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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

Modeling Wave Driven Non-linear Flow Oscillations: The Terrestrial QBO and a Solar Analog

The Quasi Biennial Oscillation (QBO) of the zonal circulation observed in the terrestrial atmosphere at low latitudes is driven by wave mean flow interaction as was demonstrated first by Lindzen and Holton (1968), shown in a laboratory experiment by Plumb and McEwan (1978), and modeled by others (e.g., Plumb, Dunkerton). Although influenced by the seasonal cycle of solar forcing, the QBO, in principle, represents a nonlinear flow oscillation that can be maintained by a steady source of upward propagating waves. The wave driven non-linearity is of third or odd order in the flow velocity, which regenerates the fundamental harmonic itself to keep the oscillation going - the fluid dynamical analog of the displacement mechanism in the mechanical clock. Applying Hines' Doppler Spread Parameterization (DSP) for gravity waves (GW), we discuss with a global-scale spectral model numerical experiments that elucidate some properties of the QBO and its possible effects on the climatology of the atmosphere. Depending on the period of the QBO, wave filtering can cause interaction with the seasonal variations to produce pronounced oscillations with beat periods around 10 years. Since the seasonal cycle and its variability influence the period of the QBO, it may also be a potent conduit of solar activity variations to lower altitudes. Analogous to the terrestrial QBO, we propose that a flow oscillation may account for the 22-year periodicity of the solar magnetic cycle, potentially answering Dicke (1978) who asked, "Is there a chronometer hidden deep inside the Sun?" The oscillation would occur below the convection region, where gravity waves can propagate. Employing a simplified, analytic model, Hines' DSP is applied to estimate the flow oscillation. Depending on the adopted horizontal wavelengths of GW's, wave amplitudes less than 10 m/s can be made to produce oscillating zonal flows of about 20 m/s that should be large enough to generate a significant oscillation in the magnetic field. For the large length scales of the Sun, the flow cycle period tends to be very long. The period, however, can be made to be 22 years, provided the buoyancy frequency (stability) is sufficiently small, thus placing the proposed flow near the base of the convection zone where a dynamo is now believed to operate.

Mayr, Hans G.↗
DOE OSTI2013

DeepCwind Consortium Research Program: January 15, 2010 - March 31, 2013 (Final Technical Report)

This is the final technical report for the U.S. Department of Energy-funded program, DE-0002981: DeepCwind Consortium Research Program. The project objective was the partial validation of coupled models and optimization of materials for offshore wind structures. The United States has a great opportunity to harness an indigenous abundant renewable energy resource: offshore wind. In 2010, the National Renewable Energy Laboratory (NREL) estimated there to be over 4,000 GW of potential offshore wind energy found within 50 nautical miles of the US coastlines (Musial and Ram, 2010). The US Energy Information Administration reported the total annual US electric energy generation in 2010 was 4,120 billion kilowatt-hours (equivalent to 470 GW) (US EIA, 2011), slightly more than 10% of the potential offshore wind resource. In addition, deep water offshore wind is the dominant US ocean energy resource available comprising 75% of the total assessed ocean energy resource as compared to wave and tidal resources (Musial, 2008). Through these assessments it is clear offshore wind can be a major contributor to US energy supplies. The caveat to capturing offshore wind along many parts of the US coast is deep water. Nearly 60%, or 2,450 GW, of the estimated US offshore wind resource is located in water depths of 60 m or more (Musial and Ram, 2010). At water depths over 60 m building fixed offshore wind turbine foundations, such as those found in Europe, is likely economically infeasible (Musial et al., 2006). Therefore floating wind turbine technology is seen as the best option for extracting a majority of the US offshore wind energy resource. Volume 1 - Test Site; Volume 2 - Coupled Models; and Volume 3 - Composite Materials.

17 WIND ENERGY↗
DOE OSTI2023

Using Ultrasound Image Augmentation and Ensemble Predictions to Prevent Machine-Learning Model Overfitting

Deep learning predictive models have the potential to simplify and automate medical imaging diagnostics by lowering the skill threshold for image interpretation. However, this requires predictive models that are generalized to handle subject variability as seen clinically. Here, we highlight methods to improve test accuracy of an image classifier model for shrapnel identification using tissue phantom image sets. Using a previously developed image classifier neural network—termed ShrapML—blind test accuracy was less than 70% and was variable depending on the training/test data setup, as determined by a leave one subject out (LOSO) holdout methodology. Introduction of affine transformations for image augmentation or MixUp methodologies to generate additional training sets improved model performance and overall accuracy improved to 75%. Further improvements were made by aggregating predictions across five LOSO holdouts. This was done by bagging confidences or predictions from all LOSOs or the top-3 LOSO confidence models for each image prediction. Top-3 LOSO confidence bagging performed best, with test accuracy improved to greater than 85% accuracy for two different blind tissue phantoms. This was confirmed by gradient-weighted class activation mapping to highlight that the image classifier was tracking shrapnel in the image sets. Overall, data augmentation and ensemble prediction approaches were suitable for creating more generalized predictive models for ultrasound image analysis, a critical step for real-time diagnostic deployment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Evolution of linear perturbations in Lemaítre-Tolman-Bondi void models

We study the evolution of linear perturbations in a Lemaítre-Tolman-Bondi (LTB) void model with realistic cosmological initial conditions. Linear perturbation theory in LTB models is substantially more complicated than in standard Friedmann universes as the inhomogeneous background causes gauge-invariant perturbations couple at first order. As shown by Clarkson et al. (2009) [1], the evolution is constrained by a system of linear partial differential equations which need to be integrated numerically. We present a new numerical scheme using finite element methods to solve this equation system and generate scalar initial conditions based on Gaussian random fields with an underlying power spectrum for the Bardeen potential. After spherical harmonic decomposition, the initial fluctuations are propagated in time and estimates of angular power spectra of each gauge invariant variable are computed as functions of redshift. This allows to analyse the coupling strength in a statistical way. We find significant couplings up to 25% for large and deep voids of Gpc scale as required to fit the distance redshift relations of SNe.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

QuGAN: A Quantum State Fidelity based Generative Adversarial Network

In the recent years, Generative Adversarial Networks (GANs) have been arguably one of the largest strides forward in Deep Learning. Many papers illustrate the use of GANs to accomplish extremely impressive goals, such as text-to-image or image augmentation. Specifically, GANs have seen this success in the computer vision domain. However, GANs are not without their own set of problems. GANs are computationally expensive, sometimes computationally prohibitive, and can suffer a multitude of convergence problems. As research on classical GANs continues to push the topic further, a branch of GANs, namely Quantum GANs, has seen research interest in the past years. In this work, we intend to push this research further with an illustration of a Quantum GAN architecture that provide stable convergence of the model and is extended onto real data sets. Furthermore, unlike many other Quantum GANs out there, our model's GAN architecture runs the discriminator and the generator primarily on Quantum hardware through the use of a Quantum-based similarity metric. When compared to the very few other Quantum GAN papers, our architecture leads to significantly better results in almost all aspects.

Stein, Samuel A.↗
DOE OSTI2025

AI‐Driven Defect Engineering for Advanced Thermoelectric Materials

Thermoelectric materials offer a promising pathway to directly convert waste heat to electricity. However, achieving high performance remains challenging due to intrinsic trade-offs between electrical conductivity, the Seebeck coefficient, and thermal conductivity, which are further complicated by the presence of defects. This review explores how artificial intelligence (AI) and machine learning (ML) are transforming thermoelectric materials design. Advanced ML approaches including deep neural networks, graph-based models, and transformer architectures, integrated with high-throughput simulations and growing databases, effectively capture structure-property relationships in a complex multiscale defect space and overcome the “curse of dimensionality”. This review discusses AI-enhanced defect engineering strategies such as composition optimization, entropy and dislocation engineering, and grain boundary design, along with emerging inverse design techniques for generating materials with targeted properties. Finally, it outlines future opportunities in novel physics mechanisms and sustainability, highlighting the critical role of AI in accelerating the discovery of thermoelectric materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Unveiling mesoscopic structures in distorted lamellar phases through deep learning-based small angle neutron scattering analysis

Hypothesis: The formation of distorted lamellar phases, distinguished by their arrangement of crumpled, stacked layers, is frequently accompanied by the disruption of long-range order, leading to the formation of interconnected network structures commonly observed in the sponge phase. Nevertheless, traditional scattering functions grounded in deterministic modeling fall short of fully representing these intricate structural characteristics. Our hypothesis posits that a deep learning method, in conjunction with the generalized leveled wave approach used for describing structural features of distorted lamellar phases, can quantitatively unveil the inherent spatial correlations within these phases. Experiments and Simulations: This report outlines a novel strategy that integrates convolutional neural networks and variational autoencoders, supported by stochastically generated density fluctuations, into a regression analysis framework for extracting structural features of distorted lamellar phases from small angle neutron scattering data. To evaluate the efficacy of our proposed approach, we conducted computational accuracy assessments and applied it to the analysis of experimentally measured small angle neutron scattering spectra of AOT surfactant solutions, a frequently studied lamellar system. Findings: The findings unambiguously demonstrate that deep learning provides a dependable and quantitative approach for investigating the morphology of wide variations of distorted lamellar phases. It is adaptable for deciphering structures from the lamellar to sponge phase including intermediate structures exhibiting fused topological features. In conclusion, this research highlights the effectiveness of deep learning methods in tackling complex issues in the field of soft matter structural analysis and beyond.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Interpretable Deep Learning for Advancing Field-Enhanced Catalysis

This DOE Early Career project developed a physics-informed, interpretable AI-and-modeling framework to understand and exploit electric-field effects in heterogeneous catalysis, with ammonia cracking and synthesis as a representative pathway. The team built and validated methods to map local electric fields on metal surfaces and nanoparticles, showing that low-coordination features (tips/edges/corners) can concentrate fields by several-fold relative to flat facets. Using DFT-generated datasets, the project created physics-guided machine learning models that rapidly predict local electric fields and field-dependent adsorption energetics with near-DFT accuracy while reducing computational cost by orders of magnitude. These predictions were integrated with microkinetic modeling to quantify how field-dipole interactions reshape reaction energetics and mechanisms, enabling large increases in predicted catalytic rates and substantial reductions in operating temperature under favorable field conditions. To accelerate discovery of earth-abundant catalysts, the project combined interpretable ML screening (with electronic-structure descriptors identified as key drivers) with a generative inverse-design workflow based on diffusion models and physics constraints. The resulting closed-loop approach, linking simulation, mechanistic modeling, and AI, provides reusable tools and datasets for designing catalysts and operating conditions in field-enhanced catalysis, with broad relevance to electrostatic catalysis, plasma catalysis, electrocatalysis, and other energy-related chemical transformations.

30 DIRECT ENERGY CONVERSION↗
NASA NTRS1991

Thermal stress tectonics on the satellites of Saturn and Uranus

Thermal stress histories of the Saturnian and Uranian satellites are investigated. To this end, the thermal evolution of an icy satellite subjected to accretional and radiogenic heating, thermal conduction, and solid-state convection is modeled, and changes in the internal stress that occur during satellite evolution are examined. Results show that internal temperature changes that occur during normal evolution of many of the satellites of Saturn and Uranus can be expected to generate large extensional stresses in the satellites' outer regions. These stresses arise from three sources: (1) radiogenic warming, causing thermal expansion of materials in the satellite's deep interior; (2) radiogenic warming in larger satellites that can induce a phase transition from ice II to ice I and to produce a volume increase in the deep interior; and (3) accretional heating depositing heat in the satellite'e outer regions.

Hillier, John↗
NASA NTRS2004

Interference from the Deep Space Network's 70-m High Power Transmitter in Goldstone, CA to 3G Mobile Users Operating in the Surrounding Area

The International Telecommunications Union (ITU) has allocated 2110-2200 MHz for the third generation (3G) mobile services. Part of the spectrum (2110-2120 MHz) is allocated for space research service and has been used by the DSN for years for sending command uplinks to deep space missions. Due to the extremely high power transmitted, potential interference to 3G users in areas surrounding DSN Goldstone exists. To address this issue, a preliminary analytical study has been performed and computer models have been developed. The goal is to provide theoretical foundation and tools to estimate the strength of interference as a function of distance from the transmitter for various interference mechanisms, (or propagation modes), and then determine the size of the area in which 3G users are susceptible to interference from the 400-kW transmitter in Goldstone. The focus is non-line-of-sight interference, taking into account of terrain shielding, anomalous propagation mechanisms, and technical and operational characteristics of the DSN and the 3G services.

propagation↗
DOE OSTI2011

Recent Developments in SHERPA

Some recent QCD-related developments in the SHERPA event generator are presented. In the past decades, event generators such as PYTHIA [1, 2] and HERWIG [3, 4] have been central for nearly all physics analyses at particle physics experiments at the high-energy frontier. This will also hold true at the LHC, where a large number of interesting signals for new particles or new phenomena (the Higgs boson or any other manifestation of the mechanism behind electro-weak symmetry breaking, supersymmetry, extra dimensions etc.) is hampered by a plethora of severe, sometimes overwhelming backgrounds. Nearly all of them are largely influenced by QCD. Therefore it seems fair to say that the success of the LHC in finding new physics may very well depend on a deep and detailed understanding of old physics, like QCD. Examples for this include, among others, the central-jet veto for the vector boson fusion channel for Higgs production or topologies, where gauge bosons emerge in association with many jets, a background for many search channels. In a reflection on increased needs by the experimental community, aiming at higher precision, incorporation of new physics models and so on, the work horses of old have undergone serious renovation efforts, resulting in new, improved versions of the respective codes, namely PYTHIA8 [5] and HERWIG++ [6]. In addition a completely new code, SHERPA [7], has been constructed and is in the process of maturing. The status of this code is the topic of this contribution. SHERPA's hallmark property is the inclusion of higher-order tree-level QCD contributions, leading to an improved modelling of jet production. They are introduced through a full-fledged matrix element generator, AMEGIC++ [8], which is capable of generating matrix elements and corresponding phase space mappings for processes with multi-particle final states in various models, including the Standard Model, anomalous gauge triple and quadruple couplings according to [9, 10], the Minimal Supersymmetric Standard Model with Feynman rules from [11], the ADD-model of extra dimensions [12, 13], and a model with an extra U(1) singlet coupling to the Higgs boson only [14]. The code has been thoroughly tested and validated [15]. This code, however, is limited, especially in the treatment of many ({ge} 6) external QCD particles. Therefore, in the near future, SHERPA will incorporate another, new matrix element generator, COMIX, which is based on Berends-Giele recursion relations [16] and color-dressing [17] rather than color-ordering. In Tabs. 1 and 2 some example cross sections for gg {yields} ng at fixed energies and pp {yields} b{bar b} + n jets obtained with this program are exhibited and compared to those from other programs. In addition, concerning the calculation of higher-order matrix elements and cross sections, there have been first steps towards an automation of such calculations at truly next-to leading order accuracy. They manifest themselves in the implementation of a procedure [19] to fully automatically construct and evaluate Catani-Seymour dipole subtraction terms [20] for the real part of such NLO calculations. The results from the matrix element calculations are merged with the subsequent parton shower through the formalism of [21, 22]. The results of its implementation in SHERPA [23] has recently been compared with other algorithms [24]. Although there remains some dispute about the theoretical equivalence of the different approaches, the overall results show satisfying agreement with each other, such that they can be used with confidence for data analysis.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Vertex finding in neutrino-nucleus interaction: a model architecture comparison

We compare different neural network architectures for machine learning algorithms designed to identify the neutrino interaction vertex position in the MINERvA detector. The architectures developed and optimized by hand are compared with the architectures developed in an automated way using the package “Multi-node Evolutionary Neural Networks for Deep Learning” (MENNDL), developed at Oak Ridge National Laboratory. While the domain-expert hand-tuned network was the best performer, the differences were negligible and the auto-generated networks performed as well. There is always a trade-off between human, and computer resources for network optimization and this work suggests that automated optimization, assuming resources are available, provides a compelling way to save significant expert time.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Wind Speed Prediction with Spatio–Temporal Correlation: A Deep Learning Approach

Wind speed prediction with spatio–temporal correlation is among the most challenging tasks in wind speed prediction. In this paper, the problem of predicting wind speed for multiple sites simultaneously is investigated by using spatio–temporal correlation. This paper proposes a model for wind speed prediction with spatio–temporal correlation, i.e., the predictive deep convolutional neural network (PDCNN). The model is a unified framework, integrating convolutional neural networks (CNNs) and a multi-layer perceptron (MLP). Firstly, the spatial features are extracted by CNNs located at the bottom of the model. Then, the temporal dependencies among these extracted spatial features are captured by the MLP. In this way, the spatial and temporal correlations are captured by PDCNN intrinsically. Finally, PDCNN generates the predicted wind speed by using the learnt spatio–temporal correlations. In addition, three error indices are defined to evaluate the prediction accuracy of the model on the wind turbine array. Experiment results on real-world data show that PDCNN can capture the spatio–temporal correlation effectively, and it outperforms the conventional machine learning models, including multi-layer perceptron, support vector regressor, decision tree, etc.

17 WIND ENERGY↗
NASA NTRS2024

Simulated Feasibility of 3D Lightning Mapping from Space

In addition to the awe it inspires, lightning can illuminate the microphysical processes hidden away within deep convection. The current generation of space-based lightning mapping uses mostly 2D optical imaging to connect overall flash characteristics to their parent storm dynamics, but are missing a dimension’s worth of information. With lightning now classified as an essential climate variable, future spaceborne mappers will need improved capabilities to take advantage of the 3D structure of lightning flashes to support meteorological and climate modeling. We report here on a study of the feasibility of implementing a radio frequency (RF)–based network of satellites for 3D lightning mapping in high resolution from low Earth orbit (LEO). Lightning sources are simulated using existing tools of the Lightning Mapping Array (LMA), modified for orbital detection, and spatially reconstructed using a Levenberg-Marquardt geolocation algorithm to assess sources of uncertainty in these solutions. We analyze the benefits and limitations of this approach compared to existing orbital and ground-based methods. Results of this study show that lightning can be mapped in 3D with a vertical location accuracy better than 2 km using as few as five satellites in LEO capable of measuring the time-of-arrival of impulsive RF signals in the very high frequency (VHF) band. The consequence of this study is that high-resolution, spaceborne 3D mapping of lightning is achievable across most of the globe, having crucial implications for our understanding of not only lightning, but also severe weather development, climate science, and more.

CubeSpark↗
DOE OSTI2016

Considering the Role of Natural Gas in the Deep Decarbonization of the U.S. Electricity Sector. Natural Gas and the Evolving U.S. Power Sector Monograph Series: Number 2

Natural gas generation in the U.S. electricity sector has grown substantially in recent years, while the sector's carbon dioxide (CO2) emissions have generally declined. This relationship highlights the concept of natural gas as a potential enabler of a transition to a lower-carbon future. This work considers that concept by using the National Renewable Energy Laboratory (NREL) Renewable Energy Deployment System (ReEDS) model. ReEDS is a long-term capacity expansion model of the U.S. electricity sector. We examine the role of natural gas within the ReEDS modeling framework as increasingly strict carbon emission targets are imposed on the electricity sector. In addition to various natural gas price futures, we also consider scenarios that emphasize a low-carbon technology in order to better understand the role of natural gas if that low-carbon technology shows particular promise. Specifically, we consider scenarios with high amounts of energy efficiency (EE), low nuclear power costs, low renewable energy (RE) costs, and low carbon capture and storage (CCS) costs. Within these scenarios we find that requiring the electricity sector to lower CO2 emissions over time increases near-to-mid-term (through 2030) natural gas generation (see Figure 1 - left). The long-term (2050) role of natural gas generation in the electricity sector is dependent on the level of CO2 emission reduction required. Moderate reductions in long-term CO2 emissions have relatively little impact on long-term natural gas generation, while more stringent CO2 emission limits lower long-term natural gas generation (see Figure 1 - right). More stringent carbon targets also impact other generating technologies, with the scenarios considered here seeing significant decreases in coal generation, and new capacity of nuclear and renewable energy technologies over time. Figure 1 also demonstrates the role of natural gas in the context of scenarios where a specific low-carbon technology is advantaged. In 2030, natural gas generation in the technology scenarios is quite similar to that in the reference scenarios, indicating relatively little change in the role of natural gas in the near-to-mid-term due to advancements in those technology areas. The 2050 natural gas generation shows more significant differences, suggesting that technology advancements will likely have substantial impacts on the role of natural gas in the longer-term timeframe. Natural gas generation differences are most strongly driven by alternative natural gas price trajectories--changes in natural gas generation in the Low NG Price and High NG Price scenarios are much larger than in any other scenario in both the 2030 and 2050 timeframes. The only low-carbon technology scenarios that showed any increase in long-term natural gas generation relative to the reference case were the Low CCS cost scenarios. Carbon capture and storage technology costs are currently high, but have the potential to allow fossil fuels to play a larger role in low-carbon grid. This work considers three CCS cost trajectories for natural gas and coal generators: a baseline trajectory and two lower cost trajectories where CO2 capture costs reach $40/metric ton and $10/metric ton, respectively. We find that in the context of the ReEDS model and with these assumed cost trajectories, CCS can increase the long-term natural gas generation under a low carbon target (see Figure 2). Under less stringent carbon targets we do not see ReEDS electing to use CCS as part of its electricity generating portfolio for the scenarios considered in this work.

03 NATURAL GAS↗
DOE OSTI2018

Cloud, Aerosol, and Complex Terrain Interactions (CACTI) Science Plan

General circulation models and downscaled regional models exhibit persistent biases in deep convective initiation location and timing, cloud top height, stratiform area and precipitation fraction, and anvil coverage. Despite important impacts on the distribution of atmospheric heating, moistening, and momentum, nearly all climate models fail to represent convective organization, while system evolution is not represented at all. Improving representation of convective systems in models requires characterization of their predictability as a function of environmental conditions, and this characterization depends on observing many cases of convective initiation, non-initiation, organization, and non-organization. The Cloud, Aerosol, and Complex Terrain Interactions (CACTI) experiment in the Sierras de Córdoba mountain range of north-central Argentina is designed to improve understanding of cloud life cycle and organization in relation to environmental conditions so that cumulus, microphysics, and aerosol parameterizations in multiscale models can be improved. The Sierras de Córdoba range has a high frequency of orographic boundary-layer clouds, many reaching congestus depths, many initiating into deep convection, and some organizing into mesoscale systems uniquely observable from a single fixed site. Some systems even grow upscale to become among the deepest, largest, and longest-lived in the world. These systems likely contribute to an observed regional trend of increasing extreme rainfall, and poor prediction of them likely contributes to a warm, dry bias in climate models downstream of the Sierras de Córdoba range in a key agricultural region. Many environmental factors influence the convective life cycle in this region, including orographic, low-level jet, and frontal circulations, surface fluxes, synoptic vertical motions influenced by the Andes, cloud detrainment, and aerosol properties. Local and long-range transport of smoke resulting from biomass burning as well as blowing dust are common in the austral spring, while changes in land surface properties as the wet season progresses impact surface fluxes and boundary-layer evolution on daily and seasonal time scales that feed back to cloud and rainfall generation. This range of environmental conditions and cloud properties coupled with a high frequency of events makes this an ideal location for improving our understanding of cloud-environment interactions. The following primary science questions will be addressed through coordinated first U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF1), mobile C-band Scanning ARM Precipitation Radar (C-SAPR2), ARM Aerial Facility (AAF) Gulfstream-1 (G-1), and guest instrument observations: How are the properties and life cycles of orographically generated cumulus humulis, mediocris, and congestus clouds affected by environmental kinematics, thermodynamics, aerosols, and surface properties? How do these cloud types alter these environmental conditions? How do environmental kinematics, thermodynamics, and aerosols impact deep convective initiation, upscale growth, and mesoscale organization? How are soil moisture, surface fluxes, and aerosol properties altered by deep convective precipitation events and seasonal accumulation of precipitation? This multi-faceted experiment involves a long-term 7-month extended operational period (EOP, 1 October, 2018–30 April, 2019) as well as a 1.5-month intensive operational period (IOP, 30 October–13 December) that will include G-1 observations coinciding with the international multi-agency Remote Sensing of Electrification, Lightning, and Meso-scale/Micro-scale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign.

Convection, Andes, Argentina, cloud life cycle, cl↗
DOE OSTI2012

Pilot Residential Deep Energy Retrofits and the PNNL Lab Homes

This report summarizes research investigating the technical and economic feasibility of several pilot deep energy retrofits, or retrofits that save 30% to 50% or more on a whole-house basis while increasing comfort, durability, combustion safety, and indoor air quality. The work is being conducted for the U.S. Department of Energy Building Technologies Program as part of the Building America Program. As part of the overall program, Pacific Northwest National Laboratory (PNNL) researchers are collecting and analyzing a comprehensive dataset that describes pre- and post-retrofit energy consumption, retrofit measure cost, health and comfort impacts, and other pertinent information for each home participating in the study. The research and data collection protocol includes recruitment of candidate residences, a thorough test-in audit, home energy modeling, and generation of retrofit measure recommendations, implementation of the measures, test-out, and continued evaluation. On some homes, more detailed data will be collected to disaggregate energy-consumption information. This multi-year effort began in October 2010. To date, the PNNL team has performed test-in audits on 51 homes in the marine, cold, and hot-humid climate zones, and completed 3 retrofits in Texas, 10 in Florida, and 2 in the Pacific Northwest. Two of the retrofits are anticipated to save 50% or more in energy bills and the others - savings are in the 30% to 40% range. Fourteen other retrofits are under way in the three climate zones. Metering equipment has been installed in seven of these retrofits - three in Texas, three in Florida, and one in the Pacific Northwest. This report is an interim update, providing information on the research protocol and status of the PNNL deep energy retrofit project as of December, 2011. The report also presents key findings and lessons learned, based on the body of work to date. In addition, the report summarizes the status of the PNNL Lab Homes that are new manufactured homes procured with minimal energy-efficiency specifications typical of existing homes in the region, and sited on the PNNL campus. The Lab Homes serve as a flexible test facility (the first of its kind in the Pacific Northwest) to rapidly evaluate energy-efficient and grid-smart technologies that are applicable to residential construction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Introducing single-crystal scattering and optical potentials into MCNPX: Predicting neutron emission from a convoluted moderator

Neutron transport simulation codes are an indispensable tool used for the design and construction of modern neutron scattering facilities and instrumentation. It has become increasingly clear that some neutron instrumentation has started to exploit physics that is not well-modelled by the existing codes. Particularly, the transport of neutrons through single crystals and across interfaces in MCNP(X), Geant4 and other codes ignores scattering from oriented crystals and refractive effects, and yet these are essential ingredients for the performance of monochromators and ultra-cold neutron transport respectively (to mention but two examples). In light of these developments, we have extended the MCNPX code to include a single-crystal neutron scattering model and neutron reflection/refraction physics. Furthermore, we have also generated silicon scattering kernels for single crystals of definable orientation with respect to an incoming neutron beam. As a first test of these new tools, we have chosen to model the recently developed convoluted moderator concept, in which a moderating material is interleaved with layers of perfect crystals to provide an exit path for neutrons moderated to energies below the crystal s Bragg cut off at locations deep within the moderator. Studies of simple cylindrical convoluted moderator systems of 100 mm diameter and composed of polyethylene and single crystal silicon were performed with the upgraded MCNPX code and reproduced the magnitude of effects seen in experiments compared to homogeneous moderator systems. Applying different material properties for refraction and reflection, and by replacing the silicon in the models with voids, we show that the emission enhancements seen in recent experiments are primarily caused by the transparency of the silicon/void layers. Finally the convoluted moderator experiments described by Iverson et al. were simulated and we find satisfactory agreement between the measurement and the results of simulations performed using the tools we have developed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗