美国阿凯提斯动力公司（Achates Power）打造的2冲程对置活塞压燃式发动机已正式计划投入市场。据阿凯提斯称，同标准的4冲程发动机相比，该款发动机能够大大提升燃油能效。而就像2017年2月号的《汽车工程》（Automotive Engineering）有关这款发动机的封面报道中曾提到的，要让这个有100多年历史的动力技术概念在本世纪付诸实践，还有很多实际问题有待解决。

毫无疑问，技术细节需要更详尽完备。为此，阿凯提斯研发团队运用先进的CAE模拟仿真技术（CAE simulations），在造价更低、运算更快的现代计算机上模拟仿真运行，以使细节方面更趋精准。

阿凯提斯的首席执行官David Johnson说，“1990年我刚加入福特时，公司有一台克雷超级计算机（Cray Supercomputer）。这在当时绝对是个宝贝，只有很少的工程师能用到它。而如今，阿凯提斯给每名工程师都配备了与之性能相当的计算机设备，公司有1/3的工程师每天都会用到。”

对于这个领域的工程师而言，阿凯提斯的这一大胆构思绝对让人激动无比，尤其是那些在较小公司工作的同行。而要弄清楚这一设计为何如此令人兴奋，我们还得先简单了解一下相关背景。

模拟仿真软件和基本原理

燃烧模型建模的复杂和困难程度远超想象，工程师需要十分精确地了解燃烧在哪个部位，什么时间发生，如何膨胀，以及热量如何释放，其精确程度需要达到毫米级别，且以微秒为时间单位。

所有现代CAE模拟仿真采用的都是离散技术。一个计算机辅助设计模型（CAD model）被分成若干个计算元细胞，再使用有限元（finite-element）或有限体积（finite-volume）法模拟出液体流动、热量释放、自动点火（爆震）、氮氧化物、烟炱以及未燃烧的碳氢化合物等状态，然后在元细胞中或元细胞之间解出“简化后”的方程。而对于阿凯提斯这样的对置活塞发动机而言，要了解技术细节，将元细胞准确定义到亚毫米级至关重要。而最终得到的则是数百万的元细胞和方程，这些数据必须在逾千步的分析中实现同步处理。

而在每个亚毫米级的元细胞内，要做出燃烧模型则需要模拟出一套复杂的化学动力学反应。由于其太过复杂，大多数的发动机厂商都会采用相对简单的燃料模拟物来代替真的燃料。毕竟，真的燃料中所包含的化学复合物多达数百种，而燃料模拟物中的化合物则只有几十种。

举例而言，我们从阿凯提斯技术开发副总裁Fabien Redon处获悉，在设计9.8升柴油发动机的模型时，阿凯提斯使用的燃料模拟物由35种物质混合而成，需要77个反应步骤。阿凯提斯的软件供应商ANSYS和Convergent Science都会为其提供燃料模拟物的数据库和详尽的化学动力反应模型。比如，ANSYS会提供汽油、柴油、航空煤油及其他燃料、天然气或合成燃气、生物燃料以及燃油添加剂等模拟物的相关数据，客户根据其具体需求合理选用。而随着时间的推移，这些数据库也将不断扩充。

但这些还不是CAE模拟的全部。由于燃料在每个时间点上的燃烧情况不尽相同，燃烧时，燃料和空气的混合物会在发动机气缸内位移和膨胀。这就需要将三维的流体力学计算代码（即CFD）和化学动力编码配对后，进行三维的燃烧分析。拿上例来说，每个元细胞需要在涡轮模型下计算77道反应步骤和配对后独立的纳维—斯托克斯方程（Navier-Stokes equations）。这也就难怪克雷超级计算机一度如此抢手了。

适应性强的智能软件

尽管如今计算机的能力已经能够逐渐满足CAE日益增长的需求，软件开发者仍需设法降低其复杂度。不过，建立能够一直运行的固定模型已经是相对容易的部分了。如今CAE软件公司已开发出了自动网格生成、多部件燃料蒸发模型、元细胞分组（用以化学动力计算）以及网格自适应改良（adaptive mesh refinement）等技术。

网格自适应改良技术可将体积小的元细胞置于温度或燃烧等变量对结果影响明显的区域，而将体积大的元细胞置于影响不明显的区域，以便进一步减轻计算的负荷。

Redon指出，网格自适应改良技术在每个时间步长都会进行运算，这一点对阿凯提斯研发团队而言尤其有帮助，因为燃烧区域处在对置的活塞之间。基于用户自定义的网络控制参数，Converge Science公司的代码会在运行环境下进行自适应改良，减少了对脚本或模板的依赖。其他代码也会进行类似操作。

尽管优质详尽的模型对于理解气缸内的燃烧动态十分重要，然而研发者最终想看到的还是发动机在各种负荷和速度状况下的表现。据Redon称，由于对置活塞发动机使用气缸扫气技术替代提升阀，测试的预估是否足够精确，完全取决于对空气、燃料以及气门关闭时未排出气体的建模。

此外，有些进出气缸的气流和燃烧无关。若要将这一因素考虑在内，就要涉及到Redon所说的三维开放式循环分析（3D Open Cycle Analysis）。这一情况下，开发人员就需要对已提交的代码做重大调整，而这通常是需要CAE公司提供支持的部分。

阿凯提斯还建了一个摩擦模型，来预估动力气缸、变速箱、曲轴轴承、发动机附件以及密封件等所带来的主要摩擦损失。Redon解释道，“用来计算动力气缸和曲轴轴承摩损的是曲轴角解算模型。这一模型的特点是在计算这些部件的摩擦时，囊括了气缸压力长期变化的影响。”

系统模型和最优化技术

要建立一个完全可行的发动机模型，阿凯提斯就要将其变成一维或系统模型。阿凯提斯的做法是把Gamma Technologies公司的GT Power技术运用到发动机上。这类一维代码并非是要从空间上模拟仿真气缸内的情况，而是对理解其热力学方面的状况大有帮助，并能提供指示扭矩和热效率数据。再将其和三维的流体力学计算代码，以及经Converge Science公司改进过的三维开放式循环分析相结合，就能生成一个三部分构成的三阶段迭代循环（参见图表）模型，来预估发动机的表现。

这一模型的另一特点是迭代循环内还嵌入了实验设计（DoE）。这种统计工具可用来进行气门倾角的几何计算，从而估计气门的方位和涡流。Redon称，从已有实验结果的长期相关性表现中可以看出，模型有足够的精确度。这在近年SAE的技术论文中多有体现。

除了精确计算燃烧和液体流动的各项数值，如今的软件还能优化气缸的几何形态和各部件的组合，以使整个系统更趋完美。设计优化的发展非常激动人心，越来越多的多领域优化、形状优化以及拓扑优化技术已得到运用，这些都有助于工程师拿出更好的设计。

“我们正在将优化技术运用到我们的燃烧CFD上，以找到活塞碗形状、燃烧时气流运动和喷油器喷雾模式之间的最佳组合。”Redon指出，“根据具体的应用和参数设计空间等不同情况，我们可以采用相应的遗传算法和实验方案进行最优化处理。”

展望CAE软件的未来，值得关注的一点是，其中大多数工具都能通过平行计算轻而易举地提高运算速度，像CFD或化学动力学编码皆是如此。如今，很多台式计算机都是四核或八核处理器，以后甚至会更多。因此使用CAE会变得越来越容易，从而对既有的理念和技术提出新的挑战，而这正是阿凯提斯动力如今正在进行的变革。

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.

Author: Bruce Morey
Source: SAE Automotive Engineering Magazine