NL2Hull: A Natural Language-Driven Constrained Ship Design Decision Framework

Wenhua Huo; Fenglei Han; Wangyuan Zhao; Jialin Wu; Jiayi Han

cs.AIOct 7, 2026

NL2Hull: A Natural Language-Driven Constrained Ship Design Decision Framework

Authors: Wenhua Huo, Fenglei Han, Wangyuan Zhao, Jialin Wu, Jiayi Han

Organizations: Harbin Engineering University, 145 Nantong Street, Nangang District, Harbin, P. R. China

Abstract

Ship-form design combines smooth geometric representation, local shape editing, and constraints on the resulting hull. We present the Natural-Language-to-Hull Framework (NL2Hull Framework), which formulates ship-form editing as a typed discrete decision problem and connects language decisions to numerical geometry. Its Constrained Free-Form Deformation Engine (CFFD Engine) represents hull waterlines with non-uniform rational B-splines (NURBS), applies free-form deformation (FFD) to their control points, reconstructs the hull, and checks geometric constraints. We construct the Ship Design Decision Dataset (SDD Dataset) with 134,558 cleaned records and evaluate compared models on its subset Ship Design Decision Benchmark (SDDBench), containing 5,000 records and 43,496 typed questions. We propose Chip, a constrained ship-design decision model for processing natural-language requests. Chip reaches 95.90% question accuracy and 99.32% FFD exact match, with a negative log-likelihood of 0.0951, an expected calibration error of 0.0032, and a Brier score of 0.0551. The NL2Hull Framework provides a reproducible interface for evaluating language-based ship-form decisions while identifying the geometry and continuous-control components that require further development. Our code and dataset is available at https://github.com/wenhuahuo/NL2Hull.

Figures & tables

Figure 1 : Overview of the NL2Hull framework and its ship-form editing workflow. (a) Natural-language requests are converted by Chip into ordered actions and applied by the CFFD Engine to a KCS benchmark hull. (b) SDD Dataset and SDDBench construction. (c) Typed decision questions and candidate probabilities in Chip. (d) NURBS representation, control-point deformation, hull reconstruction, and geometric checks.

Figure 2 : The twelve source hull geometries used to construct the SDD Dataset. The collection includes the Duisburg Test Case (DTC), DTMB 5415 surface-combatant benchmark hull, KRISO Container Ship (KCS), KRISO Very Large Crude Carrier 2 (KVLCC2), a generic container-ship hull, a generic frigate hull, NPL Round Bilge 4a and NPL Round Bilge, the ITTC S-175 container ship, a Series 60 hull, the Wigley hull, and a generic workboat hull. Side and front orthographic views use unit-length normalization while preserving each hull’s geometric aspect ratio.

Panel of Figure 3 — Figure 3 : Dataset structure and typed-question composition. (a) The distribution of the 134,558 language descriptions after embedding projection and dimensionality reduction. The six annotations identify dominant linguistic or structural patterns obtained by joining the projection to the structured records; (b) Absolute counts of the six typed-question families in the SDD Dataset and SDDBench on a common radial scale. The outer polygon is the full SDD Dataset (1,184,336 questions), and the inner polygon is SDDBench (43,496 questions).

Panel of Figure 4 — Figure 4 : NURBS-based reconstruction of the KCS hull. The input surface is represented by fitted longitudinal profiles and waterline curves, then reconstructed as a closed section-based hull mesh. The wide profile panel is placed above the two compact diagnostics to preserve the aspect ratio of each source plot.

Hull	Surface RMSE	Waterline width RMSE	Fore-profile RMSE	Aft-profile RMSE
DTC	0.01130	0.00302	0.00038	0.00121
DTMB5415	0.00524	0.00227	0.00070	0.00072
KCS	0.00623	0.00312	0.00034	0.00082
KVLCC2	0.00975	0.00140	0.00012	0.00092
Containership	0.00690	0.00202	0.00003	0.00038
Firgate	0.00658	0.00284	0.00023	0.00084

Table 1 : Reconstruction errors for the twelve-hull NURBS representation experiment. All quantities are normalized by the input hull length. Surface RMSE is computed from bidirectional nearest-neighbour distances between input and reconstructed surface vertices.

Panel of Figure 5 — Figure 5 : Representative FFD operations applied to the fitted KVLCC2 hull. Each panel compares the original fitted hull with the hull obtained after the indicated control-point deformation. In each row, the centered arrow points from the original hull to the modified hull. The right column overlays the deformed hull (blue) with red section lines from the original hull (gray); plan views are above side views.

Operation	Level	Volume ratio	Watertight	Valid volume
Bow outward	5	1.037	Yes	Yes
Bulb forward	4	1.033	Yes	Yes
Global breadth increase	5	1.120	Yes	Yes

Table 2 : Geometric measurements for representative FFD operations on KVLCC2.

Model	Question accuracy $\uparrow$	FFD exact match $\uparrow$	NLL $\downarrow$	ECE $\downarrow$	Brier $\downarrow$
Small language models
Qwen3	75.61	2.84	1.2088	0.1148	0.3803
Gemma3	66.97	15.98	1.4328	0.0225	0.4471
Llama3.2	63.94	2.16	1.4999	0.0723	0.5063
Flagship language models
Claude Opus 5	92.87	99.12	0.2385	0.0607	0.1066

Table 3 : Performance on SDDBench. Question accuracy is computed over successfully evaluated questions, and FFD exact match uses all 5,000 requested records. Accuracy values are percentages; lower values are preferred for NLL, ECE, and Brier score. Best values are shown in bold.

Model	Easy	Original	Hard	Brier $\downarrow$	ECE $\downarrow$
JEV	100.0000	98.6100	72.0700	0.3584	0.0947
Laya	95.8300	72.2200	28.8300	0.8042	0.2465
SemIf	100.0000	98.6100	61.2600	0.4980	0.1122
JevK5	100.0000	97.2200	73.8700	0.3662	0.0467
Intern-Decision-0.8B	97.9200	80.5600	52.2500	0.5295	0.0657
Chip (Ours)	100.0000	81.9444	36.9369	0.3489	0.1633

Table 4 : General typed-decision evaluation on JevBench. Accuracy values are percentages. Brier score and ECE are computed by the corresponding benchmark scorers; lower values are preferred. Chip calibration metrics are from the Original tier. Public reference calibration values follow the reporting scope of the supplied JevBench comparison table.

Panel of Figure 6 — Figure 6 : Representative end-to-end ship-form editing cases. Each panel connects a natural-language instruction to the original and modified KVLCC2 hulls in plan and side views. Each interaction category occupies one row, with the original hull on the left and the modified hull (blue) on the right; the centered arrow indicates the editing direction, and red curves trace sections of the original hull. The displayed instructions are English translations of the evaluated Chinese requests.

Interaction type	Experiments	End-to-end success	Trajectory exact	Geometry valid	Constraints satisfied	Level accuracy
Single edit	100	100.00%	100.00%	100.00%	100.00%	52.00%
Multi-region, single turn	100	60.00%	96.00%	100.00%	13.33%	70.48%
Same-region, two turns	100	98.00%	100.00%	100.00%	95.35%	81.50%

Table 5 : End-to-end editing results on the KVLCC2 hull. Magnitude-level accuracy is calculated over all labelled action levels. Constraint satisfaction is calculated over experiments containing at least one constraint.

Panel of Figure 7 — Figure 7 : Alternative representative end-to-end editing cases from the same three interaction categories. The paired views show the original hull, natural-language instruction, and reconstructed modified hull. Original hulls are on the left and modified hulls (blue) are on the right; red lines trace sections of the original hull. Arrows indicate the editing direction.

Model	Mean latency (ms)	95th percentile (ms)
Small language models
Qwen3	25280.05	46745.65
Gemma3	18407.59	43119.55
Llama3.2	22589.78	44708.37
Flagship language models
Claude Opus 5	7844.00	12531.86

Table 6 : Per-request model latency on SDDBench. Mean and 95th-percentile latency are reported in milliseconds from the recorded evaluator outputs. The API evaluators timed failed attempts as well as successful attempts; the JEV latency statistics use its 4,322 completed requests.

Variant	32 controls	Deck knot	Bulb knots	KCS stem RMSE	KCS stern RMSE	Mean stem RMSE	Mean stern RMSE
24 controls	–	–	–	0.002326	0.001581	0.001551	0.001236
32 controls	✓	–	–	0.001222	0.000818	0.000909	0.000785
Deck knot	–	✓	–	0.002187	0.001581	0.000994	0.001236
Bulb knots	–	–	✓	0.001684	0.001581	0.001396	0.001236
Combined	✓	✓	✓	0.000343	0.000818	0.000221	0.000785

Table 7 : Ablation of the center-plane profile representation. The “32 controls” operation increases the stem and stern contours from 24 to 32 control points. The “Deck knot” operation repeats the stem deck-corner knot, and “Bulb knots” densifies the interior stem-knot distribution around the bulb. “Combined” contains all three operations. Errors are normalized by the source-hull length; “Mean” is the arithmetic mean over the 12 classic hulls.

Panel of Figure 8 — Figure 8 : KCS center-plane profile reconstruction for the five controlled representation variants. Each panel compares the extracted stem and stern contours with their fitted cubic-spline curves. Variants occupy separate rows, with stem contours on the left and stern contours on the right.

Model	Completed records	Question accuracy	FFD exact match	NLL	ECE	Brier score
Chip	5,000	95.8962%	99.32%	0.0951	0.0032	0.0551
Chip-2B	5,000	95.9192%	99.30%	0.0948	0.0022	0.0548
Chip-4B	4,999	95.8794%	99.24%	0.0951	0.0025	0.0549

Table 8 : Chip model-scale ablation on SDDBench. Question accuracy uses the successfully scored questions. FFD exact match uses all requested turns. Best values in each column are bold.

Training share (%)	Training records	Question accuracy $\uparrow$	FFD exact match $\uparrow$	NLL $\downarrow$	ECE $\downarrow$	Brier $\downarrow$
0	0	80.82	72.24	0.6972	0.1995	0.3439
1	886	94.02	96.86	0.1729	0.0179	0.0850
2	1,772	94.57	97.88	0.1533	0.0110	0.0754
4	3,544	95.13	98.64	0.1331	0.0105	0.0682
8	7,088	95.39	98.82	0.1196	0.0097	0.0642
25	22,151	95.66	99.14	0.1040	0.0047	0.0584

Table 9 : Effect of SDD Dataset size on Chip. Training share is relative to the SDD Dataset training partition; the 0% row evaluates Kev-Base before ship-design adaptation. Accuracy values are percentages. Lower values are preferred for NLL, ECE, and Brier score. Best values are shown in bold.

Appendix figures & tables1 asset

Supplementary material from the paper’s appendix.

Appendix

Model	Language backbone	Batch size	Accumulation
Chip	Qwen3.5-0.8B-Base	2	4
Chip-2B	Qwen3.5-2B-Base	2	4
Chip-4B	Qwen3.5-4B-Base	1	8

Appendix

Table 10 : Variant-specific language-backbone and batch settings for ship-design adaptation. The batch size is the number of records per microbatch; accumulation is the number of gradient-accumulation steps.

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MiDShip: Multimodal Dataset of Ship Cargo Hold Structures for Engineering Design

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Ship structures govern vessel strength, safety, and manufacturability, but their design must satisfy hundreds of classification society requirements, making the process complex and iterative. Data-driven approaches are limited by the lack of structured datasets linking design geometry, structural performance, and rule-based constraints. This paper presents MiDShip, a multimodal dataset of 12,753 synthetic cargo-hold structural designs: 6,020 random, 496 generated by an SGLD-inspired procedure, and 6,237 generated by an equation-informed repair procedure. Each design includes parametric data, full and mesh-ready 3D geometry, engineering drawings and annotations, a bill of materials, and preliminary structural evaluations. Twenty-five constraints derived from a subset of ABS MVR are also evaluated. None of the random designs satisfies all constraints. Among the SGLD-inspired designs, 322 (64.9%) were fully compliant, with an average of 0.409 violations, 82.7% below the seed mean and 96.9% below the random-design mean. The repair procedure, developed through LLM-assisted code analysis, produced 4,952 fully compliant designs (79.4%), averaging 0.296 violations, 97.1% below the paired-source mean. In equal-size comparisons, mean nearest-neighbor distances in the scaled 120-parameter space were 3.495 for repaired designs, 1.144 for SGLD batches, and 3.729 for random designs. The primary contribution is the synchronized dataset and its generation and evaluation infrastructure; the generation studies demonstrate its utility rather than proposing new optimization algorithms. MiDShip supports machine learning, generative design, and automated rule-based evaluation for ship structures.

Abstract

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