A Knowledge-Based Optimization Method for Aerodynamic Design

A new aerodynamic design method, CODISC, has been developed that combines a legacy knowledge-based design method, CDISC, with a simple optimization module known as SOUP. The primary goal of this new design system is to improve the performance gains obtained using CDISC without adding significant computational time. An additional objective of this approach is to reduce the need for a priori knowledge of good initial input variable values, as well as for subsequent manual revisions of those values as the design progresses. Several test cases illustrate the development of the process to date and some of the options available at transonic and supersonic speeds for turbulent flow designs. The test cases generally start from good baseline configurations and, in all cases, were able to improve the performance. Several new guidelines for good initial values for the design variables, as well as new design rules within CDISC itself, were developed from these cases

Development of a Knowledge-Based Optimization Method for Aerodynamic Design

A new aerodynamic design method, CODISC, has been developed that combines an existing knowledgebased design method, CDISC, with a simple optimization module known as SOUP. The primary goal of this new design system is to improve the performance gains obtained using CDISC without adding significant computational time. An additional benefit of this approach is a reduction in the need for a priori knowledge of good initial input variable values as well as for subsequent manual revisions of those values as the design progresses. A series of 2D and 3D test cases are used to illustrate the development of the process and some of the options available at transonic and supersonic speeds for both laminar and turbulent flow. The test cases start from good baseline configurations and, in all cases, were able to improve the performance. Several new guidelines for good initial values for the design variables, as well new design rules within CDISC itself, were developed from these cases

Application of a Knowledge-Based Optimization Method for Aerodynamic Design

The current research is investigating the application of an optimization technique to an existing knowledge-based design tool. The optimization method, referred to as CODISC, helps improve the results from a knowledge-based design by eliminating the required advanced design knowledge, or help fine-tune a well-performing vehicle. Three CODISC designs are presented using a generic transonic transport, the Common Research Model (CRM). One design optimizes the baseline CRM to demonstrate the ability to improve a well-performing vehicle. Another design is performed from the CRM with camber and twist removed, which highlights the ability to use CODISC in the conceptual design phase. The final design implements laminar flow on the CRM, showing how CODISC can optimize the extent of laminar flow to find the best aerodynamic performance. All three CODISC designs reduced the vehicle drag compared to the baseline CRM, and highlight the new optimization techniques versatility in the aircraft design industry

The how and why of syntactic relations

Human communication takes place when one person does something that when seen or heard by another person is taken to be done with the intention to communicate, and the other person, having seen the communicator show his or her intention to communicate, then uses inference to determine what the communicator intends to communicate. This is possible because the addressee assumes that the communicator is a rational person, that is, acts with goals in mind (see Grice 1975), and so must be doing the act for a reason, and it is worth the addressee’s effort to try to determine what that reason is, that is, determine the relevance of the act

Quality of will and radical value reversals

Al Mele’s Manipulated Agents: A Window to Moral Responsibility (OUP 2019) is an extraordinarily careful and clear little book. A central recurring element is the use of examples of radical value reversals due to manipulation. In this commentary, I discuss the relevance of these examples to a simple quality of will account of blameworthiness without explicit historical conditions. Such an account, I suggest, can fairly straightforwardly explain how value reversals might mitigate blameworthiness. But I also suggest that the intuition that they completely remove blameworthiness should instead be explained away