# 3D Acoustic Mapping in Automotive Wind Tunnel: Algorithm and Problem Analysis on Simulated Data
## 7 Conclusions  
This work investigates the use of three-dimensional volumetric acoustic mapping in automotive wind tunnel testing. It discusses the arising additional complexities and proposes adequate countermeasures. The acoustic imaging technique described in this paper belongs to the branch of inverse methods, and it has been developed to match the needs of volumetric mapping. The method is based on IRLS and Bayesian Regularization, and makes it possible to obtain sparse approximation of the source field. Acoustic imaging results are affected by a large number of concurrent parameters and, in the case of inverse methods, they are strongly influenced by the problem definition. For this reason, in this paper a parametric study is conducted by means of simulated virtual experiments, through which controlling each salient aspect keeping others fixed. This allowed a detailed sensitivity analysis and a systematic comparison of the results obtained with the expected values. Such an approach enables efficiently transfer the gained insight to the real applications. The parametric study involved  
* the comparison in the use of a single array or a dual array to produce the map,
* the effect of different levels of masking effect due to the object itself,
* the trimming of the calculation grid removing potential sources inside the model,
* the effect of grid step on the final result, and
* the effect of background noise having well-defined spatial distribution. The maps obtained from the simulated data have been compared by means of objective scores that allow measuring the performance in terms of localization and quantification of the maps. Finally, a complete simulated test case has been studied, accounting for masking effect and background noise. This paper demonstrates that the localization accuracy achievable in 3D array configuration and on volumetric maps is below the grid step defined for the calculation. However, the quantification systematically produces underestimated source level, especially when masking effect is present or high level of regularization  
Figure 19: Scores of maps of the realistic test case. (**a**) Non-dimensional distance error \(D_{s}\). Average value obtained from identified sources only. Transparent bars represent the ratio between grid step and \(\lambda\). (**b**) Power concentration \(W_{\%}\). (**c**) Source power ratio \(W_{s}\). Ideal source power ratio is 0.  
is needed (e.g., in case of strong background noise). As future works, the authors will first study an approach to increase the source quantification accuracy accounting for the masking/scattering effect due to the presence of the tested object. For example, once a source is identified, the quantification step can be done using only microphone in line-of-sight condition. Second, experimental data will be analyzed to prove the efficiency of the 3D acoustic mapping method. In conclusion, although the application to an automotive wind tunnel testing has been investigated in this paper, it can be extrapolated that the mapping method proposed can also be efficiently applied to other environments, given the good results produced. The method can be easily applied to aeronautical wind tunnel testing. In addition, it can be promising for noise source localization in workplaces where the noisy environment makes the separation of each desired source from the background noise difficult. [PERSON] contributed to the conceptualization, the methodology definition, software realization, results validation, and writing the original draft. [PERSON]. ([PERSON]) contributed to the conceptualization and formal analysis. [PERSON] contributed to the manuscript writing, review, and editing, [PERSON] ([PERSON]) contributed to the investigation of the problem, resources finding and supervision. [PERSON] and [PERSON] supervised the paper preparation and finalization. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.