Results for “differential expansion” — Page 11

NASA NTRS1978

A strictly Markovian expansion for plasma turbulence theory

The collision operator that appears in the equation of motion for a particle distribution function that has been averaged over an ensemble of random Hamiltonians is non-Markovian. It is non-Markovian in that it involves a propagated integral over the past history of the ensemble averaged distribution function. All formal expansions of this nonlinear collision operator to date preserve this non-Markovian character term by term yielding an integro-differential equation that must be converted to a diffusion equation by an additional approximation. In this note we derive an expansion of the collision operator that is strictly Markovian to any finite order and yields a diffusion equation as the lowest non-trivial order. The validity of this expansion is seen to be the same as that of the standard quasi-linear expansion.

Jones, F. C.↗

DOE OSTI2025

High-temperature stability and thermal expansion behavior of equi-atomic refractory multi-principal element alloys based on MoNbTi system for Gen IV reactor applications

The present study investigates the thermal stability and thermal expansion behavior of seven equi-atomic refractory multi-principal element alloys (MPEAs) based on the MoNbTi ternary system composed of low neutron absorption cross section elements. Through an integrated approach utilizing in-situ high-temperature X-ray diffraction (HT-XRD) in conjunction with differential scanning calorimetry (DSC), dilatometry and ageing heat treatment, the thermal stability of the MPEAs was comprehensively analyzed. In-situ HT-XRD experiment confirmed the stability of the room temperature phases up to 1000 °C with no peaks observed corresponding to additional phases in the HT-XRD patterns at 500, 800 and 1100 °C. DSC thermograms showed the absence of peaks up to 1000 °C, while peaks and valleys corresponding to exothermic and endothermic events were observed above 1000 °C. Coefficient of thermal expansion (CTE) derived from second order polynomial fitting of linear thermal expansion data from the dilatometry experiment showed linear increment up to 1000 °C for all the alloys except those containing Zr. The Cr containing alloys exhibited notably higher CTE values, particularly the Al containing alloy exhibited the highest value. Ageing heat treatment at 800 and 1000 °C for 96 h and subsequent microstructural analysis revealed significant precipitation of secondary phases in MoNbTiZr, MoNbTiZrV and MoNbTiCrAl. In conclusion, a substantial increase in hardness was observed in MoNbTiZr and MoNbTiCrAl due to secondary phase precipitation, while the other alloys maintained hardness values comparable to their as-cast and homogenized states.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRSDate not supplied

End-To-End Uncertainty Quantification with Analytical Derivatives for Design Under Uncertainty

Uncertainty quantification (UQ) is a rapidly growing and evolving discipline, especially within the aerospace community. Performing analysis with UQ can provide decision makers with a wealth of information about a candidate design. However, the value of UQ is fully realized when the information gained during UQ analysis is leveraged in a feedback loop of a design optimization process, often referred to as design under uncertainty. Although design under uncertainty can be a powerful risk mitigation technique, there are a number of roadblocks that prevent its implementation. Two primary factors are computational costs and added complexity of the analysis. High fidelity simulations on the order tens of uncertain variables quickly become computationally infeasible. Also, implementing UQ into an existing multidisciplinary design and optimization (MDO) process often requires extensive knowledge of the UQ methods and careful treatment of the problem formulation. The objective of this work is to address these two primary roadblocks and enable practitioners to efficiently perform design under uncertainty with limited knowledge of the UQ discipline. Methods outlined in this paper demonstrate MDO incorporating UQ into the design process, leveraging an analytic derivative tool chain through the entire optimization. The proposed approach leverages machine learning techniques to generate a differentiable confidence interval output from polynomial chaos models. This technique, coupled with the incorporation of analytical derivatives through the Polynomial Chaos Expansion (PCE) process, eliminates the need to estimate derivatives which are usually obtained from finite difference, complex step, or similar methods. Developing a differentiable confidence interval allows mixed uncertainty problems (both epistemic and aleatory) to be modeled. Without such modeling, these problems cannot accurately predict objective functions containing statistical quantities such as mean and variance. The addition of analytic derivatives to a polynomial chaos-based UQ method decreases the computational costs of performing design under uncertainty by orders of magnitude in comparison with methods such as complex step. The method and codes developed are modular in nature and are a drop-in solution for design under uncertainty within existing MDO problems. A low-fidelity analytical multidisciplinary optimization under uncertainty for a wing design in OpenMDAO is detailed in this paper. This demonstration case will include both objective functions and constraints which are influenced by uncertain parameters.

Ben D Phillips↗

NASA NTRSDate not supplied

End-To-End Uncertainty Quantification with Analytical Derivatives for Design Under Uncertainty

Uncertainty quantification (UQ) is a rapidly growing and evolving discipline, especially within the aerospace community. Performing analysis with UQ can provide decision makers with a wealth of information about a candidate design. However, the value of UQ is fully realized when the information gained during UQ analysis is leveraged in a feedback loop of a design optimization process, often referred to as design under uncertainty. Although design under uncertainty can be a powerful risk mitigation technique, there are a number of roadblocks that prevent its implementation. Two primary factors are computational costs and added complexity of the analysis. High fidelity simulations on the order tens of uncertain variables quickly become computationally infeasible. Also, implementing UQ into an existing multidisciplinary design and optimization (MDO) process often requires extensive knowledge of the UQ methods and careful treatment of the problem formulation. The objective of this work is to address these two primary roadblocks and enable practitioners to efficiently perform design under uncertainty with limited knowledge of the UQ discipline. Methods outlined in this paper demonstrate MDO incorporating UQ into the design process, leveraging an analytic derivative tool chain through the entire optimization. The proposed approach leverages machine learning techniques to generate a differentiable confidence interval output from polynomial chaos models. This technique, coupled with the incorporation of analytical derivatives through the Polynomial Chaos Expansion (PCE) process, eliminates the need to estimate derivatives which are usually obtained from finite difference, complex step, or similar methods. Developing a differentiable confidence interval allows mixed uncertainty problems (both epistemic and aleatory) to be modeled. Without such modeling, these problems cannot accurately predict objective functions containing statistical quantities such as mean and variance. The addition of analytic derivatives to a polynomial chaos-based UQ method decreases the computational costs of performing design under uncertainty by orders of magnitude in comparison with methods such as complex step. The method and codes developed are modular in nature and are a drop-in solution for design under uncertainty within existing MDO problems. A low-fidelity analytical multidisciplinary optimization under uncertainty for a wing design in OpenMDAO is detailed in this paper. This demonstration case will include both objective functions and constraints which are influenced by uncertain parameters.

Ben Phillips↗

DOE OSTI2024

Emergence of Orchestrated and Dynamic Metabolism of Saccharomyces cerevisiae

Microbial metabolism is a fundamental cellular process that involves many biochemical events and is distinguished by its emergent properties. While the molecular details of individual reactions have been increasingly elucidated, it is not well understood how these reactions are quantitatively orchestrated to produce collective cellular behaviors. Here we developed a coarse-grained, systems, and dynamic mathematical framework, which integrates metabolic reactions with signal transduction and gene regulation to dissect the emergent metabolic traits of Saccharomyces cerevisiae. Our framework mechanistically captures a set of characteristic cellular behaviors, including the Crabtree effect, diauxic shift, diauxic lag time, and differential growth under nutrient-altered environments. It also allows modular expansion for zooming in on specific pathways for detailed metabolic profiles. Furthermore, this study provides a systems mathematical framework for yeast metabolic behaviors, providing insights into yeast physiology and metabolic engineering.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1974

Quasi-linear theory via the cumulant expansion approach

The cumulant expansion technique of Kubo was used to derive an intergro-differential equation for f , the average one particle distribution function for particles being accelerated by electric and magnetic fluctuations of a general nature. For a very restricted class of fluctuations, the f equation degenerates exactly to a differential equation of Fokker-Planck type. Quasi-linear theory, including the adiabatic assumption, is an exact theory for this limited class of fluctuations. For more physically realistic fluctuations, however, quasi-linear theory is at best approximate.

Jones, F. C.↗

NASA NTRS1985

The Formation of Preplanetary Disks from the Collapse of Rotating Molecular Cloud Cores

Solutions that describe the collapse of a molecular cloud core that is initially in unstable equilibrium, embedded within an envelope of uniform density, and rotating at the same rate as the envelope are given. Hydrodynamic equations, including self gravity, are deduced to a set of ordinary differential equations, which are solved by the method of matched asymptotic expansions. Results of these calculations are: (1) the range of stellar masses derived seems to correspond to realistic ranges of observed stellar masses and interstellar cloud parameters, (2) the proper measure of dissipation rate is the ratio of accretion time to viscous diffusion time, and (3) the pressure distribution on the surface of an accreting protostar is nonuniform in a way that favors the channeling of a stellar wind into a bipolar flow directed along the rotation axis.

Cassen, P.↗

NASA NTRS1995

Comparison of exact solution with Eikonal approximation for elastic heavy ion scattering

A first-order optical potential is used to calculate the total and absorption cross sections for nucleus-nucleus scattering. The differential cross section is calculated by using a partial-wave expansion of the Lippmann-Schwinger equation in momentum space. The results are compared with solutions in the Eikonal approximation for the equivalent potential and with experimental data in the energy range from 25A to 1000A MeV.

Dubey, Rajendra R.↗

NASA NTRS1996

Fiber-Optic Differential Displacement Sensor

Dual fiber-optic sensor measures small relative displacements of two proximate objects along common surface. Dual sensor comprises two fiber-optic sensors in differential configuration increasing sensitivity to displacement while decreasing sensitivity to thermal expansion and contraction.

Tcheng, Ping↗

NASA NTRS2013

Extended Analytic Device Optimization Employing Asymptotic Expansion

Analytic optimization of a thermoelectric junction often introduces several simplifying assumptionsincluding constant material properties, fixed known hot and cold shoe temperatures, and thermallyinsulated leg sides. In fact all of these simplifications will have an effect on device performance,ranging from negligible to significant depending on conditions. Numerical methods, such as FiniteElement Analysis or iterative techniques, are often used to perform more detailed analysis andaccount for these simplifications. While numerical methods may stand as a suitable solution scheme,they are weak in gaining physical understanding and only serve to optimize through iterativesearching techniques. Analytic and asymptotic expansion techniques can be used to solve thegoverning system of thermoelectric differential equations with fewer or less severe assumptionsthan the classic case. Analytic methods can provide meaningful closed form solutions and generatebetter physical understanding of the conditions for when simplifying assumptions may be valid.In obtaining the analytic solutions a set of dimensionless parameters, which characterize allthermoelectric couples, is formulated and provide the limiting cases for validating assumptions.Presentation includes optimization of both classic rectangular couples as well as practically andtheoretically interesting cylindrical couples using optimization parameters physically meaningful toa cylindrical couple. Solutions incorporate the physical behavior for i) thermal resistance of hot andcold shoes, ii) variable material properties with temperature, and iii) lateral heat transfer through legsides.

Thermoelectric Generators↗

DOE OSTI2026

Chromosome-scale Genome Assembly of the Most Abundant Ectomycorrhizal Fungus Cenococcum Geophilum Reveals Massive TE Expansion and RIP Defense Mechanism

Transposable elements (TEs) play crucial roles in genome evolution and ecological adaptation in fungi, yet their dynamics in ectomycorrhizal species remain poorly understood. Cenococcum geophilum, the most widespread ectomycorrhizal fungus in boreal and temperate forests with its large, repeat-rich genome, represents an ideal system to investigate TE-mediated adaptation to the physical environment and symbiotic lifestyle. However, previous studies have been limited by fragmented genome assemblies that prevented the resolution of repeat-rich regions. We assembled a telomere-to-telomere reference genome of C. geophilum strain 1.58 using PacBio HiFi and Hi-C datasets, resulting in a 178.54 Mbp genome with seven contiguous chromosomes. We identified 14,145 genes and over 78% of the genome consists of transposable elements (TEs). Of these, 94% are affected by repeat-induced point mutations (RIP), a genome defense mechanism that acts during the sexual reproduction phase, indicating cryptic or ancient sexual reproduction in this putatively asexual fungus. Long terminal repeat retrotransposons, LINEs, and DNA transposons dominate, with three TE families (Ty3, Ty1, and Tad1) contributing over 60% of the genome size, indicating recent transposition bursts. Screening of 15 additional C. geophilum strains revealed recent and lineage-specific TE expansions, implying that several TEs escaped the RIP machinery and retained potential activity. Supporting TE activity in the context of symbiosis, we found 56 TEs differentially transcribed between ectomycorrhizal and free-living mycelium tissues. An even higher number (n = 66) of TEs were differentially expressed between stress resistance morphology (i.e. sclerotia) and free-living mycelium. This supports that TEs are differentially regulated as a response to symbiotic and stress-related conditions. Our results demonstrate that the C. geophilum genome expansion was driven by a few lineage-specific TE families in recent history, with high RIP activity attesting to sexual reproduction. We also provide insights how TEs could respond to lifestyle transitions and traits associated with desiccation resistance.

Cenococcum geophilum↗

DOE OSTI2025

Karhunen–Loève deep learning method for surrogate modeling and approximate Bayesian parameter estimation

We evaluate the performance of the Karhunen-Loève Deep Neural Network (KL-DNN) framework for surrogate modeling and approximate Bayesian parameter estimation in partial differential equation models. In the surrogate model, the Karhunen-Loève (KL) expansions are used for the dimensionality reduction of the number of unknown parameters and variables, and a deep neural network is employed to relate the reduced space of parameters to that of the state variables. The KL-DNN surrogate model is used to formulate a maximum-a-posteriori-like least-squares problem, which is randomized to draw samples of the posterior distribution of the parameters. We test the proposed framework for a hypothetical unconfined aquifer via comparison with the forward MODFLOW and inverse PEST++ iterative ensemble smoother (IES) solutions as well as the state-of-the-art Fourier neural operator (FNO) and deep operator networks (DeepONets) operator learning surrogate models. Our results show that the KL-DNN surrogate model outperforms FNO and DeepONet for forward predictions. For solving inverse problems, the randomized algorithm provides the same or more accurate Bayesian predictions of the parameters than IES as evidenced by the higher log-predictive probability of both the estimated parameter field and the forecast hydraulic head. The posterior mean obtained from the randomized algorithm is closer to the reference parameter field than that obtained with FNO as the maximum a posteriori estimate.

Approximate Bayesian inference↗

NASA NTRS1977

Effect of flow on quasi-one-dimensional acoustic wave propagation in a variable area duct of finite length

The general equation for the velocity potential of quasi-one-dimensional acoustic wave motion in a variable area, finite duct with one-dimensional flow is derived by using a perturbation technique. The nonlinear second-order partial differential equation is linearized and then solved, by either a power series expansion method or the Runge-Kutta fourth-order method, for harmonic time dependence. The boundary condition taken at the duct mouth is that of matching the impedance of the duct sound field to that of the radiation field at the duct opening. Three axial Mach number variations along the duct axis are considered and the results obtained are compared with those for the case of constant Mach number, to determine the influence of the axial velocity gradient on sound propagation. The effect of flow on the radiation impedance is also considered.

Lumsdaine, E.↗

NASA NTRS1993

Dynamics of aging magnetic clouds

The dynamics of radially expanding magnetic clouds is rigorously analyzed within the framework of ideal MHD. The cloud is modelled as a cylindrically symmetric magnetic flux rope. In the force balance we include the gas pressure gradient and the Lorentz force. Interaction with the ambient solar wind due to expansion of the magnetic cloud is represented by a drag force proportional to the bulk velocity. We consider the self-similar expansion of a polytrope, and reduce the problem to an ordinary nonlinear differential equation for the evolution function. Analyzing the asymptotic behavior of the evolution function, we formulate theoretical expectations for the long-term behavior of cloud parameters. We focus on the temporal evolution of (1) the magnetic field strength; (2) the twist of the field lines; (3) the asymmetry of the total field profile; and (4) the bulk flow speed. We present data from two magnetic clouds observed at 1 AU and 2 AU, respectively, and find good agreement with theoretical expectations. For a peak magnetic field strength at 1 AU of 25 nT and a polytropic index of 0.5, we find that a magnetic cloud can be distinguished from the background interplanetary field up to a distance of about 5 AU. Taking larger magnetic fields and bigger polytropic indices this distance can double.

Osherovich, V. A.↗

NASA NTRS1980

Strain gage

A strain gage particularly suited for use in detecting and measuring mechanically induced strain in thermally strained specimens including an elongated link, adapted to be fixedly secured to the surface of a specimen, having thermal expansion characteristics matched with those of the specimen, and characterized by a differential capacitor employed for detecting strain induced motion as such motion is effected between the link and the surface of the specimen.

Harting, Darrell R.↗

NASA NTRS2016

Hall Thruster Thermal Modeling and Test Data Correlation

HERMeS - Hall Effect Rocket with Magnetic Shielding. Developed through a joint effort by NASA/GRC and the Jet Propulsion Laboratory (JPL). Design goals: High power (12.5 kW) high Isp (3000 sec), high efficiency (> 60%), high throughput (10,000 kg), reduced plasma erosion and increased life (5 yrs) to support Asteroid Redirect Robotic Mission (ARRM). Further details see "Performance, Facility Pressure Effects and Stability Characterization Tests of NASAs HERMeS Thruster" by H. Kamhawi and team. Hall Thrusters (HT) inherently operate at elevated temperatures approx. 600 C (or more). Due to electric magnetic (E x B) fields used to ionize and accelerate propellant gas particles (i.e., plasma). Cooling is largely limited to radiation in vacuum environment.Thus the hardware components must withstand large start-up delta-T's. HT's are constructed of multiple materials; assorted metals, non-metals and ceramics for their required electrical and magnetic properties. To mitigate thermal stresses HT design must accommodate the differential thermal growth from a wide range of material Coef. of Thermal Expansion (CTEs). Prohibiting the use of some bolted/torqued interfaces.Commonly use spring loaded interfaces, particularly at the metal-to-ceramic interfaces to allow for slippage.However most component interfaces must also effectively conduct heat to the external surfaces for dissipation by radiation.Thus contact pressure and area are important.

Model↗

NASA NTRS2004

Regularizing the r-mode Problem for Nonbarotropic Relativistic Stars

We present results for r-modes of relativistic nonbarotropic stars. We show that the main differential equation, which is formally singular at lowest order in the slow-rotation expansion, can be regularized if one considers the initial value problem rather than the normal mode problem. However, a more physically motivated way to regularize the problem is to include higher order terms. This allows us to develop a practical approach for solving the problem and we provide results that support earlier conclusions obtained for uniform density stars. In particular, we show that there will exist a single r-mode for each permissible combination of 1 and m. We discuss these results and provide some caveats regarding their usefulness for estimates of gravitational-radiation reaction timescales. The close connection between the seemingly singular relativistic r-mode problem and issues arising because of the presence of co-rotation points in differentially rotating stars is also clarified.

Lockitch, Keith H.↗

NASA NTRS1996

Rod Has High Tensile Strength And Low Thermal Expansion

Thoriated tungsten extension rod fabricated to replace stainless-steel extension rod attached to linear variable-differential transformer in gap-measuring gauge. Threads formed on end of rod by machining with special fixtures and carefully chosen combination of speeds and feeds.

Smith, D. E.↗