The steam table in saturated and superheated region was modeled simultaneously using the neural networks. A variable was introduced to distinguish between the saturation and the superheat. The relative errors were compared with the quadratic spline interpolation method. The relative errors by the neural networks were superior to those by the quadratic spline interpolation method over almost all ranges of temperatures and properties. The overall errors in the saturated region were better than those in the superheated region. From the analysis, it was confirmed that the neural networks could be a very powerful tool for simultaneous modeling of superheated and saturated steam table

Abstract
 1. 서론
 2. 해석
  2.1 상태량 선정
  2.2 신경회로망(neural networks)
  2.3 스플라인 보간(spline interpolation)
 3. 결과 및 검토
 4. 결론
 후기
 References

• 이태환(경남과학기술대학교 메카트로닉스공학과) | Tae-Hwan Lee
• 박진현(경남과학기술대학교 메카트로닉스공학과) | Jin-Hyun Park Corresponding Author