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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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Selective and Efficient Production of Biomass-Derived Vinylfurans

We describe a two-step catalytic process for the selective and rapid conversion of biomass-derived acetylfurans into vinylfurans. This is accomplished by nearly quantitatively reducing acetylfurans into furyl alcohols using a copper chromite catalyst and exploiting the promoter-like properties of ethanol, the solvent. Subsequently, furyl alcohol is dehydrated using solid-acid catalysts. Catalyst deactivation due to oligomeric species is a major issue, but the activity can be partially recovered by calcination. Optimization of reaction conditions showed that the selectivity could be increased by using nitrobenzene, a polar aprotic solvent. Here, the high-boiling point solvent allows the formation of vinylfurans with a selectivity of up to 85%; the product can be isolated in high purity by distillation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

State Dependent Optimization with Quantum Circuit Cutting

Quantum circuits can be reduced through optimization to better fit the constraints of quantum hardware. One such method, initial-state dependent optimization (ISDO), reduces gate count by leveraging knowledge of the input quantum states. Surprisingly, we found that ISDO is broadly applicable to the downstream circuits produced by circuit cutting. Circuit cutting also requires measuring upstream qubits and has some flexibility of selection observables to do reconstruction. Therefore, we propose a state-dependent optimization (SDO) framework that incorporates ISDO, our newly proposed measure-state dependent optimization (MSDO), and a biased observable selection strategy. Building on the strengths of the SDO framework and recognizing the scalability challenges of circuit cutting, we propose nonseparate circuit cutting-a more flexible approach that enables optimizing gates without fully separating them. We validate our methods on noisy simulations of QAOA, QFT, and BV circuits. Results show that our approach consistently mitigates noise and improves overall circuit performance, demonstrating its promise for enhancing quantum algorithm execution on near-term hardware.

Li, Xinpeng↗
DOE OSTI2024

Evaluating disease surveillance strategies for early outbreak detection in contact networks with varying community structure

Disease surveillance systems allow public health agencies to respond to emerging diseases before they become widespread. Developing such systems requires identifying optimal ways to monitor in the context of an epidemic outbreak; this problem is known as sensor selection. Contact networks represent the dynamics of interaction in a population and are used to model how a disease spreads in a population and to explore strategies of sensor selection. We evaluated five sensor selection strategies on their ability to provide an early warning of a COVID-like outbreak in synthetic contact networks encapsulated in four network scenarios. Three of these scenarios assessed different aspects of community structure. The fourth scenario employed a contact network representing the population and interactions of 6.8 million people in New York City, constructed from an agent-based simulation using census and transportation data. This scenario exemplifies how sensor selection strategies may perform in a real-world, urban context. Our findings suggest that the choice of the optimal strategy depends heavily on the community structure of the network. Strategies that select highly connected nodes or maximize network coverage are the optimal surveillance strategy for outbreak detection in many network community structures. However, a naive implementation of these strategies may fail to provide an early warning at all—including in the New York City scenario. Moreover, these methods are impractical for real-world use as they require knowledge of the underlying contact network. Instead, a selection strategy that starts with a set of random nodes and then performs a random walk through a chain of neighbors reliably provides early warnings without requiring prior knowledge of the network. We find this method, called “random chain”, to be the most pragmatic for implementation in a real-world disease surveillance context.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Optimization of signal-to-noise ratio in the in vivo{sup 31}P magnetic resonance spectra of the human brain

The main problem in {sup 31}P magnetic resonance spectroscopy is a low signal-to-noise ratio (SNR) of spectra acquired with clinical magnetic resonance imaging (MRI) scanners. Using spin-spin phosphorus-proton ({sup 31}P-{sup 1}H) decoupling and heteronuclear Overhauser effect and taking into account the effect of the longitudinal relaxation time T{sub 1} on the SNR, the method for localization and excitation of the region of interest (Image Selected in vivo Spectroscopy pulse sequence) was optimized to increase the SNR in the {sup 31}P magnetic resonance spectra of the human brain to ~50% without increasing signal acquisition time.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Design and Development of Low Weight, Titanium Aluminide Airfoils for High Performance Industrial Gas Turbines meeting 65% Combined Cycle Efficiency (Final Report)

This project funded under DOE-FOA-0001816 was performed with the intent of evaluating the potential of utilizing a low density, high specific strength Titanium Aluminide (TiAl) alloy in a large annulus last stage gas turbine blade with the intent of improving combined cycle gas turbine efficiency toward a goal of reaching 65% efficiency. The project consisted of identifying a potentially suitable TiAl material for manufacture of such a large blade by investment casting. This included estimation of design properties for the selected alloy, trade studies using a baseline design created for a nickel based alloy cast in the TiAl alloy to evaluate potential design changes required for the use of this material and optimization of a prototype design for the same baseline application using the selected TiAl alloy. Several rounds of optimization were completed with the intent of meeting the baseline design targets with a resulting design that showed at least equivalent performance in terms of creep, LCF/TMF, HCF and aerodynamic damping potential. While it was found that significant reductions in blade loading on the disk were possible, the design resulting from the optimization met most lifing targets at the baseline size, but showed a reduction in margins in some areas in comparison to the Ni-based baseline design. Additionally, it was found that the low strain tolerance of the TiAl alloy for temperatures at and below the operational temperature of the attachment resulted in marginal, although still acceptable, creep rupture performance of the design. Due to results indicating the reduced potential for increasing turbine annulus compared to expectations a Phase II award was not proposed and it is not recommended to pursue additional design optimization efforts until further material advances with TiAl materials are achieved.

03 NATURAL GAS↗
DOE OSTI2016

Discovery of 3-(3-(4-(1-Aminocyclobutyl)phenyl)-5-phenyl-3 H -imidazo[4,5- b ]pyridin-2-yl)pyridin-2-amine (ARQ 092): An Orally Bioavailable, Selective, and Potent Allosteric AKT Inhibitor

The work in this paper describes the optimization of the 3-(3-phenyl-3H-imidazo[4,5-b]pyridin-2-yl)pyridin-2-amine chemical series as potent, selective allosteric inhibitors of AKT kinases, leading to the discovery of ARQ 092 (21a). The cocrystal structure of compound 21a bound to full-length AKT1 confirmed the allosteric mode of inhibition of this chemical class and the role of the cyclobutylamine moiety. Compound 21a demonstrated high enzymatic potency against AKT1, AKT2, and AKT3, as well as potent cellular inhibition of AKT activation and the phosphorylation of the downstream target PRAS40. Compound 21a also served as a potent inhibitor of the AKT1-E17K mutant protein and inhibited tumor growth in a human xenograft mouse model of endometrial adenocarcinoma.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Baseline Fuel Fabrication Facility

PRO-RR is the research reactor focused program element of the broader Proliferation Resistance Optimization program (PRO-X) under the National Nuclear Safety Administration (NNSA) in the U.S. Department of Energy (DOE). PRO-X provides a framework for integrating proliferation resistance in nuclear system designs to minimize weapons usable nuclear materials (WUNM) production and diversion pathways while optimizing systems performance for peaceful use missions. PRO-RR applies the PRO-X mission objectives to research reactor system design. This document serves as one of the foundational documents for the PRO-RR-Fuel System Design technical team by documenting a baseline fuel fabrication facility to be used for further optimization studies. The PRO-RR-Fuel System Design technical team consists of subject matter experts from Argonne National Laboratory (Argonne) and Savannah River National Laboratory (SRNL). In order to develop specific strategies for fuel fabrication facilities to optimize proliferation resistance, performance, and safety, a baseline fuel fabrication facility design basis was developed. Having a baseline design basis allows for the qualitative and quantitative comparison of design choices in the optimization process. This report describes the baseline fuel fabrication facility and general optimization strategy. Chapter 2 describes the fuel system selected for examination, the fabrication process used as the baseline, a description of the model developed to track uranium utilization, and a generic floorplan of the fabrication facility. Chapter 3 describes the overarching optimization strategy that could be implemented for a fabrication facility.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Conductivity space isotherm behavior in quantum anomalous Hall devices

The quantum Hall effect (QHE) has enhanced accessibility to measure and disseminate electrical units, owed in part to the recently redefined International System of Units in 2019. Graphene remains one of the preferred options to realize the ohm despite the limitations of high magnetic fields to produce a robust QHE. Topological insulators, on the other hand, show promise in providing quantized resistance via the quantum anomalous Hall effect, a phenomenon that removes the need for magnetic fields during operation. To optimize future devices for metrological applications, it is important to gain a better understanding of magnetically doped topological insulators like Cr-doped bismuth antimony telluride. The application of differential conductivity space analyses offers a more sensitive way to analyze the data and distinguish between 2D and 3D transport behaviors. This is particularly important in thin films, where the transition between 2D and 3D behavior can be subtle. The ability to confidently determine the dimensionality of the transport is crucial for selecting appropriate theoretical models for future device optimization. Furthermore, this work identifies variable range hopping as the dominant transport mechanism in the 2D regime using a rigorous statistical analysis (via the Bayes factor). These elements assist in the understanding of microscopic processes that govern charge transport in these materials.

Tran, N. T. M. [National Inst. of Standards and Te↗
DOE OSTI2021

Purification of Propylene and Ethylene by a Robust Metal–Organic Framework Mediated by Host–Guest Interactions

Industrial purification of propylene and ethylene requires cryogenic distillation and selective hydrogenation over palladium catalysts to remove propane, ethane and/or trace amounts of acetylene. Here, we report the excellent separation of equimolar mixtures of propylene/propane and ethylene/ethane, and of a 1/100 mixture of acetylene/ethylene by a highly robust microporous material, MFM-520, under dynamic conditions. In situ synchrotron single crystal X-ray diffraction, inelastic neutron scattering and analysis of adsorption thermodynamic parameters reveal that a series of synergistic host-guest interactions involving hydrogen bonding and π⋯π stacking interactions underpin the cooperative binding of alkenes within the pore. Notably, the optimal pore geometry of the material enables selective accommodation of acetylene. The practical potential of this porous material has been demonstrated by fabricating mixed-matrix membranes comprising MFM-520, Matrimid and PIM-1, and these exhibit not only a high permeability for propylene (≈1984 Barrer), but also a separation factor of 7.8 for an equimolar mixture of propylene/propane at 298 K.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Equation Discovery Using Fast Function Extraction: a Deterministic Symbolic Regression Approach

Advances in machine learning (ML) coupled with increased computational power have enabled identification of patterns in data extracted from complex systems. ML algorithms are actively being sought in recovering physical models or mathematical equations from data. This is a highly valuable technique where models cannot be built using physical reasoning alone. In this paper, we investigate the application of fast function extraction (FFX), a fast, scalable, deterministic symbolic regression algorithm to recover partial differential equations (PDEs). FFX identifies active bases among a huge set of candidate basis functions and their corresponding coefficients from recorded snapshot data. This approach uses a sparsity-promoting technique from compressive sensing and sparse optimization called pathwise regularized learning to perform feature selection and parameter estimation. Furthermore, it recovers several models of varying complexity (number of basis terms). FFX finally filters out many identified models using non-dominated sorting and forms a Pareto front consisting of optimal models with respect to minimizing complexity and test accuracy. Numerical experiments are carried out to recover several ubiquitous PDEs such as wave and heat equations among linear PDEs and Burgers, Korteweg–de Vries (KdV), and Kawahara equations among higher-order nonlinear PDEs. Additional simulations are conducted on the same PDEs under noisy conditions to test the robustness of the proposed approach.

42 ENGINEERING↗
DOE OSTI2020

Power Split Supercharging: A Mild Hybrid Approach to Boost Fuel Economy

This work investigates an innovative low-voltage (<60 V) hybrid device that enables engine boosting and downsizing in addition to mild hybrid functionalities such as regenerative braking, start-stop, and torque assist. A planetary gear set and a brake permit the power split supercharger (PSS) to share a 9 kW motor between supercharging the engine and direct torque supply to the crankshaft. In contrast, most e-boosting schemes use two separate motors for these two functionalities. This single motor structure restricts the PSS operation to only one of the supercharging or parallel hybrid modes; therefore, an optimized decision making strategy is necessary to select both the device mode and its power split ratio. An adaptive equivalent consumption minimization strategy (A-ECMS), which uses the battery state of charge (SoC) history to adjust the equivalence factor, is developed for energy management of the PSS. The A-ECMS effectiveness is compared against a dynamic programming (DP) solution with full drive cycle preview through hardware-in-the-loop experiments on an engine dynamometer testbed. The experiments show that the PSS with A-ECMS reduces vehicle fuel consumption by 18.4% over standard FTP75 cycle, compared to a baseline turbocharged engine, while global optimal DP solution decreases the fuel consumption by 22.8% compared to the baseline.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Understanding Multilevel Selection May Facilitate Management of Arbuscular Mycorrhizae in Sustainable Agroecosystems

Studies in natural ecosystems show that adaptation of arbuscular mycorrhizal (AM) fungi and other microbial plant symbionts to local environmental conditions can help ameliorate stress and optimize plant fitness. This local adaptation arises from the process of multilevel selection, which is the simultaneous selection of a hierarchy of groups. Studies of multilevel selection in natural ecosystems may inform the creation of sustainable agroecosystems through developing strategies to effectively manage crop microbiomes including AM symbioses. Field experiments show that the species composition of AM fungal communities varies across environmental gradients, and that the biomass of AM fungi and their benefits for plants generally diminish when fertilization and irrigation eliminate nutrient and water limitations. Furthermore, pathogen protection by mycorrhizas is only important in environments prone to plant damage due to pathogens. Consequently, certain agricultural practices may inadvertently select for less beneficial root symbioses because the conventional agricultural practices of fertilization, irrigation, and use of pesticides can make these symbioses superfluous for optimizing crop performance. The purpose of this paper is to examine how multilevel selection influences the flow of matter, energy, and genetic information through mycorrhizal microbiomes in natural and agricultural ecosystems, and propose testable hypotheses about how mycorrhizae may be actively managed to increase agricultural sustainability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Stochastic Programming Approach for Resource Selection under Demand Uncertainty

Cost-efficient selection and scheduling of a subset of geographically distributed resources to meet the demands of a scientific workflow is a challenging problem. The problem is exacerbated by uncertainties in demand and availability of resources. In this paper, we present a stochastic optimization based framework for robust decision making in the selection of distributed resources over a planning horizon under demand uncertainty. We present a novel two-stage stochastic programming model for resource selection, and implement an L-shaped decomposition algorithm to solve this model. A Sample Average Approximation algorithm is integrated to enable stochastic optimization to solve problems with a large number of scenarios. Using the metric of stochastic solution, we demonstrate up to 30% cost reduction relative to solutions without explicit consideration of demand uncertainty for a 24-month problem. We also demonstrate up to 54% cost reduction relative to a previously developed solution for a 36-month problem. We further argue that the composition of resources selected is superior to solutions computed without explicit consideration of uncertainties. Given the importance of resource selection and scheduling of complex scientific workflows, especially in the context of commercial cloud computing, we believe that our novel stochastic programming framework will benefit many researchers as well as users of distributed computing resources.

scientific workflows, task scheduling, optimizatio↗
DOE OSTI2024

3D high-fidelity automated neutronics guided optimization of fusion blanket designs

The compact Fusion Pilot Plant (FPP) is defined in the recent National Academies of Sciences, Engineering, and Medicine report as the next step of fusion energy demonstration with a $50$ MWe peak net electricity production, $Q_e$ greater than $1$, and at least $3$ hours of continuous operation. This fusion pilot plant will be a test bed enabling materials, designs, and fuel management assessment, and it will represent an engineering challenge because of its high-fusion power and compact design targets. Previous reactor data is limited to experiments operating in different design space ranges. Therefore, design iterations and assessments should rely on high-fidelity first-principle theoretical and computational models. The high-fidelity integrated modeling of the plasma is a fundamental part of fusion energy research. However, the whole device modeling is often neglected, utilizing low-fidelity, system-level analysis. Recently, the need for high-fidelity multi-physics modeling was recognized, resulting in a selection of integrated tools. Further, autonomous design optimization requires a streamlined framework that perturbs the design point, reruns the analysis, and examines the outputs. However, high-fidelity analysis requires complex geometry specification that is difficult to perturb. This work presents the parametric CAD generation tool TRACER and a new neutronic workflow. TRACER allows the perturbation of the geometry representation, creating geometry files ready for further analysis. The streamlined neutronic workflow allows efficient and accurate calculations. The two new tools coupled together were used to perform a 3D high-fidelity multi-objective, multi-input optimization of an "ARC Class" compact tokamak design. The workflow was driven by an optimization driver for full automation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Creation of a Robust and Generalizable Machine Learning Classifier for Patient Ventilator Asynchrony

Background: As healthcare increasingly digitizes, streaming waveform data is being made available from an variety of sources, but there still remains a paucity of performant clinical decision support systems. For example, in the intensive care unit (ICU) existing automated alarm systems typically rely on simple thresholding that result in frequent false positives. Recurrent false positive alerts create distrust of alarm mechanisms that can be directly detrimental to patient health. To improve patient care in the ICU, we need alert systems that are both pervasive, and accurate so as to be informative and trusted by providers. Objective: We aimed to develop a machine learning-based classifier to detect abnormal waveform events using the use case of mechanical ventilation waveform analysis, and the detection of harmful forms of ventilation delivery to patients. We specifically focused on detecting injurious subtypes of patient-ventilator asynchrony (PVA).Methods: Using a dataset of breaths recorded from 35 different patients, we used machine learning to create computational models to automatically detect, and classify two types of injurious PVA, double trigger asynchrony (DTA), breath stacking asynchrony (BSA). We examined the use of synthetic minority over-sampling technique (SMOTE) to overcome class imbalance problems, varied methods for feature selection, and use of ensemble methods to optimize the performance of our model. Results: We created an ensemble classifier that is able to accurately detect DTA at a sensitivity/specificity of 0.960/0.975, BSA at sensitivity/specificity of 0.944/0.987, and non-PVA events at sensitivity/specificity of .967/.980. Conclusions: Our results suggest that it is possible to create a high-performing machine learning-based model for detecting PVA in mechanical ventilator waveform data in spite of both intra-patient, and inter-patient variability in waveform patterns, and the presence of clinical artifacts like cough and suction procedures. Our work highlights the importance of addressing class imbalance in clinical data sets, and the combined use of statistical methods and expert knowledge in feature selection.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Understanding Selectivity for the Electrochemical Reduction of Carbon Dioxide to Formic Acid and Carbon Monoxide on Metal Electrodes

Increases in energy demand and in chemical production, together with the rise in CO 2 levels in the atmosphere, motivate the development of renewable energy sources. Electrochemical CO 2 reduction to fuels and chemicals is an appealing alternative to traditional pathways to fuels and chemicals due to its intrinsic ability to couple to solar and wind energy sources. Formate (HCOO – ) is a key chemical for many industries; however, greater understanding is needed regarding the mechanism and key intermediates for HCOO – production. This work reports a joint experimental and theoretical investigation of the electrochemical reduction of CO 2 to HCOO – on polycrystalline Sn surfaces, which have been identified as promising catalysts for selectively producing HCOO – . Our results show that Sn electrodes produce HCOO – , carbon monoxide (CO), and hydrogen (H 2 ) across a range of potentials and that HCOO – production becomes favored at potentials more negative than –0.8 V vs RHE, reaching a maximum Faradaic efficiency of 70% at –0.9 V vs RHE. Scaling relations for Sn and other transition metals are examined using experimental current densities and density functional theory (DFT) binding energies. While *COOH was determined to be the key intermediate for CO production on metal surfaces, we suggest that it is unlikely to be the primary intermediate for HCOO – production. Instead, *OCHO is suggested to be the key intermediate for the CO 2 RR to HCOO – transformation, and Sn’s optimal *OCHO binding energy supports its high selectivity for HCOO – . Lastly, these results suggest that oxygen-bound intermediates are critical to understand the mechanism of CO 2 reduction to HCOO – on metal surfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Machine Learning-Based Temperature Prediction for Runtime Thermal Management Across System Components

Elevated temperatures limit the peak performance of systems because of frequent interventions by thermal throttling. Non-uniform thermal states across system nodes also cause performance variation within seemingly equivalent nodes leading to significant degradation of overall performance. In this paper we present a framework for creating a lightweight thermal prediction system suitable for run-time management decisions. We pursue two avenues to explore optimized lightweight thermal predictors. First, we use feature selection algorithms to improve the performance of previously designed machine learning methods. Second, we develop alternative methods using neural network and linear regression-based methods to perform a comprehensive comparative study of prediction methods. We show that our optimized models achieve improved performance with better prediction accuracy and lower overhead as compared with the Gaussian process model proposed previously. Specifically we present a reduced version of the Gaussian process model, a neural network-based model, and a linear regression-based model. Using the optimization methods, we are able to reduce the average prediction errors in the Gaussian process from 4: 2 degrees C to 2: 9 degrees C. We also show that the newly developed models using neural network and Lasso linear regression have average prediction errors of 2: 9 degrees C and 3: 8 degrees C respectively. The prediction overheads are 0.22, 0.097, and 0.026 ms per prediction for reduced Gaussian process, neural network, and Lasso linear regression models, respectively, compared with 0.57 ms per prediction for the previous Gaussian process model. We have implemented our proposed thermal prediction models on a two-node system configuration to help identify the optimal task placement. The task placement identified by the models reduces the average system temperature by up to 11: 9 degrees C without any performance degradation. Furthermore, these models respectively achieve 75, 82.5, and 74.17 percent success rates in correctly pointing to those task placements with better thermal response, compared with 72.5 percent success for the original model in achieving the same objective. Lastly, we extended our analysis to a 16-node system and we were able to train models and execute them in real time to guide task migration and achieve on average 17 percent reduction in the overall system cooling power.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

An experimental and numerical investigation of HD diesel engine DOC efficiency in oxidizing NO to NO 2

Reducing pollutant emissions from heavy-duty (HD) diesel engines is critical due to their significant environmental impact, particularly concerning NOx emissions. Understanding and optimizing modern diesel oxidation catalyst (DOC) and selective catalytic reduction (SCR) performance is essential for improving exhaust aftertreatment (EAT) system efficiency to meet stringent emissions regulations. The oxidation of nitric oxide (NO) to nitrogen dioxide (NO 2 ) in DOC plays a key role in improving SCR efficiency in reducing NO x . This study investigates the DOC performance in oxidizing NO to NO2 and its impact on the SCR efficiency of a 2021 MY Navistar E39 HD diesel engine. The influence of engine speed, load, exhaust gas temperature, and composition on DOC efficiency is experimentally investigated. The relationships between DOC inlet temperature, oxygen availability and NO 2 /NO x ratio at the DOC inlet are examined to better understand their effects on the overall DOC efficiency. The results indicate that DOC NO oxidation efficiency is highly dependent on exhaust temperature, with optimal oxidation occurring within a specific temperature range (275-350°C). Below this threshold, the chemical reactions are kinetically limited, while at higher temperatures, thermodynamic constraints reduce the efficiency of DOC in oxidizing NO to NO 2 . The experimental data further reveal that the NO 2 /NO x ratio peaks at medium loads before declining at higher loads due to reduced residence time and mass transfer effects. Additionally, the SCR NO x conversion efficiency is significantly influenced by the NO 2 /NO x ratio, achieving peak performance when the NO 2 /NO x ratio approaches 0.5. A DOC chemistry model was developed and validated against the experimental data to predict DOC oxidation behavior under various operating conditions. The findings of this study provide insights into the interdependencies between DOC and SCR performance, contributing to the optimization of SCR systems for optimized NOx reduction.

33 ADVANCED PROPULSION SYSTEMS↗