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NASA NTRS1988

Grid generation and adaptation for the Direct Simulation Monte Carlo Method

A grid generation and adaptation procedure based on the method of transfinite interpolation is incorporated into the Direct Simulation Monte Carlo Method of Bird. In addition, time is advanced based on a local criterion. The resulting procedure is used to calculate steady flows past wedges and cones. Five chemical species are considered. In general, the modifications result in a reduced computational effort. Moreover, preliminary results suggest that the simulation method is time step dependent if requirements on cell sizes are not met.

Olynick, David P.↗

DOE OSTI2023

Avoiding symmetry roadblocks and minimizing the measurement overhead of adaptive variational quantum eigensolvers

Quantum simulation of strongly correlated systems is potentially the most feasible useful application of near-term quantum computers. Minimizing quantum computational resources is crucial to achieving this goal. A promising class of algorithms for this purpose consists of variational quantum eigensolvers (VQEs). Among these, problem-tailored versions such as ADAPT-VQE that build variational ansätze step by step from a predefined operator pool perform particularly well in terms of circuit depths and variational parameter counts. However, this improved performance comes at the expense of an additional measurement overhead compared to standard VQEs. Here, we show that this overhead can be reduced to an amount that grows only linearly with the number n of qubits, instead of quartically as in the original ADAPT-VQE. We do this by proving that operator pools of size 2 n − 2 can represent any state in Hilbert space if chosen appropriately. We prove that this is the minimal size of such "complete" pools, discuss their algebraic properties, and present necessary and sufficient conditions for their completeness that allow us to find such pools efficiently. We further show that, if the simulated problem possesses symmetries, then complete pools can fail to yield convergent results, unless the pool is chosen to obey certain symmetry rules. We demonstrate the performance of such symmetry-adapted complete pools by using them in classical simulations of ADAPT-VQE for several strongly correlated molecules. Our findings are relevant for any VQE that uses an ansatz based on Pauli strings.

Shkolnikov, V. O.↗

DOE OSTI2022

The molecular mechanism of load adaptation by branched actin networks

Branched actin networks are self-assembling molecular motors that move biological membranes and drive many important cellular processes, including phagocytosis, endocytosis, and pseudopod protrusion. When confronted with opposing forces, the growth rate of these networks slows and their density increases, but the stoichiometry of key components does not change. The molecular mechanisms governing this force response are not well understood, so we used single-molecule imaging and AFM cantilever deflection to measure how applied forces affect each step in branched actin network assembly. Although load forces are observed to increase the density of growing filaments, we find that they actually decrease the rate of filament nucleation due to inhibitory interactions between actin filament ends and nucleation promoting factors. The force-induced increase in network density turns out to result from an exponential drop in the rate constant that governs filament capping. The force dependence of filament capping matches that of filament elongation and can be explained by expanding Brownian Ratchet theory to cover both processes. We tested a key prediction of this expanded theory by measuring the force-dependent activity of engineered capping protein variants and found that increasing the size of the capping protein increases its sensitivity to applied forces. In summary, we find that Brownian Ratchets underlie not only the ability of growing actin filaments to generate force but also the ability of branched actin networks to adapt their architecture to changing loads.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Conformal Hierarchical Simulation-Based Inference with Local Validity

Trustworthy and interpretable uncertainty quantification is a long-standing challenge in artificial intelligence. Simulation-based inference (SBI) comprises a broad swath of approaches for estimating latent parameters with uncertainties. Although flexible neural density estimators in SBI can be remark- ably expressive capturing highly structured, high-dimensional posteriors their credible regions can be badly mis-calibrated and are often only accompanied by heuristic coverage checks. We present the first SBI framework that delivers finite-sample local valid coverage guarantees that hold in the neighborhood of each observation. Our framework can couple any off-the-shelf hierarchical SBI engine with a confor- mal Bayesian post-processing step that operates on the posterior predictive density. A kernel-weighted conformity score adapts the conformal quantile to the local geometry of the data, yielding prediction sets that are simultaneously (i) marginally calibrated, (ii) locally valid, and (iii) hierarchical, handling global and observation-specific parameters in a single pass. Through experiments on synthetic data and benchmarks from neuroscience and physics, we show that our approach attains 1 − α coverage, where prior SBI methods under- or over-cover. Our approach also maintains a competitive, credible set size with minimal computational overhead. Finally, our approach can be used to make predictions on real data and give valid credible regions modulo weight-initialization-based model mis-specification.

Trivedi, Shubhendu [Fermilab]↗

DOE OSTI2024

Enhancing scalability and accuracy of quantum poisson solver

The Poisson equation has many applications across the broad areas of science and engineering. Most quantum algorithms for the Poisson solver presented so far either suffer from lack of accuracy and/or are limited to very small sizes of the problem and thus have no practical usage. In this regard, our previous work showed a proof-of-concept demonstration in advancing quantum Poisson solver algorithm and validated preliminary results for a simple case of 3 x 3 problem. In this work, we delve into comprehensive research details, presenting the results on up to 15 x 15 problems that include step-by-step improvements in Poisson equation solutions, scaling performance, and experimental exploration. In particular, we demonstrate the implementation of eigenvalue amplification by a factor of up to 2 8 , achieving a significant improvement in the accuracy of our quantum Poisson solver and comparing that to the exact solution. Additionally, we present success probability results, highlighting the reliability of our quantum Poisson solver. Moreover, we explore the scaling performance of our algorithm against the circuit depth and width, demonstrating how our approach scales with larger problem sizes and thus further solidifies the practicality of easy adaptation of this algorithm in real-world applications. We also discuss a multilevel strategy for how this algorithm might be further improved to explore much larger problems with greater performance. Finally, through our experiments on the IBM quantum hardware, we conclude that though overall results on the existing NISQ hardware are dominated by the error in the CNOT gates, this work opens a path to realizing a multidimensional Poisson solver on near-term quantum hardware.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Modeling pre-Exascale AMR Parallel I/O Workloads via Proxy Applications

The present work investigates the modeling of preexascale input/output (I/O) workloads of Adaptive Mesh Refinement (AMR) simulations through a simple proxy application. We collect data from the AMReX Castro framework running on the Summit supercomputer for a wide range of scales and mesh partitions for the hydrodynamic Sedov case as a baseline to provide sufficient coverage to the formulated proxy model. The non-linear analysis data production rates are quantified as a function of a set of input parameters such as output frequency, grid size, number of levels, and the Courant-Friedrichs-Lewy (CFL) condition number for each rank, mesh level and simulation time step. Linear regression is then applied to formulate a simple analytical model which allows to translate AMReX inputs into MACSio proxy I/O application parameters, resulting in a simple “kernel” approximation for data production at each time step. Results show that MACSio can simulate actual AMReX nonlinear “static” I/O workloads to a certain degree of confidence on the Summit supercomputer using the present methodology. The goal is to provide an initial level of understanding of AMR I/O workloads via lightweight proxy applications models to facilitate autotune data management strategies in anticipation of exascale systems.

Godoy, William↗

NASA NTRSDate not supplied

CFD Modeling & Validation of Single Bubble Growth During Cavitation in Zero Gravity

Cryogenic Fluid Management (CFM) in zero gravity represents a vital area of research as NASA pursues future long-duration space missions. Success of these missions relies heavily on the ability to store and transfer the cryogenic liquid fuels efficiently and safely. In the Zero Boil-Off Tank (ZBOT-1) Experiment in 2018, unexpected and intense phase change occurred at the screened Liquid Acquisition Device (LAD) during tank pressure control that was later attributed to cavitation. The cavitation ultimately resulted in pump failure. Since cryogen cavitation during storage and transfer of propellants in microgravity has been a source of serious concern for the aerospace community, a series of drop tower experiments with liquid methane were also performed at the University of Bremen to carefully study cavitation from a single bubble nucleus in an engineered micron-sized wall cavity. As the test cell is depressurized during the 10-second drop, cavitation causes bubble growth from the nucleus. Here, in a collaboration between the Case Western Reserve University and University of Bremen, we present a two-phase Computational Fluid Dynamics (CFD) model of bubble growth from a single wall cavity during depressurization in microgravity. Both 2D axisymmetric and 3D parametric simulation case studies are performed to show the evolution of the bubble growth. The model uses a Volume of Fluid (VOF) approach with and without adaptive mesh refinement and adaptive time stepping to capture the growth dynamics using an explicit formulation with algebraic reconstruction of the interface. CFD results show good agreement with corresponding experiment. Based on the velocity, temperature, and volume fraction fields generated by the validation case studies, detailed discussion and analysis of fluid flow and heat transfer during bubble growth process are presented to delineate the intricacies of the cavitation process. Strengths and shortcomings of the present numerical approach in capturing this phenomenon are also highlighted.

Two Phase Flow↗

DOE OSTI2025

Packing fraction control during additive manufacturing of powder green bodies

The demand for high performance ceramic and metal components with complex geometry necessitates developments in powder handling and green body shaping. Here, in this study, vibrational powder deposition is adapted to three-dimensional printing with the ability to modulate packing fraction during printing. Boron carbide powder with sub-micron primary particle size is printed over a packing fraction range of 25.0 % to 46.5 % (below the poured density to above the tapped density). Solid metal powders are printed at 67.5 %, which enables novel freestanding, vertical-walled features without binder. These capabilities introduce opportunities for more complex geometries in high-performance materials, e.g. large-scale uniaxial hot-pressing of ceramic ballistic armor components with graded thickness and three-dimensional curved or stepped features (by compensating for displacement differences during compaction). This technology also enables multi-material patterning of additive manufacturing powder beds with reduced feedstock quantity requirements and wastage.

36 MATERIALS SCIENCE↗

DOE OSTI2024

A Methodology for determining optimal milling parameters for low-taper microtensile sample production

Femtosecond lasers are beginning to see an increase in interest for industrial and high throughput microsample milling applications. Despite this, much of the literature regarding removal rate studies for ultrashort pulse laser milling continues to focus on single-pulse or similar experiments that have a very small time on target. The material volume removal rates of femtosecond milling can vary wildly depending on parameters like laser scan speed, hatch spacing, z-axis step size, and pulses on the material. Currently, there is no established methodology for determining the optimal values of these parameters for micromilling applications. This methodology was developed in this study. The methodology consists of a pulse study, a repeat study, and a z-step study that will return the milling parameters that resulted in the largest volume removal rate for the laser used for this research. The methodology was created for the laser system used in this study, and was tested on 316 Stainless Steel, but was developed in such a way that it may be adapted to any material, and with some tweaking of variables might be used in other laser systems. The laser system used in this study utilizes a novel positioning stage that introduces 6 degrees of freedom to the translation of laser samples, being able to tilt and move in all three axes. Using both the developed methodology and the unique capabilities of the laser microtensile samples were produced with taper angles of <1° in 316 Stainless Steel.

Barker, Zachary Wayne↗

NASA NTRSDate not supplied

A Multilevel Approach For SolvingLarge-Scale QUBO Problems With Noisy Hybrid Quantum Approximate Optimization

Quantum approximate optimization is one ofthe promising candidates for useful quantum computation,particularly in the context of finding approximate solutionsto Quadratic Unconstrained Binary Optimization (QUBO)problems. However, the existing quantum processing units(QPUs) are of relatively small size, and canonical mappingsof QUBO via the Ising model require one qubit per vari-able, rendering direct large-scale optimization infeasible.In classical optimization, a general strategy for addressingmany large-scale problems is via multilevel/multigrid meth-ods, where the large target problem is iteratively coarsenedand the global solution is constructed from multiple small-scale optimization runs. In this work, we experimentallytest how existing QPUs perform when used as a sub-solverwithin such a multilevel strategy. To this aim, we com-bine and extend (via additional classical processing steps)the recently proposed Noise-Directed Adaptive Remapping(NDAR) and Quantum Relax&Round (QRR) algorithms.We first demonstrate the effectiveness of our heuristicextensions on Rigetti’s superconducting transmon deviceAnkaa-2. We find approximate solutions to10instances offully connected82-qubit Sherrington-Kirkpatrick graphswith random integer-valued coefficients obtaining normal-ized approximation ratios (ARs) in the range∼0.98−1.0,and the same class with real-valued coefficients (ARs∼0.94−1.0). Then, we implement the extended NDAR andQRR algorithms as subsolvers in the multilevel algorithmfor6large-scale graphs with at most∼27,000variables.In practice, the QPU (with classical post-processing steps)is used to find approximate solutions to dozens of at most82-qubit problems, which are iteratively used to constructthe global solution. We observe that quantum optimizationresults are competitive in terms of the quality of solutionswhen compared to classical heuristics used as subsolverswithin the multilevel approach.Reproducibility: source code and data are available at[TBA upon acceptance]

quantum computing↗

NASA NTRS1978

Theory and applications for optimization of every part of a photovoltaic system

A general method is presented for quantitatively optimizing the design of every part and fabrication step of an entire photovoltaic system, based on the criterion of minimum cost/Watt for the system output power. It is shown that no element or process step can be optimized properly by considering only its own cost and performance. Moreover, a fractional performance loss at any fabrication step within the cell or array produces the same fractional increase in the cost/Watt of the entire array, but not of the full system. One general equation is found to be capable of optimizing all parts of a system, although the cell and array steps are basically different from the power-handling elements. Applications of this analysis are given to show (1) when Si wafers should be cut to increase their packing fraction; and (2) what the optimum dimensions for solar cell metallizations are. The optimum shadow fraction of the fine grid is shown to be independent of metal cost and resistivity as well as cell size. The optimum thicknesses of both the fine grid and the bus bar are substantially greater than the values in general use, and the total array cost has a major effect on these values. By analogy, this analysis is adaptable to other solar energy systems.

Redfield, D.↗

DOE OSTI2023

Adaptive Envelope Detector-Based Phase Fault Detection Method for Power System Grid Distortions

In this study, a phase fault detection algorithm is developed by employing the envelope detector method. The proposed method diagnoses faults among phases and defines fault areas in the incoming signal. The designed algorithm consists of three steps: analytical signal conversion, complex magnitude, and fault detection. Initially, an analytical signal is obtained from the incoming power signal to determine the instantaneous amplitude and phase of the signal. A complex magnitude operation is applied to analytical signals to display changes in amplitude. On the basis of the threshold values specified by the user, the last step identifies the distortion signal in terms of the type of error and size. The proposed method is tested with realistically simulated substation power signal data and real power system data from the Grid Event Signature Library. The obtained results revealed that the proposed method detects distortions accurately.

Alaca, Ozgur↗

DOE OSTI2025

Selective mass scaling for single-layer thick shell elements in DYNA3D

Hexahedral elements can be adapted to model thin and moderately thick structures by neglecting the coupling of through-thickness stress, resulting in a fully three-dimensional, but simplified, state of stress. These specialized elements, often referred to as “thick” or “solid” shells, are generally employed to model thin-walled structures using continuum mechanics-based material models. In explicit dynamics simulations, where computational speed is important, these elements are integrated with a single quadrature point and a set of anti-hourglassing (stabilizing) forces. Thick shells, by definition, have a thickness dimension smaller than their in-plane dimensions, and this small thickness often determines the stable time step size in simulations, despite the mechanics being approximated. To alleviate this limitation while retaining the relevant dynamics of thin-walled structures, selective mass scaling (SMS), or selective mass “augmentation,” has been proposed in the literature. In this technical report, we explore the application of SMS to single-layer thick shells in the simulation software DYNA3D.

42 ENGINEERING↗

DOE OSTI2021

Evaluation of a Variable-Aperture Full-Ring SPECT System using Large-Area Pixelated CZT Modules: A Simulation Study for Brain SPECT Applications

Single photon emission computed tomography (SPECT) scanner using cadmium zinc telluride (CZT) offers improved imaging capability over conventional NaI (Tl)-based SPECT scanner. We aim to demonstrate a full-ring SPECT system design with eight large-area CZT detectors that can be used for a broad spectrum of SPECT radiopharmaceuticals and provides higher sensitivity and better spatial resolution than those of conventional NaI (Tl)-based gamma cameras. A newly-designed full-ring SPECT system is composed of 8 large-area CZT cameras (128 mm × 179.2 mm effective area) that can be independently swiveled around their own axes of rotation independently and can have radial motion for varying aperture sizes that can be adapted to different sizes of imaging volume. Extended projection data were generated by conjoining projections of two adjacent detectors to overcome the limited field-of-view (FOV) by each CZT camera. Using Monte Carlo simulations, we evaluated this new system design with digital phantoms including a Derenzo hot-rod phantom and a Zubal brain phantom. Comparison of performance metrics such as spatial resolution, sensitivity, contrast-to-noise ratio (CNR), and contrast-recovery ratio were made between our design and a conventional SPECT scanner. The proposed scanner could result in up to about 3 times faster in acquisition time over conventional scan time at same acquisition time per step. The spatial resolution of our proposed scanner was similar or better to that of the conventional scanner, and there was significant performance improvement over the conventional scanner particularly in sensitivity (approximately 4 times). Overall, we successfully reconstructed the phantom image for both 99 mTc-based perfusion and 123 I-based dopamine transporter (DaT) brain studies simulated for our new design. In particular, the striatal/background contrast-recovery ratio in 3-to-1 reference ratio was over the 0.8 for the 123 I-based DaT study. In conclusion, we demonstrated the potential of our new full-ring CZT SPECT design, showing improved performance metrics such as improved system sensitivity and CNR while maintaining other important imaging parameters such as spatial resolution.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Probabilistic partition of unity networks for high–dimensional regression problems

We explore the probabilistic partition of unity network (PPOU-Net) model in the context of high-dimensional regression problems and propose a general framework focusing on adaptive dimensionality reduction. With the proposed framework, the target function is approximated by a mixture of experts model on a low-dimensional manifold, where each cluster is associated with a fixed-degree polynomial. We present a training strategy that leverages the expectation maximization (EM) algorithm. During the training, we alternate between (i) applying gradient descent to update the DNN coefficients; and (ii) using closed-form formulae derived from the EM algorithm to update the mixture of experts model parameters. Under the probabilistic formulation, step (ii) admits the form of embarrassingly paralleliazable weighted least-squares solves. The PPOU-Nets consistently outperform the baseline fully-connected neural networks of comparable sizes in numerical experiments of various data dimensions. Here, we also explore the proposed model in applications of quantum computing, where the PPOU-Nets act as surrogate models for cost landscapes associated with variational quantum circuits.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1994

Plant Responses to Increased UV-B Radiation: A Research Project

Ozone decrease implies more ultraviolet-B (UV-B) radiation reaching the surface of the Earth. Increased UV-B radiation triggers responses by living organisms. Despite the large potential impacts on vegetation, little is known about UV-B effects on terrestrial ecosystems. Long-term ecological studies are needed to quantify the effects of increased UV radiation on terrestrial ecosystems, asses the risks, and produce reliable data for prediction. Screening pigments are part of one of the protective mechanism in plants. Higher concentrations of screening pigments in leaves may be interpreted as a response to increased UV radiation. If the screening effect is not sufficient, important molecules will be disturbed by incoming radiation. Thus, genetics, photosynthesis, growth, plant and leaf shape and size, and pollen grains may be affected. This will have an impact on ecosystem dynamics, structure and productivity. It is necessary to monitor selected terrestrial ecosystems to permit detection and interpretation of changes attributable to global climate change and depleted ozone shield. The objectives of this project are: (1) To identify and measure indicators of the effects of increased solar UV-B radiation on terrestrial plants; (2) to select indicators with the greatest responses to UV-B exposure; (3) to test, adapt or create ecosystem models that use the information gathered by this project for prediction and to enhance our understanding of the effects of increased UV-B radiation on terrestrial ecosystems. As a first step to achieve these objectives we propose a three-year study of forest and steppe vegetation on the North slope of the Brooks Range (within the Arctic circle, in Alaska), in the Saguaro National Monument (near Tucson, Arizona) and in the forests and steppes of Patagonia (Argentina). We selected (1) vegetation north of the Polar Circle because at 70N there is 8% risk of plant damage due to increased UV-B radiation; (2) the foothills of Catalina Mountains because there is anecdotal evidence of plant damage on the saguaros that has been linked to increased UV radiation, and (3) the forests of Nothofagus spp. and the steppe of Patagonia where the risk of plant damage at 35S is 5% and increases to as much as 15% at 55S due to increased UV-B radiation. Measurements of UV-B radiation impinging on the surface at 55S largely exceed the predicted UV-B radiation values at 50 latitude and 0% ozone depletion. Preliminary HPLC analyses of UV-B absorbing compounds in Nothofagus antartica, N. pumilio, N. betuloides and Rumex sp. in natural conditions show species-specific patterns. The spectrum of N. antartica grown at 38S differs significantly from that of N. antartica in natural conditions in Ushuaia (55S). These results suggest that the selected main area (Patagonia) is appropriate for assessing the problem and its magnitude and that Nothofagus is appropriate for our study.

DAntoni, H. L.↗

DOE OSTI2026

A new self-adaptive reconstruction method to identify defects through Wigner–Seitz approach

A new self-adaptive reconstruction method based on local atomic structure at any given molecular dynamics (MD) step has been developed in this article. The method can be used in Wigner–Seitz defect analysis approach to correctly and efficiently explore the information of both point defects and complex defect clusters (e.g. dislocation loops and voids) formed after a displacement cascade where the cascade interacts with grain boundaries and/or dislocations. The algorithm and validation are provided in detail. Results for identification of radiation defects during and after cascades interacting with a dislocation network show that the new method can well recognize all simple and complex defects and defect clusters. Thus, this new method provides a totally new way to explore the density and size of radiation defects at atomic scale after complex MD evolution processes, providing correct information to understand and predict radiation damage in materials through atomic simulations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Quantum simulations of nuclear resonances with variational methods

Background: The many-body nature of nuclear physics problems poses significant computational challenges. These challenges become even more pronounced when studying the resonance states of nuclear systems, which are governed by the non-Hermitian Hamiltonian. Quantum computing, particularly for quantum many-body systems, offers a promising alternative, especially within the constraints of current noisy intermediate-scale quantum (NISQ) devices. Purpose: This work aims to simulate nuclear resonances using quantum algorithms by developing a variational framework compatible with non-Hermitian Hamiltonians and implementing it fully on a quantum simulator. Methods: We employ the complex scaling technique to extract resonance positions classically and adapt it for quantum simulations using a two-step algorithm. First, we transform the non-Hermitian Hamiltonian into a Hermitian form by using the energy variance as a cost function within a variational framework. Second, we perform 𝜃-trajectory calculations to determine optimal resonance positions in the complex energy plane. To address resource constraints on NISQ devices, we utilize Gray code (GC) encoding to reduce qubit requirements. Results: We first validate our approach using a schematic potential model that mimics a nuclear potential, successfully reproducing known resonance energies with high fidelity. We then extend the method to a more realistic 𝛼−𝛼 nuclear potential and compute the 𝐷- and 𝐺-wave resonance energies with a basis size of 𝑁=16, using only four qubits. The quantum simulation results closely match the classical values, demonstrating the feasibility of our approach. Conclusions: This study demonstrates, for the first time, that the complete 𝜃-trajectory method can be implemented on a quantum computer without relying on any classical input beyond the Hamiltonian. The results establish a scalable and efficient quantum framework for simulating resonance phenomena in nuclear systems. This work represents a significant step toward quantum simulations of open quantum systems and lays the foundation for future investigations into resonance structures in nuclear, atomic, and molecular physics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗