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DOE OSTI2026

Multipoint Aerostructural Optimization of Wind Turbine Rotors Using a Coupled Blade‐Resolved Aerostructural Solver

Physics‐based design optimization workflows thread the needle between computational cost limitations and simulation complexity, often compromising between modeling detail and the range of operating design conditions. Multipoint aerostructural optimization of wind turbine rotors has so far been confined to low‐fidelity analyses or to high‐fidelity studies with simplified structural models, leaving the most complex design trade‐offs unexplored. We close this gap by performing the first tightly coupled gradient‐based multipoint aerostructural rotor optimization using 3D aerodynamic and structural solvers with discrete coupled adjoints. The optimizer simultaneously varies blade planform, airfoil shapes, and structural thickness through more than 270 design variables, minimizing a weighted combination of rotor mass and power across multiple wind speeds. Applied to a modified DTU 10‐MW benchmark under conservative structural and aerodynamic constraints, our multipoint optimization reduces rotor mass by up to 36% and increases power by 12%–15% across the main operating conditions; biasing the objective toward power yields power gains up to 18% and a 17% mass reduction. For a nominal wind distribution, 3‐point rotor designs accounting for low RPM and high thrust conditions capture dominant trade‐offs and outperform single‐point designs. Adding two off‐design points changes individual‐condition power by less than 3% but leaves the weighted average within 0.5%, and the mass‐power bias has a stronger effect on the final design than the operating‐point weighting itself. Our framework extends naturally to richer load cases and site‐specific wind distributions, providing a basis for high‐fidelity multipoint design earlier in industrial workflows.

17 WIND ENERGY↗

DOE OSTI2016

Variational Bayesian strategies for high-dimensional, stochastic design problems

This paper is concerned with a lesser-studied problem in the context of model-based, uncertainty quantification (UQ), that of optimization/design/control under uncertainty. The solution of such problems is hindered not only by the usual difficulties encountered in UQ tasks (e.g. the high computational cost of each forward simulation, the large number of random variables) but also by the need to solve a nonlinear optimization problem involving large numbers of design variables and potentially constraints. We propose a framework that is suitable for a class of such problems and is based on the idea of recasting them as probabilistic inference tasks. To that end, we propose a Variational Bayesian (VB) formulation and an iterative VB–Expectation-Maximization scheme that is capable of identifying a local maximum as well as a low-dimensional set of directions in the design space, along which, the objective exhibits the largest sensitivity. We demonstrate the validity of the proposed approach in the context of two numerical examples involving thousands of random and design variables. In all cases considered the cost of the computations in terms of calls to the forward model was of the order of 100 or less. The accuracy of the approximations provided is assessed by information-theoretic metrics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2022

maestro

MÆSTRO stands for Multi-fidelity Adaptive Ensemble Stochastic Trust Region Optimization and it is a plug n play derivate fee stochastic optimization solver. The problem being considered in MÆSTRO involves fitting Monte Carlo simulations that describe complex phenomena to experiments. This is done by finding parameters of the resource intensive and noisy simulation that yield the least squares objective function value to the noisy experimental data. This problem is solved using a stochastic trust-region optimization algorithm where in each iteration, a local approximation of the simulation signal and of the simulation noise is constructed over data, which is obtained by running the simulation at strategically placed design points within the trust-region around the current iterate. Then the simulation components of the objective are replaced by their approximations and this analytical and closed-form optimization problem is solved to find the next iterate within the trust-region. Then the trust region is moved and the iterations continue until a satisfactory convergence criteria is met.

KRISHNAMOORTHY, MOHAN↗

DOE OSTI2020

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis: Version 6.12 User's Manual

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.13 User's Manual

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user’s manual for the Dakota software and provides capability overviews and procedures for software execution, as well as a variety of example studies.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.15 User's Manual

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

A simulation model of front-end electronics for high-precision timing measurements with low-gain avalanche detectors

Here, we report simulation results of a study aiming to optimize parameters of a detector that uses low-gain avalanche detectors (LGAD) for high-precision timing measurements. The detector is assumed to be composed of a 50μm LGAD sensor coupled to front-end readout electronics which is used to measure the time of arrival of minimum ionizing particles. The simulation includes modeling of signal fluctuations in the LGAD sensor, variations of the analog bandwidth and signal-to-noise ratio (SNR) of the front-end electronics, time bin quantization, and radiation damage of the LGAD sensors. Two approaches to measure the timestamp are considered: leading edge and constant fraction. Simulated LGAD pulses before irradiation, and after irradiation with neutron fluences of 5×10 14 n/cm 2 and 1×10 15 n/cm 2 , are studied. The time resolution for a 50μm LGADs was found to be 35 ps for front-end electronics bandwidths larger than 350 MHz and SNRs larger than 30. The time resolution at SNR of 30 for fluences of 5×10 14 n/cm 2 and 1×10 15 n/cm 2 were found to be 31 ps and 37 ps, respectively.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2015

Application of the FW-CADIS variance reduction method to calculate a precise N-flux distribution for the FRJ-2 research reactor - 15603

In order to avoid unnecessary radiation exposure and to minimize the radioactive waste and respectively cost by the decommissioning of nuclear facilities, the radiological characteristics and the activity atlases are required. Therefore, a significant step is the accurate determination of the neutron fluence distribution resulting from the operation of a reactor. For this purpose, computer codes based on the Monte-Carlo method are employed because of the flexibility in the modelling of the complex structures and physical processes. In the scope of the research project founded by the German Federal Ministry of Education and Research (BMBF), and implemented by the Institute of Nuclear Engineering and Technology Transfer (NET) -RWTH Aachen, sophisticated simulation tools are developed, which have the capability to cover the range of demands for activity and dose rate distribution using the example of the German research reactor FRJ-2. Due to the complex geometry of FRJ-2, which consists of different structures and configuration, the MCNP5 Monte Carlo Code is the optimal method for a precise neutron transport simulation. This work presents a method to achieve a precise N-Flux distribution for the FRJ-2 research reactor using hybrid deterministic Monte Carlo method. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2026

Technical Report for Bayesian Optimization and Reinforcement Learning for Beam Polarization Increase in the BNL Hadron Injectors

This project developed and evaluated physics-informed Bayesian learning and machine learning (ML)-based optimization methods for improving beam polarization preservation in the BNL hadron injector chain. The work focused on uncertainty-aware digital twin modeling, Bayesian calibration of accelerator simulations using beam measurements, and data-efficient optimization strategies including Bayesian optimization and reinforcement learning. These methods were applied to injector tuning and RF control problems in realistic accelerator settings to support improved operational robustness and readiness for RHIC operations and future Electron–Ion Collider facilities. No subject inventions were disclosed under this award.

43 PARTICLE ACCELERATORS↗

DOE OSTI2015

Laboratory Optimization Tests of Technetium Decontamination of Hanford Waste Treatment Plant Direct Feed Low Activity Waste Melter Off-Gas Condensate Simulant

The Hanford Waste Treatment and Immobilization Plant (WTP) Low Activity Waste (LAW) vitrification facility will generate an aqueous condensate recycle stream (LAW Off-Gas Condensate) from the off-gas system. The baseline plan for disposition of this stream is to send it to the WTP Pretreatment Facility, where it will be blended with LAW, concentrated by evaporation and recycled to the LAW vitrification facility. Alternate disposition of this stream would eliminate recycling of problematic components, and would enable less integrated operation of the LAW melter and the Pretreatment Facilities. Eliminating this stream from recycling within WTP would also decrease the LAW vitrification mission duration and quantity of glass waste.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

Materials Design using an Active Subspace-based Batch Bayesian Optimization Approach

Integrated computational materials engineering (ICME) calls for integrating simulation tools and/or experiments to develop new materials and materials systems. However, implementation of ICME approaches is challenging mainly due to the considerable computational expense of such frameworks and large dimensionality of the design space. Addressing these challenges is thus critical to the success of ICME initiatives. We present here a specific Bayesian optimization framework designed to address these two challenges. In particular, we propose an active subspace batch Bayesian optimization framework. The framework makes use of dimension reduction via the active subspace method and makes use of the ability to query in parallel via the batch Bayesian optimization approach. Here, the integration of these techniques leads to significant efficiency improvements while maintaining accuracy.

36 MATERIALS SCIENCE↗

DOE OSTI2025

A GPU‐Based Ocean Dynamical Core for Routine Mesoscale‐Resolving Climate Simulations

Abstract We describe an ocean hydrostatic dynamical core implemented in Oceananigans optimized for Graphical Processing Unit (GPU) architectures. On 64 A100 GPUs, equivalent to 16 computational nodes in current state‐of‐the‐art supercomputers, our dynamical core can simulate a decade of near‐global ocean dynamics per wall‐clock day at an 8‐km horizontal resolution; a resolution adequate to resolve the ocean's mesoscale eddy field. Such efficiency, achieved with relatively modest hardware resources, suggests that climate simulations on GPUs can incorporate fully eddy‐resolving ocean models. This removes a major source of systematic bias in current IPCC coupled model projections, the parameterization of ocean eddies, and represents a major advance in climate modeling. We discuss the computational strategies, focusing on GPU‐specific optimization and numerical implementation details that enable such high performance.

Silvestri, Simone [Massachusetts Institute of Tech↗

DOE OSTI2017

Advantages of Unfair Quantum Ground-State Sampling

The debate around the potential superiority of quantum annealers over their classical counterparts has been ongoing since the inception of the field. Recent technological breakthroughs, which have led to the manufacture of experimental prototypes of quantum annealing optimizers with sizes approaching the practical regime, have reignited this discussion. However, the demonstration of quantum annealing speedups remains to this day an elusive albeit coveted goal. We examine the power of quantum annealers to provide a different type of quantum enhancement of practical relevance, namely, their ability to serve as useful samplers from the ground-state manifolds of combinatorial optimization problems. We study, both numerically by simulating stoquastic and non-stoquastic quantum annealing processes, and experimentally, using a prototypical quantum annealing processor, the ability of quantum annealers to sample the ground-states of spin glasses differently than thermal samplers. We demonstrate that (i) quantum annealers sample the ground-state manifolds of spin glasses very differently than thermal optimizers (ii) the nature of the quantum fluctuations driving the annealing process has a decisive effect on the final distribution, and (iii) the experimental quantum annealer samples ground-state manifolds significantly differently than thermal and ideal quantum annealers. We illustrate how quantum annealers may serve as powerful tools when complementing standard sampling algorithms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

LBNF Material Activation Study

This poster presents a study on material activation at the Fermilab Long-Baseline Neutrino Facility (LBNF), focusing on understanding how high-energy beams interact with surrounding materials to produce radioactive isotopes. Using a simplified model of the LBNF-20 bunker and the FLUKA Monte Carlo simulation tool, the project quantifies isotope production at key locations inside and outside the shielding structures. The study employs a two-step simulation process, tracking primary and secondary particles, and explores the impact of different geometric and material configurations on activation rates. Results include preliminary comparisons of simulated activation rates and suggest methods for refining geometry, improving accuracy, and efficiently simulating future configurations. This work informs the design and operation of the LBNF facility, aiding in radiation safety and shielding optimization. Future efforts will focus on sample iteration, automated simulations, and detailed residual isotope analyses to further enhance the understanding of material activation in high-energy physics environments.

Pietraszek, Jacob [Unlisted, US]↗

DOE OSTI2016

Large-Scale Refinement of Metal-Organic Framework Structures Using Density Functional Theory

Efforts to computationally characterize large numbers of nanoporous materials often rely on databases of experimentally resolved crystal structures. The accuracy of experimental crystal structures used in such calculations has a significant impact on the reliability of the results. Here in this work, we report structures optimized using periodic density functional theory (DFT) for more than 800 experimentally synthesized metal-organic frameworks (MOFs). Many MOFs changed significantly upon structural optimization, particularly materials that were crystallographically resolved in their solvated form. For each MOF, we simulated the adsorption of CH 4 and CO 2 using grand canonical Monte Carlo both before and after DFT optimization. The DFT optimization has a large impact on simulated gas adsorption in some cases – for example, CO 2 loading at 1 bar changed by more than 25% in over 25% of the MOFs we considered.

36 MATERIALS SCIENCE↗

DOE OSTI2015

Ion-Stockmayer clusters: Minima, classical thermodynamics, and variational ground state estimates of Li{sup +}(CH{sub 3}NO{sub 2}){sub n} (n = 1–20)

Structural optimizations, classical NVT ensemble, and variational Monte Carlo simulations of ion Stockmayer clusters parameterized to approximate the Li{sup +}(CH{sub 3}NO{sub 2}){sub n} (n = 1–20) systems are performed. The Metropolis algorithm enhanced by the parallel tempering strategy is used to measure internal energies and heat capacities, and a parallel version of the genetic algorithm is employed to obtain the most important minima. The first solvation sheath is octahedral and this feature remains the dominant theme in the structure of clusters with n ≥ 6. The first “magic number” is identified using the adiabatic solvent dissociation energy, and it marks the completion of the second solvation layer for the lithium ion-nitromethane clusters. It corresponds to the n = 18 system, a solvated ion with the first sheath having octahedral symmetry, weakly bound to an eight-membered and a four-membered ring crowning a vertex of the octahedron. Variational Monte Carlo estimates of the adiabatic solvent dissociation energy reveal that quantum effects further enhance the stability of the n = 18 system relative to its neighbors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2015

SU-E-T-546: Modeling and Validation of the a New Proton Therapy System Using a Monte-Carlo Environment Optimized for Protons

8 particle histories were run. Results: Range measurements of the Monte-Carlo simulations matched the measured data within 1mm. Distal fall-off of the simulated fields matched within <1mm. Lateral penumbra and field size measurements of the standard-sized square and half-beam blocked fields matched within 1mm at all three planes compared. A small difference was seen in the in-air profiles at doses <0%. The suspected cause of the difference was the aperture shape. The measured data utilized a divergent aperture. The Monte-Carlo calculation used a non-divergent aperture. Conclusion: The validation measurements indicate that we were able to accurately model the MEVION s250 Proton therapy system using Monte-Carlo Calculations. This may reduce the commissioning time for future users. Purpose: Monte-Carlo modeling is an important tool for understanding the behavior of therapeutic proton beams in a heterogeneous media such as the patient. To gain confidence that a Monte-Carlo model is accurate in complex geometries and media, it must first be compared with measurement in simple situations. This study documents the validation of our Monte-Carlo Model. Methods: A model of the MEVION s250 Proton therapy system was created in the TOPAS Monte-Carlo environment using machine geometry and field shaping system information provided by the vendor. For each of 24 options, validation of the TOPAS model was performed by comparing the dose scored by TOPAS to the dose measurements obtained during the commissioning of the treatment planning system. The measurements compared consisted of: pristine peak depth-dose profiles, in-air profiles for a standard-sized square field (20cm×20cm or 10cm×10cm depending on the maximum field size for each option) at isocenter and at 20cm upstream and downstream of isocenter, and in-air profiles with a half-beam blocked aperture at isocenter and at 20cm upstream and downstream of isocenter. For all Monte-Carlo simulations,.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2024

Improved Accuracy for Trotter Simulations Using Chebyshev Interpolation

Quantum metrology allows for measuring properties of a quantum system at the optimal Heisenberg limit. However, when the relevant quantum states are prepared using digital Hamiltonian simulation, the accrued algorithmic errors will cause deviations from this fundamental limit. In this work, we show how algorithmic errors due to Trotterized time evolution can be mitigated through the use of standard polynomial interpolation techniques. Our approach is to extrapolate to zero Trotter step size, akin to zero-noise extrapolation techniques for mitigating hardware errors. We perform a rigorous error analysis of the interpolation approach for estimating eigenvalues and time-evolved expectation values, and show that the Heisenberg limit is achieved up to polylogarithmic factors in the error. Our work suggests that accuracies approaching those of state-of-the-art simulation algorithms may be achieved using Trotter and classical resources alone for a number of relevant algorithmic tasks.

Rendon, Gumaro↗