<p>ESA&#8217;s current Mars exploration programme consists of the flying orbiters Mars Express and the Exomars TGO, while the Exomars rover is planned for launch in 2022. The Nov 2019 ESA Council of Ministers meeting, Space19+, has approved ESA contributions to a Mars Sample Return Campaign, led by NASA, with a launch of the sample retrieval missions planned to occur as early as 2026.</p> <p>While the vast majority of ESA&#8217;s funding for Mars exploration in 2020&#8217;s is planned to be invested in Mars Sample Return, there is interest to assess, at Phase 0-level, the possibility of implementing a small mission to Mars in parallel with or soon after the completion of the MSR programme, in order to further the exploration of Mars in areas not directly addressed by MSR.</p> <p>A study was undertaken in the Concurrent Design Facility at ESA, ESTEC in order to assess low cost mission architectures for small satellite missions to Mars. Given strict programmatic constraints, the focus of the study was on a low cost, short mission development schedule with a cost-driven spacecraft design and mission architecture.</p> <p>Three mission cases were studied:</p> <ol> <li><strong>Mars Communications Constellation Mission</strong>: A three satellite constellation with an objective to provide data relay with continuous coverage to ground assets. The satellites also contain a secondary science instrument.</li> <li><strong>Mars Science Orbiter Mission</strong>: A single satellite science orbiter with a primary science objective and a secondary objective to provide a data relay.</li> <li><strong>Mars Hard Lander / Penetrator Mission</strong>: A demonstration mission of a carrier module and N hard landers/penetrators.</li> </ol> <p>The study assessed a wide range of mission architectures and focused on commercial rideshare options to GTO from which the satellite(s) would transfer to Mars utilising either chemical or electric propulsion. The study results showing the feasibility of such missions meeting the programmatic constraints and options for future work will be presented.</p> <p><img src="data:image/png;base64, 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
We present here a complete model of a Sony 18650HC lithium-ion battery developed in MATLAB/Simulink, which is adaptable to other lithium-ion cell chemistries and can be implemented into a full power system model. The model accounts for varying current rates, temperature dependencies and internal cell characteristics under both charge and discharge conditions and is primarily based on characterisation tests of the 18650HC cell with the addition of standard thermal equations and electrical relationships. The model can be adapted to fit other types of lithium-ion cell chemistries when the characterisation profile for that chemistry is created using our testing procedures. Comparisons between the model and experiments conducted on test cells have shown very accurate results and the adaptability of the model has been proven. This allows us to test and model newer cell technologies for which commercial data may not yet be available.
<p>ESA&#8217;s current Mars exploration programme consists of the flying orbiters Mars Express and the ExoMars TGO, while the ExoMars rover is planned for launch in 2022. The Nov 2019 ESA Council of Ministers meeting, Space19+, has approved ESA contributions to a Mars Sample Return Campaign, led by NASA, with a launch of the sample retrieval missions planned to occur as early as 2026.</p> <p>The ExoMars and Mars Sample Return (MSR) missions will expand our understanding of Martian history, potential for life and environmental conditions. These missions will develop European technologies and capabilities to enable access to Mars orbit, Mars surface and shallow sub-surface as well as a round-trip journey. However, key strategic knowledge gaps will still remain for the human exploration of Mars following successful ExoMars and MSR missions. These include the understanding of the long-term behaviour of the atmosphere, wind profiles, dust properties and transport and origin of dust storms.</p> <p>A study was undertaken in the Concurrent Design Facility (CDF) at ESA, ESTEC in order to address this important knowledge gap through a reference mission architecture that would be the first coordinated global, regional and local network of atmospheric science and weather stations at Mars. The reference launch date for the mission is in the early-mid 2030&#8217;s.</p> <p>The selected mission architecture comprises several elements, including:</p> <ul> <li>A Low Mars Orbiter (LMO), in a high-inclination orbit;</li> <li>An Areosynchronous Orbiter (ASO) situated at the same longitude as the landers</li> <li>Four identical Mars landers comprising: <ul> <li>3 &#8220;local&#8221; landers in a triangular configuration situated 20km-150km apart</li> <li>1 &#8220;regional&#8221; lander situated ~800km West of the local lander network</li> </ul> </li> <li>Two carrier spacecraft each carrying two Mars landers from Earth to Mars atmospheric entry.</li> </ul> <p>The primary science objectives focus on meteorology, wind and dust transport measurements from the orbital and landed assets. In addition, the landers also contain a radio science, seismology and heat flow payload suite to address secondary science objectives. Due to the scale of the mission and large number of sizeable mission elements, the architecture was spilt into a two-launch scenario where the first launch comprises the two orbiter spacecraft, and the second launch (in the next launch window opportunity) comprises the two carrier spacecraft each carrying two landers. Due to this large scope, the CDF study focussed on the design of the four Mars landers and the Areosynchronous orbiter, the definition of the science payloads, the overall concept of operations, cost and programmatics.</p> <p>The study results show the technical feasibility of such a mission to be undertaken in the 2030&#8217;s at landing sites between 0&#176;N and 20&#176;N latitude whilst utilising existing European heritage in chemical propulsion Orbiters and ballistic Entry, Descent and Landing (EDL) technology. Challenges for the lander elements include the need for radioisotope heater units (RHUs), large solar arrays and energy-efficient platform and payload operations, all driven by the need to survive for multiple Mars years on the surface including science operations through potential global dust storms. Further work to refine the mission concept is planned.</p> <p>&#160;</p> <p><img src="data:image/jpeg;base64, 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
The use of satellite NO 2 data for air quality studies is increasingly revealing the need for observations with higher spatial and temporal resolution. The study of the NO 2 diurnal cycle, global sub-urban scale observations, and identification of emission point sources are some examples of important applications not possible at the resolution provided by current instruments. One way to achieve increased spatial resolution is to reduce the spectral information needed for the retrieval, allowing both dimensions of conventional 2-D detectors to be used to record spatial information. 5In this work we investigate the use of ten discrete wavelengths with the well-established Differential Optical Absorption Spectroscopy (DOAS) technique for NO 2 slant column density (SCD) retrievals. To test the concept we use a selection of individual OMI and TROPOMI Level 1B swaths from various regions around the world which contain a mixture of clean and heavily polluted areas. To discretise the data we simulate a set of Gaussian optical filters centred at various key wavelengths of the NO 2 absorption cross section. We perform SCD retrievals of the discrete data using a simple implementation of the 10 DOAS algorithm and compare the results with the corresponding Level 2 SCD products, namely QA4ECV for OMI and the operational TROPOMI product.For OMI the overall results from our discrete-wavelength retrieval are in very good agreement with the Level 2 data (mean difference < 5 %). For TROPOMI the agreement is good (mean difference < 11 %), with lower uncertainty owing to its higher signal-to-noise ratio. These discrepancies can be mostly explained by the differences in retrieval implementation. There are 15 some larger differences around the centre of the swath and over water. While further research is needed to address specific retrieval issues, our results indicate that our method has potential. It would allow for simpler, more economic satellite instrument designs for NO 2 monitoring at high spatial and temporal resolution. Constellations of small satellites with such instruments on board would be a valuable complement to current and upcoming high-budget hyperspectral instruments.
Abstract. The use of satellite NO2 data for air quality studies is increasingly revealing the need for observations with higher spatial and temporal resolution. The study of the NO2 diurnal cycle, global sub-urban-scale observations, and identification of emission point sources are some examples of important applications not possible at the resolution provided by current instruments. One way to achieve increased spatial resolution is to reduce the spectral information needed for the retrieval, allowing both dimensions of conventional 2-D detectors to be used to record spatial information. In this work we investigate the use of 10 discrete wavelengths with the well-established differential optical absorption spectroscopy (DOAS) technique for NO2 slant column density (SCD) retrievals. To test the concept we use a selection of individual OMI and TROPOMI Level 1B swaths from various regions around the world, which contain a mixture of clean and heavily polluted areas. To discretise the data we simulate a set of Gaussian optical filters centred at various key wavelengths of the NO2 absorption cross section. We perform SCD retrievals of the discrete data using a simple implementation of the DOAS algorithm and compare the results with the corresponding Level 2 SCD products, namely QA4ECV for OMI and the operational TROPOMI product. For OMI the overall results from our discrete-wavelength retrieval are in very good agreement with the Level 2 data (mean difference <5 %). For TROPOMI the agreement is good (mean difference <11 %), with lower uncertainty owing to its higher signal-to-noise ratio. These discrepancies can be mostly explained by the differences in retrieval implementation. There are some larger differences around the centre of the swath and over water. While further research is needed to address specific retrieval issues, our results indicate that our method has potential. It would allow for simpler, more economic satellite instrument designs for NO2 monitoring at high spatial and temporal resolution. Constellations of small satellites with such instruments on board would be a valuable complement to current and upcoming high-budget hyperspectral instruments.
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