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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 811 records · Page 45
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Experimental Characterization of Additively Manufactured Nickel-Titanium Shape Memory Alloy Heat Pipes

Shape memory alloys (SMA) have been identified for use in spacecraft components as replacement for conventional deployment mechanisms. They may be used in thermal management components such as radiators to create self-deploying radiators. One SMA, NiTi, has also been developed for additive manufacturing processes. Heat pipes are a common way to create highly effective and lightweight spaceflight radiators, and heat pipes can also be made from NiTi and related alloys. The wick is the critical element of a functioning heat pipe, and recent progress over the past years has led to the development of additively manufactured heat pipe wicks in various materials. The combination of these efforts is the focus of this project: creating an additively manufactured, shape memory alloy self-deploying heat pipe radiator. This paper will focus on the experimental characterization of these additively manufactured NiTi heat pipes. The heat pipe coupons were additively manufactured by direct metal laser sintering (DMLS), with an integral liquid cooled condenser. Heat is input to the heat pipe via a thin film heater. Thermocouples were spot welded to the heat pipes to measure temperature at several axial locations. The heat pipes were tested with two working fluids: water and ethanol. Ethanol is not an ideal working fluid for heat pipes but is useful in characterizing them because it wets well to a wide variety of surfaces. Water is in general a superior working fluid for heat pipes, but its contact angle and therefore wicking performance strongly depends on the surface chemistry of the surface it is in contact with. A particular measurement of interest in this test is the evaporator to condenser thermal conductance, which will be compared in the full paper to recently published correlations for additively manufactured heat pipes. Experimental results for two straight geometry and one bellows geometry heat pipe will be presented. The bellows geometry is of interest for condenser of the self-deploying radiator design.

Additive manufacturing↗
DOE OSTI2024

Nanoengineered Shape-Memory Hemostat

Uncontrolled hemorrhage is the predominant cause of preventable combat deaths. Various biomaterials serve as hemostatic agents due to their procoagulant or absorptive activity. However, these biomaterials often lack expansion capabilities, which severely limits use in noncompressible wounds. This study combines a hemostatic nanocomposite with a shape-memory polymer foam to design a composite material with both hemostatic and physical expansion properties. This composite is fabricated in two formulations: a foam externally coated in a highly concentrated nanocomposite (“coated composite”) and a foam containing a diluted nanocomposite infused throughout its pores (“infused composite”). Both formulations retain the shape-memory foam's expansion property. Further, the coated composite shows improved fluid uptake (>2-fold) versus infused composites or foam. The nanocomposite component dissociates from the foam under degradative conditions, with the foam remaining stable for 30 days. Hemostatic studies illustrate that the coated composite reduces the clotting time by ≈20%. Alternatively, the infused composite improves clotting over a larger distance (up to ≈2× distance from the composite). These results signify a modular hemostatic ability: the coated composite reduces clotting and improves fluid uptake, while the infused composite achieves diffuse clotting and maintains mechanical properties. Thus, these materials pose a strong potential for use in noncompressible wounds.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

DeStager: feature guided in-situ data management in distributed deep memory hierarchies

In-situ analytics have been increasingly adopted by leadership scientific applications to gain fast insights into massive output data of simulations. With the current practice, systems buffer the output data in DRAM for analytics processing, constraining it to DRAM capacity un-used by the simulation. The rapid growth of data size requires alternative approaches to accommodating data-rich analytics, such as using solid-state disks to increase effective memory capacity. For this purpose, this paper explores software solutions for exploring the deep memory hierarchies expected on future high-end machines. Leveraging the fact that many analytics are sensitive to data features (regions-of-interest) hidden in the data being processed, the approach incorporates the knowledge of the data features into in-situ data management. It uses adaptive index creation/refinement to reduce the overhead of index management. In addition, it uses data features to predict data skew and improve load balance through controlling data distribution and placement on distributed staging servers. The experimental results show that such feature-guided optimizations achieve substantial improvements over state-of-the-art approaches for managing output data in-situ.

Computer Science↗
DOE OSTI2026

Toward memory-efficient melt pool monitoring: a classification framework using event-based imaging and sparse sensing technique

Vision sensors like CMOS and CCD cameras are often used for in-process monitoring of melt pools in laser-based additive and welding processes, but they require transferring large amounts of data and computational processing resources. Event-based neuromorphic imagery, on the other hand, detects only the change in pixel intensity, thus potentially reducing the data amount and latency. With an event imager, this study develops a framework for melt pool condition classification, including image construction, time scale selection, optimal pixel selection, and sparse classification, to achieve a highly memory-efficient scheme. These are based on sparse sensing techniques with singular value decomposition (SVD) and QR pivoting, the two fundamental matrix transformations for linear dimensionality reduction. The framework is then validated by classifying a controlled experiment by exciting various mode shapes of liquid gallium pools of varying depths (3, 6, and 8 mm). At 200 pixels, the classifier can reach overall accuracy of 75%, while at 2000 pixels (0.013% of the total possible pixels), the accuracy is nearly 90% (89.86%). At the same number of pixels, random selection can only achieve 46% and 67%, respectively. The memory savings of the sparsely sampled event data compared to a conventional imager is about 500 times. In addition to performance, implementation and limitations of the framework are also discussed.

42 ENGINEERING↗
DOE OSTI2018

Reachable set bounding for a class of bidirectional associative memory NNSs with Markov jump switching parameters

This paper studies the problem how to estimate the reachable set for a class of delayed bidirectional associative memory neural network systems (NNSs), which have Markov switching parameters and unit-energy or unit-peak bounded disturbance inputs. The feature of the Markov jump bidirectional associative memory NNSs shows in the following twofold: the time delay is time varying; the transition rates is time varying. Moreover, the time-varying transition rates is piecewise constant. Using the Lyapunov functional method, delay-partitioning and linear matrix inequalities techniques, the estimate problem of the reachable set depending on time delay is solved. The effectiveness of the given results is illustrated by the proposed numerical examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

H{sub ∞} memory feedback control for uncertain singular Markov jump systems with time-varying delay and input saturation

This paper concerns with the H{sub ∞} robust exponential stability and memory state feedback stabilization of singular Markov jump systems with time-delay and input saturation. Linear matrix inequality conditions are given to guarantee regularity, impulse-freeness and exponential stability for the singular Markov jump system. Moreover, sufficient conditions are presented to ensure the H{sub ∞} disturbance attenuation level, and the design method of memory feedback controller is developed by solving linear matrix inequalities optimization problem without any decompositions of system matrices and equivalent transformation. Furthermore, the function in the proof procedure belongs to multiple Lyapunov-like functions whose advantage lies in their flexibility. Finally, numerical examples are employed to verify the effectiveness of the proposed methods and to illustrate the significant improvement on the conservativeness of some reported results in the literature.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Design, characterization and shape recovery behavior of 3D/4D printed shape memory polymers (SMPs)

Shape memory polymers (SMPs) represent a paradigm shift in material science, uniquely capable of undergoing reversible shape transformations triggered by external stimuli, positioning them as pivotal in developing next-generation biomedical devices, aerospace components, and adaptive structures. Extensive research has been done on SMPs with a major focus on high-temperature programming methods, which can limit energy efficiency and applicability with temperature-sensitive materials. Additionally, while various SMP blends have demonstrated great potential, limited work has been done on the suitability for 3D printing these materials, particularly under high-strain and ambient temperature programming conditions. In this study, a three-component optimized SMP composition was evaluated by 3D printing via the Material Extrusion (MEX) technique and investigating its ambient temperature-programming behavior at high strains. The SMP formulation studied was a tailored blend of thermoplastic polyurethane (TPU), polycaprolactone (PCL), and an octadecane diol-based copolymer (OBC) that exhibits robust shape memory behavior, high strain tolerance, and efficient force generation. Rigorous thermal, mechanical, and shape recovery analyses, along with optimized printing parameters and consistent shape recovery rates of up to 90%, were achieved under dynamic mechanical analysis (DMA), even under ambient programming conditions. This work demonstrates the SMP composition’s potential for adaptive, self-deployable systems with 4D printing characteristics ideal for bio-inspired structures and artificial muscle fibers.

Sudan, Kavish [University of Louisville, KY]↗
DOE OSTI2019

A eutectic dual-phase design towards superior mechanical properties of heusler-type ferromagnetic shape memory alloys

Heusler-type ferromagnetic shape memory alloys possess attractive multifunctional properties, including magnetic field induced shape memory effect, magnetoresistance and magnetocaloric effect, owing to the unique concurrent magnetic and martensitic transformations. However, these intermetallics generally exhibit intrinsic high brittleness and low strength, which severely impede their workability for processing and applicability in real use. In this study, we demonstrate a new grain refining strategy by means of eutectic solidification to improve the mechanical properties in Ni-Mn-Sn-Fe alloys. In a fully eutectic microstructure, the average γ lamellae thickness was refined to ~170 nm and the composite showed a compressive strength of 1950 MPa, ductility of 19.5%, Young’s Modulus of 38 GPa, pseudoelasticity of 3.2% and high mechanical cyclic stability. The high mechanical performance is attributed to the effect of departmentalization of the brittle Heusler alloy by the densely distributed γ phase fine lamellae in resisting crack propagation. The eutectic Heusler composite exhibited a metamagnetic phase transformation, with a magnetic entropy change of 10.2 J/kg·K and a refrigeration capacity of 168 J/kg in a field change of 5 T.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Pushing memory bandwidth limitations through efficient implementations of Block-Krylov space solvers on GPUs

Lattice quantum chromodynamics simulations in nuclear physics have benefited from a tremendous number of algorithmic advances such as multigrid and eigenvector deflation. These improve the time to solution but do not alleviate the intrinsic memory-bandwidth constraints of the matrix-vector operation dominating iterative solvers. Batching this operation for multiple vectors and exploiting cache and register blocking can yield a super-linear speed up. Block-Krylov solvers can naturally take advantage of such batched matrix-vector operations, further reducing the iterations to solution by sharing the Krylov space between solves. However, practical implementations typically suffer from the quadratic scaling in the number of vector-vector operations. Here, using the QUDA library, we present an implementation of a block-CG solver on NVIDIA GPUs which reduces the memory-bandwidth complexity of vector-vector operations from quadratic to linear. We present results for the HISQ discretization, showing a 5x speedup compared to highly-optimized independent Krylov solves on NVIDIA's SaturnV cluster.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Memory-efficient emulation of physical tabular data using quadtree decomposition

Computationally expensive functions are sometimes replaced in simulations with an emulator that approx-imates the true function (e.g., equations of state, wavelength-dependent opacity, or composition-dependent materials properties). For functions that have a constrained domain of interest, this can be done by discretizing the domain and performing a local interpolation on the tabulated function values of each local domain. For these so-called tabular data methods, the method of discretizing the domain and mapping the input space to each subdomain can drastically influence the memory and computational costs of the emulator. This is especially true for functions that vary drastically in different regions. We present a method for domain discretization and mapping that utilizes quadtrees, which results in significant reductions in the size of the emulator with minimal increases to computational costs or loss of global accuracy. We apply our method to the electron-positron Helmholtz free energy equation of state and show over an order of magnitude reduction in memory costs for reasonable levels of numerical accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

A memory-distributed quasi-Newton solver for nonlinear programming problems with a small number of general constraints

We approach the problem of parallelizing state-of-the-art nonlinear programming optimization algorithms.Specifically, we focus on parallelizing quasi-Newton interior-point methods that use limited-memory secant Hessian approximations. Such interior-point methods are known to have better convergence properties and to be more effective on large-scale problems than gradient-based and derivative-free optimization algorithms. We target nonlinear and potentially nonconvex optimization problems with an arbitrary number of bound constraints and a small number of general equality and inequality constraints on the optimization variables. These problems occur for example in the form of optimal control, optimal design, and inverse problems governed by ordinary or partial differential equations, whenever they are expressed in a “reduced-space” optimization approach. We introduce and analyze the time and space complexity of a decomposition method for solving the quasi-Newton linear systems that leverages the fact that the quasi-Newton Hessian matrix has a small number of dense blocks that border a low-rank update of a diagonal matrix. This enables an efficient parallelization on memory-distributed computers of the iterations of the optimization algorithm, a state-of-the-art filter line-search interior-point algorithm by Wächter et. al. We illustrate the efficiency of the proposed method by solving structural topology optimization problems on up to 4608 cores on a parallel machine.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

PSelInv – A distributed memory parallel algorithm for selected inversion: The non-symmetric case

This paper generalizes the parallel selected inversion algorithm called PSelInv to sparse non-symmetric matrices. We assume a general sparse matrix A has been decomposed as $PAQ$ = $LU$ on a distributed memory parallel machine, where L, U are lower and upper triangular matrices, and P, Q are permutation matrices, respectively. The PSelInvmethod computes selected elements of $A$ -1 . The selection is confined by the sparsity pattern of the matrix A T . Here our algorithm does not assume any symmetry properties of A, and our parallel implementation is memory efficient, in the sense that the computed elements of $A$ -T overwrites the sparse matrix $L + U$ in situ . PSelInv involves a large number of collective data communication activities within different processor groups of various sizes. In order to minimize idle time and improve load balancing, tree-based asynchronous communication is used to coordinate all such collective communication. Numerical results demonstrate that PSelInv can scale efficiently to 6,400 cores for a variety of matrices.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Overcoming the Memory Bottleneck in Auxiliary Field Quantum Monte Carlo Simulations with Interpolative Separable Density Fitting

We investigate the use of interpolative separable density fitting (ISDF) as a means to reduce the memory bottleneck in auxiliary field quantum Monte Carlo (AFQMC) simulations of real materials in Gaussian basis sets. Here, we find that ISDF can reduce the memory scaling of AFQMC simulations from O(M 4 ) to O(M 2 ) . We test these developments by computing the structural properties of carbon in the diamond phase, comparing to results from existing computational methods and experiment. (LLNL-JRNL-759024)

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

Relaxor Ferroelectric-Like Spatiotemporal Memory in Field-Driven Lipid Bilayers

Lipid membranes are often regarded as passive barriers, yet their nonlinear dielectric response remains poorly understood. Using all-atom molecular dynamics, we show that fully hydrated dipalmitoylphosphatidylcholine bilayers exhibit relaxor ferroelectric-like behavior under time-dependent electric fields. Unlike crystalline relaxors, which are bipolar and display little remanent polarization, lipid bilayers exhibit a unipolar polarization response: even an alternating current field produces persistent, asymmetric polarization. Furthermore, the underlying free-energy landscape contains two distinct minima, a nonpolarized state and a unipolarly polarized state, between which stochastic thermally activated transitions occur. Directionally resolved Van Hove analysis reveals pronounced anisotropy arising from out-of-plane electric dipole alignment, interleaflet coupling, and lateral polarization domains. Each field cycle nucleates polarization at distinct sites and monitors their relaxation, marking a crossover from thermal fluctuations to field-sustained polarization. Remarkably, these polarized domains persist after field removal, generating long-lived, spatially coherent dipolar patterns that encode nanoscale polarization memory. Potassium chloride amplifies these effects via dielectric screening and a modified hydration structure, enhancing electric dipole flexibility and cooperativity. Together, these results establish protein-free bilayers as nonlinear, history-dependent dielectrics capable of sustaining field-tunable electromechanical coupling, providing an emergent physical foundation for nanoscale information storage and memory phenomena reminiscent of short- and long-term plasticity in soft neuromorphic systems.

Insulators↗
DOE OSTI2022

On-Surface Single-Molecule Identification of Mass-Selected Cyclodextrin-Supported Polyoxovanadates for Multistate Resistive-Switching Memory Applications

Polyoxovanadate anions immobilized on conducting substrates are promising candidates for molecule-based memory technologies. However, strong electronic interactions with the substrate and the formation of larger agglomerates remain bottlenecks on the way to their technical application. We demonstrate that ion soft-landing of mass selected cyclodextrin- functionalized hexavanadates enables to deposit these host-guest complexes as single memory units on surfaces without their quaternary ammonium countercations from solution. The cyclodextrin provides a stabilizing “shell” for the dianions. Further, multistate switching evidenced by the change in molecular resistance has been detected using scanning tunneling microscopy and spectroscopy and attributed to reduction of individual vanadium (+V) centers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Biobased Semi-Interpenetrating Polymer Networks of Poly(ε-caprolactone) and Epoxidized Soybean Oil with Nanoscale Morphology, Shape-Memory Effect, and Biocompatibility

Creating biobased polymer blends with outstanding properties, nanoscale morphology, shape-memory capability, and biocompatibility is very crucial and requires a fundamental understanding of the phase behavior, macromolecular structure, and biological compatibility of the polymer blends with living cells. It is very critical to understand the complex relationships among the polymer structure, morphology, and performance of multifunctional smart materials under conditions that they are likely to encounter during use, particularly in biomedical applications. Biobased semi-interpenetrating polymer networks of poly(ε-caprolactone) and epoxidized soybean oil with nanoscale morphology have been successfully synthesized via in situ cationic polymerization and compatibilization in a homogeneous solution. Varies analytical and characterization techniques, such as Fourier transform infrared spectroscopy, differential scanning calorimetry, dynamic mechanical analysis, transmission electron microscopy, X-ray scattering, cell toxicity, and shape-memory effects (SMEs), have been employed to understand the structure–properties relationship of these smart, biobased nanostructured polymer blends. The synthesized nano blends were nontoxic or biocompatible and supported attachment of human vein endothelial cells, showing their potential use in biomedical applications. The current versatile, low-cost strategy for synthesizing the nanoscale morphology of semi-interpenetrating polymer networks with SMEs and biocompatibility should be widely applicable for polymer systems. This study is also considered as a continuation to our efforts in the area of biobased polymers to develop innovative technologies to transform natural resources into smart multifunctional materials for a wide range of applications, including coatings, adhesives, and medical devices.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Intermittent fasting enhances long-term memory consolidation, adult hippocampal neurogenesis, and expression of longevity gene Klotho

Daily calorie restriction (CR) and intermittent fasting (IF) enhance longevity and cognition but the effects and mechanisms that differentiate these two paradigms are unknown. We examined whether IF in the form of every-other-day feeding enhances cognition and adult hippocampal neurogenesis (AHN) when compared to a matched 10% daily CR intake and ad libitum conditions. After 3 months under IF, female C57BL6 mice exhibited improved long-term memory retention. IF increased the number of BrdU-labeled cells and neuroblasts in the hippocampus, and microarray analysis revealed that the longevity gene Klotho (Kl) was upregulated in the hippocampus by IF only. Furthermore, we found that downregulating Kl in human hippocampal progenitor cells led to decreased neurogenesis, whereas Kl overexpression increased neurogenesis. Finally, histological analysis of Kl knockout mice brains revealed that Kl is required for AHN, particularly in the dorsal hippocampus. These data suggest that IF is superior to 10% CR in enhancing memory and identifies Kl as a novel candidate molecule that regulates the effects of IF on cognition likely via AHN enhancement.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Piezomagnetism and magnetoelastic memory in uranium dioxide

Uranium dioxide (UO 2 ) is a prime nuclear fuel and perhaps the most thoroughly studied actinide material to date. Its thermal and magnetic properties remain, however, a puzzle resulting from strong couplings between magnetism and lattice vibrations. The magnetic state of this cubic material is characterized by a non- collinear antiferromagnetic structure and multidomain Jahn-Teller distortions that could be behind novel thermal properties. Here we show that single crystals of UO 2 , subjected to magnetic fields up to 95 T in the magnetic state, exhibit the abrupt appearance of positive linear magnetostriction leading to a trigonal distortion. Upon reversal of the field the linear term also reverses sign, a hallmark of piezomagnetism. The switching phenomenon occurs at ± 18 T and persists during subsequent field reversals, demonstrating robust magneto-elastic memory. This is the first example of piezomagnetism in an actinide spin system and the magneto-elastic memory loop here is nearly an order of magnitude wider in field than those previously observed, making UO 2 the hardest piezomagnet known. The possibility of an inverse phase with reduced magnetocrystalline anisotropy is considered to explain these effects.

36 MATERIALS SCIENCE↗