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NASA NTRS2010

Systems Analyze Water Quality in Real Time

A water analyzer developed under Small Business Innovation Research (SBIR) contracts with Kennedy Space Center now monitors treatment processes at water and wastewater facilities around the world. Originally designed to provide real-time detection of nutrient levels in hydroponic solutions for growing plants in space, the ChemScan analyzer, produced by ASA Analytics Inc., of Waukesha, Wisconsin, utilizes spectrometry and chemometric algorithms to automatically analyze multiple parameters in the water treatment process with little need for maintenance, calibration, or operator intervention. The company has experienced a compound annual growth rate of 40 percent over its 15-year history as a direct result of the technology's success.

Source record↗

NASA NTRS2011

Volatile Analyzer for Lunar Polar Missions

One of the major questions remaining for the future exploration of the Moon by humans concerns the presence of volatiles on our nearest neighbor in space. Observational studies, and investigations involving returned lunar samples and using robotic spacecraft infer the existence of volatile compounds particularly water [1]. It seems very likely that a volatile component will be concentrated at the poles in circumstances where low-temperatures exist to provide cryogenic traps. However, the full inventory of species, their concentration and their origin and sources are unknown. Of particular importance is whether abundances are sufficient to act as a resource of consumables for future lunar expeditions especially if a long-term base involving humans is to be established. To address some of these issues requires a lander designed specifically for operation at a high-lunar latitude. A vital part of the payload needs to be a volatile analyzer such as the Gas Analysis Package specifically designed for identification quantification of volatile substances and collecting information which will allow the origin of these volatiles to be identified [1]. The equipment included, particularly the gas analyzer, must be capable of operation in the extreme environmental conditions to be encountered. No accurate information yet exists regarding volatile concentration even for sites closer to the lunar equator (because of contamination). In this respect it will be important to understand (and thus limit) contamination of the lunar surface by extraneous material contributed from a variety of sources. The only data for the concentrations of volatiles at the poles comes from orbiting spacecraft and whilst the levels at high latitudes may be greater than at the equator, the volatile analyzer package under consideration will be designed to operate at the highest specifications possible and in a way that does not compromise the data.

Gibons, Everett K.↗

NASA NTRS2012

International Space Station Major Constituent Analyzer On-Orbit Performance

The Major Constituent Analyzer is a mass spectrometer based system that measures the major atmospheric constituents on the International Space Station. A number of limited-life components require periodic changeout, including the ORU 02 analyzer and the ORU 08 Verification Gas Assembly. Over the past two years, two ORU 02 analyzer assemblies have operated nominally while two others have experienced premature on-orbit failures. These failures as well as nominal performances demonstrate that ORU 02 performance remains a key determinant of MCA performance and logistical support. It can be shown that monitoring several key parameters can maximize the capacity to monitor ORU health and properly anticipate end of life. Improvements to ion pump operation and ion source tuning are expected to improve lifetime performance of the current ORU 02 design.

Gardner, Ben D.↗

NASA NTRS2012

OASIS: Organics Analyzer for Sampling Icy Surfaces

Liquid chromatography mass spectrometry (LC-MS) is a well established laboratory technique for detecting and analyzing organic molecules. This approach has been especially fruitful in the analysis of nucleobases, amino acids, and establishing chirol ratios [1 -3]. We are developing OASIS, Organics Analyzer for Sampling Icy Surfaces, for future in situ landed missions to astrochemically important icy bodies, such as asteroids, comets, and icy moons. The OASIS design employs a microfabricated, on-chip analytical column to chromatographically separate liquid ana1ytes using known LC stationary phase chemistries. The elution products are then interfaced through electrospray ionization (ESI) and analyzed by a time-of-flight mass spectrometer (TOF-MS). A particular advantage of this design is its suitability for microgravity environments, such as for a primitive small body.

Getty, S. A.↗

NASA NTRS2012

Laser Ablation Mass Spectrometer (LAMS) as a Standoff Analyzer in Space Missions for Airless Bodies

A laser ablation mass spectrometer (LAMS) based on a time-of-flight (TOF) analyzer with adjustable drift length is proposed as a standoff elemental composition sensor for space missions to airless bodies. It is found that the use of a retarding potential analyzer in combination with a two-stage reflectron enables LAMS to be operated at variable drift length. For field-free drift lengths between 33 cm to 100 cm, at least unit mass resolution can be maintained solely by adjustment of internal voltages, and without resorting to drastic reductions in sensitivity. Therefore, LAMS should be able to be mounted on a robotic arm and analyze samples at standoff distances of up to several tens of cm, permitting high operational flexibility and wide area coverage of heterogeneous regolith on airless bodies.

Li, X.↗

NASA NTRS2013

A Next Generation Air Monitor: Combining Orion and ISS Requirements for a Common Major Constituent Analyzer

The Major Constituent Analyzer (MCA) is a mass spectrometer-based instrument designed to provide critical monitoring of six major atmospheric constituents; nitrogen, oxygen, hydrogen, carbon dioxide, methane, and water vapor on-board the International Space Station. The analyzer has been an integral part of the Environmental Control and Life Support System (ECLSS) since the station went on-line. The Orion Air Monitor (OAM) was derived from the MCA and heavily optimized for reduced mass, lower power, faster water vapor response, and maintenance-free operation. The resulting OAM is approximately the size of the analyzer portion of the MCA, orbital-replacement unit 02 (ORU 02), while incorporating the functions of three other modules: Data Processing and Communication (ORU 01), Verification Gas Assembly (ORU 08), and Low Voltage Power Supply (ORU 04). The overlap in MCA and OAM requirements makes it possible to derive a common Air Monitor design that spans both applications while minimally impacting the weight and power limits imposed by the Multipurpose Crew Vehicle (MPCV). Benefits to ISS include the retirement of ORUs 01, 04, and 08, reducing up-mass and eliminating EEE parts obsolescence issues through the extended ISS mission phases. Benefits to MPCV and future deployed habitats under the Constellation program include greater interchangeability across ECLSS subsystems. This paper discusses the results of the requirements development study, where a superset of ISS and Orion air monitoring requirements were distilled; evaluated against increases in OAM functionality, mass, and power; and traded-off where possible using simple operating mode modifications. A system architecture and preliminary design addressing the common requirements will be presented.

Burchfield, David E.↗

NASA NTRS2015

International Space Station Major Constituent Analyzer On-Orbit Performance

The Major Constituent Analyzer is a mass spectrometer based system that measures the major atmospheric constituents on the International Space Station. A number of limited-life components require periodic change-out, including the ORU 02 analyzer and the ORU 08 Verification Gas Assembly. Improvements to ion pump operation and ion source tuning have improved lifetime performance of the current ORU 02 design. The most recent ORU 02 analyzer assemblies, as well as ORU 08, have operated nominally. For ORU 02, the ion source filaments and ion pump lifetime continue to be key determinants of MCA performance and logistical support. Monitoring several key parameters provides the capacity to monitor ORU health and properly anticipate end of life.

Gardner, Ben D.↗

NASA NTRS2016

International Space Station Major Constituent Analyzer On-orbit Performance

The Major Constituent Analyzer (MCA) is a mass spectrometer based system that measures the major atmospheric constituents on the International Space Station. A number of limited-life components require periodic change-out, including the ORU 02 analyzer and the ORU 08 Verification Gas Assembly. The most recent ORU 02 and ORU 08 assemblies are operating nominally. For ORU 02, the ion source filaments and ion pump lifetime continue to be key determinants of MCA performance. Additionally, testing is underway to evaluate the capacity of the MCA to analyze ammonia. Finally, plans are being made to bring the second MCA on ISS to an operational configuration.

Gardner, Ben D.↗

NASA NTRS2017

Educational and Scientific Applications of Climate Model Diagnostic Analyzer

Climate Model Diagnostic Analyzer (CMDA) is a web-based information system designed for the climate modeling and model analysis community to analyze climate data from models and observations. CMDA provides tools to diagnostically analyze climate data for model validation and improvement, and to systematically manage analysis provenance for sharing results with other investigators. CMDA utilizes cloud computing resources, multi-threading computing, machine-learning algorithms, web service technologies, and provenance-supporting technologies to address technical challenges that the Earth science modeling and model analysis community faces in evaluating and diagnosing climate models. As CMDA technology and infrastructure have matured, we have developed the educational and scientific applications of CMDA. Educationally, CMDA supported the summer school of the JPL Center for Climate Sciences in 2014, 2015, and 2016. In the summer school, the students work on group research projects where CMDA provide datasets, analysis tools, and provenance support utility tools. Each student is assigned to a virtual machine with CMDA installed in Amazon Web Services. Scientifically, we have developed several science use cases of CMDA covering various topics, datasets, and analysis types. Each of the science use cases is described in terms of a scientific goal, datasets used, the analysis tools used, scientific results discovered, an analysis result such as output plots and data files, and a link to the corresponding analysis service call with all the input arguments filled.

Bao, Qihao↗

NASA NTRSDate not supplied

Py MILab: Capturing, Analyzing and Storing Test Data

Integrated Computational Materials Engineering (ICME) has recently received widespread attention due to its promises in reducing dependence on physical testing for engineering design by relying on simulation, reducing both time and cost to market for various applications. ICME however requires validated multiscale material models, which is heavily dependent on available test data with full material and test pedigree, including material processing, test and measurement equipment, raw data collection, and analysis methodology and results. Populating searchable information management systems with such rich data sets is often burdensome for data producers, resulting in a lack of findable data for modelers to validate and verify their models. To overcome these cultural barriers to ICME, NASA has developed of various database-integration toolsets that perform both data management activities within the organization’s best practices with additional functionality that relieves the effort of the data producer and promotes adoption of information management system. One such tool currently under development is Py MILab, an automatic framework for automatic capturing, analysis, maintenance, and storage of material test data. Py MILab uses a modular approach for capturing raw data, analyzing the data, and storing the data in a database, interfaced by neutral file structures, to promote plug-and-play capabilities for various analysis types. TMAnalysis is a Python-based tool that performs automatic data reduction and analysis of uniaxial thermomechanical test data. The TMAnalysis toolset can be implemented within the Analysis module of Py MILab, and thus requires a populated neutral file form the Raw Data Module of Py MILab and outputs a Analysis neutral file compatible with the Database Module of Py MILab. TMAnalysis is able to perform automatic segmentation of multistage tests and perform data analysis and reduction, including determination of point-wise properties in tension, compression, and shear, analysis of stress relaxation tests, creep analysis and zone identification, and combination of these stage types for tests with complex loading histories. The TMAnalysis code is accompanied with a graphical user interface (GUI) that allows users to easily analyze test data in bulk, verify the automatic, consistent analysis performed by the backend code, and edit stage segmentation if necessary before producing the output neutral files, ensuring data is properly analyzed and maintained with full traceability.

Data management↗

NASA NTRS2002

Science opportunity analyzer - a multi-mission tool for planning

For many years the diverse scientific community that supports JPL's wide variety ofinterplanetary space missions has needed a tool in order to plan and develop their experiments. The tool needs to be easily adapted to various mission types and portable to the user community. The Science Opportunity Analyzer, SOA, now in its third year of development, is intended to meet this need. SOA is a java-based application that is designed to enable scientists to identify and analyze opportunities for science observations from spacecraft. It differs from other planning tools in that it does not require an in-depth knowledge of the spacecraft command system or operation modes to begin high level planning. Users can, however, develop increasingly detailed levels of design. SOA consists of six major functions: Opportunity Search, Visualization, Observation Design, Constraint Checking, Data Output and Communications. Opportunity Search is a GUI driven interface to existing search engines that can be used to identify times when a spacecraft is in a specific geometrical relationship with other bodies in the solar system. This function can be used for advanced mission planning as well as for making last minute adjustments to mission sequences in response to trajectory modifications. Visualization is a key aspect of SOA. The user can view observation opportunities in either a 3D representation or as a 2D map projection. The user is given extensive flexibility to customize what is displayed in the view. Observation Design allows the user to orient the spacecraft and visualize the projection of the instrument field of view for that orientation using the same views as Opportunity Search. Constraint Checking is provided to validate various geometrical and physical aspects of an observation design. The user has the ability to easily create custom rules or to use official project-generated flight rules. This capability may also allow scientists to easily impact the cost to science if flight rule changes occur. Data Output generates information based on the spacecraft's trajectory, opportunity search results or based on a created observation. The data can be viewed either in tabular format or as a graph. Finally, SOA is unique in that it is designed to be able to communicate with a variety of existing planning and sequencing tools. From the very beginning SOA was designed with the user in mind. Extensive surveys of the potential user community were conducted in order to develop the software requirements. Throughout the development period, close ties have been maintained with the science community to insure that the tool maintains its user focus. Although development is still in its early stages, SOA is already developing a user community on the Cassini project, which is depending on this tool for their science planning. There are other tools at JPL that do various pieces of what SOA can do; however, there is no other tool which combines all these functions and presents them to the user in such a convenient, cohesive, and easy to use fashion.

SOA science planning mission operations sequence s↗

NASA NTRS2006

On-wafer vector network analyzer measurements in the 220-325 Ghz frequency band

We report on a full two-port on-wafer vector network analyzer test set for the 220-325 GHz (WR3) frequency band. The test set utilizes Oleson Microwave Labs frequency extenders with the Agilent 8510C network analyzer. Two port on-wafer measurements are made with GGB Industries coplanar waveguide (CPW) probes. With this test set we have measured the WR3 band S-parameters of amplifiers on-wafer, and the characteristics of the CPW wafer probes. Results for a three stage InP HEMT amplifier show 10 dB gain at 235 GHz [1], and that of a single stage amplifier, 2.9 dB gain at 231 GHz. The approximate upper limit of loss per CPW probe range from 3.0 to 4.8 dB across the WR3 frequency band.

coplanar waveguides↗

NASA NTRS2015

Design and Demonstration of the Mars Organic Molecule Analyzer (MOMA) on the ExoMars 2018 Rover

The Mars Organic Molecule Analyzer (MOMA) investigation is a key astrobiology experiment scheduled to launch on the joint ESA-Roscosmos ExoMars 2018 rover mission. MOMA will examine the chemical composition of geological samples acquired from depths of up to two meters below the martian surface, where fragile organic molecules may be protected from destructive cosmic radiation and/or oxidative chemical reactions. The heart of the MOMA mass spectrometer subsystem (i.e., MOMA-MS) is a miniaturized linear ion trap (LIT) that supports two distinct modes of operation to detect: i) volatile and semi-volatile, low-to-moderate mass organics (less than or equal to 500 Da) via pyrolysis coupled with gas chromatography mass spectrometry (pyr/GCMS); and, ii) more refractory, moderate-to-high mass compounds (up to 1000 Da) via laser desorption (LDMS) at ambient Mars pressures. Additionally, the LIT mass analyzer enables selective ion trapping via multi-frequency waveform ion excitation (e.g., stored waveform inverse Fourier transform, or SWIFT), and structural characterization of complex molecules using tandem mass spectrometry (MSIMS). A high-fidelity Engineering Test Unit (ETU) of MOMAMS, including the LIT subassembly, dual-gun electron ionization source, micropirani pressure gauge, solenoid-driven aperture valve, redundant detection chains, and control electronics, has been built and tested at NASA GSFC under relevant operational conditions (pressure, temperature, etc.). Spaceflight qualifications of individual hardware components and integrated subassemblies have been validated through vibration, shock, thermal, lifetime, and performance evaluations. The ETU serves as a pathfinder for the flight model buildup, integration and test, as the ETU meets the form, fit and function of the flight unit that will be delivered to MPS in late 2015. To date, the ETU of MOMA-MS has been shown to meet or exceed all functional requirements, including mass range, resolution, accuracy, instrumental drift, and Iimit-of-detection specifications, thereby enabling the primary science objectives of the MOMA investigation and ExoMars 2018 mission.

Mars Organic Molecule Analyzer↗

NASA NTRS1996

The Cosmic Dust Analyzer for Cassini

The Cosmic Dust Analyzer (CDA) is designed to characterize the dust envirenment in interplanetary space, in the Jovian and in the Saturnian systems. The instrument consists of two major components, the Dust Analyzer (DA) and the HIgh Rate Detector(HRD).

cosmic↗

DOE OSTI2024

Analyzing the impact of design factors on solar module thermomechanical durability using interpretable machine learning techniques

Solar modules in utility-scale systems are expected to maintain decades of lifetime to rival conventional energy sources. However, cyclic thermomechanical loading often degrades their long-term performance, highlighting the importance of effective design to mitigate thermal expansion mismatches between module materials. Given the complex composition of solar modules, isolating the impact of individual components on overall durability remains a challenging task. In this work, we analyze a comprehensive data set that comprises bill-of-materials (BOM) and thermal cycling power loss from 251 distinct module designs to identify the predominant design factors and their impacts on the thermomechanical durability of modules. The methodology of our analysis combines machine learning modeling (random forest) and Shapley additive explanation (SHAP) to correlate design factors with power loss and interpret the model’s decision-making. The interpretation reveals that silicon type (monocrystalline or polycrystalline), encapsulant thickness, busbar numbers, and wafer thickness predominantly influence the degradation. With lower power loss of around 0.6% on average in the SHAP analysis, monocrystalline cells present better durability than polycrystalline cells. This finding is further substantiated by statistical testing on our raw data set. The SHAP analysis also demonstrates that while thicker encapsulants lead to reduced power loss, further increasing their thickness over around 0.6 to 0.7 mm does not yield additional benefits, particularly for the front side one. In addition, other important BOM features such as the number of busbars are analyzed. This study provides a blueprint for utilizing explainable machine learning techniques in a complex material system and can potentially guide future research on optimizing the design of solar modules.

14 SOLAR ENERGY↗

DOE OSTI2025

A finite-strain rate- and pressure-dependent constitutive framework for analyzing shock compression behavior of cemented tungsten carbides to 100 GPa

In the present study a thermodynamically-consistent finite-strain rate-and-pressure-dependent constitutive framework is implemented to analyze the shock-compression behavior of cemented tungsten carbides to 100 GPa. Central to this framework is the use of logarithmic strain with a set of invariant basis that allow the Cauchy stress tensor to be expressed as a sum of three response terms that are mutually orthogonal, thus permitting a complete separation of the deviatoric and volumetric (pressure) response. An overstress viscoplasticity model that includes strain and strain rate hardening along with thermal softening is used to represent the deviatoric response, while a complete Mie-Grüneisen equation of state (EoS) is used to obtain the pressure response. Using this formulation, the shock-induced compression behavior of cemented tungsten carbide - obtained from planar plate impact experiments using a 30 mm powder gun to peak stresses of up to ~100 GPa - is analyzed to better understand the structure of the measured shock wave profiles and the associated in-material shock quantities. Of particular interest is the evolution of material inelasticity and strength, and temperature in the tungsten carbide samples during the shock compression process.

Cemented tungsten carbide↗

DOE OSTI2026

An interactive machine learning platform for analyzing multi-particle coincidence data from cold target recoil ion momentum spectroscopy

We present SCULPT (Supervised Clustering and Uncovering Latent Patterns with Training), a comprehensive software platform for analyzing tabulated high-dimensional multi-particle coincidence data from Cold Target Recoil Ion Momentum Spectroscopy (COLTRIMS) experiments. The software addresses critical challenges in modern momentum spectroscopy by integrating advanced machine learning techniques with physics-informed analysis in an interactive web-based environment. SCULPT implements uniform manifold approximation and projection for non-linear dimensionality reduction to reveal correlations in high-dimensional data. We also discuss potential extensions to deep autoencoders for feature learning and genetic programming for automated discovery of physically meaningful observables. A novel adaptive confidence scoring system provides quantitative reliability assessments by evaluating user-selected clustering quality metrics with predefined weights that reflect each metric’s robustness. The platform features configurable molecular profiles for different experimental systems, interactive visualization with selection tools, and comprehensive data filtering capabilities. Utilizing a subset of SCULPT’s capabilities, we analyze photo-double-ionization data measured using the COLTRIMS method for three-body dissociation of the D 2 O molecule, revealing distinct fragmentation channels and their correlations with physics parameters. The software’s modular architecture and web-based implementation make it accessible to the broader atomic and molecular physics community, significantly reducing the time required for complex multi-dimensional analyses. This opens the door to finding and isolating rare events exhibiting non-linear correlations on the fly during experimental measurements, which can help steer exploration and improve the efficiency of experiments.

Artificial neural networks↗

DOE OSTI2025

Analyzing the Quantum Approximate Optimization Algorithm: Ansätze, Symmetries, and Lie Algebras

The quantum approximate optimization algorithm (QAOA) has been proposed as a method to obtain approximate solutions for combinatorial optimization tasks. In this work, we study the underlying algebraic properties of three QAOA ansätze for the maximum-cut problem on connected graphs, while focusing on the generated Lie algebras as well as their invariant subspaces. Specifically, we analyze the standard QAOA ansatz as well as the orbit and multiangle ansätze. We are able to fully characterize the Lie algebras of the multiangle ansatz across arbitrary connected graphs, finding that they only fall into one of just six families. Aside from the cycle and path graphs, the Lie dimensions for every graph are exponentially large in the system size, meaning that multiangle ansätze are extremely prone to exhibiting barren plateaus. Then, a similar quasi-graph-independent Lie-algebraic characterization beyond the multiangle ansatz is impeded as the circuit exhibits additional “hidden” symmetries besides those naturally arising from a certain parity-superselection operator and all automorphisms of the considered graph. Disregarding the “hidden” symmetries, we can upper bound the dimensions of the orbit and the standard Lie algebras, and the dimensions of the associated invariant subspaces are determined via explicit character formulas. To finish, we conjecture that (for most graphs) the standard Lie algebras have only components that are either exponential or that grow, at most, polynomially with the system size. This would imply that the QAOA is either prone to barren plateaus or classically simulable. More generally, our work provides a symmetry framework and tools to analyze any desired variational quantum algorithm.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗