Below is the text version of the Co-Optima Capstone webinar, "How Can Co-Optimized Fuels and Spark-Ignition Engines Enhance Efficiency While Reducing Carbon Emissions of Light-Duty Passenger Vehicles?" Watch the webinar recording.
[Begin audio]
Dan Gaspar: We are planning to record this presentation, including the Q&A session. We just want everybody to know that before we get started. Okay, Nick. Whenever you are ready, you can start recording.
Nick Medina: Okay.
Dan Gaspar: Great. So, welcome everybody to the first Co-Optima capstone webinar in our series. Today's webinar will describe Co-Optima's efforts to identify fuel engine combinations that can increase the efficiency of light-duty or passenger vehicles while reducing carbon emissions. My name is Dan Gaspar. I am a manager at Pacific Northwest National Laboratory and have been involved with Co-Optima since the beginning, currently the principal investigator. Watch the webinar recording.
I am joined today by Jim Szybist, who has led the Co-Optima fuel property team also since the beginning of the program. Jim is also currently the head of the propulsion science section at Oak Ridge National Laboratory. So, the Co-Optimization of Fuels and Engines (or Co-Optima) Program is an initiative jointly sponsored by two offices within the U.S. Department of Energy's Office of Efficiency and Renewable Energy (or EERE). These two offices are the Bioenergy Technologies Office and the Vehicle Technologies Office. We are particularly grateful to Alicia Lindauer of the Bioenergy Technology Office and Kevin Stork and Mike Weismiller of the Vehicle Technologies Office for their guidance and support since the beginning of the program. So, Co-Optima is finishing up its second three-year cycle at the end of this fiscal year and this webinar series is aimed at disseminating some of the key lessons we have learned.
In today's presentation, we focus on light-duty vehicles and what we have learned about the opportunity to increase their efficiency by co-optimizing fuels and engines.
So, Co-Optima researchers conduct foundational research at the fuel engine interface with the aim of simultaneously optimizing fuel properties and engine technologies in order to more quickly identify technology options to increase efficiency and decrease emissions. Ultimately, Co-Optima seeks to identify biomass and waste-derived blendstocks that could be blended into a base fuel to provide a performance advantage for advanced engines. The performance advantage could then increase market pull for these fuels.
The presentation is organized as shown in the left panel. We will start by explaining a little bit more about the Co-Optima initiative and what we set out to accomplish, including providing some context on the need to increase engine efficiency for a more sustainable transportation future.
After that, we will briefly summarize the key takeaways from this work. Next, we will describe the approach that Co-Optima has taken to address this co-optimization problem. And then, finally, we will highlight the most important outcomes from Co-Optima research on turbocharged light-duty gasoline vehicles. And also, please note the disclaimer on the lower left.
The Co-Optima initiative succeeded in large part due to the strong teamwork that has developed over the years. The core of the effort is a nine-lab team, including the National Renewable Energy Laboratory, Oak Ridge, Pacific Northwest, Sandia, Argonne, Lawrence Livermore, Lawrence Berkley, and Idaho national laboratories. We are fortunate to have on the team more than 20 university and industry partners – shown on this map – from the biofuel, vehicles, and energy industries through a series of funding opportunity announcement awards and directed funding opportunity awards. Researchers on the team include experts in biofuel production, fuel properties, combustion, modeling, and analysis, as well as many other fields.
Our search is for fuel-engine combinations that offer a performance advantage, which could increase the rate of adoption of these fuel-engine combinations. The search starts with non-food-based renewable resources such as biomass, including algae, and waste that can be converted into blendstocks. A blendstock is a fuel component that would be blended into a base fuel and, in this program, we have included analysis of existing market blendstocks such as ethanol, isobutanol, and renewable diesel for comparison with new candidate blendstocks. In order to facilitate an energy transition, Co-Optima is focused on an approach where the biomass or waste-derived blendstock would be blended into a base – a traditional petroleum-derived base fuel at up to 30 percent by volume. Six years ago, this blending level would have been considered quite aggressive, and even today, such a strategy could facilitate a transition to fuels that would be mostly from renewable resources.
All of our analyses are conducted using a field-to-wheels or well-to-wheels approach to ensure that the full lifecycle is considered. And ultimately, what we provide are data, tools, and knowledge to stakeholders so that they can make the best decision based on their particular situation. And finally, while we have kept an eye on advances in engines for hybrid and plug-in hybrid electric vehicles, Co-Optima has not conducted research on fuels and engines for these vehicles. But nonetheless, the data, tools, and knowledge developed in Co-Optima could be used for further development of hybrid powertrains.
We have organized our research by segmenting it into light-duty and medium- and heavy-duty research to address both near-term and longer-term opportunities in on-road transportation. For light-duty engines, we started with turbocharged or boosted spark-ignited engines, and Jim Szybist will go into some detail on how a spark-ignited engine works in today's presentation. Co-Optima has also conducted significant research on what is called compression ignition or CI. In a CI engine, there is no spark plug. Instead, air is compressed to high temperature and pressure, the fuel is injected into the cylinder and it auto ignites as it mixes with the hot compressed air. So, in the light-duty boosted SI area, turbochargers have been around for more than 50 years.
Originally aimed at enhancing engine performance, turbocharging has become one of the primary ways that engine manufacturers have increased fuel economy. Although less than 2 percent of light-duty engines sold in 2005 included a turbocharger, more than 30 percent of light-duty engines sold in 2018 were turbocharged. So, why do we do this? We boost the engine so that we can make the engine smaller and change how it operates to increase engine efficiency while still meeting consumer needs and expectations. In the longer term for light-duty engines, Co-Optima has looked at multi-mode combustion where more than one combustion approach is used over a drive cycle.
The multi-mode approaches that we looked at in Co-Optima use advanced compression ignition under some engine operating conditions to improve overall engine efficiency. In medium- and heavy-duty transportation, where the focus is on moving goods over long and short distances, we have looked at, in the near term, both diesel combustion or mixing controlled compression ignition, and in the longer term, advance compression ignition offers promise to improve electrification and reduce emissions. More than 70 percent of all goods transported in the U.S. are moved by heavy-duty trucks so this is a really important area of research. The total cost of ownership is the key driver for the owners of these vehicles, and that total cost of ownership is impacted by vehicle durability, fuel economy, and the cost of reducing criteria pollutant emissions. Currently, these requirements are best fulfilled by diesel engines, hence our near-term focus in that area.
In the longer term, achieving diesel-like efficiency while reducing emissions more easily or further is the promise of advanced compression ignition. And you can hear more about these topics in future webinars.
So, our capstone webinar series kicks off today. In April, we will hear from Bob McCormick of the National Renewable Energy Laboratory and Chuck Mueller from Sandia National Laboratories about what we have learned about advanced engines and fuels to reduce the pollutants from future diesel engines. In May, Troy Hawkins from Argonne National Laboratory will be featured in a presentation about the economic and environmental benefits of what we have learned in these boost spark-ignited engines for light-duty vehicles; and in June, we feature Avantika Singh of NREL who will talk about the same topic but, in this case, for medium- and heavy-duty vehicles. In August, Magnus Sjöberg from Sandia National Laboratories will talk about what we have learned about what he calls here "unconventional engine/fuel combinations." That is the longer-term multi-mode and ACI fuel-engine combinations that we talked about on the previous slide.
And then, finally, we will wrap up our series with Robert Wagner from Oak Ridge National Laboratory and me, and we will talk about what we have learned overall within Co-Optima and where we might go next.
So in today's webinar, we will describe progress toward the Co-Optima goal of identifying fuel-engine combinations offering higher efficiency. We will describe our progress in connecting fuel chemical structure to fuel properties and connecting fuel properties to combustion efficiency.
As transportation is about moving people and goods, it is essential to maintaining our standard of living. Why does higher efficiency matter? So, higher efficiency matters because every increase in electrification directly reduces the carbon intensity of transportation. In the figure, the top vehicle – the blue SUV – represents the average fuel economy of the current light-duty fleet – 22 miles per gallon. The middle column is the energy used to move that fleet in exajoules – so that is about 16 exajoules – in 2017 for all light-duty transportation. Sixteen exajoules is the equivalent of more than 850 581-megawatt nuclear reactors or more than 1,200 400-megawatt combine cycle natural gas power plants.
It is a lot of energy. And that energy contributes about 1,000 teragrams of CO2 emissions. Transportation is now the largest contributing sector to GHG emissions in the U.S. So, if we increase efficiency, as shown in the green car below, we can reduce the energy used and we can reduce the carbon emissions. So how do we do that?
Well, Co-Optima started with the idea that if we can improve or change the fuel properties and design an engine to operate with those new fuel properties that we can improve the overall efficiency. And if we understand how chemical structure of the molecules in the fuel impact fuel properties and we also understand how fuel properties impact engine performance, we can thereby identify and develop more efficient fuel-engine combinations.
We do this by asking three foundational questions. The first one – "What fuels do engines really want?" – is about that latter part from the previous slide where we were talking about how the fuel properties impact combustion. The middle question – "What fuel options work best?" – really is asking the question, "What chemistries in the fuel provide us target values of these critical fuel properties?" And ultimately, we want to identify those candidates that can really work in the real world. "What are the barriers to adoption at scale?" – whether they are related to compatibility in the infrastructure, in the fueling system, or the ability to make them at scale, or to have the kinds of impacts that we are seeking in reducing emissions, improving efficiency, and thereby, providing market pull.
So, we will give you the key takeaways. And in order to do that, I will turn it over to Jim Szybist. Jim?
Jim Szybist: Thanks, Dan. One of the key takeaways in Co-Optima is that we have determined what the most important fuel properties are for efficiency in a quantitative manner. It is important to determine these effects in isolation from one another because, for alternative biobased fuels, which have different compositions relative to petroleum-based fuels, it is important to know how individual fuel properties can trade off against one another. The first of these is the research octane number, or RON as it is often referred to.
Different grades of gasoline at the filling station are differentiated by an average of two different octane rating tests – the research octane number and the motor octane number. These two tests represent different operating conditions. The operating conditions of the research octane number are more representative of modern engines. The second fuel property we have identified is octane sensitivity, which is the difference between the research octane number and motor octane number. And the third most impactful fuel property on efficiency is heat of vaporization, which is a measure of the cooling that occurs when the fuel is vaporized with the air.
While these fuel properties were widely recognized as being important for efficiency prior to Co-Optima, what Co-Optima did was to quantify each of these effects and put them on a common basis so that the fuel properties could be individually valued. We have also identified numerous chemical families that best meet these fuel properties. These are the alcohols, iso-olefins, and alkylfurans. These are fuels that can be derived from sustainable sources with reduced lifecycle greenhouse gas emissions and can enable higher efficiency. Next slide, please.
Next, to provide some insight on how we came to these takeaways, I will discuss our research approach at a high level to connect engine performance to fuel properties and ultimately, to fuel chemistry. Next slide.
So, you have already heard us mention fuel properties on several occasions, and the reason for this is that there is a central hypothesis about fuel properties and engine performance. To put simply, the hypothesis is that equivalent fuel properties result in equivalent performance. The development of this hypothesis at an early stage in Co-Optima was really essential to how the program developed. Taking a fuel-properties-based approach allowed us to proceed in a composition agnostic manner, thereby allowing us to work on both the needed engine developments and on fuel compositions to achieve those properties. If a fuel has improved properties, there will be some benefits to an existing engine, but to really take the most advantage of those properties, from an efficiency standpoint, it is going to be necessary to change the engine design.
The merit function that we developed to quantify the potential of each of these fuel properties does just this. It considers engine design changes that coincide with the more beneficial fuel properties. Next slide, please.
The merit function that we developed quantifies the efficiency potential of fuel properties as described in the paper shown on the slide. It was published in Progress in Energy and Combustion Science. This paper synthesized a large amount of work, a large number of individual journal articles that were produced within the lifespan of Co-Optima. Co-Optima currently has more than 250 journal articles in the Co-Optima Publications Database. This literature review and analysis brought together the learnings of Co-Optima as well as learnings from other outside groups into a unified merit function metric that allows fuel properties to be quantified and traded off against one another.
We considered more than 15 qualifying fuel properties in an initial screening, and then performed a detailed analysis on six individual fuel properties for engine optimization. These are the research octane number, octane sensitivity, heat of vaporization, flame speed, the particulate matter index, and the catalyst light-off temperature. Now, in order to quantify these effects, we need to also know how the engine design is changing, and for this, we considered more than six different engine technologies for the optimization. Next slide, please.
Perhaps the most useful example of this optimization is an illustration of how knock limits engine efficiency. This figure shows engine efficiency as a function of engine speed on the X-axis and engine load on the Y-axis. The top of this contour can be thought of as full acceleration. The energy efficiency shown in the contours changes significantly with the different operating conditions. Some of the highest efficiencies occur at the lowest speeds and high loads, and these are the conditions that are most limited by knock, which is an abnormal combustion event that limits efficiency. Fuels that resist knock can provide higher efficiency, particularly in these parts of the map. Next slide.
In order to understand what knock is, it is useful to understand how a normal combustion event in the combustion chamber proceeds. Combustion is initiated by a spark at the spark plug location from which a flame propagates outward to the walls. This process can take several milliseconds, which is slow relative to knock. In contrast, when knock occurs, combustion starts in the same way, but the high temperatures, pressures, and the amount of time that is available in the unburned zone causes the fuel and the air to auto-ignite before it is consumed by the flame. This causes a loud pinging noise in the combustion chamber and can cause engine damage.
Engines – modern engines, especially – are calibrated to avoid knock, and it is seldom heard in modern engines in modern vehicles. However, the avoidance of knock causes a significant efficiency penalty. Fuels that have a high research octane number, a high octane sensitivity, and a high heat of vaporization do a better job at resisting knock. Next slide, please.
The example of knock was just one way in which fuel properties can affect energy efficiency, but we must consider a lot more factors that go into engine efficiency. Knock primarily affects the theoretical thermodynamic efficiency, which is the term on the right most side, which is ultimately the largest contributor to efficiency. But fuels can also impact a number of other terms that also impact efficiency – this includes the effects on fuels on heat transfer, on air flow or pumping losses. It also includes losses due to unburned fuel and engine friction, which is associated with how much an engine size can be reduced with a given fuel. In addition to these, fuel effects on emissions compliance with gaseous and particle emissions were also quantified and included in the merit function. Next slide, please.
So, the merit function was normalized to represent a relative efficiency. A merit function score of five represents a 5 percent improvement in efficiency. I mentioned earlier that the research octane number and octane sensitivity were two of the most impactful fuel properties on efficiency. On the right-hand side, we can see the merit function score for these properties. The reference fuel, which represents regular grade gasoline, has a merit function score of 0, whereas a research octane score of a number 100 produces a merit function score of just over 5.
Interestingly, for octane sensitivity, the impact of this fuel property changes dramatically for whether you are using a naturally aspirated engine, or an engine without a turbocharger, or a boosted engine, which is an engine with a turbocharger or a super charger. For an engine without a turbocharger, octane sensitivity is not impactful for efficiency. However, for a turbocharged engine or a boosted engine, octane sensitivity is nearly as impactful as the research octane number. It is important to keep in mind that to fully realize the potential of these fuel properties, the engine design needs to be modified from the baseline case. Next slide, please.
So, Co-Optima has also had a significant focus on being able to model the combustion process to take advantage of the improved fuel properties by developing new models. The development of these models is one of the primary ways that we can hand off the knowledge that we have developed to our industrial partners to maximize the impactfulness of our work. Sometimes, the results these models produce is expected and sometimes they help clarify the net result of what can be a complex set of tradeoffs. For instance, take the net effect of heat of vaporization and flames feed on knock. When the heat of vaporization was artificially increased or decreased in the model, this changed knock in an expected way.
The plot on the left shows that a knock-limited spark advance changes relative to the baseline case. When the heat of vaporization is increased to 130 percent of the initial value, this results in a negative value, indicating that the fuel is more resistant to knock, because it caused a reduction in the in-cylinder temperature. Importantly, we are able to quantify this effect on combustion and, ultimately, on efficiency. For laminar flame speed on the plot on the right, there are a couple of different competing effects. On one hand, a higher flame speed reduces the amount of time available for reactions that cause knock to occur.
However, on the other hand, a faster flame speed and combustion process results in a higher in-cylinder temperature and pressure. These simulations help clarify that, in this case, increasing the flame speed actually increased the knock propensity, meaning that the net results of the higher temperatures and pressures was a stronger effect that the reduced time. Next, I will turn it back over to Dan to talk about the structure-property relationships.
Dan Gaspar: So, the third leg in our approach was really to try to understand the structure-property relationships. In other words – “How does the chemistry and the molecules of the fuel impact their combustion or other properties?” So, we did this through a variety of approaches. We started with a number of candidates that we could then identify, and these tools – some of these tools are available to the public. On the right, you can see the output from a particular model developed at Sandia called Feature Creature. And so, the way to read these diagrams is red means a higher contribution to a given property and blue is a lower or negative contribution to a given property.
And so, this allowed us then to estimate and to understand the impact of the different parts of the molecule on that particular fuel property. So for a molecule called Prenol – you can see on the left – when it was neat, in other words not blended into a base fuel – the primary contribution to RON was the alcohol moiety or the oxygen bonded to the hydrogen on the right side of the molecule, with some contribution from the double bond in the middle of the molecule. But when blended, the primary contribution to increasing RON became the double bond and the alcohol was less important. Similarly, for the molecule di-isobutylene shown on the bottom, the double bond – and particularly one part of the double bond – was most important neat, but when blended, contribution was spread to other parts of the molecule. So, these kinds of tools – some of which are available to the public and you can find links to them on the Co-Optima website – allow us then to make predictions about what and understand how chemical structure impacts fuel property.
So using these kinds of tools as well as lots of outputs from other groups outside Co-Optima and a long history of understanding of how hydrocarbons behave in the fuel industry, we started with a very large number of candidate blendstocks. So in order to efficiently screen them, we used what we call "the leaky funnel." This is an approach where we can look at large numbers of molecules and mixtures using high-level screening and small volumes – 0 if we're looking at them computationally up to maybe 100 milliliters – and we can then determine which have the most promise and evaluate them more carefully in blending studies as well as fuel property studies that require more volume, and then ultimately, we can select those candidates which have the most promise and look at them in engine studies of one sort or another. In that case, we might use up to gallons, which for a new molecule or a new mixture, might be quite expensive to generate at the lab scale.
And then once we have generated the data on fuel properties, in order to understand the impacts, we have used an integrated set of analysis tools. Each of these tools on the left – each of the major boxes – is a tool, all of which were developed prior to Co-Optima. But what Co-Optima has been able to do is to integrate these so that we can answer complex questions that are not really accessible via the output of a particular model. And so, the ADOPT model on the upper left is a consumer choice model that can estimate how the market might change as the properties of engines change over time. The middle on the top, BSM is the Biomass Scenario Model, and that model answers the question, "What happens with the number of biorefineries as the demand for biofuels grows?"
And then, that model inputs into JEDI on the upper right, which is a jobs and economic development model, which then can estimate the net job benefits over time. There are other models on the bottom. So, TEA is technoeconomic analysis. We do this a variety of different ways. It is a way to connect a particular production process with estimated costs.
GREET is gold standard for greenhouse gas emissions modeling developed at Argonne. We used GREET for all of our lifecycle analyses, and then those feed into bioeconomy age, which seeks to understand the overall energy and environmental impacts of the total biofuel industry. And so we do what we call a benefits analysis, which rolls all of these up into a single set of model runs based on all of the other research and analysis that was conducted within the program. The details and the outputs of those analyses will be described in two later webinars.
So, what have we learned? I think the most notable outcomes is that we have identified fuel-engine combinations that can achieve a 10 percent efficiency gain in turbocharged or boosted spark-ignited engines.
To improve efficiency and reduce greenhouse gas emissions, we have simultaneously looked at what biofuel options provide critical values or targeted values of critical fuel properties, how those impact engine efficiency and emissions, what fuel properties are critical to doing that, and then ultimately, how those fuel properties and the fuel-engine combinations can lead to greenhouse gas emissions reductions. And so, this is a durable methodology that can be used to guide future fuel development. In fact, we have applied this methodology to the other work within Co-Optima.
With that, I am going to turn it back over to Jim to describe how the merit function points the way for us.
Jim Szybist: Thanks, Dan. First, the merit function allows us to individually value each fuel property for high efficiency. The first term here in the merit function includes the research octane number and octane sensitivity. Those are in the octane index terms. Next, the heat of vaporization is combined into two different groupings.
The first illustrates the effect of heat of vaporization on an engine knock, and the second incorporates the other effects, including combustion efficiency effect and various thermal effects on engine operation. The next property includes the effect of laminar flame speed, and this is captured from the standpoint of being able to support a more dilute combustion process. And lastly, the fuel effects on emissions compliance are captured with the fuel type. With Co-Optima, we have captured the effect of the particulate emissions, which describes the need to include a gasoline particulate culture, and on gaseous emission, which focuses on the catalyst light-off temperature. Now I will hand it back over to Dan to finish off the presentation.
Dan Gaspar: Thanks, Jim. So along the way, we identified many blendstock options that can provide these target values of critical fuel properties, and we quantified their potential impact on efficiency using the merit function. You can find a detailed description of those results at the link found at the bottom in the Top 10 Bioblendstocks for Boosted SI Report. I would note that all of these candidates have high RON, high S or high octane sensitivity, and that the small molecule alcohol – shown mostly on the left – also have high heat of vaporization.
I would also note that the research octane number for all of these blendstocks blend what we call synergistically – or non-linearly – in a way that the contribution to increasing the research octane number is larger than would be expected from a linear relationship between the volume blended and the resulting RON. So let me also make a couple of notes or a few comments about the blendstocks shown here. Out of these 10, six were determined to have the fewest barriers to adoption, and those six are ethanol, isobutanol, the isopropanols, di-isobutylene, and the fusel alcohol blend. Cyclopentanone has some significant issues with compatibility with infrastructure, elastomers, and plastics, and research within Co-Optima on cyclopentanone was halted as the extent of these challenges became clear. The furans have some of the highest potential for synergistic RON blending, but they also have some significant barriers to entry, including oxidative stability and they are moderately toxic.
Methanol has very high blending RON. It has relatively low energy density, which was not one of our criteria, but is to be considered. But the main barrier to adoption for methanol is its high vapor pressure. I would note that methanol is used as a blendstock and a market fuel elsewhere in the world, and it is under consideration for use for various applications. And then, prenol – on the upper right in the alcohol box – also has exhibited some oxidative stability challenges. It has what the Co-Optima researchers who discovered this call hyper-boosting in that the neat blendstock has a lower RON than when it is blended into a base fuel. And so, that hyper-boosting potential means that it has very high synergistic RON blending and remains interesting for that reason.
I would also note that out of all of these, we did not find aromatics that met all of our criteria, except for a component within the fusel alcohol blend, but work since then has looked at minimizing the amount of that phenyl ethanol in that blend. So, you can find details on many of the other blendstocks we have evaluated and what we learned about them in the report as shown.
So what are potential impacts of adoption of these fuels? One of the things that Co-Optima has determined is that there is a wide range of potential carbon intensities for many of these fuels or blendstocks based on the pathways that we have examined. On the right, you can see a fairly large list – and I apologize for the small text – if you blow it up, it will be clear and easier to see. You can see a large number of outcomes of lifecycle analyses conducted by the Co-Optima team. At the top is petroleum gasoline with a lifecycle greenhouse gas emissions of about 90-gram CO2 equivalent per megajoule; and all of the top candidates that we evaluated would reduce that by at least 50 percent, and most of them by more than 60 percent.
You can see as well that even for a given blendstock such as ethanol, there are a fair range of greenhouse gas emissions that might be obtained – or reductions that might be obtained – depending on the specific pathway. Given the early stage of the research within Co-Optima, in order to generate these lifecycle analyses, we have often had to make a large number of simplifying assumptions in order to be able to actually generate a credible lifecycle analysis. But ultimately what we did determine, is that we can reduce the lifecycle greenhouse gas emissions significantly and that we have a number of options to do so.
So, what comes next?
In order to realize the potential of fuel-engine co-optimization, the fuels need to be available at scale and the engines need to be designed to take advantage of them. And so, for some of these, that includes overcoming other adoption barriers, but ultimately if you would like to see our assessment of what benefits might be obtained, you can find details in the paper on the right, led by Jennifer Dunn of Argonne National Laboratory, with a number of collaborators from PNNL and NREL. Sorry – this was just from NREL. But the energy, economic, environmental benefits will only be obtained if we are able to generate them and use them at scale. Right now, most of the blendstocks that we have shown are not market fuels and so they would need to be qualified for use in vehicles and the allowable blending levels would have to be increased for some of them to obtain the benefits that we are seeking.
With that, I'd like to thank the large Co-Optima team. Here's one picture from one of our all-hands meetings on the left – and particularly, thank the Department of Energy and leadership within the transportation part of the Department of Energy – so, Michael Berube, the active deputy assistant secretary for transportation; Valerie Reed, the acting director of the Bioenergy Technologies Office; Dave Howell, the acting director of the Vehicle Technologies Office; and Gurpreet Singh, the program manager for the Vehicle Technologies Office Advanced Engine and Fuel Technologies; and as I said at the beginning, Alicia, Kevin, and Mike for their continued guidance and support. With that, Jim and I would be happy to take any questions you might have.
Okay Nick, I am looking in the chat. It looks like I do not see any questions in the chat.
Nick Medina: I do not see any questions. If you do have a question, feel free to raise your hand, type something into the chat. All right, I do see a hand from Kent Hoekman. Go ahead. Unmute yourself.
Kent Hoekman: Okay. Thank you. This is Kent Hoekman from Desert Research Institute. I am wondering if the Co-Optima team is involved in any interactions with organizations and governmental bodies working to eliminate internal combustion engines.
Dan Gaspar: The Co-Optima team interacts with various parts of the government as well as industry and other stakeholders through a variety of avenues. I do not think we are working closely with – I think the short answer is no, we do interact with stakeholders in a lot of different ways and it is possible some of them are advocating for such a position, but our role is to really provide the data, tools, and knowledge and not to advocate for a particular policy. Hopefully that answers your question.
Howard Kershaw: I have a question.
Dan Gaspar: Yes, please.
Howard Kershaw: With the variable compression engines in your analyses, how important is that with the different types of fuels that you are looking at?
Dan Gaspar: I'll let Jim field this one, I think.
Jim Szybist: Yeah, so we did not specifically optimize for, say, a continuously variable compression ratio, but the compression ratio of the engine did change with different fuel properties. And I'd say that was quite important. If you have an engine that is knock limited with a particular fuel, you can change that knock limit by putting in a more knock-resistant fuel for sure, but in order to take full advantage of what that fuel can offer and really provide those efficiency benefits throughout the operating map, you do need to increase the compression ratio of the engine. So, I hope that answers your question.
Howard Kershaw: Well, it does, but another thing – with compression ignition engines, the advantages with that with gasoline – has that been looked into, the value?
Jim Szybist: Yes. So, we refer to that as – well, there are a number of different acronyms that describe it, but we have it fall under the same category of advanced compression ignition, and that is a topic that will be considered in later webinars and was part of Co-Optima. So we did include that in our work, and it will be covered in a later webinar.
Howard Kershaw: Okay, thank you.
Dan Gaspar: It looks like we have another hand up, Nick. Is that Costa Badra?
Costa Badra: Hi, yeah. This is Costa from the University of Michigan. I had a question regarding that one plot that compared the turbocharged gasoline engines to the naturally aspirated one, and why there was no dependence on the merit function with respect to sensitivity for the naturally aspirated ones?
Dan Gaspar: Jim?
Jim Szybist: Sure, I will give a short answer here, but I would refer you to that article that is in the slides that I believe will be available to you for more detail. I would tell you the short answer is that the knock propensity of a different fuel changes with the operating conditions. And the knock propensity of a naturally aspirated engine matches pretty closely to the conditions in the research octane number tests. So you are already getting a pretty good representation of how the fuels behave for a naturally aspirated engine.
What the octane sensitivity does is it modifies that research octane number rating for the conditions that are important for a boosted engine. So I would say that is the short answer – that the research octane number is really a good representation of the important knock-limited conditions in a naturally aspirated engine.
Costa Badra: I see, thank you. That answers my question.
Jim Szybist: Thanks.
Dan Gaspar: There is a question in the chat from Steve Bush, and the question is, "Are there plans to continue to apply the analysis techniques developed within Co-Optima to e-fuels?" And by "analysis/techniques" here, I am assuming he means the technoeconomic and lifecycle analyses. But if he also means the modeling work that the toolkit team does or the things that Jim referred to regarding merit function, et cetera, the short answer to all of the above is, I think, "Yes." You know, that will ultimately depend on how the department chooses to advance their research agenda in order to meet administration goals. But I think everything we have done is extensible – as we said, a durable methodology to evaluate new fuels and engine options.
Okay. Any other questions?
Howard Kershaw: Yes, Howard Kershaw. I would like to ask another question. In your laboratory testing or knowledge, I am seeing different temperatures for knocks running from 2,100 degrees in the combustion chamber to 28. Are you seeing any different figures or any laboratory figures as to what is the ideal temperature that is best for emissions?
Dan Gaspar: Jim, do you want to –
Jim Szybist: So, the particular emissions that you get and the particular emissions that you can handle in terms of cleaning up – in terms of the treatability in the emissions – are highly dependent on a lot of factors. And I would say the in-cylinder temperature is one minor consideration in a number of additional considerations. So I do not think the in-cylinder temperature is the primary driver for emissions, per se, although certainly for NOx emissions – nitrogen oxide emission – then certainly, it is a contributor and plays a role. But if you are dealing with, say, a stoichiometric spark-ignited engine or what we are describing here in this webinar, there are very well-established emission controls with a three-way catalyst that is on all passenger cars on the road today. That does a very good job of cleaning those up.
A much smaller level of NOx emissions from an engine that had a fuel-lean strategy makes emissions control more challenging. Now, there are well-established techniques to deal with that, but it is a more complex after-treatment system. So, I think it can be a complicated answer that has a lot more dependency that what we are talking about so far.
Dan Gaspar: And I might add that in later webinars, we will talk a little bit more about emissions control in the context of advanced compression emissions – and you asked questions earlier about GCI – about gasoline compression ignition – and we will talk about that. And we also have some papers published by Josh Peele and Theresa Majumdar at Oak Ridge National Laboratory that talked about the neat and blended emissions impacts of some of the blendstocks that we have looked at. I would note that they were primarily focused on particulate matter, I think in that case, and not knocks. We also have some work in the medium- and heavy-duty space that will talk specifically about knocks.
Howard Kershaw: Great, I will follow-up on that. Thank you.
Nick Medina: Okay. I see a question from Dominic Vervetto. If you want to go ahead and unmute yourself.
Domini Vervetto: The State of California has a program that most people are aware of called LCFS, which directs the fuel industry toward certain processes and certain fuel products. And most of what you talked about could be compared to where California is headed in terms of its LCFS program. And I was wondering if you compared your conclusions about these advantageous fuels with what the LCFS program is incentivizing industry to produce and how that looks.
Dan Gaspar: Yeah. So in the light-duty space, we haven't as much. The light-duty LCFS is focused on market fuels, and we have looked at a number of blendstocks that are not on the market. In the medium- and heavy-duty space, in a later presentation when we talk about diesel fuels – Bob McCormick and Chuck Mueller will talk about diesel fuels and engines – there, we actually did a direct comparison between the LCAs and that were conducted within Co-Optima and the LCAs that are published for the LCFS pathways. And so, we did compare those directly.
In some cases, some of the candidates that we have identified – which are pre-market; they're not allowed in the marketplace at this point – have lower emissions, and in other cases, they are comparable or a little bit higher. And so, that was a good benchmark for us, and it was something we used for exactly that purpose. Does that help answer your question?
Dominic Vervetto: Yeah. So have you published any of that data or comparisons available to the public to see?
Dan Gaspar: It is under review at the Department of Energy right now and we hope to have it out in the near future.
Dominic Vervetto: Okay, thank you very much.
Dan Gaspar: Okay, any more questions? Nick, do you see any hands up?
Nick Medina: I do not.
Dan Gaspar: Okay, it looks like there is nothing in the chat. Well, with that then, first of all, we would like to thank all of you for staying and listening and for the thought-provoking and interesting questions. Very interesting for us to hear what you are thinking.
And so, if you have further comments or questions, feel free to reach out to any of us – to Jim or me on this presentation – or any member of Co-Optima. We are happy to answer any questions or have a more detailed dialogue. With that, thank you again for your time and have a nice day.
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