Calculating optimal number of topics for topic modeling (LDA)
LDA being a probabilistic model, the results depend on the type of data and problem statement. There is nothing like a valid range for coherence score but having more than 0.4 makes sense. By fixing the number of topics, you can experiment by tuning hyper parameters like alpha and beta which will give you better distribution of topics.

The alpha controls the mixture of topics for any given document. Turn
it down and the documents will likely have less of a mixture of
topics. Turn it up and the documents will likely have more of a
mixture of topics.
The beta controls the distribution of words per topic. Turn it down and
the topics will likely have less words. Turn it up and the topics will
likely have more words.

The main purpose of lda is to find hidden meaning of corpus and find words which best describe that corpus.
To know more about coherence score you can refer this