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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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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Radiometer calibration methods and resulting irradiance differences

Accurate solar radiation measured by radiometers depends on instrument performance specifications, installation method, calibration procedure, measurement conditions, maintenance practices, location, and environmental conditions. This study addresses the effect of different calibration methodologies and resulting differences provided by radiometric calibration service providers such as the National Renewable Energy Laboratory (NREL) and manufacturers of radiometers. Some of these methods calibrate radiometers indoors and some outdoors. To establish or understand the differences in calibration methodologies, we processed and analyzed field-measured data from radiometers deployed for 10 months at NREL's Solar Radiation Research Laboratory. These different methods of calibration resulted in a difference of ±1% to ±2% in solar irradiance measurements. Here by analyzing these differences will ultimately assist in determining the uncertainties of the field radiometer data and will help develop a consensus on a standard for calibration. Further advancing procedures for precisely calibrating radiometers to world reference standards that reduce measurement uncertainties will help the accurate prediction of the output of planned solar conversion projects and improve the bankability of financing solar projects.

14 SOLAR ENERGY↗
DOE OSTI2018

Trends in the market structure of US residential solar PV installation, 2000 to 2016: An evolving industry

The US residential solar photovoltaic (PV) installation grew from fewer than 100 firms in 2000 to about 3000 firms by 2016. The rapid expansion of this emerging industry prompts questions about the installation industry's market structure, such as how market shares are distributed among installers and how interinstaller competition affects PV deployment. This article leverages a rich dataset to describe trends in the evolution of the US residential PV installation industry. The data show that most residential PV installers are relatively small companies, with about half of installers installing fewer than 5 systems. At the same time, a subset of high-volume installers have accumulated market share, especially from 2010 to 2016. Furthermore causes of this recent market concentration are explored, with an emphasis on the role of the emergence of alternative customer financing options in the United States.

14 SOLAR ENERGY↗
DOE OSTI2026

A Systems-Level Approach to Address Risks and Ethics in Artificial Intelligence Systems

Artificial intelligence (AI) is rapidly changing the world, from completely controlling routine or mundane tasks like text and image generation, to powering advanced algorithms that control critical systems. The recent advances in generative AI quickly overwhelmed multiple industries from education to finance as first adopters rushed (and continue to rush) to take advantage of the technology. The expanding AI ecosystem presents novel risks and ethical challenges that must be handled to ensure that technology is leveraged fairly and ethically. There are intertwined risks and ethical challenges stemming from the stochastic nature of AI (i.e., intrinsic risks), as well as from specific applications (i.e., extrinsic risks). Appropriately regulating AI requires a systems-approach to develop an integrated solution to these dependent challenges. Thus far, however, questions of risk, ethics and regulation appear to occupy separate spaces. This paper reviews the risks and ethical implications of AI and proposes a system-level approach to integrating ethics and regulation for the nascent industry.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

A comparison of methods for assessing power output in non-uniform onshore wind farms

Wind resource assessments are used to estimate a wind farm's power production during the planning process. It is important that these estimates are accurate, as they can impact financing agreements, transmission planning, and environmental targets. Here, we analyze the challenges in wind power estimation for onshore farms. Turbine wake effects are a strong determinant of farm power production. With given input wind conditions, wake losses typically cause downstream turbines to produce significantly less power than upstream turbines. These losses have been modeled extensively and are well understood under certain conditions. Most notably, validation of different model types has favored offshore farms. Models that capture the dynamics of offshore wind conditions do not necessarily perform equally as well for onshore wind farms. We analyze the capabilities of several different methods for estimating wind farm power production in 2 onshore farms with non-uniform layouts. We compare the Jensen model to a number of statistical models, to meteorological downscaling techniques, and to using no model at all. In conclusion, we show that the complexities of some onshore farms result in wind conditions that are not accurately modeled by the Jensen wake decay techniques and that statistical methods have some strong advantages in practice.

17 WIND ENERGY↗
DOE OSTI2021

Floating platform effects on power generation in spar and semisubmersible wind turbines

The design and financing of commercial-scale floating offshore wind projects require a better understanding of how power generation differs between newer floating turbines and well-established fixed-bottom turbines. In floating turbines, platform mobility causes additional rotor motion that can change the time-averaged power generation. In this work, OpenFAST simulations examine the power generated by the National Renewable Energy Laboratory's 5-MW reference turbine mounted on the OC3-UMaine spar and OC4-DeepCWind semisubmersible floating platforms, subjected to extreme irregular waves and below-rated turbulent inflow wind from large-eddy simulations of a neutral atmospheric boundary layer. For these below-rated conditions, average power generation in floating turbines is most affected by two types of turbine displacements: an average rotor pitch angle that reduces power, caused by platform pitch; and rotor motion upwind-downwind that increases power, caused by platform surge and pitch. The relative balance between these two effects determines whether a floating platform causes power gains or losses compared to a fixed-bottom turbine; for example, the spar creates modest (3.1%–4.5%) power gains, whereas the semisubmersible creates insignificant (0.1%–0.2%) power gains for the simulated conditions. Furthermore, platform surge and pitch motions must be analyzed concurrently to fully capture power generation in floating turbines, which is not yet universal practice. Finally, a simple analytical model for predicting average power in floating turbines under below-rated wind speeds is proposed, incorporating effects from both the time-averaged pitch displacement and the dynamic upwind-downwind displacements.

17 WIND ENERGY↗
DOE OSTI2019

Creating Abundance: Nexus Stress as a Driver for Innovation in Solving Energy and Water Stress

Energy and water nexus stress drives innovation in technology, financing models, business models, and partnerships. There are new platforms to accelerate nexus innovation that leverage the skills and experience of successful entrepreneurs from outside the energy and water sectors. New business and financial models are needed to solve energy and water nexus stress challenges. New partnership models are emerging to foster cross-industry and entrepreneurial platform collaboration.

abundance↗
DOE OSTI2022

Addressing Regulatory Challenges to Tribal Solar Deployment: Preprint

Although Tribal land represents more than 5% of the solar photovoltaic technical potential in the United States, this resource is largely untapped due to a range of barriers, including complex project economics, Tribal technical and human resource capacity, project funding and financing obstacles, and regulatory challenges. To identify and better understand the regulatory barriers, the National Renewable Energy Laboratory (NREL) and the Midwest Tribal Energy Resources Association (MTERA) engaged Tribes, utilities, and regulators. Funded by the U.S. Department of Energy, the 3-year effort seeks to address regulatory challenges or barriers that affect Tribal solar projects differently - specifically or disproportionately because they are located on Tribal lands. This paper, largely excerpted from a comprehensive (draft) guidebook released by NREL and MTERA, provides an overview of 13 key regulatory barriers identified through this research, as well as potential short- and long-term solutions. In addition, the paper points to potential pathways for addressing key barriers through case studies highlighting successful Tribal solar projects along with considerations for stakeholders working with Tribes. These resources can support stakeholders in creating meaningful relationships and pursuing workable solar projects.

ENERGY PLANNING, POLICY, AND ECONOMY,SOLAR ENERGY↗
DOE OSTI2013

A Model-Free No-arbitrage Price Bound for Variance Options

We suggest a numerical approximation for an optimization problem, motivated by its applications in finance to find the model-free no-arbitrage bound of variance options given the marginal distributions of the underlying asset. A first approximation restricts the computation to a bounded domain. Then we propose a gradient projection algorithm together with the finite difference scheme to solve the optimization problem. We prove the general convergence, and derive some convergence rate estimates. Finally, we give some numerical examples to test the efficiency of the algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Dynamic Programming and Error Estimates for Stochastic Control Problems with Maximum Cost

This work is concerned with stochastic optimal control for a running maximum cost. A direct approach based on dynamic programming techniques is studied leading to the characterization of the value function as the unique viscosity solution of a second order Hamilton–Jacobi–Bellman (HJB) equation with an oblique derivative boundary condition. A general numerical scheme is proposed and a convergence result is provided. Error estimates are obtained for the semi-Lagrangian scheme. These results can apply to the case of lookback options in finance. Moreover, optimal control problems with maximum cost arise in the characterization of the reachable sets for a system of controlled stochastic differential equations. Some numerical simulations on examples of reachable analysis are included to illustrate our approach.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Implications of climate change mitigation strategies on international bioenergy trade

Most climate change mitigation scenarios rely on increased use of bioenergy to decarbonize the energy system. Here we use results from the 33rd Energy Modeling Forum study (EMF-33) to investigate projected international bioenergy trade for different integrated assessment models across several climate change mitigation scenarios. Results show that in scenarios with no climate policy, international bioenergy trade is likely to increase over time, and becomes even more important when climate targets are set. More stringent climate targets, however, do not necessarily imply greater bioenergy trade compared to weaker targets, as final energy demand may be reduced. However, the scaling up of bioenergy trade happens sooner and at a faster rate with increasing climate target stringency. Across models, for a scenario likely to achieve a 2 °C target, 10–45 EJ/year out of a total global bioenergy consumption of 72–214 EJ/year are expected to be traded across nine world regions by 2050. While this projection is greater than the present trade volumes of coal or natural gas, it remains below the present trade of crude oil. This growth in bioenergy trade largely replaces the trade in fossil fuels (especially oil) which is projected to decrease significantly over the twenty-first century. As climate change mitigation scenarios often show diversified energy systems, in which numerous world regions can act as bioenergy suppliers, the projections do not necessarily lead to energy security concerns. Nonetheless, rapid growth in the trade of bioenergy is projected in strict climate mitigation scenarios, raising questions about infrastructure, logistics, financing options, and global standards for bioenergy production and trade.

09 BIOMASS FUELS↗
DOE OSTI2025

Analysis of the Financial Impacts of Building Performance Standard Penalties on Commercial Buildings in Aurora, Colorado

Buildings are responsible for 30% of total energy consumption worldwide. To address building energy, jurisdictions in the USA have enacted Building Performance Standards (BPS) legislation. The objective of BPS is to reduce energy consumption in buildings, thereby reducing the energy burden on utility infrastructure and other externalities. This is accomplished by setting mandatory energy use limits coupled with penalties for exceeding those limits. One of the key questions in BPS policymaking is how these penalties might impact the finances of building owners and tenants. This paper presents an analysis of BPS penalties in Aurora, Colorado, specifically targeting buildings impacted by the adopted statewide BPS legislation. Several BPS penalty structures were applied to the affected building stock in Aurora, and the potential impacts on building owner returns and tenant rents were estimated. The results show that for some combinations of building types and penalty structures, potential rent increases due to penalties could match or exceed typical yearly rent increases. The results also show that in most cases, for Aurora, there was no statistically significant difference in impact between buildings located in under-resourced and well-resourced areas.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

Comparison of Stochastic Correlation Models

Five models of stochastic correlation are compared on the basis of the generated associations of Wiener processes. Associations are characterised by the copulas and their K-functions. A confidence domain for the randomly changing K-functions is build on the basis of simulated Wiener process pairs. The models are ordered by the magnitude of the domains of confidence bounds. Larger confidence domains or higher bounds represent higher correlation risk when the models are applied in mathematical finance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Image encryption and watermarking combined dynamic chaotic hopping pattern with double random phase encoding DRPE

In modern civilization information, security is a crucial problem. Image encryption and watermarking can effectively protect information from unauthorized access. This paper introduces a new vigorous symmetric optical image encryption and watermarking technique. This technique depends on a novel dynamic delay chaotic hopping pattern (DDCHP) combined with double random phase encoding (DRPE). To get a robust watermark image, the plain image is first watermarked with a binary secret message using a new chaotic hopping pattern (CHP) and then encrypted using a DRPE system that is controlled by double random independent keys generated with double DDCHP. In this symmetric cryptosystem, three different chaotic maps are used to enlarge the key space of the proposed cryptosystem against brute-force attack and overcome the linearity and vulnerable of the classical DRPE. The system keys are hypersensitive and their initial parameters are selected carefully. Security scrutiny proves the efficiency of the suggested cryptosystem. Therefore, the obtained simulation results prove that the cryptosystem is robust enough against the known attacks, such as statistical attack (uniform histograms of different encrypted images, correlation coefficients between the plain and the encrypted image is as low as 5.46 × 10{sup −6}), differential attack (99.99% NPCR and 38.6% UACI), brute force attack (large enough key space), and entropy attack. Moreover, the robustness of the watermark (secret message) is proved due to the solidity of CHP. The proposed approach can be used in different implementations like secure military communications, e-finance, e-healthcare, authenticated multimedia broadcasting, etc.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Evaluating Gaussian process metamodels and sequential designs for noisy level set estimation

Abstract We consider the problem of learning the level set for which a noisy black-box function exceeds a given threshold. To efficiently reconstruct the level set, we investigate Gaussian process (GP) metamodels. Our focus is on strongly stochastic simulators, in particular with heavy-tailed simulation noise and low signal-to-noise ratio. To guard against noise misspecification, we assess the performance of three variants: (i) GPs with Student- t observations; (ii) Student- t processes (TPs); and (iii) classification GPs modeling the sign of the response. In conjunction with these metamodels, we analyze several acquisition functions for guiding the sequential experimental designs, extending existing stepwise uncertainty reduction criteria to the stochastic contour-finding context. This also motivates our development of (approximate) updating formulas to efficiently compute such acquisition functions. Our schemes are benchmarked by using a variety of synthetic experiments in 1–6 dimensions. We also consider an application of level set estimation for determining the optimal exercise policy of Bermudan options in finance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Deep learning for time series forecasting: a survey of recent advances

Time series forecasting plays a critical role in numerous real-world applications, such as finance, healthcare, transportation, and scientific computing. In recent years, deep learning has become a powerful tool for modeling complex temporal patterns and improving forecasting accuracy. This survey provides an overview of recent deep learning approaches for time series forecasting, involving various architectures including RNNs, CNNs, GNNs, transformers, large language models, MLP-based models, and diffusion models. We first identify key challenges in the field, such as temporal dependency, efficiency, and cross-variable dependency, which drive the development of forecasting techniques. Then, the general advantages and limitations of each architecture are discussed to contextualize their adaptation in time series forecasting. Furthermore, we highlight promising design trends like multi-scale modeling, decomposition, and frequency-domain techniques, which are shaping the future of the field. This paper serves as a compact reference for researchers and practitioners seeking to understand the current landscape and future trajectory of deep learning in time series forecasting.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Fast Fourier Transform for multivariate aggregate claims

The Fast Fourier Transform provides an alternative approximate method to evaluate the distribution of aggregate losses in insurance and finance. The efficiency of this method has already been proved for univariate and bivariate insurance models; therefore, in this paper, we extend it to a multivariate setting by considering its application to a particular model that includes losses of different types and dependency between them. Since the Fourier transform method works with truncated claims distributions, it can generate aliasing errors by wrapping around the probability mass that lies at the truncation point below this point. To eliminate this problem, we also discuss a suitable change of measure called exponential tilting that forces the tail of the distribution to decrease at exponential rate. Other possible errors are also discussed. We also illustrate the method on several numerical examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A two-grid penalty method for American options

In this paper we consider the pricing of American options, governed by a partial differential complementarity problem. The differential problem is first approximated by a semi-linear PDE using two distinct penalty approaches which are well known in computational finance. We then initiate the two-grid algorithm by solving the nonlinear problem on a coarse grid and further the linearized in the interpolated coarse-grid solution problem on a fine grid. By means of the maximum principle the algorithm is shown to be of fourth order convergence rate in space. Numerical experiments verify the presented two-grid approach where we draw some interesting conclusions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

On an asymptotic viscosity solution property of solutions of discrete Hamilton–Jacobi–Bellman equations

In this paper, we show that a proper limit of solutions of discrete Hamilton–Jacobi–Bellman (dHJB) equations in a random walk model becomes a viscosity solution of a Hamilton–Jacobi–Bellman (HJB) variational inequality in a continuous-time geometric Brownian model. HJB variational inequalities are used to analyze singular stochastic control problems in mathematical finance. By our result, with the help of dHJB equations, we can obtain viscosity solutions of HJB variational inequalities which are usually identified with the value functions of the singular stochastic control problems.

97 MATHEMATICS AND COMPUTING↗