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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Neural network kinetics for exploring diffusion multiplicity and chemical ordering in compositionally complex materials

Diffusion involving atom transport from one location to another governs many important processes and behaviors such as precipitation and phase nucleation. The inherent chemical complexity in compositionally complex materials poses challenges for modeling atomic diffusion and the resulting formation of chemically ordered structures. Here, we introduce a neural network kinetics (NNK) scheme that predicts and simulates diffusion-induced chemical and structural evolution in complex concentrated chemical environments. The framework is grounded on efficient on-lattice structure and chemistry representation combined with artificial neural networks, enabling precise prediction of all path-dependent migration barriers and individual atom jumps. To demonstrate the method, we study the temperature-dependent local chemical ordering in a refractory NbMoTa alloy and reveal a critical temperature at which the B2 order reaches a maximum. The atomic jump randomness map exhibits the highest diffusion heterogeneity (multiplicity) in the vicinity of this characteristic temperature, which is closely related to chemical ordering and B2 structure formation. The scalable NNK framework provides a promising new avenue to exploring diffusion-related properties in the vast compositional space within which extraordinary properties are hidden.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Emergence of catalytic function in prebiotic information-coding polymers

Life as we know it relies on the interplay between catalytic activity and information processing carried out by biological polymers. Here we present a plausible pathway by which a pool of prebiotic information-coding oligomers could acquire an early catalytic function, namely sequence-specific cleavage activity. Starting with a system capable of non-enzymatic templated replication, we demonstrate that even non-catalyzed spontaneous cleavage would promote proliferation by generating short fragments that act as primers. Furthermore, we show that catalytic cleavage function can naturally emerge and proliferate in this system. Specifically, a cooperative catalytic network with four subpopulations of oligomers is selected by the evolution in competition with chains lacking catalytic activity. The cooperative system emerges through the functional differentiation of oligomers into catalysts and their substrates. The model is inspired by the structure of the hammerhead RNA enzyme as well as other DNA- and RNA-based enzymes with cleavage activity that readily emerge through natural or artificial selection. We identify the conditions necessary for the emergence of the cooperative catalytic network. In particular, we show that it requires the catalytic rate enhancement over the spontaneous cleavage rate to be at least 10 2 –10 3 , a factor consistent with the existing experiments. The evolutionary pressure leads to a further increase in catalytic efficiency. The presented mechanism provides an escape route from a relatively simple pairwise replication of oligomers toward a more complex behavior involving catalytic function. This provides a bridge between the information-first origin of life scenarios and the paradigm of autocatalytic sets and hypercycles, albeit based on cleavage rather than synthesis of reactants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Emergence of catalytic function in prebiotic information-coding polymers

Life as we know it relies on the interplay between catalytic activity and information processing carried out by biological polymers. Here we present a plausible pathway by which a pool of prebiotic information-coding oligomers could acquire an early catalytic function, namely sequence-specific cleavage activity. Starting with a system capable of non-enzymatic templated replication, we demonstrate that even non-catalyzed spontaneous cleavage would promote proliferation by generating short fragments that act as primers. Furthermore, we show that catalytic cleavage function can naturally emerge and proliferate in this system. Specifically, a cooperative catalytic network with four subpopulations of oligomers is selected by the evolution in competition with chains lacking catalytic activity. The cooperative system emerges through the functional differentiation of oligomers into catalysts and their substrates. The model is inspired by the structure of the hammerhead RNA enzyme as well as other DNA- and RNA-based enzymes with cleavage activity that readily emerge through natural or artificial selection. We identify the conditions necessary for the emergence of the cooperative catalytic network. In particular, we show that it requires the catalytic rate enhancement over the spontaneous cleavage rate to be at least 10 2 –10 3 , a factor consistent with the existing experiments. The evolutionary pressure leads to a further increase in catalytic efficiency. The presented mechanism provides an escape route from a relatively simple pairwise replication of oligomers toward a more complex behavior involving catalytic function. This provides a bridge between the information-first origin of life scenarios and the paradigm of autocatalytic sets and hypercycles, albeit based on cleavage rather than synthesis of reactants.

Tkachenko, Alexei V. (ORCID:000000031291243X)↗
NASA NTRS2013

An Investigation of the Kinematic and Microphysical Control of Lightning Rate, Extent and NOX Production using DC3 Observations and the NASA Lightning Nitrogen Oxides Model (LNOM)

The Deep Convective Clouds and Chemistry (DC3) experiment seeks to quantify the relationship between storm physics, lightning characteristics and the production of nitrogen oxides via lightning (LNOx). The focus of this study is to investigate the kinematic and microphysical control of lightning properties, particularly those that may govern LNOx production, such as flash rate, type and extent across Alabama during DC3. Prior studies have demonstrated that lightning flash rate and type is correlated to kinematic and microphysical properties in the mixed‐phase region of thunderstorms such as updraft volume and graupel mass. More study is required to generalize these relationships in a wide variety of storm modes and meteorological conditions. Less is known about the co‐evolving relationship between storm physics, morphology and three‐dimensional flash extent, despite its importance for LNOx production. To address this conceptual gap, the NASA Lightning Nitrogen Oxides Model (LNOM) is applied to North Alabama Lightning Mapping Array (NALMA) and Vaisala National Lightning Detection NetworkTM (NLDN) observations following ordinary convective cells through their lifecycle. LNOM provides estimates of flash rate, flash type, channel length distributions, lightning segment altitude distributions (SADs) and lightning NOx production profiles. For this study, LNOM is applied in a Lagrangian sense to multicell thunderstorms over Northern Alabama on two days during DC3 (21 May and 11 June 2012) in which aircraft observations of NOx are available for comparison. The LNOM lightning characteristics and LNOX production estimates are compared to the evolution of updraft and precipitation properties inferred from dual‐Doppler and polarimetric radar analyses applied to observations from a nearby radar network, including the UAH Advanced Radar for Meteorological and Operational Research (ARMOR). Given complex multicell evolution, particular attention is paid to storm morphology, cell mergers and possible dynamical, microphysical and electrical interaction of individual cells when testing various hypotheses.

Carey, Lawrence↗
NASA NTRS2006

Following the Water: the Evolution of Ice-forming Regions in the Early Solar Nebula

The abundances of water-vapor and water-ice during the first ten million years of the protoplanetary solar nebula are simulated using a new condensation/sublimation model. This study builds on a "snow line" model reported in ApJ 627 L153 (2005); it uses a simple phenomenological model where water vapor molecules evolve from solar atomic abundance and eventually condenses to ice at colder points in the nebula once the water-vapor partial pressure exceeds a value determined by the phase diagram for water. The synthesis of water vapor from elementary species is modeled with a chemical network consisting of about 400 species and 4000 reactions. The evolution of the icy zone (and its relative abundance of solid ice) is traced from a limited region in the early hotter disk to its final state at the time when the gas is expelled and a planetary system begins to form. Possible effects of this dynamic motion on disk chemistry and organic molecule formation are also described.

Davis, Sanford S.↗
NASA NTRS2017

On the Development and Application of High Data Rate Architecture (HiDRA) in Future Space Networks

Historically, space missions have been severely constrained by their ability to downlink the data they have collected. These constraints are a result of relatively low link rates on the spacecraft as well as limitations on the time during which data can be sent. As part of a coherent strategy to address existing limitations and get more data to the ground more quickly, the Space Communications and Navigation (SCaN) program has been developing an architecture for a future solar system Internet. The High Data Rate Architecture (HiDRA) project is designed to fit into such a future SCaN network. HiDRA's goal is to describe a general packet-based networking capability which can be used to provide assets with efficient networking capabilities while simultaneously reducing the capital costs and operational costs of developing and flying future space systems.Along these lines, this paper begins by reviewing various characteristics of modern satellite design as well as relevant characteristics of emerging technologies (such as free-space optical links capable of working at 100+ Gbps). Next, the paper describes HiDRA's design, and how the system is able to both integrate and support the operation of not only today's high-rate systems, but also the high-rate systems likely to be found in the future. This section also explores both existing and future networking technologies, such as Delay Tolerant Networking (DTN) protocol (RFC4838 citeRFC:1, RFC5050citeRFC:2), and explains how HiDRA supports them. Additionally, this section explores how HiDRA is used for scheduling data movement through both proactive and reactive link management. After this, the paper moves on to explore a reference implementation of HiDRA. This implementation is currently being realized based on a Field Programmable Gate Array (FPGA) memory and interface controller that is itself controlled by a local computer running DTN software. Next, this paper explores HiDRA's natural evolution, which includes an integration path for software-defined networking (SDN) switches. This section also describes considerations for both near-Earth and deep-space instantiations of HiDRA, describing how differences in latencies between the environments will necessarily influence how the system is configured and the networks operate. Finally, this paper describes future work. This section includes a description of a potential ISS implementation which will allow rapid advancement through the technology readiness levels (TRL). This section also explores work being done to support HiDRA's successful implementation and operation in a heterogeneous network: such a network could include communications equipment spanning many vintages and capabilities, and one significant aspect of HiDRA's future development involves balancing compatibility with capability.

DTN↗
NASA NTRS2015

LightForce Photon-pressure Collision Avoidance: Efficiency Analysis in the Current Debris Environment and Long-Term Simulation Perspective

This work provides an efficiency analysis of the LightForce space debris collision avoidance scheme in the current debris environment and describes a simulation approach to assess its impact on the long-term evolution of the space debris environment. LightForce aims to provide just-in-time collision avoidance by utilizing photon pressure from ground-based industrial lasers. These ground stations impart minimal accelerations to increase the miss distance for a predicted conjunction between two objects. In the first part of this paper we will present research that investigates the short-term effect of a few systems consisting of 10kW class lasers directed by 1.5 m diameter telescopes using adaptive optics. The results found such a network of ground stations to mitigate more than 85 percent of conjunctions and could lower the expected number of collisions in Low Earth Orbit (LEO) by an order of magnitude. While these are impressive numbers that indicate LightForce's utility in the short-term, the remaining 15 percent of possible collisions contain (among others) conjunctions between two massive objects that would add large amount of debris if they collide. Still, conjunctions between massive objects and smaller objects can be mitigated. Hence we choose to expand the capabilities of the simulation software to investigate the overall effect of a network of LightForce stations on the long-term debris evolution. In the second part of this paper, we will present the planed simulation approach for that effort.

conjunction analysis↗
NASA NTRS2016

Lightforce Photon-Pressure Collision Avoidance: Efficiency Analysis in the Current Debris Environment and Long-Term Simulation Perspective

This work provides an efficiency analysis of the LightForce space debris collision avoidance scheme in the current debris environment and describes a simulation approach to assess its impact on the long-term evolution of the space debris environment. LightForce aims to provide just-in-time collision avoidance by utilizing photon pressure from ground-based industrial lasers. These ground stations impart minimal accelerations to increase the miss distance for a predicted conjunction between two objects. In the first part of this paper we will present research that investigates the short-term effect of a few systems consisting of 20-kilowatt-class lasers directed by 1.5-meter-diameter telescopes using adaptive optics. The results found such a network of ground stations to mitigate more than 85 percent of conjunctions and could lower the expected number of collisions in Low Earth Orbit (LEO) by an order of magnitude. While these are impressive numbers that indicate LightForce's utility in the short-term, the remaining 15 percent of possible collisions contain (among others) conjunctions between two massive objects that would add large amount of debris if they collide. Still, conjunctions between massive objects and smaller objects can be mitigated. Hence, we choose to expand the capabilities of the simulation software to investigate the overall effect of a network of LightForce stations on the long-term debris evolution. In the second part of this paper, we will present the planned simulation approach for that effort. For the efficiency analysis of collision avoidance in the current debris environment, we utilize a simulation approach that uses the entire Two Line Element (TLE) catalog in LEO for a given day as initial input. These objects are propagated for one year and an all-on-all conjunction analysis is performed. For conjunctions that fall below a range threshold, we calculate the probability of collision and record those values. To assess efficiency, we compare a baseline (without collision avoidance) conjunction analysis with an analysis where LightForce is active. Using that approach, we take into account that collision avoidance maneuvers could have effects on third objects. Performing all-on-all conjunction analyses for extended period of time requires significant computer resources; hence we implemented this simulation utilizing a highly parallel approach on the NASA Pleiades supercomputer.

space debris mitigation↗
DOE OSTI2023

Early self-organization of fission gas bubble superlattice formation in neutron-irradiated monolithic U-10Mo fuels

Self-organization of defect superlattices in far-from-equilibrium systems presents a promising way to mitigate swelling concerns in nuclear materials. The gas bubble superlattice (GBS) is a highly ordered, three-dimensional complex defect structure that can retain fission gasses in Uranium-Molybdenum (U-Mo) fuels. Transmission electron microscopy (TEM) investigation of monolithic U-10Mo fuel irradiated to 1.15 × 10 21 fissions/cm 3 and 1.30 × 10 21 fissions/cm 3 revealed that early-stage ordering preferentially occurs at the grain boundaries (GB) and that the critical bubble size for complete ordering is ~3 nm. Once formed at the GB, the GBS extends towards the grain interior; however, the spread in distance from the GB varies likely depending on the type and strength of the GB sink. TEM results also showed a possible correlation between the growth and evolution of the intragranular disordered bubbles and large dislocation networks. The fission product distribution in and outside of the GBS was also investigated confirming the presence of xenon in the GBS, as well as other fission products including cesium, barium, lanthanum, and cerium.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Segmental Dynamics and Vitrification in Associating Copolymer Melts: Role of Cluster Formation, Microdomains, and Cross-Linking

A microscopic statistical mechanical theory of the structure, self-assembly, and activated segmental relaxation is employed to study associating copolymer melts with high attractive sticker fractions, local clustering, and disordered microphase ordering. The stickers are dynamically pinned in a manner that does not affect equilibrium structure which mimics the much slower physical bond breaking process or postassembly cross-linking of sticky monomers. Local sticker clustering and microdomain spatial correlations significantly modify the activated relaxation of nonstickers and glass transition temperature, T g . A re-entrant glass-melting feature is predicted as sticker attraction strength is initially increased corresponding to a speed up of segmental relaxation, and hence reduction of T g relative to the cross-linked homopolymer network. A mechanistic analysis reveals three competing effects: a purely kinetic slowing down of nonstickers down due to cross-linking, disordering of the nonsticker local cage and weakening of effective forces they experience due to sticker physical clustering, and a longer range impact of microdomain scale correlations that results in nonmonotonic dynamical effects. At high enough attraction strength, a qualitative change emerges corresponding to a sticker fraction dependent elevation of T g , which eventually surpasses that of the cross-linked homopolymer network. Dynamically, the new physics arises from a complex evolution of the amplitude of the collective elastic field that dresses the large amplitude mobile segment hopping within a coupled local-nonlocal description of the alpha relaxation. Furthermore, the results are qualitatively consistent with recent experiments on associating PDMS and PPG telechelics of fixed sticker fraction but with chemically different end groups of variable attraction strengths. Finally, possible tests using simulation and the influence of material or model specific interaction potentials and other real world complications are discussed.

Copolymers↗
DOE OSTI2025

Deep Reinforcement Learning for Distribution System Operations: A Tutorial and Survey

Here, the rapid evolution of modern electric power distribution systems into complex networks of interconnected active devices, distributed generation (DG), and storage poses increasing difficulties for system operators. The large-scale integration of distributed energy resources (DERs) and the rapid exchange of measurement data via communication networks present major opportunities for advancing grid operations but also introduce greater uncertainty, higher data dimensionality, more complex network and device models, and challenging control and optimization problems. Deep reinforcement learning (DRL) algorithms are promising in addressing these challenges. However, they have not been effectively adapted for power systems applications, requiring extensive customization for implementation and evaluation. This has resulted in reproducibility challenges and a steep learning curve for researchers new to applying DRL algorithms to the power systems domain. To bridge these gaps, this tutorial aims to serve as a valuable resource for researchers interested in exploring learning-based algorithms to operate active power distribution networks. Specifically, this work presents a generalized process for translating sequential decision-making problems in power distribution systems into Markov decision process (MDP) formulations, illustrated through concrete grid service examples. Additionally, we introduce a simple environment design strategy to develop and evaluate example DRL algorithms for distribution system applications, complete with an included code repository to guide users through environment construction.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Robust Restoration From Cyber-Physical Attacks in Active Distribution Grids With Grid-Edge IBRs

The inverter-based resources (IBRs) have enabled the integration of renewable energy at the grid edge with enhanced control capabilities to support the reliable operation of power grids. Different control frameworks, such as hierarchical or distributed architecture, have been proposed with the expansion of cyber networks for real-time monitoring and control. This evolution of critical infrastructure into cyber-physical systems also brings more vulnerabilities for the broadened attack surfaces, and significantly increases the possibility of physical system failures or outages caused by cyberattacks. Among tremendous efforts in the defense-in-depth approach, it remains challenging to provide prompt detection and accurate location of attack entry points or paths. Therefore, the prevailing restoration framework may struggle to fully consider the cyber-physical interdependence, successfully isolate the compromised cyber and physical components, and safely recover the systems without the potential risks leading to secondary outages. This paper is motivated to develop a cyber-physical restoration framework for distribution grids to recover from cyber attacks by harnessing grid-edge IBRs. The framework is first built on the operational guidelines of IBRs considering the compromised cyber layer. Then, an ambiguity set is established to represent the uncertainty of attack scenarios and their possibility levels. Next, a distributionally robust optimization model is developed to provide the optimal load restoration strategy across all scenarios. The effectiveness of the proposed model is demonstrated through various use cases on the modified IEEE 13-node and 123-node test systems. Finally, simulation results demonstrate the effectiveness and advancement of developed post-attack restoration strategies.

Cybersecurity↗
DOE OSTI2022

Neural Phase Simulation

The Neural Phase Simulation (NPS) is a package of codes for simulating microstructure evolution and accelerated molecular dynamics with deep neural-networks based surrogate models. NPS is designed to offer quantitatively accurate and computationally efficient simulation capabilities by leveraging modern machine-learning techniques. The primary intended use cases of NPS are training neural network surrogate models, though performing simulations on a single node is also supported. The NPS surrogate models can be trained from ground truth simulation methods, which are supposed to be accurate but expensive, such as molecular dynamics, phase field methods, kinetic Monte Carlo and discrete dislocation dynamics.

Zhou, Fei↗
DOE OSTI2023

Wind Turbine Generator Reliability Analysis to Reduce Operations and Maintenance (O&M) Costs

Wind turbine major systems (blades, pitch, main bearing, gearbox, and generator) are integrated into a composite system. Specifications for these systems and components are developed to achieve symmetry of operation, avoiding negative interaction. For instance, the main bearing, gearbox, and generator (drivetrain) components are interdependent, functioning in unison for efficient energy production. Hence, wind resource and grid interactions affecting the drivetrain impact the performance and reliability of the turbine generator. This paper discusses generator reliability covering the technology evolution over the last 20 years. EPRI's Wind Network for Enhanced Reliability (WinNER) web-based tool and Shermco Industries databases are presented, and conclusions are drawn regarding failures specific to generator design, manufacturing, and operating conditions. Additionally, this paper compares the life expectancy of stator-fed configurations and doubly fed generator systems.

17 WIND ENERGY↗
DOE OSTI2022

A PDE Model of Breast Tumor Progression in MMTV-PyMT Mice

The evolution of breast tumors greatly depends on the interaction network among different cell types, including immune cells and cancer cells in the tumor. This study takes advantage of newly collected rich spatio-temporal mouse data to develop a data-driven mathematical model of breast tumors that considers cells’ location and key interactions in the tumor. The results show that cancer cells have a minor presence in the area with the most overall immune cells, and the number of activated immune cells in the tumor is depleted over time when there is no influx of immune cells. Interestingly, in the case of the influx of immune cells, the highest concentrations of both T cells and cancer cells are in the boundary of the tumor, as we use the Robin boundary condition to model the influx of immune cells. In other words, the influx of immune cells causes a dominant outward advection for cancer cells. We also investigate the effect of cells’ diffusion and immune cells’ influx rates in the dynamics of cells in the tumor micro-environment. Sensitivity analyses indicate that cancer cells and adipocytes’ diffusion rates are the most sensitive parameters, followed by influx and diffusion rates of cytotoxic T cells, implying that targeting them is a possible treatment strategy for breast cancer.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2008

Potential Use of a Bayesian Network for Discriminating Flash Type from Future GOES-R Geostationary Lightning Mapper (GLM) data

Continuous monitoring of the ratio of cloud flashes to ground flashes may provide a better understanding of thunderstorm dynamics, intensification, and evolution, and it may be useful in severe weather warning. The National Lighting Detection Network TM (NLDN) senses ground flashes with exceptional detection efficiency and accuracy over most of the continental United States. A proposed Geostationary Lightning Mapper (GLM) aboard the Geostationary Operational Environmental Satellite (GOES-R) will look at the western hemisphere, and among the lightning data products to be made available will be the fundamental optical flash parameters for both cloud and ground flashes: radiance, area, duration, number of optical groups, and number of optical events. Previous studies have demonstrated that the optical flash parameter statistics of ground and cloud lightning, which are observable from space, are significantly different. This study investigates a Bayesian network methodology for discriminating lightning flash type (ground or cloud) using the lightning optical data and ancillary GOES-R data. A Directed Acyclic Graph (DAG) is set up with lightning as a "root" and data observed by GLM as the "leaves." This allows for a direct calculation of the joint probability distribution function for the lighting type and radiance, area, etc. Initially, the conditional probabilities that will be required can be estimated from the Lightning Imaging Sensor (LIS) and the Optical Transient Detector (OTD) together with NLDN data. Directly manipulating the joint distribution will yield the conditional probability that a lightning flash is a ground flash given the evidence, which consists of the observed lightning optical data [and possibly cloud data retrieved from the GOES-R Advanced Baseline Imager (ABI) in a more mature Bayesian network configuration]. Later, actual GLM and NLDN data can be used to refine the estimates of the conditional probabilities used in the model; i.e., the Bayesian network is a learning network. Methods for efficient calculation of the conditional probabilities (e.g., an algorithm using junction trees), finding data conflicts, goodness of fit, and dealing with missing data will also be addressed.

Solakiewiz, Richard↗
DOE OSTI2025

DS-TIDE: Harnessing Dynamical Systems for Efficient Time-Independent Differential Equation Solving

Time-Independent Differential Equations (TIDEs) are central to modeling equilibrium behavior across a wide range of scientific and engineering domains, from electrostatics to porous media flow. Conventional numerical solvers offer reliable solutions but incur significant computational costs due to fine-grained discretization and iterative procedures. Machine learning-based approaches address this by replacing iterative solving processes with one-time inference; however, their sophisticated models require extensive training resources that often exceed those of traditional solvers. Consequently, designing a TIDE solver that achieves high accuracy, broad applicability, and exceptional computational efficiency remains a fundamental challenge. In this paper, we propose DS-TIDE, a novel hardware solver that is inspired by, and subsequently leverages, the intrinsic connection between Dynamical Systems (DS) and Differential Equations (DEs) to efficiently and accurately solve TIDEs. DS-TIDE employs a CMOS-compatible DS-based processor, whose physical states evolve under carefully designed DE-driven dynamics and naturally converge to equilibrium -- the solution of the target TIDE -- within ~1µs on a ~1-watt DS-TIDE processor. To enhance expressivity, DS-TIDE incorporates Heterogeneous Dynamics with Temporal Layering (HDTL), which solves TIDEs through a three-stage DS evolution -- conditioning, solving, and decoding -- each governed by specialized dynamics. The entire evolution process is analogous to an infinitely deep neural network temporally unrolled, offering the system the capability of representing complex equations. Furthermore, DS-TIDE is equipped with an on-device DS-DE Auto-Alignment mechanism that dynamically adapts intrinsic hardware dynamics within milliseconds, effectively aligning the system’s dynamics to diverse target DEs. Experimental results across TIDEs from a wide range of scientific and engineering domains demonstrate that DS-TIDE achieves ~10^3× speedup, ~10^5× energy savings, and competitive or superior accuracy compared to state-of-the-art numerical and ML-based solvers.

Liu, Chuan↗
DOE OSTI2023

Emissions minimization on road networks via Generic Second Order Models

In this paper we consider the problem of estimating emissions due to vehicular traffic on complex networks, and minimizing their effect by regulating traffic at junctions. For the traffic evolution, we consider a Generic Second Order Model, which encompasses the majority of two-equations (i.e., second-order) models available in the literature, and extend it to road networks with merge and diverge junctions. The dynamics on the whole network is determined by selecting a solution to the Riemann Problems at junctions, i.e., the Cauchy problems with constant initial data on each incident road. The latter are solved by assuming the maximization of the flow and assigning a traffic distribution coefficient for outgoing roads of diverges, and a priority rule for incoming roads of merges. A general emission model is considered and its parameters are tuned to the $ {\mathrm{NO_{x}}} $ emission rate. The minimization of emissions is then formulated in terms of the traffic distribution and priority parameters, taking into account travel times. A comparison is provided between roundabouts with optimized parameters and traffic lights, which correspond to time-varying traffic priorities. Our approach can be adapted to manage traffic in complex networks in order to reduce emissions while keeping travel time at acceptable levels.

33 ADVANCED PROPULSION SYSTEMS↗