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DOE OSTI2023

Viewfactor and Raytracing for AgriPV Modeling

View factor models are used in due diligence software to calculate rear irradiance for bifacial modules. An intermediate step in this calculation is the irradiance at the ground level, which can be leveraged for evaluating the Photo Active Radiation available for crops in Agrivoltaic setups. This paper presents the metrics and modifications to the model for ground irradiance study with the view factor approach, compares it to the raytracing method, and validates it with field measurements of ground irradiance. It is found that for the clearances, row-to-row setups, and tilts studied, the view factor method matches with raytracing results within 2% MBD. The comparison is performed for the nine most common agriPV configurations using high-performance computing and the NSRDB database for the whole US, with results and data made available open-source on the InSPIRE AgriPV website.

AgriPV↗

NASA NTRSDate not supplied

Unsteady Pressure Measurements on a Simplified High-Lift Configuration of the Common Research Model using Active Flow Control

The High-Lift Common Research Model (CRM-HL) is a product of the NASA Advanced Air Transport Technology (AATT) Project intended to study various high-lift aerodynamic phenomena including enhanced lift, acoustic noise generation, and optimum placement of leading and trailing-edge devices. The current study was conducted in the NASA Langley 14-by 22-Foot Subsonic Tunnel (14x22) for the purpose of evaluating enhanced lift through the use of active flow control (AFC). The current paper documents the effect of sweeping-jet actuation combined with steady blowing on a simplified high-lift configuration using unsteady pressure measurements. The time-series parameters for the measurements are documented as well as the data acquisition system used to acquire the data. An uncertainty analysis will be presented in order to document the system performance and provide an estimate of the data quality. Repeatability of the data will be shown along with trends associated with model angle of attack, and also those associated with changes in the nozzle pressure ratio (NPR) of the AFC system.

Active Flow Control↗

NASA NTRS2011

Ares I-X Flight Test Validation of Control Design Tools in the Frequency-Domain

A major motivation of the Ares I-X flight test program was to Design for Data, in order to maximize the usefulness of the data recorded in support of Ares I modeling and validation of design and analysis tools. The Design for Data effort was intended to enable good post-flight characterizations of the flight control system, the vehicle structural dynamics, and also the aerodynamic characteristics of the vehicle. To extract the necessary data from the system during flight, a set of small predetermined Programmed Test Inputs (PTIs) was injected directly into the TVC signal. These PTIs were designed to excite the necessary vehicle dynamics while exhibiting a minimal impact on loads. The method is similar to common approaches in aircraft flight test programs, but with unique launch vehicle challenges due to rapidly changing states, short duration of flight, a tight flight envelope, and an inability to repeat any test. This paper documents the validation effort of the stability analysis tools to the flight data which was performed by comparing the post-flight calculated frequency response of the vehicle to the frequency response calculated by the stability analysis tools used to design and analyze the preflight models during the control design effort. The comparison between flight day frequency response and stability tool analysis for flight of the simulated vehicle shows good agreement and provides a high level of confidence in the stability analysis tools for use in any future program. This is true for both a nominal model as well as for dispersed analysis, which shows that the flight day frequency response is enveloped by the vehicle s preflight uncertainty models.

Johnson, Matthew↗

DOE OSTI2025

A Markov chain Monte Carlo (MCMC) Bayesian inference approach to analyze apparent activation barriers and reaction orders from microreactor data

Statistical analysis of steady-state catalytic kinetic data is often limited by data sparsity due to the slow pace at which the data is collected. Data sparsity and limitations in statistical analysis make it difficult to differentiate between mechanistic models and catalytic sites. A Bayesian inference tool is reported for catalysis researchers to estimate error in the determination of reaction orders from steady state microreactor data. The benefits of a Bayesian inference approach are discussed, as an alternative to the more common frequentist approach. The approach incorporates prior knowledge of the system and the data collected to form an error estimate on reaction orders. We investigated the effects of three distinct data treatments—individual fitting of trials, pooled analysis, and constrained regression methods—on the precision and uncertainty of reaction order determinations. To assess the robustness of our findings, we conducted sensitivity analyses to evaluate the influence of Bayesian parameters on uncertainty estimation. Additionally, we utilized synthetic data to illustrate how data quality impacts the precision of uncertainty assessments. We show Bayesian analysis can obtain a more precise estimation of error with a sparse data set than a frequentist analysis. Finally, this work provides strong evidence that the adoption of Bayesian analysis of kinetic data may help researchers make more precise arguments as to the strength of their evidence for a particular mechanistic hypothesis, or in comparing across different catalysts.

42 ENGINEERING↗

DOE OSTI2026

Tunable Multisite Proton-Coupled Electron Transfer Mediators: Distinct Pathways for Substrate Reduction Versus Competing Hydrogen Evolution

Proton-coupled electron transfer (PCET) reagents have emerged as powerful tools for transferring net H atoms to organic substrates from relatively weak X–H bonds. One advantage of employing PCET reagents is the tunability of the X–H bond strength by independently varying their redox potential and/or p K a for selective substrate reductions; however, the rational development of modular catalytic PCET reagents based on these features remains underdeveloped. In this work, we address important mechanistic questions relevant to a dimethylaniline-appended cobaltocene PCET mediator that our lab has previously introduced. Specifically, we examine where protonation occurs within the reactive Co(II, NH) + intermediate of a Brønsted-base modified cobaltocene mediator, whether substrate reduction and hydrogen evolution reaction (HER) proceed by a common or bifurcated mechanistic pathway, and how the redox, acid–base, and structural properties of PCET mediators can dictate their reactivity and selectivity. We show that substrate compatibility can be tuned and, via a model study with N -aryl imine substrates, provide data pointing to a multisite PCET (MS-PCET) pathway. Moreover, we rigorously characterize the site of protonation in the reactive reduced, protonated form of the mediator, and through kinetic analysis establish that the pathway for undesired competing HER is fundamentally different and involves Cp-ring protonation. Our findings point to a high degree of flexibility in the design of reductive PCET mediators.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Systematic Benchmarking of Climate Models: Methodologies, Applications, and New Directions

As climate models become increasingly complex, there is a growing need to comprehensively and systematically assess model performance with respect to observations. Given the increasing number and diversity of climate model simulations in use, the community has moved beyond simple model intercomparison and toward developing methods capable of benchmarking a large number of simulations against a suite of climate metrics. Here, we present a detailed review of evaluation and benchmarking methods and approaches developed in the last decade, focusing primarily on scientific implications for Coupled Model Intercomparison Project (CMIP) simulations and CMIP6 results that contributed to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Based on this review, we explain the resulting contemporary philosophy of model benchmarking, and provide clear distinctions and definitions of the terms model verification, process validation, evaluation, and benchmarking. While significant progress has been made in model development based on systematic evaluation and benchmarking efforts, some climate system biases still remain. The development of open‐source community software packages has played a fundamental role in identifying areas of significant model improvement and bias reduction. We review the key features of several software packages that have been commonly used over the past decade to evaluate and benchmark global and regional climate models. Additionally, we discuss best practices for the selection of evaluation and benchmarking metrics and for interpreting the obtained results, the importance of selecting suitable sources of reference data and accurate uncertainty quantification.

Environmental sciences↗

DOE OSTI2026

The Q 10 of in situ microbial soil respiration varies with mean annual temperature, precipitation, pH, and plant cover: a meta-analysis and spatial prediction of Q 10

The temperature sensitivity of soil microbial respiration, commonly quantified using the Q 10 coefficient, is a key parameter in carbon cycle models. Uncovering how environmental factors affect in situ Q 10 values can therefore provide critical insight into potential shifts in global carbon stocks under climate change. We collected data from previously published field experiments that measured soil microbial respiration across a range of temperatures. We hypothesized that the Q 10 coefficient of in situ soil microbial respiration would vary based on environmental factors including mean annual temperature (MAT), mean annual precipitation (MAP), plant cover type, pH, soil C:N, and latitude. Linear regression revealed that Q 10 correlates negatively with MAT and MAP and positively with pH and absolute latitude. Additionally, average Q 10 varied significantly across different plant cover types; it was highest in mountain grasslands and lowest in tropical moist forests. Variation in microbial Q 10 across environmental factors may arise from underlying mechanisms such as enzyme kinetics, substrate availability and complexity, and microbial adaptation. To capture patterns in Q 10 more comprehensively, we developed a multiple linear regression model of Q 10 based on the most individually significant environmental drivers and applied it to public datasets to generate a global map of predicted Q 10 . Q 10 was higher in high-latitude and high-altitude regions, where large permafrost carbon stores are vulnerable to thawing and decomposition. We also compared fits between the Q 10 equation and a model produced from macromolecular rate theory (MMRT). We found that the MMRT model had the superior fit and may be better suited to model temperature sensitivity of complex biological reactions. Overall, our results emphasize that relationships between microbial Q 10 and environmental variables should be accounted for in climate models. Incorporating these variations in the Q 10 parameter, rather than using a fixed value, will help predict whether CO 2 emissions will be buffered or exacerbated by soil microbial respiration under climate change.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe)

Recent proliferation of distributed energy sources in distribution or sub-transmission systems necessitates close monitoring of these three-phase power grids which typically operate under unbalanced loading conditions. Unlike the transmission systems where the network equations are commonly based on the positive sequence component models, a detailed three phase model will have to be used in implementing network applications for these systems. In the specific case of the state estimator, where measurement and parameter errors may bias the solution, bad data and parameter error detection algorithms should also be incorporated. Implementing the state estimator and error detection algorithms for three-phase systems impose additional computational burden and modifications to the state estimation code. This paper proposes a practical solution to avoid these issues by using synchronized phasor measurements and modal decoupling. The previously developed parameter error detection algorithm based on the normalized Lagrange multipliers (NLM) test is applied to the measurements independently in each mode in parallel, not only saving CPU time but also avoiding new code development for a three-phase estimator. Different parameter error scenarios are created and tested to verify the effectiveness of the proposed error detection approach.

Khalili, Ramtin↗

DOE OSTI2023

Distributed Load Shedding Application Architecture and Bi-Level Predictive Estimator Algorithm

Increasing penetrations of distributed renewables are decreasing the effectiveness of traditional decentralized under-frequency load shedding (UFLS) schemes. As more distribution circuits begin to back-feed the transmission system, operation of traditional UFLS may exacerbate frequency instability. This paper presents the conceptual framework for a data-rich environment to coordinate UFLS across multiple distribution providers based on the laminar coordination framework in order to ensure optimal adaptive setting of UFLS relays. Communication and control are enabled through a distributed implementation of the IEC 61968-1 Common Information Model message bus structure. In addition to the proposed architecture, a novel adaptive UFLS scheme informed by a bi-level state estimator to create optimal relay setpoints is introduced. Initial simulation results are presented for the IEEE 14-bus test system on scenarios leading to mis-operation of traditional UFLS.

Anderson, Alexander A.↗

DOE OSTI2024

Deep Learning-based Non-Stationary Bias Correction (NSBC)

This work develops the NSBC (non-stationary bias correction) methodology to correct temperature projection bias from E3SM. The NSBC deep learning framework consists of a three-part architecture: an auto-encoder for compressing the spatial information, an LSTM for predicting annual temperature mean, and a U-Net for capturing the residual bias in temperature. The non-stationary bias correction (NSBC) framework can correct the non-stationarity of the biases of the climate models, which significantly improves the accuracy of future temperature prediction and improves the overestimation of extreme high temperatures that many existing bias correction methods suffer from. Getting started 1. Obtain the historical climate simulation and observation data. The E3SM simulation data are available through https://aims2.llnl.gov/search/cmip6/. The pseudo observations, the Geophysical Fluid Dynamics Laboratory (GFDL)-ESM4 model (Krasting et al., 2018) are available through https://aims2.llnl.gov/search/cmip6/. The spatial resolution of E3SM and pseudo observation datasets are both regridded to a common 1° resolution grid using conservative interpolation. The regridded E3SM and pseudo observation with 1° resolution can be found throught ./data/. 2. Train the Auto-encoder model. Python 0-autoencoder.py 3. Train the LSTM Python 1-LSTM.py 4. Generate the annual mean temperature based on trained LSTM Python 2-generate_annual_mean_LSTM.py 5. Train the U-Net. Python 3-unet.py 6. Evaluation and compared with the baseline Python 4_evaluation.py Is there a deadline approaching that requires the release of yo

Lucas, Donald↗

DOE OSTI2022

A Demonstration of Intelligent Container Surveillance Using Stationary and Mobile Camera Platforms

Surveillance of nuclear material storage containers is required to ensure that the container safety boundary is maintained during the service life of the container. As many of these container types are the first and only containment barrier to release protecting the worker, public, and environment, it is imperative to develop robust surveillance tools to characterize container degradation. Machine learning (ML) techniques have matured quickly in the past decade and are slowly becoming a routine application in data analysis. While more popular architectures have been developed with different applications in mind, these can be easily translated to container surveillance requirements. In this study, we developed a ML model based on the Detectron2 framework to identify slip lid containers and common exterior container defects, primarily dents. Commercial-off-the-shelf cameras are combined into a stationary camera array or are mounted to a robotic arm to provide more mobility for positioning needs. Results from the model evaluations on the images indicate that the stationary camera arrays outperform the accuracy of the mobile camera systems for both individual camera and composite image detections. Overall, however, the detection accuracies of the systems fell short of 50% and were less than satisfactory. Future efforts to improve the system involve focusing on a single camera deployable solution that combines a controllable light source that eliminates one of the biggest environmental factors that influence ML model detection performance. Additional training set collection is planned to build a more robust model that enables accurate detection from a wider range of imaging conditions.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

NASA NTRS1991

A hierarchical distributed control model for coordinating intelligent systems

A hierarchical distributed control (HDC) model for coordinating cooperative problem-solving among intelligent systems is described. The model was implemented using SOCIAL, an innovative object-oriented tool for integrating heterogeneous, distributed software systems. SOCIAL embeds applications in 'wrapper' objects called Agents, which supply predefined capabilities for distributed communication, control, data specification, and translation. The HDC model is realized in SOCIAL as a 'Manager'Agent that coordinates interactions among application Agents. The HDC Manager: indexes the capabilities of application Agents; routes request messages to suitable server Agents; and stores results in a commonly accessible 'Bulletin-Board'. This centralized control model is illustrated in a fault diagnosis application for launch operations support of the Space Shuttle fleet at NASA, Kennedy Space Center.

Adler, Richard M.↗

NASA NTRS1999

Planar Strain-Rate-Free Diffusion Flames: Initiation, Properties, and Extinction

An effectively strain-rate-free diffusion flame constitutes the most vigorous laminar combustion of initially unmixed reactive gases. Such a diffusion flame is characterized by a relatively long residence time and by a relatively large characteristic length scale. If such a flame were also planar, providing high symmetry, it would be particularly suitable for experimental and theoretical investigations of key combustion phenomena, such as multicomponent diffusion, chemical kinetics, and soot inception, growth, and oxidation. Unfortunately, a planar strain-rate-free diffusion flame is highly disrupted in earth-gravity (e.g., in a counterflow-diffusion-flame apparatus) because of the very rapid onset (approx. 100 ms) of gravity-induced instability. Accordingly, a specially dedicated apparatus was designed, fabricated, and initially checked out for the examination of a planar strain-rate-free diffusion flame in microgravity. Such a diffusion flame may be formed within a hollowed-out squat container (initially configured as 25 cm x 25 cm x 9 cm), with isothermal, noncatalytic, impervious walls. At test initiation, a thin metallic sheet (approx. 1 mm in thickness) that separates the internal volume into two equal portions, each of dimensions 25 cm x 25 cm x 4.5 cm, is withdrawn, by uniform translation (approx. 50 cm/s) in its own plane, through a tightly fitting slit in one side wall. Thereupon, diluted fuel vapor (initially confined to one half-volume of the container) gains access to diluted oxygen (initially with the same pressure, density, and temperature as the fuel, but initially confined to the other half-volume). After a brief delay (approx. 10 ms), to permit limited but sufficient-for-flammability diffusional interpenetration of fuel vapor and oxidizer, burning is initiated by discharge of a line igniter, located along that side wall from which the trailing edge of the separator withdraws. The ignition spawns a triple-flame propagation across the 25 cm x 25 cm centerplane. When a diffusion flame is emplaced in the centerplane, any subsequent travel, and change in temperature, of that planar diffusion flame may be tracked, along with the effectively spatially uniform but temporally evolving pressure within the container. Eventually, nearly complete depletion of the stoichiometrically deficient reactant, along with heat loss to the container surfaces, effects extinction. These data afford an opportunity to check theoretical models of diffusion and chemical kinetics under conditions ranging from intense burning to flame out, or, alternatively, to evolve simple empirical representations of these phenomena. Thus, the project sought to utilize microgravity testing to elucidate commonly encountered phenomenology, arising in the commonly-encountered mode of combustion (whether related to heating, manufacturing, boiling, and propulsion, or to uncontrolled, free-burning fire in structures and wildland vegetation), of those commonly utilized fuels usually categorized as gaseous fuels (such as hydrogen, natural gas, and propane, which are gaseous under atmospheric conditions).

Fendell, Francis↗

NASA NTRS2003

An Advanced Hierarchical Hybrid Environment for Reliability and Performance Modeling

The key issue we intended to address in our proposed research project was the ability to model and study logical and probabilistic aspects of large computer systems. In particular, we wanted to focus mostly on automatic solution algorithms based on a state-space exploration as their first step, in addition to the more traditional discrete-event simulation approaches commonly employed in industry. One explicitly-stated goal was to extend by several orders of magnitude the size of models that can be solved exactly, using a combination of techniques: 1) Efficient exploration and storage of the state space using new data structures that require an amount of memory sublinear in the number states; and 2) Exploitation of the existing symmetries in the matrices describing the system behavior using Kronecker operators. Not only we have been successful in achieving the above goals, but we exceeded them in many respects.

Ciardo, Gianfranco↗

NASA NTRS2003

Atmospheric Pressure Corrections in Geodesy and Oceanography: a Strategy for Handling Air Tides

Global pressure data are often needed for processing or interpreting modern geodetic and oceanographic measurements. The most common source of these data is the analysis or reanalysis products of various meteorological centers. Tidal signals in these products can be problematic for several reasons, including potentially aliased sampling of the semidiurnal solar tide as well as the presence of various modeling or timing errors. Building on the work of Van den Dool and colleagues, we lay out a strategy for handling atmospheric tides in (re)analysis data. The procedure also offers a method to account for ocean loading corrections in satellite altimeter data that are consistent with standard ocean-tide corrections. The proposed strategy has immediate application to the on-going Jason-1 and GRACE satellite missions.

Ponte, Rui M.↗

NASA NTRS2004

Development of a Solid-Oxide Fuel Cell/Gas Turbine Hybrid System Model for Aerospace Applications

Recent interest in fuel cell-gas turbine hybrid applications for the aerospace industry has led to the need for accurate computer simulation models to aid in system design and performance evaluation. To meet this requirement, solid oxide fuel cell (SOFC) and fuel processor models have been developed and incorporated into the Numerical Propulsion Systems Simulation (NPSS) software package. The SOFC and reformer models solve systems of equations governing steady-state performance using common theoretical and semi-empirical terms. An example hybrid configuration is presented that demonstrates the new capability as well as the interaction with pre-existing gas turbine and heat exchanger models. Finally, a comparison of calculated SOFC performance with experimental data is presented to demonstrate model validity. Keywords: Solid Oxide Fuel Cell, Reformer, System Model, Aerospace, Hybrid System, NPSS

Freeh, Joshua E.↗

NASA NTRS2022

A Generalized Approach to Aircraft Trajectory Prediction via Supervised Deep Learning

As research advances diverse forms and missions of aircraft, the National Airspace System (NAS) will become increasingly crowded, limiting current communications resources to accommodate aviation operations. Ongoing research proposes a paradigm of airspace communications, such that resources are autonomously and dynamically allocated via intelligent agents; this allocation requires accurate representations of the NAS, including the predicted positions of aircraft. State-of-the-art research emphasizes the importance of a hybrid-recurrent framework for trajectory prediction and compares the impact of commonly considered weather data on prediction accuracy. However, current research has been limited in its scope of efforts, frequently utilizing a unique flight route, architecture, set of weather data, and date range. This article considers the challenges of generalizing hybrid-recurrent predictive models for flight trajectories. Results illustrate an increase in error variance when identical models are trained over a generalized set of flights; this may be mitigated with careful tuning of hyperparameters, both in the network structure and optimization algorithms. Even so, an irreducible vertical error was identified, resulting from the complex takeoff and landing procedures which can not be correlated to functions of weather or additional assumptions of aircraft behavior. Finally, the use of a test route indicates that generalized models still do not possess sufficient knowledge for general aircraft predictions, with mean error increases ranging from 70-500%. These results illustrate the need for continued efforts on improving model versatility, as well as potential limitations for spectrum allocation near airports and other centers.

Nathan Schimpf↗

NASA NTRSDate not supplied

Extending CF Conventions to Enhance Data FAIRness for Atmospheric Composition Observations

The Hierarchical Data Format (HDF) and Network Common Data Form (NetCDF) are data file formats created to aid users in the creation or use of scientific data. These file formats are useful for handling large data volumes and hosting extensive metadata as global, group, or variable attributes and are popular with the modeling community. HDF and NetCDF files are widely used with atmospheric remote sensing data and have been used to support measurements from numerous field campaigns, from satellite to aircraft or ground and mobile based measurements. The files from airborne field studies, however, vary greatly in terms of the file structure and the amount and content of their metadata. Information relevant to the file that can be useful to the user such as the data producer, location where data was taken, variable descriptions, or information about the instrument might not be included in the file. Recently, the Measurements of Aerosols, Clouds, and their Interactions for Earth System Models (MACIE) group started a grassroots effort to develop a CF-based template for the HDF and NetCDF files for field studies, with the aim of making the data products more interoperable and usable. This template seeks to make the files more compliant to Climate and Forecast (CF) metadata conventions and to standardize the file structure and the global and variable attributes. The template would help to ensure that HDF and NetCDF files contain adequate metadata to better support their use for research, e.g., the modeling community, and to enhance the usability and interoperability of data for research communities at large. The draft template has been applied to recent field studies for various instruments and their merge files in support of the Atmosphere Observing System (AOS) project. The details of the revised template are to be presented, as well as examples of the implementation of these requirements for merge files and lidar observation data files and issues revealed during the implementation process.

Sean Leavor↗