The Achates Power opposed-piston, two-stroke compression-ignition engine is making its way to market, boasting significantly improved fuel efficiency versus today’s standard four-stroke units. As explained in the February 2017 Automotive Engineering cover story on the engine, many practical challenges had to be overcome before the 100-year-old power concept was ready for duty in the 21st century.
A highly detailed level of engineering was needed. The Achates development team exploited the advanced CAE simulations running on today’s cheap, fast computers to get those details precisely right.
“When I joined Ford in 1990, the company had a Cray Supercomputer that was a special thing; only a few engineers got to use it. Today, we have that same capability accessible to any of our engineers and about a third of them are using it every day at Achates,” stated company CEO David Johnson.
There is an exciting lesson here from Achates for all engineers, especially those working at smaller companies. And to fully grasp their enthusiasm, a bit of background is required.
Software simulation tools and fundamentals
Modeling combustion is exceedingly complex. Engineers need to know minute details down to millimeters of where and when combustion occurs, how it expands and how heat is released, measured in microseconds.
All modern CAE simulations use discrete techniques. They divide a CAD model into a mesh of computational cells and then solve ‘simplified’ equations in and between the cells, usually with finite-element or finite-volume mathematics to simulate fluid flow, heat release, autoignition (knock), NOx, soot and unburned hydrocarbons. For an engine like the opposed-piston Achates, specifying cells in sub-millimeters is crucial in understanding the details. The result is millions of cells and equations that must be solved simultaneously over thousands of time steps.
Within each sub-millimeter cell, modeling combustion requires a complex chemical kinetics simulation. It is so complex that most engine OEMs use less complex fuel models that represent real fuels. The chemical compounds that comprise real fuels contain hundreds of species, a model fuel surrogate a few dozen.
For example, in modeling a 9.8L diesel engine, Achates used a fuel surrogate with 35 species and 77 reaction steps, according to Fabien Redon, the Vice President of Technology Development. Software suppliers to Achates such as ANSYS and Convergent Science offer model fuel libraries along with their detailed chemical kinetics models. For example, ANSYS offers fuel models for gasoline, diesel, jet fuel, FT fuels, natural or synthetic gas, biofuels and additives, for use with its Forte code, according to the company. These libraries are expected to continue to grow.
But there's more. As the fuel is combusting differently at each point, the fuel/air mixture will move and expand in the cylinder while it is combusting. The spray of the fuel into the chamber requires its own modeling technique. This requires coupling a 3D computational fluid dynamics code, or CFD, to the chemical kinetics code for 3D combustion analysis. So, in the example above, each cell needs to compute 77 reaction steps as well as the coupled discrete Navier-Stokes equations, with a turbulence model. No wonder Crays were once needed!
Smart, adaptable software
Despite the power of today’s computers that is vital to the growth of CAE, software developers still need to help tame complexity. It is still easy to create models that will run forever. Today, CAE software companies offer automatic mesh generation, multi-component fuel vaporization models, methods to group cells for chemical kinetics computing and adaptive mesh refinement.
Adaptive mesh refinement creates small cells in places where there are steep gradients in effects, like temperature or combustion, and big cells where not much is happening, further reducing computation.
Redon notes that adaptive mesh refinement calculated at each time-step is particularly useful for his Achates team because their combustion area is squeezed between the opposed pistons. The Converge code does this at runtime based on a few user-defined grid control parameters, eliminating the need for scripts or templates, according to Convergent Sciences. Other codes perform similar operations.
While finely-detailed models are essential to understanding in-cylinder combustion dynamics, what is required at the end of the day is the specific performance of the engine at any load and speed. According to Redon, because opposed-piston engines use a scavenging process rather than poppet valves, accurate predictions depend on modeling of the air, fuel and exhaust trapped at the instant of port closing.
Also, there are flows into and out of the cylinder that do not involve combustion, what Redon refers to as 3D Open Cycle Analysis. This required them to make important adaptations to the codes as-delivered, a task usually facilitated by the CAE companies.
Achates also created a friction model, to model the important losses from the power cylinder, gearbox, crank bearings, engine auxiliaries and seals. “The power cylinder and crank-bearing friction was calculated using a crank angle resolved model which allows for impact of the cylinder pressure history to be included in assessing the friction for these components,” explained Redon.
System models and optimization
To create a fully functional engine model required Achates to turn to a 1D or system model. They adapted Gamma Technologies' GT Power to their engine. Such 1D codes do not attempt to model in-cylinder spatially, but are useful in understanding thermodynamic conditions and provide indicated torque and thermal efficiencies. Combining this with a 3D Combustion CFD code with the 3D Open Cycle adapted from CONVERGE, they created a three-part, three-step iterative loop (see figure) to predict engine performance.
Embedded in the iterative loop is a Design of Experiment, or DoE, computation to calculate the geometry of the port angle to estimate port orientation and swirl. Ongoing correlation to experimental results shows the accuracy of the models, according to Redon, such as in SAE Technical Papers presented in recent years (see SAE paper 2017-01-0638).
Beyond computing the minute details of combustion and fluid flow, today's software can also be used to optimize the best geometry of pistons and combinations of components for a system. Design Optimization is a particularly exciting field, where multidomain optimization, shape optimization and topology optimization techniques are increasingly being used to help engineers design.
“We’re applying optimization techniques in our combustion CFD all the time to identify the best combination of piston bowl shapes, charge motion during combustion and injector nozzle spray pattern,” noted Redon. “We have used genetic algorithms as well as design of experiment schemes, depending on the application and the design space of the parameters to optimize.”
A key point about the future of CAE software is that most of it, such as CFD and chemical kinetics codes, are easily speeded up through parallel computing. Many desktop computers today offer four and eight processors, and more are on the way. Using CAE is only going to get easier, enabling others to challenge the establishment and its incumbent technologies, just as Achates Power has done.