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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Mechanical Design of the Interaction Region Dipole B1pF for Electron Ion Collider

Here, the Electron-Ion Collider (EIC), planned for construction at Brookhaven National Laboratory, will consist of two intersecting beams of intense electrons and high-energy protons or heavier atomic nuclei. The collider will need many demanding components, including high field superconducting magnets for the Interaction Region (IR). It has been decided that a prototype of one such magnet, the B1pF dipole, shall be built, to validate design choices common to all Rutherford cable dipole and quadrupole IR magnets. B1pF is one of several high field magnets in the interaction region whose design is based on the 15.1 mm wide NbTi cable, a cable like the one used in the main dipole coil of the Large Hadron Collider (LHC) at CERN. The current design is based on a single layer coil with an inner diameter of 300 mm and magnetic length of ~3 m which generates a magnetic field at the center of the magnet of about 3.7 T at a current of ~11 kA at design. This paper presents the mechanical design of the B1pF dipole magnet, and the R&D effort associated with the development of the prototype, including tests for successfully winding and curing coils of the needed configuration into proper structural and magnetic elements, and including a multilayer magnetic return yoke which meets field requirements while providing an initial reduced structure suitable for operational testing in an existing test facility.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Economic Optimal Control for Minimizing Fuel Consumption of Heavy-Duty Trucks in a Highway Environment

This paper provides a comparative assessment of three economic optimal control strategies, aimed at minimizing the fuel consumption of heavy-duty trucks in a highway environment, under a representative lead vehicle model informed by traffic data. These strategies fuse a global, offline dynamic programming (DP) optimization with online model predictive control (MPC). We then show how two of the three strategies can be adapted to accommodate the presence of traffic and optimally navigate signalized intersections using infrastructure-to-vehicular (I2V) communication. The MPC optimization, which is local in nature, makes refinements to a coarsely (but globally, subject to grid resolution) optimized target velocity profile from the DP optimization. The three candidate economic MPC formulations that are evaluated include: a nonlinear time-based formulation that directly penalizes predicted fuel consumption, a nonlinear time-based formulation that penalizes braking effort as a surrogate for fuel consumption, and a linear distance-based convex formulation that maintains a tradeoff between energy expenditure and tracking of the coarsely optimized velocity profile obtained from DP. Using a medium-fidelity Simulink model, based on a Volvo truck's longitudinal and engine dynamics, we analyze the optimization's performance on four highway routes under various traffic scenarios. Results demonstrate 3.7-8.3% fuel economy improvement on highway routes without traffic and 6.5-10% on the same routes with traffic included. Furthermore, we present a detailed analysis of energy usage by "type" (aerodynamic losses, braking losses, and comparison of brake-specific fuel consumption), under each candidate control strategy.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Stability and Performance of an Undersea Kite Operating in a Turbulent Flow Field

In this paper, we examine the effects of flow disturbances resulting from turbulence on the dynamic behavior of an underwater energy-harvesting kite system that executes periodic figure-8 flight. Due to the periodic nature of the kite’s operation, we begin by assessing orbital stability using the Floquet analysis and stroboscopic intersection analysis of a Poincaré section, with the former analysis performed on a simplified “unifoil” model and the latter performed on a six-degree-of-freedom (6-DOF)/flexible tether model. With periodic stability established, a frequency-domain analysis based on a linearization about the kite’s path is used to predict the quality of flight path tracking as a function of the turbulence frequency. To validate the accuracy of these simulation-based predictions under flow disturbances, we compare the predictions of the kite’s behavior against the results of small-scale tow testing experiments performed in a controlled pool environment.

13 HYDRO ENERGY↗
DOE OSTI2025

Dopant Optimization of Donors in Semiconductor Opening Switches to Eliminate Prepulse

Semiconductor opening switches are solid-state devices capable of delivering nanosecond, hundreds of kilovolts pulses by interrupting kiloamps of current. The interruption of the current occurs in a moderately doped p-region when a high electric field region (HFR) is formed. The HFR occurs because the reverse pumping current cannot be supported by the saturation velocity and majority carrier concentration of the doping level. However, the donor profile also significantly affects the pulse performance. A secondary prepulse occurs if a secondary HFR is formed at the interface of the background n-doping and N+ doping (X n ) . By moving the location of X n deeper into the diode, the effect of the prepulse is reduced. This article investigates the effect of the donor doping profile on the performance metrics of semiconductor opening switches through technology computer-aided design (TCAD) simulations and experimental results. Through a SILVACO TCAD optimization, we designed a P + /p/n - base/n/N + where the intersection of the moderate p-region and intrinsic n-base region (X p ) is at 160 μm and X n is at 220 μm. This profile is fabricated via silicon epitaxy. Experimentally, it is shown that a deep X n (220 μm) compared with a shallow X n (300 μm) reduces the rise time by >5× . In addition, the magnitude of current density during interruption affects the prepulse foot and pulse shape. At lower current densities without the graded donor profile, high peak voltages are not achieved. Comparing the experimental results to the TCAD simulations shows that the model is predictive under high-current densities in the semiconductor opening switch (SOS) regime.

nanosecond pulse power↗
DOE OSTI2022

Background-Aware 3-D Point Cloud Segmentation With Dynamic Point Feature Aggregation

With the proliferation of LiDAR sensors and 3-D vision cameras, 3-D point cloud analysis has attracted significant attention in recent years. In this article, we propose a novel 3-D point cloud learning network, referred to as dynamic point feature aggregation network (DPFA-Net), by selectively performing the neighborhood feature aggregation (FA) with dynamic pooling and an attention mechanism. DPFA-Net has two variants for semantic segmentation and classification of 3-D point clouds. As the core module of the DPFA-Net, we propose an FA layer, in which features of the dynamic neighborhood of each point are aggregated via a self-attention mechanism. In contrast to other segmentation models, which aggregate features from fixed neighborhoods, our approach can aggregate features from different neighbors in different layers providing a more selective and broader view to the query points and focusing more on the relevant features in a local neighborhood. In addition, to further improve the performance of semantic segmentation, we exploit the background–foreground (BF) information and present two novel approaches, namely, two-stage BF-Net and BF regularization. Experimental results show that the proposed DPFA-Net achieves the state-of-the-art overall accuracy score of 89.22% for semantic segmentation on the Stanford large-scale 3-D Indoor Spaces (S3DIS) dataset and provides consistently satisfactory performance across different tasks of semantic segmentation, part segmentation, and 3-D object classification. Furthermore, our model achieves 93.1% accuracy on the ModelNet40 dataset and provides a mean shape intersection-over-union (IoU) value of 85.5% for part segmentation on the ShapeNet-Part dataset. It is a also computationally more efficient compared to other methods.

3-D↗
DOE OSTI2026

Characterization of Precipitation-Induced Radon Progeny Deposition Events Using a City-Scale Sensor Network

Networks of radiation detectors provide a platform for real-time radioactive source detection and identification in urban environments. Detection algorithms in these systems must adapt to naturally-occurring changes in background, which requires well-characterized relationships between precipitation events and their corresponding radiological signature. Here, we present a quantitative and qualitative description of rain-induced radon progeny deposition events occurring in Chicago from September 2023 to February 2024. We measure ambient gamma radiation levels, precipitation rate, temperature, pressure, and relative humidity in a network of sensor nodes. For each identified precipitation period, we decompose spectra into static- and radon-associated components as defined by a non-negative matrix factorization (NMF) algorithm. We find a consistent power-law relationship between a precipitation-dependent peak of the radon progeny proxy (RPP) and the peak strength of the radon-associated NMF component for most precipitation events. We conduct a case study of a rainfall period with abnormally high levels of implied radon progeny concentration and describe its temporal and spatial evolution. We hypothesize that this phenomenon is due to the air mass path that intersects a uranium-rich region of Wyoming. Finally, we cluster precipitation events into three distinct categories. One category roughly corresponds to events with deep low-pressure systems and high relative radon concentration, while another is characteristic of light stratiform rain with slightly higher temperatures and intermediate relative radon concentration. The third category appears to contain weak-gradient or lake breeze convection showers with intermittent precipitation and low relative radon concentration. These findings suggest that radiological anomaly detection could be improved by training unique background models corresponding to each category of meteorological event.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Trust: Triangle Counting Reloaded on GPUs

Triangle counting is a building block for a wide range of graph applications. Here, traditional wisdom suggests that i) hashing is not suitable for triangle counting, ii) edge-centric triangle counting beats vertex-centric design, and iii) communication-free and workload balanced graph partitioning is a grand challenge for triangle counting. On the contrary, we advocate that i) hashing can help the key operations for scalable triangle counting on Graphics Processing Units (GPUs), i.e., list intersection and graph partitioning, ii) vertex-centric option reduces both hash table construction cost and memory consumption, which is limited on GPUs. In addition, iii) we exploit graph and workload collaborative, and hash-based 2D partitioning to scale vertex-centric triangle counting over 1,000 GPUs with sustained scalability. In this work, we present TRUST, which performs triangle counting with the hash operation and vertex-centric paradigm. To the best of our knowledge, TRUST is the first work that achieves over one trillion Traversed Edges Per Second (TEPS) rate for triangle counting.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Identifying Redundant Flow Limits on Parallel Lines

Many power system optimization problems constrain line flows with limits specified in terms of the magnitudes of apparent power or current flows. The set of line-flow constraints may have redundancies (i.e., the feasible space may be unchanged upon removal of a subset of the line-flow constraints), which unnecessarily complicate optimization problems. This letter describes an algorithm for identifying redundant line-flow constraints corresponding to certain parallel lines. After formulating the constraints as ellipsoids in the voltage variables, redundancies are detected from the absence of intersections between pairs of ellipsoids corresponding to parallel lines. This algorithm is demonstrated using several large test cases.

Molzahn, Daniel K. (ORCID:0000000305835376)↗
DOE OSTI2017

Optimal Power Flow of Radial Networks and Its Variations: A Sequential Convex Optimization Approach

This paper proposes a sequential convex optimization method to solve broader classes of optimal power flow (OPF) problems over radial networks. The non-convex branch power flow equation is decomposed as a second-order cone inequality and a non-convex constraint involving the difference of two convex functions. Provided with an initial solution offered by an inexact second-order cone programming relaxation model, this approach solves a sequence of convexified penalization problems, where concave terms are approximated by linear functions and updated in each iteration. It could recover a feasible power flow solution, which usually appears to be very close, if not equal, to the global optimal one. Two variations of the OPF problem, in which non-cost related objectives are optimized subject to power flow constraints and the convex relaxation is generally inexact, are elaborated in detail. One is the maximum loadability problem, which is formulated as a special OPF problem that seeks the maximal distance to the boundary of power flow insolvability. The proposed method is shown to outperform commercial nonlinear solvers in terms of robustness and efficiency. The other is the bi-objective OPF problem. A non-parametric scalarization model is suggested, and is further reformulated as an extended OPF problem by convexifying the objective function. It provides a single trade-off solution without any subjective preference. The proposed computation framework also helps retrieve the Pareto front of the bi-objective OPF via the e-constraint method or the normal boundary intersection method. This paper also discusses extensions for OPF problems over meshed networks based on the semidefinite programming relaxation method.

bi-objective optimization↗
DOE OSTI2015

Critical biogeochemical functions in the subsurface are associated with bacteria from new phyla and little studied lineages

Nitrogen, sulfur and carbon fluxes in the terrestrial subsurface are determined by the intersecting activities of microbial community members, yet the organisms responsible are largely unknown. Metagenomic methods can identify organisms and functions, but genome recovery is often precluded by data complexity. To address this limitation, we developed subsampling assembly methods to re-construct high-quality draft genomes from complex samples. Here, we applied these methods to evaluate the interlinked roles of the most abundant organisms in biogeochemical cycling in the aquifer sediment. Community proteomics confirmed these activities. The eight most abundant organisms belong to novel lineages, and two represent phyla with no previously sequenced genome. Four organisms are predicted to fix carbon via the Calvin Benson Bassham, Wood Ljungdahl or 3-hydroxyproprionate/4-hydroxybutarate pathways. The profiled organisms are involved in the network of denitrification, dissimilatory nitrate reduction to ammonia, ammonia oxidation and sulfate reduction/oxidation, and require substrates supplied by other community members. An ammonium-oxidizing Thaumarchaeote is the most abundant community member, despite low ammonium concentrations in the groundwater. Finally, this organism likely benefits from two other relatively abundant organisms capable of producing ammonium from nitrate, which is abundant in the groundwater. Overall, dominant members of the microbial community are interconnected through exchange of geochemical resources.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

The Press and Pulse of Climate Change: Extreme Events in the Colorado River Basin

Extremes in temperature and precipitation are associated with damaging floods, prolonged drought, destructive wildfires, agricultural challenges, compromised human health, vulnerable infrastructure, and threatened ecosystems and species. Often, the steady and progressive trends (or presses) of rising global temperature are the central focus in how climate impacts are described. However, observations of extreme weather events (or pulses) increasingly show that the intensity, duration and/or frequency of acute events are also changing, resulting in greater impacts on communities and the environment. Describing how the influence of extreme events may shape water management in the Colorado River Basin in clear terms is critical to sound future planning and efforts to manage risk. Three scenario planning workshops in 2019 and 2020 were held as part of a Colorado River Conversations series, identifying potential impacts from multiple intersecting extreme events. Water managers identified climate-related events of concern in the Colorado River Basin that necessitate greater attention and adaptive responses. To support efforts to include consideration of climate-change-driven extremes in water management and planning, we explore the current state of knowledge at the confluence of long-term climate shifts and extreme weather in the Colorado River Basin related to the events of concern that were identified by scenario planning participants.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Identification of ancestral gnathostome Gli3 enhancers with activity in mammals

Abnormal expression of the transcriptional regulator and hedgehog (Hh) signaling pathway effector Gli3 is known to trigger congenital disease, most frequently affecting the central nervous system (CNS) and the limbs. Accurate delineation of the genomic cis-regulatory landscape controlling Gli3 transcription during embryonic development is critical for the interpretation of noncoding variants associated with congenital defects. Here, we employed a comparative genomic analysis on fish species with a slow rate of molecular evolution to identify seven previously unknown conserved noncoding elements (CNEs) in Gli3 intronic intervals (CNE15–21). Transgenic assays in zebrafish revealed that most of these elements drive activities in Gli3 expressing tissues, predominantly the fins, CNS, and the heart. Intersection of these CNEs with human disease associated SNPs identified CNE15 as a putative mammalian craniofacial enhancer, with conserved activity in vertebrates and potentially affected by mutation associated with human craniofacial morphology. Finally, comparative functional dissection of an appendage-specific CNE conserved in slowly evolving fish (elephant shark), but not in teleost (CNE14/hs1586) indicates co-option of limb specificity from other tissues prior to the divergence of amniotes and lobe-finned fish. In conclusion, these results uncover a novel subset of intronic Gli3 enhancers that arose in the common ancestor of gnathostomes and whose sequence components were likely gradually modified in other species during the process of evolutionary diversification.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

High fluid shear strain causes injury in silver shark: Preliminary implications for Mekong hydropower turbine design

Fluid shear arises when two bodies of water, travelling at different velocities, intersect. Fish entrained at the interface of these two water masses will experience shear stress; which can be harmful. The stress magnitude is dependent on waterbody mass and velocity; with the fish impact largely related to body size. Elevated shear stress occurs where rapidly flowing water passes near spillways, across screens, within turbine draft tubes or other passage routes. A flume was used to determine critical tolerances of silver shark (Balantiocheilos melanopterus) to different shear stress rates generated by a high velocity jet. Fish experienced higher levels of injury and mortality as shear stress was increased. Excessive shear forces had damaging impacts on fish. Mortality occurred at shear levels higher that 600/s. It is important that developers should attempt to model potential shear profiles expected during turbine passage in selected designs. These data will be critical to determine potential impacts on fish. If the likelihood of adverse impact is high, then alternative designs which have lower shear stress could be explored.

hydro power plants↗
DOE OSTI2020

A Method to Represent a Well in a Three‐Dimensional Discrete Fracture Network Model

Abstract In discrete fracture network (DFN) modeling, fractures are randomly generated and placed in the model domain. The rock matrix is considered impermeable. Small fractures and isolated fractures are often ignored to reduce computational expense. As a result, the rock matrix between fractures could be large and intersections may not be found between a well introduced in the model and the hydraulically connected fracture networks (fracture backbones). To overcome this issue, this study developed a method to conceptualize a well in a three‐dimensional (3D) DFN using two orthogonal rectangular fractures oriented along the well's axis. Six parameters were introduced to parameterize the well screen and skin zone, and to control the connectivity between the well and the fracture backbones. The two orthogonal fractures were discretized using a high‐resolution mesh to improve the quality of flow and transport simulations around and along the well. The method was successfully implemented within dfnWorks 2.0 (Hyman et al. 2015) to incorporate a well in a 3D DFN and to track particles leaving an injection well and migrating to a pumping well. Verification of the method against MODFLOW/MODPATH found a perfect match in simulated hydraulic head and particle tracking. Using three examples, the study showed that the method ensured the connectivity between wells and fracture backbones, and honored the physical processes of flow and transport along and around wells in DFNs. Recommendations are given for estimating the values of the six introduced well parameters in a real‐world case study.

Pham, Hai (ORCID:0000000310164394)↗
DOE OSTI2017

Linking plant hydraulics and beta diversity in tropical forests

In tropical forests, studies of xylem traits governing water transport through plants, or ‘hydraulic architecture’ (Tyree et al., 1991), and changes in species composition across environmental gradients, or ‘beta diversity’ (Gentry, 1988; Ackerly & Cornwell, 2007), have progressedmostly in parallel until recently (Hao et al., 2008; Bartlett et al., 2016). In this issue of New Phytologist, Cosme et al. (pp. 000–5 000) present a timely contribution to the intersection of plant hydraulic architecture (HA) with trait-based community ecology. Building on previous biogeographical work that demonstrated shifts in species composition (beta diversity) across a gradient from valleys to plateaus in central Amazonia (Schietti et al., 2014), Cosme et al. explore how variation in HA might underpin this sorting, sampling pairs of congeneric species restrictedmostly to either plateau or valley habitats. Valley species had significantly lower wood density and higher hydraulically-weighted vessel diameter and vessel area. By contrast, trees with some of the largest hydraulically-weighted vessel diameters existed in tall, deciduous plateau species, while the leaf: sapwood area ratio decreased with height in valley but not plateau species. These intriguing results suggest that species differentiation in water transport traits mediate edaphic filtering along the valley-toplateau gradient, in contrast to previous work where wood mechanical support mediated valley-to-plateau environmental filtering (Fortunel et al., 2014).

Christoffersen, Bradley↗
DOE OSTI2022

Let’s talk about sex: A biological variable in immune response against melanoma

As science culture gravitates toward a more holistic inclusion of both males and females in research design, the outlining of sex differences and their respective intersections with disease physiology and pathophysiology should see reciprocal expansion. Melanoma skin cancer, for example, has observed a female advantage in incidence, mortality, and overall survival since the early 1970s. The exact biological mechanism of this trend, however, is unclear and further complicated by a layering of clinical variables such as skin phototype, age, and body mass index. In this perspective, we highlight epidemiological evidence of sex differences in melanoma and summarize the landscape of their potential origin. Among several biological hallmarks, we make a note of sex-specific immune profiles—along with divergent hormonal regulation, social practices, DNA damage and oxidative stress responses, body composition, genetic variants, and X-chromosome expression—as probable drivers of disparity in melanoma initiation and progression. This review further focuses the conversation of sex as an influencing factor in melanoma development and its potential implication for disease management and treatment strategies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A Multirange Vehicle Speed Prediction With Application to Model Predictive Control-Based Integrated Power and Thermal Management of Connected Hybrid Electric Vehicles

Abstract Connectivity and automated driving technologies have opened up new research directions in the energy management of vehicles which exploit look-ahead preview and enhance the situational awareness. Despite this advancement, the vehicle speed preview that can be obtained from vehicle-to-vehicle/infrastructure (V2V/I) communications is often limited to a relatively short time-horizon. The vehicular energy systems, specifically those of the electrified vehicles, consist of multiple interacting power and thermal subsystems that respond over different time-scales. Consequently, their optimal energy management can greatly benefit from long-term speed prediction beyond that available through V2V/I communications. Accurately extending the look-ahead preview, on the other hand, is fundamentally challenging due to the dynamic nature of the traffic environment. To address this challenge, we propose a data-driven multirange vehicle speed prediction strategy for arterial corridors with signalized intersections, providing the vehicle speed preview for three different ranges, i.e., short-, medium-, and long-range. The short-range preview is obtained by V2V/I communications. The medium-range preview is realized using a neural network (NN), while the long-range preview is predicted based on a Bayesian network (BN). The predictions are updated in real-time based on the current state of traffic and incorporated into a multihorizon model predictive control (MH-MPC) for integrated power and thermal management (iPTM) of connected vehicles. The results of design and evaluation of the performance of the proposed data-informed MH-MPC for iPTM of connected hybrid electric vehicles (HEVs) using traffic data for real-world city driving are reported.

Automation & Control Systems↗