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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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Coupled Reactor Multiphysics and Mass Scalability Assessment for Crewed Megawatt-Class NEP System Architectures

Nuclear Electric Propulsion (NEP) is an in-space propulsion technology capable of enabling opposition and conjunction class crewed Mars missions. NEP subsystems include the reactor for heat generation, a power conversion system (PCS), power management and distribution, electric propulsion system, and heat rejection system. Specific mass, or α (kg/kWe), is a key performance parameter (KPP) of the propulsion system which is directly scalable with the performance and mass predictions for each individual component. To inform technology maturation planning activities, full system and component level parametric modeling is ongoing to explore the design trade space and illustrate the effect of subsystem design choices on the system KPPs. In this study, scaling of high-assay, low-enriched uranium reactor designs is assessed through coupled reactor physics and thermal hydraulics analyses. Scaling analyses evaluate the impact of system performance parameters (power level, interface temperatures) on mass for direct gas cooled, pumped liquid metal, and passively-cooled heat pipe reactor concepts. Each concept requires specific geometries, fluids, and power conversion interface conditions (temperature, pressure, flow rate) to meet desired performance and mass. The reactor assembly includes the active core (fuel, moderator, cladding, working fluid), axial and radial neutron reflectors, control drums, structural support / pressure vessel, and external radiation shielding. Each of these components are parametrically sized based on performance parameters for a megawatt-class power cycle. Results of this scaling analysis increase NEP propulsion system modeling fidelity and ultimately aim to support concept down-selection along with related technology development planning. The reactor and shield α are a function of several PCS and heat rejection system design choices, and reactor scaling with these parameters must be considered to enable an informed decision on an optimal reactor geometry and working fluid combination.

Nuclear Electric Propulsion↗
DOE OSTI2026

Ensemble Federated Machine Learning‐Based Cybersecurity Situational Awareness in Microgrid Network

Cyber-physical microgrids are vulnerable to stealthy cybersecurity threats that disguise their actions through the exploitation of system knowledge. Such actions can severely impacts microgrids deployed in defense bases, slowing the response time of military forces during national emergencies. Several machine-learning algorithms have been proposed to detect intrusions in the grid networks; however, these traditional machine-learning algorithms lack data privacy and are subject to several adversarial machine-learning threats. This paper proposes a novel federated machine learning (FML)-based three-model framework to detect and identify stealthy data-integrity attacks while ensuring data privacy in microgrid networks. The proposed architecture uses a variational mode decomposition technique to extract derived features from incoming measurement and control datasets. The extraction of these derived features allows FML models to learn minute variations in data patterns that allow them to perform significantly better than the models trained with generic datasets consisting of raw features. Our experimental results show the efficient performance of the proposed methodology against different types of data integrity attacks while considering primary and secondary controllers in microgrids. Further, the applied FML-integrated random forest ensemble algorithm outperforms the existing generic FML algorithms during noisy and noise-free datasets with prediction latencies of only 91–134 µs per sample within the 0.1 s sampling interval and requires communication bandwidth of around ∼8.25 KB/s at the control center and ∼2.7 KB/s per edge client for communication.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2011

Integration of Dust Prediction Systems and Vegetation Phenology to Track Pollen for Asthma Alerts in Public Health

Pollen can be transported great distances. Van de Water et. al., 2003 reported Juniperus pollen, a significant aeroallergen was transported 200-600 km. Hence local observations of plant phenology may not be consistent with the timing and source of pollen collected by pollen sampling instruments. The DREAM (Dust REgional Atmospheric Model, Yin 2007) is a verified model for atmospheric dust transport modeling using MODIS data products to identify source regions and quantities of dust (Yin 2007). The use of satellite data products for studying phenology is well documented (White and Nemani 2006). We are modifying the DREAM model to incorporate pollen transport. The linkages already exist with DREAM through PHAiRS (Public Health Applications in remote Sensing) to the public health community. This linkage has the potential to fill this data gap so that health effects of pollen can better be tracked for linkage with health outcome data including asthma, respiratory effects, myocardial infarction, and lost work days. DREAM is based on the SKIRON/Eta modeling system and the Eta/NCEP regional atmospheric model. The dust modules of the entire system incorporate the state of the art parameterizations of all the major phases of the atmospheric dust life such as production, diffusion, advection, and removal. These modules also include effects of the particle size distribution on aerosol dispersion. The dust production mechanism is based on the viscous/turbulent mixing, shear-free convection diffusion, and soil moisture. In addition to these sophisticated mechanisms, very high resolution databases, including elevation, soil properties, and vegetation cover are utilized. The DREAM model was modified to use pollen sources instead of dust (PREAM). Pollen release will be estimated based on satellite-derived phenology of Juniperus spp. communities. The MODIS surface reflectance product (MOD09) will provide information on the start of the plant growing season, growth stage, peak greenness, dry-down and pollen release. Ground based observational records of pollen release timing and quantities will be used as verification. Techniques developed using MOD09 surface reflectance products will be directly applicable to the next generation sensors such as VIIRS. The resulting deterministic model for predicting and simulating pollen emission and downwind concentration to study details of phenology and meteorology and their dependencies. This information will be used to support the Centers for Disease Control and Prevention (CDC)'s National Environmental Public Health Tracking Program (EPHT) and the State of New Mexico environmental public health decision support for asthma and allergies alerts

Luvall, Jeffrey C.↗
DOE OSTI2012

Small Scale Mixing Demonstration Batch Transfer and Sampling Performance of Simulated HLW - 12307

The ability to effectively mix, sample, certify, and deliver consistent batches of High Level Waste (HLW) feed from the Hanford Double Shell Tanks (DST) to the Waste treatment Plant (WTP) has been recognized as a significant mission risk with potential to impact mission length and the quantity of HLW glass produced. At the end of 2009 DOE's Tank Operations Contractor, Washington River Protection Solutions (WRPS), awarded a contract to EnergySolutions to design, fabricate and operate a demonstration platform called the Small Scale Mixing Demonstration (SSMD) to establish pre-transfer sampling capacity, and batch transfer performance data at two different scales. This data will be used to examine the baseline capacity for a tank mixed via rotational jet mixers to transfer consistent or bounding batches, and provide scale up information to predict full scale operational performance. This information will then in turn be used to define the baseline capacity of such a system to transfer and sample batches sent to WTP. The Small Scale Mixing Demonstration (SSMD) platform consists of 43'' and 120'' diameter clear acrylic test vessels, each equipped with two scaled jet mixer pump assemblies, and all supporting vessels, controls, services, and simulant make up facilities. All tank internals have been modeled including the air lift circulators (ALCs), the steam heating coil, and the radius between the wall and floor. The test vessels are set up to simulate the transfer of HLW out of a mixed tank, and collect a pre-transfer sample in a manner similar to the proposed baseline configuration. The collected material is submitted to an NQA-1 laboratory for chemical analysis. Previous work has been done to assess tank mixing performance at both scales. This work involved a combination of unique instruments to understand the three dimensional distribution of solids using a combination of Coriolis meter measurements, in situ chord length distribution measurements, and electro-resistive tomography. This current work utilized the same instruments to monitor simulated waste transfers. This paper will discuss some of the scaling compromises when it came to the scaled sampling system design, handling of large quantities of material for sampling, and present data for the discuss of likely behavior of the full scale DST based on scaling correlations using a scale ratio exponent (SRE) from 0.25 to 0.45 and the behavior observed in the SSMD platform. This does not establish a scaling factor for DST mixing using paired jet mixers but is an attempt to envelope the likely performance ranges in terms of certification sampling bias, certification sample root-mean-square-deviation, and bath to batch relative standard deviation. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

A new method to quantify the effects of baryons on the matter power spectrum

Future large-scale galaxy surveys have the potential to become leading probes for cosmology provided the influence of baryons on the total mass distribution is understood well enough. As hydrodynamical simulations strongly depend on details in the feedback implementations, no unique and robust predictions for baryonic effects currently exist. In this paper we propose a baryonic correction model that modifies the density field of dark-matter-only N-body simulations to mimic the effects of baryons from any underlying adopted feedback recipe. The model assumes haloes to consist of 4 components: 1- hot gas in hydrostatical equilibrium, 2- ejected gas from feedback processes, 3- central galaxy stars, and 4- adiabatically relaxed dark matter, which all modify the initial dark-matter-only density profiles. These altered profiles allow to define a displacement field for particles in N-body simulations and to modify the total density field accordingly. The main advantage of the baryonic correction model is to connect the total matter density field to the observable distribution of gas and stars in haloes, making it possible to parametrise baryonic effects on the matter power spectrum. We show that the most crucial quantities are the mass fraction of ejected gas and its corresponding ejection radius. The former controls how strongly baryons suppress the power spectrum, while the latter provides a measure of the scale where baryonic effects become important. A comparison with X-ray and Sunyaev-Zel'dovich cluster observations suggests that baryons suppress wave modes above k∼0.5 h/Mpc with a maximum suppression of 10-25 percent around k∼ 2 h/Mpc. More detailed observations of the gas in the outskirts of groups and clusters are required to decrease the large uncertainties of these numbers.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Artificial intelligence-based predictive modeling for imaging neutral particle analyzers on the DIII-D tokamak

The Imaging Neutral Particle Analyzer (INPA) at DIII-D is a diagnostic system used to accurately resolve the energy and spatial distributions of fast ions in fusion plasmas. A novel artificial intelligence (AI) technique named INPA-net is based on Reservoir Computing Networks and developed here to predict active and passive signals produced by charge-exchange reactions from injected and edge-cold neutrals, respectively, in magnetically confined fusion plasmas. This model is trained using a set of 21 time domain signals between 0 s to 3.35 s that includes injected beam and thermal plasma information, and 6444 real 2D experimental images of the INPA in 12 plasma discharges at DIII-D. The trained neural network is able to forecast experimental images in real-time. The model achieves an R-squared value of 0.91, which is higher than the 0.83 value achieved by a simple linear regression model. This improvement highlights the model's enhanced predictive accuracy for measured images from the validation set. This AI approach is valuable due to its rapid response times and potential for integration into real-time plasma control systems. A version of this model capable of generating syntehic images would be useful for the real-time monitoring of fast-ion transport. A comprehensive sensitivity study reveals that INPA-net maintains high performance even with variations in the input parameters, indicating the model's robustness and reliability. While developed for the INPA, the underlying architecture is adaptable and may be applied to various 2D imaging diagnostics in fusion research.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

Healthy Zero Energy Buildings (HZEB) Program - Cross-Sectional Study of Contaminant Levels, Source, Strengths, and Ventilation Rates in Retail Stores

This field study measured ventilation rates and indoor air quality parameters in 21 visits to retail stores in California. The data was collected to guide the development of new, science-based commercial building ventilation rate standards that balance the dual objectives of increasing energy efficiency and maintaining acceptable indoor air quality. Data collection occurred between September 2011 and March 2013. Three types of stores participated in this study: grocery stores, furniture/hardware stores, and apparel stores. Ventilation rates and indoor air contaminant concentrations were measured on a weekday, typically between 9 am and 6 pm. Ventilation rates measured using a tracer gas decay method exceeded the minimum requirement of California’s Title 24 Standard in all but one store. Even though there was adequate ventilation according to Title 24, concentrations of formaldehyde, acetaldehyde, and acrolein exceeded the most stringent chronic health guidelines. Other indoor air contaminants measured included carbon dioxide (CO{sub 2}), carbon monoxide (CO), ozone (O{sub 3}), and particulate matter (PM). Concentrations of CO{sub 2} were kept low by adequate ventilation, and were assumed low also because the sampling occurred on a weekday when retail stores were less busy. CO concentrations were also low. The indoor-outdoor ratios of O{sub 3} showed that the first-order loss rate may vary by store trade types and also by ventilation mode (mechanical versus natural). Analysis of fine and ultrafine PM measurements showed that a substantial portion of the particle mass in grocery stores with cooking-related emissions was in particles less than 0.3 μm. Stores without cooking as an indoor source had PM size distributions that were more similar indoors and outdoors. The whole-building emission rates of volatile organic compounds (VOCs) and PM were estimated from the measured ventilation rates and indoor and outdoor contaminant concentrations. Mass balance models were then used to determine the ventilation rates, filtration strategies, or source reductions needed to maintain indoor contaminant concentrations below reference levels. Several scenarios of potential concern were considered: (i) formaldehyde levels in furniture/hardware stores, (ii) contaminants associated with cooking (e.g., PM, acrolein, and acetaldehyde) in grocery stores, and (iii) outdoor contaminants (e.g., PM and O{sub 3}) impacting stores that use natural ventilation. Estimated formaldehyde emission rates suggest that retail stores would need to ventilate at levels far exceeding the current Title 24 requirement to lower indoor concentrations below California’s stringent formaldehyde reference level. Given the high costs of providing ventilation but only modest chronic health benefit is expected, effective source control is an attractive alternative, as demonstrated by some retail stores in this study. Predictions showed that grocery stores need MERV 13 air filters, instead of MERV 8 filters that are more commonly used, to maintain indoor PM at levels that meet the chronic health standards for PM. Exposure to acrolein is a potential health concern in grocery stores, and should be addressed by increasing the use of kitchen range hoods or improving their contaminant removal efficiency. In stores that rely on natural ventilation, indoor PM can be a health concern if the stores are located in areas with high outdoor PM. This concern may be addressed by switching to mechanical ventilation when the outdoor air quality is poor, while continuing natural ventilation when outdoor air quality is good.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Optimal pumping schedule with high-viscosity gel for uniform distribution of proppant in unconventional reservoirs

Hydrocarbon recovery from a hydraulic-fractured unconventional reservoir can be enhanced by replacing slickwater with non-Newtonian high-viscosity gel as a fracturing fluid. However, in the literature, the role of gel rheology on hydrocarbon production from a fractured reservoir is not very well understood. This lack of understanding makes it difficult for the industry to choose appropriate viscosity parameters of fracturing gels. Motivated by these limitations, first, we have developed a high-fidelity model of non-Newtonian fluid flow to simulate fracture propagation, fluid leak-off, and gel flowback processes to predict the oil production from a hydraulic fractured reservoir. Simulation results of the high-fidelity model show how fracture geometry, formation damage, and gel cleanup process varies with the fracturing fluid rheology. Therefore, the developed model is used to understand the effect of gel rheology on hydrocarbon production via a sensitivity analysis, which provides a set of rheological parameters that maximize the cumulative amount of oil production. Finally, utilizing this optimal high-viscosity gel, a model predictive controller is designed to obtain an optimal pumping schedule necessary for producing a target fracture geometry and proppant concentration. The closed-loop simulation results demonstrate that the obtained pumping schedule successfully achieved the desired fracture geometry and proppant concentration at the end of pumping, leading to the maximum oil production from an unconventional reservoir.

13 HYDRO ENERGY↗
DOE OSTI2018

Securing against malicious control of circuit breakers in electrical substations

An example method for detecting and mitigating attacks on electric power substations comprises detecting a command to open or close a circuit breaker in the electric power substation. A modified extended substation model for the electric power substation is generated, based on the detected command and based on measurements in substation, where the modified extended substation model is a power flow model for the substation and for one or more directly connected neighboring substations. A power flow analysis is performed, using the modified extended substation model, to generate a predicted voltage for each of a plurality of nodes in the substation and in the one or more directly connected neighboring substations. Each predicted voltage is compared to a corresponding allowable voltage range, and execution of the command is blocked in response to determining that one or more of the voltages is outside the corresponding allowable voltage range.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Hierarchical Model-Free Transactive Control of Building Loads to Support Grid Services

Residential buildings consume 4.4 quads of electricity annually, approximately 37% of the total electricity consumption in the United States. This represents a vast resource that can be used for demand management and other ancillary services. This project aims to develop a robust, scalable hierarchical transactional control mechanism incorporating elements of model-free control (MFC) and game theory to harness buildings to provide ancillary services to the grid. This approach is being taken to address the challenges of incorporating traditional transactional control schemes into existing buildings. The challenges include small individual building sizes requiring aggregation of many buildings, unpredictable energy usage that makes model identification difficult, and satisfying the sensitive occupant comfort constraints. In the proposed approach, by separating the control mechanism into two layers above and below the load aggregator, MFC can be used below the aggregator to modulate flexible building loads in response to pricing signals with guaranteed performance. This allows the burden of identifying an accurate model of the system to be shifted to the above-aggregator layer, where fluctuations in individual building usage have less impact on predicted building system behavior. Game theory concepts can then be used to determine pricing curves and control signals among regional aggregators. Managing this control in a game-theoretic approach will allow us to build in financial incentives that increase customer engagement. Additionally, the usage of MFC necessitates less burdensome computational and communication requirements, thus, it is easily deployable on small, embedded devices. In a broader sense, developing a strategy capable of effectively incorporating residential and small commercial buildings will allow greater throughput of existing and emerging grid services in addition to future transactive energy grid management methods. Using MFC within a hierarchical control architecture will allow the shifting of existing forecasting challenges to an aggregate level, where dynamics are slower and more predictable. This will enable a smooth interface between the grid services requests of utilities and the reliable control required by participating buildings. MFC, which supports distributed control architecture, permits a scalable solution that can be deployed to neighborhood-size systems as well as individual buildings. This project focuses on three objectives: (1) developing the mathematical framework, algorithm toolkit, and software toolset of the two-layer transactive control testbed; (2) developing a scalable solution for application over many residential and small-size commercial buildings with sparse distributed communication; and (3) field testing and implementation on hardware of the control strategies developed in the previous two objectives. The research and development activities are focused and designed to be impactful within the relevant 2025 targets timeframe. An open-source control framework for exploiting variability and dispatchability of building loads will be delivered as the outcome of the project. This capability enables greater participation of loads in electricity markets and ancillary services that are both useful for the utility and financially beneficial for building owners.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

WRF-ELM v1.0: a regional climate model to study land–atmosphere interactions over heterogeneous land use regions

Abstract. The Energy Exascale Earth System Model (E3SM) Land Model (ELM) is a state-of-the-art land surface model that simulates the intricate interactions between the terrestrial land surface and other components of the Earth system. Originating from the Community Land Model (CLM) version 4.5, ELM has been under active development, with added new features and functionality, including plant hydraulics, radiation–topography interaction, subsurface multiphase flow, and more explicit land use and management practices. This study integrates ELM v2.1 with the Weather Research and Forecasting (WRF; WRF-ELM) model through a modified Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) framework, enabling affordable high-resolution regional modeling by leveraging ELM's innovative features alongside WRF's diverse atmospheric parameterization options. This framework includes a top-level driver for variable communication between WRF and ELM and Earth System Modeling Framework (ESMF) caps for the WRF atmospheric component and ELM workflow control, encompassing initialization, execution, and finalization. Importantly, this LILAC–ESMF framework demonstrates a more modular approach compared to previous coupling efforts between WRF and land surface models. It maintains the integrity of ELM's source code structure and facilitates the transfer of future developments in ELM to WRF-ELM. To test the ability of the coupled model to capture land–atmosphere interactions over regions with a variety of land uses and land covers, we conducted high-resolution (4 km) WRF-ELM ensemble simulations over the Great Lakes region (GLR) in the summer of 2018 and systematically compared the results against observations, reanalysis data, and WRF-CTSM (WRF coupled with the Community Terrestrial Systems Model). In general, the coupled WRF-ELM model has reasonably captured the spatial distribution of surface state variables and fluxes across the GLR, particularly over the natural vegetation areas. The evaluation results provide a baseline reference for further improvements in ELM in the regional application of high-resolution weather and climate predictions. Our work serves as an example to the model development community for expanding an advanced land surface model's capability to represent fully-coupled land–atmosphere interactions at fine spatial scales. The development and release of WRF-ELM marks a significant advancement for the ELM user community, providing opportunities for fine-scale regional representation, parameter calibration in coupled mode, and examination of new schemes with atmospheric feedback.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Heat Transfer Experiments of a 1st Stage Blade Cascade for Supercritical CO2 Oxy-Combustion Turbine Application

The results of internally cooled 1st stage blade (S1B) cascade testing in a supercritical CO2 environment is presented. The turbine blade design has been previously established for the end application of an oxy-combustion turbine operating in the Allam-Fetvedt cycle with turbine inlet conditions of 305 bar and 1150°C. The internally cooled blade features leading edge (LE) region impingement cooling, mid-section ribbed serpentine passages, and a pin-finned trailing edge (TE) region before cooling ejection holes. The geometry for the tested blade cascade has a cooled central blade with un-cooled blades on either side to match flowpath areas of the actual turbine. The flowpath reuses internal components previously employed for mid-section region ribbed serpentine passage experiments that established Nusselt number enhancement ratios over a range of Reynolds numbers from 100,000-400,000. New components include flow conditioning plates upstream and downstream of the blade cascade to adequately represent the flow field and blade external heat transfer coefficient profiles for the actual turbine. The cooled central blade utilizes uniform crystal temperature sensors (UCTS) with six sensors each on the blade pressure and suction surfaces distributed radially and from LE to TE. The post-processed UCTS quantified the maximum wall temperature seen at each installed sensor location. The test procedure consisted of establishing supercritical CO2 cooling flow temperature and flow rate and maintaining it throughout the test. The flow rate aims to match that for the actual in-service turbine blade design and is maintained through an orifice restriction to keep the pressure differential between internal cooling flow and external hot flow nearly constant. For the sCO2 flow path external to the blade, temperatures were ramped throughout the test via control of the test loop’s natural gas burner heater. The maximum temperature seen was 468°C and held constant for a duration of 10 minutes at which the blade metal temperature was predicted to be at its maximum before ramping down. For the turbine blade design for service inlet conditions, external flow path computational fluid dynamics (CFD) results and an internal cooling 1-D thermal and hydraulic flow network model using experimentally validated correlations served as thermal finite element (FE) boundary conditions to predict blade metal temperatures. These predicted temperatures were subsequently utilized in a structural FE model to predict blade life ratings dictated by Haynes 282 creep strength data, having a strong dependence on temperature. The boundary conditions experienced during testing are used in the same workflow and compared to the experimental results, with the goal of validating the analysis methodology and providing insight on the uncertainty in local metal temperature predictions.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

Residual stresses in Cu matrix composite surface deposits after laser melt injection

Abstract Tungsten carbide particles reinforced metal matrix composite (MMC) coatings can significantly improve surface wear resistance owing to their increased surface hardness. However, the presence of macro‐ and micro‐residual stresses in MMC coatings can have detrimental effects, such as reducing service life. In this study, neutron diffraction was used to determine the residual stresses in spherical fused tungsten carbide (sFTC) reinforced Cu matrix composite surface deposits after laser melt injection. We also developed a thermo‐mechanical coupled finite element model to predict residual stresses. Our findings reveal that sFTC/Cu composite deposits produced with a preheating temperature of 400°C have low residual stresses, with a maximum tensile residual stress of 98 MPa in the Cu matrix on the top surface. In contrast, the sFTC/bronze (CuAl10Ni5Fe4) composite deposit exhibits very high residual stresses, with a maximum tensile residual stress in the Cu matrix on the top surface reaching 651 MPa. These results provide a better understanding of the magnitudes and distributions of residual stresses in sFTC‐reinforced Cu matrix composite surface deposits manufactured via laser melt injection.

Zhang, Xingxing↗
DOE OSTI2019

Deep learning as a tool for neural data analysis: Speech classification and cross-frequency coupling in human sensorimotor cortex

A fundamental challenge in neuroscience is to understand what structure in the world is represented in spatially distributed patterns of neural activity from multiple single-trial measurements. This is often accomplished by learning a simple, linear transformations between neural features and features of the sensory stimuli or motor task. While successful in some early sensory processing areas, linear mappings are unlikely to be ideal tools for elucidating nonlinear, hierarchical representations of higher-order brain areas during complex tasks, such as the production of speech by humans. Here, we apply deep networks to predict produced speech syllables from a dataset of high gamma cortical surface electric potentials recorded from human sensorimotor cortex. We find that deep networks had higher decoding prediction accuracy compared to baseline models. Having established that deep networks extract more task relevant information from neural data sets relative to linear models (i.e., higher predictive accuracy), we next sought to demonstrate their utility as a data analysis tool for neuroscience. We first show that deep network's confusions revealed hierarchical latent structure in the neural data, which recapitulated the underlying articulatory nature of speech motor control. We next broadened the frequency features beyond high-gamma and identified a novel high-gamma-to-beta coupling during speech production. Finally, we used deep networks to compare task-relevant information in different neural frequency bands, and found that the high-gamma band contains the vast majority of information relevant for the speech prediction task, with little-to-no additional contribution from lower-frequency amplitudes. Together, these results demonstrate the utility of deep networks as a data analysis tool for basic and applied neuroscience.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Opportunities in multiscale modeling of mosquito-borne flaviviruses

Mosquito-borne flaviviruses, such as Zika, dengue, West Nile, and yellow fever virus, represent a growing public health concern due to their widespread distribution and the severe diseases they cause. These viruses are difficult to control as climate change and urbanization help mosquitoes expand into new areas, increasing the risk of outbreaks. Mathematical models play a key role in understanding their spread, providing insights at every level—from how the virus multiplies inside cells to how it circulates through entire populations. This review examines various approaches used in modeling arboviruses, including microscale models that focus on cellular and molecular dynamics, mesoscale models that address within-host processes, and macroscale models that capture population-level transmission. We briefly summarize the methodology used for models at each scale, which primarily consists of sets of differential equations with parameters that represent physical rates of change for different subprocesses. We particularly highlight how temperature affects virus transmission, which is key to understanding the impact of climate change. We also show how multiscale models can connect viral replication, immune response, and the spread of infection at a larger scale. This is essential for developing better vaccines and treatments, evaluating disease control measures, predicting the impact of climate change, and improving public health responses to outbreaks.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2008

John F. Kennedy Space Center's Technology Development and Application 2006-2007 Report

Topics covered include: Reversible Chemochromic Hydrogen Detectors; Determining Trajectory of Triboelectrically Charged Particles, Using Discrete Element Modeling; Using Indium Tin Oxide To Mitigate Dust on Viewing Ports; High-Performance Polyimide Powder Coatings; Controlled-Release Microcapsules for Smart Coatings for Corrosion Applications; Aerocoat 7 Replacement Coatings; Photocatalytic Coatings for Exploration and Spaceport Design; New Materials for the Repair of Polyimide Electrical Wire Insulation; Commodity-Free Calibration; Novel Ice Mitigation Methods; Crack Offset Measurement With the Projected Laser Target Device; New Materials for Structural Composites and Protective Coatings; Fire Chemistry Testing of Spray-On Foam Insulation (SOFI); Using Aerogel-Based Insulation Material To Prevent Foam Loss on the Liquid-Hydrogen Intertank; Particle Ejection and Levitation Technology (PELT); Electrostatic Characterization of Lunar Dust; Numerical Analysis of Rocket Exhaust Cratering; RESOLVE Projects: Lunar Water Resource Demonstration and Regolith Volatile Characterization; Tribocharging Lunar Soil for Electrostatic Beneficiation; Numerically Modeling the Erosion of Lunar Soil by Rocket Exhaust Plumes; Trajectory Model of Lunar Dust Particles; Using Lunar Module Shadows To Scale the Effects of Rocket Exhaust Plumes; Predicting the Acoustic Environment Induced by the Launch of the Ares I Vehicle; Measuring Ultrasonic Acoustic Velocity in a Thin Sheet of Graphite Epoxy Composite; Hail Size Distribution Mapping; Launch Pad 39 Hail Monitor Array System; Autonomous Flight Safety System - Phase III; The Photogrammetry Cube; Bird Vision System; Automating Range Surveillance Through Radio Interferometry and Field Strength Mapping Techniques; Next-Generation Telemetry Workstation; GPS Metric Tracking Unit; and Space-Based Range.

Source record↗
DOE OSTI2012

The re-evaluation of the AVR melt-wire experiment with specific focus on different modeling strategies and simplifications

The AVR is a pebble-bed type reactor that operated in Germany for 21 years and was closed down in December 1988. The AVR melt-wire experiments, where graphite spheres with melt wires of different melting temperatures were introduced into the core, indicate that measured pebble temperatures significantly exceeded temperatures calculated with the analysis codes available at the time. The reason for these discrepancies are often attributed to the special design features of the AVR, in particular the control rod “noses” protruding into the core, and to inherent features of the pebble bed reactor. In a previous study different possible bypass flows were investigated. This study investigates different modeling strategies and assumptions for the solution of the core neutronics. Due to the complexities specific to the AVR there is not currently a code system that can take into account the noses while simultaneously solving the burnup and diffusion equations in three dimensions. A number of modeling simplifications were therefore made in the historic analysis of the AVR. The aim of this study is to quantify the effects these different simplifications have on the results. This includes the effects of modeling the core neutronics, burn-up and thermo-hydraulics in both two and three dimensions, as well as other simplifications of the geometry. The comparison of the most realistic case to the one comparable to historic calculations show that the difference in temperature predicted with these two models can be as high as 300 degrees C. The gas temperature distribution at the top of the core, where the maximum temperatures occur, is in fair agreement with the melt-wire experiment data even though few simplifications are introduced to the models and the power history has not been simulated. The results also serve as input to the final modeling strategy to repeat the modeling of the operational history of the AVR, which is planned for the future.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

Moore vs. Murphy: Tradeoffs between complexity and reliability in distributed energy system scheduling using software-as-a-service

Software-based optimization of building control strategies, including scheduling, has the potential to improve the performance of existing complex heating, ventilation, and air conditioning (HVAC), storage, and other systems—especially if temporally variable energy production, such as solar thermal or photovoltaics, is included. If reductions in energy bills can be achieved using optimized control strategies that take advantage of cost-saving opportunities, such as time-of-use pricing, the additional bill savings can cover further efficiency investment costs. As computer processing becomes cheaper over time (Moore's Law), opportunities to perform complex control optimization become more abundant, and these can be performed remotely as software-as-a-service (SaaS). However, by “perfecting” our control strategies, we run an increased risk that when something unexpected happens (Murphy's Law), the consequences of failure are greater. This study used simulation to explore the potential benefits of HVAC schedule optimization, delivery, and implementation using a SaaS paradigm, at various levels of complexity. Implementing optimal schedules in a model of an efficient building's HVAC system, the study predicts energy cost savings of up to 10% compared to the naïve reference control strategy. Optimizing more system control variables increases the potential energy cost savings; however, these savings could be compromised by failures in communication inherent in delivering schedules via SaaS. The additional cost of energy resulting from the risk of increased demand charges generally increased with increased communication failure to a much larger extent than the risk of increased energy use charges. This work suggests that moderate improvements in performance, achieved at low cost by simple means, may be more effective than highly optimized schemes, which are more susceptible to failure due to their dependence on complex interactions between systems.

42 ENGINEERING↗