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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 235 records · Page 13

Cordon screen: A cordon-based congestion pricing policy evaluation method for U.S. cities

Global trends toward urbanization will exacerbate traffic congestion, delays in economic productivity, and air pollution issues for growing cities. Traffic congestion pricing is one method available to help ameliorate these concerns. New York City is on the verge of implementing a cordon-based traffic congestion pricing policy around its central business district. For budget-constrained municipalities, evaluating implementation of such policy could be costly. This article proposes a sketch-planning methodology, called Cordon Screen, for major U.S. cities to evaluate the net income, traffic mitigation, and avoided pollution emissions from cordon-based traffic congestion pricing. This method relies on national datasets and limited user-specific data inputs, along with a range of user-selectable assumptions informed by academic literature to deliver order-of-magnitude results. The numerous limitations of this method are acceptable for preliminary policy evaluation to determine if greater financial investment to obtain more accurate results is justified. The Denver metropolitan area is used to demonstrate Cordon Screen capabilities, with mid-range assumption results suggesting the policy is most effective at generating net income and increasing vehicle speeds on major interstates. For Denver, the policy is comparably less effective at reducing air pollution and increasing speeds on minor roadways. Validation against early implementation results from the London cordon are acceptable. Still, users should discount revenue generation projections. Choice of cordon area may be the most difficult obstacle when using the Cordon Screen. With refinement, Cordon Screen could serve as a low-cost, open-source planning evaluation tool for growing and congested U.S. cities.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

A Method for Measuring Coupled Individual and Social Vulnerability to Environmental Hazards

Although models of social vulnerability to environmental hazards are commonly developed to support policy interventions in emergencies and disasters, their utility is hindered by a lack of contextual information on individuals exposed to and affected by hazards. We develop a novel approach to model social vulnerability that couples individuals and their varying forms of protective capacity with the social fabric of the communities in which they reside. The backbone of our model is the Public-Use Microdata Sample (PUMS), a product of the U.S. Census Bureau that preserves a representative sample of completed responses to the American Community Survey (ACS). The PUMS enables us to understand the full range of individual protective capacities against a hazard in an exposed area, which we term individual vulnerability profiles (IVPs). In this case, we examine IVPs in the Coney Island-Brighton Beach section of New York City, which suffered severe impacts during Hurricane Sandy in 2012. To manage the large number of unique IVPs in Coney Island-Brighton Beach, we perform a segmentation analysis to generalize them into thematic cohort vulnerability profiles (CVPs) representing a typology of vulnerable people in Coney Island-Brighton Beach during Sandy. From synthetic populations of CVPs, we then estimate how individuals in varying housing types were coexposed to Sandy at the census tract level by classifying these areas into community social vulnerability profiles (SVPs). Our results provide a topology of social vulnerability that simultaneously links individual, community, and population-wide concerns, enabling a more holistic understanding of resources and interventions beneficial to human security during events like Sandy than is attainable with area-level metrics.

54 ENVIRONMENTAL SCIENCES↗

Associations of Early COVID-19 Cases in San Francisco With Domestic and International Travel

In early-to-mid March 2020, 20 of 46 (43%) COVID-19 cases at a tertiary care hospital in San Francisco, California were travel related. Cases were significantly associated with travel to either Europe (odds ratio, 6.1) or New York (odds ratio, 32.9). Viral genomes recovered from 9 of 12 (75%) cases co-clustered with lineages circulating in Europe.

59 BASIC BIOLOGICAL SCIENCES↗

Characterizing Canopy Openness in Open Forests: Spherical Densiometer and Canopy Photography Are Equivalent but Less Sensitive than Direct Measurements of Solar Radiation

In forest ecosystems, canopy openness affects understory light availability, plant growth, and tree species recruitment, thus shaping future forest composition, structure, and functional diversity. Foresters must correctly and quickly measure canopy openness to meet their management objectives. To help guide the selection of an appropriate method for measuring canopy openness, we compared three common techniques that vary in cost, complexity, and time required for measurements and data processing: smartphone-based hemispherical photography, spherical densiometer measurements, and direct measurements of solar radiation (using AccuPAR ceptometer). We measured canopy openness using these three methods on 28 permanent forest health monitoring plots in pine-oak forests of the Central Pine Barrens of Long Island in New York State. By analysis of variance and regression analyses, we found the three methods (particularly densiometer and hemispherical photographs) yielded broadly equivalent and strongly positively correlated descriptions of canopy openness. The direct measurements of solar radiation seemed to have a greater potential to detect subtle variation in forest understory light. Forest managers may sufficiently characterize canopy openness using quick and cheap methods (e.g., spherical densiometers) and avoid larger costs of devices for direct light measurements (e.g., ceptometers) and the larger data processing times of hemispherical photography.

54 ENVIRONMENTAL SCIENCES↗

The Melampsora americana Population on Salix purpurea in the Great Lakes Region Is Highly Diverse with a Contributory Influence of Clonality

Shrub willows (Salix spp.) are emerging as a viable lignocellulosic, second-generation bioenergy crop with many growth characteristics favorable for marginal lands in New York State and surrounding areas. Willow rust, caused by members of the genus Melampsora, is the most limiting disease of shrub willow in this region and remains extremely understudied. In this study, genetic diversity, genetic structure, and pathogen clonality were examined in Melampsora americana over two growing seasons via genotyping-by-sequencing to identify single-nucleotide polymorphism markers. In conjunction with this project, a reference genome of rust isolate R15-033-03 was generated to aid in variant discovery. Sampling between years allowed regional and site-specific investigation into population dynamics, in the context of both wild and cultivated hosts within high-density plantings. This work revealed that this pathogen is largely panmictic over the sampled areas, with few sites showing moderate genetic differentiation. These data support the hypothesis of sexual recombination between growing seasons because no genotype persisted across the two years of sampling. Additionally, clonality was determined as a driver of pathogen populations within cultivated fields and single shrubs; however, there is also evidence of high genetic diversity of rust isolates in all settings. This work provides a framework for M. americana population structure in the Great Lakes region, providing crucial information that can aid in future resistance breeding efforts.

Plant Sciences↗

Modeling Offshore Wind Farm Performance in Coastal Low-Level Jets Using Coupled Mesoscale-Microscale Large Eddy Simulations

Accurately predicting wind farm reliability under complex offshore atmospheric conditions remains a key challenge, particularly during noncanonical meteorological events such as coastal low-level jets (LLJs). LLJs, characterized by strong nonmonotonic vertical shear and directional veer, depart significantly from the simplified inflow assumptions embedded in conventional design standards, low-fidelity engineering models, and microscale large eddy simulations of the atmospheric boundary layer. In this work, we use the virtual wind farm framework—an exascale, graphics processing unit–accelerated large eddy simulation platform coupled with high-fidelity aeroservoelastic turbine models and advanced mesoscale-microscale coupling via the ExaWind software stack—to investigate turbine responses under realistic LLJ forcing. Simulations are performed over the U.S. North Atlantic offshore domain with the use of meteorological inputs from New York State Energy Research and Development Authority buoy data, focusing on a representative LLJ case impacting the International Energy Agency 15 MW reference turbine. Our results show that LLJs can cause up to 50% power deficits in downstream turbine rows and significantly amplify low-speed shaft and tower loads through nonlinear coupling between complex inflow characteristics and turbine structural dynamics. Two primary mechanisms drive these load amplifications: (1) unique LLJ inflow features—including veer and vertical/lateral shear—and (2) the downstream evolution of the flow under stable thermal stratification, which suppresses turbulence mixing and alters wake recovery. These mechanisms produce streamwise variations in turbine loading not captured by standard hub height–based metrics or existing design load case (DLC) definitions. This study highlights the critical role of rotor-scale flow gradients in driving fatigue and system-level aeroelastic responses, challenging current DLC and control strategies. We advocate the integration of full-flow field, environment-aware wind inputs into load modeling and control algorithms. By leveraging exascale computing to resolve mesoscale-microscale coupling, this work lays the groundwork for next-generation offshore wind turbine design and operation in meteorologically complex marine environments.

17 WIND ENERGY↗

X-ray-based technologies in emerging fuel cell research

The workshop titled `X-ray-based technologies in emerging fuel cell research', organized by Vivian Stojanoff from Brookhaven National Laboratory (BNL) and Narayanasami Sukumar from Cornell University/Advanced Photon Source-Northeastern Collaborative Access Team, was a notable segment of the National Synchrotron Light Source II and Center for Functional Nanomaterials Users' Meeting held 13–17 May 2024. This one-day event, on 13 May 2024, at BNL in New York, aimed to bring together researchers, beamline scientists, management and developers to propel fuel cell technology forward using model systems inspired by natural photosynthesis and redox enzymes. Furthermore, this summary encapsulates the key discussions, advancements and future implications of the workshop.

Xray↗

ADDS-EVS: An agent-based deployment decision-support system for electric vehicle services

Rapid and sustainable development of the electric vehicle (EV) industry places the requirement for the plan of EV deployment. For public EV, existing models mainly focus on the charging facility design and fail to capture the multi-modal scenarios. In this work, we develop an agent-based decision-support system for multi-modal electric transits to locate the optimal combinations of key parameters, including the fleet size, the transit schedule, the charging facility design, and the routing strategy. We demonstrate the utilities of our system by simulating public EV services deployed to serve travel needs related to a transportation hub in New York City. To support the decision of the fleet size, we summarize system-level performances including the total satisfied demand, passengers' waiting time, vehicle idling time. The spatial and temporal patterns are extracted to serve a deeper understanding of system dynamics and service quality. Finally, we investigate the interaction between the fleet size design and the routing strategy. The results suggest a necessity of integrating the operation strategies into the planning phase.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Prediction of Climate-Specific Degradation Rate for Photovoltaic Encapsulant Discoloration

Encapsulant discoloration is a well-known field degradation mode of crystalline-silicon photovoltaic modules, particularly in the hot climate zones. The discoloration rate is influenced by several weathering factors, such as UV light, module temperature, and humidity, as well as the permeability of oxygen into the module. Here, a rate dependence model employing the modified Arrhenius equations to predict the degradation rate for encapsulant discoloration in different climates is presented. Two modeling approaches are introduced, which utilize the field and accelerated UV testing degradation data in conjunction with the field meteorological data to determine the acceleration factor for encapsulant browning. A novel method of accelerated UV stress testing at three simultaneous module temperatures in a single environmental chamber test run is implemented to estimate the activation energy for browning. The test was performed on three field-retrieved modules to capture the wear-out failure mechanism. The degradation in short-circuit current Isc rather than maximum power is used as a decisive parameter for the discoloration analysis. Furthermore, the developed model has been used to predict the Isc degradation rate for the Arizona field characterized by a hot and dry climate and is validated against the field-measured value. It has also been applied to other climate types, e.g., the cold and dry climate of New York.

14 SOLAR ENERGY↗

Hybrid Power Plant Bidding in Models of Future Electricity Systems

This paper proposes a stochastic model for hybrid power plants participation in day-ahead electricity markets, considering uncertainty in market prices and renewable generation. Additionally, it presents a methodology to incorporate this hybrid participation into existing production cost models (PCM), allowing the analysis and market design of future systems with high penetration of hybrids. These developments are illustrated using a wind-battery hybrid located in New York Independent System Operator (NYISO) footprint.

Kemp, Julie Mulvaney↗

Construction of Cryogen-Free 4.3T Superconducting Wiggler for NSLS-II Ring

With the 3 GeV electron beam energy for the National Synchrotron Light Source II (NSLS-II) ring, only superconducting wigglers (SCW) producing greater than 4T peak field can cover the photon energy range of 20 keV and 200 keV with sufficient number of photons. The High energy Engineering X-ray (HEX) Diffraction beamline, which is primarily funded by the New York State Energy Research and Development Authority (NYSERDA) and NSLS-II, will be equipped with a 1.2m-long SCW with 70 mm period length and 4.3 T on-axis field. This SCW is free from the use of liquid Helium and is cooled only with cryo-coolers. The Electron Beam Chamber (EBC) with vertical aperture of 8 mm is made from 316LN stainless steel and copper plating is applied both surface and +-12.5mm wide from the center in the inner surface. The expected heat load from the electron beam of the NSLS-II ring is estimated to be 10 W/m. Here, we describe the design principles and engineering challenges for the device.

36 MATERIALS SCIENCE↗

Integration of New Technology Considering the Trade-Offs Between Operational Benefits and Risks: A Case Study of Dynamic Line Rating

Electric grid operators are adept at handling complexity and uncertainty. However, with increasing introduction of renewable generation, distributed energy resources, and more frequent severe weather events, operators will experience new workload and challenging decision scenarios. Here, this paper quantifies risks and benefits from an operator's perspective of introducing weather based forecast Dynamic Line Ratings (DLR) using variable wind conditions in addition to ambient temperature to relieve transmission congestion and facilitating more offshore wind (OSW). A concept of operations (CONOPS) applied to a forecast DLR implementation and its integration with OSW is defined. A method for evaluating tradeoffs of derating to make the rating more conservative but decreasing the benefit was developed and applied to a case study for two existing overhead transmission lines on Long Island, New York. The CONOPS uses historical day-ahead and hour-ahead High Resolution Rapid Refresh weather forecasts and weather station data to support planning and real-time operations. The analysis determines the risk of downgrades in real-time operational rating compared to the forecast and quantifies the frequency and severity of last-minute downgrades. The risk is compared against the benefits in increased capacity to provide insights on the additional amount of uncertainty DLR and OSW will add to the operator's workload.

17 WIND ENERGY↗

A Predictive Prescription Framework for Stochastic Unit Commitment Using Boosting Ensemble Learning Algorithms

To take unit commitment (UC) decisions under uncertain load, most existing stochastic optimization (SO) frameworks adopt a generic representation of uncertainty. While load levels that materialize on a particular day are influenced by various covariates (such as the day of the week or temperature), SO frameworks typically disregard such side observations, wasting actionable information that could significantly enhance decision quality. Here, this article proposes a contextual SO (CSO) framework for UC under uncertain load, which can effectively exploit covariate observations in conjunction with a class of machine learning (ML) algorithms to improve the out-of-sample performance of UC decisions. It shows how three ML algorithms, adaptive boosting, gradient boosted trees, and extreme gradient boosting, can be used to this end, constituting the first application of these algorithms in any CSO framework. Using real-world data harvested from the New York ISO grid, we measure the out-of-sample performance of the framework in terms of total operation cost, shed load values, locational marginal prices, and total payments by the loads, against several benchmark methods proposed in the literature. The article has an online companion (Yurdakul et al.), wherein we present additional results and lay out further mathematical formulations used in this work.

42 ENGINEERING↗

A Data-Driven Methodology for Contextual Unit Commitment Using Regression Residuals

Day after day, system operators are faced with the challenge of taking unit commitment (UC) decisions under uncertain net load conditions. The standard operating procedure for taking UC decisions begins by leveraging auxiliary data on covariates (such as the day of the week or latest weather information) to generate a point prediction for net load, which is used in solving a deterministic UC problem. Such an approach, however, is known to deliver a notoriously poor out-of-sample (OOS) performance, as it completely disregards the stochastic nature of net load. While stochastic programming models explicitly represent uncertainty, they mostly do so using a generic set of scenarios that neglect covariate observations, squandering useful auxiliary data that could be harnessed to glean insights into uncertainty. In this article, we discuss a contextual stochastic optimization approach to UC, which effectively exploits covariate observations while explicitly assessing uncertainty so as to boost the OOS performance of UC decisions. The key thrust of our approach is to leverage regression models, along with their empirical residuals, to set up and solve sample average approximation problems. Not only do we prove that our approach satisfies the requisite conditions for asymptotic optimality and consistency laid out in (Kannan et al., 2022), but we also assess its performance on several case studies conducted using real-world data collected in California ISO and New York ISO grids. In conclusion, results show that the proposed approach can significantly improve OOS performance compared to alternative methods proposed in the literature under varying dataset sizes.

Yurdakul, Ogun↗

Cut–and–chip harvester material capacity and fuel performance on commercial-scale willow fields for varying ground and crop conditions

Shrub willow (Salix spp.) is capable of producing commercially attractive amounts of biomass in short rotations, but harvesting costs and logistics remain a concern. There is a particular need for information about harvesting operations on larger, commercial short–rotation woody crop systems. Another recent issue on commercial fields in northern New York is commercial growers conducting harvests during the growing season rather than the recommended dormant season when fields may be too wet to harvest. This study evaluated and modeled the in–field performance of a cut–and–chip harvester for almost 700 wagonloads of chips operating in commercial willow fields in a wider array of crop and field conditions than have been previously reported. Analysis indicated that the time of harvest (leaf–on or leaf–off) and whether site conditions were wet or dry affected the harvester's material capacity. Mean material capacity was greatest for leaf–off, dry conditions (71.8 Mg/hr) and lowest for leaf–on harvests, which were similar for wet (30.4 Mg/hr) and dry conditions (29.7 Mg/hr). Mean crop specific fuel consumption ranged between 1.3 and 3.3 L/Mg, but can get considerably higher for standing biomasses below 40 Mg/ha. Wet ground conditions and leaf–on harvests tend to decrease material capacity and increase fuel consumption as the harvester has to divert power to forward movement and material processing. Relationships for material capacity and fuel consumption based on standing biomass, time of harvest and ground conditions will be essential for evaluating and modeling the economic and environmental impacts of commercial–scale willow operations.

09 BIOMASS FUELS↗

Measurement of Convective Heat Transfer Coefficients With Supercritical CO 2 Using the Wilson-Plot Technique

This paper describes the measurement of convective heat transfer coefficients and friction factors for sCO 2 flowing in a smooth tube and compares the results with published correlations for validation. The paper also describes the Heat Exchange and Experimental Testing (HEET) rig recently designed and built at the U.S. Department of Energy’s (DoE’s) National Energy Technology Laboratory (NETL) in Morgantown, WV. Here, the Wilson-plot technique used for measuring the heat transfer coefficients is described along with the data reduction process. The Wilson-plot technique was chosen as the basis for the design of NETL’s HEET rig. Advantages of the Wilson-plot technique include the (1) ability to measure high convective heat transfer coefficients accurately, (2) ability to measure average heat transfer coefficient for complicated heat exchange geometries like those produced using additive manufacturing, (3) ability to measure heat transfer coefficients on both sides of a heat exchanger independently, and (4) simplicity of experimental setup. Capabilities of the HEET rig include pressure to 24 MPa (3500 psig), temperature to 538 °C (1000 °F), mass flow rate to 1.5 kg/s (3 lb/s), and Re to 500,000. The rig is designed to operate with pure CO 2 or a mixture of CO 2 and up to 10% N2 by volume to study the impact of gas mixtures typical of direct-fired sCO 2 power cycles on the convective heat transfer and pressure drop. Preliminary tests in the HEET rig were performed with smooth stainless-steel tube and pure CO 2 , and the results were compared with published correlations for Nusselt number (Nu) and friction factor. Over a Reynolds number (Re) range from 58,000 to 228,000, measured Nu was compared to predictions using the Dittus and Boelter equation (Kreith and Bohn, 1993, “Principles of Heat Transfer, West Publishing Company”) within 5% and measured friction factors were compared to predictions using the McAdams correlation (“McAdams, 1954, “Heat Transmission,” 3rd ed., McGraw Hill, New York)” for smooth tube to be within 5%.

42 ENGINEERING↗

Design and Analysis of a Floating-Wind Shallow-Water Mooring System Featuring Polymer Springs

This paper presents a mooring system design featuring polymer springs for the VolturnUS-S 15-MW reference floating wind turbine in site conditions for the New York Bight at a 50-m water depth. Polymer springs have a nonlinear stress-strain curve that allows a stiffer response at low loads and a more flexible response at higher loads, potentially reducing peak mooring line tensions. The mooring dynamics model MoorDyn has been extended to model springs with nonlinear tension-strain curves from a user-inputted look-up table. This MoorDyn modeling advancement is verified against OrcaFlex simulation results. Using MoorDyn's updated capabilities, a spring-equipped catenary mooring system is designed for the 15-MW floating system, along with a baseline catenary mooring system that does not use polymer springs. The floating wind turbine simulator OpenFAST is used to simulate the mooring systems in design-driving load cases to show the effect of polymer springs on key dynamic behaviours. The results show that the spring-equipped design reduces peak tensions by up to 60%, whereas the turbine offsets stay within a maximum of 7.2 m, which is still a reasonable offset limit for cable considerations. The reduction in peak tensions results in a significant decrease in damage equivalent loads - on the order of 50% for upwind lines in fully loaded conditions. These results show that polymer springs can effectively reduce peak tensions and fatigue loads in mooring systems at shallow water depths.

floating offshore wind↗