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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Inhibition of Micro-pitting by Tribofilm-Forming ZrO 2 Nanocrystal Lubricant Additives: A Micro-pitting Rig and Transmission Electron Microscope Study

The drive to reduce fuel consumption in transportation has encouraged the emergence of low viscosity lubricants to reduce viscous losses in the engine, drivetrain, and other components. However, viscosity reduction increases the risk of surface damage, thus motivating the development of new anti-wear (AW) additives. Capped ZrO 2 nanocrystals (NCs) in base oils have been shown to form AW tribofilms within microscale sliding contacts. However, the potential of ZrO 2 NCs to protect surfaces subjected to rolling-sliding contact from macroscale damage, such as micro-pitting, remains unexplored. Here, we explore the ability of ZrO 2 NCs to form protective tribofilms under harsh conditions using a micro-pitting rig (MPR), consisting of a three ring-on-roller configuration. The experiments were conducted in polyalphaolephin (PAO) base oil, with and without 5 nm diameter ZrO 2 NCs, at two levels of slide-to-roll ratio (SRR) (30% and 0%) and at variable test durations up to long durations (119 h). MPR results showed the use of ZrO 2 NCs gives rise to the formation of a tribofilm covering the roller surfaces and decreases the initiation and propagation of micro-pits compared to tests in pure PAO base stock. Transmission electron microscopy (TEM) performed on focused ion beam (FIB) milled cross-sectional samples of the roller surfaces revealed the growth of dense and 50–100 nm thick ZrO 2 -based tribofilms independent of (SRR), indicating the potential for robust micro-pitting fatigue protection. Nevertheless, small cracks localized within the near surface region of the roller tested at the most severe conditions (30% SRR and 119 h) were observed. The initiation of these cracks was directly related to the presence of manganese sulphide (MnS) inclusions in the steel, revealed using TEM combined with energy dispersive spectroscopy (EDS). Furthermore, the results highlight the benefits of the protective tribofilms formed by ZrO 2 NCs and suggest approaches for further optimizing their use.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A novel machine learning-based optimization algorithm (ActivO) for accelerating simulation-driven engine design

A novel design optimization approach (ActivO) that employs an ensemble of machine learning algorithms is presented. The proposed approach is a surrogate-based scheme, where the predictions of a weak leaner and a strong learner are utilized within an active learning loop. The weak learner is used to identify promising regions within the design space to explore, while the strong learner is used to determine the exact location of the optimum within promising regions. For each design iteration, exploration is done by randomly selecting evaluation points within regions where the weak learner-predicted fitness is high. The global optimum obtained by using the strong learner as a surrogate is also evaluated to enable rapid convergence once the most promising region has been identified. First, the performance of ActivO was compared against five other optimizers on a cosine mixture function with 25 local optima and one global optimum. In the second problem, the objective was to minimize indicated specific fuel consumption of a compression-ignition internal combustion (IC) engine while adhering to desired constraints associated with in-cylinder pressure and emissions. In this work, the efficacy of the proposed approach is compared to that of a genetic algorithm, which is widely used within the internal combustion engine community for engine optimization, showing that ActivO reduces the number of function evaluations needed to reach the global optimum, and thereby time-to-design by 80%. Furthermore, the optimization of engine design parameters leads to savings of around 1.9% in energy consumption, while maintaining operability and acceptable pollutant emissions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Experimental validation of an organic rankine-vapor compression cooling cycle using low GWP refrigerant R1234ze(E)

There is a significant global opportunity to capture and utilize low grade waste heat to reduce fossil fuel consumption, greenhouse gas emissions, and improve energy efficiency across a wide range of industries. In this work, an advanced type of thermally activated cooling system, an organic Rankine-vapor compression cycle (ORVC) with novel heat integration strategies, was designed and tested at a relevant scale for industrial waste heat recovery (300 kW th cooling capacity). The ORVC linked an organic Rankine power cycle and a vapor compression cooling cycle using a turbine and compressor that shared a single shaft. The ORVC test facility absorbed waste heat from a liquid stream at 91 °C to simulate engine coolant in diesel generator sets, rejected heat to a glycol stream at 30 °C, and generated chilled water at 7 °C. A cooling capacity of 264 kW ± 3.5 kW was experimentally validated with a COP of 0.56 ± 0.01 during steady-state operation at the design temperatures. The thermal efficiency, accounting for pump work, of the Rankine cycle was 7.7% ± 0.22% and the COP of the vapor compression cycle was 5.25 ± 0.09. The centrifugal turbo-compressor operated at 31.5 kRPM ± 0.3 kRPM, with a turbine and compressor isentropic efficiencies of 76.7% ± 0.90% and 84.8% ± 0.54%, respectively, with near-perfect power transmission between these components. The pressure drop in the piping and heat exchangers were significantly larger than expected which had a detrimental impact on the performance of the ORVC. In addition, the condenser on the cooling cycle could not deliver the subcooling as specified from the design point modeling. Furthermore, the results from the sensitivity analysis showed that the higher condenser glycol outlet temperature had the largest impact on performance, which is consistent with other analytical models in the literature. When the ORVC simulations were updated with experimental values for isentropic efficiencies of the turbomachinery, the thermal COP was 0.66 which represents an estimate of the predicted performance if test facility limitations are overcome.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2023

Enhancing flexibility for climate change using seasonal energy storage (aquifer thermal energy storage) in distributed energy systems

Long-term energy storage is expected to play a vital role in the deep decarbonization of building energy sectors, while enhancing the flexibility of buildings to withstand future climate variations. However, it is challenging to design distributed multi-energy systems (DMES) while taking into account the uncertainties introduced by climate change, since stochastic optimization of such systems is difficult. The present study introduces a stochastic optimization model to address this bottleneck, taking into account DMES including aquifer thermal energy storage (ATES) as the long-term thermal storage. For the first time, a novel optimization algorithm links ATES with the DMES optimization model with the support of a simplified geotechnical model. Subsequently, a case study was conducted, focusing on a residential district in Chicago where the impact of future climate condition, energy demand, and solar and wind energy potentials were evaluated using Weather Research and Forecasting (WRF) data (up to 2080) and the EnergyPlus model. The study revealed that ATES is an attractive way to improve the renewable energy penetration level and minimize the dependence on fossil fuels with reasonable support from the grid to assist the fluctuations in both demand and generation. Furthermore, ATES notably reduces fuel consumption and dependence while greatly enhancing the flexibility of the energy system to withstand fluctuations in demand and renewable energy generation brought by future climate variations. These qualities will make ATES an important part of distributed energy systems, even though it is not currently the lowest cost alternative due to lack of technology maturity. Furthermore, the design platform introduced in the present study can be used to design DMES enhancing flexibility to accommodate future climate variations.

25 ENERGY STORAGE↗
DOE OSTI2021

Direct Numerical Simulation of hydrogen combustion at auto-ignitive conditions: Ignition, stability and turbulent reaction-front velocity

Direct Numerical Simulations (DNS) are performed to investigate the process of spontaneous ignition of hydrogen flames at laminar, turbulent, adiabatic and non-adiabatic conditions. Mixtures of hydrogen and vitiated air at temperatures representing gas-turbine reheat combustion are considered. Adiabatic spontaneous ignition processes are investigated first, providing a quantitative characterization of stable and unstable flames. Results indicate that, in hydrogen reheat combustion, compressibility effects play a key role in flame stability and that unstable ignition and combustion are consistently encountered for reactant temperatures close to the mixture’s characteristic crossover temperature. Furthermore, it is also found that the characterization of the adiabatic processes is also valid in the presence of non-adiabaticity due to wall heat-loss. Finally, a quantitative characterization of the instantaneous fuel consumption rate within the reaction front is obtained and of its ability, at auto-ignitive conditions, to advance against the approaching turbulent flow of the reactants, for a range of different turbulence intensities, temperatures and pressure levels.

42 ENGINEERING↗
DOE OSTI2025

Optimizing on-ramp merging for connected and automated vehicles: A hierarchical approach using deep reinforcement learning and optimal control

On-ramp merging for Connected and Automated Vehicles (CAVs) presents significant challenges in dynamic traffic environments. Traditional methods and recent learning-based approaches often fail to simultaneously address decision-making complexity and execution precision under fluctuating conditions. This study introduces a novel hierarchical framework that combines: (1) a high-level Deep Reinforcement Learning (DRL) module that coordinates merging sequences through Virtual Traffic Signals (VTS) with Yield/Green phases and (2) a low-level optimal controller generating collision-free speed trajectories via pseudospectral convex optimization. A convolutional autoencoder compresses high-dimensional traffic states to enhance responsiveness. Extensive simulations demonstrate a 12.5% improvement in mainline throughput a 28% reduction in emergency braking events, and 31.66% lower fuel consumption compared to baseline methods. Furthermore, the framework’s effectiveness in coordinating CAV merges highlights its potential for real-world deployment. Future work will extend validation to multi-lane scenarios with mixed traffic and large-scale multiple merging points.

Connected and automated vehicles↗
DOE OSTI2020

Connected Vehicle-Based Traffic Signal Coordination

This study presents a connected vehicles (CVs)-based traffic signal optimization framework for a coordinated arterial corridor. The signal optimization and coordination problem are first formulated in a centralized scheme as a mixed-integer nonlinear program (MINLP). The optimal phase durations and offsets are solved together by minimizing fuel consumption and travel time considering an individual vehicle’s trajectories. Due to the complexity of the model, we decompose the problem into two levels: an intersection level to optimize phase durations using dynamic programming (DP), and a corridor level to optimize the offsets of all intersections. In order to solve the two-level model, a prediction-based solution technique is developed. The proposed models are tested using traffic simulation under various scenarios. Compared with the traditional actuated signal timing and coordination plan, the signal timing plans generated by solving the MINLP and the two-level model can reasonably improve the signal control performance. When considering varies vehicle types under high demand levels, the proposed two-level model reduced the total system cost by 3.8% comparing to baseline actuated plan. MINLP reduced the system cost by 5.9%. It also suggested that coordination scheme was beneficial to corridors with relatively high demand levels. For intersections with major and minor street, coordination conducted for major street had little impacts on the vehicles at the minor street.

42 ENGINEERING↗
DOE OSTI2024

Comparative Evaluation of Control-Oriented Heavy Duty Vehicle Air Drag Coefficient Models

Heavy-duty vehicles (HDVs) are a significant source of fuel consumption and greenhouse gas emissions, prompting solutions such as HDV platooning to mitigate these negative impacts through air drag reduction. The intervehicle distance in an HDV platoon needs to be carefully selected, such that the platoon-level energy efficiency and safety considerations can be well balanced. Underlying this problem lies in accurately modeling the relationship between HDV air drag coefficient and intervehicle distance. Through comprehensive evaluation and comparison, we analyze five control-oriented HDV air drag coefficient models, including the polynomial model, rational polynomial model, rational model, semi-quadratic model, and ridge model. Leveraging Scipy Curve-Fit toolbox and our previously compiled air drag coefficient datasets, we optimally identify the parameters inside each model. The calibrated models are then thoroughly evaluated via five complementary metrics. The comparison results reveal that the semi-quadratic model has the highest overall performance, while the widely adopted rational model only exhibits suboptimal performance.

Best, Micah↗
DOE OSTI2021

Molecular dynamics simulations of energy accommodation between gases and polymers for ultra-low thermal conductivity insulation

Determining the energy accommodation between gases and solids is essential to developing porous thermal insulation materials with ultra-low effective thermal conductivity that reduce energy use, greenhouse gas emissions, and fossil fuel consumption. The energy accommodation coefficients of most gases, however, have been rarely studied, especially with respect to solids that have relatively high thermal resistivity, e.g., polymers. Here, by using all-atom nonequilibrium molecular dynamics simulations, we reveal the accommodation coefficients of He, Ar, N 2 , and O 2 with polymers, mainly polystyrene. We find that their values are around 0.51, 0.72, 0.79, and 0.90, respectively, suggesting a critical reexamination of the commonly used theoretical maximum value of 1. We have also conducted experiments and validated the value for air, which is about 0.81. Such a change in accommodation coefficients can lead to a reduction of about 70%, 50%, 35%, and 20% in the thermal conductivity of He, Ar, N 2 , and O 2 gases in nano pores (below 100 nm) or at low pressures (below 1 millibar). With these new accommodation coefficients, we find that in a 10 nm pore with ambient pressure at 300 K, the gas thermal conductivity of He, Ar, N 2 , and O 2 in porous polystyrene can be as low as 9.7 × 10 –4 , 3.4 × 10 –4 , 7.3 × 10 –4 , and 8.5 × 10 –4 W·m –1 ·K –1 , respectively, which are two to three orders of magnitude lower than their bulk values, promising higher thermal resistivity of insulation materials. This work reveals the fundamental energy exchange between gases and polymers, providing important guidance for designing high-performance thermal insulation materials for various applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Scalability analysis of heavy-duty gas turbines using data-driven machine learning

With the increasing integration of variable renewable energy sources into power systems, the role of flexible power generation technologies like gas turbines (GT) in rapid grid balancing remains crucial. This sustained importance underscores the need for scaled and precise modeling of GT to ensure effective integration within evolving energy frameworks. While physics-driven GT models integrate thermodynamics, fluid dynamics, and combustion principles, they often rely on approximate mathematical representations to accommodate scaling that may not capture the actual complex dynamics for GTs and inertial effects associated to GTs with different ratings. In this study, a data-driven model is proposed using machine learning (ML) techniques to conduct GT scalability analysis and performance evaluation with high accuracy. The ML model, trained on data from various operating conditions and performance parameters, aims to uncover intricate relationships and patterns, resembling GT characteristics at different scales (ratings). The model is developed to capture complex system interaction and to adapt to changing operational scenarios at different capacities, providing valuable insights of power system dynamics. In this study, the real-time digital simulator platform was employed to generate training data for the ML model and assess its dynamic characteristics. The ultimate objective was to develop a detailed modeling framework based on governing equations and data-driven ML capable of predicting key performance indicators, in thermal systems such as GTs, including power output, speed, fuel consumption, and exhaust temperature under diverse operating conditions at different scales. The developed ML framework demonstrated high accuracy, with mean relative errors for GT power prediction, reference speed, exhaust temperature, and compressor pressure ratio (CPR) parameters consistently below 0.1% across typical load fluctuation scenarios. Maximum deviations were limited to approximately 0.5 K for exhaust temperature and 0.009 for CPR, underscoring the model’s ability to replicating dynamic GT behavior with high precision. The adaptability of the ML model enables its application across diverse operational conditions and its extension to other thermal systems. By leveraging advanced ML techniques, this study presents a robust and scalable modeling framework that enhances GT simulation precision, facilitating improved integration into evolving power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Styrene Thermal Decomposition and Its Reaction with Acetylene under Shock Tube Pyrolysis Conditions: an Experimental and Kinetic Modeling Study

Styrene is an important compound for polymer production and a key intermediate in gas-phase kinetics of polycyclic aromatic hydrocarbons (PAHs). For the first time, the pyrolysis of styrene with and without the presence of acetylene is investigated in a single-pulse shock tube coupled to gas chromatography and mass spectrometry. For each reaction system, quantitative speciation profiles are probed within the temperature range of 1100-1730 K, nominal pressure of 20 bar, and reaction duration of similar to 4 ms. A kinetic model is built to simulate the results. The model explains how styrene is consumed under high-pressure pyrolytic conditions, how the secondary chemistry of intermediate products affect subsequent PAH formation, and how acetylene addition alters the reaction pathways. Throughout the temperature range, styrene breakdown is dominated by the bimolecular interaction between styrene and hydrogen atom, which produces benzene+vinyl or phenyl and ethylene through the stabilization of 2-phenylethyl and its subsequent dissociation. As a result, large amounts of phenyl accumulate, which react with styrene to form C14H12 species while simultaneously releasing H atoms through addition/elimination reactions. The reactivity of fuel consumption is preserved by the regeneration of H atoms as chain carriers. Several C14H10 compounds are formed as a result of the following breakdown of the C14H12 isomers, particularly stilbene, 1,1-diphenyl ethylene, and 9-methyl-9H-fluorene. The presence of acetylene as a co-reactant with styrene allows the Hydrogen-Abstraction-Acetylene-Addition (HACA) pathway to proceed from phenyl radical to enhance the production of phenylacetylene at very low temperatures and acenaphthylene. This hinders the formation of C14H12 isomers, exclusive products from pure styrene dissociation by competing with the styrene+phenyl routes.

Kinetic Modeling↗
DOE OSTI2024

High resolution numerical simulations of methane pool fires using adaptive mesh refinement

The ability to accurately predict the structure and dynamics of pool fires using computational simulations is of great interest in a wide variety of applications, including accidental and wildland fires. However, the presence of physical processes spanning a broad range of spatial and temporal scales poses a significant challenge for simulations of such fires, particularly at conditions near the transition between laminar and turbulent flow. Here, in this study, we examine the transition to turbulence in methane pool fires using high-resolution simulations with multi-step finite rate chemistry, where adaptive mesh refinement (AMR) is used to directly resolve small-scale flow phenomena. We perform three simulations of methane pool fires, each with increasing diameter, corresponding to increasing inlet Reynolds and Richardson numbers. As the diameter increases, the flow transitions from organized vortex roll-up via the puffing instability to much more chaotic mixing associated with finger formation along the shear layer and core collapse near the inlet. These effects combine to create additional mixing close to the inlet, thereby enhancing fuel consumption and causing more rapid acceleration of the fluid above the pool. We also make comparisons between the transition to turbulence and core collapse in the present pool fires and in inert helium plumes, which are often used as surrogates for the study of buoyant reacting flows.

42 ENGINEERING↗
DOE OSTI2024

Assessment of the polygeneration approach in wastewater treatment plants for enhanced energy efficiency and green hydrogen/ammonia production

Wastewater treatment plants (WWTPs) offer opportunities to optimize resource utilization and enhance energy efficiency. Here, this study provides a comprehensive analysis of using the polygeneration approach in WWTPs to reduce grid energy dependence, optimize energy distribution, and utilize surplus energy for hydrogen (H 2 ) and ammonia (NH 3 ) production. Several models were employed, including photovoltaic (PV) cells, parabolic trough collectors (PTCs), steam methane reforming, and polymer electrolyte membranes, to assess the feasibility of this approach. Three scenarios were evaluated and compared: Scenario 1 (Baseline) represents the current situation, Scenario 2 maximizes the Net Present Value (NPV), and Scenario 3 minimizes NH 3 production costs. Real data from As-Samra WWTP in Jordan was used to accurately assess the feasibility of each scenario. The results show that Scenario 2 offers the highest profitability and efficiency, with a NPV of 87.48 million USD and an annual NH 3 production of 15,417 tons, reducing both grid dependency and biogas fuel consumption. Both Scenarios 2 and 3 demonstrate the ability to meet thermal demands efficiently while generating significant revenue from NH 3 production. Scenario 3, in particular, achieves competitive H 2 and NH 3 production costs. Environmentally, Scenario 2 significantly reduces annual greenhouse gas emissions by 12.66 kilotons of CO 2eq , with near-zero carbon intensity for thermal energy due to solar reliance. In conclusion, the polygeneration approach offers a promising pathway for WWTPs to achieve greater sustainability, economic gains, and reduced environmental impact, providing valuable insights for decision-makers.

42 ENGINEERING↗
DOE OSTI2023

Engineering bimetallic interfaces and revealing the mechanism for carbon dioxide electroreduction to C 3+ liquid chemicals

Reduction reaction of CO 2 (CO 2 RR) to liquid C 3+ chemicals is a net-zero-carbon process and can increase local resiliency to power outages and fuel consumption. The mechanism and the catalyst design rules of CO 2 RR-to-C 3+ are unknown. Engineering bimetallic interface (e.g., Pd/Au) to tune the intermediate adsorption is promising for promoting C 3+ formation. Our density functional theory (DFT) calculations find that *CH 2 could be the key intermediate and C 1 -CH 2 coupling could be the rate-limiting step to generate C 3+ . High CO surface coverages can promote the bimetallic interfacial sites, lower the energetics of the C 1 -CH 2 coupling step, and enhance the C 3+ formation. We further construct a volcano plot of C 1 -CH 2 kinetics as a function of the binding strength of key intermediate *CH 2 via engineering the d-band center of the interfacial site. Our findings could guide the rational design of bimetallic interfaces and their near-surface microenvironment for enhancing CO 2 RR-to-C 3+ .

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Sensitivity of Anthropogenic Cloud Droplet Number Change to Preindustrial Emission Inventories and Physics Parameterizations

Understanding the preindustrial (PI) to present-day (PD) change in cloud droplet number concentration (ΔNd) is important to constrain the anthropogenic influence on clouds and aid future model projections. Perturbed parameter ensemble simulations of PI and PD conditions in the Energy Exascale Earth System Model (E3SMv3) reveal two locations with consistent negative ΔNd signal across parameter space–the Southeastern US and the United Kingdom–despite a trend of positive ΔNd globally from increased fossil fuel consumption. These negative ΔNd signals are driven by higher anthropogenic biofuel emissions in the PI. By varying the PI aerosol mass and/or number emissions across these data sets' uncertainty ranges, we can change the magnitude and sign of ΔNd and the effective radiative forcing from aerosol-cloud interactions (ERFaci) regionally and globally, with near-zero ΔNd leading to near-zero ERFaci. These results highlight the need to constrain PI emission uncertainty to better understand Earth system responses to aerosol-cloud interactions.

Brown, Hunter Y. [University of Wyoming, Laramie, ↗
DOE OSTI2023

Mechanistic origins of corrosion protection of aluminum alloys by graphene/polyetherimide nanocomposite coatings

Light-weighting vehicular components through adoption of light-metal structural alloys holds promise for reducing the fuel consumption of internal combustion engine vehicles and increasing the range of battery electric vehicles. However, the alloyed microstructure and surface precipitates of aluminum alloys render these materials susceptible to corrosion under modest excursions from neutral pH. Traditional chromium-based anodic passivation layers are subject to increasingly stringent environmental regulations, whereas options for sacrificial cathodic films are sparse for electropositive metals. While hybrid nanocomposite coatings have shown initial promise, mechanistic underpinnings remain poorly understood. Here, a fully imidized polyetherimide (PEI) resin is utilized as the continuous phase with inclusion of unfunctionalized exfoliated graphite (UFG). A comprehensive investigation of the mechanisms of corrosion protection reveals key fundamental design principles underpinning corrosion inhibition. First, strong interfacial adhesion, which for PEI is facilitated by binding of imide carbonyl moieties to Lewis acidic sites on Al surfaces. Second, the miscibility of ion-impervious nanoscopic UFG fillers and stabilization of a substantial interphase region at UFG/PEI boundaries that result in minimizing the free volume at the filler/polymer interface. Finally, extended tortuosity of ion diffusion pathways imbued by the below-percolation-threshold 2D fillers. These three design principles help govern and modulate ion transport from electrolyte/coating interfaces to the coating/metal interface and are crucial for the extended preservation of barrier properties. The results suggest an approach to systematically activate multiple modes of corrosion inhibition through rational design of hybrid nanocomposite coatings across hard-to-abate sectors where light metal alloys are likely to play an increasingly prominent role.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Mapping classes of carbon

With rising fossil fuel consumption and ongoing land cover change, humanity is burning through its remaining carbon budget. Here, recent work puts a ‘Do not disturb’ sign on biospheric carbon we can’t afford to lose.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Connected Traffic Signal Coordination Optimization Framework through Network-Wide Adaptive Linear Quadratic Regulator–Based Control Strategy

Traffic congestion in metropolitan areas causes several significant challenges, such as longer travel times, decreased productivity, increased fuel consumption and vehicle emissions, and even severe injuries during crashes. Traffic signal control is a management approach to reduce traffic congestion and allocate the appropriate right of way for safety and mobility efficiency, both in temporal and spatial domains. Here, this study proposes a network-wide adaptive signal control coordination optimization framework based on the linear quadratic regulator algorithm. The traffic flow conditions driven by signal control inputs are formulated based on their network-wide state-space representation. After modeling traffic control regulation constraints, an adaptive linear quadratic regulator algorithm is designed to maximize the network-wide total throughput under the current conditions. Optimal signal control split time durations for multiple intersections in the network are derived by solving the algebraic Riccati equation. Furthermore, the recursive least square parameter estimation method is employed to quantify dynamic traffic condition changes. To verify the effectiveness of this proposed signal control framework, both simulation and real-world experimental tests are conducted for multiple intersections in downtown Chattanooga, Tennessee, United States. In preparation for real-world experimental tests, pipelines for real-time data processing implementation and historical traffic flow data analysis are conducted. The test results demonstrate that the proposed control framework achieves a decrease in travel time by up to 19.4%, total time spent (TTS) by up to 11.9%, and relative queue balance (RQB) by up to 15.6%. The research findings indicate that the proposed signal control framework can be generalized to handle large scale signal control optimization network-wide.

97 MATHEMATICS AND COMPUTING↗