Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “robust parameter design”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Development of coated heat flux gauges for fast responding measurements

Thermal systems often exhibit transient behaviors that have important implications for the operation of the system and can be difficult to predict. For these reasons, experimental testing is often required to ensure system durability requirements are achieved. One important parameter governing the survivability of components in hot, high-stress environments is the heat flux into the part that dictates the temperature distribution for the component. However, sensors required to experimentally characterize heat fluxes in extreme environments must also be resilient. Here, this study presents the development of coated heat transfer gauges capable of robust, high-frequency measurements in turbine research facilities. The addition of a protective coating increases the durability of the gauge, but inherent of that coating is the attenuation of high-frequency temperature penetrations. As a result, this study first outlines the use of analytical solutions to define a gauge design for a specific frequency range and heat transfer, ensuring that subsurface signals can be rectified to surface conditions through inverse methods. Then, the fabrication of polyimide substrate sensors with a parylene-F coating is described. Micro surface heaters added to the custom sensors were used to determine important geometric and thermal properties necessary to calculate accurate surface heat flux. Ultimately, this work shows increased sensor robustness in a turbine test bed and experimentally validates that the frequency response of the fabricated sensors meet the design intent.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Measurement of ion transport properties in ion exchange membranes for photoelectrochemical water splitting

Photoelectrochemical (PEC) water-splitting systems have the unique ability to produce renewable hydrogen directly from sunlight, independent of the electrical grid. These systems are therefore appealing technological options for resilient long-term energy storage. Ion selective membranes, such as monopolar and bipolar membranes, are a vital component of PEC water-splitting systems. These membranes allow for ionic conduction between the cathode and anode chambers, separation of products, and improved catalyst environments for reactions. In order to measure key properties and to study the performance of these ion exchange membranes, it is imperative to develop a robust testing protocol that can be used across the field. This paper introduces two standard electrochemical cells designed to directly measure ion transport properties in monopolar and bipolar membranes. The first electrochemical cell uses commercially available Pt disk electrodes to preform electrochemical impedance spectroscopy (EIS) and reliably measure through-plane conductivity of monopolar membranes. The second electrochemical cell uses four-point measurements with Luggin capillaries and a series of membrane configurations to perform current density-voltage and Faradaic efficiency (FE) measurements for water dissociation (WD) reactions on bipolar membranes. The cell designs and techniques laid out below allow for accurate measurement of ion transport parameters in ion exchange membranes, direct comparison of membranes being developed across the field, and in turn, greater advancements in ion exchange membranes and PEC water-splitting systems.

cation exchange membrane↗
DOE OSTI2026

Designing the Protocols for Programmable Ammonia Catalysis

Programmable catalysis can provide a more energy-efficient and cost-effective route to enhancing commercial ammonia production, a key process in the advancement of renewable energy technologies and the manufacture of fertilizers and basic chemicals. This work explores the computational discovery of optimal forcing protocols to drive such dynamic catalysis models. By employing matrix-free time-stepper methods, coupled with an optimization approach, that integrates Bayesian optimization with a Bayesian continuation strategy to efficiently discover the periodic steady states of such periodically forced systems, we enable the discovery of complex optimal catalyst strain waveforms, while ensuring robust solver convergence. We demonstrate the flexibility of our approach to discover optimized forcing protocols under varying physical constraints on strain modulation or other catalyst operating parameters. We show that these can have a temporal structure more complex than simple step functions. In order to detect undesirable catalytic loops that may correlate with overall reduced performance, we perform a study using graph-theoretical analysis to investigate the dynamics of catalytic kinetic networks formed.

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

ROSE: A reduced-order scattering emulator for optical models

A new generation of phenomenological optical potentials requires robust calibration and uncertainty quantification, motivating the use of Bayesian statistical methods. These Bayesian methods usually require calculating observables for thousands or even millions of parameter sets, making fast and accurate emulators highly desirable or even essential. Emulating scattering across different energies or with interactions such as optical potentials is challenging because of the nonaffine parameter dependence, meaning the parameters do not all factorize from individual operators. Here we introduce and demonstrate the reduced-order scattering emulator ( ROSE ) framework, a reduced basis emulator that can handle nonaffine problems. ROSE is fully extensible and works within the publicly available band framework software suite for calibration, model mixing, and experimental design. As a demonstration problem, we use ROSE to calibrate a realistic nucleon-target scattering model through the calculation of elastic cross sections. This problem shows the practical value of the ROSE framework for Bayesian uncertainty quantification with controlled trade-offs between emulator speed and accuracy as compared to high-fidelity solvers. Finally, planned extensions of ROSE are discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Dark Energy Survey Year 3 results: $w$CDM cosmology from simulation-based inference with persistent homology on the sphere

We present cosmological constraints from Dark Energy Survey Year 3 (DES Y3) weak lensing data using persistent homology, a topological data analysis technique that tracks how features like clusters and voids evolve across density thresholds. For the first time, we apply spherical persistent homology to galaxy survey data through the algorithm TopoS2, which is optimized for curved-sky analyses and HEALPix compatibility. Employing a simulation-based inference framework with the Gower Street simulation suite, specifically designed to mimic DES Y3 data properties, we extract topological summary statistics from convergence maps across multiple smoothing scales and redshift bins. After neural network compression of these statistics, we estimate the likelihood function and validate our analysis against baryonic feedback effects, finding minimal biases (under $0.3σ$) in the $Ω_\mathrm{m}-S_8$ plane. Assuming the $w$CDM model, our combined Betti numbers and second moments analysis yields $S_8 = 0.821 \pm 0.018$ and $Ω_\mathrm{m} = 0.304\pm0.037$-constraints 70% tighter than those from cosmic shear two-point statistics in the same parameter plane. Our results demonstrate that topological methods provide a powerful and robust framework for extracting cosmological information, with our spherical methodology readily applicable to upcoming Stage IV wide-field galaxy surveys.

Prat, J. [Nordita; Royal Inst. Tech., Sodertalje; ↗
DOE OSTI2026

Multi‐Model Ensembles in Ecosystem Modeling: Challenges and Best Practices for Decision‐Making

Ecosystem models are increasingly central to the decision-making for environmental policy, conservation planning, and climate-related investments. Yet, the growing reliance on Multi-Model Ensembles (MMEs) of ecosystem models by practitioners and policymakers, sometimes under tight timelines and imperfect information, has frequently outpaced the scientific rigor required to ensure ensemble reliability. Here, MMEs refer to approaches that combine targeted predictions from multiple models with the expectation of improving robustness and quantifying predictive uncertainty. Poorly designed MMEs may create a false sense of confidence and lead to suboptimal policy and market decisions. This perspective argues that robust decision-making-relevant MMEs must be grounded on two pillars: (1) rigorous Model Intercomparison Projects (MIPs), which identify inter-model agreement and disagreement, characterize model uncertainties, and evaluate robustness with observationally based benchmarks—MIPs' diagnostic evaluation is so critical that it must be needed to drive MME's decision in model selection and weighting, especially when only a limited number of models available; and (2) co-design by both stakeholders and scientists to ensure that scenarios, metrics and uncertainty requirements provide decision-relevant information. Building upon the past success and lessons from the existing MIPs-MMEs efforts (e.g., climate/Earth system/crop), we derived the theoretical basis for MMEs, addressed their specific challenges in ecosystem modeling, and highlighted proper consideration of model numbers and diversity, risk of model inter-dependence, effective calibration of model parameters, possible overdue of some ecosystem model development, critical roles of open benchmark data across a wide range of conditions, and suggested use of Artificial Intelligence to support MIPs-MMEs. We highlighted the under-recognized opportunity for MIPs and MMEs to drive scientific progress and innovation through identifying better performing models, systematic benchmarking, feedback loops, and targeted model improvement. By following actionable best practice guidelines, MMEs can evolve from ad hoc aggregation of models into a trusted backbone of environmental policy and decision-making.

ecosystem modeling↗
DOE OSTI2023

Assessment of design and location of an active prechamber igniter to enable mixing-controlled combustion of ethanol in heavy-duty engines

Here, this numerical study focuses on assessing the key design parameters of interest for use of an active prechamber igniter as an ignition assistance device to enable mixing-controlled combustion (MCC) of ethanol (E100) in a heavy-duty Caterpillar C9.3B engine. Computational fluid dynamic (CFD) simulations of a baseline diesel and prechamber retrofitted C9.3B at a gross indicated mean effective pressure (IMEPg) of 5 bar and 1800 rpm are carried out using CONVERGE. In particular, the sizing of the prechamber volume, sizing of total orifice cross sectional area (orifice diameter), and location of the prechamber relative to a centrally mounted common rail direct injector are varied to discern the appropriate operational and design characteristics to achieve robust ignition assistance at the selected conditions. Simulation results indicate that use of an active prechamber igniter with E100 as a standalone fuel source can replicate ignition delays and thermal efficiencies similar to diesel combustion at the same engine boundary conditions, thus not requiring any changes to the engine’s air handling system. Igniter mounting location, orifice sizing, and jet targeting were found to have the strongest influence on ignition assistance with preference toward larger orifice diameters that appropriately located heating contributions in near vicinity to the direct injector.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Preconditioned least‐squares Petrov–Galerkin reduced order models

Abstract In this article, we introduce a methodology for improving the accuracy and efficiency of reduced order models (ROMs) constructed using the least‐squares Petrov–Galerkin (LSPG) projection method through the introduction of preconditioning. Unlike prior related work, which focuses on preconditioning the linear systems arising within the ROM numerical solution procedure to improve linear solver performance, our approach leverages a preconditioning matrix directly within the minimization problem underlying the LSPG formulation. Applying preconditioning in this way has the potential to improve ROM accuracy for several reasons. First, preconditioning the LSPG formulation changes the norm defining the residual minimization, which can improve the residual‐based stability constant bounding the ROM solution's error. The incorporation of a preconditioner into the LSPG formulation can have the additional effect of scaling the components of the residual being minimized to make them roughly of the same magnitude, which can be beneficial when applying the LSPG method to problems with disparate scales (e.g., dimensional equations, multi‐physics problems). Importantly, we demonstrate that an “ideal preconditioned” LSPG ROM (a ROM in which the preconditioner is the inverse of the Jacobian of its corresponding full order model) emulates projection of the full order model solution increment onto the reduced basis. This quantity defines a lower bound on the error of a ROM solution for a given reduced basis. By designing preconditioners that approximate the Jacobian inverse—as is common in designing preconditioners for solving linear systems—it is possible to obtain a ROM whose error approaches this lower bound. The proposed approach is evaluated on several mechanical and thermo‐mechanical problems implemented within the Albany HPC code and run in the predictive regime, with prediction across material parameter space. We demonstrate numerically that the introduction of simple Jacobi, Gauss‐Seidel, and ILU preconditioners into the proper orthogonal decomposition/LSPG formulation reduces significantly the ROM solution error, the reduced Jacobian condition number, the number of nonlinear iterations required to reach convergence, and the wall time (thereby improving efficiency). Moreover, our numerical results reveal that the introduction of preconditioning can deliver a robust and accurate solution for test cases in which the unpreconditioned LSPG method fails to converge.

Lindsay, Payton↗
DOE OSTI2023

Validation of Robustness in TCR Design Strategies

Advanced Manufacturing (AM) has been identified as one of key pathways in accelerating deployment of next generation of nuclear power plants. The nuclear industry continues to predominately rely on fabrication techniques and materials that their codes and standards were approved decades ago. This legacy approach in the highly regulated nuclear sector has brought uncertainty to commercial viability of nuclear energy in the U.S. The Transformational Challenge Reactor (TCR), sought to utilize AM methods for fabrication of a High Temperature Gas Reactor (HTGR). The program sought to leverage AM to construct a non-conventional geometry made up of TRISO fuel and hydride moderator in SiC matrix. This project objective was to provide independent analysis of the core configuration with separate set of tools and approaches than utilized by the TCR team in a 2-year time frame. This NEUP project also tackled a question: Can AM be a cost-effective tool in changing the commercial paradigm of nuclear. We investigated this question in-context of a micro HTGR. While respecting the inherit uncertainty in our input parameters, overall we saw that AM has potential in improving economic competitiveness of nuclear energy when employed on multiple fronts (i.e. few percentage cost improvement per component). Given realizing AM requires mass orders and large R&D investment for commercial utilization, Department of Energy should focus its support on few key enabling technologies and support their codes and standards development and qualification cost (including irradiation as applicable) rather than taking a diluted approach.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Liquid Crystal Orientation and Shape Optimization for the Active Response of Liquid Crystal Elastomers

Liquid crystal elastomers (LCEs) are responsive materials that can undergo large reversible deformations upon exposure to external stimuli, such as electrical and thermal fields. Controlling the alignment of their liquid crystals mesogens to achieve desired shape changes unlocks a new design paradigm that is unavailable when using traditional materials. While experimental measurements can provide valuable insights into their behavior, computational analysis is essential to exploit their full potential. Accurate simulation is not, however, the end goal; rather, it is the means to achieve their optimal design. Such design optimization problems are best solved with algorithms that require gradients, i.e., sensitivities, of the cost and constraint functions with respect to the design parameters, to efficiently traverse the design space. In this work, a nonlinear LCE model and adjoint sensitivity analysis are implemented in a scalable and flexible finite element-based open source framework and integrated into a gradient-based design optimization tool. To display the versatility of the computational framework, LCE design problems that optimize both the material, i.e., liquid crystal orientation, and structural shape to reach a target actuated shapes or maximize energy absorption are solved. Multiple parameterizations, customized to address fabrication limitations, are investigated in both 2D and 3D. The case studies are followed by a discussion on the simulation and design optimization hurdles, as well as potential avenues for improving the robustness of similar computational frameworks for applications of interest.

42 ENGINEERING↗
DOE OSTI2025

Braiding for the win: Harnessing braiding statistics in topological states to play quantum games

Nonlocal quantum games provide proof of principle that quantum resources can confer an advantage at certain tasks. They also provide a compelling way to explore the computational utility of phases of matter on quantum hardware. In a recent paper [O. Hart et al., Phys. Rev. Lett. 134, 130602 (2025)], we demonstrated that a toric code resource state conferred advantage at a certain nonlocal game, which remained robust to small deformations of the resource state. In this paper we demonstrate that this robust advantage is a generic property of resource states drawn from topological or fracton ordered phases of quantum matter. To this end, we illustrate how several other states from paradigmatic topological and fracton ordered phases can function as resources for suitably defined nonlocal games, notably the three-dimensional toric-code phase, the X-cube fracton phase, and the double-semion phase. The key in every case is to design a nonlocal game that harnesses the characteristic braiding processes of a quantum phase as a source of contextuality. We unify the strategies that take advantage of mutual statistics by relating the operators to be measured to order and disorder parameters of an underlying generalized symmetry-breaking phase transition. Additionally, by connecting the win probability to twist products, we show that success at the game serves as a many-body entanglement witness. Namely, if the players implement a perfect quantum strategy on large length scales, the quantum state they share cannot be connected to a trivial product state via a constant-depth local unitary circuit. Lastly, we massively generalize the family of games that admit perfect strategies when codewords of homological quantum error-correcting codes are used as resources.

Fractons↗
DOE OSTI2024

Effect of sintering temperature on feature resolution and flexural strength of ceramics fabricated through vat photopolymerization additive manufacturing

Although ceramic additive manufacturing (AM) could be used to fabricate complex, high-resolution parts for diverse, functional applications, one ongoing challenge is optimizing the post-process, particularly sintering, conditions to consistently produce geometrically accurate and mechanically robust parts. This study aims to investigate how sintering temperature affects feature resolution and flexural properties of silica-based parts formed by vat photopolymerization (VPP) AM. Test artifacts were designed to evaluate features of different sizes, shapes and orientations, and three-point bend specimens printed in multiple orientations were used to evaluate mechanical properties. Sintering temperatures were varied between 1000°C and 1300°C. Deviations from designed dimensions often increased with higher sintering temperatures and/or larger features. Higher sintering temperatures yielded parts with higher strength and lower strain at break. Many features exhibited defects, often dependent on geometry and sintering temperature, highlighting the need for further analysis of debinding and sintering parameters. To the best of the authors’ knowledge, this is the first time test artifacts have been designed for ceramic VPP. This work also offers insights into the effect of sintering temperature and print orientation on flexural properties. These results provide design guidelines for a particular material, while the methodology outlined for assessing feature resolution and flexural strength is broadly applicable to other ceramics, enabling more predictable part performance when considering the future design and manufacture of complex ceramic parts.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Sterile-neutrino search based on 259 days of KATRIN data

Neutrinos are the most abundant fundamental matter particles in the Universe and play a crucial part in particle physics and cosmology. Neutrino oscillation, discovered about 25 years ago, shows that the three known species mix with each other. Anomalous results from reactor and radioactive-source experiments suggest a possible fourth neutrino state, the sterile neutrino, which does not interact through the weak force. The Karlsruhe Tritium Neutrino (KATRIN) experiment, primarily designed to measure the neutrino mass using tritium β-decay, also searches for sterile neutrinos suggested by these anomalies. A sterile-neutrino signal would appear as a distortion in the β-decay energy spectrum, characterized by a discontinuity in curvature (kink) related to the sterile-neutrino mass. This signature, which depends only on the shape of the spectrum rather than its absolute normalization, offers a robust, complementary approach to reactor experiments. Here we report the analysis of the energy spectrum of 36 million tritium β-decay electrons recorded in 259 measurement days within the last 40 eV below the endpoint. The results exclude a substantial part of the parameter space suggested by the gallium anomaly and challenge the Neutrino-4 claim. Together with other neutrino-disappearance experiments, KATRIN probes sterile-to-active mass splittings from a fraction of an eV 2 to several hundred eV 2 , excluding light sterile neutrinos with mixing angles above a few per cent.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Bayesian calibration of irradiated graphite property models under high temperatures

Graphite under high temperatures and irradiation is central to advanced reactors. We develop a Bayesian calibration framework for graphite property models that explicitly represents model-data mismatch via a Gaussian-process discrepancy. The approach propagates uncertainty from parameters, experimental noise, and model form, with a hierarchical variance structure to capture group and cross-group noise. Using two predictive models across five grades (IG-110, NBG-18, PCEA, NBG-17, 2114) and four properties-irradiation-induced dimension change, creep, Young’s modulus change ratio, and coefficient of thermal expansion change ratio-we obtain average predictive-error reductions of 54%, 65%, 17%, and 17% when discrepancy is included. We illustrate engineering impact with a multiphysics model of a very-high-temperature reactor prismatic reflector brick, analyzing stresses under high fluence and temperature. Accounting for model discrepancy markedly improves predictive accuracy and provides a robust basis for reliable graphite component design in advanced reactors.

36 - MATERIALS SCIENCE↗
DOE OSTI2026

A hybrid Penman-Monteith and machine learning model for simulating evapotranspiration and its components

Integrating physical processes with machine learning has advanced evapotranspiration (ET) simulation, yet most hybrid models fail to partition total ET into its components: soil evaporation (E) and vegetation transpiration (T). This study introduces Residual Neural Network–Penman–Monteith (RNN-PM), a novel hybrid dual-source ET model designed to overcome this limitation. The model synergizes the physically-based Penman–Monteith framework with three specialized residual neural networks trained to estimate key conductance parameters (canopy conductance, soil surface conductance, and aerodynamic conductance). Furthermore this explicit parameterization allows for the direct partitioning of total ET. Validation at National Ecological Observatory Network (NEON) flux sites using high-frequency partitioned E and T shows that RNN-PM reliably reproduces ET and the transpiration fraction (T/ET). For ET, the model achieves an average Kling–Gupta efficiency (KGE) of 0.89 and a root-mean-square error (RMSE) of 0.55 mm/day; for T/ET, the KGE is 0.87 with an RMSE of 0.06. Furthermore, RNN-PM demonstrates robust generalization, accurately simulating ET and its components well beyond the initial training dataset, even under extreme climatic conditions. This study extended the analysis by comparing the RNN-PM model with seven established dual-source ET models. The results indicate that RNN-PM outperforms both conventional machine learning models and purely physical process-based models in simulating ET components in most cases. Among the purely physical process-based dual-source models, those based on surface temperature decomposition showed improved performance as the leaf area index (LAI) decreased when evaluated against high-frequency ET component datasets. In contrast, the performance of conductance-based dual-source models declined with decreasing LAI. Although purely machine learning-based models can produce relatively accurate simulations of ET components, they often exhibit limited generalization capability, an issue that the RNN-PM model effectively overcomes. Ultimately, the RNN-PM model represents a significant advance in simulating ET components, offering a novel and scalable approach for improving the representation of land–atmosphere interactions in Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Kinetics-Informed Neural Networks

Chemical kinetics and reaction engineering consists of the phenomenological framework for the disentanglement of reaction mechanisms, optimization of reaction performance and the rational design of chemical processes. Here, we utilize feed-forward artificial neural networks as basis functions to solve ordinary differential equations (ODEs) constrained by differential algebraic equations (DAEs) that describe microkinetic models (MKMs). We present an algebraic framework for the mathematical description and classification of reaction networks, types of elementary reaction, and chemical species. Under this framework, we demonstrate that the simultaneous training of neural nets and kinetic model parameters in a regularized multi-objective optimization setting leads to the solution of the inverse problem through the estimation of kinetic parameters from synthetic experimental data. We analyze a set of scenarios to establish the extent to which kinetic parameters can be retrieved from transient kinetic data, and assess the robustness of the methodology with respect to statistical noise. Furthermore, this approach to inverse kinetic ODEs can assist in the elucidation of reaction mechanisms based on transient data.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Prototype High-Voltage Pulsed Power Supply for Control of the ITER Shattered Pellet Injection System Flyer Plate Valve

A high-voltage pulsed power supply (HVPPS) has been designed, prototyped, and tested for driving an eddy current actuated propellant valve for the International Thermonuclear Experimental Reactor (ITER) disruption mitigation system. The high-voltage (HV) dc supply output voltage is software programmable, and the energy storage capacitor bank can be readily reconfigured as 200, 400, 600, and 800 μ F, enabling testing and optimization of both the valve drive and valve systems. Multiple system parameters are monitored before, during, and after each firing of the valve. The system parameters are both displayed and stored for further analysis. Control of the setup, firing sequence, and data collection is automated using a LabVIEW-based control program. The programmability and reconfigurability of this system collectively provide a flexible and robust platform for system refinement and optimization. In this article, a summary of the system will be provided including operational sequences, HV switching and associated triggering methods and circuits, and results measured while firing a solid frozen pellet. Additionally, planned refinement activities toward meeting all requirements for ITER integration will be discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Description and Use of SCALE Sampler Parametric Capability for Engineering Analysis and Optimization

The Sampler sequence was introduced into the SCALE nuclear modeling and simulation suite in SCALE 6.2 to perform uncertainty quantification via random sampling of nuclear data, material number densities, and dimensions. Sampler was expanded with the introduction of a parametric capability in SCALE 6.2.2. This paper discusses input for the Sampler parametric sequence and presents two case studies of analyses performed using the sequence. These case studies include preconceptual design of a package for transporting high assay low-enriched uranium (HALEU) oxide and scoping calculations to support subcritical limit development for a future update of the ANSI/ANS-8.1 (ANS-8.1) standard. The parametric capability within Sampler provides many benefits to analysts. For instance, parametric sweeps are frequently used to identify optimum parameter values as part of safety analysis or system design, but such sweeps can require substantial engineering time or may rely on custom-written scripts or scripts such as Write One, Run Many (or WORM) developed outside of any software quality assurance program. With the parametric capabilities in Sampler, however, a large number of inputs can be generated automatically without recourse to scripting by individual analysts. The parametric capability can also be used in lieu of the CSAS5S search sequence to identify optimum parameters more simply with straightforward inputs and outputs. Sampler can also be used to calculate input parameters from engineering specifications. For example, diameters can be converted to radii, or masses can be used to calculate number densities. Overall, the Sampler parametric capability provides a robust feature within SCALE, eliminating the need for user-developed scripting.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗