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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Physics guided machine learning for multi-material decomposition of tissues from dual-energy CT scans of simulated breast models with calcifications

We introduce a physics guided data-driven method for image-based multi-material decomposition for dual-energy computed tomography (CT) scans. The method is demonstrated for CT scans of virtual human phantoms containing more than two types of tissues. The method is a physics-driven supervised learning technique. We take advantage of the mass attenuation coefficient of dense materials compared to that of muscle tissues to perform a preliminary extraction of the dense material from the images using unsupervised methods. We then perform supervised deep learning on the images processed by the extracted dense material to obtain the final multi-material tissue map. The method is demonstrated on simulated breast models with calcifications as the dense material placed amongst the muscle tissues. The physics-guided machine learning method accurately decomposes the various tissues from input images, achieving a normalized root-mean-squared error of 2.75%.

Gopalakrishnan Meena, Murali↗
DOE OSTI2023

COMSOL Models and Simulation Results for Closed-Loop Geothermal Systems with Thermal Enhancements

The Closed-Loop Geothermal Working Group and the National Renewable Energy Laboratory (NREL) have studied the performance of disc-shaped and linear thermal enhancements (conductive material introduced around a wellbore) in closed-loop geothermal systems. COMSOL models were developed to investigate the increase in heat production in systems with thermal enhancements compared to those without, across a range of scenarios. This dataset includes two example COMSOL models, a summary of the simulation results, and a link to an accompanying paper. Studied are idealized versions of two thermal enhancement designs: (1) existing or newly created fractures surrounding the wellbore, filled with highly conductive material, and (2) a 'fishbone structure,' where short wellbores are drilled radially outward from the main wellbore and filled with highly conductive material.

15 GEOTHERMAL ENERGY↗
NASA NTRS2017

Classifying Multi-Model Wheat Yield Impact Response Surfaces Showing Sensitivity to Temperature and Precipitation Change

Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (minus 2 to plus 9 degrees Centigrade) and precipitation (minus 50 to plus 50 percent). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses. The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern. The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description. Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index. Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities.

Classification; Climate change; Crop model; Ensemb↗
DOE OSTI2023

Plant-specific Model and Data Analysis using Dynamic Security Modeling and Simulation

The requirements for U.S. nuclear power plants to maintain a large on-site physical security force contribute to their high operational costs. The cost of maintaining the current physical security posture is approximately 10% of the overall operation and maintenance budget for commercial nuclear power plants. The goal of the Light Water Reactor Sustainability (LWRS) program’s physical security pathway is to develop tools, methods, and technologies and provide the technical basis for an optimized physical security posture. The conservatisms built into current security postures may be analyzed and minimized in order to reduce security costs while still ensuring adequate security and operational safety. The research performed at Idaho National Laboratory within LWRS program’s physical security pathway has successfully developed a dynamic force-on-force modeling framework using various computer simulation tools and integrating them with the dynamic assessment Event Modeling Risk Assessment using Linked Diagrams (EMRALD) tool. This document provides an update on the progress in applying a dynamic computational framework that links results from a commercially available force-on-force simulation tool, a commercially available thermal-hydraulic tool, and EMRALD to an operating commercial nuclear power plant. This report is only a summary of the progress and does not contain specific modeling results as those contain sensitive security information. This process of including plant procedures and multiple analysis results is being called Modeling and Analysis for Safety Security using Dynamic EMRALD Framework or MASS-DEF. Previous reports described how a user could integrate their plant-specific force-on-force models with the dynamic simulation tool EMRALD, model operator actions, integrate with probabilistic risk assessment tools, such as CAFTA (Computer Aided Fault Tree Analysis System) or SAPHIRE (Systems Analysis Programs for Hands-on Integrated Reliability Evaluations), and with thermal-hydraulic tools, such as RELAP-5. Previous reports applied various combinations of available simulations codes with EMRALD using generic plant models to demonstrate how to perform the analysis. This report documents the results of applying the dynamic computational framework to an actual nuclear facility using their security scenarios and timelines. This report does not contain any plant's sensitive information and/or Safeguards Information. The purpose of this study was to verify that results achieved using generic models are similar to actual plant results and to refine our guidance on the use of the framework. This assessment enables further analysis, such as what-if scenarios and staff-reduction evaluation, thereby optimizing physical security at plants.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2018

Multi-model Comparison of the Volcanic Sulfate Deposition from the 1815 Eruption of Mt. Tambora

The eruption of Mt. Tambora in 1815 was the largest volcanic eruption of the past 500 years. The eruption had significant climatic impacts, leading to the 1816 "year without a summer", and remains a valuable event from which to understand the climatic effects of large stratospheric volcanic sulfur dioxide injections. The eruption also resulted in one of the strongest and most easily identifiable volcanic sulfate signals in polar ice cores, which are widely used to reconstruct the timing and atmospheric sulfate loading of past eruptions. As part of the Model Intercomparison Project on the climatic response to Volcanic forcing (VolMIP), five state-of-the-art global aerosol models simulated this eruption. We analyze both simulated background (no Tambora) and volcanic (with Tambora) sulfate deposition to polar regions and compare to ice core records. The models simulate overall similar patterns of background sulfate deposition, although there are differences in regional details and magnitude. However, the volcanic sulfate deposition varies considerably between the models with differences in timing, spatial pattern and magnitude. Mean simulated deposited sulfate on Antarctica ranges from 19 to 264 kgkm-2 and on Greenland from 31 to 194 kgkm-2, as compared to the mean ice-core derived estimates of roughly 50 kgkm-2 for both Greenland and Antarctica. The ratio of the hemispheric atmospheric sulfate aerosol burden after the eruption to the average ice sheet deposited sulfate varies between models by up to a factor of 15. Sources of this inter-model variability include differences in both the formation and the transport of sulfate aerosol. Our results suggest that deriving relationships between sulfate deposited on ice sheets and atmospheric sulfate burdens from model simulations may be associated with greater uncertainties than previously thought.

Marshall, Lauren↗
NASA NTRS2006

Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry: Improved Method and Uncertainties - Part 1

A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and nonconvective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud-resolving model simulations, and from the Bayesian formulation itself. Synthetic rain-rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in TMI instantaneous rain-rate estimates at 0.5 -resolution range from approximately 50% at 1 mm/h to 20% at 14 mm/h. Errors in collocated spaceborne radar rain-rate estimates are roughly 50%-80% of the TMI errors at this resolution. The estimated algorithm random error in TMI rain rates at monthly, 2.5deg resolution is relatively small (less than 6% at 5 mm day.1) in comparison with the random error resulting from infrequent satellite temporal sampling (8%-35% at the same rain rate). Percentage errors resulting from sampling decrease with increasing rain rate, and sampling errors in latent heating rates follow the same trend. Averaging over 3 months reduces sampling errors in rain rates to 6%-15% at 5 mm day.1, with proportionate reductions in latent heating sampling errors.

Olson, William S.↗
NASA NTRS2004

Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry: Method and Uncertainties - Part 1

A revised Bayesian algorithm for estimating surface rain rate, convective rain proportion, and latent heating/drying profiles from satellite-borne passive microwave radiometer observations over ocean backgrounds is described. The algorithm searches a large database of cloud-radiative model simulations to find cloud profiles that are radiatively consistent with a given set of microwave radiance measurements. The properties of these radiatively consistent profiles are then composited to obtain best estimates of the observed properties. The revised algorithm is supported by an expanded and more physically consistent database of cloud-radiative model simulations. The algorithm also features a better quantification of the convective and non-convective contributions to total rainfall, a new geographic database, and an improved representation of background radiances in rain-free regions. Bias and random error estimates are derived from applications of the algorithm to synthetic radiance data, based upon a subset of cloud resolving model simulations, and from the Bayesian formulation itself. Synthetic rain rate and latent heating estimates exhibit a trend of high (low) bias for low (high) retrieved values. The Bayesian estimates of random error are propagated to represent errors at coarser time and space resolutions, based upon applications of the algorithm to TRMM Microwave Imager (TMI) data. Errors in instantaneous rain rate estimates at 0.5 deg resolution range from approximately 50% at 1 mm/h to 20% at 14 mm/h. These errors represent about 70-90% of the mean random deviation between collocated passive microwave and spaceborne radar rain rate estimates. The cumulative algorithm error in TMI estimates at monthly, 2.5 deg resolution is relatively small (less than 6% at 5 mm/day) compared to the random error due to infrequent satellite temporal sampling (8-35% at the same rain rate).

Olson, William S.↗
NASA NTRS2010

A Combined Observational and Modeling Approach to Study Modern Dust Transport from the Patagonia Desert to East Antarctica

The understanding of present atmospheric transport processes from Southern Hemisphere (SH) landmasses to Antarctica can improve the interpretation of stratigraphic data in Antarctic ice cores. In addition, long range transport can deliver key nutrients normally not available to marine ecosystems in the Southern Ocean and may trigger or enhance primary productivity. However, there is a dearth of observational based studies of dust transport in the SH. This work aims to improve current understanding of dust transport in the SH by showing a characterization of two dust events originating in the Patagonia desert (south end of South America). The approach is based on a combined and complementary use of satellite retrievals (detectors MISR, MODIS, GLAS ,POLDER, OMI,), transport model simulation (HYSPLIT) and surface observations near the sources and aerosol measurements in Antarctica (Neumayer and Concordia sites). Satellite imagery and visibility observations confirm dust emission in a stretch of dry lakes along the coast of the Tierra del Fuego (TdF) island (approx.54deg S) and from the shores of the Colihue Huapi lake in Central Patagonia (approx.46deg S) in February 2005. Model simulations initialized by these observations reproduce the timing of an observed increase in dust concentration at the Concordia Station and some of the observed increases in atmospheric aerosol absorption (here used as a dust proxy) in the Neumayer station. The TdF sources were the largest contributors of dust at both sites. The transit times from TdF to the Neumayer and Concordia sites are 6-7 and 9-10 days respectively. Lidar observations and model outputs coincide in placing most of the dust cloud in the boundary layer and suggest significant de- position over the ocean immediately downwind. Boundary layer dust was detected as far as 1800 km from the source and approx.800 km north of the South Georgia Island over the central sub-Antarctic Atlantic Ocean. Although the analysis suggests the presence of dust at approx.1500 km SW of South Africa five days after, the limited capabilities of existing satellite platforms to differentiate between aerosol types do not permit a definitive conclusion. In addition, the model simulations show dust lifting to the free troposphere as it travels south but it could not be confirmed by the satellite observations due to cloudiness. This work demonstrates that complementary information from existing transport models, satellite and surface data can yield a consistent picture of the dust transport from the Patagonia desert to Antarctica. It also illustrates the limitation of using any of these approaches individually to characterize the transport of dust in a heavily cloudy area.

Gasso, S.↗
NASA NTRS2021

Evaluation of High Mountain Asia-Land Data Assimilation System (Version 1) from 2003 to 2016, Part I: A Hyper-Resolution Terrestrial Modeling System

This first paper of the two-part series focuses on demonstrating the accuracy of a hyper-resolution, offline terrestrial modeling system used for the High Mountain Asia (HMA) region. To this end, this study systematically evaluates four sets of model simulations at point scale, basin scale, and domain scale obtained from different spatial resolutions including 0.01° (∼1-km) and 0.25° (∼25-km). The assessment is conducted via comparisons against ground-based observations and satellite-derived reference products. The key variables of interest include surface net shortwave radiation, surface net longwave radiation, skin temperature, near-surface soil temperature, snow depth, snow water equivalent, and total runoff. In the evaluation against ground-based measurements, the superiority of the 0.01° estimates are mostly demonstrated across relatively complex terrain. Specifically, hyper-resolution modeling improves the skill in meteorological forcing estimates (except precipitation) by 9% relative to coarse-resolution estimates. The model forced by downscaled forcings in its entirety yields the highest skill in model output states as well as precipitation, which improves the skill obtained by coarse-resolution estimates by 7%. These findings, on one hand, corroborate the importance of employing the hyper-resolution versus coarse-resolution modeling in areas characterized by complex terrain. On the other hand, by evaluating four sets of model simulations forced with different precipitation products, this study emphasizes the importance of accurate hyper-resolution precipitation products to drive model simulations.

Yuan Xue↗
DOE OSTI2026

Thermal-Hydraulic-Mechanical Modeling and Simulation of Sodium-Potassium–Cooled MARVEL Microreactor Core

The U.S. Department of Energy's Microreactor Program, with Idaho National Laboratory's development of a nuclear microreactor applications test bed named MARVEL, aims to support R&D for the deployment of small, transportable reactors across civilian, industrial, and defense sectors. The MARVEL microreactor, an 85-kWth thermal fission reactor, incorporates TRIGA nuclear fuel and a sodium-potassium eutectic as its primary coolant, designed for safety and efficiency, with natural circulation eliminating the risk of critical heat flux conditions. The reliance on natural circulation for primary cooling means the reactor avoids using fuel spacers to minimize core pressure drop, which could disrupt the primary coolant's natural flow. However, the reactor core’s tight P/D ratio of 1.05, in the absence of fuel spacers, could pose a risk of fuel rod contact and increased peak cladding temperatures. To ensure the reactor safety, this study conducted computational modeling and simulations to investigate the reactor's thermal-hydraulic-mechanical characteristics, including the reactor core heat transfer coefficients, the potential for rod-to-rod contact, and assessed its impact on peak cladding temperature and overall reactor safety. The computational analyses of the MARVEL microreactor core revealed that the thermal deformation of fuel rods under worst-case scenario may lead to the fuel rod contact, but the peak cladding temperatures will remain significantly lower than the safety criteria, ensuring the safety operation reactor without fuel spacers under normal operating conditions.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
NASA NTRS2021

Mission Concept Design for Autonomous Space Missions using Mission-Level Modeling and Simulation

NASA’s Europa Lander mission is to search for biosignatures on Europa based on in-situ science using a lander architecture. This mission presents a set of challenges that requires a high level of autonomy on the lander system, leading to the need for a new operational paradigm that supports better collaboration and coordination between the lander and the ground operations team. M\&S is used for both designing the onboard system-level autonomy and the ground operations paradigm that allows effective and efficient collaboration and coordination between the lander and the ground operations team. In this paper, M\&S as applied to the design of new mission and operational concepts will be discussed. At its current early stage of the mission development for Europa Lander, the M\&S is used to explore different mission concepts and gain insights for design (formative) rather than to verify and validate fully designed mission concepts quantitatively (summative). Organically, we established a new approach to mission and operational concept exploration using high-fidelity modeling and simulation. M\&S has been an integral part of the approach of defining constraints and assertions, designing mission concepts, assessing (i.e., simulating them), and discovering insights, which feeds back to the definition an design steps. This organically-established approach provided important benefits to the project at its early phase of the development by enabling the project team to be able to build shared understanding of impacts from design characteristics, constraints, and their interactions on the mission performance.

Ye, Sean↗
DOE OSTI2020

P-model v1.0: an optimality-based light use efficiency model for simulating ecosystem gross primary production

Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth system model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions diverge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a GPP (photosynthesis per unit ground area) model, the P-model, that combines the Farquhar–von Caemmerer–Berry model for C 3 photosynthesis with an optimality principle for the carbon assimilation–transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C 3 vegetation type. The model builds on the theory developed in Prentice et al. (2014) and Wang et al. (2017a) and is extended to include low temperature effects on the intrinsic quantum yield and an empirical soil moisture stress factor. The model is forced with site-level data of the fraction of absorbed photosynthetically active radiation (fAPAR) and meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs, the R 2 for predicted versus observed GPP based on the full model setup is 0.75 (8 d mean, 126 sites) – similar to comparable satellite-data-driven GPP models but without predefined vegetation-type-specific parameters. The R 2 is reduced to 0.70 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R 2 for the P-model-predicted LUE is 0.32 (means by site) and 0.48 (means by vegetation type). Applying this model for global-scale simulations yields a total global GPP of 106–122 Pg C yr –1 (mean of 2001–2011), depending on the fAPAR forcing data. The P-model provides a simple but powerful method for predicting – rather than prescribing – light use efficiency and simulating terrestrial photosynthesis across a wide range of conditions. The model is available as an R package (rpmodel).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Evaluation of E3SM land model snow simulations over the western United States

Abstract. Seasonal snow has crucial impacts on climate, ecosystems, and humans, but it is vulnerable to global warming. The land component (ELM) of the Energy Exascale Earth System Model (E3SM) mechanistically simulates snow processes from accumulation, canopy interception, compaction, and snow aging to melt. Although high-quality field measurements, remote sensing snow products, and data assimilation products with high spatio-temporal resolution are available, there has been no systematic evaluation of the snow properties and phenology in ELM. This study comprehensively evaluates ELM snow simulations over the western United States at 0.125∘ resolution during 2001–2019 using the Snow Telemetry (SNOTEL) in situ networks, MODIS remote sensing products (i.e., MCD43 surface albedo product), the spatially and temporally complete (STC) snow-covered area and grain size (MODSCAG) and MODIS dust and radiative forcing in snow (MODDRFS) products (STC-MODSCAG/STC-MODDRFS), and the snow property inversion from remote sensing (SPIReS) product and two data assimilation products of snow water equivalent and snow depth – i.e., University of Arizona (UA) and SNOw Data Assimilation System (SNODAS). Overall the ELM simulations are consistent with the benchmarking datasets and reproduce the spatio-temporal patterns, interannual variability, and elevation gradients for different snow properties including snow cover fraction (fsno), surface albedo (αsur) over snow cover regions, snow water equivalent (SWE), and snow depth (Dsno). However, there are large biases of fsno with dense forest cover and αsur in the Rocky Mountains and Sierra Nevada in winter, compared to the MODIS products. There are large discrepancies of snow albedo, snow grain size, and light-absorbing particle-induced snow albedo reduction between ELM and the MODIS products, attributed to uncertainties in the aerosol forcing data, snow aging processes in ELM, and remote sensing retrievals. Against UA and SNODAS, ELM has a mean bias of −20.7 mm (−35.9 %) and −20.4 mm (−35.5 %), respectively, for spring, and −13.8 mm (−27.8 %) and −10.2 mm (−22.2 %), respectively, for winter. ELM shows a relatively high correlation with SNOTEL SWE, with mean correlation coefficients of 0.69 but negative mean biases of −122.7 mm. Compared to the snow phenology of STC-MODSCAG and SPIReS, ELM shows delayed snow accumulation onset dates by 17.3 and 12.4 d, earlier snow end dates by 35.5 and 26.8 d, and shorter snow durations by 52.9 and 39.5 d, respectively. This study underscores the need for diagnosing model biases and improving ELM representations of snow properties and snow phenology in mountainous areas for more credible simulation and future projection of mountain snowpack.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

X-Ray Energy Deposition Model for Simulating Asteroid Response to a Nuclear Planetary Defense Mitigation Mission

Abstract In the event of a potentially catastrophic asteroid impact, with sufficient warning time, deploying a nuclear device remains a powerful option for planetary defense if a kinetic impactor or other means of deflection proves insufficient. Predicting the effectiveness of a potential nuclear deflection or disruption mission depends on accurate multiphysics simulations of the device's X-ray energy deposition into the asteroid and the resulting material ablation. The relevant physics in these simulations span many orders of magnitude, require a variety of different complex physics packages, and are computationally expensive. Having an efficient and accurate way of modeling this system is necessary for exploring a mission's sensitivity to the asteroid's range of physical properties. To expedite future simulations, we present a completed X-ray energy deposition model developed using the radiation-hydrodynamics code Kull that can be used to initiate a nuclear mitigation mission calculation. The model spans a wide variety of possible mission initial conditions: four different asteroid-like materials at a range of porosities, two different source spectra, and a broad range of radiation fluences, source durations, and angles of incidence. Using blowoff momentum as the primary metric, the model-initiated simulation results match the full radiation-hydrodynamics results to within 10%.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1991

The influence of dynamics on two-dimensional model results - Simulations of C-14 and stratospheric aircraft NO(x) injections

The influence of dynamics on model results is investigated via a 2D photochemical model. Three representations of atmospheric transport are tested in order to simulate total ozone and C-14 amounts after nuclear tests in the early 1960s. Three scenarios of NO(x) injections from a proposed fleet of stratospheric aircraft and their effects on ozone are simulated. The three dynamical formulations used are Dynamics A, a base dynamics used in previous work with this model; Dynamics B, a strong circulation dynamics discussed by Jackman et al. (1989); and Dynamics C, the dynamics used by Shia et al. (1989). The advective component of the stratosphere to the troposphere mass exchange rate is largest for Dynamics B (5.8 x 10 exp 17 kg/yr) and smallest for Dynamics C (1.4 x 10 exp 17 kg/yr), with the advective strat/trop exchange rate for Dynamics A (2.4 x 10 exp 17 kg/yr) being these two extremes. Ozone depletion from NO(x) injections of stratospheric aircraft showed a strong sensitivity to dynamics.

Jackman, Charles H.↗
DOE OSTI2023

Field validation of isotropic analytical models for simulating fabric shades

Fabric roller shades are common shading materials used in commercial and residential buildings. Accurately characterizing and modeling shades helps practitioners select the appropriate product and its control strategy based on climate and occupants' priorities, such as visual comfort and view to outdoors. Previous studies established a generalized method for modeling complex fenestration systems using data-driven tabulated bidirectional scattering distribution functions. However, deploying such a method at scale to all fabric shading products on the market is too costly and time-consuming. Analytical models that are based on a limited set of measurements (e.g., normal-normal and normal-hemispherical visible transmittance and reflectance, and directional cut-off angles) can be used to model the wide variety of shading products on the market. This study evaluates the performance of two isotropic analytical models, Roos-Wienold and Modified-Kotey, for modeling fabric roller shades, with a focus on the model's ability to predict occupant visual comfort. The performance evaluation was conducted through laboratory and field measurements and simulations. The results showed that both models are sufficient for predicting vertical illuminance at seated eye-level. Roos-Wienold model was able to predict binary visual comfort classification (glare/no-glare) under a wide range of luminance conditions, while Modified-Kotey model did not perform as well under high-contrast low-adaptation conditions. Both models are insufficient in predicting visual comfort at a four-point scale (e.g., imperceptible, perceptible, disturbing, intolerable). The two isotropic models become less accurate when the fabric exhibits high anisotropy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1986

A lumped parameter mathematical model for simulation of subsonic wind tunnels

Equations for a lumped parameter mathematical model of a subsonic wind tunnel circuit are presented. The equation state variables are internal energy, density, and mass flow rate. The circuit model is structured to allow for integration and analysis of tunnel subsystem models which provide functions such as control of altitude pressure and temperature. Thus the model provides a useful tool for investigating the transient behavior of the tunnel and control requirements. The model was applied to the proposed NASA Lewis Altitude Wind Tunnel (AWT) circuit and included transfer function representations of the tunnel supply/exhaust air and refrigeration subsystems. Both steady state and frequency response data are presented for the circuit model indicating the type of results and accuracy that can be expected from the model. Transient data for closed loop control of the tunnel and its subsystems are also presented, demonstrating the model's use as a control analysis tool.

Krosel, S. M.↗