Datasets

After you create an idealized model on paper and determine the units you want to work in, you are ready to create your model in Adams Solver by entering statements and function expressions in the dataset. Most statements define a model element. These include the specification of mass and inertia properties, geometry, and connectivity, as well as analysis control and output specification. Each function expression, in general, evaluates a mathematical equation and returns that value to the statement.

The following statements allow you to use the function expressions as part of the element definition:

If you can develop an equation describing the desired characteristic and are familiar with FORTRAN syntax, you have the knowledge to write a correct Adams Solver function expression.

You use a text editor to enter the statements and function expressions in a file. The file is called the Adams model definition file or the dataset. Each statement consists of an identifier and a set of keywords that qualify the statement. You can specify most system properties and data in the dataset.

You always start your dataset with a TITLE statement and end it with an END statement. The statements, function expressions, and comments between the TITLE and END statements constitute the body of the dataset. You can define statements in any order without affecting the results of an analysis. Carefully arranging them, however, can greatly increase dataset readability and simplify debugging, mode sharing, and modification. This section suggests two methods for organizing datasets and discusses the implications of each.

The following sample structure shows a dataset for a piston-crank mechanical system. The geometric information in the dataset describes the model in the top dead-center position.

The TITLE statement in the example is PISTON COMPRESSOR SAMPLE DATASET. Note that the statements are in blocks or sections separated by blank lines. In some cases, the comments occupy entire lines; and in other cases, the comments occupy parts of lines following executable statements. Adams Solver requires neither sections nor comments, but both increase dataset readability.

You can organize the dataset into six major sections:

When you have part drawings that provide the markers with respect to separate body coordinate systems (BCS), this organization is particularly convenient. The major disadvantage of this organization is that it separates statements for joints, forces, and requests from the MARKER statements to which they refer. For example, the two MARKER statements that define a joint are beneath the PART statements and not with the JOINT statement. In short, this organization makes it necessary to search the dataset to find all the information you need concerning a particular system element, but it is hierarchically organized.

The following sample structure is an alternate organization scheme for the piston-crank mechanical system. This structure emphasizes a relational organization of data.

Although the second sample dataset defines the same model as the first, the two differ in their organization. Note that in the second sample dataset, the statements and comments are in four sections rather than in six. The four sections are for:

Note that at time zero, the BCS of each part has the same location and orientation as the global coordinate system or ground.

The major advantage of this dataset organization is that it groups MARKER statements with the statements for parts, constraints, forces, and requests to which they refer. In addition, the position and orientation of each BCS is the same; so to superimpose two markers for a joint, you simply input the same geometric data for them. To describe any point in more than one part, constraint, force, or request, this organization requires that you use multiple MARKER statements so that each MARKER statement is used only once. This dataset organization decreases the time you spend searching for data and makes the dataset easier to modify.

In general, when you define parts with respect to the BCS, an organization like the one in the first sample dataset is easier to use. However, if you are superimposing all the BCS on the global coordinate system, an organization like the one in the second sample dataset is easier to use. The primary objective, of course, is to find an organization that works best for you and for the types of models you are defining. In some cases, you may find it useful to employ a dataset organization other than these two.