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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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15 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A Privacy-Aware Federated Learning Framework for Distributed Energy Resource Analytics in Constrained Environments

To be resilient against extreme weather events, the rural communities in Puerto Rico are leveraging distributed energy resources (DER). However, computing frameworks sup-porting the grid in critical decision-making are still largely centralized. Sensitive consumer data are transmitted over the Internet or cellular networks to a secondary or tertiary node. It guarantees better situational awareness at the cost of a wider attack surface, jeopardizing user privacy, as more DER come online. Cloud, Edge, and Fog computing all require data aggregation at some level. This paper introduces a privacy-aware federated learning framework that leverages the Fog model by pushing analytics all the way to the DER and load assets. These local models train on individual asset data and transmit only learned parameters (such as weights) over secure communications to a global decision-maker. By abstracting personally identifiable consumer data without impacting decision optimality, this framework better aligns with distributed power generation paradigm.

Sundararajan, Aditya↗
DOE OSTI2023

Energy-efficient cooperative resource allocation and task scheduling for Internet of Things environments

Offloading Internet of Things (IoT) tasks to the cloud for further processing might not always lead to an optimal execution time, particularly in situations such as resource contention, under-provisioning, over-provisioning, and fragmentation. In addition, dynamically optimizing the number of Virtual Machines (VMs) for resource scheduling in order to meet application requirements remains a major research challenge. Further, existing resource scheduling algorithms focus primarily on minimizing operational costs while maximizing resource sharing and utilization. Considering energy utilization as part of the resource allocation and scheduling process as an optimization objective for maintaining load balancing has often been neglected. To address these challenges and more, we propose a cooperative energy-aware resource allocation and scheduling strategy based on a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria decision-making method. Here we used the Grid Workloads Archive dataset to evaluate our proposed approach named TOPREAL. Experimental results with respect to the allocation of VM resources when considering processing a large segment of tasks indicate that TOPREAL outperforms existing algorithms in terms of energy savings, with an average improvement of 40.25%, while maintaining an average improvement of 16.21% when it comes to execution time. Results also demonstrate that our method can save an average of 78.06 processing hours and 63,215kJ of energy when compared to existing scheduling algorithms. These results demonstrate the effectiveness of our proposed model and the viability of using multi-criteria decision-making techniques such as TOPSIS to solve the resource allocation and scheduling problem in edge environments.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Computational Imaging for Intelligence in Highly Scattering Aerosols (Final Report)

Natural and man-made degraded visual environments pose major threats to national security. The random scattering and absorption of light by tiny particles suspended in the air reduces situational awareness and causes unacceptable down-time for critical systems and operations. To improve the situation, we have developed several approaches to interpret the information contained within scattered light to enhance sensing and imaging in scattering media. These approaches were tested at the Sandia National Laboratory Fog Chamber facility and with tabletop fog chambers. Computationally efficient light transport models were developed and leveraged for computational sensing. The models are based on a weak angular dependence approximation to the Boltzmann or radiative transfer equation that appears to be applicable in both the moderate and highly scattering regimes. After the new model was experimentally validated, statistical approaches for detection, localization, and imaging of objects hidden in fog were developed and demonstrated. A binary hypothesis test and the Neyman-Pearson lemma provided the highest theoretically possible probability of detection for a specified false alarm rate and signal-to-noise ratio. Maximum likelihood estimation allowed estimation of the fog optical properties as well as the position, size, and reflection coefficient of an object in fog. A computational dehazing approach was implemented to reduce the effects of scatter on images, making object features more readily discernible. We have developed, characterized, and deployed a new Tabletop Fog Chamber capable of repeatably generating multiple unique fog-analogues for optical testing in degraded visual environments. We characterized this chamber using both optical and microphysical techniques. In doing so we have explored the ability of droplet nucleation theory to describe the aerosols generated within the chamber, as well as Mie scattering theory to describe the attenuation of light by said aerosols, and correlated the aerosol microphysics to optical properties such as transmission and meteorological optical range (MOR). This chamber has proved highly valuable and has supported multiple efforts inclusive to and exclusive of this LDRD project to test optics in degraded visual environments. Circularly polarized light has been found to maintain its polarization state better than linearly polarized light when propagating through fog. This was demonstrated experimentally in both the visible and short-wave infrared (SWIR) by imaging targets made of different commercially available retroreflective films. It was found that active circularly polarized imaging can increase contrast and range compared to linearly polarized imaging. We have completed an initial investigation of the capability for machine learning methods to reduce the effects of light scattering when imaging through fog. Previously acquired experimental long-wave images were used to train an autoencoder denoising architecture. Overfitting was found to be a problem because of lack of variability in the object type in this data set. The lessons learned were used to collect a well labeled dataset with much more variability using the Tabletop Fog Chamber that will be available for future studies. We have developed several new sensing methods using speckle intensity correlations. First, the ability to image moving objects in fog was shown, establishing that our unique speckle imaging method can be implemented in dynamic scattering media. Second, the speckle decorrelation over time was found to be sensitive to fog composition, implying extensions to fog characterization. Third, the ability to distinguish macroscopically identical objects on a far-subwavelength scale was demonstrated, suggesting numerous applications ranging from nanoscale defect detection to security. Fourth, we have shown the capability to simultaneously image and localize hidden objects, allowing the speckle imaging method to be effective without prior object positional information. Finally, an interferometric effect was presented that illustrates a new approach for analyzing speckle intensity correlations that may lead to more effective ways to localize and image moving objects. All of these results represent significant developments that challenge the limits of the application of speckle imaging and open important application spaces. A theory was developed and simulations were performed to assess the potential transverse resolution benefit of relative motion in structured illumination for radar systems. Results for a simplified radar system model indicate that significant resolution benefits are possible using data from scanning a structured beam over the target, with the use of appropriate signal processing.

58 GEOSCIENCES↗
DOE OSTI2022

A Multi-Objective Approach for Optimizing Edge-Based Resource Allocation Using TOPSIS

Existing approaches for allocating resources on edge environments are inefficient and lack the support of heterogeneous edge devices, which in turn fail to optimize the dependency on cloud infrastructures or datacenters. To this extent, we propose in this paper OpERA, a multi-layered edge-based resource allocation optimization framework that supports heterogeneous and seamless execution of offloadable tasks across edge, fog, and cloud computing layers and architectures. By capturing offloadable task requirements, OpERA is capable of identifying suitable resources within nearby edge or fog layers, thus optimizing the execution process. Throughout the paper, we present results which show the effectiveness of our proposed optimization strategy in terms of reducing costs, minimizing energy consumption, and promoting other residual gains in terms of processing computations, network bandwidth, and task execution time. We also demonstrate that by optimizing resource allocation in computation offloading, it is then possible to increase the likelihood of successful task offloading, particularly for computationally intensive tasks that are becoming integral as part of many IoT applications such robotic surgery, autonomous driving, smart city monitoring device grids, and deep learning tasks. The evaluation of our OpERA optimization algorithm reveals that the TOPSIS MCDM technique effectively identifies optimal compute resources for processing offloadable tasks, with a 96% success rate. Moreover, the results from our experiments with a diverse range of use cases show that our OpERA optimization strategy can effectively reduce energy consumption by up to 88%, and operational costs by 76%, by identifying relevant compute resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

High sensitivity of simulated fog properties to parameterized aerosol activation in case studies from ParisFog

Aerosols influence fog properties such as visibility and lifetime by affecting fog droplet number concentrations (N d ). Numerical weather prediction (NWP) models often represent aerosol–fog interactions using highly simplified approaches. Incorporating prognostic size-resolved aerosol microphysics from climate models could allow them to simulate N d and aerosol–fog interactions without incurring excessive computational expense. However, microphysics code designed for coarse spatial resolution may struggle with sub-kilometer-scale grid spacings. Here, we test the ability of the UK Met Office Unified Model to simulate aerosol and fog properties during case studies from the ParisFog field campaign in 2011. We examine the sensitivity of fog properties to variations in N d caused by modifications to simulated aerosol activation. Our model, with a 500 m horizontal resolution and interactive aerosol and cloud microphysics, significantly underpredicts N d , although it only slightly underestimates the cloud condensation nuclei concentration. With an updated version of the Abdul-Razzak and Ghan (2000) activation scheme, we produce N d that are more consistent with those predicted by a cloud parcel model under fog-like conditions. We activate droplets only by adiabatic cooling. We incorporate more realistic hygroscopicities for sulfate and organic aerosols and explore the sensitivity of simulated N d to unresolved updrafts. We find that both N d and simulated fog liquid water content are very sensitive to the updated activation scheme but remain less affected by the update to hygroscopicities. Our improvements offer insights into the physical processes regulating N d in stable conditions, potentially laying foundations for improved operational fog forecasts that incorporate interactive aerosol simulations or aerosol climatologies.

Ghosh, Pratapaditya [Carnegie Mellon University, P↗
DOE OSTI2022

Incorporating the effects of objects in an approximate model of light transport in scattering media

A computationally efficient radiative transport model is presented that predicts a camera measurement and accounts for the light reflected and blocked by an object in a scattering medium. The model is in good agreement with experimental data acquired at the Sandia National Laboratory Fog Chamber Facility (SNLFC). The model is applicable in computational imaging to detect, localize, and image objects hidden in scattering media. Here, a statistical approach was implemented to study object detection limits in fog.

42 ENGINEERING↗
DOE OSTI2026

From Reproducible Edge–Cloud Experimentation to Real-World Practice: The E2Clab Experience

Reproducibility is already difficult in distributed systems; on the computing continuum, it becomes substantially harder. Applications that span sensing devices, edge and fog resources, and cloud platforms must be evaluated across heterogeneous hardware, variable network conditions, cross-layer orchestration decisions, and long-running workflow lifecycles. We use E2Clab as a case study to examine these challenges and their implications for experimental methodology. We explain why reproducible experimentation is harder on the continuum, then revisit E2Clab as an initial response based on explicit modeling of infrastructure, workflow lifecycle, and artifacts. Lastly, we discuss how its evolution toward more realistic application settings can be understood through the lens of Translational Computer Science. We argue that reproducible continuum experimentation requires methods that are rigorous enough for research while remaining adaptable to real-world practice.

42 ENGINEERING↗
DOE OSTI2022

Additively manufactured multiplexed inertial coalescence filters

Multiphase flows often pose a significant challenge to the efficient and reliable design of thermofluidic systems. This paper describes multiplexed inertial coalescence filters composed of parallel helical pathways, designed to capture fine droplets (<40 µm) through inertial separation while maintaining a low pressure drop (<400 Pa). Filtration efficiencies for 7 µm and 30 µm droplets were characterized for varying flow conditions, with complete capture observed above a threshold flow rate. Models for filtration efficiency and pressure drop were developed and validated against experimental results to allow system design and optimization, enabled by the tunable additive manufacturing approach used to fabricate the filters. Filter quality factor was computed for varying droplet sizes, showcasing exceptional quality factor when compared to state-of-the-art filters documented in the literature. In conclusion, this multiplexed inertial coalescence filtration approach could find use in dehumidification systems, fog harvesting, chemical reactors, and microgravity droplet capture.

3D Printing↗
DOE OSTI2022

Anti-Icing Coatings using Ionomer Film Layer Structuring

This research effort examined the application of Nafion polymers in alcohol solvents as an anti-ice surface coating, as a mixture with hydrophilic polymers and freezing point depressant salt systems. Co-soluble systems of Nafion, polymer and salt were applied using dip coating methods to create smooth films for frost observation over a Peltier plate thermal system in ambient laboratory conditions. Cryo-DSC was applied to examine freezing events of the Nafion-surfactant mixtures, but the sensitivity of the measurement was insufficient to determine frost behavior. Collaborations with the Fog Chamber at Sandia-Albuquerque, and in environmental SAXS measurements with CINT-LANL were requested but were not able to be performed under the research duration. Since experimental characterization of these factors is difficult to achieve directly, computational modeling was used to guide the scientific basis for property improvement. Computational modeling was performed to improve understanding of the dynamic association between ionomer side groups and added molecules and deicing salts. The polyacrylic acid in water system was identified at the start of the project as a relevant system for exploring the effect of varying counterions on the properties of fully deprotonated polyacrylic acid (PAA) in the presence of water. Simulations were modeled with four different counterions, two monovalent counterions (K+ and Na+) and two divalent counterions (Ca2+ and Mg2+). The wt% of PAA in these systems was varied from ~10 to 80 wt% PAA for temperatures from 250K to 400K. In the second set of simulations, the interpenetration of water into a dry PAA film was studied for Na+ or Ca2+ counterions for temperatures between 300K and 400K. The result of this project is a sprayable Nafion film composite which resists ice nucleation at -20 °C for periods of greater than three hours. It is composed of Nafion polymer, hydrophilic polyethylene oxide polymer and CaCl2 anti-ice crosslinker. Durability and field performance properties remain to be determined.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Analytical advances to study the air – water interfacial chemistry in the atmosphere

Formation of aqueous secondary organic aerosol (aqSOA) at the air – liquid interface recently has attracted a lot of attention in atmospheric chemistry. The discrepancies in mass distributions, aerosol oxidative capacity, liquid water content, hygroscopic growth of aerosols, and formation of clouds and fogs suggest that interfacial chemistry play a more important role than previously deemed. However, detailed mechanisms at the air–water interface remain unclear owing to the lack of comprehensive understanding that underpins complicated interfacial phenomena, which are not easily measurable from field campaigns, laboratory measurements, or computational simulations. Here, this review highlights relevant and recent technical advancement employed to study aqSOA encompassing spectroscopy and mass spectrometry. The current knowledge on the aqSOA processes is digested with an emphasis on recent research of interfacial aqSOA formation including laboratory studies and model simulations. Finally, future directions of the interfacial chemistry are recommended for field and laboratory studies as well as theoretical efforts to resolve interfacial challenges in atmospheric chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

The Application of Machine Learning Techniques to Meteorological Forecasting

Fog and inland-penetrating sea-breezes occur often at SRS and have a strong impact on site operations. Site personnel therefore require accurate forecasts of these events, but both are difficult to forecast using traditional techniques. Our goal is to apply machine learning (ML) techniques to the problem of forecasting fog and the sea breeze at the Savannah River Site. We apply several such techniques - decision trees, regression, and a series of classification/regression techniques – and train them using the large datasets collected by our group at SRS and from external organizations that maintain databases of regional meteorological variables.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Quantitative cross-species translators of cardiac myocyte electrophysiology: Model training, experimental validation, and applications

Animal experimentation is key in the evaluation of cardiac efficacy and safety of novel therapeutic compounds. However, interspecies differences in the mechanisms regulating excitation-contraction coupling can limit the translation of experimental findings from animal models to human physiology and undermine the assessment of drugs’ efficacy and safety. Here, we built a suite of translators for quantitatively mapping electrophysiological responses in ventricular myocytes across species. We trained these statistical operators using a broad dataset obtained by simulating populations of our biophysically detailed computational models of action potential and Ca 2+ transient in mouse, rabbit, and human. We then tested our translators against experimental data describing the response to stimuli, such as ion channel block, change in beating rate, and β-adrenergic challenge. We demonstrate that this approach is well suited to predicting the effects of perturbations across different species or experimental conditions and suggest its integration into mechanistic studies and drug development pipelines.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Studies of Alternative Ventilation Configurations to Mitigate Airborne Exposure Risks in Office Spaces

The objective of this study was to evaluate the impact of alternative ventilation configurations on airflow patterns and potential exposure risks in office spaces. Two existing conference rooms at Sandia NM were modeled using Computational Fluid Dynamics (CFD) simulations to characterize airflow patterns and potential airborne exposure risks in well-mixed and once-through (through-flow) ventilation conditions. Multiple scenarios were studied to evaluate the impact of occupancy, Plexiglass barriers, and a modified-return airflow configuration. Experimental and visualization tests were also conducted to validate the well-mixed and through-flow models and findings. The simulations demonstrated that the modified-return airflow configuration that promoted through-flow conditions reduced pathogen concentrations within the space compared to the well-mixed airflow configuration; occupancy reduction only reduced the number of exposed individuals, and Plexiglass barriers had almost no effect. The experimentally measured air speeds at nine anemometer locations generally matched the simulated airflow velocities, and a fog-purge visualization test was also consistent with simulated results of plume movement and dissipation. The visualization tests demonstrated improvements in air change rate with the modified return, which promoted through-flow conditions, versus the original well-mixed ventilation configuration. The results of this study demonstrate that minor modifications to a space that promote through-flow conditions can improve air quality and reduce pathogen concentrations. Additional airflow modeling and testing of alternative occupied space configurations are recommended to further inform room designs that mitigate airborne exposure risks for occupants.

42 ENGINEERING↗