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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Processing behavior evolution of recycled polypropylene: An integrated experimental and Computer-Aided engineering simulation study

Polypropylene (PP) comprises 21% of global plastics production and 18% of plastics waste, yet less than 1% of solid-waste PP is recycled in the United States (U.S.), representing significant environmental and economic challenges. Mechanical recycling, the most prevalent recycling method, subject's materials to thermomechanical stresses, which typically degrade polymer properties, affecting the quality of polymer products. This study replicates the impact of mechanical recycling through multiple extrusion cycles to examine the effects on PP's processing behavior. Dynamic scanning calorimetry (DSC) measurements showed stable melting behavior across all processing conditions, while crystallization analysis exhibited consistent shifts in kinetic parameters. Rheological characterization demonstrated progressive viscosity reductions through successive cycles, particularly pronounced at elevated reprocessing temperatures. Here, the integration of this experimental data into injection molding simulations showed that recycled PP maintains viable processing characteristics. Our findings establish quantitative correlations between processing history and material behavior, enabling optimization of processing parameters directly rather than relying on trial-and-error approaches. While these results reflect idealized recycling conditions with minimal contamination, they provide a framework for understanding fundamental property evolution during mechanical recycling.

42 ENGINEERING↗
DOE OSTI2026

Full-wave modeling of RF waves in fusion plasmas with finite element method: Progress in past decades and its future role

This paper reviews the progress in computing radio frequency (RF) wave fields in fusion plasmas, specifically focusing on simulations utilizing the finite element method (FEM) over the past few decades. Computing RF wave fields in fusion plasmas presents unique challenges due to large simulation domains, complex antenna structures, non-local dielectric properties, and wide ranges of spatial scales. It highlights key developments and outlines future directions, primarily addressing waves in the ion cyclotron (IC) to lower hybrid (LH) frequency range. We begin with briefly revisiting earlier developments before the widespread availability of modern computer-aided engineering (CAE) software based on FEM. This historical perspective illuminates early progress and the physics difficulties that motivated ongoing work within the community. Modern wave simulations for RF antennas based on FEM are characterized by the use of detailed 3D antenna model geometry generated from engineering CAD software and localized wave dielectric model. Significant advancements have also been made in improving physics models to include phenomena such as RF sheath rectification and wave scattering. FEM-based RF simulations have also been applied to compute wave propagation in the core region, where the inclusion of non-local dielectric response is crucial. This is a challenging goal, and several promising approaches have been proposed in this area. Additionally, RF simulation development initiatives based on open-source libraries have gained popularity, demonstrating scalability and flexibility in extending physics models. This paper will discuss the advantages and disadvantages of using such a publicly available FEM library.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Accelerating kinetic plasma simulations with machine-learning-generated initial conditions

Computational models of plasma technologies often solve for the system operating conditions by time-stepping an initial value problem to a quasi-steady solution. However, the strongly nonlinear and multi-timescale nature of plasma dynamics often necessitate millions, or even hundreds of millions, of steps to reach convergence, reducing the effectiveness of these simulations for computer-aided engineering. We consider acceleration of kinetic plasma simulations via data-driven machine-learning-generated initial conditions, which initialize the simulations close to their final quasi-steady-state, thereby reducing the number of steps to reach convergence. Three machine-learning models are developed to predict the density and ion kinetic profiles of capacitively coupled plasma discharges relevant to the microelectronics industry. The models are trained on kinetic simulations over a range of device operating frequencies and pressures. Best performance was observed when simulations were initialized with ion kinetic profiles generated by a convolutional neural network, reducing the mean number of steps to reach convergence by 17.1× when compared to initialization with a zero-dimensional global model. We also outline a workflow for continuous data-driven model improvement and simulation speedup, with the aim of generating sufficient data for full device digital twins.

Artificial neural networks↗
DOE OSTI2025

A simple introduction to the SiMPL method for density-based topology optimization

We introduce a novel method for solving density-based topology optimization problems: Sigmoidal Mirror descent with a Projected Latent variable (SiMPL). The SiMPL method (pronounced as “the simple method”) optimizes a design using only first-order derivative information of the objective function. The bound constraints on the density field are enforced with the help of the (negative) Fermi–Dirac entropy, which is also used to define a non-symmetric distance function called a Bregman divergence on the set of admissible designs. This Bregman divergence leads to a simple update rule that is further simplified with the help of a so-called latent variable. Because the SiMPL method involves discretizing the latent variable, it produces a sequence of pointwise-feasible iterates, even when high-order finite elements are used in the discretization. Numerical experiments demonstrate that the method outperforms other popular first-order optimization algorithms. In conclusion, to outline the general applicability of the technique, we include examples with (self-load) compliance minimization and compliant mechanism optimization problems.

Calculus of Variations and Optimization↗
DOE OSTI2024

Dual Enhancement of Thermostability and Activity of Xylanase through Computer-Aided Rational Design

In the realm of enzyme engineering, the dual enhancement of thermostability and activity remains a challenge. Herein, we employed a computer-aided approach integrating folding free energy calculations and evolutionary analysis to engineer Paecilomyces thermophila xylanase into a hyperthermophilic enzyme for application in the paper and pulp industry. Through the computational rational design, XynM9 with superior thermostability and enhanced activity was designed. Its optimal reaction temperature increases by 10 °C to 85 °C, its T m increases by 10 °C to 93 °C, and its half-life increases 11-fold to 5.8 h. Additionally, its catalytic efficiency improves by 57% to 3926 s –1 mM –1 . Molecular dynamics simulations revealed that XynM9 is stabilized by more hydrogen bonds and salt bridges than wild-type xylanase. The mutant’s narrower catalytic cleft enhances the substrate-binding affinity, thus improving the catalytic efficiency. In harsh conditions at 80 °C and pH 10, using XynM9 significantly reduced both hemicellulose and lignin, which makes it a good candidate for use in the paper and pulp process. Finally, our study presents an accurate and efficient strategy for the dual enhancement of enzyme properties, guiding further improvement of computational tools for protein stabilization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Software For Advanced Large-scale Analysis Of Magnetic Confinement For Numerical Design, Engineering & Research (salamander)

As magnetic confinement fusion energy gains traction internationally to enable abundant energy production, designing components for fusion systems is a pressing challenge. During the planned lifetime of a fusion device, components evolve in extreme environments and must withstand large, repeated thermal loads and bombardment by 14 MeV neutrons, plasma ions, and neutral particles (deuterium, tritium, and helium), corrosive conditions, etc. All these physical processes take place simultaneously, interact in intricate ways, and impose important constraints that can affect performance. Experimental data is rare and costly to obtain, making design particularly challenging. Predictive computational frameworks must be an integral part of an accelerated and cost-effective design process by modeling fusion system performance in simulated environments. To better understand component degradation and operational impacts on their performance, the Software for Advanced Large-scale Analysis of MAgnetic confinement for Numerical Design, Engineering & Research (SALAMANDER) is designed as an open-source, fully integrated, multiphysics, multiscale, NQA-1 compliant framework facilitating 3D, high-fidelity fusion system modeling. To that end, SALAMANDER is a MOOSE-based framework, and therefore leverages MOOSE upstream libraries such as PETSc and libMesh to deliver sophisticated finite element, finite volume, and nonlinear solver technology for fusion energy simulations. SALAMANDER couples MOOSE physics module capabilities—such as thermal hydraulics, heat conduction, Navier-Stokes, and thermomechanics—with tritium transport via TMAP8, neutronics via Cardinal, and nascent particle-in-cell capabilities. Direct simulation Monte Carlo methods will be used to address neutral transport near the walls. By coupling all these physics in an integrated application, SALAMANDER will enable high-fidelity modeling of irradiation levels and plasma exposure conditions of plasma facing components and their impact on heat and tritium distributions, as well as the resulting mechanical constraints experienced by the plasma facing components and performance of blanket systems. Furthermore, SALAMANDER will be particularly suited for engineering studies thanks to the stochastic tool module readily available in MOOSE, allowing for extended uncertainty quantification and risk analysis studies. It is also able to use computer-aided design (CAD) meshes to model complex geometries, which is indispensable for fusion systems. SALAMANDER therefore supports design, safety, engineering, and research projects for magnetic confinement fusion systems

Simon, Pierre-Clement [Idaho National Laboratory (↗
DOE OSTI2026

Synthetic Biology of Plants and Microbes for Agriculture, Environment, and Future Applications

Agriculture is under pressure to provide food for a growing population and the feedstock required to drive the bioeconomy. Methods to breed and genetically modify plants are inadequate to keep pace. When engineering crops, traits are painstakingly introduced into plants one-at-a-time, combine unpredictably, and are continuously expressed. Synthetic biology is changing these paradigms with new genome construction tools, computer aided design (CAD), and artificial intelligence (AI). “Smart plants” contain circuits that respond to environmental change, alter morphology, or respond to threats. Further, the plant and associated microbes (fungi, bacteria, archaea) are now being viewed by genetic engineers as a holistic system. Historically, plant health has been enhanced by many natural and laboratory-evolved soil microbes marketed to enhance growth, provide nutrients, or confer pest/stress resistance. Synthetic biology has expanded the number of species that can be engineered, increased the complexity of engineered functions, controlled environmental release, and assembled stable consortia. New CAD tools will manage genetic engineering projects spanning multiple plant genomes (nucleus, chloroplast, mitochondrion) and the thousands of genomes of associated bacteria/fungi. Here, this review covers advanced genetic engineering techniques to drive the next agricultural revolution, as well as push plant engineering into new realms for manufacturing, infrastructure, sensing, and remediation.

Clauer, Phillip [Massachusetts Inst. of Technology↗
DOE OSTI2025

Wide-ranging predictions of new stable compounds powered by recommendation engines

The computational search for new stable inorganic compounds is faster than ever, thanks to high-throughput density functional theory (DFT). However, stable compound searches remain highly expensive because of the enormous search space and the cost of DFT calculations. To aid these searches, recommendation engines have been developed. We conduct a systematic comparison of the performance of previously developed recommendation engines, specifically ones based on elemental substitution, data mining, and neural network prediction of formation enthalpy. After identifying ways to improve the recommendation engines, we find the neural network to be superior at recommending stable Heusler compounds. Armed with improved recommendation engines, we identify tens of thousands of compounds that are stable at zero temperature and pressure, now available in the Open Quantum Materials Database. We summarize this diverse pool of compounds, including the elusive mixed anion compounds, and two of their many applications: thermoelectricity and solar thermochemical fuel production.

Science & Technology - Other Topics↗
DOE OSTI2024

Results from a synthetic model of the ITER XRCS-Core diagnostic based on high-fidelity x-ray ray tracing

A high-fidelity synthetic diagnostic has been developed for the ITER core x-ray crystal spectrometer diagnostic based on x-ray ray tracing. This synthetic diagnostic has been used to model expected performance of the diagnostic, to aid in diagnostic design, and to develop engineering tolerances. The synthetic model is based on x-ray ray tracing using the recently developed xicsrt ray tracing code and includes a fully three-dimensional representation of the diagnostic based on the computer aided design. The modeled components are: plasma geometry and emission profiles, highly oriented pyrolytic graphite pre-reflectors, spherically bent crystals, and pixelated x-ray detectors. Plasma emission profiles have been calculated for Xe 44+ , Xe 47+ , and Xe 51+ , based on an ITER operational scenario available through the Integrated Modelling & Analysis Suite database, and modeled within the ray tracing code as a volumetric x-ray source; the shape of the plasma source is determined by equilibrium geometry and an appropriate wavelength distribution to match the expected ion temperature profile. All individual components of the x-ray optical system have been modeled with high-fidelity producing a synthetic detector image that is expected to closely match what will be seen in the final as-built system. Particular care is taken to maintain preservation of photon statistics throughout the ray tracing allowing for quantitative estimates of diagnostic performance.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Nuclear Materials Packaging, Transportation, and Systems Analysis Group Software Quality Assurance Plan: ANSYS Mechanical Finite Element Analysis Software Version 2023R1

ANSYS Inc. develops and markets engineering simulation software and services used in the aerospace, automotive, manufacturing, electronics, biomedical, energy, defense, and many other industries. ANSYS is dedicated to engineering simulation and is the world’s leading software provider. ANSYS was founded in 1970 and is headquartered in Canonsburg, Pennsylvania. ANSYS provides an engineering analysis tool combining structural, thermal, computational fluid dynamics, acoustic, and electromagnetic simulation capabilities. ANSYS has two main programs, which use the same solvers: (1) Mechanical APDL (ANSYS Design Parametric Language), a Fortran-based coding platform, and (2) ANSYS Workbench, which uses a graphical user interface to aid in finite element analysis implementation. This plan covers both APDL and Workbench. The ANSYS computer program is a large-scale, multipurpose finite element program that can be used to solve several classes of engineering analyses. The analysis capabilities of ANSYS include the ability to solve static and dynamic structural analyses, steady-state and transient heat transfer problems, mode-frequency and buckling eigenvalue problems, static or time-varying magnetic analyses, and various types of field and coupled-field applications. The program contains many special features that allow nonlinearities or secondary effects such as plasticity, large strain, hyperelasticity, creep, swelling, large deflections, contact, stress stiffening, temperature dependency, material anisotropy, and radiation to be included in the solution. As ANSYS has been developed, other special capabilities such as substructuring, submodeling, random vibration, kinetostatics, kinetodynamics, free convection fluid analysis, acoustics, magnetics, piezoelectrics, coupled-field analysis, and design optimization have been added to the program. These capabilities contribute further to making ANSYS a multipurpose analysis tool for varied engineering disciplines. The ANSYS program has been in commercial use for over 50 years and has been used extensively in the aerospace, automotive, construction, electronic, energy services, manufacturing, nuclear, plastics, oil, and steel industries. Additionally, many consulting firms and hundreds of universities have used ANSYS for analysis, research, and educational purposes. ANSYS is recognized worldwide as one of the most widely used and capable programs of its type. Ansys design analysis software is the first created within a quality system with ISO 9001 certification, the internationally accepted quality standard. Product development, testing, maintenance and support processes also meet the United States Nuclear Regulatory Commission's quality requirements, as they have for nearly four decades. The Quality Assurance Service Agreement is suitable for the customers working in the nuclear industry who need to meet specific federal regulations including 10CRF50 Appendix B and provisions of 10CFR21. ANSYS has retained its original International Organization for Standardization (ISO) 9001 accreditation certificate since1995-05-04, It’s current certificate is valid until 2027-05-29.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

CFD modeling of non-catalytic, partial-oxidation engine reformer for flare mitigation

Flaring associated natural gas is commonly employed in the oil and gas industry to reduce methane (CH 4 ) emissions but generates carbon dioxide (CO 2 ) and harmful pollutants, significantly contributing to air pollution and posing risks to public health. To mitigate this impact, M2X Energy Inc. has developed a small-scale, modular gas-to-methanol system. This system features an engine reformer that performs fuel-rich partial oxidation of wellhead gas to produce syngas—a mixture of carbon monoxide (CO) and hydrogen (H 2 )—followed by a downstream reactor for methanol synthesis. This study focused on computational fluid dynamics (CFD) modeling of the engine reformer to simulate partial oxidation chemistry, predict the rich-burn operating limit, and assess syngas quality, ultimately aiding in design and operational optimization. The CFD model, developed within a Reynolds-Averaged Navier-Stokes (RANS) turbulence framework, incorporated sub-models for turbulent combustion, a chemical mechanism with polycyclic aromatic hydrocarbon (PAH) pathways, and soot emissions to accurately capture the fuel-rich, turbulent jet ignition and combustion processes. Model validation against experimental data showed good agreement across pre- and main-chamber pressures, apparent heat release rates, and exhaust gas concentrations of key species (H 2 , CO, CO 2 , CH 4 ) for varying intake equivalence ratios. Here, the model identified a rich-burn operating limit near a fuel-air equivalence ratio of 2.35, consistent with experimental observations. Furthermore, syngas quality analysis revealed that extending the rich-burn limit through engine reformer optimization could enhance syngas production, contributing to higher methanol synthesis efficiency.

Computational Fluid Dynamics↗
DOE OSTI2024

Numerical simulation projects in micromagnetics with Jupyter

We report a case study where an existing materials science course was modified to include numerical simulation projects on the micromagnetic behavior of materials. The Ubermag micromagnetic simulation software package is used in order to solve problems computationally. The simulation software is controlled through the Python code in Jupyter notebooks. Our experience is that the self-paced problem-solving nature of the project work can facilitate a better in-depth exploration of the course contents. We discuss which aspects of the Ubermag and the project Jupyter ecosystem have been beneficial for the students' learning experience and which could be transferred to similar teaching activities in other subject areas.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Cyber-Informed Engineering Workbook: Substations

This workbook contains a case study based on a hypothetical substation project, intended to aid in the conversation and utilization of principles related to Cyber-Informed Engineering.

42 ENGINEERING↗
DOE OSTI2025

Integrating an Industrial Source and Commercial Algae Farm with Innovative CO 2 Transfer Membrane and Improved Strain Technologies

This report describes the overall findings of the research project “Integrating an Industrial Source and Commercial Algae Farm with Innovative CO 2 Transfer Membrane and Improved Strain Technologies”. The motivation and goal for this project were to increase the carbon utilization efficiency (CUE) and areal productivity for algal cultivations, thereby reducing CO 2 costs to cultivation operations and improving economics. This was achieved through a combination of enhanced delivery of inorganic carbon and improved strains of Nannochloropsis oceanica capable of higher rates of bicarbonate uptake and metabolism. The project succeeded in these goals. First, a bubble-free, membrane-based technology was developed for delivering CO 2 to cultivations, which increased the CUE from the 15-20% that is standard in the industry to more than 65%. In addition, N. oceanica was modified to express a bicarbonate transporter protein, BicA, which enabled the cells to grow more quickly. In addition, advances were made in the protein engineering of carbonic anhydrase (CA) for enhanced stability and catalytic performance, the computational fluid dynamics modeling of algal cultivation systems, and in the life-cycle assessment and technoeconomic analysis of algal production. Together, these outcomes contribute to advancing algal cultivation as an economically viable platform for production of fuels, materials, and other chemical products. The project results aid in addressing the dual challenges of reducing atmospheric CO 2 levels and achieving green energy solutions.

09 BIOMASS FUELS↗
DOE OSTI2026

Evaluation of Howard A. Hanson Dam Juvenile Fish Passage and Survival Study Live Fish Injury Assessment, Sensor Fish, and BioPA Modeling Tasks

The live fish injury assessment, Sensor Fish, and BioPA modeling study tasks were conducted by researchers from Pacific Northwest National Laboratory (PNNL). The four tasks were part of the larger Evaluation of Howard A. Hanson Dam (HAHD) Juvenile Fish Passage and Survival study, which had six total tasks. To achieve study objectives for each of the four tasks, field work occurred at Green Peter Dam (GPR) to evaluate the highest elevation steep slope bypass pipe, at HAHD to evaluate baseline conditions of the horseshoe tunnel, and at PNNL’s Aquatic Research Laboratory (ARL) to evaluate simulated dam passage conditions (i.e., shear forces and collision). Each of these evaluations utilized live fish injury assessment, Sensor Fish, and BioPA modeling. Live fish injury assessment and survival (tagged with and without balloon or passive integrated transponder [PIT] tags) was correlated with Sensor Fish to determine thresholds. The CFD analyses were then performed, and the computed values were compared to the corresponding measured values of Sensor Fish data. The results of the overall injury and survival of fish was also used in the validation of the CFD modeling method. Collectively, the results will aid in future modeling of fish passage at HAHD. Results from these tasks can be used by biologists, engineers, resource managers, and regional decision-makers to inform baseline conditions under current operations and the engineering design of the new FPF at HAHD. This draft report contains initial data and results from the four tasks. Table 8 1, Table 8 2, and Table 8 3, and Figure 8 1, Figure 8 2, and Figure 8 3 depict the CFD modeling findings for the GPR steep slope bypass, HAHD horseshoe tunnel, and laboratory testing. Table 8 4, Table 8 5, and Table 8 6 depict the Sensor Fish findings for the GPR steep slope bypass and HAHD horseshoe tunnel testing. The Mv values observed in the HAHD were significantly lower compared to the laboratory experiments conducted at PNNL. Currently, investigations are underway to understand the reasons for this disparity and to establish an appropriate threshold value for Mv. Survival predictions presented in the tables below should be considered preliminary and should not be used until further analyses and adjustments are completed. The next steps for modeling will include the flow regime, (i.e., density of flow regimes due to water and air mixing ) to continue to improve on the threshold value for Mv.

13 HYDRO ENERGY↗
DOE OSTI2025

SigTime: Learning and Visually Explaining Time Series Signatures

Understanding and distinguishing temporal patterns in time series data is essential for scientific discovery and decision-making. For example, in biomedical research, uncovering meaningful patterns in physiological signals can improve diagnosis, risk assessment, and patient outcomes. However, existing methods for time series pattern discovery face major challenges, including high computational complexity, limited interpretability, and difficulty in capturing meaningful temporal structures. Here, to address these gaps, we introduce a novel learning framework that jointly trains two Transformer models using complementary time series representations: shapelet-based representations to capture localized temporal structures and traditional feature engineering to encode statistical properties. The learned shapelets serve as interpretable signatures that differentiate time series across classification labels. Additionally, we develop a visual analytics system—SigTime—with coordinated views to facilitate exploration of time series signatures from multiple perspectives, aiding in useful insights generation. We quantitatively evaluate our learning framework on eight publicly available datasets and one proprietary clinical dataset. Additionally, we demonstrate the effectiveness of our system through two usage scenarios along with the domain experts: one involving public ECG data and the other focused on preterm labor analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Evaluation of a 5kW Solid Oxide Electrolysis Cell Stack

Solid oxide electrolysis cells (SOECs) are a developing technology for hydrogen production. They are promising due to their utilization of thermal energy to reduce their electricity consumption. The Department of Energy (DOE) has set goals to reduce the price per kilogram of hydrogen, and Idaho National Laboratory (INL) is conducting research to develop and demonstrate advanced methods and technologies to achieve making hydrogen a more affordable, efficient, and sustainable energy source. In this project, INL is collaborating with a vendor to evaluate and validate the design of a 5kW SOEC stack. The test aims to demonstrate a safe startup, operation, and shutdown on INL’s 5kW test stand, providing third-party validation in a different environment. A successful 50-hour test with stable hydrogen production will pave the way for a subsequent 1000-hour test. This project emphasized learning about electrolysis and the various support systems, referred to as balance of plant (BOP) equipment. These systems required modification while swapping to the vendor 5kW SOEC stack, most prominently the furnace door design and the controls system. It also included gaining skills in creating computer-aided design (CAD) drawings for the new door while using available materials and ensuring it met high-temperature requirements. Upon completion of modifications to the test stand, the SOEC will be test-fitted with respective wiring and piping. Instrumentation and piping will be checked for leaks and quality. This must be completed before the vendor’s engineers arrive to observe the test plan, startup, and a collection of 50 hours of data. The vendor’s engineers will ensure their stack performed sufficiently in the 50-hour test to enable the progression to a 1000-hour test.

08 - HYDROGEN↗