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The ShadowCam instrument on the Danuri spacecraft provides high-resolution views of shadowed portions of the Moon, which are illuminated by naturally scattered light from nearby sunlit terrain. The sensitive time-delay integration detector captures high signal-to-noise observations within the permanently shadowed regions and areas in shadow for part of the year. We characterized the geometric properties of the images, enabling accurate placement of observations within the lunar cartographic framework. This work describes the internal and external orientation parameters using laboratory observations and images collected during the cruise and commissioning phase of the mission. We identified a radial distortion in the cross-track direction from these observations, which is correctable during our standard calibration pipeline procedures. We also calculated the pointing of the camera relative to the spacecraft bus within ~0.001°. Using these models, corrections, and the initial ephemeris provided by the Korea Aerospace Research Institute, images can be aligned within 60 m on the surface (95% confidence interval). This calibration and a precise radiometric model will enable reliable interpretation of ShadowCam images and the development of future derived products, including precisely mapped mosaics and meter-scale digital elevation models.

The Korea Pathfinder Lunar Orbiter (KPLO), officially named as Danuri, is the Republic of Korea's first spacecraft to orbit the Moon. The goal of KPLO is to secure core deep space technology for future space exploration in Korea and contribute to lunar science by gathering data from the lunar orbit for one year. This paper presents the results of KPLO's Lunar Orbit Acquisition (LOA) phase in detail, with a particular focus on the operational results of Flight Dynamics (FD). During the LOA phase, the KPLO FD team experienced many expected and unexpected issues. These issues were mitigated by making real-time updates to the LOA phase Design Reference Mission (DRM). The paper also presents the results of the Orbit Determination (OD) and performance of each Lunar Orbit Insertion (LOI) maneuver conducted during the actual flight operation. The importance of considering operational constraints when designing the DRM is emphasized by presenting lessons learned based on actual flight experiences. Unlike the original KPLO LOA phase DRM, KPLO successfully achieved the final mission orbit using only three LOI burns, instead of the planned five LOI burns and an Orbit Trim Maneuver (OTM). Despite the extensive revisions made to the DRM, all mission requirements were still fulfilled during the final lunar orbit insertion, taking into account the significant operational constraints. • Real flight operation results of the Korea Pathfinder Lunar Orbiter's lunar orbit acquisition phase are presented. • Orbit determination and lunar orbit insertion maneuver performance are analyzed based on actual flight data. • Unexpected issues during the actual flight operation were mitigated through real-time updates on the design reference mission. • Considering real-world operational constraints while designing the design reference mission is very critical. [ABSTRACT FROM AUTHOR]

Korea Pathfinder Lunar Orbiter (KPLO), also known as Danuri, was successfully launched on 4 Aug. from Cape Canaveral Space Force Station using a Space-X Falcon-9 rocket. Flight dynamics (FD) operational readiness was one of the critical parts to be checked before the flight. To demonstrate FD software’s readiness and enhance the operator’s contingency response capabilities, KPLO FD specialists planned, organized, and conducted four simulations and two rehearsals before the KPLO launch. For the efficiency and integrity of FD simulation and rehearsal, different sets of blind test data were prepared, including the simulated tracking measurements that incorporated dynamical model errors, maneuver execution errors, and other errors associated with a tracking system. This paper presents the simulation and rehearsal results with lessons learned for the KPLO FD operational readiness checkout. As a result, every functionality of FD operation systems is firmly secured based on the operation procedure with an enhancement of contingency operational response capability. After conducting several simulations and rehearsals, KPLO FD specialists were much more confident in the flight teams’ ability to overcome the challenges in a realistic flight and FD software’s reliability in flying the KPLO. Moreover, the results of this work will provide numerous insights to the FD experts willing to prepare deep space flight operations.

This study presents data-driven reduced-order models (ROMs) of a lunar orbiter based on principal component analysis (PCA) and artificial neural networks (ANNs) for a ground thermal vacuum test to simulate space thermal environments. We employed a radial basis function network (RBFN) and deep neural network (DNN) from among the various types of ANNs. PCA extracts features from high-dimensional data, such as thermal analysis data. It is utilized in machine learning algorithms as a preprocessing step before inputting the data into neural networks. This process improves the convergence speed and training performances compared to using neural networks alone. The coefficients of the extracted principal component modes were regressed using the RBFN and DNN. Twenty thermal design parameters comprising infrared emissivity, effective thermal conductivity, thermal contact conductance coefficients, and thermal conductance were used to train the ROMs. We conducted training and test of the proposed models during the cold and hot balance phases of the ground test. Consequently, the temperature map can be estimated in seconds for the new design parameters, and the model results are consistent with thermal analysis and measurement data. [ABSTRACT FROM AUTHOR]