Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Engine Design”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Healable Coatings as a Mechanism to Repair Leading Edge Erosion in Wind Energy

Wind turbine blades are highly engineered structures designed to face temperature extremes and high winds. However, erosion of the blade's leading edge and subsequent repair remains a significant and costly challenge for the wind energy industry. Repair of these leading edges can lead to large amounts of downtime for the turbine and significant operational inefficiencies. In this work, the strength of adhesion and healing ability of a commercially available vitrimer (Mallinda's VITRIMAX) was compared to that of a thermoplastic resin, which has previously been demonstrated in wind energy applications (Arkema's Elium) to evaluate their efficacy as surface coatings for wind turbine blades, particularly their leading edges. Vitrimers are a class of inherently reprocessable thermosets, and it was theorized that vitrimer-based leading edge coatings could enable more robust and efficient wind turbine blades with decreased operational downtime and safer maintenance practices. It was found that the VITRIMAX adhered better to the wind blades' surfaces than both the manufacturer's paint and Elium, with increases in pull-off strength of adhesion ranging from 24% to 83% above that of the original paint. Furthermore, the VITRIMAX adhered strongly to the underlying composite of each blade with strength of adhesion values increasing in ranges from 42% to 97% above that of the original paint. Finally, the vitrimer coating showed an 88% decrease in surface roughness compared to end-of-life blade materials, and initial healing demonstrations in which coatings were manually scratched and subsequently healed exhibited an ~84.5% decrease in scratch depths.

Hubbard, Amber [ORNL]↗
DOE OSTI2023

Anticipating gelation and vitrification with medium amplitude parallel superposition (MAPS) rheology and artificial neural networks

Abstract Anticipating qualitative changes in the rheological response of complex fluids (e.g., a gelation or vitrification transition) is an important capability for processing operations that utilize such materials in real-world environments. One class of complex fluids that exhibits distinct rheological states are soft glassy materials such as colloidal gels and clay dispersions, which can be well characterized by the soft glassy rheology (SGR) model. We first solve the model equations for the time-dependent, weakly nonlinear response of the SGR model. With this analytical solution, we show that the weak nonlinearities measured via medium amplitude parallel superposition (MAPS) rheology can be used to anticipate the rheological aging transitions in the linear response of soft glassy materials. This is a rheological version of a technique called structural health monitoring used widely in civil and aerospace engineering. We design and train artificial neural networks (ANNs) that are capable of quickly inferring the parameters of the SGR model from the results of sequential MAPS experiments. The combination of these data-rich experiments and machine learning tools to provide a surrogate for computationally expensive viscoelastic constitutive equations allows for rapid experimental characterization of the rheological state of soft glassy materials. We apply this technique to an aging dispersion of Laponite ® clay particles approaching the gel point and demonstrate that a trained ANN can provide real-time detection of transitions in the nonlinear response well in advance of incipient changes in the linear viscoelastic response of the system.

Lennon, Kyle R. (ORCID:0000000212515461)↗
DOE OSTI2024

Interpreting test temperature and loading rate effects on the fracture toughness of polymer-metal interfaces via time–temperature superposition

Here, in this letter, we present interfacial fracture toughness data for a polymer-metal interface where tests were conducted at various test temperatures T and loading rates $\dot{δ}$. An adhesively bonded asymmetric double cantilever beam (ADCB) specimen was utilized to measure toughness. ADCB specimens were created by bonding a thinner, upper adherend to a thicker, lower adherend (both 6061 T6 aluminum) using a thin layer of epoxy adhesive, such that the crack propagated along the interface between the thinner adherend and the epoxy layer. The specimens were tested at T from 25 to 65 °C and $\dot{δ}$ from 0.002 to 0.2 mm/s. The measured interfacial toughness Γ increased as both T and $\dot{δ}$ increased. For an ADCB specimen loaded at a constant $\dot{δ}$, the energy release rate G increases as the crack length a increases. For this reason, we defined rate effects in terms of the rate of change in the energy release rate $\dot{G}$. Although not rigorously correct, a formal application of time–temperature superposition (TTS) analysis to the Γ data provided useful insights on the observed dependencies. In the TTS-shifted data, Γ decreased and then increased for monotonically increasing $\dot{G}$. Thus, the TTS analysis suggests that there is a minimum value of Γ. This minimum value could be used to define a lower bound in Γ when designing critical engineering applications that are subjected to T and $\dot{δ}$ excursions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Formal verification and validation of run-to-completion style state charts using Event-B

State chart notations with ‘run to completion’ semantics are popular with engineers for designing controllers that react to environment events with a sequence of state transitions but lack formal refinement and rigorous verification methods. State chart models are typically used to design complex control systems that respond to environmental triggers with a sequential process. The model is usually constructed at a concrete level and verified and validated using animation techniques relying on human judgement. Event-B, on the other hand, is based on refinement from an initial abstraction and is designed to make formal verification by automatic theorem provers feasible. Abstraction and formal verification provide greater assurance that critical (e.g. safety or security) properties are not violated by the control system. In this paper, we introduce a notion of refinement into a ‘run to completion’ state chart modelling notation and leverage Event-B’s tool support for theorem proving. We describe the difficulties in translating ‘run to completion’ semantics into Event-B refinements and suggest a solution. We illustrate our approach and show how models can be validated at different refinement levels using our scenario checker animation tools. We show how critical invariant properties can be verified by proof despite the reactive nature of the system and how behavioural aspects of the system can be verified by testing the expected reactions using a temporal logic, model checking approach. To verify liveness, we outline a proof that the run to completion is deadlock-free and converges to complete the run.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Exploring novel deformation mechanisms in aluminum–copper alloys using in situ 4D nanomechanical testing

Even after nearly a century of extensive use of aluminum alloys in structural applications, our understanding of such precipitation-strengthened materials is far from complete. With the advent of next generation advanced characterization techniques, our ability to probe materials in unique ways and at different length scales has established a new paradigm for devising new pathways to alloy design by engineering materials and tailoring specific properties at the nanoscale. Here, we perform in situ nanomechanical testing in conjunction with synchrotron -based hard X-ray nanotomography to capture initiation and evolution of damage in 3D in Al-Cu alloys. Precipitates in these alloys are seen to exhibit unprecedented localized deformation in compression, which is attributed to novel observations of kinking in these brittle second-phase particles, accompanied with the generation of a fine polycrystalline texture in the adjacent matrix. We observe a size-dependent transition in precipitate deformation behavior that has been thoroughly investigated using a comprehensive correlative approach.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Low-cost crushed-rock heat storage with oil or salt heat transfer

The demand for variable electricity and heat is met by fossil-fuel power plants because the power plant capital costs and the cost of storing fossil fuels are low. A low-carbon economy requires replacement of the storage function of fossil fuels to provide variable heat and electricity as needed. The Crushed Rock Ultra-large Stored Heat (CRUSH) system is a new technology with the goal to provide heat storage at an incremental capital cost of $2–4/kWh at scales of 10 s to 100 s of GWhs that enables economic daily to multi-week storage. CRUSH can be coupled to nuclear power plants, concentrated solar power (CSP) plants and thermal energy batteries to provide variable electricity and heat on demand. Sensible heat is stored in crushed rock—the lowest-cost heat-storage material. Heat is transferred to and from the crushed rock using nitrate salt or heat-transfer oils but these fluids do not store heat. Heat from a nuclear reactor, CSP plant or conversion of low-price electricity into heat is used to heat the salt or oil. The hot fluid is poured over the crushed rock, trickles downward by gravity through the rock, heats the rock and is collected by the drain pans under the crushed rock. To recover heat cold salt or oil is poured over hot crushed rock, trickles down by gravity while being heated and is collected by the drain pans under the crushed rock. The system design and engineering trade-offs are described. The technology is in the early stages of development.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

Empirical validation and comparison of methodologies to simulate micro and macro-encapsulated PCMs in the building envelope

Thermal Energy Storage (TES) has the potential to shift peak electricity demand. Passive TES is usually implemented in building envelope as micro and macro encapsulated phase change materials (PCM) to shift electric energy demand and therefore requires careful heat transfer analysis. Whole building energy modelling with simplified heat transfer analysis has become extremely important for designers, architects, engineers, and researchers to predict energy performance of buildings. It is important to validate PCM modelling algorithms used in building energy programs to quantify their error and prove their capacity to model different PCM encapsulation types. This study uses data from a microencapsulated PCM and two macroencapsulated PCMs (Bio based PCM and hydrate salts) tested in full-scale using the Advanced Multiscale Building Energy Research (AMBER) Lab located at the Colorado School of Mines and is used to validate a numerical algorithm written in MATLAB language. To approximate the heat transfer through a wall assembly with macroencapsulated PCM pouches, several modelling techniques that can reduce 3D heat transfer characteristics to 1D are explored in this research. A parallel path heat transfer modelling approach is found to give the closest agreement with the experimental data for the pouched PCMs in building envelope applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

AutoTandemML: Active Learning Enhanced Tandem Neural Networks for Inverse Design Problems

Inverse design in science and engineering involves determining optimal design parameters that achieve desired performance outcomes, a process often hindered by the complexity and high dimensionality of design spaces, leading to significant computational costs. To tackle this challenge, we propose a novel hybrid approach that combines active learning with Tandem Neural Networks to enhance the efficiency and effectiveness of solving inverse design problems. Active learning allows to selectively sample the most informative data points, reducing the required dataset size without compromising accuracy. We investigate this approach using three benchmark problems: airfoil inverse design, photonic surface inverse design, and scalar boundary condition reconstruction in diffusion partial differential equations. We demonstrate that integrating active learning with Tandem Neural Networks outperforms standard approaches across the benchmark suite, achieving better accuracy with fewer training samples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Mechanically graded granular scaffolds for osteochondral tissue engineering

Engineered scaffolds designed to approximate the mechanical microenvironment of the osteochondral unit often address this complexity using discrete, two-phase architectures that introduce mechanical discontinuities and interfacial stress concentrations rather than a contiguous stiffness transition. To address this challenge, we created a photoannealed polyethylene glycol (PEG) granular scaffold with a spatially controlled stiffness gradient within a cell-permissive, macroporous architecture. Stiffness was dictated by photoannealing microgels using a photomask. We tuned void volume and available surface area by varying microgel diameter and tested how mesenchymal stromal cells (MSCs) interpret local mechanical environments. MSCs exhibited position-dependent differences in morphology, cytoskeletal structure, matrix deposition, and lineage-specific gene expression within the gradient scaffolds. Softer regions supported rounded cell morphology and deposition of a glycosaminoglycan-rich matrix, whereas stiffer regions promoted cell elongation, increased cytoskeletal tension, and expression of mineral-associated markers. Gradients formed from smaller microgels magnified these spatial responses by increasing cellular confinement and adhesion site availability. Disruption of actomyosin contractility eliminated these regional differences, demonstrating that MSCs rely on tension-dependent mechanotransduction to interpret the gradient. These findings reveal that coupling microgel architecture with continuous stiffness transitions provides a tractable platform to study multiscale mechanobiologic regulation and spatially guide osteochondral tissue formation.

Biological and medical sciences↗
DOE OSTI2026

Discovery and engineering of enzymes for new-to-nature photobiocatalysis

Photobiocatalysis integrates enzymatic catalysis with photochemistry, enabling challenging radical transformations with high selectivity under mild conditions. Early developments in this field were largely driven by the discovery that enzyme-bound cofactors can form photoactive charge–transfer complexes with substrates, thereby initiating radical chemistry upon light irradiation. Recent advances, however, have substantially expanded the mechanistic landscape of photobiocatalysis through diverse mechanisms. This review summarizes major developments in photobiocatalysis reported since 2024. Rather than cataloging individual reactions, we focus on the fundamental mechanisms of radical generation and interception within enzyme active sites, and discuss how these mechanistic principles guide the discovery, engineering, and design of enzymes for new-to-nature photobiocatalysis.

Bai, Zibo [University of Illinois Urbana-Champaign↗
DOE OSTI2016

Microbial communities for bioprocessing: lessons learned from nature

Microbial bioprocessing has evolved from the use of undefined natural consortia to the construction of synthetic communities tailored to specific processes and bioproducts. This evolution is enabled by recent advances in biotechnology, including cultivation of non-model microbes, metabolic engineering, bioinformatics, and numerical modeling. Equipped with these powerful tools, engineers have designed co-cultures and consortia with an expanded set of capabilities, mainly via ‘bottom-up’ approaches that tether isolates together in culture. We present a brief review of the opportunities, challenges, and recent developments in consortia-based bioprocessing with a focus on lignocellulosic biomass conversion. With improved understanding of microbial community composition and function, we further present a vision to harness defined consortia down-selected from nature via ‘top down’ approaches.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Effect of patterned inclusions on the fracture behavior of ceramic composites

Developments in additive manufacturing have enabled the reinforcement of ceramic composites across the scales. Understanding the fracture behavior of such composites is critical for structural design and engineering. However, systematic investigations on the relationship between the patterning characteristics and the effective fracture properties have been scarce. In this work, we propose a mixed-mode phase field model to investigate the effects of elastic modulus and pattern characteristics of circular inclusions on the fracture behavior of a ceramic composite beam. The modeling results have shown that the stiff and compliant inclusions can either repel or attract the crack. The extent of repelling and attraction can be controlled by altering the pattern characteristics. In addition, utilizing the relationship between the pattern characteristics and resulting fracture path, fracture growth can be controlled with appropriate inclusion properties. The targeted enhancement in fracture toughness can also be predicted using an effective fracture model linked with the underlying fracture path computed by the proposed mixed-mode phase field model.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Field validation of data-driven BSDF and peak extraction models for light-scattering fabric shades

Shading and daylighting systems affect cooling, heating, and lighting energy use by modulating solar radiation through the building façade. Characterizing shading systems holistically and accurately helps designers and engineers evaluate shading systems to achieve energy and non-energy performance goals. These complex fenestration systems can be modeled using Bidirectional Scattering Distribution Functions (BSDF), which map incident radiation to hemispherical distributions of outgoing radiation. Data-driven, tabulated BSDFs are derived from interpolated goniophotometer measured data, then sampled during the raytracing calculation. A peak extraction (PE) algorithm was developed to circumvent limits in BSDF angular resolution, where the specular peak is extracted during simulation by evaluating the BSDF in the through direction and surrounding region. The objective of this study was to validate this measurement and modeling workflow using field monitored data from a full scale testbed with eleven installed fabrics of different weaves, openness factors, and colors and assess the accuracy of the workflow under different adaptation and contrast conditions. Test conditions were limited to clear sky conditions with the sun in the field of view. Results showed that, for tensor tree datasets, vertical illuminance, solar luminance (2.5° apex), and daylight glare probability (DGP) were predicted to within a mean bias error (MBE) error of -456 lx (-12.3%), -3.46e5 (-38.4%), and -0.042 (-7.8%) when full PE occurred. With a binary classification of glare/ no glare, DGP was predicted accurately with a true positive rate of 0.98 and true negative rate of 1.0 using tensor tree data and less accurately with Klems BSDF data, particularly for cases of no glare. The workflow may be of insufficient accuracy to distinguish borderline performance between fabrics using the four-point glare scale, particularly under low adaptation, high contrast daylit conditions. Errors were due to reductions in peak shape and intensity across the BSDF interpolation and data reduction workflow. Future work is needed to better preserve measurement fidelity during interpolation and sampling, which in turn will improve PE performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Novel artificial neural network model for instantaneous power losses and operational efficiency mapping of MW-scale vanadium redox flow battery for improved technoeconomic analysis

A novel data-driven, machine-learning-based method for modeling the instantaneous power losses of a distribution-sited 2 MW/8MWh vanadium redox flow battery (VRFB), a grid-scale electrochemical storage technology, is introduced and compared against benchmark empirical modeling approaches, including symmetric and asymmetric models, as well as a recent convex hull modeling approach. The novel loss modeling method introduces several advantages over the benchmark models and over simplistic efficiency estimates, the most significant of which is that the model can accurately reflect the stepwise and non-linear parasitic losses associated with the duty cycles of mechanical auxiliary systems like pump motor drives and blower fans. Residuals of the models are compared; the proposed data driven model features significantly improved accuracy over the benchmark models. The model's coefficient of determination is also improved relative to that of the benchmark models. Furthermore, a novel method for visualization of operational efficiency of the grid-scale storage technology is introduced. To demonstrate the benefits of the novel data-driven method for modeling the VRFB, the benchmark models and the proposed models are embedded into an Open DSS distribution network model to study two applications of the grid-scale electrical storage system: load leveling for grid support and energy arbitrage. This article demonstrates that the accuracy of the instantaneous power loss model significantly impacts the understanding of the state of charge of the VRFB. In turn, the accuracy of the efficiency modeling of the VRFB impacts the understanding of the potential economic value and technical benefits to the distribution network operators. In conclusion, the presented power loss modeling approach is, therefore, highly relevant for utility-stakeholders, battery asset owners, system engineers, system designers, and financial planners interested in evaluating or optimizing the operation of grid-scale VRFBs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Real-time visualization of impact damage in monolithic silicon carbide and fibrous silicon carbide ceramic composite

Impact resistance from foreign debris is a critical requirement for brittle ceramic and ceramic composite materials intended for use in the hot-section of gas turbine engines. A design to mitigate against such impact failures necessitates a detailed understanding of the driving failure mechanisms. The present study introduces a unique time-resolved experimental method which advances the latter effort. Specifically, a pulsed synchrotron X-ray source, in phase contrast imaging (PCI) configuration, is used as a medium, to visually characterize the evolution of damage inside the target materials during impact. As a proof of concept, two types of ceramic materials were tested: monolithic silicon carbide and fibrous silicon carbide composite. Impact was performed using a light-gas gun and 1.5 mm diameter spheres of partially stabilized zirconia (PSZ) and silicon nitride (Si 3 N 4 ). The retrieved dynamic X-ray image sequences provided clear outlines of the damage features. In the case of the monolithic ceramic, the impact by a PSZ projectile initially produced a cone crack and complete failure resulted by extension of a median crack. By contrast, the fibrous composite deformed readily prior to cone crack formation. Nearly identical damage features were observed in the monolith for the Si 3 N 4 projectile, with the exception of the added vertical tensile crack. For this same projectile, the fibrous ceramic showed very limited surface deformation and enhanced cone cracking and kinking of laminates along the crack path. The latter response is attributed to the change in projectile properties. Some of the target materials were recovered, and post-mortem analysis via scanning electron microscopy (SEM) showed correlation with observed X-ray damage profiles. Furthermore, simple Hertzian contact was used to estimate damage for the elastic portion of impact. This approach was found to yield a reasonable match with experimental results for the surface displacement near the contact interface.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Impacts of ruthenium valence state on the electrocatalytic activity of ruthenium ion-complexed graphitic carbon nitride/reduced graphene oxide nanosheets towards hydrogen evolution reaction

Design and engineering of effective electrode catalysts represents a critical first step for hydrogen production by electrochemical water splitting. Nanocomposites based on ruthenium atomically dispersed within a carbon scaffold have emerged as viable candidates. In the present study, ruthenium metal centers are atomically embedded within graphitic carbon nitride/reduced graphene oxide nanosheets by thermal refluxing. Subsequent chemical reduction/oxidation leads to ready manipulation of the ruthenium valence state, as evidenced in microscopic and spectroscopic measurements, and hence enhancement/diminishment of the electrocatalytic activity towards hydrogen evolution reaction in both acidic and alkaline media. Further, this is largely ascribed to the increased/reduced contribution of the Ru valence electrons to the density of state near the Fermi level which dictates the binding and reduction of hydrogen. Results from this study highlight the significance of the valence state of metal centers in the manipulation and optimization of the catalytic performance of single atom catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier–Stokes simulations: A data-driven, physics-informed Bayesian approach

Despite their well-known limitations, Reynolds-Averaged Navier–Stokes (RANS) models are still the workhorse tools for turbulent flow simulations in today's engineering analysis, design and optimization. While the predictive capability of RANS models depends on many factors, for many practical flows the turbulence models are by far the largest source of uncertainty. As RANS models are used in the design and safety evaluation of many mission-critical systems such as airplanes and nuclear power plants, quantifying their model-form uncertainties has significant implications in enabling risk-informed decision-making. In this work we develop a data-driven, physics-informed Bayesian framework for quantifying model-form uncertainties in RANS simulations. Uncertainties are introduced directly to the Reynolds stresses and are represented with compact parameterization accounting for empirical prior knowledge and physical constraints (e.g., realizability, smoothness, and symmetry). An iterative ensemble Kalman method is used to assimilate the prior knowledge and observation data in a Bayesian framework, and to propagate them to posterior distributions of velocities and other Quantities of Interest (QoIs). We use two representative cases, the flow over periodic hills and the flow in a square duct, to evaluate the performance of the proposed framework. Both cases are challenging for standard RANS turbulence models. Simulation results suggest that, even with very sparse observations, the obtained posterior mean velocities and other QoIs have significantly better agreement with the benchmark data compared to the baseline results. At most locations the posterior distribution adequately captures the true model error within the developed model form uncertainty bounds. The framework is a major improvement over existing black-box, physics-neutral methods for model-form uncertainty quantification, where prior knowledge and details of the models are not exploited. This approach has potential implications in many fields in which the governing equations are well understood but the model uncertainty comes from unresolved physical processes. - Highlights: • Proposed a physics–informed framework to quantify uncertainty in RANS simulations. • Framework incorporates physical prior knowledge and observation data. • Based on a rigorous Bayesian framework yet fully utilizes physical model. • Applicable for many complex physical systems beyond turbulent flows.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗