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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Bio-inspired alula-based winglet design for enhanced heat transfer in high temperature fin-and-tube heat exchangers

Fin-and-tube heat exchangers (FTHEs) are widely used for high-temperature flue-gas heat recovery, but their performance is often limited by wake regions and non-uniform fin-surface temperatures. This study proposes and numerically evaluates four bio-inspired longitudinal vortex generator (VG) configurations in a high-temperature FTHE with flue-gas inlet temperature ∼1230 K: double-delta, curved double-delta, alula, and a new curved-alula geometry. The reference fin is not hydraulically plain; it already incorporates leading-edge separation columns and convex protrusions, so the alula-type winglets are assessed as downstream add-ons acting on a strongly disturbed flow. In a second step, perforations (one, two and three circular holes) are introduced into the curved-alula VGs to further tailor the flow field. Three-dimensional simulations with the Shear Stress Transpor (SST) $k - ω$ model, temperature-dependent flue-gas properties and conjugate conduction are carried out for gas-side Reynolds numbers $Re_g ≈ 8.0$ x $10^2 - 3.6$ x $10^3$ (mass flow rates 0.5 – 2.5 g/s), and the designs are compared in terms of surface heat flux, Nusselt number, friction factor and hydrothermal performance factor (HTPF). For this already-promoted fin, the additional downstream winglets provide moderate, incremental hydrothermal gains. At the highest Reynolds number, the best non-perforated design (curved-alula) increases surface heat flux from 1630.9 to 1794.7 kW/m² (∼ 10 % gain) and the Nusselt number from 227.6 to 242.6 (∼ 7 % gain), while the friction factor rises from 0.26 to about 0.30, yielding HTPF values close to unity (∼ 0.9 – 1.0). Introducing circular perforations into the curved-alula winglets acts mainly as a wake-bleeding refinement: the three-hole configuration provides a heat flux of 1824.7 kW/m² and a pressure drop of 127.9 Pa, with HTPF in the range ∼ 1.03 – 1.14 and a small (∼ 1 – 3 %) improvement over the solid curved-alula design. Flow-field analysis shows that the perforated curved-alula VGs shrink tube-wake regions, thin the thermal boundary layer and homogenize the fin-surface temperature (outlet-gas temperature ∼ 510 – 520 K and fin-surface temperature ∼ 420 – 421 K for the three-hole case). An optimal flue-gas mass flow rate of ∼ 1 g/s ($Re_g ≈ 1.5$ x $10^3$) is identified, beyond which additional heat-transfer gains are offset by rapidly increasing pressure losses. Overall, the results highlight that initial fin geometry and VG placement are as important as VG shape: alula-based winglets are expected to yield larger relative gains on simpler flat-fin layouts or when positioned closer to the fin leading edge and tube

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Beyond interpolation: Physics-inspired gating transformers for extrapolating irradiation conditions to novel nuclear fuels

The qualification of advanced nuclear fuels relies on irradiation experiments in test reactors that emulate commercial conditions. Designing these tests requires accurate prediction of key irradiation quantities, particularly heat generation rate and burnup, yet obtaining them typically involves computationally expensive multi-step simulation workflows. We propose a physics-inspired gating transformer (PIGT) that integrates an inverse-square, distance-based attenuation into the encoder representation to bias attention toward physically relevant spatial relationships while retaining data-driven flexibility. Using MiniFuel irradiation data from the High Flux Isotope Reactor at Oak Ridge National Laboratory, we benchmark against ensemble methods, feedforward and recurrent networks, convolutional models, and standard transformers. While baseline models perform well under interpolation, they exhibit a pronounced generalization gap when evaluated on fuels not included in the training set. The proposed model consistently improves extrapolative accuracy and stability, yielding the strongest performance on unseen fuel configurations. These results indicate that a lightweight physics structure embedded within attention mechanisms can substantially improve robustness, enabling more reliable surrogate predictions to accelerate the design of nuclear fuel irradiation experiments.

Fuel qualification↗
DOE OSTI2025

Hydraulic performance and wave transmission through nature-inspired perforated hollow-base piles breakwater

Nature-based Solutions (NbS) are critical to achieving long-term sustainable coastlines in deltaic coastlines, particularly susceptible to severe impacts under climate change. In recent years, coastal erosion has accelerated in the Vietnam Mekong Delta (VMD), particularly in the West Sea (from Ca Mau to Kien Giang). Due to the characteristics of muddy coastlines and eroded mangrove forests in this region, only a few engineering solutions have effectively reduced waves and promoted sediment accumulation to facilitate beach accretion. However, these solutions require significant investment and cannot be relocated or reused. Therefore, this study introduces a coastal mangrove-inspired perforated hollow-base piles breakwater (PHBPB), which uses the principle of biomimicry by studying the ability of mangrove groups to protect the coastline, stimulate sediment deposition, and over time, restore mangrove forests in the VMD. The hydraulic parameters of PHBPB were tested in 260 scenarios using a physical model, which included varying parameters such as crest width (B), pile row numbers (n c ), crest freeboard (R c ), and wave parameters (H s , T p ) in determining the capabilities of wave transmission, reflection, and wave dissipation. The results showed that the process of wave energy dissipation through perforated hollow-base breakwaters without piles (PHBBWP) is governed by three main parameters, including the relative water depth of the crest (R c /H m0,i ), the relative width of the crest (B/H m0,i ), and the wave slope at the structure location (s m = H m0,i /L m ). In contrast, for PHBPB, the primary influencing factors are the relative submergence depth or the length of the submerged pile portion (R c /H m0,i ) and the relative width of the pile system (X b /L m ). Finally, the hollow structure is suitable for the soft, weak mud geology in this region with poor load-bearing capacity, allows for rapid installation, is reasonably cost-effective, and can be relocated and reused once regeneration has been completed in each stretch of coastline.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Understanding the Conductivity and Transference Trade-Off in Polymer Electrolytes Using a Robeson-Inspired Upper Bound

The development of high-performance electrolytes is crucial for advancing next-generation lithium and sodium battery technologies. Since the cation is the working ion in both technologies, electrolytes exhibiting the rapid cation transport are essential for making progress. Pathways to optimize electrolytes are unclear due to the inherent trade-off between conductivity and cation transference. While this trade-off is sometimes recognized, there are no well-accepted methodologies for quantifying it. Inspired by the Robeson upper bound for the permeability–selectivity trade-off in gas separation membranes, we propose an approach for quantifying the trade-off in electrolytes using Newman’s concentrated solution theory. We suggest calling this the Newman upper bound. By analyzing published data from 30 polymer electrolytes containing univalent lithium and sodium salts, the Newman upper bound is expressed as κ = 2.0­(1/ρ+ – 1) where κ (mS/cm) is conductivity and ρ+ is the current fraction measured in a symmetric cell as first described by Bruce et al. [J. Electroanal. Chem. Interfacial Electrochem. 1987, 225 (1), 1–17]. This formulation of the upper bound introduces a critical guiding metric for designing next-generation polymer electrolytes; it highlights factors underlying the trade-off, including the salt diffusion coefficient (D), cation transference number relative to solvent velocity ( t + 0 ), and thermodynamic factor (1 + (d lnγ+–)/(d lnm)), where γ+– is the mean molar activity coefficient and m is the molality. These parameters have been measured for very few electrolytes. We posit that establishing the molecular properties that govern these parameters will lead to improved electrolytes that greatly exceed the current upper bound.

He, Zirong↗
DOE OSTI2026

Catalytic Triad-Inspired Nanozyme Catalysts for Ester Hydrolysis in Organic Solvent Mixtures

We synthesized a series of molecular nanozyme catalysts containing functional groups (imidazole, carboxylic acid, and hydroxyl groups) inspired by the catalytic triad found in natural serine hydrolases. Different structural features were incorporated using two distinct synthesis routes to investigate the influence of interactions beyond the active site on catalytic activity in designing molecular nanozymes. Molecular dynamics simulations suggest that nanozyme activity is affected by structural features that influence nanozyme hydrophilicity and the organization of the local solvent environment. The most active nanozyme showed activity comparable to the enzyme α-chymotrypsin for the hydrolysis of a model ester (4-nitrophenyl 4- hydroxybenzoate) in a 95:5 (v/v%) water/acetonitrile mixture at ambient temperature. Increasing the temperature and organic solvent content decreases the activity of α-chymotrypsin while enhancing the activity of the nanozymes. The nanozymes can be immobilized on supported metal nanoparticles using a dithiol self-assembled monolayer, which facilitates their removal from the postreaction solution. These results demonstrate the potential in creating solvent-tolerant bioinspired catalysts, thereby combining the advantages of biocatalysts and chemical catalysts as next-generation industrial catalysts.

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

Investigation into the Synthesis of Several TATB-Inspired Energetic Materials

The synthesis and characterization of several TATB-inspired energetic materials was explored. Despite their structural similarity to TATB, their behavior toward traditional synthetic pathways deviates significantly from that of the parent material. This change in expected reactivity is discussed and an efficient alternative synthesis is reported. The development of a simple and effective route to these TATB-type compounds opens the door to a potential new class of insensitive high explosives.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Neuromorphic intermediate representation: A unified instruction set for interoperable brain-inspired computing

Abstract Spiking neural networks and neuromorphic hardware platforms that simulate neuronal dynamics are getting wide attention and are being applied to many relevant problems using Machine Learning. Despite a well-established mathematical foundation for neural dynamics, there exists numerous software and hardware solutions and stacks whose variability makes it difficult to reproduce findings. Here, we establish a common reference frame for computations in digital neuromorphic systems, titled Neuromorphic Intermediate Representation (NIR). NIR defines a set of computational and composable model primitives as hybrid systems combining continuous-time dynamics and discrete events. By abstracting away assumptions around discretization and hardware constraints, NIR faithfully captures the computational model, while bridging differences between the evaluated implementation and the underlying mathematical formalism. NIR supports an unprecedented number of neuromorphic systems, which we demonstrate by reproducing three spiking neural network models of different complexity across 7 neuromorphic simulators and 4 digital hardware platforms. NIR decouples the development of neuromorphic hardware and software, enabling interoperability between platforms and improving accessibility to multiple neuromorphic technologies. We believe that NIR is a key next step in brain-inspired hardware-software co-evolution, enabling research towards the implementation of energy efficient computational principles of nervous systems. NIR is available atneuroir.org

Science & Technology - Other Topics↗
DOE OSTI2025

High-throughput micro-scale bandgap mapping for perovskite-inspired materials with complex composition space

Abstract To realize the full promise of high-throughput experimental workflows, the rate of sample synthesis must be matched by that of characterization. Of growing interest are contactless optical techniques that can rapidly measure material homogeneity and properties. Here, we present a hyperspectral imaging method to measure local optical bandgap distributions within samples, utilizing spatially-resolved reflectance spectra coupled with automated data analysis. We collect approximately one million optical bandgap data across the compositional space of Cs 3 (Bi x Sb 1-x ) 2 (Br y I 1-y ) 9 perovskite-inspired materials. Our results show non-monotonic bandgap variations (i.e., bandgap bowing) along six composition gradient sequences, in addition to identifying samples with multiple bandgaps in statistics. High-throughput transient absorption spectroscopy reveals that within these compositions, the depletion of the ground state carriers to excited states occurred at discrete energy levels with independent carrier dynamics, consistent with the bandgap observation and indicative of phase separation. This work demonstrates the potential for rapid optical measurements to assess material quality and homogeneity in a high-throughput experimental setting, supporting screening and recipe optimization of optoelectronic material candidates with desired carrier dynamics and optical properties.

Science & Technology - Other Topics↗
DOE OSTI2024

Unleashed from constrained optimization: quantum computing for quantum chemistry employing generator coordinate inspired method

Hybrid quantum-classical approaches offer potential solutions to quantum chemistry problems, yet they often manifest as constrained optimization problems. Here, we explore the interconnection between constrained optimization and generalized eigenvalue problems through the Unitary Coupled Cluster (UCC) excitation generators. Inspired by the generator coordinate method, we employ these UCC excitation generators to construct non-orthogonal, overcomplete many-body bases, projecting the system Hamiltonian into an effective Hamiltonian, which bypasses issues such as barren plateaus that heuristic numerical minimizers often encountered in standard variational quantum eigensolver (VQE). Diverging from conventional quantum subspace expansion methods, we introduce an adaptive scheme that robustly constructs the many-body basis sets from a pool of the UCC excitation generators. This scheme supports the development of a hierarchical ADAPT quantum-classical strategy, enabling a balanced interplay between subspace expansion and ansatz optimization to address complex, strongly correlated quantum chemical systems cost-effectively, setting the stage for more advanced quantum simulations in chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

High performance long chain polyesters via melt copolymerization of cutin-inspired monomers

Biopolymers have exhibited potential as sustainable and circular replacements to existing commodity thermoplastic polymers. However, current biopolymers are limited by poor thermomechanical performance compared with their petroleum-derived counterparts. Herein, we report a simple strategy to achieve good mechanical properties in bio-inspired long-chain polyesters via melt copolymerization. By combining mono- and poly-hydroxyl functionalized long chain fatty acids, we show that tough, semi-crystalline materials can be produced that outperform related biopolymers in terms of their thermomechanical behavior. Finally, we envision that long-chain polyesters derived from hydroxylated fatty acids represent an ideal platform to create the next generation of commodity thermoplastics that possess advantaged properties, inherent biodegradability, and feedstock stability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Synthesis and characterization of 5,15-bis(hydroxymethyl)porphyrins – simple compounds distantly inspired by the chlorosomal bacteriochlorophylls

A self-assembly paradigm is provided in green photosynthetic bacteria by the chlorin macrocycle bacteriochlorophyll (BChl) c, which contains a 3-(1-hydroxyethyl) substituent, central magnesium ion, and 13-keto group. The assembled BChl c structure is a powerful light-harvesting apparatus that can support life even under extreme low-light conditions. Here, inspired by the work of Balaban, two far simpler porphyrins have been synthesized, 5,15-bis(hydroxymethyl)-10,20-diphenylporphinatozinc(II) (Ph/CH 2 OH) and 5,15-bis(hydroxymethyl)porphinatozinc(II) (H/CH 2 OH), and analogues wherein ethyl replaces hydroxymethyl (Ph/Et and H/Et). Examination of Ph/CH 2 OH and H/CH 2 OH by time-resolved spectroscopy showed an ∼2-fold enhancement in the singlet excited-state lifetime compared to meso-tetraphenylporphinatozinc(II) (ZnTPP). The single-crystal X-ray diffraction revealed distinct packing patterns. Porphyrin Ph/CH 2 OH exhibited double staircases wherein (1) each zinc is pentacoordinate (by apical coordination of one hydroxymethyl group of a porphyrin in the same staircase), (2) the second hydroxymethyl group is hydrogen-bonded to an apically coordinated hydroxymethyl oxygen atom in the adjacent staircase, (3) the porphyrins in a given staircase are coplanar but cofacially offset with each other, and (4) the adjacent staircases are oriented approximately 72° relative to each other. Porphyrin H/CH 2 OH assembled wherein (1) each zinc is hexacoordinate by ligation of hydroxymethyl moieties, (2) each hydroxymethyl –OH is hydrogen-bonded with an acetonitrile solvent molecule in the lattice, and (3) the planes of the four nearest neighbor porphyrins are essentially perpendicular to a given porphyrin. Study of the solid-state packing patterns of sparsely substituted porphyrins enables insights into how the structural design of tetrapyrroles can guide their aggregate self-assembly.

Tran, Vy-Phuong [North Carolina State University, ↗
DOE OSTI2025

Quantum many-body linear algebra, Hamiltonian moments, and a coupled-cluster inspired framework

Here, we propose a general strategy to develop quantum many-body approximations of primitives in linear algebra algorithms. As a practical example, we introduce a coupled-cluster inspired framework to produce approximate Hamiltonian moments and demonstrate its application in various linear algebra algorithms for ground state estimation. Through numerical examples, we illustrate the difference between the ground-state energies arising from quantum many-body linear algebra and those from the analogous many-body perturbation theory. Our results support the general idea of designing quantum many-body approximations outside of perturbation theory, providing a route to new algorithms and approximations.

Algorithms and data structure↗
DOE OSTI2025

RANGE: A robust adaptive nature-inspired global explorer of potential energy surfaces

With the growing demand for realistic representations of chemical structures and the advent of exascale computing, the intelligent sampling of potential energy surfaces and efficient identification of global minima have become more essential but also more feasible. Building on prior studies demonstrating the efficiency of the Artificial Bee Colony (ABC) swarm intelligence algorithm, we report a hybrid metaheuristic framework that integrates the adaptive exploration capabilities of ABC coupled with the exploitation strengths of genetic algorithms (GA) in a scalable, Python-based implementation. The resulting tool, RANGE (Robust Adaptive Nature-inspired Global Explorer), provides seamless interfaces to multiple potential energy evaluators, either directly or via widely used Python libraries, and is designed for high-performance computing environments. We describe the implementation details of RANGE and evaluate its performance, relative to ABC- or GA-alone based algorithms, on a variety of chemical systems, including molecular clusters and heterogeneous surfaces. In conclusion, our results demonstrate RANGE’s efficiency, robustness, and broad applicability in addressing challenging global optimization problems in computational chemistry and materials science.

Algorithms and data structure↗
DOE OSTI2025

Logical error rates for the surface code under a mixed coherent and stochastic circuit-level noise model inspired by trapped ions

With fault-tolerant quantum computing (FTQC) on the horizon, it is critical to understand sources of logical errors in plausible hardware implementations of quantum error-correcting codes. Detailed error modeling of computational instructions on particular FTQC architectures will enable the better prediction of error propagation in FT-encoded quantum circuits while revealing where greater attention is needed in hardware design. In this work, we consider logical error rates for the surface code implemented on a hypothetical grid-based trapped-ion quantum charge-coupled device architecture. Specifically, we construct logical channels for the idling surface code and examine its diamond error under a mixed coherent and stochastic circuit-level noise model inspired by trapped ions. We include the coherent dephasing noise that is known to accumulate during physical qubit idling and transport in these systems, determining idling and transport durations using the time-resolved output of an open-source trapped-ion surface code compiler. To estimate expectation values of logical Pauli observables following hardware circuits containing non-Clifford sources of noise, we utilize a Monte Carlo technique to sample from an underlying quasiprobability distribution of Clifford circuits that we independently simulate in a phase-sensitive fashion. We verify error suppression up to code distance 𝑑 = 11 at coherent dephasing rates near and below those of current-generation trapped-ion quantum computers and find that logical error rates align with those of analogous fully stochastic simulations in this regime. Exploring higher dephasing rates at 𝑑 = 3−5, we find evidence for growing coherent rotations about all three logical Pauli axes, increased diagonal logical error process matrix elements relative to those of stochastic simulations, and a reduced dephasing rate threshold. Overall, our work paves a way toward realistic hardware emulation of small fault-tolerant quantum processes, e.g., members of an FTQC instruction set.

Quantum benchmarking↗
DOE OSTI2024

Feedback-based quantum algorithm inspired by counterdiabatic driving

In recent quantum algorithmic developments, a feedback-based approach has shown promise for preparing quantum many-body system ground states and solving combinatorial optimization problems. This method utilizes quantum Lyapunov control to iteratively construct quantum circuits. Here, we propose a substantial enhancement by implementing a protocol that uses ideas from quantum Lyapunov control and the counterdiabatic driving protocol, a key concept from quantum adiabaticity. Our approach introduces an additional control field inspired by counterdiabatic driving. We apply our algorithm to prepare ground states in one-dimensional quantum Ising spin chains. Comprehensive simulations demonstrate a remarkable acceleration in population transfer to low-energy states within a significantly reduced time frame compared to conventional feedback-based quantum algorithms. This acceleration translates to a reduced quantum circuit depth, a critical metric for potential quantum computer implementation. We validate our algorithm on the IBM cloud computer, highlighting its efficacy in expediting quantum computations for many-body systems and combinatorial optimization problems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Quantifying uncertainty in physics-based predictions of rare-isotope production cross sections via Bayesian-inspired model averaging across nuclear mass tables

Accurate prediction of fragmentation cross sections is essential for rare-isotope beam production, planning new-isotope searches, and designing experiments to study the most exotic regions of the nuclear chart. However, existing reaction models and phenomenological cross-section parametrizations often exhibit significant deviations over broad regions of mass and charge. In this work, a Bayesian-inspired model-averaging framework is developed to combine abrasion-ablation (AA) calculations based on multiple nuclear mass tables into a single statistically weighted estimate. For the calibrated systems, the model weights are assigned empirically according to the relative quality of fit to measured cross sections, thereby reducing systematic model bias while preserving the underlying physics content of the AA description. The weights are constrained using proton-rich fragmentation data for the 78 Kr and 124 Xe projectiles. The resulting parameter trends are then propagated to the 92 Mo and 144 Sm systems through a controlled scaling procedure. In the present implementation, the excitation-energy prescription is fixed, while the averaging is performed across nuclear-mass inputs; the framework provides both weighted cross sections and associated uncertainty estimates. Applied to proton-rich fragmentation, the present approach provides a practical basis for interpolation and limited extrapolation in regions relevant to rare-isotope production. The resulting predictions are used to assess the production of very proton-rich nuclei, and candidate new isotopes are discussed.

Bayesian methods↗
DOE OSTI2025

Enhanced Power Grid Maintenance Planning and Quantum-Inspired Combinatorial Prospects

Efficient and reliable scheduling of maintenance for power generation and transmission infrastructure is essential for minimizing operational costs and ensuring grid stability. This paper introduces an integrated optimization framework for coordinated maintenance scheduling of generators and transmission lines under resource and reliability constraints. The model minimizes a composite cost function including maintenance and generation costs, as well as penalties for delayed maintenance, while satisfying N−1 security constraints, operational limits, and crew availability. Case studies on the IEEE 300-bus test system demonstrate the effectiveness of the proposed approach in producing feasible and cost-effective maintenance schedules. To address scalability and combinatorial complexity, the model is mapped into a Quadratic Unconstrained Binary Optimization (QUBO) problem, enabling exploration of solution approaches based on Quantum Imaginary Time Evolution (QITE). While the QUBO reformulation provides a foundation for future quantum-inspired optimization, this study focuses primarily on the development and demonstration of the classical optimization framework and illustrates the potential applicability of QITE in large-scale maintenance scheduling.

Chen, Yang [ORNL] (ORCID:0000000271693874)↗