This study used a Systemic Modeling technique, based on the methodologies of Churchman and Ackoff, to integrate and assess the subsystems regulating the functionality of a Rocket Candy (R-Candy) motor. The nozzle and combustion chamber design was improved using a five-phase systemic architecture to assure the coherent interplay of essential factors, including pressure, temperature, and velocity fields. The principles of experimental rocketry are elucidated through the examination of impulse performance throughout class A to class C engines. A preliminary design was developed in SolidWorks 2024, incorporating the engine’s three main components: the igniter, the combustion chamber, and a convergent–divergent nozzle that enhances the acceleration of the exhaust gases. The system model was validated using simulations in FEATool and verified through experimentation. This allowed for the analysis of fluid behavior, as well as the geometry of the structures, initial parameters, and boundary conditions. The results demonstrate a strong correlation between the simulations and the experimental data, with discrepancies of less than 1.5%, confirming the reliability and feasibility of the nozzle design. The findings indicate that systemic modeling, in conjunction with CFD and experimentation, can provide a strategic framework for iterative refinement, optimization of key performance metrics, and the development of cost-effective, high-performance R-Candy engines for educational and experimental purposes.

Regional-scale gravity imaging is limited by the size of the sensitivity matrix: a direct three-dimensional formulation of the gravity operator requires on the order of ${10}^{13}$ coefficients, making explicit storage infeasible on standard hardware. We exploit two symmetry properties of the gravitational sensitivity kernel: translational invariance, giving the operator a Block Toeplitz with Toeplitz Blocks (BTTB) structure, and central symmetry, halving the number of unique kernel evaluations, to reformulate the Linear Back-Projection (LBP) adjoint operator as depth-wise two-dimensional convolutions. With finite-support truncation at cutoff radius $R_{\max }$, memory requirements drop from terabyte-scale dense arrays to compact per-layer kernels. We implement and benchmark two equivalent backends: an OpenMP spatial-domain stencil and an FFT convolution via BTTB circulant embedding. For a production-scale model ($701\times 401\times 512$ cells, $R_{\max }=150$), the stencil completed the LBP computation in 7187 s on one thread and 354 s on 32 threads ($S_{32}=20.29$), while the FFT backend required 13.8 s; both agree to within an invariant checksum ($3.3464\times {10}^{10}$). Applied to Bouguer gravity data over the Chicxulub impact crater, northern Yucatán Peninsula, the raw LBP amplitudes were calibrated to physical density contrasts through a two-parameter rescaling solved in closed form as a box-constrained convex quadratic program and cross-validated against an independent simulated-annealing search, reducing the RMS misfit from $27.3$ to $5.3$ mGal (an $81\%$ reduction). The resulting model shows density patterns consistent with the crater’s known structural organization. As a single-pass adjoint method, LBP does not resolve the non-uniqueness of the gravity inverse problem, and the model is interpreted for regional structure rather than for pointwise density values; the formulation extends directly to iterative regularized inversion.