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In recent designs of safety-critical pyro control circuitry, latching circuits, used to store the state of control signals, have been found to have sensitivity to noise that could lead to inadvertent firing. This technical bulletin describes the sensitive circuit, and provides best practice recommendations to improve the design.
The NASA Engineering and Safety Center (NESC) was requested to support the development of a governing philosophy to provide a consistent and systematic approach for fracture control certification of additive manufacturing (AM) hardware that cannot be reliably inspected. The overall goal of the NESC assessment was to make progress toward a framework for general certification of un-inspectable fracture critical AM hardware and toward approaches for evaluating risks associated with these components. This report contains the results of the assessment.
We summarize tentative findings from multi air quality model ensembles for the years 2009 and 2010 in Europe (EU), and 2010 and 2016 in North America (NA), under AQMEII-4. The model predictions of sulphur and nitrogen deposition were used to estimate exceedances of critical loads for acidification and eutrophication, to show the extent to which the ensemble members agree in the magnitude and the trend of ecologically meaningful impacts. Model exceedance variability was analyzed using AQMEII-4 diagnostics. Evaluation against concentration and wet deposition observations, coupled with these diagnostics, identified specific process representations as the causes for variability between model predictions and for reduced model performance. All models predicted reductions in ecosystem acidification impacts in North America between the years 2010 and 2016, in accord with SO2 emissions reduction legislation which started in 2010 (SO2 SIP) However, all models in EU and NA domains had net negative biases for wet deposition of sulphur and nitrogen relative to observations. The wet S deposition average mean bias for the NA ensemble was -0.17 eq ha-1 d-1, and for the EU ensemble -1.15 eq ha-1 d-1. The NA daily wet deposition average mean bias for NH4+ was -0.37 eq ha-1d-1; EU -1.19 eq ha-1 d-1. The daily NA wet NO3- deposition average mean bias was -0.24 eq ha-1d-1; EU -0.69 eq ha-1 d-1. The members of the ensemble diverged (factor of 10) in their North American predictions for Ndep and consequently their eutrophication exceedances. The models with the highest eutrophication predictions also predicted the highest levels of gas-phase ammonia dry deposition (standard deviation of ammonia dry deposition flux across ensemble members was larger than the ensemble average). These models also had negative biases of predicted ammonia concentrations; average mean biases of -0.63 (satellite NH3) and -0.85 ppbv (surface NH3) compared to ensemble averages of -0.30 and -0.34 ppbv. Diagnostics showed that these differences resulted from the manner in which bidirectional ammonia fluxes were parameterized within these models. The second largest source of NA eutrophication prediction variability were models with positive biases in particulate ammonium and nitrate concentrations, and higher particle nitrogen deposition levels ( particle ammonium concentration bias +0.35 ug m-3; ensemble bias +0.15 ug m-3). We believe two factors may have led to these latter overestimates: higher levels of fine mode particle nitrate formation compared to other models (due to the use of an inorganic heterogeneous chemistry algorithm which did not take base cation chemistry into account), and updates to particle dry deposition velocities carried out in the absence of concurrent updates to wet scavenging algorithms. The relative importance of dry gas, dry particulate, and wet deposition towards total sulphur and nitrogen deposition totals differed between EU and North American domains, though all models had negative biases in wet deposition as noted above. Parallel and subsequent work suggests that multiphase hydrometeor scavenging may improve model wet deposition performance. An increased research focus is recommended for four model processes: multiphase hydrometeor scavenging, ammonia bidirectional fluxes, base cation chemistry and emissions, and particle dry deposition.
Multiple human spaceflight programs are underway at NASA including Orion, Space Launch System, Gateway, Human Landing System, and EVA and Lunar Surface Mobility programs. Achieving success in these programs requires NASA to collaborate with a variety of commercial partners, including both new spaceflight companies and robotic spaceflight companies pursuing crewed spaceflight for the first time. It is not always clear to these organizations how to show their systems are safe for human spaceflight. This is particularly true for avionics systems, which are responsible for performing some of a crewed spacecraft’s most critical functions. NASA recently published guidance describing how to show the design of an avionic system meets safety requirements for crewed missions.
Closed Brayton super-critical CO2 power cycles are well suited to waste heat bottoming cycles, due to their increased efficiency and compactness, relative to Rankine steam bottoming cycles. Since waste heat applications would be retrofits, the power system compactness is important. To achieve high efficiency, these power cycles require high pressure recuperative type heat exchangers, of substantial heat duty. Current Printed Circuit Heat Exchangers (PCHE), originally developed for high pressure gas and oil applications, can be used as recuperators, but costs are higher than desired. Altex is developing a purpose-built high pressure and effectiveness recuperator to provide the reliability, compactness, performance and pressure capability of current recuperators, but at a reduced cost. The High Effectiveness Low Cost (HELC) recuperative heat exchanger design yields volume and weight metrics of .0024 m3/UA and 10.2 kg/UA, which are 4% and 77.3% below recuperator target metrics, respectively. A 50 kW test article was designed and fabricated. Performance tests on water and oil showed that the HELC design model could predict heat transfer, to within 10% of the measured value. This model was then used to project HELC performance, when operating on supercritical CO2. Besides performance tests, the test article was hydrostatically tested, for integrity at up to 4,000 psi pressure. At these conditions, some distortion of channels was encountered. To mitigate distortion, the inserts were redesigned and these results were used to project the cost of 500 kW and 5,083 kW HELC units. The cost metric for the 5,083 kW unit was determined to be $1,349/UA, which is 10% lower than the recuperator desired cost metric desired target of $1,500/UA. In addition, the 5,083 kW unit HELC cost of $61.09/kW is 33.6% lower than the $92/kW estimated cost for a PCHE. Hydrostatic pressure tests, at up to 4,000psi, showed that the unit did not leak. However, channels were distorted at this pressure differential. Design updates to minimize stress concentrations and distortion were prepared and analyzed, to show that distortion could be controlled, but to date tests have not been run to prove the design. Project results show the potential of the HELC approach, but more work is required to confirm this potential, at the larger scales of interest.
The derivation of a non-invasive prompt neutron decay constant sensitivity coefficient is provided in this work. The computation of the sensitivity coefficient derived in this work does not require modification of Monte Carlo source code and is based on capabilities available in Monte Carlo N-Particle R© Code Version 6.2. The prompt neutron decay constant sensitivity coefficients are calculated for 44-group and 252-group energy structures for specific nuclide-reaction pairs in the Jezebel benchmark experiment. The nuclide-reaction pairs investigated in this work include Pu- 239(n,f), Pu-240(n,f), and Pu-241(n,f). Physical explanations of the sensitivity profiles exhibited by the 252-group energy structure are investigated for the prompt neutron multiplication factor, mean neutron lifetime, and prompt neutron decay constant. The prompt neutron decay constant sensitivity coefficients calculated for the 44-group and 252-group energy structure of Pu-239(n,f) are compared. Lastly, the 44-group energy structure sensitivity coefficients calculated are used for nuclear-data induced uncertainty quantification of the neutron multiplication factor. This work shows that a reduction in the nuclear data-induced uncertainty of the neutron multiplication factor is possible for all nuclide-reaction pairs investigated when prompt neutron decay constant sensitivity coefficients are utilized. This is important for new critical experiment design optimization studies of measurement configurations. This work provides a basis for more detailed sensitivity analysis and uncertainty quantification of nuclide, reaction, and energy-specific cross section data.
Los Alamos National Laboratory (LANL) has been working on creating a new centralized repository for MCNP models of critical benchmark experiments. The initial model of U233-COMP-THERM-004 was derived from the Whisper Suite provided with MCNP6.2, and was compared against the ICSBEP handbook chapter for the benchmark. Many notable errors were found in the initial model and were revised accordingly. Comparing the computational model of the old and new models confirmed that any Whisper results that relied upon the old model are still valid.
Modern distribution networks are undergoing several technical challenges, such as voltage fluctuations, because of high penetration of distributed energy resources (DERs). This paper proposes a deep reinforcement learning (DRL)-based Volt VAR co-optimization technique for reducing voltage fluctuations as well as power loss under high penetration of DERs. In addition, the proposed approach minimizes the operational cost of the grid. A stochastic policy optimization based soft actor critic (SAC) agent is proposed to configure the optimal set-points of the reactive power outputs of the inverters. The performance of the proposed model is verified on the modified IEEE 34- and 123-bus systems and compared with a base case scenario with no reactive supply by inverters, and a local droop control approach. The results demonstrate that the proposed framework outperforms the conventional droop control method in improving the voltage profile, minimizing the network power loss, and reducing grid operational cost.
The Criticality Accident Alarm System (CAAS) for the Uranium Processing Facility (UPF) provides coverage for two primary processing facilities. For UPF, detailed documentation and drawings of the design are readily available. While this design information allows for some CAAS modeling features to match the actual design with a high level of accuracy, assumptions and simplifications are necessary as it is unrealistic and unnecessary to replicate many aspects of the design. A set of modeling guidelines and techniques were used to develop consistency between the MCNP6 models. While ensuring conservatism, overly conservative choices must be avoided to demonstrate the minimum accident of concern can be detected. To ensure proper modeling choices were made for the final models, a group of parametric studies were performed to evaluate the impact on the dose rates detected at the individual detectors. Some of these modeling choices and/or associated parametric studies include: - Modeling the grid steel area above the detectors as homogeneous stainless steel and determining the impact on detectability with an increasing density. - Modeling the presence of equipment in close proximity to the CAAS detectors. - Determining an appropriate material density to represent miscellaneous items not specifically modeled. - Evaluating the impact of sprinkler activation. The results of UPF CAAS analyses show that the modeling assumptions made based on the detailed design and parametric studies are appropriate and demonstrate accurate yet conservative models have been developed for detecting minimum accident of concern within the UPF.
The Republic of Colombia is operating a small 100 kW(t) research reactor. The reactor was provided to Colombia under the U.S. Atoms for Peace Program, and which was fueled with MTR HEU fuel enriched nominally to 93% U{sup 235}. With the cooperation of the International Atomic Energy Agency IAEA, it was prepared a new safety analysis report for performing an HEU to LEU conversion of the IAN-R1 reactor and manufacture TRIGA type LEU (19.7%, enriched) fuel to replace the original MTR-HEU fuel plate assemblies. This paper describes the activities which were achieved during the approach to nuclear criticality. (authors)
Due to the ever-changing risk environment that faces the Nation’s critical infrastructure, it is essential that a comprehensive, collaborative approach is taken to enhance the resilience of the infrastructure assets and systems that are relied upon by the American people. To address the gaps that exist in infrastructure resilience research and development, Idaho National Laboratory (INL) created the Resilience Optimization Center (IROC) to bring together multi-disciplinary subject matter experts internally across the laboratory, as well as from public and private entities, other national laboratories, and academia to address some of the Nation’s more challenging infrastructure problems. These experts are working to provide easier access to subject matter experts; providing feasible, optimized solutions that yield observable results; and creating collaborative teams that apply a cyber-physical-dependencies approach. Though a variety of research initiatives, IROC is striving to provide end-to-end solutions, bridging the gap between cyber and physical infrastructure through research, analysis, testing, and validation.
The SCALE code system has been widely used for nuclear criticality safety, reactor physics, radiation shielding, source term generation, and inventory analyses by researchers, industry, and regulatory bodies. Although limited support for shared- and distributed-memory parallel processing was introduced via C++ threading, OpenMP, and MPI, a hybrid parallel programming model with both distributed- and shared-memory parallelism has not been fully supported in the SCALE code system.
Nuclear criticality safety (NCS) training is vital and mandatory for NCS professionals to ensure the safe processing, handling, storage, and transportation of fissionable materials outside nuclear reactors. The requirement for this training is prescribed in several international and domestic guidance documents.
This study investigates the application of generative artificial intelligence techniques, particularly conditional generative adversarial networks (cGAN), in real-world engineering contexts, with a specific focus on synthetic data generation for critical heat flux (CHF). Utilizing a dataset comprising more than 20,000 real experimental CHF measurements, we conduct a series of experiments to examine cGAN’s behavior. These experiments encompass varying sizes of the training dataset, training cGAN on data from diverse experimental sources to generate new data on unseen experimental setups, and assessing the impact of excluding various input features on cGAN’s data generation accuracy. Our findings underscore the pronounced data dependency of cGAN for reliable performance, with decreased efficacy observed with smaller training dataset sizes. Notably, cGAN exhibits varying performance when trained on data from different experiments, with superior predictive capabilities observed for certain experiment sources compared to others. For instance, when cGAN was trained on data from Smolin et al.’s experiments or Zenkevich et al., it exhibited relatively good performance in generating the data from Becker et al., Kirillov et al., and Alekseev et al. experiments. In contrast, when trained with Alekseev et al.’s data and tasked with generating other experimental setups, cGAN showed notably poor performance. In both scenarios, cGAN’s performance was inferior compared to training on samples from all experiments concurrently. A feature importance analysis highlights the significant influence of parameters such as mass flux and heated length on accurate CHF generation, while other parameters like diameter and pressure have less impact. Inlet temperature is identified as a moderating factor by cGAN.
The Advanced Test Reactor (ATR) is a versatile nuclear research reactor located at the Idaho National Laboratory (INL) in Eastern Idaho, United States of America. It is one of the most powerful and flexible research reactors in the world and is primarily used for materials testing, isotope production and basic nuclear science research. The ATR is a light-water-cooled, beryllium-moderated reactor with a nominal thermal power capacity of 250 MW utilizing specially designed fuel, arranged in a serpentine pattern to create 9 flux traps. The special design of ATR allows for the neutron flux within each of the flux traps providing flexibility for experiments, programs and allowing experiments with different dose requirements to be irradiated simultaneously. The Advance Test Reactor Critical (ATRC) is a full-scale replica of the ATR core but is housed in a pool instead of a pressure vessel. ATRC is usually operated at less than 600 watts [1]. There are 12 dry instrument thimbles located around the outside of the core tank where a variety of measurement equipment is housed to facilitate reactor operations. Currently Instrument Thimble 11 (IT-11) is not utilized in ATR or ATRC. A range of measurements is ongoing to qualify IT-11 as a test platform for nuclear instrumentation research, development and further experimentation.
Accurate prediction of critical heat flux (CHF) is crucial for the safe and efficient operation of nuclear reactors. Traditional CHF modeling methods often require extensive experimental data, which are hard to obtain. This study introduces the Aided Active Learning (AAL) framework, which strategically minimizes data requirements without sacrificing model accuracy. Unlike conventional Active Learning (AL), AAL introduces an additional step of randomly selecting a subset from the sample pool before applying the query strategy. To evaluate the performance of AAL, two query strategies—uncertainty-based sampling and error-reduction sampling—were evaluated across the following models: random forest (RF), feedforward neural network (FNN), and variational feedforward neural network (vFNN). The proposed framework demonstrated that AAL effectively reduces the number of training samples needed to achieve comparable predictive accuracy. For the RF model, AL required only 710 samples to achieve an R2 score of 0.98, as compared to the 4,785 samples needed by random sampling. Similarly, the FNN model achieved the same R2 score with just 355 samples when using AL, a significant improvement over the 825 samples required by random sampling. In case of uncertainty-based sampling strategy, vFNN attained an R2 of 0.98 with 3,420 samples, reducing the sample requirement by 47% relative to the 6,440 samples needed for random sampling. Its performance suggests that larger training data are required to fully leverage its uncertainty quantification capabilities.
Electrochemical hydrogenation of nitrate to ammonia using renewable electricity is a promising route for sustainability but lacks catalysts that can deliver balanced selectivity, activity, and durability. For this work, a new family of noble metal-free and high-performing Chevrel phase Ni 2 Mo 6 T 8 (T = S, Se, and Te) catalysts that have similar structural and textural properties and differ presumably only in chalcogenide anion is systematically studied. The side-by-side comparisons allow the uncovering of the critical roles of chalcogenide anions in impacting kinetic activities and long-term durability. The incorporation of anions with larger size and smaller electronegativity from sulfide to selenide and telluride invokes stronger inhibition of the otherwise competing hydrogen evolution reaction (HER) and steers the hydrogenation toward the selective formation of ammonia, thus improving both Faradic selectivity and the turnover frequency to high levels of 99.4% and 21.5 s –1 , respectively, on the Ni 2 Mo 6 Te 8 catalyst. More significantly, the bulkier anion in the Ni 2 Mo 6 T 8 catalyst kinetically inhibited the intercalation of electrolyte cations, a major degradation mechanism in the catalyst family examined here and delivered several times improved durability. Therefore, this study introduces novel active motifs for selective nitrate reduction and provides insights into the catalyst degradation mechanism and practical ways to improve durability.
Abstract Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics and chemistry. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, and accelerate the discovery of materials. Here, an in‐depth review of the application of ML to energy materials, including rechargeable alkali‐ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors, is presented. A conceptual framework is first provided for ML in materials science, with a broad overview of different ML techniques as well as best practices. This is followed by a critical discussion of how ML is applied in energy materials. This review is concluded with the perspectives on major challenges and opportunities in this exciting field.