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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 1,423 records · Page 79
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Cummins R-SOFC System Development

The overall purpose of this project was to reduce the Reversible-Solid Oxide Fuel Cell (R-SOFC) system cost by developing two technologies, an improved cell design and the incorporation of an ejector in the fuel recycle loop instead of a blower. A Simulink model of the baseline SOFC system was developed and calibrated with experimental test data. The R-SOFC system model was built by integrating GT Suite developed models of the steam generation components into the baseline Simulink SOFC system model. The ability to run the stack in SOEC operating mode was also added to the model. The system model was used to explore the ability of the R-SOFC system to meet operational constraints on Steam/Carbon ratio and H2 concentration on the fuel side electrode. A CFD ejector model was developed and used to explore a range of ejector design parameters, leading to the final ejector design that was prototyped for testing. A prototype steam ejector was first tested in a laboratory environment using room temperature air. The steam ejector was subsequently tested using the full hot recycle loop with all relevant heat exchangers and steam generation components. The test conditions utilized temperatures, pressures, and flow rates expected in an R-SOFC application. Throughout the experimental testing work, ejector performance test data was used to improve and then validate the CFD ejector model. A CFD cell model was developed and used to optimize thermal gradients, voltage, and cost of a new cell substrate design. A CFD comparison of co-flow and cross-flow cell designs informed the decision to use a co-flow design for the new cell substrate. Multiple rounds of CFD simulation were used to improve the cell design to minimize the variation in air and fuel distribution across cell channels and to minimize the variation in air and fuel distribution across different cells in the stack. A few prototypes of the new cell substrate design were produced and validated in a laboratory environment by thermally spraying and verifying that they met established manufacturing specifications. The cell manufacturing process was adjusted in order to bring these metrics within acceptable tolerances. Cummins’ internal calculations show that the new cell design reduces cost ~50% compared to the baseline cell, while the ejector + superheater/boiler concept reduces cost of the recycle loop by ~40%. The impact of these cost reductions on the cost of producing H2 will depend on the specific system where they are applied. Therefore, a Techno-Economic Analysis was completed using system cost as a variable, and showing how the NREL Current and Future system costs translate into H2 production cost.

Henrichsen, Lars↗
DOE OSTI2023

Quantifying Charge Carrier Localization in PBTTT Using Thermoelectric and Spectroscopic Techniques

Chemically doped poly[2,5-bis(3-alkylthiophen-2-yl)thieno[3,2- b ]thiophene] (PBTTT) shows promise for many organic electronic applications, but rationalizing its charge transport properties is challenging because conjugated polymers are inhomogeneous, with convoluted optical and solid-state transport properties. Herein, we use the semilocalized transport (SLoT) model to quantify how the charge transport properties of PBTTT change as a function of iron(III) chloride (FeCl 3 ) doping level. We use the SLoT model to calculate fundamental transport parameters, including the carrier density needed for metal-like electrical conductivities and the position of the Fermi energy level with respect to the transport edge. We then contextualize these parameters with other polymer-dopant systems and previous PBTTT reports. Additionally, we use grazing incidence wide-angle X-ray scattering and spectroscopic ellipsometry techniques to better characterize inhomogeneity in PBTTT. Our analyses indicate that PBTTT obtains high electrical conductivities due to its quickly rising reduced Fermi energy level, and this rise is afforded by its locally high carrier densities in highly ordered microdomains. Ultimately, this report sets a benchmark for comparing transport properties across polymer-dopant-processing systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Revisiting particle dry deposition and its role in radiative effect estimates

Wet and dry deposition remove aerosols from the atmosphere, and these processes control aerosol lifetime and thus impact climate and air quality. Dry deposition is a significant source of aerosol uncertainty in global chemical transport and climate models. Dry deposition parameterizations in most global models were developed when few particle deposition measurements were available. However, new measurement techniques have enabled more size-resolved particle flux observations. We combined literature measurements with data that we collected over a grassland in Oklahoma and a pine forest in Colorado to develop a dry deposition parameterization. We find that relative to observations, previous parameterizations overestimated deposition of the accumulation and Aitken mode particles, and underestimated in the coarse mode. These systematic differences in observed and modeled accumulation mode particle deposition velocities are as large as an order of magnitude over terrestrial ecosystems. As accumulation mode particles form most of the cloud condensation nuclei (CCN) that influence the indirect radiative effect, this model-measurement discrepancy in dry deposition alters modeled CCN and radiative forcing. We present a revised observationally driven parameterization for regional and global aerosol models. Using this revised dry deposition scheme in the Goddard Earth Observing System (GEOS)-Chem chemical transport model, we find that global surface accumulation-mode number concentrations increase by 62% and enhance the global combined anthropogenic and natural aerosol indirect effect by −0.63 W m −2 . Our observationally constrained approach should reduce the uncertainty of particle dry deposition in global chemical transport models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Application of a Physics-Informed Convolutional Neural Network for Monitoring the Temperature Fields in High-Temperature Gas Reactors

Here, this work presents current advances in applying a physics-informed convolutional neural network (CNN) to evaluate temperature distributions in advanced reactors. Our goal is to demonstrate that the CNN can reconstruct temperature fields within the solid region of a prismatic fuel assembly in a high-temperature gas reactor (HTGR) with sensor data available in only a few cooling channels. Before that, we showcase the superior performance of the physics-informed CNN in comparison to a purely data-driven multilayer perceptron (MLP), considering a canonical heated channel setup. This analysis shows the advantages of our approach and justifies its choice. The datasets employed here are obtained upon numerical simulations performed with codes under the Nuclear Energy Advanced Modeling and Simulation program. This work is important, as industry experience indicates that the assembly material in HTGR concepts is prone to large thermal-mechanical loads nearing operational limits. This makes it crucial to characterize peak temperatures and their distributions near hot spots. Modern thermocouples are unreliable in these types of harsh environments because of the high neutron fluxes and elevated temperatures involved. The CNN-based field reconstruction represents an attractive solution, enabling sensor arrays in less aggressive locations and augmenting indirect predictions for less accessible regions. The results show that the CNN reduces prediction errors by orders of magnitude in comparison to the MLP, considering the simple yet well-representative heated channel case. In the case of the HTGR fuel assembly, the CNN can successfully reconstruct temperature fields over various cooling regimes. Furthermore, we also explore the algorithm’s ability to detect abnormalities. Interestingly, the CNN proves it has the capacity to detect blockage in one of the noninstrumented cooling channels.

Machine learning↗
DOE OSTI2022

Quantum simulation of quantum phase transitions using the convex geometry of reduced density matrices

Transitions of many-particle quantum systems between distinct phases at absolute-zero temperature, known as quantum phase transitions, require an exacting treatment of particle correlations. Here in this work, we present a general quantum-computing approach to quantum phase transitions that exploits the geometric structure of reduced density matrices. While typical approaches to quantum phase transitions examine discontinuities in the order parameters, the origin of phase transitions—their order parameters and symmetry breaking—can be understood geometrically in terms of the set of two-particle reduced density matrices (2-RDMs). The convex set of 2-RDMs provides a comprehensive map of the quantum system including its distinct phases as well as the transitions connecting these phases. Because 2-RDMs can potentially be computed on quantum computers at nonexponential cost, even when the quantum system is strongly correlated, they are ideally suited for a quantum-computing approach to quantum phase transitions. We compute the convex set of 2-RDMs for a Lipkin-Meshkov-Glick spin model on IBM superconducting-qubit quantum processors. Even though computations are limited to few-particle models due to device noise, comparisons with a classically solvable 1000-particle model reveal that the finite-particle quantum solutions capture the key features of the phase transitions including the strong correlation and the symmetry breaking.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Oxidation of 3D-printed SiC in air and steam environments

Here, the high-temperature oxidation of additively manufactured and chemically vapor infiltrated (3D-printed SiC) has been compared to chemical vapor deposited (CVD) SiC. 100-h isothermal exposures were conducted at 1425° and 1300°C at 1 atm under both dry air and steam environments. A SiC reaction tube was utilized to reduce silica volatility. After steam oxidation at 1425° and 1300°C, on the 3D-printed SiC surface, which was intrinsically rougher than the CVD surface, scales were 70%–90% thicker at the convex regions compared to concave/flat regions. In the convex regions, large cracks perpendicular to the oxidizing interface were observed. After dry air oxidation, scale thicknesses were comparable between 3D-printed SiC and CVD SiC, regardless of geometry. Finite element modeling, conducted to elucidate the relationship between SiC geometry and ß- to α-cristobalite transformation stress, determined cristobalite transformation tensile stresses to be on the order of 103 MPa during cool down, assuming a 6 vol% reduction. Compared to flat SiC substrates, tensile transformation stresses were elevated at concave regions and relaxed at convex regions. Combined with specimen mass gain (accounting for the rougher surface) of 3D-printed SiC being 15%–32% higher for 3D-printed SiC after 1300°C and 1425°C steam oxidation, the work presented concludes that the increased oxidation of 3D-printed SiC is primarily caused by tensile hoop stresses driven by oxidation volume expansion. Lastly, the efficacy of the 3D-printing method is demonstrated through the production of tristructural isotropic imbedded 3D-printed SiC fuel forms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Accelerating the solution of the S N equations with highly anisotropic scattering using the Fokker-Planck approximation

The discrete ordinates method can model forward-peaked transport problems accurately. However, convergence of discrete ordinates solution can become arbitrarily slow upon use of standard iterative procedures like source iteration. Standard zeroth and first moment-based acceleration methods like nonlinear diffusion acceleration and diffusion synthetic acceleration are ineffective in accelerating such problems because these methods do not correct higher order Legendre-moments of angular flux. In this paper, we explore the idea of using the Fokker-Planck approximation as a preconditioner to accelerate the convergence of solution of forward-peaked transport problems. Finally, we observe that such preconditioning can successfully reduce the iteration count by up to four orders of magnitude, and solver runtime by up to three orders of magnitude when compared to diffusion synthetic acceleration.

42 ENGINEERING↗
DOE OSTI2022

Comparison of spherical harmonics method and discrete ordinates method for radiative transfer in a turbulent jet flame

Here, in this study, we systematically compared the accuracy and computational cost of two popular solution methods for the radiative transfer equation (RTE): the spherical harmonics method (P N ) and the discrete ordinates method (DOM). We first investigated convergence characteristics of different orders of P N and DOM in a series of 1D homogeneous configurations with varying optical thicknesses. Both solvers perform better for optically thicker cases. The accuracy of P N methods increases with its order, , but the gain in accuracy reduces with the increase in , i.e., improvement of P 7 over P 5 is less than that of P 3 over P 1 . This decreasing trend becomes more prominent as the optical thickness decreases. On the other hand, DOM’s accuracy increases almost linearly with the increase in the number of ordinates (or polar angles in this study) in all cases. While comparing the directional profile of radiative intensity, both solvers perform better when the radiative intensity is more isotropic. These solvers were then connected with a full spectrum k-distribution (FSK) spectral model and used to perform radiation-coupled simulations of a turbulent jet flame in an axi-symmetric cylindrical domain. Results obtained from P 1 to P 7 approximations for P N , and 2 x 4, 4 x 4, 4 x 8, 8 x 8 finite angles for DOM are compared with that from an optically thin model, and a reference solution from line-by-line (LBL) photon Monte Carlo (PMC) method. The choice of radiation solver shows a noticeable impact on the temperature distribution of the flame. The P N solvers lead to slightly higher radiant fractions and the DOM solvers lead to slightly lower radiant fractions than the PMC benchmark solution. Finally, the computational costs of each of these solvers are also reported and an intermittent evaluation / time blending scheme to improve the computational efficiency of radiation solvers in radiation-coupled simulations are also demonstrated.

42 ENGINEERING↗
DOE OSTI2020

Effect of ammonia addition on suppressing soot formation in methane co-flow diffusion flames

Due to issues surrounding carbon dioxide emissions from carbon-containing fuels, there is growing interest in ammonia (NH 3 ) as an alternative combustion fuel. One attractive method of burning NH 3 is to co-fire it with hydrocarbons, such as natural gas, and in this case soot formation is possible. To begin understanding the influence of NH 3 on soot formation when co-fired with hydrocarbons, soot volume fractions and mole fractions of gas-phase species were computationally and experimentally interrogated for CH 4 flames with up to 40% NH 3 by volumetric fuel fraction. Mole fractions of gas-phase species, including C 2 H 2 and C 6 H 6 , were measured with on-line electron impact mass spectrometry, and soot volume fractions were obtained via color-ratio pyrometry. The simulations employed a detailed chemical mechanism developed for capturing nitrogen interactions with hydrocarbons during combustion. The results are compared to findings in N 2 -CH 4 flames, in order to separate thermal and dilution effects from the chemical influence of NH 3 on soot formation. Experimentally, C 2 H 2 concentrations were found to decrease slightly for the NH 3 -CH 4 flames relative to N 2 -CH 4 flames, and a stronger suppression of C 6 H 6 was found for NH 3 relative to N 2 additions. The measured results show a strong suppression of soot with the addition of NH 3 , with soot concentrations reduced by over a factor of 10 with addition of up to 20% or more NH 3 by mole fraction. The model satisfactorily captured relative differences in maximum centerline C 2 H 2 , C 6 H 6 , and soot concentrations with addition of N 2 , but was unable to match measured differences in NH 3 -CH 4 flames. Furthermore, these results highlight the need for an improved understanding of fuel-nitrogen interactions with higher hydrocarbons to enable accurate models for predicting particulate emissions from NH 3 /hydrocarbon combustion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Experimental Quantification of Spin–Phonon Coupling in Molecular Qubits Using Inelastic Neutron Scattering

Electronic spin superposition states enable nanoscale sensing through their sensitivity to the local environment, yet their sensitivity to vibrational motion also limits their coherence times. In molecular spin systems, chemical tunability and atomicscale resolution are accompanied by a dense, thermally accessible phonon spectrum that introduces efficient spin relaxation pathways. Despite extensive theoretical work, there is little experimental consensus on which vibrational energies dominate spin relaxation or how molecular structure controls spin−phonon coupling (SPC). We present a fully experimental method to quantify SPC coefficients by combining temperature-dependent vibrational spectra from inelastic neutron scattering with spin relaxation rates measured by electron paramagnetic resonance. We apply this framework to two model S = 1/2 systems, copper(II) phthalocyanine (CuPc) and copper(II) octaethylporphyrin (CuOEP). Two distinct relaxation regimes emerge: below 40 K, weakly coupled lattice modes below 50 cm −1 dominate, whereas above 40 K, optical phonons above ∼185 cm −1 become thermally populated and drive relaxation with SPC coefficients nearly 3 orders of magnitude larger. Structural distortions in CuOEP that break planar symmetry soften the crystal lattice and enhance anharmonic scattering but also raise the energy of stretching modes at the molecular core where the spins reside. This redistributes vibrational energy toward the molecular periphery and out of plane, ultimately reducing SPC relative to CuPc and enabling room-temperature spin coherence in CuOEP. Although our method does not provide mode-specific SPC coefficients, it quantifies contributions from distinct spectral regions and establishes a broadly applicable, fully experimental link between crystal structure, lattice dynamics, and spin relaxation.

Lohaus, Stefan H. [California Institute of Technol↗
DOE OSTI2022

Reduced models for ETG transport in the tokamak pedestal

This paper reports on the development of reduced models for electron temperature gradient (ETG) driven transport in the pedestal. Model development is enabled by a set of 61 nonlinear gyrokinetic simulations with input parameters taken from pedestals in a broad range of experimental scenarios. The simulation data have been consolidated in a new database for gyrokinetic simulation data, the multiscale gyrokinetic database (MGKDB), facilitating the analysis. The modeling approach may be considered a generalization of the standard quasilinear mixing length procedure. The parameter η, the ratio of the density to temperature gradient scale length, emerges as the key parameter for formulating an effective saturation rule. With a single order-unity fitting coefficient, the model achieves an error of 15%. A similar model for ETG particle flux is also described. We also present simple algebraic expressions for the transport informed by an algorithm for symbolic regression.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Medium amplitude parallel superposition (MAPS) rheology. Part 2: Experimental protocols and data analysis

An experimental protocol is developed to directly measure the new material functions revealed by medium amplitude parallel superposition (MAPS) rheology. This protocol measures the medium amplitude response of a material to a simple shear deformation composed of three sine waves at different frequencies, revealing a rich dataset consisting of up to 19 measurements of the third-order complex modulus at distinct three-frequency coordinates. We discuss how the choice of input frequencies influences the features of the MAPS domain studied by the experiment. A polynomial interpolation method for reducing the bias of measured values from spectral leakage and reducing variance due to noise is discussed, including a derivation of the optimal range of amplitudes for the input signal. This leads to the conclusion that conducting the experiment in a stress-controlled fashion possesses a distinct advantage to the strain-controlled mode. The experimental protocol is demonstrated through measurements of the MAPS response of a model complex fluid: a surfactant solution of wormlike micelles. The resulting dataset is indeed large and feature-rich, while still acquired in a time comparable to similar medium amplitude oscillatory shear (MAOS) experiments. We demonstrate that the data represent measurements of an intrinsic material function by studying its internal consistency, compatibility with low-frequency predictions for Coleman–Noll simple fluids, and agreement with data obtained via MAOS amplitude sweeps. Finally, the data are compared to predictions from the corotational Maxwell model to demonstrate the power of MAPS rheology in determining whether a constitutive model is consistent with a material’s time-dependent response.

Lennon, Kyle R. (ORCID:0000000212515461)↗
DOE OSTI2021

Comprehensive Material Characterization and Simultaneous Model Calibration for Improved Computational Simulation Credibility

Computational simulation is increasingly relied upon for high-consequence engineering decisions, and a foundational element to solid mechanics simulations is a credible material model. Our ultimate vision is to interlace material characterization and model calibration in a real-time feedback loop, where the current model calibration results will drive the experiment to load regimes that add the most useful information to reduce parameter uncertainty. The current work investigated one key step to this Interlaced Characterization and Calibration (ICC) paradigm, using a finite load-path tree to incorporate history/path dependency of nonlinear material models into a network of surrogate models that replace computationally-expensive finite-element analyses. Our reference simulation was an elastoplastic material point subject to biaxial deformation with a Hill anisotropic yield criterion. Training data was generated using either a space-filling or adaptive sampling method, and surrogates were built using either Gaussian process or polynomial chaos expansion methods. Surrogate error was evaluated to be on the order of 10 ⁻5 and 10 ⁻3 percent for the space-filling and adaptive sampling training data, respectively. Direct Bayesian inference was performed with the surrogate network and with the reference material point simulator, and results agreed to within 3 significant figures for the mean parameter values, with a reduction in computational cost over 5 orders of magnitude. These results bought down risk regarding the surrogate network and facilitated a successful FY22-24 full LDRD proposal to research and develop the complete ICC paradigm.

36 MATERIALS SCIENCE↗
DOE OSTI2023

The Development of the Pooled Rideshare Acceptance Model (PRAM)

Due to the advancements in real-time information communication technologies and sharing economies, rideshare services have gained significant momentum by offering dynamic and/or on-demand services. Rideshare service companies evolved from personal rideshare, where riders traveled solo or with known individuals, into pooled rideshare (PR), where riders can travel with one to multiple unknown riders. Similar to other shared economy services, pooled rideshare is beneficial as it efficiently utilizes resources, resulting in reduced energy usage, as well as reduced costs for the riders. However, previous research has demonstrated that riders have concerns about using pooled rideshare, especially regarding personal safety. A U.S. national survey with 5385 participants was used to understand human factor-related barriers and user preferences to develop a novel Pooled Rideshare Acceptance Model (PRAM). This model used a covariance-based structural equation model (CB-SEM) to identify the relationships between willingness to consider PR factors (time/cost, privacy, safety, service experience, and traffic/environment) and optimizing one’s experience of PR factors (vehicle technology/accessibility, convenience, comfort/ease of use, and passenger safety), resulting in the higher-order factor trust service. We examined the factors’ relative contribution to one’s willingness/attitude towards PR and user acceptance of PR. Privacy, safety, trust service, and convenience were statistically significant factors in the model, as were the comfort/ease of use factor and the service experience, traffic/environment, and passenger safety factors. The only two non-significant factors in the model were time/cost and vehicle technology/accessibility; it is only when a rider feels safe that individuals then consider the additional non-significant variables of time, cost, technology, and accessibility. Privacy, safety, and service experience were factors that discouraged the use of PR, whereas the convenience factor greatly encouraged the acceptance of PR. Despite the time/cost factor’s lack of significance, individual items related to time and cost were crucial when viewed within the context of convenience. This highlights that while user perceptions of privacy and safety are paramount to their attitude towards PR, once safety concerns are addressed, and services are deemed convenient, time and cost elements significantly enhance their trust in pooled rideshare services. This study provides a comprehensive understanding of user acceptance of PR services and offers actionable insights for policymakers and rideshare companies to improve their services and increase user adoption.

dynamic rideshare system↗
DOE OSTI2020

Energy demand science for a decarbonized society in the context of the residential sector

To develop a decarbonized society, two contradictory requirements must be met: (1) reducing energy demand and (2) creating flexibility in energy demand in order to respond to fluctuations in renewable electricity generation. To help meet these requirements, conventional energy efficiency studies should be extended to incorporate “energy demand science.” This paper presents a definition of “energy demand science” and then reviews the related history and research questions of energy demand science in the context of the residential sector. It then examines three key areas that must be integrated into the next-generation energy demand science: (1) energy demand measurement with detailed granularity and analysis using cutting-edge technology, (2) energy demand modeling that helps clarify the formation mechanism of energy demand, and (3) identification of the factors that influence people's decision making, which represents typical human-dimension research. Energy demand science consists of technical, human, natural environment, demographic, and land-use dimensions, and their integration is key for the establishment of a decarbonized society.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Sparsity-Independent Lyapunov Exponent in the Sachdev-Ye-Kitaev Model

The saturation of a recently proposed universal bound on the Lyapunov exponent has been conjectured to signal the existence of a gravity dual. This saturation occurs in the low-temperature limit of the dense Sachdev-Ye-Kitaev (SYK) model, N Majorana fermions with q body ( q > 2 ) infinite-range interactions. We calculate certain out-of-time-order correlators (OTOCs) for N ≤ 64 fermions for a highly sparse SYK model and find no significant dependence of the Lyapunov exponent on sparsity up to near the percolation limit where the Hamiltonian breaks up into blocks. This provides strong support to the saturation of the Lyapunov exponent in the low-temperature limit of the sparse SYK. A key ingredient to reaching N = 64 is the development of a novel quantum spin model simulation library that implements highly optimized matrix-free Krylov subspace methods on graphical processing units. This leads to a significantly lower simulation time as well as vastly reduced memory usage over previous approaches, while using modest computational resources. Strong sparsity-driven statistical fluctuations require both the use of a much larger number of disorder realizations with respect to the dense limit and a careful finite size scaling analysis. The saturation of the bound in the sparse SYK points to the existence of a gravity analog that would enlarge substantially the number of field theories with this feature. Published by the American Physical Society 2024

Physics↗
DOE OSTI2022

Efficient Region of Attraction Characterization for Control and Stabilization of Load Tap Changer Dynamics

In this article, we study the monitoring and control of long-term voltage stability considering load tap changer (LTC) dynamics. We show that under generic conditions, the LTC dynamics admit a unique stable equilibrium. For the stable equilibrium, we characterize an explicit inner approximation of its largest region of attraction (ROA). Compared to existing results, the computational complexity of the ROA characterization is drastically reduced. We propose a quadratically constrained linear program formulation for the ROA characterization problem. In addition, we formulate a second-order cone program for online voltage stability monitoring and control exploiting the proposed ROA characterization. Finally, we demonstrate the efficacy of the proposed formulations on the ROA characterization and stability monitoring and control using a standard IEEE test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

US Fallout Shelter

Being indoors (sheltering) reduces radiation exposures resulting from nuclear fallout or a power-plant accident. However, most US building types, particularly non-residential buildings, lack quantitative estimates. We provide here a high-level modeling analysis of modern US building protection based on a novel radiation protection building attribute taxonomy, a new building protection model, compilation of prior experimental results, and nationally representative building survey data. This approach provides a consistent, quantitative understanding of US building protection and, when combined with the distribution of people among different buildings, can assist in selecting emergency response strategies. We find that indoor radiation protection varies by orders of magnitude. Most people in non-residential buildings are adequately protected, particularly below ground or in the building center. Residential buildings are less protective and those with lighter weight walls lack adequate, above ground protection. This work also highlights key areas where further investigation will improve the current results. These include the improving the understanding of residential basement protection, additional experimental data on non-residential buildings (particularly schools and industrial buildings), and the frequency of brick veneer and stucco exterior residential buildings.

61 RADIATION PROTECTION AND DOSIMETRY↗