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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

EpiK: A Knowledge Base for Epidemiological Modeling and Analytics of Infectious Diseases

Computational epidemiology seeks to develop computational methods to study the distribution and determinants of health-related states or events (including disease), and the application of this study to the control of diseases and other health problems. Recent advances in computing and data sciences have led to the development of innovative modeling environments to support this important goal. The datasets used to drive the dynamic models as well as the data produced by these models presents unique challenges owing to their size, heterogeneity and diversity. These datasets form the basis of effective and easy to use decision support and analytical environments. As a result, it is important to develop scalable data management systems to store, manage and integrate these datasets. In this paper, we develop EpiK —a knowledge base that facilitates the development of decision support and analytical environments to support epidemic science. An important goal is to develop a framework that links the input as well as output datasets to facilitate effective spatio-temporal and social reasoning that is critical in planning and intervention analysis before and during an epidemic. The data management framework links modeling workflow data and its metadata using a controlled vocabulary. The metadata captures information about storage, the mapping between the linked model and the physical layout, and relationships to support services. EpiK is designed to support agent-based modeling and analytics frameworks—aggregate models can be seen as special cases and are thus supported. We use semantic web technologies to create a representation of the datasets that encapsulates both the location and the schema heterogeneity. The choice of RDF as a representation language is motivated by the diversity and growth of the datasets that need to be integrated. A query bank is developed—the queries capture a broad range of questions that can be posed and answered during a typical case study pertaining to disease outbreaks. The queries are constructed using SPARQL Protocol and RDF Query Language (SPARQL) over the EpiK. EpiK can hide schema and location heterogeneity while efficiently supporting queries that span the computational epidemiology modeling pipeline: from model construction to simulation output. As a result, we show that the performance of benchmark queries varies significantly with respect to the choice of hardware underlying the database and resource description framework (RDF) engine.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Diversity of Active Viral Infections within the Sphagnum Microbiome

Sphagnum -dominated peatlands play an important role in global carbon storage and represent significant sources of economic and ecological value. While recent efforts to describe microbial diversity and metabolic potential of the Sphagnum microbiome have demonstrated the importance of its microbial community, little is known about the viral constituents. We used metatranscriptomics to describe the diversity and activity of viruses infecting microbes within the Sphagnum peat bog. The vegetative portions of six Sphagnum plants were obtained from a peatland in northern Minnesota, and the total RNA was extracted and sequenced. Metatranscriptomes were assembled and contigs were screened for the presence of conserved virus marker genes. Using bacteriophage capsid protein gp23 as a marker for phage diversity, we identified 33 contigs representing undocumented phages that were active in the community at the time of sampling. Similarly, RNA-dependent RNA polymerase and the nucleocytoplasmic large DNA virus (NCLDV) major capsid protein were used as markers for single-stranded RNA (ssRNA) viruses and NCLDV, respectively. In total, 114 contigs were identified as originating from undescribed ssRNA viruses, 22 of which represent nearly complete genomes. An additional 64 contigs were identified as being from NCLDVs. Finally, 7 contigs were identified as putative virophage or polinton-like viruses. We developed co-occurrence networks with these markers in relation to the expression of potential-host housekeeping gene rpb1 to predict virus-host relationships, identifying 13 groups. Together, our approach offers new tools for the identification of virus diversity and interactions in understudied clades and suggests that viruses may play a considerable role in the ecology of the Sphagnum microbiome. Sphagnum -dominated peatlands play an important role in maintaining atmospheric carbon dioxide levels by modifying conditions in the surrounding soil to favor the growth of over that of other plant species. This lowers the rate of decomposition and facilitates the accumulation of fixed carbon in the form of partially decomposed biomass. The unique environment produced by Sphagnum enriches for the growth of a diverse microbial consortia that benefit from and support the moss's growth, while also maintaining the hostile soil conditions. While a growing body of research has begun to characterize the microbial groups that colonize Sphagnum , little is currently known about the ecological factors that constrain community structure and define ecosystem function. Top-down population control by viruses is almost completely undescribed. Furthermore, this study provides insight into the significant viral influence on the Sphagnum microbiome and identifies new potential model systems to study virus-host interactions in the peatland ecosystem.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

SiC-Based 5-kV Universal Modular Soft-Switching Solid-State Transformer (M-S4T) for Medium-Voltage DC Microgrids and Distribution Grids

Medium-voltage DC (MVDC) grids are attractive for electric aircraft and ship power systems, battery energy storage system (BESS), fast charging electric vehicle (EV), etc. Such EV or BESS applications need isolated bidirectional MVDC to LVDC or LVAC converters. However, the existing Si-based solutions cannot fulfill the requirements of a high-efficiency and robust converter for MVDC grids. This paper presents a 5 kV SiC-based universal modular solid-state transformer (SST). This universal current-source SST can interface either a LVAC or LVDC grid with a MVDC grid in single-stage power conversion, while the conventional dual active bridge (DAB) converter needs an additional inverter. The proposed SST module using 3.3 kV SiC MOSFETs and diodes is bidirectional and can serve as a building block in series or parallel for higher-voltage higher-power systems. The topology of each module is based on the soft-switching solid-state transformer (S4T) with reduced conduction loss, which features reduced EMI through controlled dv/dt, and high efficiency with full-range ZVS for main devices and ZCS for auxiliary devices. Operation principle of the modular S4T (M-S4T), capacitor voltage balancing control between the cascaded modules, design of components including a medium-voltage (MV) medium-frequency transformer (MFT) to realize a 50 kVA 5 kV DC to 600 V DC or 480 V AC M-S4T are presented. Importantly, the MV MFT prototype achieves very low leakage inductance (0.13%) and 15 kV insulation with coaxial cables and nanocrystalline cores. Here, the proposed universal modular SST is compared against the DAB solution and verified with DC-DC and DC-AC simulation and 4 kV experimental results. Significantly, the MV experimental results of a modular DC transformer with each module at MVDC are rarely covered in the literature and reported for the first time.

14 SOLAR ENERGY↗
DOE OSTI2018

Charging Electric Vehicles in Smart Cities: An EVI-Pro Analysis of Columbus, Ohio

With the support of the U.S. Department of Energy's Vehicle Technologies Office, the National Renewable Energy Laboratory (NREL) worked with the City of Columbus, Ohio, to develop a plan for the expansion of the region's network of charging stations to support increased adoption of plug-in electric vehicles (PEVs) in the local market. NREL's Electric Vehicle Infrastructure Projection (EVI-Pro) model was used to generate scenarios of regional charging infrastructure to support consumer PEV adoption. Results indicate that approximately 400 Level 2 plugs at multi-unit dwellings and 350 Level 2 plugs at non-residential locations are required to support Columbus' primary PEV goal of 5,300 PEVs on the road by the end of 2019. This analysis finds that while consumer demand for fast charging is expected to remain low (due to modest anticipated adoption of short-range battery electric vehicles), a minimum level of fast charging coverage across the city is required to ease consumer range anxiety concerns by providing a safety net for unexpected charging events. Sensitivity analyses around some key assumptions have also been performed; of these, consumer preference for PHEV versus BEV and for their electric driving range, ambient conditions, and availability of residential charging at multi-unit dwellings were identified as key determinants of the non-residential PEV charging infrastructure required to support PEV adoption. The results discussed in this report can be leveraged by similar U.S. cities as part of a strategy to accelerate PEV adoption in the light-duty vehicle market.

25 ENERGY STORAGE↗
DOE OSTI2019

Computational Bayesian Framework for Quantification and Reduction of Predictive Uncertainty in Subsurface Environmental Modeling

Subsurface environmental systems are open and complex, in which intricate hydrologic, microbiologic, and geochemical processes occur and interact at multiple scales. Understanding and predicting system responses to natural forces (e.g., climate changes) and human activities (e.g., contaminant remediation and CO 2 sequestration) is indispensable for managing water resources, cleaning subsurface contamination at many sites of the Department of Energy (DOE), and providing expert analysis to inform policy-making on long-term stewardship of nuclear waste disposal and CO 2 storage sites. Groundwater reactive transport modeling is necessary for understanding and predicting the complex subsurface environmental system. However, as complex systems are never completely predictable, model predictions of the subsurface system are inherently uncertain, and uncertainty is one of the greatest obstacles in simulating the subsurface environment. Quantifying predictive uncertainty of the complex system is critical to test model predictive power, support decision-making, and guide experimental design and data collection for uncertainty reduction. This project tackled two major obstacles to efficient and effective quantification and reduction of predictive uncertainty in simulating the complex subsurface environment. One obstacle is that existing methods of uncertainty quantification are too fragmented and incomplete for understanding and predicting the complex subsurface environment as a whole. The other obstacle is that existing methods of uncertainty quantification are computationally demanding and may not be applicable to groundwater reactive transport modeling that is always computationally expensive. The above two obstacles to uncertainty quantification directly affect the design of data collection schemes for uncertainty reduction. In this project, we developed a comprehensive Bayesian framework to remove the first obstacle. The second obstacle was tackled by using the sparse grid methods, which were originally developed and analyzed for numerical integration in the field of applied mathematics. We integrated the sparse grid methods into the Bayesian framework seamlessly, which led to a sparse-grid-based method of Bayesian uncertainty analysis. The developed computational Bayesian framework has been applied to a number of subsurface environmental problems through collaborating with scientists at the Pacific Northwest National Laboratory and Oak Ridge National Laboratory. The applications demonstrated that the computational Bayesian framework is general and compatible with any models and numerical codes of subsurface modeling. It is thus can be applied to gain insights for subsurface environmental modeling. This project helped the PI achieve his career goal to advance the state-of-the-art computational methods in uncertainty quantification for subsurface environmental modeling. This project supported two doctoral students, two master students, and one post-doc. The two doctoral students, after their graduation, worked as post-docs at the Pacific Northwest National Laboratory and the Oak Ridge National Laboratory. The project produced a total of 29 journal articles (four under review), one book chapter, and a number of conference proceedings and abstracts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Life cycle assessment and tempo-spatial optimization of deploying dynamic wireless charging technology for electric cars

Dynamic wireless power transfer (DWPT), or dynamic wireless charging technology, enables charging-while-driving and offers opportunities for eliminating range anxiety, stimulating market penetration of electric vehicles (EVs), and enhancing the sustainability performance of electrified transportation. However, the deployment of wireless charging lanes on highways and urban road networks can be costly and resource-intensive. A life cycle assessment (LCA) is conducted here to compare the sustainability performance of DWPT applied in a network of highways and urban roads for charging electric passenger cars. The assessment compares DWPT to stationary wireless charging and to conventional plug-in charging using a case study of Washtenaw County in Michigan, USA over 20 years. The LCA is based on three key sustainability metrics: costs, greenhouse gas (GHG) emissions, and energy burdens, encompassing not only the use-phase burdens from electricity and fuel, but also the upfront deployment burdens of DWPT infrastructure. A genetic algorithm is applied to optimize the rollout of DWPT infrastructure both spatially and temporally in order to minimize life cycle costs, GHG, and energy burdens: (1) spatial optimization selects road segments to deploy DWPT considering traffic volume, speed, and pavement remaining service life (RSL); (2) temporal optimization determines in which year to deploy DWPT on a particular road segment considering EV market share growth as a function of DWPT coverage rate, future DWPT cost reduction, and charging efficiency improvement. Results indicate that optimal deployment of DWPT electrifying up to about 3% of total roadway lane-miles reduces life cycle GHG emissions and energy by up to 9.0% and 6.8%, respectively, and enables downsizing of the EV battery capacity by up to 48%, compared to the non-DWPT scenarios. Roadside solar panels and storage batteries are essential to significantly reduce life cycle energy and GHG burdens but bring additional costs. Breakeven analysis indicates a breakeven year for solar charging benefits to pay back the DWPT infrastructure burdens can be less than 20 years for GHG and energy burdens but longer than 20 years for costs. A monetization of carbon emissions of at least $250 per metric tonne of CO 2 is required to shift the optimal “pro-cost” deployment to the optimal “pro-GHG” deployment. A roadway segment with volume greater than about 26,000 vehicle counts per day, speed slower than 55 miles per hour (1 mile ≈ 1.609 km), and pavement RSL shorter than 3 years should be given a high priority for early-stage DWPT deployment.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Deciphering the Solvation Structure of Aqueous ZnCl 2 Solutions from X-ray Absorption Spectra Using the Interpretable Graph Neural Network

Machine learning (ML) provides powerful pathways for predicting spectroscopic observables from atomic structures, but its broader impact depends on making model predictions interpretable in terms of physical and chemical principles. Here, we introduce a physics-guided graph neural network (GNN) model that predicts Zn K-edge X-ray spectroscopy (XAS) spectra of aqueous ZnCl 2 solutions. Training data are generated from ab initio XAS calculations on molecular dynamics snapshots obtained using a machine learning interatomic potential. The GNN reproduces experimental spectra across concentrations from dilute (<0.1 m) to highly concentrated (30 m, “water-in-salt”) regimes and scales efficiently to large, disordered liquid systems beyond the reach of conventional ab initio approaches. Gradient-based attribution analysis reveals that the model learns physically meaningful structure-spectrum relationships. Ligand-specific attributions reflect orbital hybridization patterns and the origin of the excitations derived from the density functional theory. Bond-length attributions recover spectral shifts consistent with multiple-scattering theory. Finally, this work bridges data-driven prediction with electronic-structure theory, establishing a general paradigm for interpretable ML that links atomic structure, electronic structure, and spectroscopic observables.

25 ENERGY STORAGE↗
DOE OSTI2019

Subtask 1.1 – Advanced Characterization of Unconventional Oil and Gas Reservoirs to Enhance CO2 Storage Resource Estimates

The Energy & Environmental Research Center (EERC) developed methods and approaches for advanced characterization of unconventional oil and gas reservoirs to enhance CO2 storage resource estimates using samples collected from organic-rich shales of the Bakken Formation. The project was funded under U.S. Department of Energy (DOE) Cooperative Agreement No. DE-FE0024233, with nonfederal cost share provided by Hitachi High Technologies America. The project participants included the EERC, Hitachi High Technologies America, the team at the National Energy Technology Laboratory’s (NETL’s) computed tomography (CT) scanning laboratory in Morgantown, West Virginia, and several NETL staff who have worked on CO2 storage resource estimation. Investigations on the use of subsurface geologic formations for CO2 storage have been ongoing for many years. In the last decade, improvements in directional drilling technology and hydraulic fracturing of organic-rich shale and other tight rock formations have opened new opportunities for CO2 storage, with the potential cobenefit of enhancing gas and oil recovery. Gas-bearing, organic-rich shales have been of particular interest as a carbon storage target because their ability to store large volumes of CO2 is implied by their ability to store methane; however, estimating the CO2 storage resource of organic-rich shales and other unconventional reservoirs is challenging because of the lack of detailed geologic and petrophysical data that are needed to develop improved volumetric equations. According to NETL’s method for estimating regional-scale CO2 storage resources, the key factors that control CO2 storage in geologic formations include void space within induced fractures, natural fractures and matrix pores, as well as the occurrence and distribution of organic matter (OM) and clay minerals. A key goal of this effort was to use field emission scanning electron microscopy (FESEM) analysis of fine-grain unconventional reservoir rocks coupled with advanced image analysis and machine learning techniques to quantify the occurrence and distribution of porosity, OM, and clay minerals. By better understanding the potential migration pathways of CO2 within tight rocks and quantifying the occurrence of sorptive components (OM and clays) along potential flow pathways, methods to estimate the CO2 storage resource of such rocks can be refined. The Bakken petroleum system (Bakken) was used as the source of rock samples for this effort because it includes organic-rich shales with contrasting degrees of thermal maturity as well as tight, nonshale clastic rocks with varying clay mineral content. To better understand the key components of interest in tight rock samples, the EERC worked closely with Hitachi to optimize the FESEM settings necessary to resolve the sample mineralogy, porosity and OM content at micro- to nanoscale resolution. The EERC also worked closely with Hitachi to apply and improve its advanced mineral identification and characterization system (AMICS) software so that it is better suited for mineral identification at the high resolution necessary to resolve features of interest in fine-grained rocks such as organic-rich shales. The EERC also applied advanced image analysis software packages, including Ilastik, to classify and quantify the key components of interest within the samples using FESEM imagery. These advanced techniques allowed for quantification and differentiation of the void space within fractures, pores and OM, as well as a technique to determine and quantify the key mineral and OM components surrounding pore spaces. Being able to better quantify the void space and the potential accessibility of CO2 to sorptive minerals or OM within tight rocks allows for improved estimation of the CO2 storage resource potential of organic-rich shales and other unconventional formations. The information derived from the image analysis techniques was used to refine NETL’s method for estimating regional-scale CO2 storage resources in collaboration with staff from NETL. An additional goal of this work was to better understand rates of CO2 permeation, flow pathways, and sorption in organic-rich shales and nonshale reservoir rocks and to evaluate potential changes in CO2 flow over time. The EERC conducted long-duration (several weeks) CO2 flow-through testing at reservoir conditions using Bakken shale and nonshale samples to assess CO2 permeation rates and retention/sorption within core plugs to represent bulk matrix behavior. Conventional isothermal adsorption testing over a range of pressure conditions was also performed on pulverized Bakken samples to better assess potential maximum sorptive capacity of the samples. The results of this testing illustrated the higher sorptive capacity of the Bakken shales when compared to the sorptive capacity of the nonshale reservoir rock, which contains very little OM when compared to the Bakken shales. The results also illustrate the differences in permeation behavior as a function of fracture flow versus matrix flow. In an attempt to visualize CO2 permeation and flow behavior in Bakken shale samples over time, the EERC collaborated with NETL’s CT Lab in Morgantown, West Virginia. A variety of tests were performed at the NETL lab on unfractured and fractured samples of the Bakken shale. The unfractured Bakken shale samples did not result in CO2 migration within the samples over the testing duration; thus imagery of CO2 migration pathways within the matrix of Bakken samples was not obtained. The results of the flow-through testing within fractured samples illustrate changes in CO2 permeation rates over time, with a trend of decreasing CO2 flow over time. The results of this effort suggest that FESEM analysis coupled with advanced image analysis and machine learning techniques are effective in characterizing and quantifying the mineralogy, porosity, fractures, and OM content of 2-D imagery at the high resolution needed to identify features of interest in tight rocks. In addition, the image analysis techniques allow for identification and quantification of different porosity types as well as the key mineral components and/or OM that CO2 would contact in the reservoir as it migrates through pores and fracture networks. This is key information needed to better estimate the CO2 storage resource potential of organic-rich shales and other tight reservoirs. Additional work is needed to determine the sorptive capacity of the individual sample components (OM and clay minerals) at reservoir conditions so that this approach and the refined CO2 storage resource equation can be applied to other shale and unconventional reservoirs. In addition, subsequent work should be performed to better understand the number of images needed to use stereological approaches to estimate the volume of sample components using 2-D imagery, to explore machine learning techniques to differentiate between induced vs. natural fractures, and to estimate the connectivity of pores and fracture networks using 2-D imagery. Being able to derive this information at the microscale would facilitate scaling up of the sample characterization data used for reservoir modeling and simulation to estimate the CO2 storage resource potential of tight formations.

03 NATURAL GAS↗
NASA NTRSDate not supplied

A Notional Artemis Lunar Surface Exploration Package (ArLSEP) based on the Gandalf Staff Platform

Introduction: The Artemis program is planning to deliver crew and cargo to the lunar surface, but there is no current package for supporting lunar in-struments and experiments similar to the Apollo Lunar Surface Exploration Package (ALSEP). This abstract provides a possible concept for such a package using the Gandalf Staff Platform as a common core. Gandalf Staff: The Gandalf Staff is an early prototype system developed over FY’21/FY’22 using NASA Science Technology Mission Directorate (STMD) Center Information Fund (CIF) grants to de-sign, build and test “proof-of-concept” components. These components include a 24v battery powered monopole that powers a suite of subsystems, including a Graphical User Interface (GUI) for crew, surface voice and data communications, Lunar Search and Rescue (LunaSAR) navigation and communications, LiDAR, field site external lighting, 360-degree camera, and a geothermal instrument for measuring sub-surface temperature gradient. The staff can be carried independently by an Extra-Vehicular Activity (EVA) astronaut, or can be mounted into a tripod for “hands free” support at a surface site being investigated. The staff can be attached to an external solar array and power storage system for long-duration operations. [1,2] ALSEP: An ASLEP flew on each mission Apollo 12 to Apollo 17. For Apollo 11, a simplified packaged called the Early Apollo Scientific Experiments Pack-age (EASEP) was flown. Each package included a “Central Station” that provided the power and communications connected to a variety of instruments and sensors. The power was provided by a Radioisotope Thermoelectric Generator (RTG) fueled by Plutoni-um-238 generating 70 watts of power (initially, decayed over time) [3]. The communications system provide for direct to Earth data transfer from the lunar surface. Each pack-age was stowed externally in the Lunar Module (LM) Scientific Equipment (SEQ) bay with a mass up to 163 kg (Apollo 17). The crew unloaded the ALSEP from the LM and deployed the instruments on the lunar surface. Although designed to operate for only 1 year, many sites operated for up to 8 years successfully [4]. The Active Seismic Experiment (ASE) included 3 geophones for detecting seismic waves created by mortars and thumpers deployed by the crew. Other active experiments measured the lunar atmosphere, the heat flow in the subsurface, the lunar gravity and potential gravity waves, the lunar magnetic field, the solar wind and plasma interactions in cislunar space. Passive experiments included collectors for dust and cosmic rays, and retroreflectors for precise measurements of distance using a laser from Earth. The ALSEP program continues to generate insights into lunar formation and evolution. ArLSEP Concepts: The lunar surface science package for the Artemis program will hopefully exceed the capability of the ALSEP. There are multiple issues for discussion leading to the design of a new ArLSEP, needing requirements definition from the science community, NASA mission architecture, and NASA budget planners. 1. Delivery Mechanism Two possible projects currently provide capability to deliver scientific cargo to the lunar surface: 1) the Commercial Lunar Payload Services (CLPS) [5] and the Human Landing System (HLS) [6, 7]. Each project is controlled by a different organization within NASA and budgeted with different criteria although both support lunar exploration. The HLS system delivers crew (and potentially cargo) to human landing sites. If an ArLSEP is “predeployed” to such a site, the design must include power (either from the vehicle or independently) to keep the electronics functioning until deployed by the crew. If an ArLSEP is delivered on a vehicle after the crew is present on the lunar surface, safety protocols require adequate distance from the humans for impact from descent propelled sur-face regolith ejecta. This distance can not exceed the capability of the crew to walk (if no rover) to the vehicle for ArLSEP deployment. 2. Overall Guidelines The general design of ArLSEP will likely follow the ALSEP with a common system for communications and power; however, significant architecture differences between Apollo and Artemis exist. Power: The RTG will not be available for early Artemis missions nor likely follow-on Lunar Exploration Transportation Services (LETS) missions [8]. Thus, ArLSEP power must be supplied by solar arrays with sufficient battery capability to “keep alive” necessary electronics during any lunar surface eclipse period. Communication: The Artemis program is developing a series of communications satellites for lunar orbit to provide surface transmission of data and voice to Earth. Called “LunaNET”, this network is component useful for ArLSEP since south polar locations may not always have direct “line-of-sight” to Earth [9]. 3. Concept of Operations (ConOps) The general ConOps for ArLSEP is to deliver the package to lunar surface before the crew arrives, and then have the crew deploy the package after some period of time. This requires coordinated design (for power systems) and launch window (for schedule) on both the cargo and crew missions. Once the ArLSEP is deployed, it will operate autonomously for a number of years. It should be designed to be EVA compatible for crew maintenance and upgrade. 4. Notional Design (for discussion purpose only) The landing site near the South Pole is expected to have no eclipse cycle exceeding 5 days, so the “keep alive” power is 144 hours (6 days to include margin). A 12v ArLSEP will use rechargeable LiFePO4 cells, which are common in the Electric Vehicle (EV) industry. With a current of 5 amps and a 125 watt system, the mass is about 90kg. The comm. system and structure adds another 10kg, thus the “Central Station” is approximately 100kg. The solar power is collected on four arrays (each 2m above the surface), and the entire ArLSEP is designed to stow in a 2m x 1m x 1m volume. The experiment and instrument design will vary for each installation and add mass to the total (although they are expected to fit within the 2m3 volume). Seismic wave generation will likely not be provided with mortars, thus an electric “thumper” will be required. Active instruments such as imaging systems and sensing instruments will benefit from the additional power and communication capability provided by ArLSEP. Passive systems such as retroreflectors, witness plates, and cosmic dust collectors can be added to either the landing vehicle and/or the ArLSEP. With repeated HLS missions to the same human site, the ArLSEP can be expanded and easily maintained for long duration science collection on the lunar surface.

ALSEP↗