2015 SAE Thermal Management Symposium wrapped up few days ago to its highest attendance ever in Troy, MI. I presented a joint work we did with Ricardo titled “Cold-ambient Warm-up Predictions and Improvements Using a 1D Computational Model”. (email me for the pdf if interested)
In addition, these are the three takeaways from the conference that made my visit really worth it.
$100 per 1% of fuel economy gain
Among many numbers and ideas from the conference, if there was one number that was really talked about, it was one that came from one of our keynote speakers. The OEMs are willing to pay upto $100 per 1% gain in fuel economy. I heard a similar metric from the VTMS in UK earlier this year. Even though this metric is rather straightforward, it provides a good yardstick for those not directly at the OEMs to gauge the viability of the technologies they are working on and how it will be perceived by the OEMs. The keynote speaker did state that once all the low hanging fruit are gone, he wouldn’t be surprised if this number went up to $300 per 1% fuel economy gain.
Is ORC an orphan?
I ask this because the talks from the passenger car OEMs indicated that ORC (Organic Rankine Cycle) is too bulky for small cars and it is better suited for larger trucks. Logical. And we’ve known this. However, our keynote from the heavy truck industry indicated that ORC is unlikely to be integrated into the trucks in the next 3-5 years as the customers are unwilling to pay for it. Besides, he suggested that other mature technologies when combined cleverly can achieve the CO2 reduction without ORC. Bottom line, they had no plans for the ORC!
Having said that, there seems to tons of work ongoing on the Organic Rankine Cycle, as indicated by the steady stream of papers at the conferences by govt labs and OEMs. I’ve seen many papers on the technology of ORC but what I want to see on this topic is an honest gloves-off discussion on maturity, cost-benefit and practicality.
If you want to improve the fidelity of your simulation, grade your suppliers
John Deere did a very interesting presentation on the process of getting accurate simulations. The paper talked about how sensitive the systems were to small errors. 2% error in airflow prediction led to 1 deg C change in critical ambient operating temperature. Their simulations were untrustworthy when they just used supplier data in their models without critical assessment. They couldn’t blindly trust the supplier data of components. So they tested most critical components themselves in their labs (sometimes multiple samples) and compared it with how close that test data was to the supplier provided information. This helped them decide whether they could use the supplier data as is (because that particular supplier provided fairly accurate data) or whether to ask for more information. This led them to a higher confidence and maturity of the simulation process and a 42% reduction in testing costs! Wow. Isn’t this the justification we all use for simulation?
Look forward to next year’s conference in Arizona!