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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9

Portable inference engine: An extended CLIPS for real-time production systems

The present C-Language Integrated Production System (CLIPS) architecture has not been optimized to deal with the constraints of real-time production systems. Matching in CLIPS is based on the Rete Net algorithm, whose assumption of working memory stability might fail to be satisfied in a system subject to real-time dataflow. Further, the CLIPS forward-chaining control mechanism with a predefined conflict resultion strategy may not effectively focus the system's attention on situation-dependent current priorties, or appropriately address different kinds of knowledge which might appear in a given application. Portable Inference Engine (PIE) is a production system architecture based on CLIPS which attempts to create a more general tool while addressing the problems of real-time expert systems. Features of the PIE design include a modular knowledge base, a modified Rete Net algorithm, a bi-directional control strategy, and multiple user-defined conflict resolution strategies. Problems associated with real-time applications are analyzed and an explanation is given for how the PIE architecture addresses these problems.

Le, Thach↗

Structured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES): a method for populating knowledge bases using zero-shot learning

Abstract Motivation Creating knowledge bases and ontologies is a time consuming task that relies on manual curation. AI/NLP approaches can assist expert curators in populating these knowledge bases, but current approaches rely on extensive training data, and are not able to populate arbitrarily complex nested knowledge schemas. Results Here we present Structured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES), a Knowledge Extraction approach that relies on the ability of Large Language Models (LLMs) to perform zero-shot learning and general-purpose query answering from flexible prompts and return information conforming to a specified schema. Given a detailed, user-defined knowledge schema and an input text, SPIRES recursively performs prompt interrogation against an LLM to obtain a set of responses matching the provided schema. SPIRES uses existing ontologies and vocabularies to provide identifiers for matched elements. We present examples of applying SPIRES in different domains, including extraction of food recipes, multi-species cellular signaling pathways, disease treatments, multi-step drug mechanisms, and chemical to disease relationships. Current SPIRES accuracy is comparable to the mid-range of existing Relation Extraction methods, but greatly surpasses an LLM’s native capability of grounding entities with unique identifiers. SPIRES has the advantage of easy customization, flexibility, and, crucially, the ability to perform new tasks in the absence of any new training data. This method supports a general strategy of leveraging the language interpreting capabilities of LLMs to assemble knowledge bases, assisting manual knowledge curation and acquisition while supporting validation with publicly-available databases and ontologies external to the LLM. Availability and implementation SPIRES is available as part of the open source OntoGPT package: https://github.com/monarch-initiative/ontogpt.

59 BASIC BIOLOGICAL SCIENCES↗

Flutter Prediction for Aircraft Conceptual Design

Flutter prediction is usually a knowledge-based analysis process that aims to reduce the cost of aeroelastic stability margin certification. However, early detection of flutter problems is beneficial in the development of unconventional aircraft. The recently developed automation tool ConceptFEA for structural sizing of aircraft concepts paves the way for rapid physics-based flutter prediction of aircraft concepts. A match-point iteration procedure using the p-k method is implemented for ConceptFEA with minimum user input requirements to generate flutter boundary points. A subsonic business jet concept and its high aspect-ratio wing variant are used to demonstrate how the newly developed flutter prediction capability can be used during aircraft conceptual design. Sized structures, flutter boundary curves, and flutter sensitivity analysis results are generated for these two concepts using ConceptFEA. The relevant equivalent plate theory is provided to show the quantitative relationships between a stiffened panel and its equivalent NASTRAN PSHELL panel. The rapid flutter prediction capability of ConceptFEA makes multidisciplinary collaborations between systems analysts and aeroelasticity experts feasible in practice.

Li, Wu↗

A Tutor That's Up to the Task

With assistance from NASA s Marshall Space Flight Center, a new breed of ITS for technical training and complex problem-solving has hit the market to provide students and trainees with the decision-making skills necessary to succeed to the next level. The Task Tutor Toolkit (T3), developed by Stottler Henke Associates, Inc., of San Mateo, California, is a generic tutoring system shell and scenario authoring tool that emulates expert instructors and lowers the cost and difficulty of creating scenario-based ITS for technical training. The functionality of Stottler Henke Associates T3 far exceeds that of traditional computer-based training systems, which test factual recall and narrow skills by prompting students to answer multiple-choice or fill-inthe- blank questions. The T3, on the contrary, lets students assess situations, generate solutions, make decisions, and carry out actions in realistically complex scenarios.At the beginning of each scenario, the T3 tutoring system presents a briefing that describes the situation and the goals the students should pursue. Each scenario contains a solution template that specifies a partially-ordered sequence of action patterns that match correct sequences of student actions. During each scenario, the built-in simulator notifies the tutoring system of each student action. The T3 uses this information to evaluate the student action by comparing it with the scenario s solution template and with error rules that detect incorrect actions.

Source record↗

JPSS-3 VIIRS Version 2 at-Launch Relative Spectral Response Characterization

The JPSS-3 VIIRS sensor has completed its pre-launch test program including measurements for characterizing the VIIRS relative spectral response (RSR) in support of the Sensor and Environmental Data Records (SDR and EDR, resp.) that will be generated from VIIRS on-orbit observations. Government team subject matter experts of the VIIRS DAWG have analyzed the VIIRS spectral measurements and produced the VIIRS spectral characterization, in the form of band-average and supporting detector level RSR for each VIIRS band. The characterization is based upon the analysis of independent SpMA dual monochromator (all bands) and GSFC GLAMR laser system (reflectance bands only) spectral measurements. The SpMA and GLAMR measurements for reflectance bands (DNB LGS and MGS, I1-I3, M1-M11) were combined to produce a “fused” RSR. For emissive bands (I4, I5, M12-M16), the SpMA measurements provide the characterization. The effort has led to the VIIRS Version 2 RSR release, the official at-launch RSR characterization for the JPSS-3 VIIRS mission. The JPSS-3 RSR are a close match to those of JPSS-2. An assessment on compliance with spectral performance metrics finds that VIIRS band-average RSR are compliant on nearly all metrics, with only a single minor exception. The Version 2 RSR release is available under EAR99 restrictions to the science community on the restricted access NASA Sharepoint.

Chris Moeller↗

Fast and Flexible Multivariate Time Series Subsequence Search

Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical monitoring, and financial systems. Domain experts are often interested in searching for interesting multivariate patterns from these MTS databases which often contain several gigabytes of data. Surprisingly, research on MTS search is very limited. Most of the existing work only supports queries with the same length of data, or queries on a fixed set of variables. In this paper, we propose an efficient and flexible subsequence search framework for massive MTS databases, that, for the first time, enables querying on any subset of variables with arbitrary time delays between them. We propose two algorithms to solve this problem (1) a List Based Search (LBS) algorithm which uses sorted lists for indexing, and (2) a R*-tree Based Search (RBS) which uses Minimum Bounding Rectangles (MBR) to organize the subsequences. Both algorithms guarantee that all matching patterns within the specified thresholds will be returned (no false dismissals). The very few false alarms can be removed by a post-processing step. Since our framework is also capable of Univariate Time-Series (UTS) subsequence search, we first demonstrate the efficiency of our algorithms on several UTS datasets previously used in the literature. We follow this up with experiments using two large MTS databases from the aviation domain, each containing several millions of observations. Both these tests show that our algorithms have very high prune rates (>99%) thus needing actual disk access for only less than 1% of the observations. To the best of our knowledge, MTS subsequence search has never been attempted on datasets of the size we have used in this paper.

Bhaduri, Kanishka↗

A Scala DSL for RETE-Based Runtime Verification

Runtime verification (RV) consists in part of checking execution traces against formalized specifications. Several systems have emerged, most of which support specification notations based on state machines, regular expressions, temporal logic, or grammars. The field of Artificial Intelligence (AI) has for an even longer period of time studied rule-based production systems, which at a closer look appear to be relevant for RV, although seemingly focused on slightly different application domains, such as for example business processes and expert systems. The core algorithm in many of these systems is the Rete algorithm. We have implemented a Rete-based runtime verification system, named LogFire (originally intended for offline log analysis but also applicable to online analysis), as an internal DSL in the Scala programming language, using Scala's support for defining DSLs. This combination appears attractive from a practical point of view. Our contribution is in part conceptual in arguing that such rule-based frameworks originating from AI may be suited for RV.

matching problem↗

MRI-alone radiation therapy planning for prostate cancer: Automatic fiducial marker detection

Purpose: The feasibility of radiation therapy treatment planning using substitute computed tomography (sCT) generated from magnetic resonance images (MRIs) has been demonstrated by a number of research groups. One challenge with an MRI-alone workflow is the accurate identification of intraprostatic gold fiducial markers, which are frequently used for prostate localization prior to each dose delivery fraction. This paper investigates a template-matching approach for the detection of these seeds in MRI. Methods: Two different gradient echo T1 and T2* weighted MRI sequences were acquired from fifteen prostate cancer patients and evaluated for seed detection. For training, seed templates from manual contours were selected in a spectral clustering manifold learning framework. This aids in clustering “similar” gold fiducial markers together. The marker with the minimum distance to a cluster centroid was selected as the representative template of that cluster during training. During testing, Gaussian mixture modeling followed by a Markovian model was used in automatic detection of the probable candidates. The probable candidates were rigidly registered to the templates identified from spectral clustering, and a similarity metric is computed for ranking and detection. Results: A fiducial detection accuracy of 95% was obtained compared to manual observations. Expert radiation therapist observers were able to correctly identify all three implanted seeds on 11 of the 15 scans (the proposed method correctly identified all seeds on 10 of the 15). Conclusions: An novel automatic framework for gold fiducial marker detection in MRI is proposed and evaluated with detection accuracies comparable to manual detection. When radiation therapists are unable to determine the seed location in MRI, they refer back to the planning CT (only available in the existing clinical framework); similarly, an automatic quality control is built into the automatic software to ensure that all gold seeds are either correctly detected or a warning is raised for further manual intervention.

60 APPLIED LIFE SCIENCES↗

Remote Sensing and Fluxes Upscaling for Real-world Impact (Workshop Report)

The "Remote Sensing and Fluxes Upscaling for Real-world Impact" workshop, held on July 9-10, 2024, at Lawrence Berkeley National Lab, was a collaborative effort led by the AmeriFlux Management Project, NEON, and the Carbon Dew Community of Practice. The event brought together over 200 registrants and approximately 100 attendees each day, including leading experts, researchers, and practitioners. The primary focus was on bridging the gap between cutting-edge research and practical applications in environmental monitoring by integrating remote sensing and flux data. Key themes included the importance of site-level measurements for validating remote sensing products, providing nature-based climate solutions, and addressing challenges such as instrument costs and the need for standardized methods. At the regional scale, discussions centered on addressing spatial heterogeneity and using high-resolution remote sensing and machine learning methods to enhance data interpretation. Global scale challenges included data consistency, gap filling, and accurate emission source identification, with opportunities for international collaboration and standardized practices to improve global carbon budget assessments. The workshop emphasized the critical need for integrating data across local, regional, and global scales through explicit scale-matching and developed a workflow for scaling flux data using "straight shot" and "explicit nesting" approaches. The event highlighted the importance of connecting scientific research with real-world applications in carbon, energy, and water management, ensuring that advancements translate into tangible societal benefits. These insights will guide future research, technology transfer, and collaboration, maximizing the potential of environmental fluxes to address real-world challenges.

97 MATHEMATICS AND COMPUTING↗

ChemGraph as an agentic framework for computational chemistry workflows

Atomistic simulations are essential in chemistry and materials science but remain challenging to run due to the expert knowledge required for the setup, execution, and validation stages of these calculations. We present ChemGraph, an agentic framework powered by artificial intelligence and state-of-the-art simulation tools to streamline and automate computational chemistry and materials science workflows. ChemGraph leverages graph neural network-based foundation models for accurate yet computationally efficient calculations and large language models (LLMs) for natural language understanding, task planning, and scientific reasoning to provide an intuitive and interactive interface. We evaluate ChemGraph across 13 benchmark tasks and demonstrate that smaller LLMs (GPT-4o-mini, Claude-3.5-haiku, Qwen-2.5-14B) perform well on simple workflows, while more complex tasks benefit from using larger models. Importantly, we show that decomposing complex tasks into smaller subtasks through a multi-agent framework enables GPT-4o to reach perfect accuracy and smaller LLMs to match or exceed single-agent GPT-4o's performance in these benchmarks.

Computational chemistry↗

Statistical Methods for Effective Spatial and Temporal Scaling in Support of Probabilistic Performance Assessments - 17340

Performance assessments (PAs) for the disposal of low-level radioactive waste have traditionally been performed in a deterministic manner, with inputs specified as single values as if they carry no uncertainty. Modern PAs, however, explicitly incorporate uncertainty through use of probability distributions for inputs instead of single values. While this is clearly an improvement, challenges remain in specifying appropriate distributions, many of which relate to capturing the appropriate spatial and/or temporal scale given the PA model, and matching the scale(s) of data and information to that specified by the model. More careful attention to scaling will lead to more defensible characterizations of physical variability and uncertainty in model inputs, thus leading to more defensible information regarding outcomes of interest to be used in decision making, and hence more defensible decisions. This work introduces general strategies and considerations for explicitly incorporating considerations of scale into distribution development. The strategies are explained as a series of steps and then presented with an example that starts to illustrate the new statistical concepts. This work frames the problem in terms of hierarchical statistical modeling, providing important connections to current statistical approaches to formally and rigorously account for temporal and spatial scaling. The larger goal is to build a more formal and unified approach to distribution development to help teams of modelers, subject matter experts, and statisticians work together to develop distributions and justify the choices made in the process, ultimately leading to more informed decision-making. Without this rigor, statistical distributions are not specified correctly and can have unexpected consequences for modeling and sensitivity analysis. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

JPSS-4 VIIRS Version 2 at-Launch Relative Spectral Response Characterization

The JPSS-4 VIIRS sensor, the last in a JPSS program that will eventually span some 25+ years of on-orbit data collection, has completed its pre-launch test program. The test program included measurements for characterizing the VIIRS relative spectral response (RSR) in support of the Sensor and Environmental Data Records that will be generated from JPSS-4 VIIRS on-orbit observations after launch. Subject matter experts of the Government Team’s VIIRS DAWG have analyzed the VIIRS spectral measurements and produced the VIIRS spectral characterization, in the form of band averaged and supporting detector level RSR for each VIIRS band. The characterization is based upon the analysis of independent SpMA dual monochromator (all bands) and GSFC GLAMR laser system (reflectance bands only) spectral measurements. The SpMA and GLAMR measurements for reflectance bands (DNBLGS and DNBMGS, I1-I3, M1-M11) were combined to produce a “fused” RSR. For emissive bands (I4, I5, M12-M16), the SpMA measurements provide the entire characterization. The effort has led to the VIIRS Version 2 RSR product, the official at-launch RSR characterization for the JPSS-4 VIIRS mission, which is currently slated to be the next launch (expected Fall 2027) of the JPSS program. As expected, the JPSS-4 RSR are a close match to those of JPSS-3 and JPSS-2 (NOAA-21) VIIRS, while showing some spectral position and shape differences with SNPP and JPSS-1 (NOAA-20) VIIRS. An assessment on compliance with spectral performance metrics finds that VIIRS bandaverage RSR are compliant on nearly all metrics, with only a single minor exception. The Version 2 RSR will be available under EAR99 restrictions to the science community on the restricted access NASA Sharepoint.

SDR↗

Expert system technologies for Space Shuttle decision support: Two case studies

This paper addresses the issue of integrating the C Language Integrated Production System (CLIPS) into distributed data acquisition environments. In particular, it presents preliminary results of some ongoing software development projects aimed at exploiting CLIPS technology in the new mission control center (MCC) being built at NASA Johnson Space Center. One interesting aspect of the control center is its distributed architecture; it consists of networked workstations which acquire and share data through the NASA/JSC-developed information sharing protocol (ISP). This paper outlines some approaches taken to integrate CLIPS and ISP in order to permit the development of intelligent data analysis applications which can be used in the MCC. Three approaches to CLIPS/IPS integration are discussed. The initial approach involves clearly separating CLIPS from ISP using user-defined functions for gathering and sending data to and from a local storage buffer. Memory and performance drawbacks of this design are summarized. The second approach involves taking full advantage of CLIPS and the CLIPS Object-Oriented Language (COOL) by using objects to directly transmit data and state changes from ISP to COOL. Any changes within the object slots eliminate the need for both a data structure and external function call thus taking advantage of the object matching capabilities within CLIPS 6.0. The final approach is to treat CLIPS and ISP as peer toolkits. Neither is embedded in the other; rather the application interweaves calls to each directly in the application source code.

Ortiz, Christopher J.↗

Human Performance Contributions to Safety in Commercial Aviation

Every day in aviation, pilots, air traffic controllers, and other front-line personnel perform countless correct judgments and actions in a variety of operational environments. These judgments and actions are often the difference between an accident and a non-event. Ironically, data on these behaviors are rarely collected or analyzed. Data-driven decisions about safety management and design of safety-critical systems are limited by the available data, which influence how decision makers characterize problems and identify solutions. Large volumes of data are collected on the failures and errors that result in infrequent incidents and accidents, but in the absence of data on behaviors that result in routine successful outcomes, safety management and system design decisions are based on a small sample of nonrepresentative safety data. This assessment aimed to find and document “safety successes” made possible by human operators. With many Aeronautics Research Mission Directorate (ARMD) Programs and Projects focusing on increased automation and autonomy and decreased human involvement, failure to fully consider the human contributions to successful system performance in civil aviation represents a significant risk — a risk that has not been recognized to date. Without understanding how humans contribute to safety, any estimate of predicted safety of autonomous capabilities is incomplete and inherently suspect. Furthermore, understanding the ways in which humans contribute to safety can promote strategic interactions among safety technologies, functions, procedures and the people using them. Without this understanding, the full benefits of an integrated, optimized human/technology or autonomous system will not be realized. Historically, safety has been consistently defined in terms of the occurrence of accidents or recognized risks (i.e., in terms of things that go wrong). These adverse outcomes are explained by identifying their causes, and safety is restored by eliminating or mitigating these causes. An alternative to this approach is to focus on what goes right and identify how to replicate that process. Focusing on the rare cases of failures attributed to “human error” provides little information about why human performance routinely prevents adverse events. Hollnagel has proposed that things go right because people continuously adjust their work to match their operating conditions. These adjustments become increasingly important as systems continue to grow in complexity. Thus, the definition of safety should reflect not only “avoiding things that go wrong” but “ensuring that things go right.” The basis for safety management requires developing an understanding of everyday activities. However, few mechanisms to monitor everyday work exist in the aviation domain, which limits opportunities to learn how designs function in reality. This concept of safety thinking and safety management is reflected in the emerging field of resilience engineering. According to Hollnagel, a system is resilient if it can sustain required operations under expected and unexpected conditions by adjusting its functioning prior to, during, or following changes, disturbances, and opportunities. To explore “positive” behaviors that contribute to resilient performance in commercial aviation, the assessment team examined a range of existing sources of data about pilot and air traffic control (ATC) tower controller performance, including subjective interviews with domain experts and objective aircraft flight data records. These data were used to identify strategies that support resilient performance, methods for exploring and refining those strategies in existing data, and proposed methods for capturing and analyzing new data.

Null, Cynthia H.↗

SSME fault monitoring and diagnosis expert system

An expert system, called LEADER, has been designed and implemented for automatic learning, detection, identification, verification, and correction of anomalous propulsion system operations in real time. LEADER employs a set of sensors to monitor engine component performance and to detect, identify, and validate abnormalities with respect to varying engine dynamics and behavior. Two diagnostic approaches are adopted in the architecture of LEADER. In the first approach fault diagnosis is performed through learning and identifying engine behavior patterns. LEADER, utilizing this approach, generates few hypotheses about the possible abnormalities. These hypotheses are then validated based on the SSME design and functional knowledge. The second approach directs the processing of engine sensory data and performs reasoning based on the SSME design, functional knowledge, and the deep-level knowledge, i.e., the first principles (physics and mechanics) of SSME subsystems and components. This paper describes LEADER's architecture which integrates a design based reasoning approach with neural network-based fault pattern matching techniques. The fault diagnosis results obtained through the analyses of SSME ground test data are presented and discussed.

Ali, Moonis↗

Natural Fiber Simulations and Analysis for Composites

As the U.S. government and the aviation industry strive to achieve net-zero carbon emissions by 2050, the Sustainable Manufacturing of Aircraft (SUMAC) project, supported by the Convergent Aeronautics Solutions (CAS) program of the National Aeronautics and Space Administration (NASA), aims to develop sustainably derived thermoplastic composites and manufacturing technologies for future aviation. A multidisciplinary team of experts drives this initiative through a collective effort. The team’s key focus areas are sustainably derived thermoplastic resins, natural fiber reinforcement, composite manufacturing, structural health monitoring, computational materials modeling, and systems analysis. This presentation will give an overview of the SUMAC project. Fiber-scale microscopy analysis characterized fiber cross sections finding that hemp and flax fibers are very deformable and non-circular (circularities of 0.2 were observed), and diameters range from submicron to 20 μm. Fiber shapes range from flattened, with aspect ratios Rlong/Rshort up to 5, to round, Rlong/Rshort =1. And Fiber core perimeters, which likely drives deformability, were calculated Pcore=7 μm. Explicit-fiber yarn simulations, parameterized from single-fiber measurements, resolved and identified slack as a cause for yarn strain-hardening tensile behavior. The bonded network model of PLA, PHA and PA11 resins captures tensile behavior, and can model an infused weave. Computational models of the weave unit cells matched the pattern (including plain, twill and triaxial braid), yarn ply (1to 2), and yarn twist from optical microscopy.

thermoplastic↗

Natural Fiber Simulations and Analysis for Composites

As the U.S. government and the aviation industry strive to achieve net-zero carbon emissions by 2050, the Sustainable Manufacturing of Aircraft (SUMAC) project, supported by the Convergent Aeronautics Solutions (CAS) program of the National Aeronautics and Space Administration (NASA), aims to develop sustainably derived thermoplastic composites and manufacturing technologies for future aviation. A multidisciplinary team of experts drives this initiative through a collective effort. The team’s key focus areas are sustainably derived thermoplastic resins, natural fiber reinforcement, composite manufacturing, structural health monitoring, computational materials modeling, and systems analysis. This presentation will give an overview of the SUMAC project. Fiber-scale microscopy analysis characterized fiber cross sections finding that hemp and flax fibers are very deformable and non-circular (circularities of 0.2 were observed), and diameters range from submicron to 20 μm. Fiber shapes range from flattened, with aspect ratios Rlong/Rshort up to 5, to round, Rlong/Rshort =1. And Fiber core perimeters, which likely drives deformability, were calculated Pcore=7 μm. Explicit-fiber yarn simulations, parameterized from single-fiber measurements, resolved and identified slack as a cause for yarn strain-hardening tensile behavior. The bonded network model of PLA, PHA and PA11 resins captures tensile behavior, and can model an infused weave. Computational models of the weave unit cells matched the pattern (including plain, twill and triaxial braid), yarn ply (1to 2), and yarn twist from optical microscopy.

thermoplastic↗

Solar vs. Fission Surface Power for Mars

A multi-discipline team of experts from the National Aeronautics and Space Administration (NASA) developed Mars surface power system point design solutions for two conceptual missions. The primary goal of this study was to compare the relative merits of solar- versus fission-powered versions of each surface mission. First, the team compared three different solar power options against a fission power system concept for a sub-scale, uncrewed demonstration mission. The 4.5 meter (m) diameter pathfinder lander's primary mission would be to demonstrate Mars entry, descent, and landing techniques. Once on the Martian surface, the lander's In Situ Resource Utilization (ISRU) payload would demonstrate liquid oxygen propellant production using atmospheric resources. For the purpose of this exercise, location was assumed to be at the Martian equator. The three solar concepts considered included a system that only operated during daylight hours (at roughly half the daily propellant production rate of a round-the-clock fission design), a battery-augmented system that operated through the night (matching the fission concept's propellant production rate), and a system that operated only during daylight, but at a higher rate (again, matching the fission concept's propellant production rate). Including 30% mass growth allowance, total payload masses for the three solar concepts ranged from 1,116 to 2,396 kg, versus the 2,686 kg fission power scheme. However, solar power masses are expected to approach or exceed the fission payload mass at landing sites further from the equator, making landing site selection a key driver in the final power system decision. The team also noted that detailed reliability analysis should be performed on daytime-only solar power schemes to assess potential issues with frequent ISRU system on/off cycling. Next, the team developed a solar-powered point design solution for a conceptual four-crew, 500-day surface mission consisting of up to four landers per crewed expedition mission. Unlike the demonstration mission, a lengthy power outage due to the global dust storms that are known to occur on Mars would pose a safety hazard to a crewed mission. A similar fission versus solar power trade study performed by NASA in 2007 concluded that fission power was more reliable-with a much lower mass penalty-than solar power for this application. However, recent advances in solar cell and energy storage technologies and changes in operational assumptions prompted NASA to revisit the analysis. For the purpose of this exercise a particular landing site at Jezero Crater, located at 18o north latitude, was assumed. A fission power system consisting of four each 10 kW Kilopower fission reactors was compared to a distributed network of Orion-derived Ultraflex solar arrays and Lithium ion batteries mounted on every lander. The team found that a solar power system mass of about 9,800 kg would provide the 22 kilowatts (kW) keep-alive power needed to survive a dust storm lasting up to 120-days at average optical depth of 5, and 35 kW peak power for normal operations under clear skies. Although this is less than half the mass estimated during the 2007 work (which assumed latitudes up to 30o) it is still more than the 7,000 kg mass of the fission system which provides full power regardless of dust storm conditions.

Rucker, Michelle A.↗