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All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Strengthening Retrofit Markets for Comprehensive Savings in Multifamily Buildings (Interim Final Report)

Energy efficiency is vitally important in Maine. Nearly 70% of Maine households rely on fuel oil as their primary energy source for home heating, a higher share than in any other state. Coupled with the state's long, cold winters, Maine's dependence on oil renders homeowners particularly vulnerable to fluctuating fuel costs. With $\$$4.5 million in seed funding from the Energy Department's Better Buildings Neighborhood Program, the Governor's Energy Office (GEO), through Efficiency Maine Trust (the Trust), is spurring Maine landlords to lower their monthly energy bills and improve comfort for their tenants during the state's cold winter months and increasingly warmer summers. Maine's aging multifamily housing stock can be expensive to heat and costly to maintain. It is not unusual to find buildings with little or no insulation, drafty windows, and significant air leaks, making them ideal candidates for energy efficiency upgrades. Maine modeled its Multifamily Efficiency Program (MEP) after the state's highly successful Home Energy Savings Program (HESP) for single-family homes. HESP provided cash incentives and financing opportunities to owners of one-to four-unit structures, which resulted in thousands of energy assessments and whole-house energy upgrades in 225 communities. Maine's new MEP multifamily energy efficiency upgrade and weatherization initiative focuses on small to medium-sized (i.e., five to 20 units) apartment buildings. The program's energy efficiency upgrades will provide at least 20% energy savings for each upgraded multifamily unit. The Trust’s MEP relies on a network of approved program partners who help move projects through the pipeline from assessment to upgrade. MEP has two components: benchmarking and development of an Energy Reduction Plan (ERP). Using the ENERGY STAR® Portfolio Manager benchmarking tool, MEP provides an assessment of current energy usage in the building, establishes a baseline for future energy efficiency improvements, and enables tracking and monitoring of future energy usage at the building— all at no cost to the building owner. The ERP is developed by a program partner using either the Trust’s approved modeling or prescriptive tools; it provides detailed information about the current energy related conditions in the building and recommends energy efficiency, health, and safety improvements. The Trust's delivery contractor provides quality assurance and controls throughout the process. Through this effort, MEP's goal is to establish a self-sustaining, market-driven program, demonstrating the value of energy efficiency to other building owners. The increasing value of properties across the state will help incentivize these owners to continue upgrades after the grant period has ended. Targeting urban areas in Maine with dense clusters of multifamily units—such as Portland, Lewiston- Auburn, Bangor, and Augusta—MEP engaged a variety of stakeholder groups early on to design its multifamily program. Through direct emails and its website, program officials invited lending institutions, building professionals, engineering firms, equipment distributors, and local property owners associations to attend open meetings around the state to learn about the goals of the multifamily program and to help define its parameters. These meetings helped program administrators understand the diversity of the customer base: some owners are individuals with a single building, while other owners are groups of people or management companies with an entire portfolio of multifamily buildings. The diversity of the customer base notwithstanding, owners see MEP as an opportunity to make gains in their respective properties. Consistently high turnouts at stakeholder meetings fueled greater customer interest as awareness of the program spread through word of mouth. The program also gained traction by utilizing the program partner networks and building on the legacy of the Trust’s successful HESP for single-family residences. MEP offers significant incentives for building owners to participate in the upgrade program. Wholebuilding benchmarking services are available to most multifamily housing buildings free of charge. The service provides the building owner with an assessment of the building's current energy efficiency as compared to other multifamily buildings on a national scale, establishes a baseline to measure future improvements, and enables owners to track monthly energy consumption using the ENERGY STAR Portfolio Manager. Once the benchmarking process is complete, the program links building owners with approved program partners (e.g., energy professionals, home performance contractors) to identify and implement specific energy-saving opportunities in the building. Program partners can also provide project quotes with estimated financing incentives and payback period calculations that enable building owners to make informed decisions. What's more, the Trust provides two financial incentives for successful completion of program milestones. The first is a per-unit incentive for completion of an approved ERP (i.e., $\$$100 per unit if a prescriptive path is followed, and $\$$200 per unit for a modeled ERP). Upon final inspection of the installed project scope of work, an incentive of $\$$1,400 per unit or 50% of installed cost—whichever is less—is paid. The Trust originally established a $\$$1 million loan-loss reserve fund (LLRF) to further enhance financing opportunities for qualified multifamily building owners. This funding mechanism was designed to connect building owners with lenders that retain the mortgages for their properties and encourages the lenders to offer financing for energy efficiency improvements. However, there has been no interest in the LLRF and therefore the LLRF has been reduced. Ultimately, MEP plans to build an online tool for building owners to assess opportunities to make upgrades in their multifamily units. The tool will include a performance rating system to provide a way for building owners to more easily understand energy use in their building, and how it could be improved with energy efficiency upgrades. Prospective tenants will also be able to use the rating system to make informed decisions about where to rent. Furthermore, the rating can be incorporated into real estate listings as a way for prospective home buyers and the real estate financial community to evaluate a home's operating costs. The Trust’s MEP has identified the state's most experienced energy professionals, vendors, suppliers, and contractors that install energy efficiency equipment in the multifamily sector to be qualified program partners. To be eligible for partnership, energy assessment professionals and contractors are required to have demonstrated experience in the multifamily sector and hold associated professional certifications, such as Building Operator Certification (BOC), Certified Energy Manager (CEM), Professional Engineer (PE), or Building Performance Institute (BPI) Multifamily Building Analyst. Widespread program interest has enabled the Trust to redirect funds that might otherwise be needed for program promotion to building capacity through contractor training. In addition to boosting professional training and certification opportunities, MEP teaches its partners how to market the multifamily program to prospective multifamily homeowners.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Influence of ENSO and the NAO on terrestrial carbon uptake in the Texas-northern Mexico region

Climate extremes such as drought and heat waves can cause substantial reductions in terrestrial carbon uptake. Advancing projections of the carbon uptake response to future climate extremes depends on (1) identifying mechanistic links between the carbon cycle and atmospheric drivers, (2) detecting and attributing uptake changes, and (3) evaluating models of land response and atmospheric forcing. Here, we combine model simulations, remote sensing products, and ground observations to investigate the impact of climate variability on carbon uptake in the Texas-northern Mexico region. Specifically, we (1) examine the relationship between drought, carbon uptake, and variability of El Niño–Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO) using the Joint UK Land-Environment Simulator (JULES) biosphere simulations from 1950–2012, (2) quantify changes in carbon uptake during record drought conditions in 2011, and (3) evaluate JULES carbon uptake and soil moisture in 2011 using observations from remote sensing and a network of flux towers in the region. Long-term simulations reveal systematic decreases in regional-scale carbon uptake during negative phases of ENSO and NAO, including amplified reductions of gross primary production (GPP) (-0.42 ± 0.18 Pg C yr -1 ) and net ecosystem production (NEP) (-0.14 ± 0.11 Pg C yr -1 ) during strong La Niña years. The 2011 megadrought caused some of the largest declines of GPP (-0.50 Pg C yr -1 ) and NEP (-0.23 Pg C yr -1 ) in our simulations. In 2011, consistent declines were found in observations, including high correlation of GPP and surface soil moisture (r = 0.82 ± 0.23, p = 0.012) in remote sensing-based products. These results suggest a large-scale response of carbon uptake to ENSO and NAO, and highlight a need to improve model predictions of ENSO and NAO in order to improve predictions of future impacts on the carbon cycle and the associated feedbacks to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning

We assume that we are given a time series of data from a dynamical system and our task is to learn the flow map of the dynamical system. We present a collection of results on how to enforce constraints coming from the dynamical system in order to accelerate the training of deep neural networks to represent the flow map of the system as well as increase their predictive ability. In particular, we provide ways to enforce constraints during training for all three major modes of learning, namely supervised, unsupervised and reinforcement learning. In general, the dynamic constraints need to include terms which are analogous to memory terms in model reduction formalisms. Such memory terms act as a restoring force which corrects the errors committed by the learned flow map during prediction. For supervised learning, the constraints are added to the objective function. For the case of unsupervised learning, in particular generative adversarial networks, the constraints are introduced by augmenting the input of the discriminator. Finally, for the case of reinforcement learning and in particular actor-critic methods, the constraints are added to the reward function. In addition, for the reinforcement learning case, we present a novel approach based on homotopy of the action-value function in order to stabilize and accelerate training. We use numerical results for the Lorenz system to illustrate the various constructions.

Stinis, Panagiotis↗
DOE OSTI2023

Evaluation of Rail Decarbonization Alternatives: Framework and Application

The Northwestern University Freight Rail Infrastructure & Energy Network Decarbonization (NUFRIEND) framework is a comprehensive industry-oriented tool for simulating the deployment of new energy technologies including biofuels, e-fuels, battery-electric, and hydrogen locomotives. By classifying fuel types into two categories based on deployment requirements, the associated optimal charging/fueling facility location and sizing problem are solved with a five-step framework. Life-cycle analysis (LCA) and techno-economic analysis (TEA) are used to estimate carbon reduction, capital investments, cost of carbon reduction, and operational impacts, enabling sensitivity analysis with operational and technological parameters. Here, the framework is illustrated on lower-carbon drop-in fuels as well as battery-electric technology deployments for the US Eastern and Western Class I railroad networks. Drop-in fuel deployments are modeled as admixtures with diesel in existing locomotives, while battery-electric deployments are shown for varying technology penetration levels and locomotive ranges. When mixed in a 50% ratio with diesel, results show biodiesel’s capacity to reduce emissions at 36% with a cost of $\$$ 0.13 per kilogram of CO 2 reduced, while e-fuels offer a potential reduction of 50% of emissions at a cost of $\$$ 0.22 per kilogram of CO 2 reduced. Battery-electric results for 50% deployment over all ton-miles highlight the value of future innovations in battery energy densities as scenarios assuming 800-mi range locomotives show an estimated emissions reduction of 46% with a cost of $\$$ 0.06 per kilogram of CO 2 reduced, compared with 16% emissions reduction at a cost of $\$$ 0.11 per kilogram of CO 2 reduced for 400-mi range locomotives. The NUFRIEND framework provides a systematic method for comparing different alternative energy technologies and identifying potential challenges and benefits in their future deployments.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

Dynamic model-based sensor network design algorithm for system efficiency maximization

Here, a dynamic model-based sensor network design (DMSND) algorithm has been developed for maximizing system efficiency for an estimator-based control system. The algorithm synthesizes the optimal sensor network in the face of disturbances or set point changes. Computational expense of the large-scale combinatorial optimization problem is significantly reduced by parallel computing and by using combination of three novel strategies: multi-rate sampling frequency, model order reduction, and use of an incumbent solution that enables early termination of evaluation of infeasible sensor sets. The developed algorithm is applied to an acid gas removal unit as part of an integrated gasification combined cycle power plant with carbon capture. Even though there are more than thousand process states and more than hundred candidate sensor locations, the optimal sensor network design problem for maximizing process efficiency could be solved within couple of hours for a given budget.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Electrospun Hybrid Perfluorosulfonic Acid/Sulfonated Silica Composite Membranes

Electrospinning was employed to fabricate composite membranes containing perfluorosulfonic acid (PFSA) ionomer, poly(vinylidene fluoride) (PVDF) reinforcement and a sulfonated silica network, where the latter was incorporated either in the PFSA matrix or in the PVDF fibers. The best membrane, in terms of proton conductivity, was made by incorporating the sulfonated silica network in PFSA fibers (Type-A) while the lowest conductivity membrane was obtained when sulfonated silica was incorporated into the reinforcing PVDF fibers (Type-B). A Type-A membrane containing 65 wt.% PFSA with an embedded sulfonated silica network (at 15 wt.%) and with 20 wt.% PVDF reinforcing fibers proved superior to the pristine PFSA membrane in terms of both the proton conductivity in the 30–90% RH at 80 °C (a 25–35% increase) and lateral swelling (a 68% reduction). In addition, it was demonstrated that a Type-A membrane was superior to that of a neat 660 EW perfluoroimide acid (PFIA, from 3M Co.) films with respect to swelling and mechanical strength, while having a similar proton conductivity vs. relative humidity profile. This study demonstrates that an electrospun nanofiber composite membrane with a sulfonated silica network added to moderately low EW PFSA fibers is a viable alternative to an ultra-low EW fluorinated ionomer PEM, in terms of properties relevant to fuel cell applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Missing Wedge Completion via Unsupervised Learning with Coordinate Networks

Cryogenic electron tomography (cryoET) is a powerful tool in structural biology, enabling detailed 3D imaging of biological specimens at a resolution of nanometers. Despite its potential, cryoET faces challenges such as the missing wedge problem, which limits reconstruction quality due to incomplete data collection angles. Recently, supervised deep learning methods leveraging convolutional neural networks (CNNs) have considerably addressed this issue; however, their pretraining requirements render them susceptible to inaccuracies and artifacts, particularly when representative training data is scarce. To overcome these limitations, we introduce a proof-of-concept unsupervised learning approach using coordinate networks (CNs) that optimizes network weights directly against input projections. This eliminates the need for pretraining, reducing reconstruction runtime by 3–20× compared to supervised methods. Our in silico results show improved shape completion and reduction of missing wedge artifacts, assessed through several voxel-based image quality metrics in real space and a novel directional Fourier Shell Correlation (FSC) metric. Our study illuminates benefits and considerations of both supervised and unsupervised approaches, guiding the development of improved reconstruction strategies.

42 ENGINEERING↗
DOE OSTI2020

A neural network for determination of latent dimensionality in Nonnegative Matrix Factorization

Non-negative Matrix Factorization (NMF) has proven to be a powerful unsupervised learning method for uncovering hidden features in complex and noisy datasets with applications in data mining, text recognition, dimension reduction, face recognition, anomaly detection, blind source separation, and many other fields. An important input for NMF is the latent dimensionality of the data, that is, the number of hidden features, K, present in the explored dataset. Unfortunately, and this quantity is rarely known a priori. The existing methods for determining latent dimensionality, such as Automatic Relevance Determination (ARD), are mostly heuristic and utilize different characteristics to estimate the number of hidden features. However, all of them require human presence to make a final determination of K. Here we utilize a supervised machine learning approach in combination with a recent method for model determination, called NMFk, to determine the number of hidden features automatically. NMFk performs a set of NMF simulations on an ensemble of matrices, obtained by bootstrapping the initial dataset, and estimates which K produces stable groups of latent features that reconstruct the initial dataset well. We then train a Multi-Layter Perceptron (MLP) classifier network to determine the correct number of latent features utilizing the statistics and characteristics of the NMF solution, obtained from NMFk. In order to train the MLP classifier, a training set of 58,660 matrices with predetermined latent features were factorized with NMFk. The MLP classifier in conjunction with NMFk maintains a greater than 95% success rate when applied to a held out test set. Additionally, when applied to two well-known benchmark datasets, the swimmer and MIT face data, NMFk/MLP correctly recovers the established number of hidden features. Finally, we compare the accuracy of our method to the ARD, AIC and Stability-based methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Adaptive Client Selection in Federated Learning: A Network Anomaly Detection Use Case

Federated Learning (FL) has become a ubiquitous approach for training machine learning models on decentralized data, addressing the myriad privacy concerns inherent in traditional centralized methods. However, the efficiency of FL depends on effective client selection and robust privacy preservation mechanisms. Inadequate client selection may lead to suboptimal model performance, while insufficient privacy measures risk exposing sensitive data. This paper proposes a client selection framework for FL that integrates differential privacy and fault tolerance. Our adaptive approach dynamically adjusts the number of selected clients based on model performance and system constraints, ensuring privacy through calibrated noise addition. We evaluate our method on a network anomaly detection use case using the UNSW-NB15 and ROAD datasets. Results show up to a 7% increase in accuracy and a 25% reduction in training time compared to FedL2P. Moreover, we highlight the trade-offs between privacy budgets and model performance, with higher privacy budgets reducing noise and improving accuracy. Our fault tolerance mechanism, while causing a slight performance drop, enhances robustness to client failures. Statistical validation using Mann-Whitney U tests confirms the significance of these improvements (p < 0.05).

Marfo, William [University of Texas at El Paso,Dep↗
DOE OSTI2018

Microstructural Materials Design Via Deep Adversarial Learning Methodology

Identifying the key microstructure representations is crucial for computational materials design (CMD). However, existing microstructure characterization and reconstruction (MCR) techniques have limitations to be applied for microstructural materials design. Some MCR approaches are not applicable for microstructural materials design because no parameters are available to serve as design variables, while others introduce significant information loss in either microstructure representation and/or dimensionality reduction. In this work, we present a deep adversarial learning methodology that overcomes the limitations of existing MCR techniques. In the proposed methodology, generative adversarial networks (GAN) are trained to learn the mapping between latent variables and microstructures. Thereafter, the low-dimensional latent variables serve as design variables, and a Bayesian optimization framework is applied to obtain microstructures with desired material property. Due to the special design of the network architecture, the proposed methodology is able to identify the latent (design) variables with desired dimensionality, as well as capturing complex material microstructural characteristics. The validity of the proposed methodology is tested numerically on a synthetic microstructure dataset and its effectiveness for microstructural materials design is evaluated through a case study of optimizing optical performance for energy absorption. Additional features, such as scalability and transferability, are also demonstrated in this work. In essence, the proposed methodology provides an end-to-end solution for microstructural materials design, in which GAN reduces information loss and preserves more microstructural characteristics, and the GP-Hedge optimization improves the efficiency of design exploration.

Engineering↗
DOE OSTI2022

Experimental study of UV induced tensile properties deterioration and chemical aging of polyurea–POSS composites

Ultraviolet (UV) radiation present in natural sunlight degrades the chemical and mechanical properties of polymeric matrices in composites. Polyurea possess a unique set of chemical and mechanical properties due to its complex microstructure comprising of hard and soft segments (phases). The primary objective of the work presented here is to characterize the chemical and mechanical degradation of pure polyurea and polyurea - polyhedral oligomeric silsesquioxane (POSS) nanocomposites subjected to UV radiation exposure for 45 days. Control specimen in as-received condition (reference specimen) and UV-aged tensile test specimen are tested in the study. Analysis of Fourier transformed infrared (FTIR) spectra for UV-aged polyurea and polyurea-POSS composites reveals that the addition of POSS nanoparticles accelerate the deterioration of polyurea matrix by breaking the hard phase network and altering the mechanism of degradation for the soft phase present in polyurea microstructure. The deterioration of hard and soft segments cause a significant reduction in tensile properties of polyurea-POSS composites.

Materials Science↗
DOE OSTI2017

Should the next standby power target be 0-watt?

The standby power use of appliances continues to consume large amounts of electricity. Considerable success has been made in reducing each device’s use, but these savings have been offset by a huge increase in the number of products using standby power and new power requirements for maintaining network connections. Current strategies to reduce standby have limitations and may not be most appropriate for emerging energy consumption trends. A new strategy for further reductions in standby, the “Standzero” option, encourages electrical products to be designed to operate for short periods without relying on mains-supplied electricity. Energy savings are achieved through enhanced efficiency and by harvesting ambient energy. A sensitivity analysis suggests many appliances could be designed to operate for at least an hour without relying on mains power and, in some cases, may be able to operate indefinitely at zero watts until activated.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Lossy checkpoint compression in full waveform inversion: a case study with ZFPv0.5.5 and the overthrust model

This paper proposes a new method that combines checkpointing methods with error-controlled lossy compression for large-scale high-performance full-waveform inversion (FWI), an inverse problem commonly used in geophysical exploration. This combination can significantly reduce data movement, allowing a reduction in run time as well as peak memory. In the exascale computing era, frequent data transfer (e.g., memory bandwidth, PCIe bandwidth for GPUs, or network) is the performance bottleneck rather than the peak FLOPS of the processing unit. Like many other adjoint-based optimization problems, FWI is costly in terms of the number of floating-point operations, large memory footprint during backpropagation, and data transfer overheads. Past work for adjoint methods has developed checkpointing methods that reduce the peak memory requirements during backpropagation at the cost of additional floating-point computations. Combining this traditional checkpointing with error-controlled lossy compression, we explore the three-way tradeoff between memory, precision, and time to solution. We investigate how approximation errors introduced by lossy compression of the forward solution impact the objective function gradient and final inverted solution. Empirical results from these numerical experiments indicate that high lossy-compression rates (compression factors ranging up to 100) have a relatively minor impact on convergence rates and the quality of the final solution.

58 GEOSCIENCES↗
DOE OSTI2025

Advanced Transmission Technologies – GETs and HPCs Session 2: Advanced Power Flow Control and Transmission Topology Optimization

The INL TADA GETs Cohort Session 2, held on November 7, 2025, conducted in collaboration with ScottMadden, focused on two core Advanced Transmission Technologies (ATTs): Advanced Power Flow Control (APFC) and Transmission Topology Optimization (TTO). These technologies are pivotal in enhancing grid flexibility, reliability, and cybersecurity resilience. APFC, particularly through modular FACTS devices like Modular Static Synchronous Series Compensators (M-SSSCs), enables dynamic voltage injection to reroute power flows. The session highlighted the deployment benefits of APFC, such as rapid installation, minimal civil works, and re-deployability. Regulatory drivers like FERC Order 2023 mandate the inclusion of Grid-Enhancing Technologies (GETs) in interconnection studies. Case studies from Central Hudson, CAISO, and National Grid (UK) demonstrated APFC’s effectiveness in congestion relief and cost savings. The session also addressed cybersecurity concerns, including firmware vulnerabilities, SCADA integration risks, and supply chain dependencies. Participants engaged in interactive exercises to rank cybersecurity and supply chain risks, emphasizing the need for robust digital assurance strategies. TTO involves software-based reconfiguration of transmission networks to optimize power flow without new infrastructure. The session showcased its operational value, with examples from SPP, PJM, and MISO showing significant congestion cost reductions. Cybersecurity vulnerabilities were discussed, particularly in API security and software supply chains, referencing incidents like SolarWinds and attacks on Danish utilities. Digital assurance exercises explored worst-case scenarios, attack paths, and mitigation responsibilities between vendors and utilities. Reliability challenges such as algorithm stability, vendor dependency, and operator trust were also examined. Cross-cutting themes emphasized the importance of digital assurance tools, including Software Bills of Materials (SBOMs) and hardware-in-loop testing. Human performance, training, and operational confidence were identified as critical enablers of technology adoption. The session concluded with a preview of Session 3, which will focus on High Performance Conductors (HPCs) and risk-based cybersecurity tools. Session 2 of 3.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Self-Sacrificial Template Synthesis of Fe-N-C Catalysts with Dense Active Sites Deposited on A Porous Carbon Network for High Performance in PEMFC

In this study, iron-nitrogen-carbon (Fe-N-C) single-atom catalysts are promising sustainable alternatives to the costly and scarce platinum (Pt) to catalyze the oxygen reduction reactions (ORR) at the cathode of proton exchange membrane fuel cells (PEMFCs). However, Fe-N-C cathodes for PEMFC are made thicker than Pt/C ones, in order to compensate for the lower intrinsic ORR activity and site density of Fe-N-C materials. The thick electrodes are bound with mass transport issues that limit their performance at high current densities, especially in H 2 /air PEMFCs. Practical Fe-N-C electrodes must combine high intrinsic ORR activity, high site density, and fast mass transport. Herein, it has achieved an improved combination of these properties with a Fe-N-C catalyst prepared via a two-step synthesis approach, constructing first a porous zinc-nitrogen-carbon (Zn-N-C) substrate, followed by transmetallating Zn by Fe via chemical vapor deposition. A cathode comprising this Fe-N-C catalyst has exhibited a maximum power density of 0.53 W cm -2 in H 2 /air PEMFC at 80 °C. The improved power density is associated with the hierarchical porosity of the Zn-N-C substrate of this work, which is achieved by epitaxial growth of ZIF-8 onto g-C 3 N 4 , leading to a micro-mesoporous substrate.

25 ENERGY STORAGE↗
DOE OSTI2019

An Innovative Secure mmWave M2M Communication Network for Operating Drones

Analyze RF (Radio Frequency) coverage in the sky with the innovative use of mmWave phased arrays aimed towards the drone corridor to harness the power of 5G – multiplicative improvement in data throughput & reduction in latency, and support of massive M2M communications. Apply MA (Multiple Access) technology to increase the spectral efficiency by covering a swarm of drones with a single mmWave beam and associated radio resources. Design energy efficient low-bit quantization to form mmWave beams for drone operation. Use drones’ navigation capabilities to significantly improve security by e.g. evading attackers. Utilize intra-drone communication to improve reliability as well as security. Demonstrate the simulated recovery of a critical infrastructure such as a power grid that requires a black restart with mmWave modeling and experiments .

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2015

U.S. NO₂ trends (2005–2013): EPA air quality system (AQS) data versus improved observations from the Ozone Monitoring Instrument (OMI)

Emissions of nitrogen oxides (NO x ) and, subsequently, atmospheric levels of nitrogen dioxide (NO₂) have decreased over the U.S. due to a combination of environmental policies and technological change. Consequently, NO₂ levels have decreased by 30–40% in the last decade. We quantify NO₂ trends (2005–2013) over the U.S. using surface measurements from the U.S. Environmental Protection Agency (EPA) Air Quality System (AQS) and an improved tropospheric NO₂ vertical column density (VCD) data product from the Ozone Monitoring Instrument (OMI) on the Aura satellite.We demonstrate that the current OMI NO₂ algorithm is of sufficient maturity to allow a favorable correspondence of trends and variations in OMI and AQS data. Our trend model accounts for the non-linear dependence of NO₂ concentration on emissions associated with the seasonal variation of the chemical lifetime, including the change in the amplitude of the seasonal cycle associated with the significant change in NO x emissions that occurred over the last decade. The direct relationship between observations and emissions becomes more robust when one accounts for these non-linear dependencies. We improve the OMI NO₂ standard retrieval algorithm and, subsequently, the data product by using monthly vertical concentration profiles, a required algorithm input, from a high-resolution chemistry and transport model (CTM) simulation with varying emissions (2005-2013). The impact of neglecting the time-dependence of the profiles leads to errors in trend estimation, particularly in regions where emissions have changed substantially. For example, trends calculated from retrievals based on time-dependent profiles offer 18% more instances of significant trends and up to 15% larger total NO₂ reduction versus the results based on profiles for 2005. Using a CTM, we explore the theoretical relation of the trends estimated from NO₂ VCDs to those estimated from ground-level concentrations. The model-simulated trends in VCDs strongly correlate with those estimated from surface concentrations (r = 0.83, N = 355). We then explore the observed correspondence of trends estimated from OMI and AQS data. We find a significant, but slightly weaker, correspondence (i.e., r = 0.68, N = 208) than predicted by the model and discuss some of the important factors affecting the relationship, including known problems (e.g., NO z interferents) associated with the AQS data. This significant correspondence gives confidence in trend and surface concentration estimates from OMI VCDs for locations, such as the majority of the U.S. and globe, that are not covered by surface monitoring networks. Using our improved trend model and our enhanced OMI data product, we find that both OMI and AQS data show substantial downward trends from 2005 to 2013, with an average reduction of 38% for each over the U.S. The annual reduction rates inferred from OMI and AQS measurements are larger (–4.8 ± 1.9%/yr, –3.7 ± 1.5%/yr) from 2005 to 2008 than 2010 to 2013 (–1.2 ± 1.2%/yr, –2.1 ± 1.4%/yr). We quantify NO₂ trends for major U.S. cities and power plants; the latter suggest larger negative trend (–4.0 ± 1.5%/yr) between 2005 and 2008 and smaller or insignificant changes (–0.5 ± 1.2%/yr) during 2010-2013.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Deliver Signal Phase and Timing (SPAT) for Energy Optimization of Vehicle Cohort Via Cloud-Computing and LTE Communications

Predictive Signal Phase and Timing (SPAT) message set is one fundamental building block for vehicle-to-infrastructure (V2I) applications such as Eco-Approach and Departure (EAD) at traffic signal controlled urban intersections. Among the two complementary communication methods namely short-range sidelink (PC5) and long-range cellular radio link (Uu), this paper documents the work with long-range link: the complete data chain includes connecting to the traffic signals via existing backhaul communication network, collecting the raw signal phase state data, predicting the signal state changes and delivering the SPAT data via a geofenced service to requests over HTTP protocols. An Application Programming Interface (API) library is developed to support various cellular data transmission reduction and latency improvement techniques. An emulation-based algorithm is applied to predict the traffic signal state changes to provide adequate prediction horizon (e.g., at minimum 2 minutes) for the cohort energy optimization. In fact, the same connectivity and SPAT delivery methodology has been applied to traffic signalized intersections nationwide in the United States upon public agency approvals for access to their firewalled traffic control network and signal control systems or directly to individual controllers. This methodology proves its effectiveness and potential for rapid growth of such SPAT deliveries at mass production scale without needing infrastructure hardware retrofit or excessive communication means. To support the energy optimization of light and heavy-duty vehicle cohorts of mixed automation and propulsion systems (EV, ICE and hybrid), the connection and SPAT deliveries at two sites were completed, including public roads in Washtenaw County, Michigan and closed track test sites at American Center for Mobility (ACM) in Ypsilanti, Michigan. However, only closed test track results at ACM will be presented in this paper. A neuroevolution based optimizer is developed and implemented to control the speed of a vehicle cohort with different propulsion systems and automation levels. Closed track tests showed significant energy savings of the cohort operation.

99 GENERAL AND MISCELLANEOUS↗