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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Quantifying Investment Risk: Analysis of the Purchase Decision of a Nuclear Power Plant (Presentation)

Cost overruns are an ill-fated part of the deployment history of nuclear power plants (NPPs) in the United States, and yet studies increasingly show the important role nuclear technologies must play in decarbonizing the U.S. economy. Paradoxically, then, a key piece of a coherent decarbonization strategy depends on attracting investor action to a purchase where historical cost overruns have been sizable. To address this challenge, this study aims to develop a financial model that quantifies the risk of cost overruns in the decision-making process for purchasing advanced reactor concepts. Using the concept of value at risk (VaR), the model is built to evaluate financial risk nuclear construction with the aim to identify risk mitigation strategies. The objective is to identify strategies to mitigate cost-risk challenges and assess the potential reduction in investor risk exposure. This paper presents the initial development and preliminary verification of the financial risk analysis model. The development of this model involved a comprehensive approach to estimating financial risk over the operating life of NPP that stems from construction uncertainties. By utilizing net present value (NPV) with discounted cash flows, the model captures the complex interconnections of project costs, construction timelines, revenue, and uncertainties. Verifying the model involved testing historical data from previous reactor construction projects against the construction project of Vogtle 3 and 4. This paper’s results present the comparison of the preconstruction cost overrun prediction with the current cost estimates from a nearly complete Vogtle 3 and 4.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Quantifying Investment Risk: Analysis of the Purchase Decision of a Nuclear Power Plant

Cost overruns are an ill-fated part of the deployment history of nuclear power plants (NPPs) in the United States, and yet studies increasingly show the important role nuclear technologies must play in decarbonizing the U.S. economy. Paradoxically, then, a key piece of a coherent decarbonization strategy depends on attracting investor action to a purchase where historical cost overruns have been sizeable. To address this challenge, this study aims to develop a financial model that quantifies risk of cost overruns in the decision-making process for purchasing advanced reactor concepts. Using the concept of Value at Risk (VaR), the model is built to evaluate financial risk nuclear construction with the aim to identify risk mitigation strategies. The objective is to identify strategies to mitigate cost-risk challenges and to assess the potential reduction in investor risk exposure. The paper presents the initial development and preliminary verification of the financial risk analysis model. The development of this model involved a comprehensive approach to estimating financial risk over the operating life of NPP that stems from construction uncertainties. By utilizing net present value (NPV) with discounted cash flows, the model captures the complex interconnections of project costs, construction timelines, revenue, and uncertainties. Verification of the model involved testing historical data from previous reactor construction projects against the construction project of Vogtle 3 and 4. The results of this paper present the comparison of the preconstruction cost overrun prediction with the current cost estimates from a nearly complete Vogtle 3 and 4.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2017

Analysis of the decision-support function of policy assessment in real-world policy making in the field of poverty and social inequalities. Case study on migrant integration policies in the Brussels-Capital Region

Despite its high potential to support decision-making, the role of policy assessment in real-world policy making in the field of poverty and social inequalities remains largely questioned. In this study, we analyse policy assessment's role in a context of real-world policymaking, by means of a case study on a legislative proposal on integration policy for immigrant newcomers in the Brussels-Capital Region, for which we evaluate the potential effects on poverty and social inequalities. We first analyse the policy process surrounding the policy proposal – a process that is often treated as a black box within policy assessment research. Understanding the factors that influence and determine the decision-making process, enables us to gain insight into the potential decision-support function(s). Second, we develop an approach to policy assessment that aims to fully exploit its potential to contribute to the functions of both instrumental and conceptual learning. For this purpose, we propose to introduce the approach of realist evaluation and to focus on evaluating the underlying policy intervention theory from the perspective of poverty and social inequalities. Finally, we illustrate this new approach and its added value by applying it to the legislative proposal on integration policy and analyse its contribution to policy-oriented learning. - Highlights: •The field of policy assessment should draw on insights from policy studies. •We unpacked the policymaking black-box to identify the mechanisms of policy change. •The policy process is driven by an interaction of ideas, interests and institutions. •Policy assessment's potential lies in both instrumental and conceptual learning. •We propose to integrate realist evaluation's logic of inquiry within policy assessment.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Comparative Analysis of ML Techniques for Data-Driven Anomaly Detection, Classification and Localization in Distribution System

High penetration of Distributed Energy Resources (DERs), fundamental load behavior changes, controllable loads, and significant increase in Electrical Vehicles (EVs) lead to complex dynamic behavior of the electric distribution system. Increasing number of components also means more measurements, more data and more data anomalies. Detecting, classifying and localizing these anomalies are important for situational awareness, and at the same time, very challenging given increasing complexity of the system. Highly accurate and high-resolution analytical techniques are needed to support anomaly detection, classification and localization (AD-C-L) for monitoring, root cause analysis and decision making. This paper provides comprehensive review and analysis of the existing spatio-temporal AD-C-L techniques within the distribution system. Challenges for specific problems in AD-C- L have been also discussed in this paper. Existing AD-C- L techniques have been categorized and synthesized for specific merits and limitations of multiple Machine Learning (ML) methodologies using common developed metrics of performance. The comparative analysis is summarized and presented with the open research challenges and path forward for future research needs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2010

Decision Engines for Software Analysis Using Satisfiability Modulo Theories Solvers

The area of software analysis, testing and verification is now undergoing a revolution thanks to the use of automated and scalable support for logical methods. A well-recognized premise is that at the core of software analysis engines is invariably a component using logical formulas for describing states and transformations between system states. The process of using this information for discovering and checking program properties (including such important properties as safety and security) amounts to automatic theorem proving. In particular, theorem provers that directly support common software constructs offer a compelling basis. Such provers are commonly called satisfiability modulo theories (SMT) solvers. Z3 is a state-of-the-art SMT solver. It is developed at Microsoft Research. It can be used to check the satisfiability of logical formulas over one or more theories such as arithmetic, bit-vectors, lists, records and arrays. The talk describes some of the technology behind modern SMT solvers, including the solver Z3. Z3 is currently mainly targeted at solving problems that arise in software analysis and verification. It has been applied to various contexts, such as systems for dynamic symbolic simulation (Pex, SAGE, Vigilante), for program verification and extended static checking (Spec#/Boggie, VCC, HAVOC), for software model checking (Yogi, SLAM), model-based design (FORMULA), security protocol code (F7), program run-time analysis and invariant generation (VS3). We will describe how it integrates support for a variety of theories that arise naturally in the context of the applications. There are several new promising avenues and the talk will touch on some of these and the challenges related to SMT solvers. Proceedings

Bjorner, Nikolaj↗
NASA NTRS2011

NASA Earth Science Image Analysis for Climate Change Decisions

This talk will briefly outline the ways in which NASA observes the Earth, then describes the NASA satellite measurements, and then proceeds to show how these measurements are used to understand the changes that are occurring as Earth's climate warms.

Hilderbrand, Peter H.↗
NASA NTRSDate not supplied

Leveraging Nurse Informaticists for Development of the Impact Tool

The American Nurses Association (ANA) defines nursing informatics as the integration of “nursing science, computer science, and information science to manage and communicate data, information, knowledge, and wisdom in nursing practice. [1]” In additional to nursing education and clinical experience, this field requires additional education in areas of data analysis, database management, and clinical information systems. Nursing informaticists help bridge the communication gap between clinical and technical stakeholders which help align common goals. NASA is currently developing a new medical system trade analysis and decision support tool called IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces). The development team is comprised of multiple specialties including but not limited to clinicians (pharmacists, physicians, and nurse informaticists), software and program developers, human factors experts, and engineers. The nurse informaticists play a significant role in the development, operation, and results interpretation of IMPACT. Their expert knowledge and skills in both clinical nursing, information science, and database management place them in a unique position to collaborate and integrate with the clinical, engineering, and software development members of the team to achieve a cohesive, functional, and user-friendly model.

Lynn Boley↗
DOE OSTI2022

Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation

The ever-increasing demand for novel materials with superior properties inspires retrofitting traditional research paradigms in the era of artificial intelligence and automation. An autonomous experimental platform (AEP) has emerged as an exciting research frontier that achieves full autonomy via integrating data-driven algorithms such as machine learning (ML) with experimental automation in the material development loop from synthesis, characterization, and analysis, to decision making. In this review, we started with a primer to describe how to develop data-driven algorithms for solving material problems. Then, we systematically summarized recent progress on automated material synthesis, ML-enabled data analysis, and decision-making. Finally, we discussed the challenges and opportunities in an endeavor to develop the next-generation AEP for ultimately realizing an autonomous or self-driving laboratory. In conclusion, this review will provide insights for researchers aiming to learn the frontier of ML in materials science and deploy AEP in their labs for accelerating material development.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Assessing decision boundaries under uncertainty

In order to make design decisions, engineers may seek to identify regions of the design domain that are acceptable in a computationally efficient manner. A design is typically considered acceptable if its reliability with respect to parametric uncertainty exceeds the designer’s desired level of confidence. Despite major advancements in reliability estimation and in design classification via decision boundary estimation, the current literature still lacks a design classification strategy that incorporates parametric uncertainty and desired design confidence. To address this gap, this paper offers a novel interpretation of the acceptance region by defining the decision boundary as the hypersurface which isolates the designs that exceed a user-defined level of confidence given parametric uncertainty. This work addresses the construction of this novel decision boundary using computationally efficient algorithms that were developed for reliability analysis and decision boundary estimation. The approach proposed in this paper is verified on two physical examples from structural and thermal analysis using Support Vector Machines and Efficient Global Optimization-based contour estimation.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2011

Fast Image Texture Classification Using Decision Trees

Texture analysis would permit improved autonomous, onboard science data interpretation for adaptive navigation, sampling, and downlink decisions. These analyses would assist with terrain analysis and instrument placement in both macroscopic and microscopic image data products. Unfortunately, most state-of-the-art texture analysis demands computationally expensive convolutions of filters involving many floating-point operations. This makes them infeasible for radiation- hardened computers and spaceflight hardware. A new method approximates traditional texture classification of each image pixel with a fast decision-tree classifier. The classifier uses image features derived from simple filtering operations involving integer arithmetic. The texture analysis method is therefore amenable to implementation on FPGA (field-programmable gate array) hardware. Image features based on the "integral image" transform produce descriptive and efficient texture descriptors. Training the decision tree on a set of training data yields a classification scheme that produces reasonable approximations of optimal "texton" analysis at a fraction of the computational cost. A decision-tree learning algorithm employing the traditional k-means criterion of inter-cluster variance is used to learn tree structure from training data. The result is an efficient and accurate summary of surface morphology in images. This work is an evolutionary advance that unites several previous algorithms (k-means clustering, integral images, decision trees) and applies them to a new problem domain (morphology analysis for autonomous science during remote exploration). Advantages include order-of-magnitude improvements in runtime, feasibility for FPGA hardware, and significant improvements in texture classification accuracy.

Thompson, David R.↗
NASA NTRS2004

"ATLAS" Advanced Technology Life-cycle Analysis System

Making good decisions concerning research and development portfolios-and concerning the best systems concepts to pursue - as early as possible in the life cycle of advanced technologies is a key goal of R&D management This goal depends upon the effective integration of information from a wide variety of sources as well as focused, high-level analyses intended to inform such decisions Life-cycle Analysis System (ATLAS) methodology and tool kit. ATLAS encompasses a wide range of methods and tools. A key foundation for ATLAS is the NASA-created Technology Readiness. The toolkit is largely spreadsheet based (as of August 2003). This product is being funded by the Human and Robotics The presentation provides a summary of the Advanced Technology Level (TRL) systems Technology Program Office, Office of Exploration Systems, NASA Headquarters, Washington D.C. and is being integrated by Dan O Neil of the Advanced Projects Office, NASA/MSFC, Huntsville, AL

Lollar, Louis F.↗
NASA NTRS2014

Real-Time Science Operations to Support a Lunar Polar Volatiles Rover Mission

Future human exploration of the Moon will likely rely on in situ resource utilization (ISRU) to enable long duration lunar missions. Prior to utilizing ISRU on the Moon, the natural resources (in this case lunar volatiles) must be identified and characterized, and ISRU demonstrated on the lunar surface. To enable future uses of ISRU, NASA and the CSA are developing a lunar rover payload that can (1) locate near subsurface volatiles, (2) excavate and analyze samples of the volatile-bearing regolith, and (3) demonstrate the form, extractability and usefulness of the materials. Such investigations are important both for ISRU purposes and for understanding the scientific nature of these intriguing lunar volatile deposits. Temperature models and orbital data suggest near surface volatile concentrations may exist at briefly lit lunar polar locations outside persistently shadowed regions. A lunar rover could be remotely operated at some of these locations for the approx. 2-14 days of expected sunlight at relatively low cost. Due to the limited operational time available, both science and rover operations decisions must be made in real time, requiring immediate situational awareness, data analysis, and decision support tools. Given these constraints, such a mission requires a new concept of operations. In this paper we outline the results and lessons learned from an analog field campaign in July 2012 which tested operations for a lunar polar rover concept. A rover was operated in the analog environment of Hawaii by an off-site Flight Control Center, a rover navigation center in Canada, a Science Backroom at NASA Ames Research Center in California, and support teams at NASA Johnson Space Center in Texas and NASA Kennedy Space Center in Florida. We find that this type of mission requires highly efficient, real time, remotely operated rover operations to enable low cost, scientifically relevant exploration of the distribution and nature of lunar polar volatiles. The field demonstration illustrated the need for science operations personnel in constant communications with the flight mission operators and the Science Backroom to provide immediate and continual science support and validation throughout the mission. Specific data analysis tools are also required to enable immediate data monitoring, visualization, and decision making. The field campaign demonstrated that this novel methodology of real-time science operations is possible and applicable to providing important new insights regarding lunar polar volatiles for both science and exploration.

Moon; Volatiles; Rover; Missions↗
NASA NTRS2011

Investigating On-Orbit Attitude Determination Anomalies for the Solar Dynamics Observatory Mission

The Solar Dynamics Observatory (SDO) was launched on February 11, 2010 from Kennedy Space Center on an Atlas V launch vehicle into a geosynchronous transfer orbit. SDO carries a suite of three scientific instruments, whose observations are intended to promote a more complete understanding of the Sun and its effects on the Earth's environment. After a successful launch, separation, and initial Sun acquisition, the launch and flight operations teams dove into a commissioning campaign that included, among other things, checkout and calibration of the fine attitude sensors and checkout of the Kalman filter (KF) and the spacecraft s inertial pointing and science control modes. In addition, initial calibration of the science instruments was also accomplished. During that process of KF and controller checkout, several interesting observations were noticed and investigated. The SDO fine attitude sensors consist of one Adcole Digital Sun Sensor (DSS), two Galileo Avionica (GA) quaternion-output Star Trackers (STs), and three Kearfott Two-Axis Rate Assemblies (hereafter called inertial reference units, or IRUs). Initial checkout of the fine attitude sensors indicated that all sensors appeared to be functioning properly. Initial calibration maneuvers were planned and executed to update scale factors, drift rate biases, and alignments of the IRUs. After updating the IRU parameters, the KF was initialized and quickly reached convergence. Over the next few hours, it became apparent that there was an oscillation in the sensor residuals and the KF estimation of the IRU bias. A concentrated investigation ensued to determine the cause of the oscillations, their effect on mission requirements, and how to mitigate them. The ensuing analysis determined that the oscillations seen were, in fact, due to an oscillation in the IRU biases. The low frequencies of the oscillations passed through the KF, were well within the controller bandwidth, and therefore the spacecraft was actually following the oscillating biases, resulting in movement of the spacecraft on the order of plus or minus 20 arcsec. Though this level of error met the ACS attitude knowledge requirement of [35, 70, 70] arcsec, 3 sigma, the desire of the ACS and instrument teams was to remove as much of the oscillation as possible. The Kearfott IRUs have an internal temperature controller, designed to maintain the IRU temperature at a constant temperature of approximately 70 C, thus minimizing the change in the bias drift and scale factors of the mechanical gyros. During ground testing of the observatory, it was discovered that the 83-Hz control cycle of the IRU heaters put a tremendous amount of stress on the spacecraft battery. Analysis by the power systems team indicated that the constant charge/discharge on the battery due to the IRU thermal control cycle could potentially limit the life of the battery. After much analysis, the decision was made not to run the internal IRU heaters. Analysis of on orbit data revealed that the oscillations in the IRU bias had a connection to the temperature of the IRU; changes in IRU temperature resulted in changes in the amplitude and period of the IRU biases. Several mitigating solutions were investigated, the result of which was to tune the KF with larger IRU noise assumptions which allows the KF to follow and correct for the time-varying IRU biases.

Vess, Melissa F.↗
DOE OSTI2016

PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING

Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques that fit parametric models to curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k -nearest neighbors, support vector machines, artificial neural networks, and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Modeling the Swift BAT trigger algorithm with machine learning

To draw inferences about gamma-ray burst (GRB) source populations based on Swift observations, it is essential to understand the detection efficiency of the Swift burst alert telescope (BAT). This study considers the problem of modeling the Swift/BAT triggering algorithm for long GRBs, a computationally expensive procedure, and models it using machine learning algorithms. A large sample of simulated GRBs from Lien et al. is used to train various models: random forests, boosted decision trees (with AdaBoost), support vector machines, and artificial neural networks. The best models have accuracies of ≳97% (≲3% error), which is a significant improvement on a cut in GRB flux, which has an accuracy of 89.6% (10.4% error). These models are then used to measure the detection efficiency of Swift as a function of redshift z, which is used to perform Bayesian parameter estimation on the GRB rate distribution. We find a local GRB rate density of n{sub 0}∼0.48{sub −0.23}{sup +0.41} Gpc{sup −3} yr{sup −1} with power-law indices of n{sub 1}∼1.7{sub −0.5}{sup +0.6} and n{sub 2}∼−5.9{sub −0.1}{sup +5.7} for GRBs above and below a break point of z{sub 1}∼6.8{sub −3.2}{sup +2.8}. This methodology is able to improve upon earlier studies by more accurately modeling Swift detection and using this for fully Bayesian model fitting.

79 ASTRONOMY AND ASTROPHYSICS↗