Results for “ENGINES” — Page 74

DOE OSTI2011

Biodiesel Impact on Engine Lubricant Dilution During Active Regeneration of Aftertreatment Systems

Experiments were conducted with ultra low sulfur diesel (ULSD) and 20% biodiesel blends (B20) to compare lube oil dilution levels and lubricant properties for systems using late in-cylinder fuel injection for aftertreatment regeneration. Lube oil dilution was measured by gas chromatography (GC) following ASTM method D3524 to measure diesel content, by Fourier transform infrared (FTIR) spectrometry following a modified ASTM method D7371 to measure biodiesel content, and by a newly developed back-flush GC method that simultaneously measures both diesel and biodiesel. Heavy-duty (HD) engine testing was conducted on a 2008 6.7L Cummins ISB equipped with a diesel oxidation catalyst (DOC) and diesel particle filter (DPF). Stage one of engine testing consisted of 10 consecutive repeats of a forced DPF regeneration event. This continuous operation with late in-cylinder fuel injection served as a method to accelerate lube-oil dilution. Stage two consisted of 16 hours of normal engine operation over a transient test cycle, which created an opportunity for any accumulated fuel in the oil sump to evaporate. Light duty (LD) vehicle testing was conducted on a 2010 VW Jetta equipped with DOC, DPF and a NOx storage catalyst (NSC). Vehicle testing comprised approximately 4,000 miles of operation on a mileage-accumulation dynamometer (MAD) using the U.S. Environmental Protection Agency's Highway Fuel Economy Cycle because of the relatively low engine oil and exhaust temperatures, and high DPF regeneration frequency of this cycle relative to other cycles examined. Comparison of the lube oil dilution analysis methods suggests that D3524 does not measure dilution by biodiesel. The new back-flush GC method provided analysis for both diesel and biodiesel, in a shorter time and with lower detection limit. Thus all lube oil dilution results in this paper are based on this method. Analysis of the HD lube-oil samples showed only 1.5% to 1.6% fuel dilution for both fuels during continuous operation under DPF regeneration events. During the second stage of HD testing, the ULSD lube-oil dilution levels fell from 1.5% to 0.8%, while for B20, lube-oil dilution levels fell from 1.6% to 1.0%, but the fuel in the oil was 36% biodiesel. For the LD vehicle tests, the frequency of DPF regeneration events was observed to be the same for both ULSD and B20. No significant difference between the two fuels' estimated soot loading was detected by the engine control unit (ECU), although a 23% slower rate of increase in differential pressure across DPF was observed with B20. It appears that the ECU estimated soot loading is based on the engine map, not taking advantage of the lower engine-out particulate matter from the use of biodiesel. After 4,000 miles of LD vehicle operation with ULSD, fuel dilution in the lube-oil samples showed total dilution levels of 4.1% diesel. After 4,000 miles of operation with B20, total fuel in oil dilution levels were 6.7% consisting of 3.6% diesel fuel and 3.1% biodiesel. Extrapolation to the 10,000-mile oil drain interval with B20 suggests that the total fuel content in the oil could reach 12%, compared to 5% for operation on ULSD. Analysis of the oil samples also included measurement of total acid number, total base number, viscosity, soot, metals and wear scar; however, little difference in these parameters was noted.

09 BIOMASS FUELS↗

DOE OSTI2018

Numerical Methodology for Optimization of Compression-Ignited Engines Considering Combustion Noise Control

It is challenging to develop highly efficient and clean engines while meeting user expectations in terms of performance, comfort, and drivability. One of the critical aspects in this regard is combustion noise control. Combustion noise accounts for about 40 percent of the overall engine noise in typical turbocharged diesel engines. The experimental investigation of noise generation is difficult due to its inherent complexity and measurement limitations. Therefore, it is important to develop efficient numerical strategies in order to gain a better understanding of the combustion noise mechanisms. In this work, a novel methodology was developed, combining computational fluid dynamics (CFD) modeling and genetic algorithm (GA) technique to optimize the combustion system hardware design of a high-speed direct injection (HSDI) diesel engine, with respect to various emissions and performance targets including combustion noise. The CFD model was specifically set up to reproduce the unsteady pressure field inside the combustion chamber, thereby allowing an accurate prediction of the acoustic response of the combustion phenomena. The model was validated by simulating several steady operating conditions and comparing the numerical results against experimental data, in both temporal and frequency domains. Thereafter, a GA optimization was performed with the goal of minimizing indicated specific fuel consumption (ISFC) and combustion noise, while restricting pollutant (soot and NOx) emissions to their respective baseline values. Eight design variables were selected pertaining to piston bowl geometry, nozzle inclusion angle, number of injector nozzle holes, and in-cylinder swirl. An objective merit function (MF) based on the emissions, ISFC, and combustion noise was constructed to quantify the strength of the engine designs and was determined using the CFD model as the function evaluator. The in-cylinder noise level was characterized by the total resonance energy of local pressure oscillations. The optimum engine configuration thus obtained showed a significant improvement in terms of efficiency and combustion noise compared to the baseline system, along with both soot and NOx emissions within their respective constraints. This optimum configuration included a deeper and tighter bowl geometry with higher swirl and larger number of nozzle holes. Subsequently, a more detailed acoustics analysis based on proper orthogonal decomposition (POD) technique was carried out to further explore the combustion noise benefits achieved by the GA optimum. In conclusion, this computational study is a first of its kind (to the best of the authors’ knowledge), which demonstrates a comprehensive framework to incorporate combustion noise into a numerical optimization strategy for engine design.

42 ENGINEERING↗

DOE OSTI2019

Analysis of the Effect of Vehicle Platooning on the Optimal Control of a Heavy Duty Engine Thermal System

One promising method for reducing fuel consumption and emissions, particularly in heavy duty trucks, is platooning. As the distance between vehicles decreases, the following vehicles will experience less aerodynamic drag on the front of the vehicle. However, reducing the velocity of the air contacting the front of the vehicle could have adverse effects on the temperature of the engine. To compensate for this effect, the energy consumption of the engine cooling system might increase, ultimately limiting the overall improvements obtained with platooning. Understanding the coupling between drag reduction and engine cooling load requirement is key for successfully implementing platooning strategies. Additionally, in a Connected and Automated Vehicle (CAV) environment, where information of the future engine load becomes available, the operation of the cooling system can be optimized in order to achieve the maximum fuel consumption reduction. In this paper, a control-oriented physics-based model for the engine cooling loop of a Volvo engine is developed and validated against road data. Starting from the validated model, an optimal control problem for the coolant system is formulated considering the tradeoff between the tracking of the engine temperature setpoint and the corresponding fuel consumption under different trailing distances. To compare the coolant system performance, Dynamic Programming (DP) is used to determine the global optimal solution for the coolant system actuator. The coupling between optimal cooling system operation and reduction in ram air are evaluated by comparing the results obtained from the DP under different platooning conditions against the unrestricted scenario. In addition, the paper analyzes the changes in the tradeoff between fuel consumption and setpoint tracking for different vehicle distances. This analysis will provide useful insight on the sensitivity of the coolant system controller calibration to the platoon distance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Achieving Diesel-Like Efficiency in a High Stroke-to-Bore Ratio DISI Engine under Stoichiometric Operation

This work explores pathways to achieve diesel-like, high-efficiency combustion with stoichiometric 3-way catalyst compatible spark ignition (SI). A high stroke-to-bore engine design (1.5:1) with cooled exhaust gas recirculation (EGR) and high compression ratio (r c ) was used to improve engine efficiency by up to 30% compared with a production turbocharged gasoline direct injection spark ignition engine. To achieve efficiency improvements, engine experiments were coupled with computational fluid dynamics simulations to guide and explain experimental trends between the original engine and the high stroke-to-bore ratio design (1.5:1). The effects of EGR and late intake valve closing (IVC) and fuel characteristics are investigated through their effects on knock mitigation. Direct injection of 91 RON E10 gasoline, 99 RON E0 gasoline, and liquified petroleum gas (i.e., propane/autogas) were evaluated with geometric rc ranging from 13.3:1 to 16.8:1. Finally, engine experiments demonstrated 47% gross thermal efficiency, and 45% net thermal efficiency at stoichiometric engine operation, at up to 17 bar IMEP and 2000 r/min with 16.8:1 rc.

42 ENGINEERING↗

NASA NTRS2004

Engineering Complex Embedded Systems with State Analysis and the Mission Data System

It has become clear that spacecraft system complexity is reaching a threshold where customary methods of control are no longer affordable or sufficiently reliable. At the heart of this problem are the conventional approaches to systems and software engineering based on subsystem-level functional decomposition, which fail to scale in the tangled web of interactions typically encountered in complex spacecraft designs. Furthermore, there is a fundamental gap between the requirements on software specified by systems engineers and the implementation of these requirements by software engineers. Software engineers must perform the translation of requirements into software code, hoping to accurately capture the systems engineer's understanding of the system behavior, which is not always explicitly specified. This gap opens up the possibility for misinterpretation of the systems engineer s intent, potentially leading to software errors. This problem is addressed by a systems engineering methodology called State Analysis, which provides a process for capturing system and software requirements in the form of explicit models. This paper describes how requirements for complex aerospace systems can be developed using State Analysis and how these requirements inform the design of the system software, using representative spacecraft examples.

mission data systems↗

NASA NTRS2011

Update - Concept of Operations for Integrated Model-Centric Engineering at JPL

The increasingly ambitious requirements levied on JPL's space science missions, and the development pace of such missions, challenge our current engineering practices. All the engineering disciplines face this growth in complexity to some degree, but the challenges are greatest in systems engineering where numerous competing interests must be reconciled and where complex system level interactions must be identified and managed. Undesired system-level interactions are increasingly a major risk factor that cannot be reliably exposed by testing, and natural-language single-viewpoint specifications areinadequate to capture and expose system level interactions and characteristics. Systems engineering practices must improve to meet these challenges, and the most promising approach today is the movement toward a more integrated and model-centric approach to mission conception, design, implementation and operations. This approach elevates engineering models to a principal role in systems engineering, gradually replacing traditional document centric engineering practices.

The Concept of Operations (ConOps)↗

NASA NTRS2014

Detailed Post-Soft Impact Progressive Damage Assessment for Hybrid Structure Jet Engines

Currently, certification of engine designs for resistance to bird strike is reliant on physical tests. Predictive modeling of engine structural damage has mostly been limited to evaluation of individual forward section components, such as fan blades within a fixed frame of reference, to direct impact with a bird. Such models must be extended to include interactions among engine components under operating conditions to evaluate the full extent of engine damage. This paper presents the results of a study aim to develop a methodology for evaluating bird strike damage in advanced propulsion systems incorporating hybrid composite/metal structures. The initial degradation and failure of individual fan blades struck by a bird were investigated. Subsequent damage to other fan blades and engine components due to resultant violent fan assembly vibrations and fragmentation was further evaluated. Various modeling parameters for the bird and engine components were investigated to determine guidelines for accurately capturing initial damage and progressive failure of engine components. Then, a novel hybrid structure modeling approach was investigated and incorporated into the crashworthiness methodology. Such a tool is invaluable to the process of design, development, and certification of future advanced propulsion systems.

bird strike↗

NASA NTRS2014

Ice Crystal Icing Engine Testing in the NASA Glenn Research Center's Propulsion Systems Laboratory: Altitude Investigation

The National Aeronautics and Space Administration (NASA) conducted a full scale ice crystal icing turbofan engine test using an obsolete Allied Signal ALF502-R5 engine in the Propulsion Systems Laboratory (PSL) at NASA Glenn Research Center. The test article used was the exact engine that experienced a loss of power event after the ingestion of ice crystals while operating at high altitude during a 1997 Honeywell flight test campaign investigating the turbofan engine ice crystal icing phenomena. The test plan included test points conducted at the known flight test campaign field event pressure altitude and at various pressure altitudes ranging from low to high throughout the engine operating envelope. The test article experienced a loss of power event at each of the altitudes tested. For each pressure altitude test point conducted the ambient static temperature was predicted using a NASA engine icing risk computer model for the given ambient static pressure while maintaining the engine speed.

Altitude Testing↗

NASA NTRS2022

An Analysis of the Impact of Extreme Attitude Operation on a Turbofan Engine in a Regional Jet Aircraft

The objective of this work is to characterize the variability in engine performance at extreme attitudes, resulting from the use of different standard control variables, evaluated at different points in the engine’s lifecycle. This paper describes the procedure used to create a dynamic model of an aft-mounted jet engine on a T-tail regional jet aircraft. This model enables simulation of engine operation at extreme attitudes. The model was subsequently evaluated at various altitudes, Mach numbers, and power settings over a range of angles of attack and sideslip, and deterioration levels. Each case was simulated using both fan speed and engine pressure ratio as the engine control variable. The results show that the wing has a very large impact on the engine operation, overwhelming other sources of variation.

loss of control - inflight↗

NASA NTRSDate not supplied

Transient Optimization of an Electrified Gas Turbine Engine Using Machine Learning

Gas turbine engines are designed with sufficient margin to prevent stall under normal operating conditions throughout their life. This compromise ensures that during rapid accelerations, compressor operation remains stable, but at the cost of efficiency and thrust responsiveness. The design margin encompasses multiple sources of uncertainty and systematic deviances from the operating line, the largest of which is the transient allowance. This set-aside accounts for the temporary incoordination of the engine spools during an acceleration while still enabling it to meet the certification requirement to accelerate from low to high power within a specified time, and without experiencing overtemperature, surge, stall, or other detrimental factors. Electrification of the powertrain provides the opportunity to address this reserve and truly optimize the design. The addition of electric machines inherent in hybrid propulsion concepts offers a means to interact with the engine shafts such that the necessary margin can be reduced, which can positively impact the engine design. By adjusting the amount of power extracted from or injected to the engine spools by the electric machines during transient operation, excursions from the operating line can be minimized. Past work using a dynamic engine model has shown that optimization of the fuel flow schedule during acceleration can reduce the required margin while still meeting the time requirement, and results are further improved when combined with power injection and extraction. The current work uses machine learning through a genetic algorithm to address the problem holistically by concurrently optimizing the electric machine power command and fuel flow acceleration schedule using an updated, higher fidelity version of the original engine model.

Stall Margin↗

NASA NTRSDate not supplied

Transient Optimization of an Electrified Gas Turbine Engine Using Machine Learning

Gas turbine engines are designed with sufficient margin to prevent stall under normal operating conditions throughout their life. This compromise ensures that during rapid accelerations, compressor operation remains stable, but at the cost of efficiency and thrust responsiveness. The design margin encompasses multiple sources of uncertainty and systematic deviances from the operating line, the largest of which is the transient allowance. This set-aside accounts for the temporary incoordination of the engine spools during an acceleration while still enabling it to meet the certification requirement to accelerate from low to high power within a specified time, and without experiencing overtemperature, surge, stall, or other detrimental factors. Electrification of the powertrain provides the opportunity to address this reserve and truly optimize the design. The addition of electric machines inherent in hybrid propulsion concepts offers a means to interact with the engine shafts such that the necessary margin can be reduced, which can positively impact the engine design. By adjusting the amount of power extracted from or injected to the engine spools by the electric machines during transient operation, excursions from the operating line can be minimized. Past work using a dynamic engine model has shown that optimization of the fuel flow schedule during acceleration can reduce the required margin while still meeting the time requirement, and results are further improved when combined with power injection and extraction. The current work uses machine learning through a genetic algorithm to address the problem holistically by concurrently optimizing the electric machine power command and fuel flow acceleration schedule using an updated, higher fidelity version of the original engine model.

Stall Margin↗

NASA NTRS2024

Transient Optimization of an Electrified Gas Turbine Engine Using Machine Learning

Gas turbine engines are designed with sufficient margin to prevent stall under normal operating conditions throughout their life. This compromise ensures that during rapid accelerations, compressor operation remains stable, but at the cost of efficiency and thrust responsiveness. The design margin encompasses multiple sources of uncertainty and systematic deviances from the operating line, the largest of which is the transient allowance. This set-aside accounts for the temporary incoordination of the engine spools during an acceleration while still enabling it to meet the certification requirement to accelerate from low to high power within a specified time, and without experiencing overtemperature, surge, stall, or other detrimental factors. Electrification of the powertrain provides the opportunity to address this reserve and truly optimize the design. The addition of electric machines inherent in hybrid propulsion concepts offers a means to interact with the engine shafts such that the necessary margin can be reduced, which can positively impact the engine design. By adjusting the amount of power extracted from or injected to the engine spools by the electric machines during transient operation, excursions from the operating line can be minimized. Past work using a dynamic engine model has shown that optimization of the fuel flow schedule during acceleration can reduce the required margin while still meeting the time requirement, and results are further improved when combined with power injection and extraction. The current work uses machine learning through a genetic algorithm to address the problem holistically by concurrently optimizing the electric machine power command and fuel flow acceleration schedule using an updated, higher fidelity version of the original engine model.

Stall Margin↗

NASA NTRSDate not supplied

Development and Implementation of a Small Satellite Systems Engineering Webinar Series: A Collaboration Between United Nations Office for Outer Space Affairs and National Aeronautics and Space Administration

The United Nations Office for Outer Space Affairs (UNOOSA) in collaboration with the National Aeronautics and Space Administration (NASA) established a webinar series on NASA systems engineering standards and practices for the purpose of sharing knowledge in this area through UNOOSA’s “Access to Space for All” which provides free and accessible educational content. Through NASA’s Small Spacecraft Systems Virtual Institute (S3VI), the four-part series was designed to cover basic systems engineering and project management skills that are fundamental for planning, developing, and implementing an experiment or a space project, and that serve as critical knowledge for those engaged in space activities, whether as a designer, builder, or manager of space infrastructure and services. This first series of webinars was convened over the course of November 2023 through February 2024. The webinar series was segmented into four one and a half hour webinars which covered topics related to the Fundamentals of Systems Engineering, Requirements and Systems Engineering, System Assembly, Integration and Test, and Spacecraft Handling, as well as an Introduction to the Small Spacecraft Systems Virtual Institute (S3VI). The agendas for the first three webinars focused on addressing a number of questions relevant to a particular topic. Examples of these questions include: What is systems engineering and what does a systems engineer do? Why does NASA use systems engineering? How do you define requirements for a small spacecraft mission? Why are requirements important to space missions? How are trade studies used to determine parts selection and why are they important? The final webinar of the first series provided interactive discussions and demonstrations of the S3VI tools; shared information on additional webinar opportunities offered by the S3VI; and reviewed databases curated by the S3VI that include the Small Satellite Reliability Initiative (SSRI) Knowledge Base Tool, Mission Design Tools, Small Spacecraft Information Search, and the State-of-the-Art (SoA) Small Spacecraft Report. Future webinar series will be conducted with topics are to be determined. The presenter will discuss details related to the first webinar series and future plans for additional series.

systems engineering↗

NASA NTRSDate not supplied

The Development and Deployment of Machine Learning Models for Aircraft Engine Concept Assessment

In today's competitive landscape, the effective development and utilization of machine-learning (ML) applications have become imperative across various sectors. This study presents an outline of the procedure involved in creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models leverage supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. The main areas of focus encompass crucial engine parameters like thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While the creation of ML models is fundamental for their utilization, ensuring their seamless deployment holds equal significance. To address this aspect, a conversational AI chatbot is constructed, utilizing natural language processing (NLP) techniques, to facilitate the deployment of these ML models. The comprehensive workflow encompasses several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine assessment.

Development↗

NASA NTRSDate not supplied

The Development and Deployment of Machine Learning Models for Aircraft Engine Concept Assessment

In today's competitive landscape, the effective development and utilization of machine-learning (ML) applications have become imperative across various sectors. This study presents an outline of the procedure involved in creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models leverage supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. The main areas of focus encompass crucial engine parameters like thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While the creation of ML models is fundamental for their utilization, ensuring their seamless deployment holds equal significance. To address this aspect, a conversational AI chatbot is constructed, utilizing natural language processing (NLP) techniques, to facilitate the deployment of these ML models. The comprehensive workflow encompasses several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine assessment.

Development↗

NASA NTRSDate not supplied

Machine Learning Application in Aircraft Engine Conceptual Design

In the current competitive environment, the successful creation and application of machine learning (ML) technologies have become crucial across multiple industries. This study outlines the process of creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models use supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. Key focus areas include crucial engine parameters such as thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While developing ML models is fundamental, ensuring their seamless deployment is equally important. To address this, a conversational AI chatbot is constructed using natural language processing (NLP) techniques to facilitate the deployment of these ML models. The comprehensive workflow includes several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine conceptual design.

Aircraft Engine↗

NASA NTRS2024

The Development and Deployment of Machine Learning Models for Aircraft Engine Concept Assessment

In today's competitive landscape, the effective development and utilization of machine-learning (ML) applications have become crucial across diverse economic sectors. This study presents an outline of the procedure involved in creating and implementing ML models for conceptualizing and evaluating aircraft engines. These models leverage supervised deep-learning algorithms to analyze patterns within an open-source repository containing data on both production and research conventional turbofan engines. The main areas of focus encompass crucial engine parameters like thrust-specific fuel consumption (TSFC), engine weight, engine diameter, and turbomachinery stage counts. While the creation of ML models is fundamental for their utilization, ensuring their seamless deployment holds equal significance. To address this aspect, a conversational AI chatbot that specifically focuses on propulsion has been developed. Leveraging natural language processing (NLP) techniques, this chatbot simplifies the deployment of machine learning (ML) models. The comprehensive workflow encompasses several key stages: gathering and enhancing engine data, training and cross validating the ML models, testing and evaluating their performance, and finally, deploying, monitoring, and updating the ML models. By following this systematic approach, the aim is to streamline the development and deployment process of ML models tailored for aircraft engine assessment.

AI Chatbot↗

DOE OSTI2020

Dicarboxylic acid emissions from a GDI engine equipped with a catalytic gasoline particulate filter

Dicarboxylic acids play an important role in atmospheric chemistry, yet their emissions from primary sources, such as internal combustion engines, has not been extensively studied. In this paper, KOH impregnated quartz filters were loaded with exhaust gases from a gasoline direct injection (GDI) engine equipped with a catalytic gasoline particulate filter (GPF). All filters were analyzed for carboxylic and dicarboxylic acids using a derivatized gas chromatography-mass spectroscopy method. Exhaust gas was sampled from pre-GPF and post-GPF locations to determine the performance of the GPF regarding acid conversion. Lean and stoichiometric engine modes were considered with non-oxygenated gasoline and 10% splash blended ethanol in gasoline (E10) to examine the impact of stoichiometry and fuel type. Acid emissions represented as much as 0.51% of total unburned hydrocarbon emissions for total monocarboxylic acids and as much as 0.40% for total dicarboxylic acids. Individual acid concentrations were as high as 38 mg/kg-fuel for monocarboxylic acids and as high as 29 mg/kg-fuel for dicarboxylic acids. Overall, the study found that fuel oxygenates had mixed impact on the acid emissions. Engine-out monocarboxylic acids were reduced when using the E10 fuel by approximately 30–45% for the stoichiometric condition and increased marginally for the lean condition. Dicarboxylic acid emissions were generally insensitive to ethanol content. However, the engine condition significantly affected the acid emissions. Lean operation produced a factor of two to an order of magnitude higher emissions rates of both monocarboxylic and dicarboxylic acids than the stoichiometric condition. The catalytic GPF eliminated between 80 and 92% of the acids emitted from the engine, allowing some acids to be emitted into the environment.

42 ENGINEERING↗