In closed cattle-fattening barns, the indoor climate and herd growth are mutually interdependent. Temperature, relative humidity, airflow, and ventilation affect thermal comfort, feed intake, metabolic heat production, daily growth, feed efficiency, and energy consumption, while body-weight gain alters the future heat and moisture loads of the barn and, consequently, its ventilation, heating, and energy requirements. This article proposes a trans-domain digital twin framework with single-episode learning capability, customized for bio-aware climate and energy control in a closed cattle-fattening barn. The framework integrates a mechanistic climate simulator, a livestock growth simulator, model predictive control, lightweight reinforcement learning, and structured knowledge memory within a multi-rate temporal-loop architecture. The fast temporal loop operates every five minutes to evaluate actuator decisions and maintain short-term thermal comfort, safety, and energy efficiency, whereas the slow temporal loop provides biological guidance based on daily climatic conditions, feed efficiency, heat production, and growth-limiting factors. The results show that climate, growth, energy, feed, biological guidance, and memory can be linked within a single executable control cycle. Remaining limitations include the need for field validation, improved management of feed pressure, and reduction of abrupt actuator-command variations.

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