A Unified Methodology for Direct and Inverse Problems in Steady-State Thermal–Hydraulic Networks

Steady-state thermal–hydraulic network models are widely used for the analysis, design, and operation of energy systems. While direct problems with prescribed boundary conditions can often be solved efficiently, inverse problems such as set-point tracking and parameter identification are commonly addressed through repeated solution of the corresponding direct problem. For large-scale networks with strong nonlinear couplings, such nested strategies can become computationally expensive and numerically burdensome. This paper presents a unified methodology for the solution of direct and inverse steady-state thermal–hydraulic problems within a single modeling workflow. In contrast to classical nested approaches, inverse problems are formulated in a simultaneous analysis and design framework, in which system states and selected system inputs are treated as unknowns simultaneously. The methodology combines externally causal component representations with acausal network balance relations in order to expose the structural dependencies of the assembled system and enable graph-based tearing reduction. Component-local evaluations, including possible component-internal nonlinear calculations, are encapsulated within the component models, while the nonlinear network closure problem is restricted to a reduced set of tearing variables.. Direct problems are solved by nonlinear root finding on the tearing-reduced residual system, whereas inverse problems are posed as tearing-reduced residual-constrained nonlinear programs with equality, inequality, and bound constraints. The methodology is demonstrated on a vapor-compression refrigeration cycle, where compressor speed and expansion valve opening are adjusted to satisfy prescribed cooling-load and superheat targets under varying condenser inlet temperatures. Implemented in Python, the proposed methodology supports transparent and reproducible modeling and provides a practical basis for simulation, set-point tracking, and constrained optimization of coupled thermal–hydraulic networks.