First, some background: There are four typical pieces to the general "modeling & simulation" problem: simulation, stimulation, emulation, and modeling.
Simulation is the use of modeling to create a controllable, representative stand in for a complex system. Simulations are, by definition, always incomplete.
Stimulation is the use of artificial environmental inputs to a well defined interface to drive, examine and test the behavior of a real world device.
Emulation is the replacement of a real world device with an model at a well defined interface for the purposes of allowing controlled responses from the emulated real world device. The emulation is "complete" if all the interfaces are present, and the resulting observed behavior matches that of the real world device. Emulation (almost) never matches the real world device in speed.
Modeling is the use of mathematical techniques, specialized hardware, and engineering judgment to create a representative stand in for a real world environment, device, system, or behavior.
With that as a basis...the "Modeling and Simulation" of any interesting problem is going to rely on a detailed understanding of the physics and logical behavior of that problem domain. While there are general "frameworks" that can prove helpful, there is no general way to cover the problem specific methods that will be required to get a reasonable stand in for any given problem domain.
Within M&S, there are a few big pieces that typically come into play: dynamic systems (time driven simulation), discrete systems (event based simulation), and general numerical methods. I have a book recommendation for each of those.
Modeling and Simulation of Dynamic Systems I studied under Dr Woods many years ago as an undergrad and grad student. This text covers most of the common physical systems: electrical, thermal, and mechanical. The book has a reasonable review of differential equations, linear and non-linear systems, and enough numerical methods to get started.
Discrete-Event System Simulation I used a much earlier edition of this book to learn about Discrete Even Simulations. This book focused on the theory, rather than specific tools (which was uncommon when I was learning the subject). The sections of verification and validation were particularly helpful.
Numerical Methods in Engineering with Python I am not generally a fan of language or tool specific books for topics like there. However, in this case I will make an exception. Numerical methods form the backbone of any modeling and simulation effort. Most of the math centric texts are likely too dense for your purposes. This book (specifically the second edition) strikes a reasonable balance. If you are unfamiliar with Python, it reads enough like pseudo-code that taking the ideas to other languages is reasonable straightforward. If you are able to use Python for your project, then so much the better.