a variety of PSH interests to transparently characterize costs of alternative PSH sites and designs. The NLR PSH cost model was designed originally to consider only upfront capital costs only. This slide deck describes methodology to expand the cost model to include operations and maintenance (OM) costs. OM costs are characterized as five distinct components with unique sources and methods for cost estimation. By combining methods for each of these components into a cumulative OM cost estimate, these methods allow a more complete estimation of total OM costs that agrees with existing literature values. The methods are scalable and transparent, allowing them to be readily to applied to any prospective PSH facility for a representative preliminary OM cost estimate in advance of detailed site-specific engineering and other studies.
13 HYDRO ENERGY↗
Validation of the NLR Pumped Storage Hydropower Cost Model
The National Laboratory of the Rockies (NLR) first released its pumped storage hydropower (PSH) cost model in 2023 as the most detailed bottom-up PSH cost model available to the public. It is available both as a spreadsheet and an interactive web tool, enabling users with a variety of PSH interests to transparently characterize costs of alternative PSH sites and designs. The PSH cost model cannot replace detailed site-level studies and design, but it is important to validate it against other industry PSH cost estimates. The initial model methodology report validated the cost model for a single proposed site, the Eagle Mountain Project in California. This slide deck documents an expanded validation exercise using cost data from six other sites: Goldendale (Washington), Seminoe (Wyoming), Gordon Butte (Montana), Swan Lake (Oregon), White Pine (Oregon), and Lewis Ridge (Kentucky). It compares itemized costs from Federal Energy Regulatory Commission (FERC) applications and other reported costs with NLR PSH cost model outputs after customizing inputs for each site. The validation exercise finds that the NLR model's conservative indirect cost assumptions often drive overall cost overestimation, with direct cost comparisons typically agreeing more closely. All cost model estimates are well within an Association for the Advancement of Cost Engineering (AACE) Class 5 estimation range (-50% to +100%), with five within the AACE Class 4 range (-30% to +50%) and four being within 15%. This result is considered reasonable performance for a parametric model applied at a preliminary design stage.
13 HYDRO ENERGY↗
Materials for the Photoluminescence-Based Detection of Economically Critical Metals
Critical metals, such as rare earth elements (REEs), cobalt, lithium, aluminum, nickel, and others, are essential to advanced technologies and renewable energy in particular. Widespread global adoption of renewable energy technologies has spurred dramatic demand increases for these metals; however, the global supply of these metals is highly monopolistic and conventional mining poses economic and environmental challenges. As a result, there is increasing interest in domestic production from alternative resources such as coal and its utilization byproducts. Slow and expensive characterization costs remain a significant barrier for domestic production. Here, luminescent sensing materials and platforms are presented that provide an alternative to the current state-of-the-art characterization methods; highly sensitive and selective sensing materials for cobalt, aluminum, and rare earth elements are presented, as well as compact, inexpensive platforms capable of analyzing signal from these materials for rapid characterization of critical metal content.
Crawford, Scott↗
Materials for the Photoluminescence-Based Detection of Economically Critical Metals
Critical metals, such as rare earth elements (REEs), cobalt, lithium, aluminum, nickel, and others, are essential to advanced technologies and renewable energy in particular. Widespread global adoption of renewable energy technologies has spurred dramatic demand increases for these metals; however, the global supply of these metals is highly monopolistic and conventional mining poses economic and environmental challenges. As a result, there is increasing interest in domestic production from alternative resources such as coal and its utilization byproducts. Slow and expensive characterization costs remain a significant barrier for domestic production. Here, luminescent sensing materials and platforms are presented that provide an alternative to the current state-of-the-art characterization methods; highly sensitive and selective sensing materials for cobalt, aluminum, and rare earth elements are presented, as well as compact, inexpensive platforms capable of analyzing signal from these materials for rapid characterization of critical metal content.
Crawford, Scott↗
Integration of Large-Scale Electrical Imaging into Geological Framework Development and Refinement
Geologic framework models (GFMs) are critical to the construction of reliable simulation models of groundwater flow and contaminant transport. To support GFM development, direct information (e.g., core samples, fluid samples, hydraulic testing) tends to be sparse and separated by large distances relative to the spatial scales of aquifer heterogeneity. There are additional challenges associated with highly contaminated legacy waste sites, where drilling is particularly costly, and invasive sampling requires specialized handling and disposal of hazardous materials. At these sites in particular, non-invasive geophysical imaging can play an important role in filling spatial gaps between boreholes and reducing characterization costs by optimizing and minimizing the number of necessary boreholes. Here this paper presents a case study demonstrating the use of large-scale (> 30 km 2 ) electrical mapping to identify hydrostratigraphy and potential paleochannels at the Hanford Site, located in Washington State, USA. In two field campaigns, over 36 line-kilometers of electrical resistivity tomography (ERT) data were collected along 14 transects. ERT surveys were sited and performed to image critical aspects (e.g., paleochannels, stratigraphic contacts) of the subsurface, demonstrating a general workflow for integrating ERT with GFM development. Inconsistencies between the GFM and ERT were catalogued to provide a basis for future site characterization using complementary geophysical methods and (or) direct sampling.
58 GEOSCIENCES↗
An approach for spent nuclear fuel containment integrity verification using gas tagging
Verification of containment integrity is required for spent nuclear fuel (SNF) managed by the commercial nuclear industry and U.S. Department of Energy (DOE), especially after extended storage. Certain SNF storage systems, such as the DOE road-ready dry storage system, hold several packaged containments within a welded over-canister. These packaged containments are called Department of Energy Standard Canisters (DOESCs). DOESC leakage identification is challenging because their containment boundary cannot be accessed for testing and their contents (i.e., SNF and fill gas) are often similar. There are concerns that this could result in costly characterization and repackaging operations of DOE road-ready dry storage systems if compromised DOESCs are suspected. Here, to address these concerns, this paper presents an approach for applying a gas tagging process using xenon to uniquely identify compromised inaccessible containments following extended storage. The containments considered for this application are seven DOESCs, each packaged within a single over-canister. Two different SNF loading configurations from the Advanced Test Reactor and Fort Saint Vrain nuclear power plant are considered. These configurations are used to represent research reactor aluminum-clad spent nuclear fuel (ASNF) and TRi-structural ISOtropic (TRISO) SNF types. Results for this application show that for ASNF and TRISO type fuels for which the selected fuels are representative, the volume of taggant required at loading is determined primarily by the lower detection limit and leak rate of taggant from a compromised DOESC, rather than the amount of fission-generated xenon in the loaded fuel. While the application presented is suited for larger leaks, smaller leaks could be detected by modifying certain design parameters. This gas tagging approach can also be applied to other DOE containments and advanced reactor SNF storage systems.
07 - ISOTOPES AND RADIATION SOURCES↗
Development of Luminescent Materials and Instrumentation for Critical Mineral Discovery and Monitoring
Presentation discussing career paths in chemistry as well as how chemical research is applied to address real-world problems. Here, a variety of different materials (metal-organic frameworks, carbon dots, thin films, etc.) are used as luminescent sensors to detect trace quantities of economically critical metals, which are used in applications ranging from renewable energy to national defense. In an application particularly relevant to Western Pennsylvania, the sensor technologies are deployed in coal utilization byproducts including fly ash leachates and acid mine drainage. Taken together, this research presents an exciting path towards reducing the characterization costs associated with metals prospecting and process monitoring, facilitating domestic production of these metals.
analytical chemistry↗
The INSTEP Monitoring Network: Merging High-and-Low Cost Measurements to Characterize California Wildfires
Despite challenges with data quality and scope, low-cost sensor networks have skyrocketed in popularity over the last 15 years, making air quality data available on refined spatial scales. More recently, studies have leveraged both high and low-quality instruments to create stronger “hybrid” models, with most studies focusing on particulate matter. Low-cost measurements typically represent ground-level emissions only, providing context for human health issues from climate change-driven events such as wildfires. Since low-cost sensors’ capabilities are localized, daily events and microclimates tend to dominate the data rather than larger regional or atmospheric trends. Likewise, their low cost explains their high uncertainty. In contrast, some regulatory-grade instruments produce column measurements as well, providing reliable information on a broader scope. To bridge this gap while expanding into gas-phase measurements, we deployed 12 air quality sensor packages in California, USA during the 2022 wildfire season. These INSTEP (Inexpensive Network Sensor Technology Exploring Pollution) monitors measure carbon monoxide (CO), carbon dioxide (CO2), ozone (O3), nitrogen dioxide (NO2), and several hydrocarbons including methane (CH4) and formaldehyde (HCHO). Half of the monitors were co-located with remote sensing spectrometers: NASA Pandora and Total Column Carbon Observing Network (TCCON). The overlap in pollutants includes NO2, O3, and HCHO between the INSTEP monitors and the Pandora column measurements. TCCON covers column CO, CO2, and CH4, rounding out our comparison. Most of the monitors were distributed throughout the San Francisco Bay area, and an additional three were located within 100 km of Los Angeles. The sites ranged in geographic and population characteristics, including desert, mountainous, coastal, and urban locations. Since varying environmental conditions such as temperature and pressure are known to challenge sensor performance, we will apply newer sensor “calibration” techniques meant to combat this. We will normalize our sensor signals by z-scoring them prior to applying a single calibration model in the form of multivariate linear regression or an artificial neural network. While this technique has been validated for the hydrocarbon and ozone sensor types (metal oxide), it has not yet been tested on electrochemical and non-dispersive infrared sensors, which are also used in the INSTEP monitors. This will serve as a test to see if this normalization technique – or another – is most effective in accounting for environmental differences among sensors. Related data analysis efforts have found success with a variety of geospatial analysis techniques, including weighted network models in which high-quality instruments are given higher weights than their low-cost counterparts. Our preliminary analysis will focus on kriging, which uses a Gaussian algorithm to assign weights, providing estimated pollution levels at locations between monitors. Smoke trajectory and evolution will also be considered using both measurement types. We also aim to baseline subtract our emission estimates from each region to determine which portion of emissions are regional and local, further characterizing burn differences in northern and southern California fires. Future directions include using INSTEP jointly with TEMPO satellite data, and mobile deployments on aircraft and uncrewed aerial vehicles (UAV).
Low-cost sensors↗
A Methodology for Cost Effective Radiation Characterization of COTS Hardware for Space Use
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Jeremy Myers↗
AlgaSens: An AI-enabled Platform for Cost-effective Automated Characterization of Algae and Other Micro-objects for Optimizing Research and Cultivation in Bioproducts & Biofuels.
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algae bioproducts↗
Characterizing and Modeling the Cost of Rework in a Library of Reusable Software Components
In this paper we characterize and model the cost of rework in a Component Factory (CF) organization. A CF is responsible for developing and packaging reusable software components. Data was collected on corrective maintenance activities for the Generalized Support Software reuse asset library located at the Flight Dynamics Division of NASA's GSFC. We then constructed a predictive model of the cost of rework using the C4.5 system for generating a logical classification model. The predictor variables for the model are measures of internal software product attributes. The model demonstrates good prediction accuracy, and can be used by managers to allocate resources for corrective maintenance activities. Furthermore, we used the model to generate proscriptive coding guidelines to improve programming, practices so that the cost of rework can be reduced in the future. The general approach we have used is applicable to other environments.
Basili, Victor R.↗
A Novel Method for Characterizing Spacesuit Mobility Through Metabolic Cost
Historically, spacesuit mobility has been characterized by directly measuring both range of motion and joint torque of individual anatomic joints. The work detailed herein aims to improve on this method, which is often prone to uncertainly, lack of repeatability, and a general lack of applicability to real-world functional tasks. Specifically, the goal of this work is to characterize suited mobility performance by directly measuring the metabolic performance of the occupant. Pilot testing was conducted in 2013, employing three subjects performing a range of functional tasks in two different suits prototypes, the Mark III and Z-1. Cursory analysis of the results shows the approach has merit, with consistent performance trends toward one suit over the other. Forward work includes the need to look at more subjects, a refined task set, and another suit in a different mass/mobility regime to validate the approach.
McFarland, Shane M.↗
A Novel Method for Characterizing Spacesuit Mobility through Metabolic Cost
Spacesuit mobility has historically been defined and characterized by a combination of range of motion and joint torque of the individual anatomical joints when performing isolated motions meant to drive that joint only in a given orthogonal plane. While this has been the standard approach for several decades, there are numerous shortcomings that suit designers and engineers would like to see rectified. First, the lack of a standardized method for collecting both range of motion and joint torque translates to many different test setups, procedures and methods of data analysis. Second, all of these previously used methods for data collection lack some degree of repeatability, even within the same test setup and the same conductor; in addition, attempts at higher fidelity data collection techniques require high overhead and cost with minimal improvement. Lastly, isolated motions in standard anatomical planes are not representative of real‐world tasks that a crewmember would be performing during an EVA, be it microgravity or surface exploration based. To address these shortcomings, options are being explored within the Space Suit and Crew Survival Systems Branch to ascertain the feasibility of an alternative approach to defining mobility - one that is more repeatable, lower overhead, and more tied to functional EVA tasks. This paper serves to document the first attempt at such an alternative option - one that looks at the metabolic energy‐cost of a spacesuit. In other words, can we objectively compare the mobility of a spacesuit by evaluating the metabolic cost of that suit to the wearer while performing a battery of functional EVA tasks?
McFarland, Shane↗
Autonomous elemental characterization enabled by a low cost robotic platform built upon a generalized software architecture
Despite the rapidly growing applications of robots in industry, the use of robots to automate tasks in scientific laboratories is less prolific due to the lack of generalized methodologies and the high cost of hardware. This paper focuses on the automation of characterization tasks necessary for reducing cost while maintaining generalization and proposes a software architecture for building robotic systems in scientific laboratory environments. A dual-layer (Socket.IO and ROS) action server design is the basic building block, which facilitates the implementation of a web-based front end for user-friendly operation and the use of ROS Behavior Trees for convenient task planning and execution. A robotic platform for automating mineral and material sample characterization is built upon the architecture, with an open-source, low-cost three-axis computer numerical control gantry system serving as the main robot. A handheld laser induced breakdown spectroscopy (LIBS) analyzer is integrated with a 3D printed adapter, enabling (1) automated 2D chemical mapping and (2) autonomous sample measurement (with the support of an RGB-Depth camera). We demonstrate the utility of automated chemical mapping by scanning the surface of a spodumene-bearing pegmatite core sample with a 1071-point dense hyperspectral map acquired at a rate of 1520 bits per second. Furthermore, we showcase the autonomy of the platform in terms of perception, dynamic decision-making, and execution, through a case study of LIBS measurement of multiple mineral samples. The platform enables controlled and autonomous chemical quantification in the laboratory that complements field-based measurements acquired with the same handheld device, linking resource exploration and processing steps in the supply chain for lithium-based battery materials.
Cao, Xuan [Lawrence Berkeley National Laboratory (↗
Distribution of Cost Growth in Robotic Space Science Missions
Cost growth characterization is a critical factor for effective cost risk analysis and project planning. This study analyzed low level budget changes in Jet Propulsion Laboratory-managed space science missions, which occurred during the development of the project. The data was then curve fit, according to cost distribution categories, to provide a reference set of distribution parameters with sufficient granularity to effectively model cost growth in robotic space science missions.
cost↗