Crossed & nested random effects Mixed Effects [migrated]

Here comes my case:

I conducted an experiment with roughly the following design:

30 participants, each with a unique id, were asked to rank using a likert scale how much they liked images of forests. All the participants ranked the first 8 images and then the following 5 images were randomly drawn from a pool of 15 images. Therefore, in total each participant viewed 13 images of forests, but not all images were viewed by each participant. As the responses are ordinal I have gone with a cumulative link mixed effects model to preserve the structure of the data.

And here comes when I need your expertise. So far, I believe that the random terms of my model should take into account that:

Participants (id) Image (id)

However, Im confused about how to incorporate the random effects as each participants views some but not all of the same images. Thus-far I have come to the conclusion and coded it as:

clmm(likert_Rating ~ Experience + X.4 + X.3 + (1|part_id)+(1|Plot_ID), data=TotalF)

However, no matter how many models I try, I am never sure about what terms would be reasonable to include as randoms.

I would really appreciate if some of you could point me in the right direction as I am struggling to decide.

Many thanks for any help.