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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Identifying Vehicle Signals in Continuous Seismic Data Using Unsupervised Machine-Learning Techniques

Seismic sensors deployed near roadways effectively capture ground vibrations generated by passing vehicles. Although both traditional and machine‐learning algorithms have been utilized for analyzing such signals, independent validation of detected vehicle events remains limited. We applied two unsupervised machine‐learning algorithms, uniform manifold approximation and projection for dimension reduction, and hierarchical density‐based spatial clustering of applications with noise, to continuous seismic data collected along a road on the main campus of Oak Ridge National Laboratory. The algorithms identified seven distinct cluster labels across the entire dataset. By comparing these cluster labels with precipitation records from a nearby weather station and image‐derived labels from a local camera system, we identified one cluster associated with rainfall and another with vehicle activity. Our algorithms identified a greater number of vehicle‐related labels compared to the camera‐derived labels because seismic data are unaffected by poor lighting conditions. The arrival times of the newly detected vehicle signals corresponded well with the road’s speed limit, supporting our findings. Our algorithm outperformed the short‐term average/long‐term average method and k‐means clustering. Our results suggest that seismic data, when analyzed with machine‐learning algorithms, can complement existing vehicle monitoring systems, particularly under challenging environmental conditions.

Chai, Chengping [Oak Ridge National Laboratory (OR↗
DOE OSTI2024

Teaching Nonradiative Transitions with MATLAB and Python

Nonradiative transitions are changes in energy states of atoms, ions, or molecules that do not involve the emission or absorption of photons. Despite their importance in understanding luminescent properties and photochemical reaction mechanisms, nonradiative transitions are rarely given more than a qualitative overview in undergraduate and even graduate physical chemistry curricula. To supplement the coverage of nonradiative transition topics, we provide here a set of active learning exercises to help students develop an intuitive understanding of the factors that determine the rate of nonradiative transitions. Here, we start by outlining the theoretical background through the formulations of the Franck–Condon factor and its relation to the rate of nonradiative transition. We then introduce three teaching modules, with associated MATLAB and Python codes, to explore how (1) the excited state nuclear displacement, (2) the electronic energy gap between excited and ground state and (3) the excited/ground state vibrational mode frequencies affect the magnitude of the Franck–Condon factor and thereby the rate of nonradiative transitions. The wave function overlap plots that accompany all teaching modules provide direct visualization of the effect of input parameters on the magnitude of Franck–Condon overlap integral.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1992

A neural network prototyping package within IRAF

We outline our plans for incorporating a Neural Network Prototyping Package into the IRAF environment. The package we are developing will allow the user to choose between different types of networks and to specify the details of the particular architecture chosen. Neural networks consist of a highly interconnected set of simple processing units. The strengths of the connections between units are determined by weights which are adaptively set as the network 'learns'. In some cases, learning can be a separate phase of the user cycle of the network while in other cases the network learns continuously. Neural networks have been found to be very useful in pattern recognition and image processing applications. They can form very general 'decision boundaries' to differentiate between objects in pattern space and they can be used for associative recall of patterns based on partial cures and for adaptive filtering. We discuss the different architectures we plan to use and give examples of what they can do.

Bazell, D.↗
NASA NTRS2005

Auxiliary Propulsion Activities in Support of NASA's Exploration Initiative

The Space Launch Initiative (SLI) procurement mechanism NRA8-30 initiated the Auxiliary Propulsion System/Main Propulsion System (APS/MPS) Project in 2001 to address technology gaps and development risks for non-toxic and cryogenic propellants for auxiliary propulsion applications. These applications include reaction control and orbital maneuvering engines, and storage, pressure control, and transfer technologies associated with on-orbit maintenance of cryogens. The project has successfully evolved over several years in response to changing requirements for re-usable launch vehicle technologies, general launch technology improvements, and, most recently, exploration technologies. Lessons learned based on actual hardware performance have also played a part in the project evolution to focus now on those technologies deemed specifically relevant to the Exploration Initiative. Formal relevance reviews held in the spring of 2004 resulted in authority for continuation of the Auxiliary Propulsion Project through Fiscal Year 2005 (FY05), and provided for a direct reporting path to the Exploration Systems Mission Directorate. The tasks determined to be relevant under the project were: continuation of the development, fabrication, and delivery of three 870 lbf thrust prototype LOX/ethanol reaction control engines; the fabrication, assembly, engine integration and testing of the Auxiliary Propulsion Test Bed at White Sands Test Facility; and the completion of FY04 cryogenic fluid management component and subsystem development tasks (mass gauging, pressure control, and liquid acquisition elements). This paper presents an overview of those tasks, their scope, expectations, and results to-date as carried forward into the Exploration Initiative.

Best, Philip J.↗
NASA NTRS2009

The Human in Space: Lesson from ISS

This viewgraph presentation reviews the lessons learned from manned space flight on the International Space Station. The contents include: 1) Overview of space flight effects on crewmembers; 2) General overview of immune system; 3) How does space flight alter immune system? 4) What factors associated with space flight inteact with crewmember immune function and impact health risks? 5) What is the current understanding of space flight effects on the immune system? and 6) Why should NASA be interested in immunology? Why is it significant?

Sams, Clarence F.↗
NASA NTRS2014

Mapping by VESGEN of Blood Vessels in the Retinas of ISS Crew Members and Bed Rest Subjects for Increased Understanding of VIIP

Research by NASA has established that significant risks for visual impairment in association with increased intracranial pressure (VIIP) are incurred by microgravity spaceflight, especially long-duration missions. Impairments include decreased near visual acuity, posterior globe flattening, choroidal folds, optic disc edema, and cotton wool spots. Much remains to be learned about the etiology of VIIP before effective countermeasures can be developed. Contributions of retinal vascular remodeling to the etiology of VIIP have not yet been investigated, primarily due to the current lack of ophthalmic tools for precisely measuring progressive pathophysiological remodeling of the retinal microvasculature. Although ophthalmic science and clinical practice are now highly sophisticated at detecting indirect, secondary signs of vascular remodeling such as cotton wool spots that arise during the progression of retinal vascular diseases, methods for quantifying direct, primary vascular changes are not yet established. To help develop insightful analysis of retinal vascular remodeling for aerospace medicine, we will map and quantify by our innovative VESsel GENeration Analysis (VESGEN) software the remodeling status of retinal blood vessels in crew members before and after ISS missions, and in healthy human subjects before and after head-down tilt bed rest. For this proof-of-concept study, we hypothesize that pathophysiological remodeling of retinal blood vessels occurs in coordination with microgravity-induced fluid shifts prior to development of visual impairments. VESGEN analysis in previous research supported by the US National Institutes of Health identified surprising new opportunities to regenerate retinal vessels during early-stage progression of the visually impairing, potentially blinding disease, diabetic retinopathy.

ISS Crew↗
NASA NTRSDate not supplied

ISS External Microorganisms: Collecting Planetary Protection Samples During Extravehicular Activity

Introduction: We have designed1, built, and tested a sampling kit (Fig. 1) to aseptically collect microbiological samples from exterior surfaces on the ISS (International Space Station). The kit was flown to ISS as part of the NG-19 commercial cargo mission in August of 2023. Astronauts will use the kit to collect samples from six exterior surfaces on ISS. These samples will be frozen at -80°C after collection and returned to the ground for next generation DNA sequencing. The results of this experiment will help inform planetary protection requirements for crewed missions. We hypothesize that there are detectable microbial communities outside ISS and that these communities originate from inside ISS. Current life support systems do not include any components for reducing microbial leakage. Gases are vented from inside ISS without filtration and there are currently no protocols in place to minimize bioburden on space suit exteriors prior to use. It is important to quantify the bioburden on current vehicles so that achievable limits can be set for crewed missions to astrobiologically relevant locations like Mars. Figure 1: The sampling kit contains eight swab canisters in total (six on the top and two on the bottom). A resuable end effector (1) is used to remove and reinstall the swabs. The kit also contains a handrail (2), tether loop (3), and, bayonet probes(4) to allow easy manipulation during EVA. Methods: We have designed a kit to carry 8 commercially available foam swabs into and out of vacuum without compromising the swabs’ sterility. The specially designed swab canisters contain a 0.2 µm Teflon filter that allows the canister to accommodate pressure changes without introducing unwanted contaminants. The kit meets existing EVA (Extravehicular Activity) safety requirements and has been used by human test subjects in the neutral buoyance lab and at vacuum in test chambers at the Johnson Space Center. In the early part of 2024, astronauts will use this sampling kit to swab six surfaces on the exterior of ISS. We will collect samples from: the airlock vestibule, the interior surface of the airlock thermal cover, an exterior handrail, a vent connected to the CO2 removal system, and a vent connected to the payload vacuum system inside ISS. Approximately 300 cm2 will be swabbed at each location. A seventh swab will be exposed to the vacuum of space without touching any surfaces as a blank. The eighth swab will remain sealed as a process control. After the EVA the swab kit will be frozen at -80°C and returned to earth at the earliest possible opportunity. The samples will remain frozen until they are thawed for next generation DNA sequencing on earth. We will use amplicon sequencing to identify any bacteria, archaea or fungi present in the samples. If there is enough DNA present, we will use shotgun metagenomic sequencing to further characterize the microbial ecology outside ISS. Preliminary Results: Results from ground-based testing demonstrate that the swab kit is capable of cycling in and out of vacuum without contaminating the swabs. Test subjects, wearing flight-like EVA gloves could remove swabs from the canister and sample discrete locations without inadvertently touching any other surfaces. Several types of bacteria and fungi were collected during these ground tests and survived up to 6 hours at vacuum. This includes non-spore forming bacterial like Staphyloccus capitis that are not traditionally considered extremophiles. Shotgun metagenomic sequencing of the ground-test samples revealed bacteria associated with human skin and airways on the exterior of space suits used during these tests 2 In addition to these results we will present preliminary results from our space-flight samples. We will also present lessons learned from attempting to collect microbiological samples during an EVA and describe how our results will affect planetary protection requirements for future crewed missions.

Aaron B. Regberg↗
NASA NTRSDate not supplied

ISS External Microorganisms: Collecting Planetary Protection Samples During Extravehicular Activity

Introduction: We have designed1, built, and tested a sampling kit (Fig. 1) to aseptically collect microbiological samples from exterior surfaces on the ISS (International Space Station). The kit was flown to ISS as part of the NG-19 commercial cargo mission in August of 2023. Astronauts will use the kit to collect samples from six exterior surfaces on ISS. These samples will be frozen at -80°C after collection and returned to the ground for next generation DNA sequencing. The results of this experiment will help inform planetary protection requirements for crewed missions. We hypothesize that there are detectable microbial communities outside ISS and that these communities originate from inside ISS. Current life support systems do not include any components for reducing microbial leakage. Gases are vented from inside ISS without filtration and there are currently no protocols in place to minimize bioburden on space suit exteriors prior to use. It is important to quantify the bioburden on current vehicles so that achievable limits can be set for crewed missions to astrobiologically relevant locations like Mars. Figure 1: The sampling kit contains eight swab canisters in total (six on the top and two on the bottom). A resuable end effector (1) is used to remove and reinstall the swabs. The kit also contains a handrail (2), tether loop (3), and, bayonet probes(4) to allow easy manipulation during EVA. Methods: We have designed a kit to carry 8 commercially available foam swabs into and out of vacuum without compromising the swabs’ sterility. The specially designed swab canisters contain a 0.2 µm Teflon filter that allows the canister to accommodate pressure changes without introducing unwanted contaminants. The kit meets existing EVA (Extravehicular Activity) safety requirements and has been used by human test subjects in the neutral buoyance lab and at vacuum in test chambers at the Johnson Space Center. In the early part of 2024, astronauts will use this sampling kit to swab six surfaces on the exterior of ISS. We will collect samples from: the airlock vestibule, the interior surface of the airlock thermal cover, an exterior handrail, a vent connected to the CO2 removal system, and a vent connected to the payload vacuum system inside ISS. Approximately 300 cm2 will be swabbed at each location. A seventh swab will be exposed to the vacuum of space without touching any surfaces as a blank. The eighth swab will remain sealed as a process control. After the EVA the swab kit will be frozen at -80°C and returned to earth at the earliest possible opportunity. The samples will remain frozen until they are thawed for next generation DNA sequencing on earth. We will use amplicon sequencing to identify any bacteria, archaea or fungi present in the samples. If there is enough DNA present, we will use shotgun metagenomic sequencing to further characterize the microbial ecology outside ISS. Preliminary Results: Results from ground-based testing demonstrate that the swab kit is capable of cycling in and out of vacuum without contaminating the swabs. Test subjects, wearing flight-like EVA gloves could remove swabs from the canister and sample discrete locations without inadvertently touching any other surfaces. Several types of bacteria and fungi were collected during these ground tests and survived up to 6 hours at vacuum. This includes non-spore forming bacterial like Staphyloccus capitis that are not traditionally considered extremophiles. Shotgun metagenomic sequencing of the ground-test samples revealed bacteria associated with human skin and airways on the exterior of space suits used during these tests 2 In addition to these results we will present preliminary results from our space-flight samples. We will also present lessons learned from attempting to collect microbiological samples during an EVA and describe how our results will affect planetary protection requirements for future crewed missions.

Aaron B. Regberg↗
DOE OSTI2024

Elucidating Abnormal Grain Growth in Thermomagnetic Processed Materials with Transfer Learning and Reinforcement Learning

The goal of this research program is to establish the mechanism governing local grain boundary motion, which is needed to design and process desirable microstructures for better performance, by identifying the relative contributions of grain boundary (GB) energy and mobility to grain growth. Classical models for grain growth assume that the primary mechanism for reducing the total interfacial energy is area reduction and that GB restructuring is not significant. This assumption implies that grain growth is locally driven by curvature. However, recent experimental observations using new non-destructive 3D x-ray diffraction microscopy techniques (3D-XRM) reveal that classic descriptors (i.e., curvature, number of neighbors, grain size) do not predict real grain growth. Instead, local GB motion appears to be governed by its energy relative to its neighbors such that low-energy boundaries replace those of higher energy. However, simulations that incorporate GB energy anisotropy still fail to reproduce these observations. These discrepancies suggest that the common assumption for grain growth theory must be re-examined to predict and, thus, control microstructure evolution in real polycrystals. A significant challenge to testing this assumption is due to anisotropic GB mobility. Mobility may cause abnormal grain growth or affect the final grain shapes or growth rate but its true contributions are unknown because it is difficult to measure. For example, observations in Fe have found that grains associated with high energy and high mobility boundaries tend to experience abnormal grain growth, whereas abnormal grain growth is associated with low energy and high mobility boundaries in alumina. As mobility and energy both control GB motion, it is challenging to isolate the local driving forces necessary to test the common assumption that the primary mechanism is area reduction. The novelty of this work is the use of machine learning tools to capture GB mobility and energy from 3D-XRM measurements in polycrystals to test the common assumption used in grain growth models. Machine learning can capture high-order correlations in dynamic systems like those found in the evolving GB topology. The PIs have developed a physics-regularized interpretable machine learning microstructure evolution (PRIMME) model that accurately replicates the grain growth behavior of its trained data set.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Characterizing climate pathways using feature importance on echo state networks

The 2022 National Defense Strategy of the United States listed climate change as a serious threat to national security. Climate intervention methods, such as stratospheric aerosol injection, have been proposed as mitigation strategies, but the downstream effects of such actions on a complex climate system are not well understood. The development of algorithmic techniques for quantifying relationships between source and impact variables related to a climate event (i.e., a climate pathway) would help inform policy decisions. Data-driven deep learning models have become powerful tools for modeling highly nonlinear relationships and may provide a route to characterize climate variable relationships. In this paper, we explore the use of an echo state network (ESN) for characterizing climate pathways. ESNs are a computationally efficient neural network variation designed for temporal data, and recent work proposes ESNs as a useful tool for forecasting spatiotemporal climate data. However, ESNs are noninterpretable black-box models along with other neural networks. The lack of model transparency poses a hurdle for understanding variable relationships. We address this issue by developing feature importance methods for ESNs in the context of spatiotemporal data to quantify variable relationships captured by the model. We conduct a simulation study to assess and compare the feature importance techniques, and we demonstrate the approach on reanalysis climate data. In the climate application, we consider a time period that includes the 1991 volcanic eruption of Mount Pinatubo. This event was a significant stratospheric aerosol injection, which acts as a proxy for an anthropogenic stratospheric aerosol injection. Furthermore, we are able to use the proposed approach to characterize relationships between pathway variables associated with this event that agree with relationships previously identified by climate scientists.

black-box models↗
DOE OSTI2025

Gradient flow based phase-field modeling using separable neural networks

Allen–Cahn equation is a reaction–diffusion equation and is widely used for modeling phase separation. Machine learning methods for solving the Allen–Cahn equation in its strong form suffer from inaccuracies in collocation techniques, errors in computing higher-order spatial derivatives, and the large system size required by the space–time approach. To overcome these challenges, we propose solving the gradient flow of the Ginzburg–Landau free energy functional, which is equivalent to the Allen–Cahn equation, thereby avoiding the second-order spatial derivatives associated with the Allen–Cahn equation. A minimizing movement scheme is employed to solve the gradient flow problem, eliminating the complexities of a space–time approach. We utilize a separable neural network that efficiently represents the phase field through low-rank tensor decomposition. As we use the minimizing movement scheme to numerically solve the gradient flow problem, we thus, refer to the proposed method as the Separable Deep Minimizing Movement (SDMM) method. The evaluation of the functional in the minimizing movement scheme using the Gauss quadrature technique bypasses the inaccuracies associated with collocation techniques traditionally used to solve partial differential equations. A hyperbolic tangent transformation is introduced on the phase field prior to the evaluation of the functional to ensure that it remains strictly bounded within the values of the two phases. For this transformation, theoretical guarantee for energy stability of the minimizing movement scheme is established. Our results suggest that this transformation helps to improve the accuracy and efficiency significantly. The proposed method resolves the challenges faced by state-of-the-art machine learning techniques, outperforming them in both accuracy and efficiency. It is also the first machine learning method to achieve an order of magnitude speed improvement over the finite element method. In addition to its formulation and computational implementation, several case studies illustrate the applicability of the proposed method.

42 ENGINEERING↗
DOE OSTI2025

Preliminary Results on Bayesian Inverse UQ for OECD/NEA WPNCS Subgroup 14 Benchmark Exercise for Error Recovery and Experimental Coverage

The Organization for Economic Cooperation and Development (OECD) Working Party on Nucelar Criticality Safety (WPNCS) has proposed a benchmark exercise representative of neutronic behavior in criticality experiments. Here, the goal is to develop confidence in data assimilation techniques used to adjust nuclear data. Participants are given synthetic experimental models with associated measured data and asked to estimate the model parameters given the model and measurements as well as provide predictions for separate application models. In this work, we performed data assimilation using Bayesian inverse Uncertainty Quantification (UQ) with machine learning surrogate models to produce posterior parameter distributions for the requested parameters and posterior predictive distributions for the requested responses. Several experimental models are shown to insufficiently inform the posterior parameter distributions for the applications involved. However, given sufficient experimental data, posterior parameter estimates yielded reduced uncertainty in the response predictions of interest while covering the experimental data.

Bayesian Inference↗
NASA NTRS1989

A comparison of two neural network schemes for navigation

Neural networks have been applied to tasks in several areas of artificial intelligence, including vision, speech, and language. Relatively little work has been done in the area of problem solving. Two approaches to path-finding are presented, both using neural network techniques. Both techniques require a training period. Training under the back propagation (BPL) method was accomplished by presenting representations of (current position, goal position) pairs as input and appropriate actions as output. The Hebbian/interactive activation (HIA) method uses the Hebbian rule to associate points that are nearby. A path to a goal is found by activating a representation of the goal in the network and processing until the current position is activated above some threshold level. BPL, using back-propagation learning, failed to learn, except in a very trivial fashion, that is equivalent to table lookup techniques. HIA, performed much better, and required storage of fewer weights. In drawing a comparison, it is important to note that back propagation techniques depend critically upon the forms of representation used, and can be sensitive to parameters in the simulations; hence the BPL technique may yet yield strong results.

Munro, Paul W.↗
NASA NTRS1990

A comparison of two neural network schemes for navigation

Neural networks have been applied to tasks in several areas of artificial intelligence, including vision, speech, and language. Relatively little work has been done in the area of problem solving. Two approaches to path-finding are presented, both using neural network techniques. Both techniques require a training period. Training under the back propagation (BPL) method was accomplished by presenting representations of current position, goal position pairs as input and appropriate actions as output. The Hebbian/interactive activation (HIA) method uses the Hebbian rule to associate points that are nearby. A path to a goal is found by activating a representation of the goal in the network and processing until the current position is activated above some threshold level. BPL, using back-propagation learning, failed to learn, except in a very trivial fashion, that is equivalent to table lookup techniques. HIA, performed much better, and required storage of fewer weights. In drawing a comparison, it is important to note that back propagation techniques depend critically upon the forms of representation used, and can be sensitive to parameters in the simulations; hence the BPL technique may yet yield strong results.

Munro, Paul↗
NASA NTRS2005

Constraint-based Temporal Reasoning with Preferences

Often we need to work in scenarios where events happen over time and preferences are associated to event distances and durations. Soft temporal constraints allow one to describe in a natural way problems arising in such scenarios. In general, solving soft temporal problems require exponential time in the worst case, but there are interesting subclasses of problems which are polynomially solvable. In this paper we identify one of such subclasses giving tractability results. Moreover, we describe two solvers for this class of soft temporal problems, and we show some experimental results. The random generator used to build the problems on which tests are performed is also described. We also compare the two solvers highlighting the tradeoff between performance and robustness. Sometimes, however, temporal local preferences are difficult to set, and it may be easier instead to associate preferences to some complete solutions of the problem. To model everything in a uniform way via local preferences only, and also to take advantage of the existing constraint solvers which exploit only local preferences, we show that machine learning techniques can be useful in this respect. In particular, we present a learning module based on a gradient descent technique which induces local temporal preferences from global ones. We also show the behavior of the learning module on randomly-generated examples.

Khatib, Lina↗
DOE OSTI2025

Unraveling Hydrogen Induced Geochemical Reaction Mechanisms through Coupled Geochemical Modeling and Machine Learning

Underground hydrogen storage (UHS) provides a promising large-scale, long-term energy storage solution. A reasonable recovery of stored hydrogen is critical for a successful storage scheme. However, in subsurface reservoirs hydrogen is subject to active geochemical reactions that might result in hydrogen loss. In this study, we implemented a geochemical modeling approach coupled with an unsupervised machine learning technique called non-negative matrix factorization (NMF) to unravel the complex brine-rock-H 2 geochemical processes responsible for hydrogen losses, with particular focus on sulfate reduction reactions. NMF is applied to modeled mineral evolution and fluid component profiles to retrieve profiles that can be interpreted to more easily assess competing processes. NMF decouples simulated competing equilibrium reactions. This facilitates separation of overlapping reaction profiles from redox processes, dissolution fronts, and secondary precipitation while considering the effects of simulation parameters such as salinity, temperature, and total H 2 pressure. NMF successfully discriminates these competing effects in nonlinear ways, allowing robust interpretation. In addition, NMF reveals subtle coupled mineral associations and reaction fronts that are invisible to conventional model analysis. This integrated approach strengthens the conceptual understanding of complex nonlinear hydrogen-brine-rock interactions and advances geochemical research on UHS systems to resolve complexities in modeled geochemical systems without the need for direct experiments or prior knowledge. Furthermore, this study highlights the efficacy of combining geochemical modeling with machine learning techniques to enhance the interpretability of the intricate geochemical simulation output through deciphering the overlapping reaction path that cannot be achieved only using conventional analysis of geochemical models alone.

08 HYDROGEN↗
DOE OSTI2024

FEW questions, many answers: using machine learning to assess how students connect food–energy–water (FEW) concepts

There is growing support and interest in postsecondary interdisciplinary environmental education which integrate concepts and disciplines in addition to providing varied perspectives. There is a need to assess student learning in these programs as well as rigorous evaluation of educational practices, especially of complex synthesis concepts. This work tests a text classification machine learning model as a tool to assess student systems thinking capabilities using two questions anchored by the Food-Energy-Water (FEW) Nexus phenomena by answering two questions (1) Can machine learning models be used to identify instructor-determined important concepts in student responses? (2) What do college students know about the interconnections between food, energy and water, and how have students assimilated systems thinking into their constructed responses about FEW? Reported here are a broad range of model performances across 26 text classification models associated with two different assessment items, with model accuracy ranging from 0.755 to 0.992. Expert-like responses were infrequent in our dataset compared to responses providing simpler, incomplete explanations of the systems presented in the question. For those students moving from describing individual effects to multiple effects, their reasoning about the mechanism behind the system indicates advanced systems thinking ability. Specifically, students exhibit higher expertise for explaining changing water usage than discussing tradeoffs for such changing usage. This research represents one of the first attempts to assess the links between foundational, discipline-specific concepts and systems thinking ability. These text classification approaches to scoring student FEW Nexus Constructed Responses (CR) indicate how these approaches can be used, in addition to several future research priorities for interdisciplinary, practice-based education research. Development of further complex question items using machine learning would allow evaluation of the relationship between foundational concept understanding and integration of those concepts as well as more nuanced understanding of student comprehension of complex interdisciplinary concepts.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗