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<divclass="headertitle"><divclass="title">gridfire::screening::ScreeningModel Class Reference<spanclass="mlabels"><spanclass="mlabel abstract">abstract</span></span></div></div>
<areahref="classgridfire_1_1screening_1_1_bare_screening_model.html"title="A screening model that applies no screening effect."alt="gridfire::screening::BareScreeningModel"shape="rect"coords="292,56,574,80"/>
<areahref="classgridfire_1_1screening_1_1_weak_screening_model.html"title="Implements the weak screening model based on the Debye-Hückel approximation."alt="gridfire::screening::WeakScreeningModel"shape="rect"coords="876,56,1158,80"/>
<trclass="memdesc:a107ff2897f040d6f27f69d56a0bdd28d"><tdclass="mdescLeft"> </td><tdclass="mdescRight">Alias for CppAD Automatic Differentiation type for double precision. <br/></td></tr>
<trclass="memdesc:ab08c4490f9da18254a7c6f29be1f62ce"><tdclass="mdescLeft"> </td><tdclass="mdescRight">Calculates screening factors for a set of reactions. <br/></td></tr>
<trclass="memdesc:a1da8242c7591ab32453d1e0109999d6c"><tdclass="mdescLeft"> </td><tdclass="mdescRight">Calculates screening factors using CppAD types for automatic differentiation. <br/></td></tr>
<divclass="textblock"><p>An abstract base class for plasma screening models. </p>
<p>This class defines the interface for models that calculate the enhancement factor for nuclear reaction rates due to the electrostatic screening of interacting nuclei by the surrounding plasma. Concrete implementations of this class will provide specific screening prescriptions (e.g., WEAK, BARE, STRONG, etc.).</p>
<p>The interface provides methods for calculating screening factors for both standard double-precision inputs and for CppAD's automatic differentiation types, allowing the screening contributions to be included in Jacobian calculations. </p>
<p>Calculates screening factors using CppAD types for automatic differentiation. </p>
<p>This is a pure virtual function that provides an overload of <code>calculateScreeningFactors</code> for use with CppAD. It allows the derivatives of the screening factors with respect to abundances, temperature, and density to be computed automatically.</p>
<dlclass="params"><dt>Parameters</dt><dd>
<tableclass="params">
<tr><tdclass="paramname">reactions</td><td>The set of logical reactions in the network. </td></tr>
<tr><tdclass="paramname">species</td><td>A vector of all atomic species involved in the network. </td></tr>
<p>Implemented in <aclass="el"href="classgridfire_1_1screening_1_1_bare_screening_model.html#a0ac3685a90c1e65a84e03cfb2fed29e5">gridfire::screening::BareScreeningModel</a>, <aclass="el"href="classgridfire_1_1screening_1_1_intermediate_screening_model.html#a765e369256282f0f25e66e13c33dcbd9">gridfire::screening::IntermediateScreeningModel</a>, <aclass="el"href="classgridfire_1_1screening_1_1_weak_screening_model.html#a8b0590814e7058b4ff3f0cd6f4124773">gridfire::screening::WeakScreeningModel</a>, and <aclass="el"href="class_py_screening.html#a68a126de903e1a87a1d7d1cfec8add0f">PyScreening</a>.</p>
<p>Calculates screening factors for a set of reactions. </p>
<p>This is a pure virtual function that must be implemented by derived classes. It computes the screening enhancement factor for each reaction in the provided set based on the given plasma conditions.</p>
<dlclass="params"><dt>Parameters</dt><dd>
<tableclass="params">
<tr><tdclass="paramname">reactions</td><td>The set of logical reactions in the network. </td></tr>
<tr><tdclass="paramname">species</td><td>A vector of all atomic species involved in the network. </td></tr>
<tr><tdclass="paramname">T9</td><td>The temperature in units of 10^9 K. </td></tr>
<tr><tdclass="paramname">rho</td><td>The plasma density in g/cm^3. </td></tr>
</table>
</dd>
</dl>
<dlclass="section return"><dt>Returns</dt><dd>A vector of screening factors (dimensionless), one for each reaction in the <code>reactions</code> set, in the same order.</dd></dl>
<p><b>Pre-conditions</b></p><ul>
<li>The size of the <code>Y</code> vector must match the size of the <code>species</code> vector.</li>
<li><code>T9</code> and <code>rho</code> must be positive.</li>
</ul>
<p><b>Post-conditions</b></p><ul>
<li>The returned vector will have the same size as the <code>reactions</code> set.</li>
<li>Each element in the returned vector will be >= 1.0.</li>
</ul>
<p><b>Usage</b></p><divclass="fragment"><divclass="line"><spanclass="comment">// Assume 'model' is a std::unique_ptr<ScreeningModel> to a concrete implementation</span></div>
<divclass="line"><spanclass="comment">// and other parameters (reactions, species, Y, T9, rho) are initialized.</span></div>
<p>Implemented in <aclass="el"href="classgridfire_1_1screening_1_1_bare_screening_model.html#ad252ce1de82401bab24747aabf0c8a3a">gridfire::screening::BareScreeningModel</a>, <aclass="el"href="classgridfire_1_1screening_1_1_intermediate_screening_model.html#a86a218ce82306ed582296cbeb3de0250">gridfire::screening::IntermediateScreeningModel</a>, <aclass="el"href="classgridfire_1_1screening_1_1_weak_screening_model.html#a6a215d082b0d05126fb2b19fd1983446">gridfire::screening::WeakScreeningModel</a>, and <aclass="el"href="class_py_screening.html#aa67b5f0528006d9c742033b8c5ab3a05">PyScreening</a>.</p>
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