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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Machine Learning-Based Process Control for Injection Molding of Recycled Polypropylene

The increased interest in artificial intelligence in manufacturing has driven the adoption of machine learning to optimize processes and improve efficiency. A key challenge in injection molding is the variability of recycled materials, which affects part quality and processing stability. This study presents a novel closed-loop process control approach for injection molding, leveraging machine learning to adaptively predict processing inputs and quality outcomes. The methodology was tested on five blends of recycled polypropylene (rPP), using artificial neural networks (ANNs), linear regression, and polynomial regression to model the relationships between material properties and process parameters. The dataset was split 80/20 into training and testing sets. The ANN model was implemented using TensorFlow and Keras, with six hidden layers of 32 neurons per layer, ReLU activation, and an Adam optimizer. Empirical tuning and early stopping were used to optimize performance and prevent overfitting. Predictions were evaluated based on mean absolute error (MAE), mean squared error (MSE), and percentage error. The results showed that yield stress, ultimate elongation, and part weight were accurately predicted within a 5% error for linear and polynomial regression models and within a 10% error for the ANN. However, modulus predictions were less reliable, with errors of ~11% for ANN and linear regression and ~40% for polynomial regression, reflecting the inherent variability of this property in rPP blends. Predictions of processing inputs had errors ranging from 3% to 25%, depending on the model and response variable. No single modeling approach was consistently superior across all responses, highlighting the complexity of the relationship between material properties, process parameters, and quality metrics. Overall, the work demonstrates that closed-loop process control, powered by machine learning, can effectively predict key quality parameters in injection molding of recycled materials. The proposed approach can improve process stability and material utilization, facilitating increased adoption of sustainable materials.

Krantz, Joshua↗
DOE OSTI2026

Closing the loop on plastics: Biological and hybrid routes for converting plastic waste to polyhydroxyalkanoates

Polyhydroxyalkanoate (PHA) production from plastic-derived substrates offers a promising route to mitigate plastic pollution while reducing dependence on conventional PHA feedstocks. Plastic waste represents an abundant carbon source for microbial fermentation, but efficient conversion remains limited by incomplete deconstruction, inhibitory intermediates, low carbon recovery, and challenges in process integration. Plastic-derived streams contain diverse compounds, including fatty acids, hydrocarbons, fatty alcohols, aldehydes, esters, and aromatic compounds generated during depolymerization. These intermediates can be metabolized by selected microorganisms, particularly Pseudomonas species with versatile fatty-acid and hydrocarbon pathways, as well as Cupriavidus necator and mixed microbial cultures. Unlike reviews that address plastic upcycling or PHA biosynthesis separately, this review focuses on the deconstruction–fermentation interface that governs plastic-to-PHA conversion. It consolidates current progress in plastic deconstruction, substrate conditioning, microbial metabolism, fermentation control, polymer recovery, and techno-economic and life-cycle considerations. Here, by emphasizing substrate composition, biological compatibility, plastic‑carbon recovery, and final polymer quality, the review identifies priorities for scalable and environmentally sustainable PHA production from plastic-derived substrates.

42 ENGINEERING↗
DOE OSTI2022

Closing the loop between microstructure and charge transport in conjugated polymers by combining microscopy and simulation

A grand challenge in materials science is to identify the impact of molecular composition and structure across a range of length scales on macroscopic properties. We demonstrate a unified experimental–theoretical framework that coordinates experimental measurements of mesoscale structure with molecular-level physical modeling to bridge multiple scales of physical behavior. Here we apply this framework to understand charge transport in a semiconducting polymer. Spatially-resolved nanodiffraction in a transmission electron microscope is combined with a self-consistent framework of the polymer chain statistics to yield a detailed picture of the polymer microstructure ranging from the molecular to device relevant scale. Using these data as inputs for charge transport calculations, the combined multiscale approach highlights the underrepresented role of defects in existing transport models. Short-range transport is shown to be more chaotic than is often pictured, with the drift velocity accounting for a small portion of overall charge motion. Local transport is sensitive to the alignment and geometry of polymer chains. At longer length scales, large domains and gradual grain boundaries funnel charges preferentially to certain regions, creating inhomogeneous charge distributions. While alignment generally improves mobility, these funneling effects negatively impact mobility. The microstructure is modified in silico to explore possible design rules, showing chain stiffness and alignment to be beneficial while local homogeneity has no positive effect. This combined approach creates a flexible and extensible pipeline for analyzing multiscale functional properties and a general strategy for extending the accesible length scales of experimental and theoretical probes by harnessing their combined strengths.

42 ENGINEERING↗
DOE OSTI2020

Interstate-24 MOTION: Closing the Loop on Smart Mobility

We introduce the I-24 Mobility Technology Interstate Observation Network (MOTION), a transportation cyber-physical systems testbed under development in Tennessee. It consists of a six-mile freeway segment instrumented with 400 4K resolution cameras, processed by a real-time compute system to enable continuous performance monitoring of freeway traffic. The testbed is being developed to support next generation connected and autonomous vehicle technologies and advanced traffic management. When complete, the testbed will be the longest continuously observed freeway segment in the world. This article introduces the testbed, discusses the core design choices, and outlines the preliminary work conducted to support the design.

Gloudemans, Derek↗
DOE OSTI2022

Advanced Metering Infrastructure for Distribution Planning and Operation: Closing the Loop on Grid-Edge Visibility

In the recent history of electric utilities, the potential to have visibility of the grid edge is gaining significance for the reliable planning and operation of a clean energy smart grid. Before the recent large-scale customer adoption of distributed energy resources (DERs), distribution networks were planned with a simpler fit-and-forget philosophy. The importance of customer-sited DERs to achieve climate change goals marks a major shift for utility operations. Changes to traditional fit-and-forget planning paradigms require better availability of grid-edge data. Smart metering is an enterprise-wide tool that is enabling visibility when and where it has been most needed. As of 2020, the rollout of smart metering, or advanced metering infrastructure (AMI), had reached more than 100 million meters in the United States, and nearly half of all electricity customers are now equipped with a smart meter (Figure 1). Smart meters are quickly becoming a ubiquitous data capture feature of smart grids. AMI enables utilities to record and measure electricity usage and power-flow metrics at a minimum of hourly intervals and at least once a day. At a minimum, AMI enables interval metering, automatic meter reading enabling accurate and time-interval billing, and the ability to provide feedback on customer energy consumption.

advanced metering infrastructure↗
DOE OSTI2023

Closing the loop between in-situ stress complexity and EGS fracture complexity

The goal of the project is to employ a combination of high-fidelity simulations and true-triaxial block fracturing tests at high temperature to explore the intricate relationship between in-situ stress and hydraulic fracture patterns and better characterize the in-situ stress at Utah FORGE. Laboratory experiments are used to validate numerical models and better understand the impact of various parameters (e.g., temperature, well orientation, stress, etc) on the hydraulic fracture nucleation process in EGS. The project will have a significant impact as it will (1) improve the characterization of the in-situ stress field at FORGE; (2) demonstrate how high-fidelity modeling tools can be employed to analyze data for EGS; (3) provide a unique set of high-temperature hydraulic fracturing experimental results for EGS to help identifying the most important components for in-situ stress estimation and to validate the current in-situ stress estimation theory and establish a strong foundation for proposing a new one; (4) provide a validated set of numerical tools in an open source simulation framework (GEOS) that can be used to model EGS.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Closing the Loop Between In Situ Stress Complexity and EGS Fracture Complexity (Quarterly Research Performance Progress Report: Q3Y3)

As part of Task 1, we have started by testing our modeling capabilities by reproducing isothermal DFIT simulations presented in the literature. To start, we have been working towards adjusting numerical parameters to reproduce DFIT tests with different fracture geometries. Then, we have focused modeling the DFIT conducted at well 58-32 (cycle 4 in zone 2). Specifically, we have carefully analyzed the influence of the aperture-contact stress relationship and investigated how two different laws affect model results. We have also introduced thermal effects in the model and rerun the simulations to estimate the effect of cooling on the DFIT response. Due to the small amount of fluid injected during a DFIT, thermal effects seem to be moderate.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Closing the Loop Between In Situ Stress Complexity and EGS Fracture Complexity: Quarterly Research Performance Progress Report

As part of Task 1, we have started by testing our modeling capabilities by reproducing isothermal DFIT simulations presented in the literature. To start, we have been working towards adjusting numerical parameters to reproduce DFIT tests with different fracture geometries. Then, we have focused modeling the DFIT conducted at well 58-32 (cycle 4 in zone 2). Specifically, we have carefully analyzed the influence of the aperture-contact stress relationship and investigated how two different laws affect model results.

58 GEOSCIENCES↗
DOE OSTI2025

Closing the Loop between In Situ Stress Complexity and EGS Fracture Complexity

We present an agent-guided approach to CAD geometry decomposition that automates hex/hybrid meshing with graph neural networks (GNNs) to accelerate next-generation ModSim workflows. Our end-to-end pipeline (i) reduces 3D boundary-representation (B-Rep) models to a 2D chordal axis skeleton (CAT) and then to a 1D bipartite graph of surface and curve nodes, (ii) assigns per node labels as Cubit® WebCut actions, (iii) trains a multi-action GNN under supervised learning, and (iv) predicts five surface-node and three curve-node actions on out-of-distribution test geometries. Each graph node carries geometric, topological, and meshing attributes drawn from the B-Rep “skin” and CAT “skeleton,” with two-way mappings across 3D↔2D↔1D representations to maintain traceability back to 3D CAD. The supervised learning model exhibits stable convergence of the binary cross-entropy loss and achieves 98.7% accuracy on unseen lattice models. To operationalize decision-making, we rank predicted commands by geometric significance and prototyped the agent-guided workflow through the Cubit® Meshing PowerTool GUI. As a stretch goal, we explore reinforcement learning (RL) to reduce or remove label requirements and to learn policies for action sequences that maximize total reward (e.g., size of hex-meshable regions and resulting hex mesh quality). When all-hex meshing is not feasible, the agent assists in producing hybrid meshes—prioritizing hex in critical regions and transitioning to tetrahedral elements (tets) elsewhere—maintaining fidelity while ensuring robustness. The overarching objective is to replace manual, heuristics-based decomposition with data-driven, reproducible automation, cutting meshing turnaround time by orders of magnitude. We anticipate direct impact on simulation workflows through intelligent, scalable decomposition of complex CAD models into hex-meshable subdomains.

42 ENGINEERING↗
DOE OSTI2026

Final Report for Closing the Loop Between In Situ Stress Complexity and EGS Fracture Complexity

Characterizing in situ stress is essential in Enhanced Geothermal System (EGS) for both risk assessment and operation design. Standard stress measurement techniques such as diagnostic fracture injection tests (DFITs) require creating hydraulic fractures and apply analytical methods such as the G -function analysis to estimate the stress. These classical approaches rely on several assumptions, including but not limited to: (i) the induced fracture is planar and persistent, and (ii) the fracture plane is normal to the minimum horizontal stress. However, these assumptions may not hold in EGS environments like Utah FORGE, where thermo-hydro-mechanical (THM) coupling, wellbore deviation, and rock heterogeneity can alter the local stress condition and consequently lead to fracture complexities in both near-well and far-field regions.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Closing the Loops on Solar Photovoltaics Modules: An Agent-Based Modeling Approach for the Study of Circular Economy Strategies

Solar photovoltaics (PV) installed capacity have grown exponentially since the early 2000s (average annual growth rate of 50%). With 4,700 GW projected installed capacity by 2050, the volume of PV panels waste is also expected to become substantial. Though renewables are a sine qua non to the establishment of a truly circular economy (CE), the issue arising from their end-of-life management needs to be resolved. The management of PV end-of-life also represent a singular opportunity to create value, from recovered valuable, rare or critical materials (e.g. silver, tellurium, indium). Moreover, the PV case illustrates some of the current barriers to CE; for instance, the need for a common definition of waste (PV are defined as e-waste in the European Union but as general waste in the United States) and potential loss of innovations' advantages (PV average efficiency has continuously grown the past 10 years). In this study, an agent-based modeling (ABM) approach is proposed to simulate PV end-of-life management in the United States. The model explores how the decisions of the various actors involved in handling PV waste affect the quantities of PV that are reused, recycled, or land-filled. In the model, manufacturers, residential, and nonresidential PV owners, installers, and recyclers are represented by agents while governmental policies and regulations are treated as exogenous variables. PV-market data are used to define agents' characteristics (e.g., installed PV capacities or producing costs). Agents' decision rules draw on the literature related to industrial symbiosis (IS) and peoples' waste behaviors as they appear as the most widely used model to implement CE principles at a meso level. Specifically, the concepts of mutual trust between IS actors and knowledge about the IS philosophy are yielded to define the agents' decision process. The primary outputs of the ABM are the costs and volume of waste associated with each end-of-life pathway. Preliminaries results indicate recycling of residential PV is affected by the cost, perceived difficulty of recycling behaviors, and social norms. Moreover, the type of network connecting agents and the initial recycling rate strongly influence results. The model also highlights the crucial role of recycling behavior adoption: in case of early failure of PV, recycled volumes increase by about 40%. This represents an additional 5.7 billion USD from potential recovered silver. Finally, although results for the PV case are presented here, the developed ABM aims at being a general tool for the study of CE strategies and the CE transition. Furthermore, steps of this research include extending the model to encompass circular economy strategies (e.g., design for recycling or lifetime extension) and validating its general architecture from various case studies.

14 SOLAR ENERGY↗
DOE OSTI2022

Techno-Economic Performance of Eavor Loop 2.0

This project evaluated techno-economic performance for a sample Eavor-Loop 2.0 design for electricity production and direct-use heating. The Eavor-Loop 2.0 design investigated is a 7.5-km deep closed-loop geothermal system consisting of 12 laterals for a total of more than 90 km of downhole well and lateral length. Both a high geothermal gradient scenario of 60 degrees C/km and a low geothermal gradient scenario of 30 degrees C/km were considered. With pure water injected at 60 degrees C and 80 kg/s, reservoir simulations with the Slender-Body Theory simulator indicate average production temperatures over a 30-year lifetime of ~125 degrees C and ~210 degrees C for the low and high geothermal gradient scenario, respectively. These correspond to heat production of ~22 M Wth and ~51 M Wth, respectively. Using IPSEpro simulations, we find average power production of ~2.2 M We and ~8.6 M We, respectively, for a subcritical organic Rankine cycle power plant with air-cooled condensers. Cost estimates indicate the overall capital and levelized costs are dominated by the lateral drilling cost. Obtaining a levelized cost of electricity below $70/M Wh requires a geothermal gradient of 60 degrees C/km, a discount rate below 9%, and lateral drilling cost below $400/m. A well cost model indicates that ~$400/m for the Eavor-Loop 2.0 design investigated can be obtained for a drilling rate of penetration about 40 ft/hr (with bit life of 50 hours), and omitting casing and cement. Traditional (geothermal) well drilling has achieved these drilling rate conditions, including the Utah FORGE project where the rate of penetration has exceeded 50 ft/hr in granite. However, it is unclear if these conditions are still valid for drilling the Eavor-Loop 2.0 laterals (i.e., ~82 km of laterals at 4 to 7.5-km vertical depth with rock temperatures up to 460 degrees C), as such downhole completion has never been developed before. Competitive levelized cost of heat values ($1.2-$8.2/GJ) are calculated, even for the low geothermal gradient scenario (30 degrees C/km) and lateral drilling cost of $600/m.

advanced geothermal system↗
DOE OSTI2022

Status of LBNF/DUNE near site liquid argon proximity and external cryogenics systems development.

The Deep Underground Neutrino Experiment (DUNE) near site located at Fermilab will host the neutrino beam complex. It includes a high voltage liquid argon time projection chamber located 60-meter underground used as beamline instrumentation. The LAr cryogenic system provided by the Long-Baseline Neutrino Facility (LBNF) regulates thermohydraulic conditions of the membrane cryostat hosting the detector with 285 Ton purified LAr. The purification system uses molecular sieve and copper oxide pellets to manage the argon contamination below 100 ppt (parts per trillion) oxygen equivalent. The detector and its cryogenics are capable to move a stroke of 30 meters on/off beam. The cryogenic system modes of operation include the cryostat pressure test, purge in open loop, detector cooldown, cryostat fill, closed loop purification, liquid empty, and purification system activation/regeneration process. The system design status and schedule, and technical details such as operating modes and interfaces are reported in this paper.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

A CHIL Validation of Machine Learning-Assisted Methods for Real-Time Controls of Solar PV for Grid Services

Recent research has highlighted the potential for solar to act as a zero-marginal-cost and zero-emission flexibility resource on the bulk power system when operated with advanced control systems. To increase the performance of these systems, leading technologies, including machine learning (ML) and hierarchical inverter set point allocation, have been proposed; however, these technologies lack comprehensive validation under real-world application scenarios. This paper addresses this gap by designing and developing a controller-hardware-in-the-loop framework to evaluate the performance of different flexible solar technologies in responding to automatic generation control signals in a closed-loop fashion. Simulation results indicate the superior performance of an ML-based approach compared to the conventional reference-control grouping-based approach, showcasing its potential to support grid stability and operational efficiency.

closed-loop validation↗
DOE OSTI2024

A CHIL Validation of Machine Learning-Assisted Methods for Real-Time Controls of Solar PV for Grid Services: Preprint

Recent research has highlighted the potential for solar to act as a zero-marginal-cost and zero-emission flexibility resource on the bulk power system when operated with advanced control systems. To increase the performance of these systems, leading technologies, including machine learning (ML) and hierarchical inverter set point allocation, have been proposed; however, these technologies lack comprehensive validation under real-world application scenarios. This paper addresses this gap by designing and developing a controller-hardware-in-the-loop framework to evaluate the performance of different flexible solar technologies in responding to automatic generation control signals in a closed-loop fashion. Simulation results indicate the superior performance of an ML-based approach compared to the conventional reference-control grouping-based approach, showcasing its potential to support grid stability and operational efficiency.

closed-loop validation↗