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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Data-driven model validation for neutrino-nucleus cross section measurements

Neutrino-nucleus cross section measurements are needed to improve interaction modeling to meet the precision needs of neutrino experiments in efforts to measure oscillation parameters and search for physics beyond the Standard Model. We review the difficulties associated with modeling neutrino-nucleus interactions that lead to a dependence on event generators in oscillation analyses and cross section measurements alike. We then describe data-driven model validation techniques intended to address this model dependence. The method relies on utilizing various goodness-of-fit tests and the correlations between different observables and channels to probe the model for defects in the phase space relevant for the desired analysis. These techniques shed light on relevant mismodeling, allowing it to be detected before it begins to bias the cross section results. We compare more commonly used model validation methods which directly validate the model against alternative ones to these data-driven techniques and show their efficacy with fake data studies. These studies demonstrate that employing data-driven model validation in cross section measurements represents a reliable strategy to produce robust results that will stimulate the desired improvements to interaction modeling.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

An efficient computational algorithm for pricing European, barrier and American options

In this work, we use the spectral collocation technique for spatial derivatives and predictor–corrector method for time integration to solve the Black–Scholes (B–S) equation. If the spectral collocation method is worked properly, then we get high accuracy in the numerical solutions. Firstly, theory of application of Chebyshev spectral collocation technique and domain decomposition method on the B–S equation is presented. This method gets a stiff system of differential equations. Secondly, by using the predictor–corrector method with variable step size, we obtain the high accuracy approximate solution in the numerical integration of the stiff system of DEs. We present the order of accuracy for the proposed method. The numerical results show the efficiency and validity of the method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Framework For Spatial Agricultural Crop Yield Prediction Model Development

This framework was developed to provide data preprocessing for spatiotemporal agricultural yield data and remote sensing data for modelling using artificial neural networks (ANNs) to predict subfield crop yield estimates. The software includes methods to train, validate, and test ANN models. It also include methods to infer on new remote sensing data.

Griffel, LloydM.↗
DOE OSTI2021

Determination of Seven Organic Impurities in FD&C Yellow No. 6 by Ultra-High-Performance Liquid Chromatography

The U.S. Food and Drug Administration (FDA) batch certifies FD&C Yellow No. 6 (Y6) to ensure that the color additive meets requirements published in the Code of Federal Regulations (CFR), including specifications for seven organic manufacturing impurities consisting of two intermediates, a reaction by-product, an impurity originating from an intermediate, and three subsidiary colors. An ultra-high-performance liquid chromatography (UHPLC) method was developed and validated for determining seven organic impurities in Y6. For comparison, the currently used high-performance liquid chromatography (HPLC) method was also validated. The new UHPLC method uses a 1.7 µm particle size biphenyl column with aqueous ammonium formate and methanol as eluants. Analytes are identified by comparing their retention times and UV-visible spectra to those of reference standards. Calibration is performed in the presence of the dye matrix and analyte levels are determined from their peak areas. UHPLC and HPLC validation studies obtained linear calibration curves and excellent values for limits of detection, limits of quantitation, recovery, repeatability, and intermediate precision for all analytes. Survey analyses of 30 samples using the UHPLC and HPLC methods yielded results that were consistent within experimental error; however, the UHPLC method provided more accurate results for two analytes that coelute using the HPLC method. The UHPLC method satisfies the accuracy and precision requisites for routine certification of Y6. The UHPLC method is faster and generates less waste than the HPLC method while delivering better sensitivity, separation, and accuracy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Validation of the single-event method for low-energy electron transport via stopping power calculations with $\mathrm{MCNP}$

Monte Carlo simulations of low-energy ( <50 keV) electron transport in matter are essential for a broad range of application fields. Several Monte Carlo codes have developed specialized treatments for this case, but a comprehensive validation of low-energy electron transport for general-purpose simulations remains lacking in the literature. One approach to accomplish this validation is calculation of stopping power using low-energy electron transport physics, as stopping power is a fundamental radiation transport quantity which must be simulated accurately for nearly any application. Here in this work, we use the Monte Carlo N-Particle (MCNP) radiation transport code with the single-event method for electron transport to calculate stopping powers of low-energy electrons (50 eV to 30 keV) in 41 elemental solids, 14 compound solids, and five rare gas solids, comparing simulation results to published semi-empirical stopping power calculations from optical measurements. In general, the simulations give good agreement (typically within ±10%) with semi-empirical stopping power values at higher energies: 300 eV and above for most elemental solids, 1 keV and above for compound solids, and 400 eV and above for rare gas solids. Agreement between MCNP and semi-empirical values is worse below these energies. The most significant source of error is the EPRDATA14 cross section data, which does not account for changes in electronic structure due to solid-state bonding, particularly in compound materials. The simplistic model of atomic excitation used to generate the EPRDATA14 cross sections is another key source of error. Additionally, the breakdown of the continuous slowing-down approximation introduces significant uncertainty at low energies, although this is a limitation of the calculation method and not of the simulation procedure. Accounting for these and other uncertainty sources, the single event method in MCNP is robust and able to give good accuracy for a variety of low-energy electron transport problems through diverse kinds of materials.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2024

Photovoltaic inverter-based quantification of snow conditions and power loss

Snow is a significant challenge for photovoltaic (PV) systems at northern latitudes, where the pace of deployment is rapid but snow-related power losses can exceed 30% of annual production. Accurate snow-related power loss estimation methods for utility-scale sites can support snow mitigation strategies, inform resource planning and validate predictive snow-loss models. This study builds on our previous work on inverter-based detection of snow, and its implications for utility-scale power production, by validating the accuracy of our snow-loss method across different PV sites and system designs and highlighting its value in bringing greater visibility to PV plant operations in winter. Our estimation method is both novel and scalable, requiring only standard monitoring data to correlate snow-related losses with meteorological data. As demonstrated here, our validation method involved three main steps: 1) estimation of performance losses for multiple systems by comparing measured inverter data to modeled data; 2) application of a detection framework to identify which performance losses are snow-related; and 3) comparison of snow-related losses among three utility-scale sites differing in tilt angle. Results show that utility-scale systems at higher tilt angles consistently shed snow more quickly/completely than their lower-tilt counterparts. Further, monthly and seasonal snow losses are inversely and non-linearly correlated with tilt angle when normalized for cumulative snowfall. These results are consistent with the findings of previous studies and support the broad applicability of this method to fixed-tilt utility-scale PV systems around the world that routinely experience snow-related performance losses.

Cooper, Emma C. (ORCID:0000000190554098)↗
DOE OSTI2016

Inter-Disciplinary Collaboration in Support of the Post-Standby TREAT Mission

Although analysis methods have advanced significantly in the last two decades, high fidelity multi- physics methods for reactors systems have been under development for only a few years and are not presently mature nor deployed. Furthermore, very few methods provide the ability to simulate rapid transients in three dimensions. Data for validation of advanced time-dependent multi- physics is sparse; at TREAT, historical data were not collected for the purpose of validating three-dimensional methods, let alone multi-physics simulations. Existing data continues to be collected to attempt to simulate the behavior of experiments and calibration transients, but it will be insufficient for the complete validation of analysis methods used for TREAT transient simulations. Hence, a 2018 restart will most likely occur without the direct application of advanced modeling and simulation methods. At present, the current INL modeling and simulation team plans to work with TREAT operations staff in performing reactor simulations with MAMMOTH, in parallel with the software packages currently being used in preparation for core restart (e.g., MCNP5, RELAP5, ABAQUS). The TREAT team has also requested specific measurements to be performed during startup testing, currently scheduled to run from February to August of 2018. These startup measurements will be crucial in validating the new analysis methods in preparation for ultimate application for TREAT operations and experiment design. This document describes the collaboration between modeling and simulation staff and restart, operations, instrumentation and experiment development teams to be able to effectively interact and achieve successful validation work during restart testing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Total Protein Analysis in Algae via Bulk Amino Acid Detection: Optimization of Amino Acid Derivatization after Hydrolysis with O -Phthalaldehyde 3-Mercaptopropionic Acid (OPA-3MPA)

The analysis of protein in algal biomass is one of the most critical areas of commercial development of algae characterization for nutritional or other high value applications. A new rapid and accurate method is required that can be widely implemented and that is free from interferences from the complex algal biomass matrix. We developed a simple spectrophotometric method for primary amino acid quantification bulk measurement in an acid hydrolyzed algal biomass preparation, as an alternative to the more labor-intensive amino HPLC acid analysis or less specific nitrogen-to-protein conversion. We have validated an O -phthalaldehyde (OPA)-based derivatization method, showing accurate and linear quantification for standard reference amino acids as well as mixtures, mimicking the amino acid complexity found in algal biomass. The presence of interferences that may be derived from the complex biomass biochemical composition was tested during the method validation phase. We document the application of a novel method of OPA derivatization with 3-mercaptopropionic acid (3MPA) to determine the total amino acid content of harvested algal biomass collected from different, controlled cultivation conditions and demonstrated a within 10% accuracy against a reference measurement of amino acid content in at least 4 species and 10 algal biomass samples, across early, mid, and late-stages of cultivation.

09 BIOMASS FUELS↗
DOE OSTI2019

Development and validation of a quantitative PCR assay method of assessing relative resistance of oat ( Avena sativa ) to crown rust ( Puccinia coronata f. sp. avenae )

Evaluation of oat crown rust resistance is usually based on visual assessment of disease severity or infection types. Visual assessment is subjective, prone to rater bias and requires expert knowledge. PCR‐based quantitative assays can overcome challenges associated with visual assessment. New TaqMan primers and probes were designed from Puccinia coronata f. sp. avenae (Pca) sequences. The primer–probe sets were specific to Pca, amplified using as little as 0.5 pg fungal DNA (fDNA) and allowed for scaling to variation in sample total DNA quantity. The quantitative PCR (qPCR) assay was validated using oat recombinant inbred lines (RILs) from the Provena × 94197A1‐9‐2‐2‐2‐5 cross evaluated under a controlled environment. For comparison with fDNA load, inoculation with the Pca race LCBB provided segregation data on the hypersensitive response, while Pca race LSLG provided data on segregation for reduced pustule number. fDNA content was positively correlated with both pustule number and infection type (IT). Composite interval mapping identified two quantitative trait loci (QTLs) on oat linkage groups Mrg12 and Mrg20 using visual and qPCR assessments (pustule number, IT and fDNA). In this study a qPCR assay method that can be used to assess the relative resistance of oat to crown rust was refined and validated, and single nucleotide polymorphisms (SNPs) closely linked with two QTLs derived from the crown rust resistant line 94197A1‐9‐2‐2‐2‐5 were identified.

Admassu‐Yimer, B.↗
DOE OSTI2023

Investigating Similarity Differences for Light-Water-Moderated and Polyethylene-Moderated Systems

Oak Ridge National Laboratory has performed validation studies in support of Pacific Northwest Laboratory’s analysis of the Sum-of-Fractions method. Work in a companion paper examines the validation methods and results for various mixtures of fissile isotopes in various concentrations moderated and reflected by light-water and polyethylene. Those results indicate a greater coverage of critical experiments for validating light water moderated and reflected systems vs. those with polyethylene. The work presented here details the analysis of differences between light-water and polyethylene systems developed from the different concentration mixtures utilized in the Sum-of-Fractions validation study. Differences in actinide contributions are detailed, explaining the differences in ck for the various systems. The available tools and analysis techniques in TSUNAMI are described to illustrate how these types of differences can be understood by practitioners.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2022

Tracking Volumetric Units in Modular Factories for Automated Progress Monitoring Using Computer Vision

The construction industry is increasingly adopting off-site and prefabricated methods due to advantages offered in safety, quality, and lead time. Applying industrialized methods for plant management in offsite construction factories requires the collection of large volumes of production process data, which is a tedious task when performed manually. Recent attempts to automate this process have relied on sensor-based data collection methods which are susceptible to noise, expensive, and difficult to validate. Computer vision methods, however, enable process data collection from videos without the limitations of the other sensor-based methods. This technology has not been applied for offsite construction except in very few instances and therefore, this study proposes a novel method to reliably collect the production process data using computer vision method in near real-time from widely used surveillance cameras in offsite construction. The proposed method allows the user to annotate the workstations of interest on the video as ground truths and process these areas throughout the entire video to track the units entering and leaving stations, while continuously updating a near real-time schedule of the production line. This framework was validated by implementing on the surveillance videos of the production process of modular home manufacturing in a factory. The results consistently provided 100% accuracy, after denoising, for all the videos processed including 60 h of work for a station. The developed method enables real-time tracking of station performance, which can enable continuous improvement methods for factory management and resource allocation.

computer vision↗
DOE OSTI2018

Chance-Constrained Day-Ahead Hourly Scheduling in Distribution System Operation

This paper aims to propose a two-step approach for day-ahead hourly scheduling in a distribution system operation, which contains two operation costs, the operation cost at substation level and feeder level. In the first step, the objective is to minimize the electric power purchase from the day-ahead market with the stochastic optimization. The historical data of day-ahead hourly electric power consumption is used to provide the forecast results with the forecasting error, which is presented by a chance constraint and formulated into a deterministic form by Gaussian mixture model (GMM). In the second step, the objective is to minimize the system loss. Considering the nonconvexity of the three-phase balanced AC optimal power flow problem in distribution systems, the second-order cone program (SOCP) is used to relax the problem. Then, a distributed optimization approach is built based on the alternating direction method of multiplier (ADMM). The results shows that the validity and effectiveness method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Feed-Forward Compensation for Model Predictive Control in Tri-port Current-Source Medium-Voltage String Inverters for PV-Plus-Storage Farms

The novel tri-port current-source medium-voltage string inverter (TCS-MVSI) is a promising candidate for large-scale PV-plus-storage (PVS) farms owing to its galvanic isolation, easy storage integration, soft-switching capability across entire load range, controlled low dv/dt and EMI, benign fault tolerance, etc. Due to its low inertia feature, traditional PI-based control cannot manage large transients effectively. Instead, a model-based predictive control (MPC) is proposed to achieve robust and stable operation. As is well known, the control performance of MPC is compromised by the sampling and computational delay during implementation significantly, if not well addressed. This paper analyzed and quantified these delays and then proposes feed-forward compensation (FFC) for the MPC method to compensate the delays and the large parameter variations due to the low-inertia nature. In addition, this method also compensates for the high dc-link ripple within each switching cycle, a unique issue for low-inertia converters. Here, the proposed method requires low computational effort, allowing it to be extended for multiple ports. The effectiveness of the proposed method has been validated in experiments. In 10 kW test, the proposed method decreases the average dc-link current by 17%, leading to ~20% conduction losses and ~0.5% increase in converter efficiency. In addition, the peak dc-link current also decreases by 15%, resulting in reduced transformer size. As a result, an increased power density can be achieved with the proposed method. Similar improvements have been observed across the power range from 2 kW to 10 kW.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2023

Investigating Similarity Differences for Light-water-Moderated and Polyethylene-Moderated Systems

Oak Ridge National Laboratory has performed validation studies in support of Pacific Northwest National Laboratory’s analysis of the Sum-of-Fractions method. Work in a companion paper examines the validation methods and results for various mixtures of fissile isotopes in various concentrations moderated and reflected by water and polyethylene. Those results indicate a greater coverage of critical experiments for validating water moderated and reflected systems versus those moderated and reflected with polyethylene.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2026

Stochastic Microgrid Scheduling With Chance‐Constrained Resilience Consideration

Traditionally, it is assumed that microgrids transition seamlessly from grid‐connected operation to islanded mode in the event of sudden main grid outages. In reality, the islanding process, especially unintentional islanding, is rarely seamless. Instead, it is subject to voltage and frequency fluctuations caused by the instantaneous disconnection of the point of common coupling (PCC) switch, variations in loads and renewable generation output and even the protection tripping of distributed energy resources (DERs). To mitigate these fluctuations and facilitate a smooth islanding process, we propose a stochastic microgrid scheduling model that incorporates chance‐constrained resilience measures. Specifically, the resilience measure is defined as the probability of successful islanding (PSI), that is, the probability that a microgrid can mitigate the generation‐demand imbalance caused by the disconnection of the PCC switch, variations in load and renewable generation and DER tripping. This measure is modelled using chance constraints. Unlike existing reliability and resilience indices, which typically neglect the possibility of microgrid/DER failure under extreme events and assume their survival while primarily focussing on reducing impact duration or magnitude, the proposed PSI‐based framework explicitly addresses microgrid and DER survival during the islanding transition. The formulated nonlinear chance constraints are approximated using a multiinterval approach and equivalently represented as a mixed‐integer linear programming (MILP) formulation. Case study results validate the proposed method, showing that the PSI estimation error is reduced to less than 8%, compared to approximately 28% with existing methods. Various sensitivity analyses on the DER tripping rate and PSI settings were performed to validate the robustness of the proposed method. In particular, the necessity of accounting for DER tripping in the PSI calculation was demonstrated.

chance constrained optimization↗
DOE OSTI2016

Flow of fresh concrete through reinforced elements: Experimental validation of the porous analogy numerical method

Numerical simulations of concrete castings are complex and time consuming. In order to decrease simulation time and to simplify simulation procedure, an innovative modelling approach, which treats reinforced sections in a formwork as porous media, was proposed. In the previous studies, this numerical model was proved suitable to simulate casting of model yield-stress fluids through reinforced elements. This article focuses on the experimental validation of the proposed model at the concrete scale. For this purpose, a large-scale laboratory casting of a highly reinforced beam is performed. The casting process is numerically simulated and the numerical results are compared to the experimental measurements.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A CHIL Validation of Machine Learning-Assisted Methods for Real-Time Controls of Solar PV for Grid Services

Recent research has highlighted the potential for solar to act as a zero-marginal-cost and zero-emission flexibility resource on the bulk power system when operated with advanced control systems. To increase the performance of these systems, leading technologies, including machine learning (ML) and hierarchical inverter set point allocation, have been proposed; however, these technologies lack comprehensive validation under real-world application scenarios. This paper addresses this gap by designing and developing a controller-hardware-in-the-loop framework to evaluate the performance of different flexible solar technologies in responding to automatic generation control signals in a closed-loop fashion. Simulation results indicate the superior performance of an ML-based approach compared to the conventional reference-control grouping-based approach, showcasing its potential to support grid stability and operational efficiency.

closed-loop validation↗