To resolve the two major problems of the conventional CFD-based shape optimization technology: (1)dependence of the outcome on the selection of design parameters (2)high computational costs, two types of innovative inverse analysis technologies based on the mathematical theory called the Adjoint Method were developed in previous study: surface geometry deformation sensitivity analysis to identify the locations to be modified, and topology optimization to generate an optimal shape, for maximizing the arbitrary hydrodynamic performance as the cost function. And furthermore, we have extended these technologies to transient flows by introducing the theory of the Transient Adjoint Method. However, there are many cases around flow path shapes in automobile where the performance with respect to the heat or concentration such as the total amount of heat transfer or the flow rate of the specific gas component are very important. Therefore, a new Inverse Analysis Technology for CFD which can select arbitrary heat or concentration performance as the cost function was developed. By extending our formulations to the transport equation of the scalar (i.e. temperature or concentration), and expanding the Adjoint variables, and by improving the solution methodologies, the calculation of the surface geometry sensitivity distribution targeting above has become possible both for steady state and transient problems including fluid-solid conjugate problem. The validity of the sensitivities calculated by developed program was verified through test cases including the steady state heat transfer problem and the transient concentration problem. The definite effectiveness of the surface geometry sensitivities to design changes was confirmed about the same as targeting hydrodynamic performance, by applying developed technology to the actual components including the cooling flow performance of PCU assembly, and improvement of multi-cylinder distributing balance of EGR gas in the engine intake flow system followed by the trial shape modifications.